other fix

fix(embed): daemon process XPC connection crash on macos
2026-01-28 14:43:35 +01:00 · 2026-01-28 14:34:42 +01:00
549 changed files with 7657 additions and 76727 deletions
@@ -2,7 +2,7 @@
 # Copy this file to .env and fill in your values

 # LLM Configuration (Required)
-# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai
+# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio
 HINDSIGHT_API_LLM_PROVIDER=openai
 HINDSIGHT_API_LLM_API_KEY=your-api-key-here
 HINDSIGHT_API_LLM_MODEL=o3-mini
@@ -13,13 +13,6 @@ HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 # HINDSIGHT_API_LLM_API_KEY=your-anthropic-api-key
 # HINDSIGHT_API_LLM_MODEL=claude-sonnet-4-20250514

-# Example: Google Vertex AI configuration
-# HINDSIGHT_API_LLM_PROVIDER=vertexai
-# HINDSIGHT_API_LLM_MODEL=google/gemini-2.0-flash-001
-# HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=your-gcp-project-id
-# HINDSIGHT_API_LLM_VERTEXAI_REGION=us-central1
-# HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/path/to/service-account-key.json  # Optional, uses ADC if not set
-
 # Example: LM Studio local configuration (Qwen 2.5 32B recommended)
 # HINDSIGHT_API_LLM_PROVIDER=lmstudio
 # HINDSIGHT_API_LLM_API_KEY=lmstudio
@@ -31,15 +24,8 @@ HINDSIGHT_API_HOST=0.0.0.0
 HINDSIGHT_API_PORT=8888
 HINDSIGHT_API_LOG_LEVEL=info

-# Base Path / Reverse Proxy Support (Optional)
-# Set these when deploying behind a reverse proxy with path-based routing
-# Example: To deploy at example.com/hindsight/, set both to "/hindsight"
-# HINDSIGHT_API_BASE_PATH=/hindsight
-# NEXT_PUBLIC_BASE_PATH=/hindsight
-
 # Database (Optional - uses embedded pg0 by default)
 # HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@host:5432/db
-# HINDSIGHT_API_DATABASE_SCHEMA=public  # PostgreSQL schema name (default: public)

 # Embeddings Configuration (Optional - uses local by default)
 # Provider: "local" (default) or "tei" (HuggingFace Text Embeddings Inference)
@@ -56,18 +42,3 @@ HINDSIGHT_API_LOG_LEVEL=info
 # HINDSIGHT_API_RERANKER_LOCAL_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
 # For TEI provider:
 # HINDSIGHT_API_RERANKER_TEI_URL=http://localhost:8081
-
-# Observability & Tracing (Optional - disabled by default)
-# Enable OpenTelemetry tracing for LLM calls (GenAI semantic conventions)
-# HINDSIGHT_API_OTEL_TRACES_ENABLED=true
-#
-# Local development with Grafana LGTM stack (recommended - see scripts/dev/grafana/README.md)
-# HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318
-#
-# Cloud backends (Grafana Cloud, Langfuse, DataDog, etc.)
-# HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT=https://your-backend-url
-# HINDSIGHT_API_OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer your-token"
-#
-# Custom service name and environment (optional, defaults: hindsight-api, development)
-# HINDSIGHT_API_OTEL_SERVICE_NAME=hindsight-production
-# HINDSIGHT_API_OTEL_DEPLOYMENT_ENVIRONMENT=production
@@ -139,104 +139,6 @@ jobs:
        path: hindsight-clients/typescript/*.tgz
        retention-days: 1

-  release-openclaw-integration:
-    runs-on: ubuntu-latest
-    environment: npm
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '22'
-        registry-url: 'https://registry.npmjs.org'
-
-    - name: Install dependencies
-      working-directory: ./hindsight-integrations/openclaw
-      run: npm ci
-
-    - name: Build
-      working-directory: ./hindsight-integrations/openclaw
-      run: npm run build
-
-    - name: Publish to npm
-      working-directory: ./hindsight-integrations/openclaw
-      run: |
-        set +e
-        OUTPUT=$(npm publish --access public 2>&1)
-        EXIT_CODE=$?
-        echo "$OUTPUT"
-        if [ $EXIT_CODE -ne 0 ]; then
-          if echo "$OUTPUT" | grep -q "cannot publish over"; then
-            echo "Package version already published, skipping..."
-            exit 0
-          fi
-          exit $EXIT_CODE
-        fi
-      env:
-        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
-
-    - name: Pack for GitHub release
-      working-directory: ./hindsight-integrations/openclaw
-      run: npm pack
-
-    - name: Upload artifacts
-      uses: actions/upload-artifact@v4
-      with:
-        name: openclaw-integration
-        path: hindsight-integrations/openclaw/*.tgz
-        retention-days: 1
-
-  release-ai-sdk-integration:
-    runs-on: ubuntu-latest
-    environment: npm
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '22'
-        registry-url: 'https://registry.npmjs.org'
-
-    - name: Install dependencies
-      working-directory: ./hindsight-integrations/ai-sdk
-      run: npm ci
-
-    - name: Build
-      working-directory: ./hindsight-integrations/ai-sdk
-      run: npm run build
-
-    - name: Publish to npm
-      working-directory: ./hindsight-integrations/ai-sdk
-      run: |
-        set +e
-        OUTPUT=$(npm publish --access public 2>&1)
-        EXIT_CODE=$?
-        echo "$OUTPUT"
-        if [ $EXIT_CODE -ne 0 ]; then
-          if echo "$OUTPUT" | grep -q "cannot publish over"; then
-            echo "Package version already published, skipping..."
-            exit 0
-          fi
-          exit $EXIT_CODE
-        fi
-      env:
-        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
-
-    - name: Pack for GitHub release
-      working-directory: ./hindsight-integrations/ai-sdk
-      run: npm pack
-
-    - name: Upload artifacts
-      uses: actions/upload-artifact@v4
-      with:
-        name: ai-sdk-integration
-        path: hindsight-integrations/ai-sdk/*.tgz
-        retention-days: 1
-
  release-control-plane:
    runs-on: ubuntu-latest
    environment: npm
@@ -340,7 +242,6 @@ jobs:
        retention-days: 1

  release-docker-images:
-    name: Release Docker (${{ matrix.image_name }}${{ matrix.tag_suffix }})
    runs-on: ubuntu-latest
    permissions:
      contents: read
@@ -350,28 +251,10 @@ jobs:
        include:
          - target: api-only
            image_name: hindsight-api
-            tag_suffix: ""
-            build_args: ""
-          - target: api-only
-            image_name: hindsight-api
-            tag_suffix: "-slim"
-            build_args: |
-              INCLUDE_LOCAL_MODELS=false
-              PRELOAD_ML_MODELS=false
          - target: cp-only
            image_name: hindsight-control-plane
-            tag_suffix: ""
-            build_args: ""
          - target: standalone
            image_name: hindsight
-            tag_suffix: ""
-            build_args: ""
-          - target: standalone
-            image_name: hindsight
-            tag_suffix: "-slim"
-            build_args: |
-              INCLUDE_LOCAL_MODELS=false
-              PRELOAD_ML_MODELS=false

    steps:
    - uses: actions/checkout@v4
@@ -409,9 +292,6 @@ jobs:
      uses: docker/metadata-action@v5
      with:
        images: ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}
-        flavor: |
-          latest=auto
-          suffix=${{ matrix.tag_suffix }}
        tags: |
          type=semver,pattern={{version}},value=${{ steps.get_version.outputs.VERSION }}
          type=semver,pattern={{major}}.{{minor}},value=${{ steps.get_version.outputs.VERSION }}
@@ -437,7 +317,7 @@ jobs:
    # - name: Smoke test - verify container starts
    #   env:
    #     GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-    #   run: ./docker/test-image.sh "${{ matrix.image_name }}:test" "${{ matrix.target }}"
+    #   run: ./scripts/docker-smoke-test.sh "${{ matrix.image_name }}:test" "${{ matrix.target }}"

    # Build multi-platform and push to release tags
    - name: Build and push release images
@@ -446,7 +326,6 @@ jobs:
        context: .
        file: docker/standalone/Dockerfile
        target: ${{ matrix.target }}
-        build-args: ${{ matrix.build_args }}
        push: true
        platforms: linux/amd64,linux/arm64
        tags: ${{ steps.meta.outputs.tags }}
@@ -487,7 +366,7 @@ jobs:

  create-github-release:
    runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-ai-sdk-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
+    needs: [release-python-packages, release-typescript-client, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
    permissions:
      contents: write

@@ -510,18 +389,6 @@ jobs:
        name: typescript-client
        path: ./artifacts/typescript-client

-    - name: Download OpenClaw Integration
-      uses: actions/download-artifact@v4
-      with:
-        name: openclaw-integration
-        path: ./artifacts/openclaw-integration
-
-    - name: Download AI SDK Integration
-      uses: actions/download-artifact@v4
-      with:
-        name: ai-sdk-integration
-        path: ./artifacts/ai-sdk-integration
-
    - name: Download Control Plane
      uses: actions/download-artifact@v4
      with:
@@ -563,10 +430,6 @@ jobs:
        cp artifacts/python-packages/hindsight-embed/dist/* release-assets/ || true
        # TypeScript client
        cp artifacts/typescript-client/*.tgz release-assets/ || true
-        # OpenClaw Integration
-        cp artifacts/openclaw-integration/*.tgz release-assets/ || true
-        # AI SDK Integration
-        cp artifacts/ai-sdk-integration/*.tgz release-assets/ || true
        # Control Plane
        cp artifacts/control-plane/*.tgz release-assets/ || true
        # Rust CLI binaries
@@ -9,11 +9,42 @@ concurrency:
  cancel-in-progress: true

 jobs:
+  build-python-packages:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - name: hindsight-all
+            path: hindsight
+          - name: hindsight-api
+            path: hindsight-api
+          - name: hindsight-client
+            path: hindsight-clients/python
+          - name: hindsight-embed
+            path: hindsight-embed
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Build ${{ matrix.name }}
+      working-directory: ./${{ matrix.path }}
+      run: uv build
+
  build-api-python-versions:
    runs-on: ubuntu-latest
    strategy:
      matrix:
-        python-version: ['3.11', '3.12', '3.13', '3.14']
+        python-version: ['3.11', '3.12', '3.13']

    steps:
    - uses: actions/checkout@v4
@@ -51,52 +82,6 @@ jobs:
    - name: Build TypeScript client
      run: npm run build --workspace=hindsight-clients/typescript

-  build-openclaw-integration:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '22'
-
-    - name: Install dependencies
-      working-directory: ./hindsight-integrations/openclaw
-      run: npm ci
-
-    - name: Run tests
-      working-directory: ./hindsight-integrations/openclaw
-      run: npm test
-
-    - name: Build
-      working-directory: ./hindsight-integrations/openclaw
-      run: npm run build
-
-  build-ai-sdk-integration:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '22'
-
-    - name: Install dependencies
-      working-directory: ./hindsight-integrations/ai-sdk
-      run: npm ci
-
-    - name: Run tests
-      working-directory: ./hindsight-integrations/ai-sdk
-      run: npm test
-
-    - name: Build
-      working-directory: ./hindsight-integrations/ai-sdk
-      run: npm run build
-
  build-control-plane:
    runs-on: ubuntu-latest

@@ -277,35 +262,16 @@ jobs:
      run: helm lint helm/hindsight

  build-docker-images:
-    name: Build Docker (${{ matrix.name }})
    runs-on: ubuntu-latest
    strategy:
      matrix:
        include:
          - target: api-only
            name: api
-            variant: full
-            build_args: ""
-          - target: api-only
-            name: api-slim
-            variant: slim
-            build_args: |
-              INCLUDE_LOCAL_MODELS=false
-              PRELOAD_ML_MODELS=false
          - target: cp-only
            name: control-plane
-            variant: full
-            build_args: ""
          - target: standalone
            name: standalone
-            variant: full
-            build_args: ""
-          - target: standalone
-            name: standalone-slim
-            variant: slim
-            build_args: |
-              INCLUDE_LOCAL_MODELS=false
-              PRELOAD_ML_MODELS=false

    steps:
    - uses: actions/checkout@v4
@@ -324,31 +290,20 @@ jobs:
    - name: Set up Docker Buildx
      uses: docker/setup-buildx-action@v3

-    - name: Build ${{ matrix.name }} image (${{ matrix.variant }})
+    - name: Build ${{ matrix.name }} image
      uses: docker/build-push-action@v6
      with:
        context: .
        file: docker/standalone/Dockerfile
        target: ${{ matrix.target }}
-        build-args: ${{ matrix.build_args }}
        push: false
-        load: ${{ matrix.variant == 'slim' }}
-        tags: hindsight-${{ matrix.name }}:test
-        # Removed GitHub Actions cache (type=gha) - it frequently returns 502 errors
-        # causing buildx to fail with "failed to parse error response 502"
-        # Build will be slower but more reliable
+        load: false

-    # Only test slim variants to save disk space (they're much smaller)
-    # Slim variants require external embedding providers
-    - name: Smoke test - verify container starts
-      if: matrix.variant == 'slim'
-      env:
-        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-        HINDSIGHT_API_EMBEDDINGS_PROVIDER: openai
-        HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-        HINDSIGHT_API_RERANKER_PROVIDER: cohere
-        HINDSIGHT_API_COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
-      run: ./docker/test-image.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"
+    # TODO: Re-enable smoke test when disk space issue is resolved
+    # - name: Smoke test - verify container starts
+    #   env:
+    #     GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+    #   run: ./scripts/docker-smoke-test.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"

  test-api:
    runs-on: ubuntu-latest
@@ -771,9 +726,9 @@ jobs:
  test-embed:
    runs-on: ubuntu-latest
    env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_EMBED_LLM_PROVIDER: groq
+      HINDSIGHT_EMBED_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_EMBED_LLM_MODEL: openai/gpt-oss-20b
      # Prefer CPU-only PyTorch in CI
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

@@ -804,62 +759,10 @@ jobs:
          ${{ runner.os }}-huggingface-embed-
          ${{ runner.os }}-huggingface-

-    - name: Run unit and integration tests
-      working-directory: ./hindsight-embed
-      run: uv run pytest tests/ -v
-
    - name: Run smoke test
      working-directory: ./hindsight-embed
      run: ./test.sh

-  test-hindsight-all:
-    runs-on: ubuntu-latest
-    env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
-      # For test_server_integration.py compatibility
-      HINDSIGHT_LLM_PROVIDER: groq
-      HINDSIGHT_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_LLM_MODEL: openai/gpt-oss-20b
-      # Prefer CPU-only PyTorch in CI
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-        prune-cache: false
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Build hindsight-all
-      working-directory: ./hindsight
-      run: uv build
-
-    - name: Install dependencies
-      working-directory: ./hindsight
-      run: uv sync --frozen --extra test --index-strategy unsafe-best-match
-
-    - name: Cache HuggingFace models
-      uses: actions/cache@v4
-      with:
-        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-all-${{ hashFiles('hindsight/pyproject.toml') }}
-        restore-keys: |
-          ${{ runner.os }}-huggingface-all-
-          ${{ runner.os }}-huggingface-
-
-    - name: Run unit tests
-      working-directory: ./hindsight
-      run: uv run pytest tests/ -v
-
  test-doc-examples:
    runs-on: ubuntu-latest
    needs: test-rust-cli
@@ -941,11 +844,30 @@ jobs:
          sleep 1
        done

+    - name: Run Python doc examples
+      working-directory: ./hindsight-clients/python
+      run: |
+        for f in ../../hindsight-docs/examples/api/*.py; do
+          echo "Running $f..."
+          uv run python "$f"
+        done
+
+    - name: Run Node.js doc examples
+      run: |
+        for f in hindsight-docs/examples/api/*.mjs; do
+          echo "Running $f..."
+          node "$f"
+        done
+
    - name: Configure CLI
      run: hindsight configure --api-url http://localhost:8888

-    - name: Run all doc examples
-      run: ./scripts/test-doc-examples.sh
+    - name: Run CLI doc examples
+      run: |
+        for f in hindsight-docs/examples/api/*.sh; do
+          echo "Running $f..."
+          bash "$f"
+        done

    - name: Show API server logs
      if: always()
@@ -1013,18 +935,6 @@ jobs:
      working-directory: ./hindsight-dev
      run: uv run pytest upgrade_tests/ -v --tb=short

-    - name: Show upgrade test logs
-      if: always()
-      run: |
-        echo "=== Upgrade Test Server Logs ==="
-        for log in /tmp/upgrade-test-*.log; do
-          if [ -f "$log" ]; then
-            echo ""
-            echo "--- $log ---"
-            tail -500 "$log"
-          fi
-        done
-
  verify-generated-files:
    runs-on: ubuntu-latest
    env:
@@ -1095,52 +1005,4 @@ jobs:
          git diff --stat
          exit 1
        fi
-        echo "✓ All generated files are up to date"
-
-  check-openapi-compatibility:
-    runs-on: ubuntu-latest
-    env:
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
-
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        fetch-depth: 0  # Fetch full git history to access base branch
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Install hindsight-dev dependencies
-      run: |
-        cd hindsight-dev && uv sync --frozen --index-strategy unsafe-best-match
-
-    - name: Check OpenAPI compatibility with base branch
-      run: |
-        # Get the base branch (usually main)
-        BASE_BRANCH="${{ github.base_ref }}"
-
-        if [ -z "$BASE_BRANCH" ]; then
-          echo "⚠️  Warning: No base branch found (not a PR?). Skipping compatibility check."
-          exit 0
-        fi
-
-        echo "Checking OpenAPI compatibility against base branch: $BASE_BRANCH"
-
-        # Extract the old OpenAPI spec from base branch
-        git show "origin/$BASE_BRANCH:hindsight-docs/static/openapi.json" > /tmp/old-openapi.json
-
-        if [ ! -s /tmp/old-openapi.json ]; then
-          echo "⚠️  Warning: Could not find OpenAPI spec in base branch. Skipping compatibility check."
-          exit 0
-        fi
-
-        # Check compatibility using our tool
-        cd hindsight-dev
-        uv run check-openapi-compatibility /tmp/old-openapi.json ../hindsight-docs/static/openapi.json
+        echo "✓ All generated files are up to date"
@@ -45,15 +45,9 @@ hindsight-docs/static/llms-full.txt

 hindsight-dev/benchmarks/locomo/results/
 hindsight-dev/benchmarks/longmemeval/results/
-hindsight-dev/benchmarks/consolidation/results/
-hindsight-dev/benchmarks/perf/results/
-benchmarks/results/
 hindsight-cli/target
 hindsight-clients/rust/target
 .claude
 whats-next.md
 TASK.md
-# Changelog is now tracked in hindsight-docs/src/pages/changelog.md
-# CHANGELOG.md
-
-blog-post*
+CHANGELOG.md
@@ -45,7 +45,6 @@ cd hindsight-control-plane && npm run dev
 ./scripts/dev/start-docs.sh
 ```

-
 ### Generating Clients/OpenAPI
 ```bash
 # Regenerate OpenAPI spec after API changes (REQUIRED after changing endpoints)
@@ -57,15 +56,8 @@ cd hindsight-control-plane && npm run dev

 ### Benchmarks
 ```bash
-# Accuracy benchmarks
 ./scripts/benchmarks/run-longmemeval.sh
 ./scripts/benchmarks/run-locomo.sh
-
-# Performance benchmarks
-./scripts/benchmarks/run-consolidation.sh
-./scripts/benchmarks/run-retain-perf.sh --document <path>  # Requires API server running
-
-# Results viewer
 ./scripts/benchmarks/start-visualizer.sh  # View results at localhost:8001
 ```

@@ -245,61 +237,26 @@ def process(data: UserData) -> str:

 ### Adding New API Configuration Flags

-Configuration follows a hierarchical system: **Global (env vars) → Tenant (via extension) → Bank (database)**.
-
-Fields must be categorized as either **hierarchical** (can be overridden per-tenant/bank) or **static** (server-level only).
-
-#### Adding a New Configuration Field
+When adding a new environment variable configuration:

 1. **config.py** (`hindsight-api/hindsight_api/config.py`):
-   - Add `ENV_*` constant for the environment variable name (e.g., `ENV_MY_SETTING = "HINDSIGHT_API_MY_SETTING"`)
+   - Add `ENV_*` constant for the environment variable name
   - Add `DEFAULT_*` constant for the default value
-   - Add field to `HindsightConfig` dataclass with type annotation
-   - **Mark as hierarchical or static** by adding to `_HIERARCHICAL_FIELDS` set (hierarchical) or leaving it out (static)
+   - Add field to `HindsightConfig` dataclass
   - Add initialization in `from_env()` method

-   ```python
-   # Hierarchical field (can be overridden per-bank)
-   _HIERARCHICAL_FIELDS = {
-       ...,
-       "my_setting",  # Add here for hierarchical
-   }
-
-   # Static field - just don't add to _HIERARCHICAL_FIELDS
-   ```
-
 2. **main.py** (`hindsight-api/hindsight_api/main.py`):
   - Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")

-3. **Use hierarchical config in MemoryEngine**:
-   ```python
-   # Config is resolved automatically per bank via ConfigResolver
-   config_dict = await self._config_resolver.get_bank_config(bank_id, context)
-   value = config_dict["my_setting"]
-   ```
-
-4. **Use static config** (non-hierarchical):
+3. **Use the config** in code:
   ```python
   from ...config import get_config
   config = get_config()
-   value = config.my_static_field
+   value = config.your_new_field
   ```

-5. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
+4. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
   - Add to appropriate section table with Variable, Description, Default
-   - Mark if it's hierarchical (can be overridden per-bank)
-
-#### Hierarchical vs Static Guidelines
-
-**Hierarchical** (per-bank overridable):
- LLM settings (provider, model, API key, base URL)
- Operation-specific settings (retain mode, chunk size, etc.)
- Feature flags that vary by customer/bank
-
-**Static** (server-level only):
- Infrastructure settings (database URL, port, host)
- Global limits (max concurrent operations)
- System-wide feature flags

 ## Environment Setup

@@ -323,4 +280,3 @@ Optional (uses local models by default):
 - `HINDSIGHT_API_EMBEDDINGS_PROVIDER`: local (default) or tei
 - `HINDSIGHT_API_RERANKER_PROVIDER`: local (default) or tei
 - `HINDSIGHT_API_DATABASE_URL`: External PostgreSQL (uses embedded pg0 by default)
- `HINDSIGHT_API_ENABLE_BANK_CONFIG_API`: Enable per-bank config API (default: false, disabled for security)
@@ -93,34 +93,6 @@ uv run ty check hindsight_api  # Type check
 3. Run tests to ensure nothing breaks
 4. Submit a PR with a clear description of changes

-## Release Process
-
-The project uses `scripts/release.sh` for creating releases. This script automates the entire release workflow:
-
-1. Bumps version in all components (API, clients, CLI, control plane, Helm)
-2. **Regenerates OpenAPI spec and client SDKs** (Python, TypeScript, Rust)
-3. Updates documentation versioning
-4. Creates a commit and git tag
-5. Pushes to GitHub (triggers CI/CD to publish packages)
-
-### Usage
-
-```bash
-./scripts/release.sh <version>
-```
-
-**Example:**
-```bash
-./scripts/release.sh 0.5.0
-```
-
-### Important for Developers
-
- During development, version bumps in `__init__.py` do NOT require client regeneration
- Clients are only regenerated during releases
- Do not manually run `./scripts/generate-clients.sh` unless testing generation changes
- Client version comments will reflect the API version from the latest release
-
 ## Reporting Issues

 Open an issue on GitHub with:
@@ -1,8 +1,8 @@
 <div align="center">

-![Hindsight Banner](./hindsight-docs/static/img/hindsight-github-banner.png)
+![Hindsight Banner](./hindsight-docs/static/img/banner.svg)

-[Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://ui.hindsight.vectorize.io/signup)
+[Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://vectorize.io/hindsight/cloud)

 [![CI](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml/badge.svg)](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml)
 [![Slack Community](https://img.shields.io/badge/Slack-Join%20Community-4A154B?logo=slack)](https://join.slack.com/t/hindsight-space/shared_invite/zt-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg)
@@ -17,76 +17,76 @@

 ## What is Hindsight?

-Hindsight™ is an agent memory system built to create smarter agents that learn over time. Most agent memory systems focus on recalling conversation history. Hindsight is focused on making agents that learn, not just remember.
+Hindsight™ is an agent memory system built to create smarter agents that learn over time. It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.

+Hindsight addresses common challenges that have frustrated AI engineers building agents to automate tasks and assist users with conversational interfaces. Many of these challenges stem directly from a lack of memory.

-<video src="https://github.com/user-attachments/assets/923b798d-3581-4897-bb62-9cfa5a931682" controls></video>
+- **Inconsistency:** Agents complete tasks successfully one time, then fail when asked to complete the same task again. Memory gives the agent a mechanism to remember what worked and what didn't and to use that information to reduce errors and improve consistency.
+- **Hallucinations:** Long term memory can be seeded with external knowledge to ground agent behavior in reliable sources to augment training data.
+- **Cognitive Overload:** As workflows get complex, retrievals, tool calls, user messages and agent responses can grow to fill the context window leading to context rot. Short term memory optimization allows agents to reduce tokens and focus context by removing irrelevant details.

-It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.
+## How is Hindsight Different From Other Memory Systems?
+
+![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
+
+Most agent memory implementation rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:
+
+- **World:** Facts about the world ("The stove gets hot")
+- **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
+- **Opinion:** Beliefs with confidence scores ("I shouldn't touch the stove again" - .99 confidence)
+- **Observation:** Complex mental models derived by reflecting on facts and experiences ("Curling irons, ovens, and fire are also hot. I shouldn't touch those either.")
+
+Memories in Hindsight are stored in banks (i.e. memory banks). When memories are added to Hindsight, they are pushed into either the world facts or experiences memory pathway. They are then represented as a combination of entities, relationships, and time series with sparse/dense vector representations to aid in later recall.
+
+Hindsight provides three simple methods to interact with the system:
+
+- **Retain:** Provide information to Hindsight that you want it to remember
+- **Recall:** Retrieve memories from Hindsight
+- **Reflect:** Reflect on memories and experiences to generate new observations and insights from existing memories.
+
+### Agent Memory That Learns
+
+A key goal of Hindsight is to build agent memory that enables agents to learn and improve over time. This is the role of the `reflect` operation which provides the agent to form broader opinions and observations over time.
+
+For example, imagine a product support agent that is helping a user troubleshoot a problem. It uses a `search-documentation` tool it found on an MCP server. Later in the conversation, the agent discovers that the documentation returned from the tool wasn't for the product the user was asking about. The agent now has an experience in its memory bank. And just like humans, we want that agent to learn from its experience.
+
+As the agent gains more experiences, `reflect` allows the agent to form observations about what worked, what didn't, and what to do differently the next time it encounters a similar task.
+
+---

 ## Memory Performance & Accuracy

-Hindsight is the most accurate agent memory system ever tested according to benchmark performance. It has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of January 2026 is shown here:
+Hindsight has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational
+AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of December 2025 is shown here:

 ![Overview](./hindsight-docs/static/img/hindsight-bench.jpg)

-The benchmark performance data for Hindsight has been independently reproduced by research collaborators at the Virginia Tech [Sanghani Center for Artificial Intelligence and Data Analytics](https://sanghani.cs.vt.edu/) and The Washington Post. Other scores are self-reported by software vendors.
+The benchmark performance data for Hindsight and GPT-4o (full context) have been reproduced by research collaborators at the Virginia Tech [Sanghani Center for Artificial Intelligence and Data Analytics](https://sanghani.cs.vt.edu/) and The Washington Post. Other scores are self-reported by software vendors.

-Hindsight is being used in production at Fortune 500 enterprises and by a growing number of AI startups. 
-
-## Adding Hindsight to Your AI Agents
-
-The easiest way use Hindsight with an existing agent is with the LLM Wrapper. You can add memory to your agent with 2 lines of code. That will swap your current LLM client out with the Hindsight wrapper. After that, memories will be stored and retrieved automatically as you make LLM calls.
-
-If you need more control over how and when your agent stores and recalls memories, there's also a simple API you can integrate with using the SDKs or directly via HTTP.
-
-![Hindsight Banner](./hindsight-docs/static/img/migration-code.png)
-
---
-
-> 🤖 **Using a coding agent?** Install the Hindsight documentation skill for instant access to docs while you code:
-> ```bash
-> npx skills add https://github.com/vectorize-io/hindsight --skill hindsight-docs
-> ```
-> Works with Claude Code, Cursor, and other AI coding assistants.
-
---
+A thorough examination of the techniques implemented in Hindsight and detailed breakdowns of benchmark performance are [available on arXiv](https://arxiv.org/abs/2512.12818). This research is currently being prepared for conference submission and the wider peer review process.

+The benchmark results from this research can be inspected in our [visual benchmark explorer](https://hindsight-benchmarks.vercel.app). As additional improvements are made to Hindsight, new benchmark data will be available for review using this same tool.

 ## Quick Start

 ### Docker (recommended)

 ```bash
-export OPENAI_API_KEY=sk-xxx
+export OPENAI_API_KEY=your-key

 docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
+  -e HINDSIGHT_API_LLM_MODEL=o3-mini \
  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
  ghcr.io/vectorize-io/hindsight:latest
 ```

->API: http://localhost:8888
->UI: http://localhost:9999
-
 You can modify the LLM provider by setting `HINDSIGHT_API_LLM_PROVIDER`. Valid options are `openai`, `anthropic`, `gemini`, `groq`, `ollama`, and `lmstudio`. The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).

+API: http://localhost:8888  
+UI: http://localhost:9999

-
-### Docker (external PostgreSQL)
-
-```bash
-export OPENAI_API_KEY=sk-xxx
-export HINDSIGHT_DB_PASSWORD=choose-a-password
-cd docker/docker-compose
-docker compose up 
-```
-
-
->API: http://localhost:8888
->UI: http://localhost:9999
-
-### Client
+Install client:

 ```bash
 pip install hindsight-client -U
@@ -94,7 +94,7 @@ pip install hindsight-client -U
 npm install @vectorize-io/hindsight-client
 ```

-#### Python
+Python example:

 ```python
 from hindsight_client import Hindsight
@@ -111,29 +111,7 @@ client.recall(bank_id="my-bank", query="What does Alice do?")
 client.reflect(bank_id="my-bank", query="Tell me about Alice")
 ```

-#### Node.js / TypeScript
-
-```bash
-npm install @vectorize-io/hindsight-client
-```
-
-```javascript
-const { HindsightClient } = require('@vectorize-io/hindsight-client');
-
-const main = async () => {
-  const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });
-
-  await client.retain('my-bank', 'Alice loves hiking in Yosemite');
-
-  const results = await client.recall('my-bank', 'What does Alice like?');
-  console.log(results);
-}
-
-main();
-```
-
-
-### Python Embedded (no server required)
+### Python (embedded, no Docker)

 ```bash
 pip install hindsight-all -U
@@ -153,48 +131,25 @@ with HindsightServer(
    results = client.recall(bank_id="my-bank", query="Where does Alice work?")
 ```

+### Node.js / TypeScript

---
+```bash
+npm install @vectorize-io/hindsight-client
+```

-## Use Cases
+```javascript
+const { HindsightClient } = require('@vectorize-io/hindsight-client');

+const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });

-Hindsight is built to support conversational AI agents as well as agents that are intended to perform tasks autonomously. The ideal use case for Hindsight are agents that require a blend of these features such as AI employees that need to handle open-ended tasks, change behavior based on user feedback, and learn to perform complex tasks to automate work at a level that approximates a human work. Hindsight can be used with simple AI workflows like those built with n8n and other similar tools, but may be overkill for such applications.
-
-### Per-User Memories and Chat History
-
-One of the simpler use cases you can use Hindsight for is to personalize AI chatbots and other conversational agents by storing and recalling memories associated with individual users.
-
-The requirements for this use case usually look something like this:
-
-![Per-User Memories](./hindsight-docs/static/img/per-user-memory-requirements.png)
-
-<video src="https://github.com/user-attachments/assets/4805e8e1-e7d1-47c6-a4f8-2344a5ec8906" controls></video>
-
-Satisfying these requirements in Hindsight is straightforward. When new user inputs and tool calls are ingested into Hindsight using the retain operation, custom metadata can be used to enrich the new memories. Metadata provides a convenient way to isolate memories that need to be restricted to a given user. Once these are fed into the retain operation, any raw memories and mental models that get created can be filtered when retrieving relevant memories. 
-
-![Per-User Memories](./hindsight-docs/static/img/per-user-memory-howto.png)
+await client.retain('my-bank', 'Alice loves hiking in Yosemite');
+await client.recall('my-bank', 'What does Alice like?');
+```

 ---

 ## Architecture & Operations

-![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
-
-Most agent memory implementation rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:
-
- **World:** Facts about the world ("The stove gets hot")
- **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
- **Mental Models:** Learned understanding of the agent's world formed by reflecting on raw memories and experiences.
-
-Memories in Hindsight are stored in banks (i.e. memory banks). When memories are added to Hindsight, they are pushed into either the world facts or experiences memory pathway. They are then represented as a combination of entities, relationships, and time series with sparse/dense vector representations to aid in later recall.
-
-Hindsight provides three simple methods to interact with the system:
-
- **Retain:** Provide information to Hindsight that you want it to remember
- **Recall:** Retrieve memories from Hindsight
- **Reflect:** Reflect on memories and experiences to generate new observations and insights from existing memories.
-
 ### Retain

 The `retain` operation is used to push new memories into Hindsight. It tells Hindsight to _retain_ the information you pass in as an input.
@@ -253,7 +208,7 @@ The final output is trimmed as needed to fit within the token limit.

 ### Reflect

-The reflect operation is used to perform a more thorough analysis of existing memories. This allows the agent to form new connections between memories and build a more thorough understanding of its world.
+The reflect operation is used to perform a more thorough analysis of existing memories. This allows the agent to form new connections between memories which are then persisted as opinions and/or observations. When building agents, the reflect operation is a key capability to enable the agent to learn from its experiences. 

 For example, the `reflect` operation can be used to support use cases such as:

@@ -1,54 +0,0 @@
-# Docker Compose file for Hindsight with PostgreSQL and pgvector
-#
-# Make sure to set the required environment variables before running:
-# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
-# - Configure LLM provider variables as needed (see below in the hindsight service)
-#
-# Usage:
-# docker compose up -d
-#
-# Optional environment variables with defaults:
-# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
-# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
-# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
-# - HINDSIGHT_DB_VERSION: PostgreSQL version (default: 18)
-
-services:
-  db:
-    # Use a PostgreSQL-Image with pgvector extension pre-installed
-    # see https://hub.docker.com/r/pgvector/pgvector 
-    image: pgvector/pgvector:pg${HINDSIGHT_DB_VERSION:-18}
-    container_name: hindsight-db
-    restart: always
-    # Expose PostgreSQL port
-    # ports:
-      # - "5432:5432"
-    environment:
-      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
-      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:?Please set the HINDSIGHT_DB_PASSWORD env variable}
-      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
-    volumes:
-      - pg_data:/var/lib/postgresql/${HINDSIGHT_DB_VERSION:-18}/docker
-    networks:
-      - hindsight-net
-
-  hindsight:
-    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
-    container_name: hindsight-app
-    ports:
-      - "8888:8888"
-      - "9999:9999"
-    environment:
-      - HINDSIGHT_API_LLM_API_KEY=${OPENAI_API_KEY?Please set the OPENAI_API_KEY env variable}
-      - HINDSIGHT_API_DATABASE_URL=postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:?Please set the HINDSIGHT_DB_PASSWORD env variable}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
-    depends_on:
-      - db
-    networks:
-      - hindsight-net
-
-networks:
-  hindsight-net:
-    driver: bridge
-
-volumes:
-  pg_data:
@@ -1,96 +0,0 @@
-# Nginx Reverse Proxy with Custom Base Path
-
-Deploy Hindsight API under `/hindsight` (or any custom path) using Nginx reverse proxy.
-
-## Quick Start (Published Image - API Only)
-
-```bash
-docker-compose up
-```
-
- **API:** http://localhost:8080/hindsight/docs
- **Control Plane:** http://localhost:9999 (direct access, not proxied)
-
-## Full Stack with Custom Base Path (Requires Build)
-
-**Important:** You cannot rebuild from the published image with build args. You must build from source.
-
-### Build from Source with Custom Base Path
-
-1. **Clone the repository** (if you haven't):
-```bash
-git clone https://github.com/vectorize-io/hindsight.git
-cd hindsight
-```
-
-2. **Build with base path**:
-```bash
-docker build \
-  --build-arg NEXT_PUBLIC_BASE_PATH=/hindsight \
-  -f docker/standalone/Dockerfile \
-  -t hindsight:custom \
-  .
-```
-
-3. **Update docker-compose.yml** to use your built image:
-```yaml
-services:
-  hindsight:
-    image: hindsight:custom  # ← Change this
-    environment:
-      HINDSIGHT_API_BASE_PATH: /hindsight
-      NEXT_PUBLIC_BASE_PATH: /hindsight
-```
-
-4. **Update nginx.conf** to handle Control Plane routes (see below)
-
-5. **Run**:
-```bash
-docker-compose up
-```
-
-### Required nginx.conf for Full Stack
-
-Replace the current `nginx.conf` with this to proxy both API and Control Plane:
-
-```nginx
-events { worker_connections 1024; }
-
-http {
-    include /etc/nginx/mime.types;
-    default_type application/octet-stream;
-
-    upstream hindsight_api { server hindsight:8888; }
-    upstream hindsight_cp { server hindsight:9999; }
-
-    server {
-        listen 80;
-
-        # API
-        location ~ ^/hindsight/(docs|openapi\.json|health|metrics|v1|mcp) {
-            proxy_pass http://hindsight_api;
-            proxy_set_header Host $http_host;
-        }
-
-        # Control Plane static files
-        location ~ ^/hindsight/_next/ {
-            proxy_pass http://hindsight_cp;
-            proxy_set_header Host $http_host;
-        }
-
-        # Control Plane UI
-        location /hindsight {
-            proxy_pass http://hindsight_cp;
-            proxy_set_header Host $http_host;
-        }
-
-        location = / { return 301 /hindsight; }
-    }
-}
-```
-
-### Why Build is Required
-
-Next.js requires `basePath` at **build time**. The published image was built without a custom base path, so you must rebuild from source with the `NEXT_PUBLIC_BASE_PATH` build arg to deploy the Control Plane under a subpath.
-
-The API works without rebuild because `HINDSIGHT_API_BASE_PATH` is a runtime environment variable.
@@ -1,88 +0,0 @@
-# Hindsight API deployment with Nginx reverse proxy (API-only)
-#
-# This example deploys Hindsight API under the path /hindsight with:
-# - Hindsight standalone image (API + Control Plane + embedded pg0)
-# - Nginx reverse proxy (API only)
-#
-# Quick Start:
-#   docker-compose -f docker/docker-compose/nginx/docker-compose.yml up
-#
-# Access:
-#   API (via nginx):         http://localhost:8080/hindsight/docs
-#   Control Plane (direct):  http://localhost:9999
-#
-# For full stack deployment (API + Control Plane both under /hindsight):
-#   See README.md in this directory for instructions on building with basePath.
-#
-# Note: This configuration uses the published image (no build required).
-#       Control Plane is served directly because Next.js basePath requires
-#       build-time configuration. See README.md for the full stack option.
-
-services:
-  # Hindsight (API + Control Plane + embedded pg0)
-  hindsight:
-    image: ghcr.io/vectorize-io/hindsight:latest
-    ports:
-      - "9999:9999"  # Control Plane (direct access, not proxied)
-    environment:
-      # API base path for reverse proxy
-      HINDSIGHT_API_BASE_PATH: /hindsight
-
-      # LLM configuration
-      # Using mock provider for testing (no API key needed)
-      # For production, set OPENAI_API_KEY or ANTHROPIC_API_KEY and use a real provider
-      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-mock}
-      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-not-needed-for-mock}
-      HINDSIGHT_API_LLM_MODEL: ${HINDSIGHT_API_LLM_MODEL:-mock-model}
-
-      # Production examples (uncomment and set appropriate API key):
-      # HINDSIGHT_API_LLM_PROVIDER: openai
-      # HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY}
-      # HINDSIGHT_API_LLM_MODEL: gpt-4o-mini
-
-      # HINDSIGHT_API_LLM_PROVIDER: anthropic
-      # HINDSIGHT_API_LLM_API_KEY: ${ANTHROPIC_API_KEY}
-      # HINDSIGHT_API_LLM_MODEL: claude-sonnet-4-20250514
-
-      # Server config
-      HINDSIGHT_API_HOST: 0.0.0.0
-      HINDSIGHT_API_PORT: 8888
-      HINDSIGHT_API_LOG_LEVEL: info
-
-      # Control Plane config
-      HINDSIGHT_CP_DATAPLANE_API_URL: http://localhost:8888
-    volumes:
-      # Persist embedded pg0 database
-      - hindsight_data:/app/data
-    # Note: Ports not exposed - access via Nginx at localhost:8080/hindsight/
-    # To debug directly, uncomment these ports:
-    # ports:
-    #   - "8888:8888"  # API
-    #   - "9999:9999"  # Control Plane
-    healthcheck:
-      test: ["CMD", "curl", "-f", "http://localhost:8888/hindsight/health"]
-      interval: 10s
-      timeout: 5s
-      retries: 3
-      start_period: 30s
-    networks:
-      - hindsight
-
-  # Nginx reverse proxy
-  nginx:
-    image: nginx:alpine
-    ports:
-      - "8080:80"
-    volumes:
-      - ./nginx.conf:/etc/nginx/nginx.conf:ro
-    depends_on:
-      hindsight:
-        condition: service_healthy
-    networks:
-      - hindsight
-
-volumes:
-  hindsight_data:
-
-networks:
-  hindsight:
@@ -1,40 +0,0 @@
-# Nginx configuration for API-only reverse proxy
-# Control Plane accessed directly (not through nginx)
-
-events {
-    worker_connections 1024;
-}
-
-http {
-    include /etc/nginx/mime.types;
-    default_type application/octet-stream;
-
-    # Logging
-    access_log /var/log/nginx/access.log;
-    error_log /var/log/nginx/error.log;
-
-    # Upstream - Hindsight API
-    upstream hindsight_api {
-        server hindsight:8888;
-    }
-
-    server {
-        listen 80;
-        server_name _;
-
-        # API endpoints - forward with /hindsight prefix
-        location /hindsight/ {
-            proxy_pass http://hindsight_api;
-
-            proxy_set_header Host $http_host;
-            proxy_set_header X-Real-IP $remote_addr;
-            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
-            proxy_set_header X-Forwarded-Proto $scheme;
-        }
-
-        # Redirect root to API docs
-        location = / {
-            return 301 /hindsight/docs;
-        }
-    }
-}
@@ -1,32 +0,0 @@
-# PostgreSQL with pgvector and pg_textsearch extensions
-# Note: pg_textsearch requires PostgreSQL 17+
-FROM postgres:17
-
-# Install build dependencies
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    git \
-    postgresql-server-dev-17 \
-    libpq-dev \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install pgvector
-RUN cd /tmp && \
-    git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git && \
-    cd pgvector && \
-    make && \
-    make install
-
-# Install pg_textsearch
-RUN cd /tmp && \
-    git clone https://github.com/timescale/pg_textsearch.git && \
-    cd pg_textsearch && \
-    make && \
-    make install
-
-# Clean up source files and build dependencies
-RUN rm -rf /tmp/pgvector /tmp/pg_textsearch && \
-    apt-get purge -y --auto-remove build-essential git postgresql-server-dev-17
-
-# Ensure extensions are preloaded
-RUN echo "shared_preload_libraries = 'pg_textsearch'" >> /usr/share/postgresql/postgresql.conf.sample
@@ -1,91 +0,0 @@
-name: hindsight
-# Docker Compose file for Hindsight with PostgreSQL and Timescale pg_textsearch
-# docker compose -f docker/docker-compose/pg_textsearch/docker-compose.yaml down && sleep 2 && docker compose -f docker/docker-compose/pg_textsearch/docker-compose.yaml up -d
-# Make sure to set the required environment variables before running:
-# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
-# - Configure LLM provider variables as needed (see below in the hindsight service)
-#
-# Usage:
-# docker compose up -d
-#
-# Optional environment variables with defaults:
-# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
-# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
-# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
-
-services:
-  db:
-    # Use custom PostgreSQL image with pgvector and pg_textsearch extensions
-    build:
-      context: .
-      dockerfile: Dockerfile
-    container_name: hindsight-db
-    restart: always
-    # Expose PostgreSQL port
-    ports:
-      - "5437:5432"
-    environment:
-      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
-      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
-      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
-    volumes:
-      - pg_data:/var/lib/postgresql/data
-    networks:
-      - hindsight-net
-
-  pg-textsearch-init:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    depends_on:
-      - db
-    environment:
-      - PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
-    command: >
-      bash -c "
-        echo 'Waiting for PostgreSQL to be ready...';
-        until pg_isready -h hindsight-db -p 5432 -U hindsight_user; do
-          echo 'PostgreSQL is unavailable - sleeping';
-          sleep 2;
-        done;
-        echo 'PostgreSQL is ready - creating hindsight_db database';
-        psql -h hindsight-db -p 5432 -U hindsight_user -c 'CREATE DATABASE hindsight_db;' 2>/dev/null || echo 'Database already exists';
-        echo 'Creating extensions in hindsight_db database';
-        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vector CASCADE;';
-        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE;';
-        echo 'Database and extensions created successfully';
-      "
-    restart: "no"
-    networks:
-      - hindsight-net
-
-  hindsight:
-    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
-    container_name: hindsight-app
-    ports:
-      - "8888:8888"
-      - "9999:9999"
-    environment:
-      # LLM Configuration
-      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
-      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
-
-      # Database Configuration
-      HINDSIGHT_API_DATABASE_URL: postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:-hindsight_password}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
-
-      # Vector and Text Search Extensions
-      HINDSIGHT_API_VECTOR_EXTENSION: pgvector
-      HINDSIGHT_API_TEXT_SEARCH_EXTENSION: pg_textsearch
-
-    depends_on:
-      - db
-    networks:
-      - hindsight-net
-
-
-networks:
-  hindsight-net:
-    driver: bridge
-
-volumes:
-  pg_data:
@@ -1,93 +0,0 @@
-name: hindsight
-# Docker Compose file for Hindsight with PostgreSQL and vectorchord
-# docker compose -f docker/docker-compose/docker-compose.yaml down && sleep 2 && docker compose -f docker/docker-compose/docker-compose.yaml up -d
-# Make sure to set the required environment variables before running:
-# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
-# - Configure LLM provider variables as needed (see below in the hindsight service)
-#
-# Usage:
-# docker compose up -d
-#
-# Optional environment variables with defaults:
-# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
-# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
-# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
-# - HINDSIGHT_DB_VERSION: PostgreSQL version (default: 18)
-
-services:
-  db:
-    # Use a PostgreSQL-Image with vectorchord extension pre-installed
-    image: tensorchord/vchord-suite:pg${HINDSIGHT_DB_VERSION:-18-latest}
-    container_name: hindsight-db
-    restart: always
-    # Expose PostgreSQL port
-    ports:
-      - "5436:5432"
-    environment:
-      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
-      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
-      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
-    volumes:
-      - pg_data:/var/lib/postgresql/${HINDSIGHT_DB_VERSION:-18}/docker
-    networks:
-      - hindsight-net
-
-  vectorchord-init:
-    image: tensorchord/vchord-suite:pg18-latest
-    #container_name: vectorchord-init
-    depends_on:
-      - db
-    environment:
-      - PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
-    command: >
-      bash -c "
-        echo 'Waiting for PostgreSQL to be ready...';
-        until pg_isready -h hindsight-db -p 5432 -U hindsight_user; do
-          echo 'PostgreSQL is unavailable - sleeping';
-          sleep 2;
-        done;
-        echo 'PostgreSQL is ready - creating hindsight_db database';
-        psql -h hindsight-db -p 5432 -U hindsight_user -c 'CREATE DATABASE hindsight_db;' 2>/dev/null || echo 'Database already exists';
-        echo 'Creating extensions in hindsight_db database';
-        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vchord CASCADE;';
-        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS pg_tokenizer CASCADE;';
-        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE;';
-        echo 'Creating llmlingua2 tokenizer';
-        psql -h hindsight-db -p 5432 -U hindsight_user -d hindsight_db -c \"SELECT create_tokenizer('llmlingua2', \\$\\$ model = \\\"llmlingua2\\\" \\$\\$);\" 2>/dev/null || echo 'Tokenizer already exists or creation skipped';
-        echo 'Database and extensions created successfully';
-      "
-    restart: "no"
-    networks:
-      - hindsight-net
-
-  hindsight:
-    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
-    container_name: hindsight-app
-    ports:
-      - "8888:8888"
-      - "9999:9999"
-    environment:
-      # LLM Configuration (uses OpenAI for testing vchord)
-      # LLM configuration
-      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
-      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
-
-      # Database Configuration
-      HINDSIGHT_API_DATABASE_URL: postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:-hindsight_password}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
-
-      # Vector and Text Search Extensions
-      HINDSIGHT_API_VECTOR_EXTENSION: vchord
-      HINDSIGHT_API_TEXT_SEARCH_EXTENSION: vchord
-
-    depends_on:
-      - db
-    networks:
-      - hindsight-net
-
-
-networks:
-  hindsight-net:
-    driver: bridge
-
-volumes:
-  pg_data:
@@ -8,7 +8,6 @@
 #                                     Set to false when using external providers (TEI, OpenAI, Cohere)
 #   PRELOAD_ML_MODELS=true/false   - Pre-download ML models during build (default: true)
 #                                     Only effective when INCLUDE_LOCAL_MODELS=true
-#                                     NOTE: tiktoken encodings are ALWAYS preloaded (required for air-gapped deployments)
 #
 # Examples:
 #   docker build -t hindsight .                                          # Both (standalone)
@@ -112,10 +111,6 @@ RUN rm -f package-lock.json && sed -i '/"@vectorize-io\/hindsight-client":/d' pa
 # Copy built SDK directly into node_modules (more reliable than npm link in Docker)
 COPY --from=sdk-builder /app/hindsight-clients/typescript ./node_modules/@vectorize-io/hindsight-client

-# Accept base path as build argument for reverse proxy deployments
-# Usage: docker build --build-arg NEXT_PUBLIC_BASE_PATH=/hindsight ...
-ARG NEXT_PUBLIC_BASE_PATH=""
-
 # Build Control Plane - run next build first, then custom standalone copy
 # (The build:standalone script expects a specific path structure that differs in Docker)
 RUN npm exec -- next build
@@ -172,57 +167,18 @@ USER hindsight

 ENV PATH="/app/api/.venv/bin:${PATH}"

-# Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
-# Tiktoken is a core runtime dependency, not an optional ML model
-RUN MAX_RETRIES=3; \
-    RETRY_DELAY=5; \
-    for i in $(seq 1 $MAX_RETRIES); do \
-      echo "Attempt $i/$MAX_RETRIES: Downloading tiktoken encoding..."; \
-      /app/api/.venv/bin/python -c "\
-import tiktoken; \
-print('Downloading cl100k_base encoding...'); \
-tiktoken.get_encoding('cl100k_base'); \
-print('Tiktoken encoding cached successfully')" && break; \
-      if [ $i -lt $MAX_RETRIES ]; then \
-        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
-        sleep $RETRY_DELAY; \
-        RETRY_DELAY=$((RETRY_DELAY * 2)); \
-      fi; \
-    done; \
-    if [ $i -eq $MAX_RETRIES ]; then \
-      echo "ERROR: Failed to download tiktoken encoding after $MAX_RETRIES attempts"; \
-      exit 1; \
-    fi
-
 # Pre-download ML models to avoid runtime download (conditional)
 # Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
-# Includes retry logic with exponential backoff for transient network failures
 ARG PRELOAD_ML_MODELS
 ARG INCLUDE_LOCAL_MODELS
-ENV HF_HUB_DOWNLOAD_TIMEOUT=600
 RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-    MAX_RETRIES=3; \
-    RETRY_DELAY=10; \
-    for i in $(seq 1 $MAX_RETRIES); do \
-      echo "Attempt $i/$MAX_RETRIES: Downloading ML models..."; \
-      /app/api/.venv/bin/python -c "\
-import os; os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '600'; \
+    /app/api/.venv/bin/python -c "\
 from sentence_transformers import SentenceTransformer, CrossEncoder; \
 print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Models cached successfully')" && break; \
-      if [ $i -lt $MAX_RETRIES ]; then \
-        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
-        sleep $RETRY_DELAY; \
-        RETRY_DELAY=$((RETRY_DELAY * 2)); \
-      fi; \
-    done; \
-    if [ $i -eq $MAX_RETRIES ] && ! /app/api/.venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" 2>/dev/null; then \
-      echo "ERROR: Failed to download models after $MAX_RETRIES attempts"; \
-      exit 1; \
-    fi; \
+print('Models cached successfully')"; \
    elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
    else echo "Skipping ML model preload"; fi

@@ -234,10 +190,6 @@ ENV HINDSIGHT_API_LOG_LEVEL=info
 ENV HINDSIGHT_ENABLE_API=true
 ENV HINDSIGHT_ENABLE_CP=false
 ENV PYTHONUNBUFFERED=1
-# Suppress verbose transformers/HuggingFace model loading warnings
-ENV TRANSFORMERS_VERBOSITY=error
-ENV HF_HUB_VERBOSITY=error
-ENV TOKENIZERS_PARALLELISM=false

 CMD ["/app/start-all.sh"]

@@ -323,57 +275,18 @@ USER hindsight

 ENV PATH="/app/api/.venv/bin:${PATH}"

-# Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
-# Tiktoken is a core runtime dependency, not an optional ML model
-RUN MAX_RETRIES=3; \
-    RETRY_DELAY=5; \
-    for i in $(seq 1 $MAX_RETRIES); do \
-      echo "Attempt $i/$MAX_RETRIES: Downloading tiktoken encoding..."; \
-      /app/api/.venv/bin/python -c "\
-import tiktoken; \
-print('Downloading cl100k_base encoding...'); \
-tiktoken.get_encoding('cl100k_base'); \
-print('Tiktoken encoding cached successfully')" && break; \
-      if [ $i -lt $MAX_RETRIES ]; then \
-        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
-        sleep $RETRY_DELAY; \
-        RETRY_DELAY=$((RETRY_DELAY * 2)); \
-      fi; \
-    done; \
-    if [ $i -eq $MAX_RETRIES ]; then \
-      echo "ERROR: Failed to download tiktoken encoding after $MAX_RETRIES attempts"; \
-      exit 1; \
-    fi
-
 # Pre-download ML models to avoid runtime download (conditional)
 # Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
-# Includes retry logic with exponential backoff for transient network failures
 ARG PRELOAD_ML_MODELS
 ARG INCLUDE_LOCAL_MODELS
-ENV HF_HUB_DOWNLOAD_TIMEOUT=600
 RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-    MAX_RETRIES=3; \
-    RETRY_DELAY=10; \
-    for i in $(seq 1 $MAX_RETRIES); do \
-      echo "Attempt $i/$MAX_RETRIES: Downloading ML models..."; \
-      /app/api/.venv/bin/python -c "\
-import os; os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '600'; \
+    /app/api/.venv/bin/python -c "\
 from sentence_transformers import SentenceTransformer, CrossEncoder; \
 print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Models cached successfully')" && break; \
-      if [ $i -lt $MAX_RETRIES ]; then \
-        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
-        sleep $RETRY_DELAY; \
-        RETRY_DELAY=$((RETRY_DELAY * 2)); \
-      fi; \
-    done; \
-    if [ $i -eq $MAX_RETRIES ] && ! /app/api/.venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" 2>/dev/null; then \
-      echo "ERROR: Failed to download models after $MAX_RETRIES attempts"; \
-      exit 1; \
-    fi; \
+print('Models cached successfully')"; \
    elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
    else echo "Skipping ML model preload"; fi

@@ -387,10 +300,6 @@ ENV HINDSIGHT_CP_DATAPLANE_API_URL=http://localhost:8888
 ENV HINDSIGHT_ENABLE_API=true
 ENV HINDSIGHT_ENABLE_CP=true
 ENV PYTHONUNBUFFERED=1
-# Suppress verbose transformers/HuggingFace model loading warnings
-ENV TRANSFORMERS_VERBOSITY=error
-ENV HF_HUB_VERBOSITY=error
-ENV TOKENIZERS_PARALLELISM=false

 CMD ["/app/start-all.sh"]

@@ -1,51 +0,0 @@
-#!/bin/bash
-#
-# Local Test Script for Slim Docker Images
-#
-# This script makes it easy to test slim images locally with external providers.
-# It expects API keys to be set in environment variables.
-#
-# Usage:
-#   export GROQ_API_KEY=gsk_xxx
-#   export OPENAI_API_KEY=sk-xxx
-#   export COHERE_API_KEY=xxx
-#   ./docker/test-slim-local.sh
-#
-# Or inline:
-#   GROQ_API_KEY=gsk_xxx OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
-#
-
-set -euo pipefail
-
-# Check for required API keys
-if [ -z "${GROQ_API_KEY:-}" ]; then
-    echo "❌ Error: GROQ_API_KEY environment variable is required"
-    echo "Set it with: export GROQ_API_KEY=gsk_xxx"
-    exit 1
-fi
-
-if [ -z "${OPENAI_API_KEY:-}" ]; then
-    echo "❌ Error: OPENAI_API_KEY environment variable is required"
-    echo "Set it with: export OPENAI_API_KEY=sk-xxx"
-    exit 1
-fi
-
-if [ -z "${COHERE_API_KEY:-}" ]; then
-    echo "❌ Error: COHERE_API_KEY environment variable is required"
-    echo "Set it with: export COHERE_API_KEY=xxx"
-    exit 1
-fi
-
-# Configuration
-IMAGE="${1:-hindsight-slim:test}"
-echo "Testing image: $IMAGE"
-echo ""
-
-# Set up external providers
-export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
-export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=$OPENAI_API_KEY
-export HINDSIGHT_API_RERANKER_PROVIDER=cohere
-export HINDSIGHT_API_COHERE_API_KEY=$COHERE_API_KEY
-
-# Run the test
-exec "$(dirname "$0")/test-image.sh" "$IMAGE" standalone
@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.11
-appVersion: "0.4.11"
+version: 0.3.0
+appVersion: "0.3.0"
 keywords:
  - ai
  - memory
@@ -127,38 +127,6 @@ API URL for control plane
 {{- printf "http://%s-api:%d" (include "hindsight.fullname" .) (.Values.api.service.port | int) }}
 {{- end }}

-{{/*
-TEI reranker labels
-*/}}
-{{- define "hindsight.tei.reranker.labels" -}}
-{{ include "hindsight.labels" . }}
-app.kubernetes.io/component: tei-reranker
-{{- end }}
-
-{{/*
-TEI reranker selector labels
-*/}}
-{{- define "hindsight.tei.reranker.selectorLabels" -}}
-{{ include "hindsight.selectorLabels" . }}
-app.kubernetes.io/component: tei-reranker
-{{- end }}
-
-{{/*
-TEI embedding labels
-*/}}
-{{- define "hindsight.tei.embedding.labels" -}}
-{{ include "hindsight.labels" . }}
-app.kubernetes.io/component: tei-embedding
-{{- end }}
-
-{{/*
-TEI embedding selector labels
-*/}}
-{{- define "hindsight.tei.embedding.selectorLabels" -}}
-{{ include "hindsight.selectorLabels" . }}
-app.kubernetes.io/component: tei-embedding
-{{- end }}
-
 {{/*
 Get the name of the secret to use
 */}}
@@ -33,7 +33,7 @@ spec:
      - name: api
        securityContext:
          {{- toYaml .Values.securityContext | nindent 10 }}
-        image: "{{ .Values.api.image.repository }}:{{ .Values.api.image.tag | default .Values.version | default .Chart.AppVersion }}"
+        image: "{{ .Values.api.image.repository }}:{{ .Values.api.image.tag | default .Values.version }}"
        imagePullPolicy: {{ .Values.api.image.pullPolicy }}
        ports:
        - name: http
@@ -60,25 +60,10 @@ spec:
        - name: HINDSIGHT_API_WORKER_ENABLED
          value: "false"
        {{- end }}
-        {{- /* Explicitly set port to override K8s service discovery env var (HINDSIGHT_API_PORT) */}}
-        - name: HINDSIGHT_API_PORT
-          value: {{ .Values.api.service.targetPort | quote }}
        {{- range $key, $value := .Values.api.env }}
        - name: {{ $key }}
          value: {{ $value | quote }}
        {{- end }}
-        {{- if .Values.tei.reranker.enabled }}
-        - name: HINDSIGHT_API_RERANKER_PROVIDER
-          value: "tei"
-        - name: HINDSIGHT_API_RERANKER_TEI_URL
-          value: "http://{{ include "hindsight.fullname" . }}-tei-reranker:{{ .Values.tei.reranker.port }}"
-        {{- end }}
-        {{- if .Values.tei.embedding.enabled }}
-        - name: HINDSIGHT_API_EMBEDDINGS_PROVIDER
-          value: "tei"
-        - name: HINDSIGHT_API_EMBEDDINGS_TEI_URL
-          value: "http://{{ include "hindsight.fullname" . }}-tei-embedding:{{ .Values.tei.embedding.port }}"
-        {{- end }}
        {{- /* Only use api.secrets when not using existingSecret (for chart-managed secrets) */}}
        {{- if not .Values.existingSecret }}
        {{- range $key, $value := .Values.api.secrets }}
@@ -99,7 +84,7 @@ spec:
      nodeSelector:
        {{- toYaml . | nindent 8 }}
      {{- end }}
-      {{- with (.Values.api.affinity | default .Values.affinity) }}
+      {{- with .Values.affinity }}
      affinity:
        {{- toYaml . | nindent 8 }}
      {{- end }}
@@ -33,7 +33,7 @@ spec:
      - name: control-plane
        securityContext:
          {{- toYaml .Values.securityContext | nindent 10 }}
-        image: "{{ .Values.controlPlane.image.repository }}:{{ .Values.controlPlane.image.tag | default .Values.version | default .Chart.AppVersion }}"
+        image: "{{ .Values.controlPlane.image.repository }}:{{ .Values.controlPlane.image.tag | default .Values.version }}"
        imagePullPolicy: {{ .Values.controlPlane.image.pullPolicy }}
        ports:
        - name: http
@@ -71,7 +71,7 @@ spec:
      nodeSelector:
        {{- toYaml . | nindent 8 }}
      {{- end }}
-      {{- with (.Values.controlPlane.affinity | default .Values.affinity) }}
+      {{- with .Values.affinity }}
      affinity:
        {{- toYaml . | nindent 8 }}
      {{- end }}
@@ -1,56 +0,0 @@
-{{- if and .Values.api.enabled .Values.api.podDisruptionBudget.enabled }}
-apiVersion: policy/v1
-kind: PodDisruptionBudget
-metadata:
-  name: {{ include "hindsight.fullname" . }}-api
-  labels:
-    {{- include "hindsight.api.labels" . | nindent 4 }}
-spec:
-  {{- if .Values.api.podDisruptionBudget.minAvailable }}
-  minAvailable: {{ .Values.api.podDisruptionBudget.minAvailable }}
-  {{- end }}
-  {{- if .Values.api.podDisruptionBudget.maxUnavailable }}
-  maxUnavailable: {{ .Values.api.podDisruptionBudget.maxUnavailable }}
-  {{- end }}
-  selector:
-    matchLabels:
-      {{- include "hindsight.api.selectorLabels" . | nindent 6 }}
-{{- end }}
---
-{{- if and .Values.controlPlane.enabled .Values.controlPlane.podDisruptionBudget.enabled }}
-apiVersion: policy/v1
-kind: PodDisruptionBudget
-metadata:
-  name: {{ include "hindsight.fullname" . }}-control-plane
-  labels:
-    {{- include "hindsight.controlPlane.labels" . | nindent 4 }}
-spec:
-  {{- if .Values.controlPlane.podDisruptionBudget.minAvailable }}
-  minAvailable: {{ .Values.controlPlane.podDisruptionBudget.minAvailable }}
-  {{- end }}
-  {{- if .Values.controlPlane.podDisruptionBudget.maxUnavailable }}
-  maxUnavailable: {{ .Values.controlPlane.podDisruptionBudget.maxUnavailable }}
-  {{- end }}
-  selector:
-    matchLabels:
-      {{- include "hindsight.controlPlane.selectorLabels" . | nindent 6 }}
-{{- end }}
---
-{{- if and .Values.worker.enabled .Values.worker.podDisruptionBudget.enabled }}
-apiVersion: policy/v1
-kind: PodDisruptionBudget
-metadata:
-  name: {{ include "hindsight.fullname" . }}-worker
-  labels:
-    {{- include "hindsight.worker.labels" . | nindent 4 }}
-spec:
-  {{- if .Values.worker.podDisruptionBudget.minAvailable }}
-  minAvailable: {{ .Values.worker.podDisruptionBudget.minAvailable }}
-  {{- end }}
-  {{- if .Values.worker.podDisruptionBudget.maxUnavailable }}
-  maxUnavailable: {{ .Values.worker.podDisruptionBudget.maxUnavailable }}
-  {{- end }}
-  selector:
-    matchLabels:
-      {{- include "hindsight.worker.selectorLabels" . | nindent 6 }}
-{{- end }}
@@ -1,76 +0,0 @@
-{{- if .Values.tei.embedding.enabled }}
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: {{ include "hindsight.fullname" . }}-tei-embedding
-  labels:
-    {{- include "hindsight.tei.embedding.labels" . | nindent 4 }}
-spec:
-  replicas: {{ .Values.tei.embedding.replicaCount }}
-  selector:
-    matchLabels:
-      {{- include "hindsight.tei.embedding.selectorLabels" . | nindent 6 }}
-  template:
-    metadata:
-      {{- with .Values.podAnnotations }}
-      annotations:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-      labels:
-        {{- include "hindsight.tei.embedding.selectorLabels" . | nindent 8 }}
-    spec:
-      {{- if .Values.serviceAccount.create }}
-      serviceAccountName: {{ include "hindsight.serviceAccountName" . }}
-      {{- end }}
-      securityContext:
-        {{- toYaml .Values.podSecurityContext | nindent 8 }}
-      containers:
-      - name: tei-embedding
-        securityContext:
-          {{- toYaml .Values.securityContext | nindent 10 }}
-        image: "{{ .Values.tei.embedding.image.repository }}:{{ .Values.tei.embedding.image.tag }}"
-        imagePullPolicy: {{ .Values.tei.embedding.image.pullPolicy }}
-        args:
-          - "--model-id"
-          - {{ .Values.tei.embedding.model | quote }}
-          - "--hostname"
-          - "0.0.0.0"
-          {{- range .Values.tei.embedding.args }}
-          - {{ . | quote }}
-          {{- end }}
-        ports:
-        - name: http
-          containerPort: {{ .Values.tei.embedding.port }}
-          protocol: TCP
-        env:
-        - name: PORT
-          value: {{ .Values.tei.embedding.port | quote }}
-        {{- range $key, $value := .Values.tei.embedding.env }}
-        - name: {{ $key }}
-          value: {{ $value | quote }}
-        {{- end }}
-        livenessProbe:
-          {{- toYaml .Values.tei.embedding.livenessProbe | nindent 10 }}
-        readinessProbe:
-          {{- toYaml .Values.tei.embedding.readinessProbe | nindent 10 }}
-        resources:
-          {{- toYaml .Values.tei.embedding.resources | nindent 10 }}
-        volumeMounts:
-        - name: model-cache
-          mountPath: /data
-      volumes:
-      - name: model-cache
-        emptyDir: {}
-      {{- with .Values.nodeSelector }}
-      nodeSelector:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-      {{- with .Values.affinity }}
-      affinity:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-      {{- with .Values.tolerations }}
-      tolerations:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-{{- end }}
@@ -1,17 +0,0 @@
-{{- if .Values.tei.embedding.enabled }}
-apiVersion: v1
-kind: Service
-metadata:
-  name: {{ include "hindsight.fullname" . }}-tei-embedding
-  labels:
-    {{- include "hindsight.tei.embedding.labels" . | nindent 4 }}
-spec:
-  type: ClusterIP
-  ports:
-  - port: {{ .Values.tei.embedding.port }}
-    targetPort: http
-    protocol: TCP
-    name: http
-  selector:
-    {{- include "hindsight.tei.embedding.selectorLabels" . | nindent 4 }}
-{{- end }}
@@ -1,76 +0,0 @@
-{{- if .Values.tei.reranker.enabled }}
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: {{ include "hindsight.fullname" . }}-tei-reranker
-  labels:
-    {{- include "hindsight.tei.reranker.labels" . | nindent 4 }}
-spec:
-  replicas: {{ .Values.tei.reranker.replicaCount }}
-  selector:
-    matchLabels:
-      {{- include "hindsight.tei.reranker.selectorLabels" . | nindent 6 }}
-  template:
-    metadata:
-      {{- with .Values.podAnnotations }}
-      annotations:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-      labels:
-        {{- include "hindsight.tei.reranker.selectorLabels" . | nindent 8 }}
-    spec:
-      {{- if .Values.serviceAccount.create }}
-      serviceAccountName: {{ include "hindsight.serviceAccountName" . }}
-      {{- end }}
-      securityContext:
-        {{- toYaml .Values.podSecurityContext | nindent 8 }}
-      containers:
-      - name: tei-reranker
-        securityContext:
-          {{- toYaml .Values.securityContext | nindent 10 }}
-        image: "{{ .Values.tei.reranker.image.repository }}:{{ .Values.tei.reranker.image.tag }}"
-        imagePullPolicy: {{ .Values.tei.reranker.image.pullPolicy }}
-        args:
-          - "--model-id"
-          - {{ .Values.tei.reranker.model | quote }}
-          - "--hostname"
-          - "0.0.0.0"
-          {{- range .Values.tei.reranker.args }}
-          - {{ . | quote }}
-          {{- end }}
-        ports:
-        - name: http
-          containerPort: {{ .Values.tei.reranker.port }}
-          protocol: TCP
-        env:
-        - name: PORT
-          value: {{ .Values.tei.reranker.port | quote }}
-        {{- range $key, $value := .Values.tei.reranker.env }}
-        - name: {{ $key }}
-          value: {{ $value | quote }}
-        {{- end }}
-        livenessProbe:
-          {{- toYaml .Values.tei.reranker.livenessProbe | nindent 10 }}
-        readinessProbe:
-          {{- toYaml .Values.tei.reranker.readinessProbe | nindent 10 }}
-        resources:
-          {{- toYaml .Values.tei.reranker.resources | nindent 10 }}
-        volumeMounts:
-        - name: model-cache
-          mountPath: /data
-      volumes:
-      - name: model-cache
-        emptyDir: {}
-      {{- with .Values.nodeSelector }}
-      nodeSelector:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-      {{- with .Values.affinity }}
-      affinity:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-      {{- with .Values.tolerations }}
-      tolerations:
-        {{- toYaml . | nindent 8 }}
-      {{- end }}
-{{- end }}
@@ -1,17 +0,0 @@
-{{- if .Values.tei.reranker.enabled }}
-apiVersion: v1
-kind: Service
-metadata:
-  name: {{ include "hindsight.fullname" . }}-tei-reranker
-  labels:
-    {{- include "hindsight.tei.reranker.labels" . | nindent 4 }}
-spec:
-  type: ClusterIP
-  ports:
-  - port: {{ .Values.tei.reranker.port }}
-    targetPort: http
-    protocol: TCP
-    name: http
-  selector:
-    {{- include "hindsight.tei.reranker.selectorLabels" . | nindent 4 }}
-{{- end }}
@@ -32,7 +32,7 @@ spec:
      - name: worker
        securityContext:
          {{- toYaml .Values.securityContext | nindent 10 }}
-        image: "{{ .Values.worker.image.repository }}:{{ .Values.worker.image.tag | default .Values.version | default .Chart.AppVersion }}"
+        image: "{{ .Values.worker.image.repository }}:{{ .Values.worker.image.tag | default .Values.version }}"
        imagePullPolicy: {{ .Values.worker.image.pullPolicy }}
        command: ["hindsight-worker"]
        ports:
@@ -99,7 +99,7 @@ spec:
      nodeSelector:
        {{- toYaml . | nindent 8 }}
      {{- end }}
-      {{- with (.Values.worker.affinity | default .Values.affinity) }}
+      {{- with .Values.affinity }}
      affinity:
        {{- toYaml . | nindent 8 }}
      {{- end }}
@@ -1,8 +1,7 @@
 # Default values for hindsight

-# Global version override - use this to set a consistent image tag across all components
-# If not set, defaults to Chart.appVersion from Chart.yaml
-# version: ""
+# Chart version - use this to set a consistent image tag across all components
+version: "0.1.1"

 # Use an existing secret instead of creating one from values
 # When set, all keys from this secret are injected as environment variables via envFrom
@@ -58,15 +57,6 @@ api:
    timeoutSeconds: 3
    failureThreshold: 3

-  # Pod disruption budget
-  podDisruptionBudget:
-    enabled: false
-    minAvailable: 1
-    # maxUnavailable: 1
-
-  # Pod affinity/anti-affinity (overrides global affinity for this component)
-  # affinity: {}
-
  # Environment variables
  env:
    #HINDSIGHT_API_LLM_PROVIDER: "groq"
@@ -85,7 +75,7 @@ worker:
  image:
    repository: ghcr.io/vectorize-io/hindsight-api
    pullPolicy: IfNotPresent
-    # tag: ""  # defaults to .Values.version, then Chart.appVersion if not specified
+    # tag defaults to .Values.version if not specified

  service:
    # Service for metrics scraping (headless for StatefulSet)
@@ -131,15 +121,6 @@ worker:
    # HTTP port for metrics/health (matches service.targetPort)
    HINDSIGHT_API_WORKER_HTTP_PORT: "8889"

-  # Pod disruption budget
-  podDisruptionBudget:
-    enabled: false
-    minAvailable: 1
-    # maxUnavailable: 1
-
-  # Pod affinity/anti-affinity (overrides global affinity for this component)
-  # affinity: {}
-
  # Secret environment variables (inherited from api.secrets if not specified)
  secrets: {}

@@ -183,15 +164,6 @@ controlPlane:
    timeoutSeconds: 3
    failureThreshold: 3

-  # Pod disruption budget
-  podDisruptionBudget:
-    enabled: false
-    minAvailable: 1
-    # maxUnavailable: 1
-
-  # Pod affinity/anti-affinity (overrides global affinity for this component)
-  # affinity: {}
-
  # Environment variables
  env:
    NODE_ENV: "production"
@@ -290,87 +262,9 @@ nodeSelector: {}
 # Tolerations
 tolerations: []

-# Affinity (applied to all components unless overridden per-component)
+# Affinity
 affinity: {}

-# TEI (Text Embeddings Inference) - optional standalone deployments
-# for reranking and/or embedding models
-tei:
-  reranker:
-    enabled: false
-    replicaCount: 1
-    image:
-      repository: ghcr.io/huggingface/text-embeddings-inference
-      tag: cpu-1.8.3
-      pullPolicy: IfNotPresent
-    model: "cross-encoder/ms-marco-MiniLM-L-6-v2"
-    port: 8090
-    args:
-      - "--auto-truncate"
-    env:
-      PAYLOAD_LIMIT: "10000000"
-      MAX_CLIENT_BATCH_SIZE: "256"
-    resources:
-      limits:
-        cpu: 2000m
-        memory: 2Gi
-      requests:
-        cpu: 500m
-        memory: 1Gi
-    livenessProbe:
-      httpGet:
-        path: /health
-        port: 8090
-      initialDelaySeconds: 30
-      periodSeconds: 10
-      timeoutSeconds: 5
-      failureThreshold: 6
-    readinessProbe:
-      httpGet:
-        path: /health
-        port: 8090
-      initialDelaySeconds: 15
-      periodSeconds: 5
-      timeoutSeconds: 3
-      failureThreshold: 3
-
-  embedding:
-    enabled: false
-    replicaCount: 1
-    image:
-      repository: ghcr.io/huggingface/text-embeddings-inference
-      tag: cpu-1.8.3
-      pullPolicy: IfNotPresent
-    model: "sentence-transformers/all-MiniLM-L6-v2"
-    port: 8091
-    args: []
-    env:
-      PAYLOAD_LIMIT: "10000000"
-      MAX_CLIENT_BATCH_SIZE: "256"
-    resources:
-      limits:
-        cpu: 2000m
-        memory: 2Gi
-      requests:
-        cpu: 500m
-        memory: 1Gi
-    livenessProbe:
-      httpGet:
-        path: /health
-        port: 8091
-      initialDelaySeconds: 30
-      periodSeconds: 10
-      timeoutSeconds: 5
-      failureThreshold: 6
-    readinessProbe:
-      httpGet:
-        path: /health
-        port: 8091
-      initialDelaySeconds: 15
-      periodSeconds: 5
-      timeoutSeconds: 3
-      failureThreshold: 3
-
 # Autoscaling
 autoscaling:
  enabled: false
@@ -46,4 +46,4 @@ __all__ = [
    "RemoteTEICrossEncoder",
    "LLMConfig",
 ]
-__version__ = "0.4.11"
+__version__ = "0.1.0"
@@ -6,13 +6,11 @@ Create Date: 2025-11-27 11:54:19.228030

 """

-import os
 from collections.abc import Sequence

 import sqlalchemy as sa
 from alembic import op
 from pgvector.sqlalchemy import Vector
-from sqlalchemy import text
 from sqlalchemy.dialects import postgresql

 # revision identifiers, used by Alembic.
@@ -22,91 +20,11 @@ branch_labels: str | Sequence[str] | None = None
 depends_on: str | Sequence[str] | None = None


-def _detect_vector_extension() -> str:
-    """
-    Detect or validate vector extension: 'vchord' or 'pgvector'.
-    Respects HINDSIGHT_API_VECTOR_EXTENSION env var if set.
-    """
-    conn = op.get_bind()
-    vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
-
-    # Validate configured extension is installed
-    if vector_extension == "vchord":
-        vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
-        if not vchord_check:
-            raise RuntimeError(
-                "Configured vector extension 'vchord' not found. Install it with: CREATE EXTENSION vchord CASCADE;"
-            )
-        return "vchord"
-    elif vector_extension == "pgvector":
-        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
-        if not pgvector_check:
-            raise RuntimeError(
-                "Configured vector extension 'pgvector' not found. Install it with: CREATE EXTENSION vector;"
-            )
-        return "pgvector"
-    else:
-        raise ValueError(f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector' or 'vchord'")
-
-
-def _detect_text_search_extension() -> str:
-    """
-    Detect or validate text search extension: 'native', 'vchord', or 'pg_textsearch'.
-    Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
-    Creates the extension if needed.
-    """
-    text_search_extension = os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
-
-    if text_search_extension == "vchord":
-        # Create vchord_bm25 extension if not exists
-        try:
-            op.execute("CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE")
-        except Exception:
-            # Extension might already exist or user lacks permissions - verify it exists
-            conn = op.get_bind()
-            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord_bm25'")).fetchone()
-            if not result:
-                # Extension truly doesn't exist - re-raise the error
-                raise
-        return "vchord"
-    elif text_search_extension == "pg_textsearch":
-        # Create pg_textsearch extension if not exists
-        try:
-            op.execute("CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE")
-        except Exception:
-            # Extension might already exist or user lacks permissions - verify it exists
-            conn = op.get_bind()
-            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_textsearch'")).fetchone()
-            if not result:
-                # Extension truly doesn't exist - re-raise the error
-                raise
-        return "pg_textsearch"
-    elif text_search_extension == "native":
-        return "native"
-    else:
-        raise ValueError(
-            f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. Must be 'native', 'vchord', or 'pg_textsearch'"
-        )
-
-
 def upgrade() -> None:
    """Upgrade schema - create all tables from scratch."""

-    # Note: pgvector extension is installed globally BEFORE migrations run
-    # See migrations.py:run_migrations() - this ensures the extension is available
-    # to all schemas, not just the one being migrated
-
-    # We keep this here as a fallback for backwards compatibility
-    # This may fail if user lacks permissions, which is fine if extension already exists
-    try:
-        op.execute("CREATE EXTENSION IF NOT EXISTS vector")
-    except Exception:
-        # Extension might already exist or user lacks permissions - verify it exists
-        conn = op.get_bind()
-        result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).fetchone()
-        if not result:
-            # Extension truly doesn't exist - re-raise the error
-            raise
+    # Enable required extensions
+    op.execute("CREATE EXTENSION IF NOT EXISTS vector")

    # Create banks table
    op.create_table(
@@ -234,29 +152,11 @@ def upgrade() -> None:
    )

    # Add search_vector column for full-text search
-    # Type depends on configured text search backend
-    text_search_ext = _detect_text_search_extension()
-
-    if text_search_ext == "vchord":
-        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT)
-        # Note: vchord_bm25 extension creates types in bm25_catalog schema
-        op.execute("""
-            ALTER TABLE memory_units
-            ADD COLUMN search_vector bm25_catalog.bm25vector
-        """)
-    elif text_search_ext == "pg_textsearch":
-        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
-        op.execute("""
-            ALTER TABLE memory_units
-            ADD COLUMN search_vector TEXT
-        """)
-    else:  # native
-        # Native PostgreSQL: tsvector with automatic generation
-        op.execute("""
-            ALTER TABLE memory_units
-            ADD COLUMN search_vector tsvector
-            GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
-        """)
+    op.execute("""
+        ALTER TABLE memory_units
+        ADD COLUMN search_vector tsvector
+        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
+    """)

    op.create_index("idx_memory_units_bank_id", "memory_units", ["bank_id"])
    op.create_index("idx_memory_units_document_id", "memory_units", ["document_id"])
@@ -286,47 +186,19 @@ def upgrade() -> None:
        ["bank_id", sa.text("event_date DESC")],
        postgresql_where=sa.text("fact_type = 'observation'"),
    )
-    # Create vector index - conditional based on available extension
-    vector_ext = _detect_vector_extension()
+    op.create_index(
+        "idx_memory_units_embedding",
+        "memory_units",
+        ["embedding"],
+        postgresql_using="hnsw",
+        postgresql_ops={"embedding": "vector_cosine_ops"},
+    )

-    if vector_ext == "vchord":
-        # Use vchordrq index for vchord (supports high-dimensional embeddings)
-        op.execute("""
-            CREATE INDEX idx_memory_units_embedding ON memory_units
-            USING vchordrq (embedding vector_l2_ops)
-        """)
-    else:  # pgvector
-        # Use HNSW index for pgvector
-        op.create_index(
-            "idx_memory_units_embedding",
-            "memory_units",
-            ["embedding"],
-            postgresql_using="hnsw",
-            postgresql_ops={"embedding": "vector_cosine_ops"},
-        )
-
-    # Create full-text search index on search_vector
-    # Index type depends on text search backend
-    if text_search_ext == "vchord":
-        # VectorChord BM25 index
-        op.execute("""
-            CREATE INDEX idx_memory_units_text_search ON memory_units
-            USING bm25 (search_vector bm25_catalog.bm25_ops)
-        """)
-    elif text_search_ext == "pg_textsearch":
-        # Timescale pg_textsearch BM25 index on text column
-        # Note: pg_textsearch doesn't support expressions, so we index the main text column
-        op.execute("""
-            CREATE INDEX idx_memory_units_text_search ON memory_units
-            USING bm25(text)
-            WITH (text_config='english')
-        """)
-    else:  # native
-        # Native PostgreSQL GIN index
-        op.execute("""
-            CREATE INDEX idx_memory_units_text_search ON memory_units
-            USING gin(search_vector)
-        """)
+    # Create BM25 full-text search index on search_vector
+    op.execute("""
+        CREATE INDEX idx_memory_units_text_search ON memory_units
+        USING gin(search_vector)
+    """)

    op.execute("""
        CREATE MATERIALIZED VIEW memory_units_bm25 AS
@@ -10,11 +10,9 @@ This migration:
 3. Adds consolidation tracking columns to the 'banks' table
 """

-import os
 from collections.abc import Sequence

 from alembic import context, op
-from sqlalchemy import text

 # revision identifiers, used by Alembic.
 revision: str = "n9i0j1k2l3m4"
@@ -29,83 +27,10 @@ def _get_schema_prefix() -> str:
    return f'"{schema}".' if schema else ""


-def _detect_vector_extension() -> str:
-    """
-    Detect or validate vector extension: 'vchord' or 'pgvector'.
-    Respects HINDSIGHT_API_VECTOR_EXTENSION env var if set.
-    """
-    conn = op.get_bind()
-    vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
-
-    # Validate configured extension is installed
-    if vector_extension == "vchord":
-        vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
-        if not vchord_check:
-            raise RuntimeError(
-                "Configured vector extension 'vchord' not found. Install it with: CREATE EXTENSION vchord CASCADE;"
-            )
-        return "vchord"
-    elif vector_extension == "pgvector":
-        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
-        if not pgvector_check:
-            raise RuntimeError(
-                "Configured vector extension 'pgvector' not found. Install it with: CREATE EXTENSION vector;"
-            )
-        return "pgvector"
-    else:
-        raise ValueError(f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector' or 'vchord'")
-
-
-def _detect_text_search_extension() -> str:
-    """
-    Detect or validate text search extension: 'native', 'vchord', or 'pg_textsearch'.
-    Respects HINDSIGHT_API_TEXT_SEARCH_EXTENSION env var.
-    Creates the extension if needed.
-    """
-    text_search_extension = os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
-
-    if text_search_extension == "vchord":
-        # Create vchord_bm25 extension if not exists
-        try:
-            op.execute("CREATE EXTENSION IF NOT EXISTS vchord_bm25 CASCADE")
-        except Exception:
-            # Extension might already exist or user lacks permissions - verify it exists
-            conn = op.get_bind()
-            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord_bm25'")).fetchone()
-            if not result:
-                # Extension truly doesn't exist - re-raise the error
-                raise
-        return "vchord"
-    elif text_search_extension == "pg_textsearch":
-        # Create pg_textsearch extension if not exists
-        try:
-            op.execute("CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE")
-        except Exception:
-            # Extension might already exist or user lacks permissions - verify it exists
-            conn = op.get_bind()
-            result = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'pg_textsearch'")).fetchone()
-            if not result:
-                # Extension truly doesn't exist - re-raise the error
-                raise
-        return "pg_textsearch"
-    elif text_search_extension == "native":
-        return "native"
-    else:
-        raise ValueError(
-            f"Invalid HINDSIGHT_API_TEXT_SEARCH_EXTENSION: {text_search_extension}. Must be 'native', 'vchord', or 'pg_textsearch'"
-        )
-
-
 def upgrade() -> None:
    """Create learnings and pinned_reflections tables."""
    schema = _get_schema_prefix()

-    # Detect which vector extension is available
-    vector_ext = _detect_vector_extension()
-
-    # Detect which text search extension to use
-    text_search_ext = _detect_text_search_extension()
-
    # 1. Create learnings table
    op.execute(f"""
        CREATE TABLE {schema}learnings (
@@ -132,48 +57,18 @@ def upgrade() -> None:

    # Indexes for learnings
    op.execute(f"CREATE INDEX idx_learnings_bank_id ON {schema}learnings(bank_id)")
-
-    # Create vector index based on detected extension
-    if vector_ext == "vchord":
-        op.execute(f"""
-            CREATE INDEX idx_learnings_embedding ON {schema}learnings
-            USING vchordrq (embedding vector_l2_ops)
-        """)
-    else:  # pgvector
-        op.execute(f"""
-            CREATE INDEX idx_learnings_embedding ON {schema}learnings
-            USING hnsw (embedding vector_cosine_ops)
-        """)
-
+    op.execute(f"""
+        CREATE INDEX idx_learnings_embedding ON {schema}learnings
+        USING hnsw (embedding vector_cosine_ops)
+    """)
    op.execute(f"CREATE INDEX idx_learnings_tags ON {schema}learnings USING GIN(tags)")

    # Full-text search for learnings
-    if text_search_ext == "vchord":
-        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT)
-        # Note: vchord_bm25 extension creates types in bm25_catalog schema
-        op.execute(f"""
-            ALTER TABLE {schema}learnings ADD COLUMN search_vector bm25_catalog.bm25vector
-        """)
-        op.execute(f"""
-            CREATE INDEX idx_learnings_text_search ON {schema}learnings
-            USING bm25 (search_vector bm25_catalog.bm25_ops)
-        """)
-    elif text_search_ext == "pg_textsearch":
-        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
-        op.execute(f"""
-            ALTER TABLE {schema}learnings ADD COLUMN search_vector TEXT
-        """)
-        op.execute(f"""
-            CREATE INDEX idx_learnings_text_search ON {schema}learnings
-            USING bm25(text) WITH (text_config='english')
-        """)
-    else:  # native
-        # Native PostgreSQL: tsvector with automatic generation
-        op.execute(f"""
-            ALTER TABLE {schema}learnings ADD COLUMN search_vector tsvector
-            GENERATED ALWAYS AS (to_tsvector('english', text)) STORED
-        """)
-        op.execute(f"CREATE INDEX idx_learnings_text_search ON {schema}learnings USING gin(search_vector)")
+    op.execute(f"""
+        ALTER TABLE {schema}learnings ADD COLUMN search_vector tsvector
+        GENERATED ALWAYS AS (to_tsvector('english', text)) STORED
+    """)
+    op.execute(f"CREATE INDEX idx_learnings_text_search ON {schema}learnings USING gin(search_vector)")

    # 2. Create pinned_reflections table
    op.execute(f"""
@@ -199,52 +94,21 @@ def upgrade() -> None:

    # Indexes for pinned_reflections
    op.execute(f"CREATE INDEX idx_pinned_reflections_bank_id ON {schema}pinned_reflections(bank_id)")
-
-    # Create vector index based on detected extension
-    if vector_ext == "vchord":
-        op.execute(f"""
-            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
-            USING vchordrq (embedding vector_l2_ops)
-        """)
-    else:  # pgvector
-        op.execute(f"""
-            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
-            USING hnsw (embedding vector_cosine_ops)
-        """)
-
+    op.execute(f"""
+        CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+        USING hnsw (embedding vector_cosine_ops)
+    """)
    op.execute(f"CREATE INDEX idx_pinned_reflections_tags ON {schema}pinned_reflections USING GIN(tags)")

    # Full-text search for pinned_reflections
-    if text_search_ext == "vchord":
-        # VectorChord BM25: bm25vector type (no GENERATED - tokenization happens on INSERT/UPDATE)
-        # Note: vchord_bm25 extension creates types in bm25_catalog schema
-        op.execute(f"""
-            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector bm25_catalog.bm25vector
-        """)
-        op.execute(f"""
-            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
-            USING bm25 (search_vector bm25_catalog.bm25_ops)
-        """)
-    elif text_search_ext == "pg_textsearch":
-        # Timescale pg_textsearch: dummy TEXT column for consistency (indexes operate on base columns directly)
-        op.execute(f"""
-            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector TEXT
-        """)
-        op.execute(f"""
-            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
-            USING bm25(content)
-            WITH (text_config='english')
-        """)
-    else:  # native
-        # Native PostgreSQL: tsvector with automatic generation
-        op.execute(f"""
-            ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector tsvector
-            GENERATED ALWAYS AS (to_tsvector('english', COALESCE(name, '') || ' ' || content)) STORED
-        """)
-        op.execute(f"""
-            CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
-            USING gin(search_vector)
-        """)
+    op.execute(f"""
+        ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector tsvector
+        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(name, '') || ' ' || content)) STORED
+    """)
+    op.execute(f"""
+        CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
+        USING gin(search_vector)
+    """)

    # 3. Add consolidation tracking columns to banks table
    op.execute(f"""
@@ -1,60 +0,0 @@
-"""Fix mental_models primary key to be scoped per bank
-
-Revision ID: w8r9s0t1u2v3
-Revises: v7q8r9s0t1u2
-Create Date: 2026-02-05
-
-This migration fixes a critical bank isolation bug where mental_models.id was
-globally unique across all banks instead of being scoped per bank. This caused
-conflicts when different banks tried to use the same custom ID.
-
-CRITICAL FIX: Changes primary key from (id) to (bank_id, id) to ensure proper isolation.
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "w8r9s0t1u2v3"
-down_revision: str | Sequence[str] | None = "v7q8r9s0t1u2"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (required for multi-tenant support)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    """Change mental_models primary key from (id) to (bank_id, id) for proper bank isolation."""
-    schema = _get_schema_prefix()
-
-    # Drop the old primary key constraint (just id)
-    # Note: The constraint might be named differently on different DBs
-    # Try both old names (pinned_reflections_pkey from original, mental_models_pkey from rename)
-    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS pinned_reflections_pkey")
-    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS mental_models_pkey")
-
-    # Create the new composite primary key (bank_id, id)
-    # This ensures IDs are scoped per bank, not globally
-    op.execute(f"""
-        ALTER TABLE {schema}mental_models
-        ADD CONSTRAINT mental_models_pkey PRIMARY KEY (bank_id, id)
-    """)
-
-
-def downgrade() -> None:
-    """Revert mental_models primary key from (bank_id, id) to (id)."""
-    schema = _get_schema_prefix()
-
-    # Drop the composite primary key
-    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS mental_models_pkey")
-
-    # Restore the old primary key (just id)
-    # WARNING: This downgrade will fail if there are duplicate IDs across banks
-    op.execute(f"""
-        ALTER TABLE {schema}mental_models
-        ADD CONSTRAINT mental_models_pkey PRIMARY KEY (id)
-    """)
@@ -1,64 +0,0 @@
-"""Add config JSONB column to banks table for hierarchical configuration
-
-Revision ID: x9s0t1u2v3w4
-Revises: w8r9s0t1u2v3
-Create Date: 2026-02-09
-
-This migration adds a `config` JSONB column to the banks table to support
-per-bank configuration overrides. This enables hierarchical configuration where:
- Global config is loaded from environment variables
- Tenant config is provided via TenantExtension
- Bank config overrides are stored in banks.config JSONB column
-
-The config column stores overrides for hierarchical fields (LLM settings,
-retention parameters, retrieval settings, etc.) in Python field name format.
-"""
-
-from collections.abc import Sequence
-
-import sqlalchemy as sa
-from alembic import context, op
-from sqlalchemy.dialects.postgresql import JSONB
-
-revision: str = "x9s0t1u2v3w4"
-down_revision: str | Sequence[str] | None = "w8r9s0t1u2v3"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (required for multi-tenant support)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    """Add config JSONB column to banks table with GIN index."""
-    schema = _get_schema_prefix()
-
-    # Add config column to banks table
-    op.execute(f"""
-        ALTER TABLE {schema}banks
-        ADD COLUMN config JSONB NOT NULL DEFAULT '{{}}'::jsonb
-    """)
-
-    # Add GIN index for efficient JSONB queries
-    op.execute(f"""
-        CREATE INDEX idx_banks_config
-        ON {schema}banks
-        USING gin(config)
-    """)
-
-
-def downgrade() -> None:
-    """Remove config column and index from banks table."""
-    schema = _get_schema_prefix()
-
-    # Drop index first
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_banks_config")
-
-    # Drop column
-    op.execute(f"""
-        ALTER TABLE {schema}banks
-        DROP COLUMN IF EXISTS config
-    """)
@@ -1,49 +0,0 @@
-"""Add GIN index on async_operations.result_metadata for parent_operation_id queries
-
-Revision ID: y0t1u2v3w4x5
-Revises: x9s0t1u2v3w4
-Create Date: 2026-02-13
-
-This migration adds a GIN index on the result_metadata JSONB column in the
-async_operations table to support efficient queries for child operations by
-parent_operation_id.
-
-The index enables fast lookups when querying for child operations:
-  SELECT * FROM async_operations
-  WHERE result_metadata::jsonb @> '{"parent_operation_id": "uuid"}'::jsonb
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-revision: str = "y0t1u2v3w4x5"
-down_revision: str | Sequence[str] | None = "x9s0t1u2v3w4"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (required for multi-tenant support)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def upgrade() -> None:
-    """Add GIN index on result_metadata for efficient parent_operation_id queries."""
-    schema = _get_schema_prefix()
-
-    # Add GIN index for JSONB containment queries (@> operator)
-    op.execute(f"""
-        CREATE INDEX idx_async_operations_result_metadata
-        ON {schema}async_operations
-        USING gin(result_metadata)
-    """)
-
-
-def downgrade() -> None:
-    """Remove GIN index on result_metadata."""
-    schema = _get_schema_prefix()
-
-    # Drop index
-    op.execute(f"DROP INDEX IF EXISTS {schema}idx_async_operations_result_metadata")
@@ -6,6 +6,7 @@ Provides both HTTP REST API and MCP (Model Context Protocol) server.

 import logging
 from contextlib import asynccontextmanager
+from typing import Optional

 from fastapi import FastAPI

@@ -45,14 +46,14 @@ def create_app(
        # Both HTTP and MCP
        app = create_app(memory, mcp_api_enabled=True)
    """
-    mcp_servers = None
+    mcp_app = None

-    # Create MCP servers first if enabled (we need their lifespans for chaining)
+    # Create MCP app first if enabled (we need its lifespan for chaining)
    if mcp_api_enabled:
        try:
-            from .mcp import MCPMiddleware, create_mcp_servers
+            from .mcp import create_mcp_app

-            mcp_servers = create_mcp_servers(memory=memory)
+            mcp_app = create_mcp_app(memory=memory)
        except ImportError as e:
            logger.error(f"MCP server requested but dependencies not available: {e}")
            logger.error("Install with: pip install hindsight-api[mcp]")
@@ -69,41 +70,30 @@ def create_app(
        app = FastAPI(title="Hindsight API", version="0.0.7")
        logger.info("HTTP REST API disabled")

-    # Add MCP middleware and chain its lifespan if enabled
-    if mcp_servers is not None:
-        multi_bank_server, single_bank_server, multi_bank_starlette_app, single_bank_starlette_app = mcp_servers
+    # Mount MCP server and chain its lifespan if enabled
+    if mcp_app is not None:
+        # Get the MCP app's underlying Starlette app for lifespan access
+        mcp_starlette_app = mcp_app.mcp_app

        # Store the original lifespan
        original_lifespan = app.router.lifespan_context

        @asynccontextmanager
        async def chained_lifespan(app_instance: FastAPI):
-            """Chain both MCP lifespans with the main app lifespan."""
-            # Start both MCP lifespans (multi-bank and single-bank)
-            async with multi_bank_starlette_app.router.lifespan_context(multi_bank_starlette_app):
-                async with single_bank_starlette_app.router.lifespan_context(single_bank_starlette_app):
-                    logger.info("MCP lifespans started (multi-bank and single-bank)")
-                    # Then start the original app lifespan
-                    async with original_lifespan(app_instance):
-                        yield
-                logger.info("MCP lifespans stopped")
+            """Chain the MCP lifespan with the main app lifespan."""
+            # Start MCP lifespan first
+            async with mcp_starlette_app.router.lifespan_context(mcp_starlette_app):
+                logger.info("MCP lifespan started")
+                # Then start the original app lifespan
+                async with original_lifespan(app_instance):
+                    yield
+            logger.info("MCP lifespan stopped")

        # Replace the app's lifespan with the chained version
        app.router.lifespan_context = chained_lifespan

-        # Add MCP as a wrapping middleware — intercepts /mcp* requests directly,
-        # passes everything else through to the FastAPI app. No Starlette Mount
-        # means no 307 redirect for /mcp (no trailing slash).
-        app.add_middleware(
-            MCPMiddleware,
-            memory=memory,
-            prefix=mcp_mount_path,
-            multi_bank_app=multi_bank_starlette_app,
-            single_bank_app=single_bank_starlette_app,
-            multi_bank_server=multi_bank_server,
-            single_bank_server=single_bank_server,
-        )
-
+        # Mount the MCP middleware
+        app.mount(mcp_mount_path, mcp_app)
        logger.info(f"MCP server enabled at {mcp_mount_path}/")

    return app
@@ -5,7 +5,6 @@ This module provides the create_app function to create and configure
 the FastAPI application with all API endpoints.
 """

-import asyncio
 import json
 import logging
 import uuid
@@ -32,47 +31,11 @@ def _parse_metadata(metadata: Any) -> dict[str, Any]:
    return {}


-from typing import Callable
-
 from pydantic import BaseModel, ConfigDict, Field, field_validator

 from hindsight_api import MemoryEngine
-
-
-def FieldWithDefault(default_factory: Callable, **kwargs) -> Any:
-    """
-    Field wrapper that ensures default_factory values appear in OpenAPI schema.
-
-    Pydantic doesn't include default_factory in OpenAPI schemas, causing OpenAPI
-    Generator to make fields Optional with default=None instead of non-optional
-    with the correct default value.
-
-    This wrapper adds json_schema_extra to include the default in the schema.
-    """
-    # Determine the default value for the schema based on the factory
-    if default_factory is list:
-        schema_default = []
-    elif default_factory is dict:
-        schema_default = {}
-    else:
-        # For custom factories (like IncludeOptions), use empty dict as placeholder
-        schema_default = {}
-
-    # Add or merge json_schema_extra
-    json_extra = kwargs.pop("json_schema_extra", {})
-    if isinstance(json_extra, dict):
-        json_extra["default"] = schema_default
-    else:
-        # If json_schema_extra was a function, we can't merge easily
-        # Fall back to just setting default
-        json_extra = {"default": schema_default}
-
-    return Field(default_factory=default_factory, json_schema_extra=json_extra, **kwargs)
-
-
-from hindsight_api.config import get_config
 from hindsight_api.engine.db_utils import acquire_with_retry
-from hindsight_api.engine.memory_engine import Budget, _get_tiktoken_encoding, fq_table
+from hindsight_api.engine.memory_engine import Budget, fq_table
 from hindsight_api.engine.reflect.observations import Observation
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, TokenUsage
 from hindsight_api.engine.search.tags import TagsMatch
@@ -82,8 +45,6 @@ from hindsight_api.models import RequestContext

 logger = logging.getLogger(__name__)

-MAX_QUERY_TOKENS = 500  # Maximum tokens allowed in recall query
-

 class EntityIncludeOptions(BaseModel):
    """Options for including entity observations in recall results."""
@@ -131,7 +92,8 @@ class RecallRequest(BaseModel):
    query: str
    types: list[str] | None = Field(
        default=None,
-        description="List of fact types to recall: 'world', 'experience', 'observation'. Defaults to world and experience if not specified.",
+        description="List of fact types to recall: 'world', 'experience', 'observation'. Defaults to world and experience if not specified. "
+        "Note: 'opinion' is accepted but ignored (opinions are excluded from recall).",
    )
    budget: Budget = Budget.MID
    max_tokens: int = 4096
@@ -139,8 +101,8 @@ class RecallRequest(BaseModel):
    query_timestamp: str | None = Field(
        default=None, description="ISO format date string (e.g., '2023-05-30T23:40:00')"
    )
-    include: IncludeOptions = FieldWithDefault(
-        IncludeOptions,
+    include: IncludeOptions = Field(
+        default_factory=IncludeOptions,
        description="Options for including additional data (entities are included by default)",
    )
    tags: list[str] | None = Field(
@@ -542,6 +504,13 @@ class ReflectRequest(BaseModel):
    )


+class OpinionItem(BaseModel):
+    """Model for an opinion with confidence score."""
+
+    text: str
+    confidence: float
+
+
 class ReflectFact(BaseModel):
    """A fact used in think response."""

@@ -559,10 +528,8 @@ class ReflectFact(BaseModel):
    )

    id: str | None = None
-    text: str = Field(
-        description="Fact text. When type='observation', this contains markdown-formatted consolidated knowledge"
-    )
-    type: str | None = None  # fact type: world, experience, observation
+    text: str
+    type: str | None = None  # fact type: world, experience, opinion
    context: str | None = None
    occurred_start: str | None = None
    occurred_end: str | None = None
@@ -606,16 +573,18 @@ class ReflectLLMCall(BaseModel):
 class ReflectBasedOn(BaseModel):
    """Evidence the response is based on: memories, mental models, and directives."""

-    memories: list[ReflectFact] = FieldWithDefault(list, description="Memory facts used to generate the response")
-    mental_models: list[ReflectMentalModel] = FieldWithDefault(list, description="Mental models used during reflection")
-    directives: list[ReflectDirective] = FieldWithDefault(list, description="Directives applied during reflection")
+    memories: list[ReflectFact] = Field(default_factory=list, description="Memory facts used to generate the response")
+    mental_models: list[ReflectMentalModel] = Field(
+        default_factory=list, description="Mental models used during reflection"
+    )
+    directives: list[ReflectDirective] = Field(default_factory=list, description="Directives applied during reflection")


 class ReflectTrace(BaseModel):
    """Execution trace of LLM and tool calls during reflection."""

-    tool_calls: list[ReflectToolCall] = FieldWithDefault(list, description="Tool calls made during reflection")
-    llm_calls: list[ReflectLLMCall] = FieldWithDefault(list, description="LLM calls made during reflection")
+    tool_calls: list[ReflectToolCall] = Field(default_factory=list, description="Tool calls made during reflection")
+    llm_calls: list[ReflectLLMCall] = Field(default_factory=list, description="LLM calls made during reflection")


 class ReflectResponse(BaseModel):
@@ -624,7 +593,7 @@ class ReflectResponse(BaseModel):
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
-                "text": "## AI Overview\n\nBased on my understanding, AI is a **transformative technology**:\n\n- Used extensively in healthcare\n- Discussed in recent conversations\n- Continues to evolve rapidly",
+                "text": "Based on my understanding, AI is a transformative technology...",
                "based_on": {
                    "memories": [
                        {"id": "123", "text": "AI is used in healthcare", "type": "world"},
@@ -652,9 +621,7 @@ class ReflectResponse(BaseModel):
        }
    )

-    text: str = Field(
-        description="The reflect response as well-formatted markdown (headers, lists, bold/italic, code blocks, etc.)"
-    )
+    text: str
    based_on: ReflectBasedOn | None = Field(
        default=None,
        description="Evidence used to generate the response. Only present when include.facts is set.",
@@ -827,55 +794,6 @@ class CreateBankRequest(BaseModel):
    background: str | None = Field(default=None, description="Deprecated: use mission instead")


-class BankConfigUpdate(BaseModel):
-    """Request model for updating bank configuration."""
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "updates": {
-                    "llm_model": "claude-sonnet-4-5",
-                    "retain_extraction_mode": "verbose",
-                    "retain_custom_instructions": "Extract technical details carefully",
-                }
-            }
-        }
-    )
-
-    updates: dict[str, Any] = Field(
-        description="Configuration overrides. Keys can be in Python field format (llm_provider) "
-        "or environment variable format (HINDSIGHT_API_LLM_PROVIDER). "
-        "Only hierarchical fields can be overridden per-bank."
-    )
-
-
-class BankConfigResponse(BaseModel):
-    """Response model for bank configuration."""
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "bank_id": "my-bank",
-                "config": {
-                    "llm_provider": "openai",
-                    "llm_model": "gpt-4",
-                    "retain_extraction_mode": "verbose",
-                },
-                "overrides": {
-                    "llm_model": "gpt-4",
-                    "retain_extraction_mode": "verbose",
-                },
-            }
-        }
-    )
-
-    bank_id: str = Field(description="Bank identifier")
-    config: dict[str, Any] = Field(
-        description="Fully resolved configuration with all hierarchical overrides applied (Python field names)"
-    )
-    overrides: dict[str, Any] = Field(description="Bank-specific configuration overrides only (Python field names)")
-
-
 class GraphDataResponse(BaseModel):
    """Response model for graph data endpoint."""

@@ -953,7 +871,6 @@ class ListDocumentsResponse(BaseModel):
                        "updated_at": "2024-01-15T10:30:00Z",
                        "text_length": 5420,
                        "memory_unit_count": 15,
-                        "tags": ["user_a", "session_123"],
                    }
                ],
                "total": 50,
@@ -1025,7 +942,7 @@ class DocumentResponse(BaseModel):
    created_at: str
    updated_at: str
    memory_unit_count: int
-    tags: list[str] = FieldWithDefault(list, description="Tags associated with this document")
+    tags: list[str] = Field(default_factory=list, description="Tags associated with this document")


 class DeleteDocumentResponse(BaseModel):
@@ -1149,7 +1066,7 @@ class DirectiveResponse(BaseModel):
    content: str
    priority: int = 0
    is_active: bool = True
-    tags: list[str] = FieldWithDefault(list)
+    tags: list[str] = Field(default_factory=list)
    created_at: str | None = None
    updated_at: str | None = None

@@ -1167,7 +1084,7 @@ class CreateDirectiveRequest(BaseModel):
    content: str = Field(description="The directive text to inject into prompts")
    priority: int = Field(default=0, description="Higher priority directives are injected first")
    is_active: bool = Field(default=True, description="Whether this directive is active")
-    tags: list[str] = FieldWithDefault(list, description="Tags for filtering")
+    tags: list[str] = Field(default_factory=list, description="Tags for filtering")


 class UpdateDirectiveRequest(BaseModel):
@@ -1201,12 +1118,10 @@ class MentalModelResponse(BaseModel):
    bank_id: str
    name: str
    source_query: str
-    content: str = Field(
-        description="The mental model content as well-formatted markdown (auto-generated from reflect endpoint)"
-    )
-    tags: list[str] = FieldWithDefault(list)
+    content: str
+    tags: list[str] = Field(default_factory=list)
    max_tokens: int = Field(default=2048)
-    trigger: MentalModelTrigger = FieldWithDefault(MentalModelTrigger)
+    trigger: MentalModelTrigger = Field(default_factory=MentalModelTrigger)
    last_refreshed_at: str | None = None
    created_at: str | None = None
    reflect_response: dict | None = Field(
@@ -1227,7 +1142,6 @@ class CreateMentalModelRequest(BaseModel):
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
-                "id": "team-communication",
                "name": "Team Communication Preferences",
                "source_query": "How does the team prefer to communicate?",
                "tags": ["team"],
@@ -1237,21 +1151,17 @@ class CreateMentalModelRequest(BaseModel):
        }
    )

-    id: str | None = Field(
-        None, description="Optional custom ID for the mental model (alphanumeric lowercase with hyphens)"
-    )
    name: str = Field(description="Human-readable name for the mental model")
    source_query: str = Field(description="The query to run to generate content")
-    tags: list[str] = FieldWithDefault(list, description="Tags for scoped visibility")
+    tags: list[str] = Field(default_factory=list, description="Tags for scoped visibility")
    max_tokens: int = Field(default=2048, ge=256, le=8192, description="Maximum tokens for generated content")
-    trigger: MentalModelTrigger = FieldWithDefault(MentalModelTrigger, description="Trigger settings")
+    trigger: MentalModelTrigger = Field(default_factory=MentalModelTrigger, description="Trigger settings")


 class CreateMentalModelResponse(BaseModel):
    """Response model for mental model creation."""

-    mental_model_id: str | None = Field(None, description="ID of the created mental model")
-    operation_id: str = Field(description="Operation ID to track refresh progress")
+    operation_id: str = Field(description="Operation ID to track progress")


 class UpdateMentalModelRequest(BaseModel):
@@ -1357,16 +1267,6 @@ class CancelOperationResponse(BaseModel):
    operation_id: str


-class ChildOperationStatus(BaseModel):
-    """Status of a child operation (for batch operations)."""
-
-    operation_id: str
-    status: str
-    sub_batch_index: int | None = None
-    items_count: int | None = None
-    error_message: str | None = None
-
-
 class OperationStatusResponse(BaseModel):
    """Response model for getting a single operation status."""

@@ -1391,13 +1291,6 @@ class OperationStatusResponse(BaseModel):
    updated_at: str | None = None
    completed_at: str | None = None
    error_message: str | None = None
-    result_metadata: dict[str, Any] | None = Field(
-        default=None,
-        description="Internal metadata for debugging. Structure may change without notice. Not for production use.",
-    )
-    child_operations: list[ChildOperationStatus] | None = Field(
-        default=None, description="Child operations for batch operations (if applicable)"
-    )


 class AsyncOperationSubmitResponse(BaseModel):
@@ -1422,7 +1315,6 @@ class FeaturesInfo(BaseModel):
    observations: bool = Field(description="Whether observations (auto-consolidation) are enabled")
    mcp: bool = Field(description="Whether MCP (Model Context Protocol) server is enabled")
    worker: bool = Field(description="Whether the background worker is enabled")
-    bank_config_api: bool = Field(description="Whether per-bank configuration API is enabled")


 class VersionResponse(BaseModel):
@@ -1431,12 +1323,11 @@ class VersionResponse(BaseModel):
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
-                "api_version": "0.4.0",
+                "api_version": "1.0.0",
                "features": {
                    "observations": False,
                    "mcp": True,
                    "worker": True,
-                    "bank_config_api": False,
                },
            }
        }
@@ -1502,26 +1393,6 @@ def create_app(
            app.state.prometheus_reader = None
            # Metrics collector is already initialized as no-op by default

-        # Initialize OpenTelemetry tracing if enabled
-        if config.otel_traces_enabled:
-            if not config.otel_exporter_otlp_endpoint:
-                logging.warning("OTEL tracing enabled but no endpoint configured. Tracing disabled.")
-            else:
-                from hindsight_api.tracing import create_span_recorder, initialize_tracing
-
-                try:
-                    initialize_tracing(
-                        service_name=config.otel_service_name,
-                        endpoint=config.otel_exporter_otlp_endpoint,
-                        headers=config.otel_exporter_otlp_headers,
-                        deployment_environment=config.otel_deployment_environment,
-                    )
-                    create_span_recorder()
-                    logging.info("OpenTelemetry tracing enabled and configured")
-                except Exception as e:
-                    logging.error(f"Failed to initialize tracing: {e}")
-                    logging.warning("Continuing without tracing")
-
        # Startup: Initialize database and memory system (migrations run inside initialize if enabled)
        if initialize_memory:
            await memory.initialize()
@@ -1535,31 +1406,19 @@ def create_app(

        # Start worker poller if enabled (standalone mode)
        if config.worker_enabled and memory._pool is not None:
-            from ..config import DEFAULT_DATABASE_SCHEMA
-
            worker_id = config.worker_id or socket.gethostname()
-            # Convert default schema to None for SQL compatibility (no schema prefix)
-            schema = None if config.database_schema == DEFAULT_DATABASE_SCHEMA else config.database_schema
            poller = WorkerPoller(
                pool=memory._pool,
                worker_id=worker_id,
                executor=memory.execute_task,
                poll_interval_ms=config.worker_poll_interval_ms,
+                batch_size=config.worker_batch_size,
                max_retries=config.worker_max_retries,
-                schema=schema,
-                tenant_extension=memory._tenant_extension,
-                max_slots=config.worker_max_slots,
-                consolidation_max_slots=config.worker_consolidation_max_slots,
+                tenant_extension=getattr(memory, "_tenant_extension", None),
            )
            poller_task = asyncio.create_task(poller.run())
            logging.info(f"Worker poller started (worker_id={worker_id})")

-        # Call tenant extension startup hook (e.g. JWKS fetch for Supabase)
-        tenant_extension = memory.tenant_extension
-        if tenant_extension:
-            await tenant_extension.on_startup()
-            logging.info("Tenant extension started")
-
        # Call HTTP extension startup hook
        if http_extension:
            await http_extension.on_startup()
@@ -1578,11 +1437,6 @@ def create_app(
                    pass
            logging.info("Worker poller stopped")

-        # Call tenant extension shutdown hook
-        if tenant_extension:
-            await tenant_extension.on_shutdown()
-            logging.info("Tenant extension stopped")
-
        # Call HTTP extension shutdown hook
        if http_extension:
            await http_extension.on_shutdown()
@@ -1593,9 +1447,6 @@ def create_app(
        logging.info("Memory system closed")

    from hindsight_api import __version__
-    from hindsight_api.config import get_config
-
-    config = get_config()

    app = FastAPI(
        title="Hindsight HTTP API",
@@ -1609,7 +1460,6 @@ def create_app(
            "url": "https://www.apache.org/licenses/LICENSE-2.0.html",
        },
        lifespan=lifespan,
-        root_path=config.base_path,
    )

    # IMPORTANT: Set memory on app.state immediately, don't wait for lifespan
@@ -1716,21 +1566,16 @@ def _register_routes(app: FastAPI):

        Returns version info and feature flags that can be used by clients
        to determine which capabilities are available.
-
-        Note: observations flag shows the global default. Individual banks
-        may override this setting via bank-specific configuration.
        """
-        from hindsight_api import __version__
-        from hindsight_api.config import _get_raw_config
+        from hindsight_api.config import get_config

-        config = _get_raw_config()
+        config = get_config()
        return VersionResponse(
-            api_version=__version__,
+            api_version="1.0.0",
            features=FeaturesInfo(
                observations=config.enable_observations,
                mcp=config.mcp_enabled,
                worker=config.worker_enabled,
-                bank_config_api=config.enable_bank_config_api,
            ),
        )

@@ -1861,7 +1706,9 @@ def _register_routes(app: FastAPI):
        description="Recall memory using semantic similarity and spreading activation.\n\n"
        "The type parameter is optional and must be one of:\n"
        "- `world`: General knowledge about people, places, events, and things that happen\n"
-        "- `experience`: Memories about experience, conversations, actions taken, and tasks performed",
+        "- `experience`: Memories about experience, conversations, actions taken, and tasks performed\n"
+        "- `opinion`: The bank's formed beliefs, perspectives, and viewpoints\n\n"
+        "Set `include_entities=true` to get entity observations alongside recall results.",
        operation_id="recall_memories",
        tags=["Memory"],
    )
@@ -1874,18 +1721,11 @@ def _register_routes(app: FastAPI):
        handler_start = time.time()
        metrics = get_metrics_collector()

-        # Validate query length to prevent expensive operations on oversized queries
-        encoding = _get_tiktoken_encoding()
-        query_tokens = len(encoding.encode(request.query))
-        if query_tokens > MAX_QUERY_TOKENS:
-            raise HTTPException(
-                status_code=400,
-                detail=f"Query too long: {query_tokens} tokens exceeds maximum of {MAX_QUERY_TOKENS}. Please shorten your query.",
-            )
-
        try:
-            # Default to world and experience if not specified (exclude observation)
+            # Default to world and experience if not specified (exclude observation and opinion)
+            # Filter out 'opinion' even if requested - opinions are excluded from recall
            fact_types = request.types if request.types else list(VALID_RECALL_FACT_TYPES)
+            fact_types = [ft for ft in fact_types if ft != "opinion"]

            # Parse query_timestamp if provided
            question_date = None
@@ -1997,15 +1837,6 @@ def _register_routes(app: FastAPI):
            raise HTTPException(status_code=e.status_code, detail=e.reason)
        except (AuthenticationError, HTTPException):
            raise
-        except (asyncio.TimeoutError, TimeoutError):
-            handler_duration = time.time() - handler_start
-            logger.error(
-                f"[RECALL TIMEOUT] bank={bank_id} handler_duration={handler_duration:.3f}s - database query timed out"
-            )
-            raise HTTPException(
-                status_code=504,
-                detail="Request timed out while searching memories. Try a shorter or more specific query.",
-            )
        except Exception as e:
            import traceback

@@ -2026,7 +1857,8 @@ def _register_routes(app: FastAPI):
        "2. Retrieves world facts relevant to the query\n"
        "3. Retrieves existing opinions (bank's perspectives)\n"
        "4. Uses LLM to formulate a contextual answer\n"
-        "5. Returns plain text answer and the facts used",
+        "5. Extracts and stores any new opinions formed\n"
+        "6. Returns plain text answer, the facts used, and new opinions",
        operation_id="reflect",
        tags=["Memory"],
    )
@@ -2063,17 +1895,17 @@ def _register_routes(app: FastAPI):
                directives = []
                for fact_type, facts in core_result.based_on.items():
                    if fact_type == "directives":
-                        # Directives are dicts with id, name, content (not MemoryFact objects)
+                        # Directives have different structure (id, name, content)
                        for directive in facts:
                            directives.append(
                                ReflectDirective(
-                                    id=directive["id"],
-                                    name=directive["name"],
-                                    content=directive["content"],
+                                    id=directive.id,
+                                    name=directive.name,
+                                    content=directive.content,
                                )
                            )
-                    elif fact_type == "mental-models":
-                        # Mental models are MemoryFact with type "mental-models" (note: hyphen, not underscore)
+                    elif fact_type == "mental_models":
+                        # Mental models are MemoryFact with type "mental_models"
                        for fact in facts:
                            mental_models.append(
                                ReflectMentalModel(
@@ -2471,12 +2303,9 @@ def _register_routes(app: FastAPI):
            )
            if mental_model is None:
                raise HTTPException(status_code=404, detail=f"Mental model '{mental_model_id}' not found")
-
            return MentalModelResponse(**mental_model)
        except (AuthenticationError, HTTPException):
            raise
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
        except Exception as e:
            import traceback

@@ -2507,7 +2336,6 @@ def _register_routes(app: FastAPI):
                name=body.name,
                source_query=body.source_query,
                content="Generating content...",
-                mental_model_id=body.id if body.id else None,
                tags=body.tags if body.tags else None,
                max_tokens=body.max_tokens,
                trigger=body.trigger.model_dump() if body.trigger else None,
@@ -2519,13 +2347,11 @@ def _register_routes(app: FastAPI):
                mental_model_id=mental_model["id"],
                request_context=request_context,
            )
-            return CreateMentalModelResponse(mental_model_id=mental_model["id"], operation_id=result["operation_id"])
+            return CreateMentalModelResponse(operation_id=result["operation_id"])
        except ValueError as e:
            raise HTTPException(status_code=400, detail=str(e))
        except (AuthenticationError, HTTPException):
            raise
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
        except Exception as e:
            import traceback

@@ -2558,8 +2384,6 @@ def _register_routes(app: FastAPI):
            raise HTTPException(status_code=404, detail=str(e))
        except (AuthenticationError, HTTPException):
            raise
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
        except Exception as e:
            import traceback

@@ -3384,112 +3208,6 @@ def _register_routes(app: FastAPI):
            logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/observations: {error_detail}")
            raise HTTPException(status_code=500, detail=str(e))

-    @app.get(
-        "/v1/default/banks/{bank_id}/config",
-        response_model=BankConfigResponse,
-        summary="Get bank configuration",
-        description="Get fully resolved configuration for a bank including all hierarchical overrides (global → tenant → bank). "
-        "The 'config' field contains all resolved config values. The 'overrides' field shows only bank-specific overrides.",
-        operation_id="get_bank_config",
-        tags=["Banks"],
-    )
-    async def api_get_bank_config(bank_id: str, request_context: RequestContext = Depends(get_request_context)):
-        """Get configuration for a bank with all hierarchical overrides applied."""
-        if not get_config().enable_bank_config_api:
-            raise HTTPException(
-                status_code=404,
-                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
-            )
-        try:
-            # Get resolved config from config resolver
-            config_dict = await app.state.memory._config_resolver.get_bank_config(bank_id, request_context)
-
-            # Get bank-specific overrides only
-            bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
-
-            return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
-        except (AuthenticationError, HTTPException):
-            raise
-        except Exception as e:
-            import traceback
-
-            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(f"Error in GET /v1/default/banks/{bank_id}/config: {error_detail}")
-            raise HTTPException(status_code=500, detail=str(e))
-
-    @app.patch(
-        "/v1/default/banks/{bank_id}/config",
-        response_model=BankConfigResponse,
-        summary="Update bank configuration",
-        description="Update configuration overrides for a bank. Only hierarchical fields can be overridden (LLM settings, retention parameters, etc.). "
-        "Keys can be provided in Python field format (llm_provider) or environment variable format (HINDSIGHT_API_LLM_PROVIDER).",
-        operation_id="update_bank_config",
-        tags=["Banks"],
-    )
-    async def api_update_bank_config(
-        bank_id: str, request: BankConfigUpdate, request_context: RequestContext = Depends(get_request_context)
-    ):
-        """Update configuration overrides for a bank."""
-        if not get_config().enable_bank_config_api:
-            raise HTTPException(
-                status_code=404,
-                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
-            )
-        try:
-            # Update config via config resolver (validates configurable fields and permissions)
-            await app.state.memory._config_resolver.update_bank_config(bank_id, request.updates, request_context)
-
-            # Return updated config
-            config_dict = await app.state.memory._config_resolver.get_bank_config(bank_id, request_context)
-            bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
-
-            return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
-        except ValueError as e:
-            # Validation error (e.g., trying to override static field)
-            raise HTTPException(status_code=400, detail=str(e))
-        except (AuthenticationError, HTTPException):
-            raise
-        except Exception as e:
-            import traceback
-
-            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(f"Error in PATCH /v1/default/banks/{bank_id}/config: {error_detail}")
-            raise HTTPException(status_code=500, detail=str(e))
-
-    @app.delete(
-        "/v1/default/banks/{bank_id}/config",
-        response_model=BankConfigResponse,
-        summary="Reset bank configuration",
-        description="Reset bank configuration to defaults by removing all bank-specific overrides. "
-        "The bank will then use global and tenant-level configuration only.",
-        operation_id="reset_bank_config",
-        tags=["Banks"],
-    )
-    async def api_reset_bank_config(bank_id: str, request_context: RequestContext = Depends(get_request_context)):
-        """Reset bank configuration to defaults (remove all overrides)."""
-        if not get_config().enable_bank_config_api:
-            raise HTTPException(
-                status_code=404,
-                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
-            )
-        try:
-            # Reset config via config resolver
-            await app.state.memory._config_resolver.reset_bank_config(bank_id)
-
-            # Return updated config (should match defaults now)
-            config_dict = await app.state.memory._config_resolver.get_bank_config(bank_id, request_context)
-            bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
-
-            return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
-        except (AuthenticationError, HTTPException):
-            raise
-        except Exception as e:
-            import traceback
-
-            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/config: {error_detail}")
-            raise HTTPException(status_code=500, detail=str(e))
-
    @app.post(
        "/v1/default/banks/{bank_id}/consolidate",
        response_model=ConsolidationResponse,
@@ -8,11 +8,7 @@ from contextvars import ContextVar
 from fastmcp import FastMCP

 from hindsight_api import MemoryEngine
-from hindsight_api.engine.memory_engine import _current_schema
-from hindsight_api.extensions import MCPExtension, load_extension
-from hindsight_api.extensions.tenant import AuthenticationError
 from hindsight_api.mcp_tools import MCPToolsConfig, register_mcp_tools
-from hindsight_api.models import RequestContext

 # Configure logging from HINDSIGHT_API_LOG_LEVEL environment variable
 _log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
@@ -33,49 +29,21 @@ logger = logging.getLogger(__name__)
 # Default bank_id from environment variable
 DEFAULT_BANK_ID = os.environ.get("HINDSIGHT_MCP_BANK_ID", "default")

-# Legacy MCP authentication token (for backwards compatibility)
-# If set, this token is checked first before TenantExtension auth
-MCP_AUTH_TOKEN = os.environ.get("HINDSIGHT_API_MCP_AUTH_TOKEN")
-
 # Context variable to hold the current bank_id
 _current_bank_id: ContextVar[str | None] = ContextVar("current_bank_id", default=None)

-# Context variable to hold the current API key (for tenant auth propagation)
-_current_api_key: ContextVar[str | None] = ContextVar("current_api_key", default=None)
-
-# Context variables for tenant_id and api_key_id (set by authenticate, used by usage metering)
-_current_tenant_id: ContextVar[str | None] = ContextVar("current_tenant_id", default=None)
-_current_api_key_id: ContextVar[str | None] = ContextVar("current_api_key_id", default=None)
-

 def get_current_bank_id() -> str | None:
    """Get the current bank_id from context."""
    return _current_bank_id.get()


-def get_current_api_key() -> str | None:
-    """Get the current API key from context."""
-    return _current_api_key.get()
-
-
-def get_current_tenant_id() -> str | None:
-    """Get the current tenant_id from context."""
-    return _current_tenant_id.get()
-
-
-def get_current_api_key_id() -> str | None:
-    """Get the current api_key_id from context."""
-    return _current_api_key_id.get()
-
-
-def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
+def create_mcp_server(memory: MemoryEngine) -> FastMCP:
    """
    Create and configure the Hindsight MCP server.

    Args:
        memory: MemoryEngine instance (required)
-        multi_bank: If True, expose all tools with bank_id parameters (default).
-                   If False, only expose bank-scoped tools without bank_id parameters.

    Returns:
        Configured FastMCP server instance with stateless_http enabled
@@ -86,134 +54,36 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
    # Configure and register tools using shared module
    config = MCPToolsConfig(
        bank_id_resolver=get_current_bank_id,
-        api_key_resolver=get_current_api_key,  # Propagate API key for tenant auth
-        tenant_id_resolver=get_current_tenant_id,  # Propagate tenant_id for usage metering
-        api_key_id_resolver=get_current_api_key_id,  # Propagate api_key_id for usage metering
-        include_bank_id_param=multi_bank,
-        tools=None
-        if multi_bank
-        else {
-            "retain",
-            "recall",
-            "reflect",
-            "list_mental_models",
-            "get_mental_model",
-            "create_mental_model",
-            "update_mental_model",
-            "delete_mental_model",
-            "refresh_mental_model",
-        },  # Scoped tools for single-bank mode (excludes bank management: list_banks, create_bank)
+        include_bank_id_param=True,  # HTTP MCP supports multi-bank via parameter
+        tools=None,  # All tools
        retain_fire_and_forget=False,  # HTTP MCP supports sync/async modes
    )

    register_mcp_tools(mcp, memory, config)

-    # Load and register additional tools from MCP extension if configured
-    mcp_extension = load_extension("MCP", MCPExtension)
-    if mcp_extension:
-        logger.info(f"Loading MCP extension: {mcp_extension.__class__.__name__}")
-        mcp_extension.register_tools(mcp, memory)
-
-    # Make all tools tolerant of extra arguments from LLMs (e.g., "explanation")
-    _make_tools_tolerant(mcp)
-
    return mcp


-def _make_tools_tolerant(mcp: FastMCP) -> None:
-    """Wrap all tool run methods to strip unknown arguments before validation.
-
-    LLMs frequently add extra fields like "explanation" or "reasoning" to tool calls.
-    FastMCP's Pydantic TypeAdapter rejects these with "Unexpected keyword argument".
-    This wraps each tool's run() to filter arguments to only known parameters.
-    """
-    try:
-        for name, tool in mcp._tool_manager._tools.items():
-            if hasattr(tool, "parameters") and tool.parameters:
-                allowed = set(tool.parameters.get("properties", {}).keys())
-                original_run = tool.run
-
-                async def _tolerant_run(arguments, _allowed=allowed, _orig=original_run):
-                    extra_keys = set(arguments.keys()) - _allowed
-                    if extra_keys:
-                        logger.debug(f"Stripping unknown arguments from tool call: {extra_keys}")
-                        arguments = {k: v for k, v in arguments.items() if k in _allowed}
-                    return await _orig(arguments)
-
-                # FunctionTool is a Pydantic model with extra='forbid', so use
-                # object.__setattr__ to bypass Pydantic's setter validation.
-                object.__setattr__(tool, "run", _tolerant_run)
-    except (AttributeError, KeyError) as e:
-        logger.warning(f"Could not make tools tolerant of extra arguments: {e}")
-
-
 class MCPMiddleware:
-    """ASGI middleware that intercepts MCP requests and routes to appropriate MCP server.
+    """ASGI middleware that extracts bank_id from header or path and sets context.

-    This middleware wraps the main FastAPI app and intercepts requests matching the
-    configured prefix (default: /mcp). Non-MCP requests pass through to the inner app.
+    Bank ID can be provided via:
+    1. X-Bank-Id header (recommended for Claude Code)
+    2. URL path: /mcp/{bank_id}/
+    3. Environment variable HINDSIGHT_MCP_BANK_ID (fallback default)

-    Authentication:
-        1. If HINDSIGHT_API_MCP_AUTH_TOKEN is set (legacy), validates against that token
-        2. Otherwise, uses TenantExtension.authenticate_mcp() from the MemoryEngine
-           - DefaultTenantExtension: no auth required (local dev)
-           - ApiKeyTenantExtension: validates against env var
-
-    Two modes based on URL structure:
-
-    1. Multi-bank mode (for /mcp/ root endpoint):
-       - Exposes all tools: retain, recall, reflect, list_banks, create_bank
-       - All tools include optional bank_id parameter for cross-bank operations
-       - Bank ID from: X-Bank-Id header or HINDSIGHT_MCP_BANK_ID env var
-
-    2. Single-bank mode (for /mcp/{bank_id}/ endpoints):
-       - Exposes bank-scoped tools only: retain, recall, reflect
-       - No bank_id parameter (comes from URL)
-       - No bank management tools (list_banks, create_bank)
-       - Recommended for agent isolation
-
-    Bank ID resolution priority:
-        1. URL path (e.g., /mcp/{bank_id}/) → single-bank mode
-        2. X-Bank-Id header → multi-bank mode
-        3. HINDSIGHT_MCP_BANK_ID env var → multi-bank mode (default: "default")
-
-    Examples:
-        # Single-bank mode (recommended for agent isolation)
-        claude mcp add --transport http my-agent http://localhost:8888/mcp/my-agent-bank/ \\
-            --header "Authorization: Bearer <token>"
-
-        # Multi-bank mode (for cross-bank operations)
+    For Claude Code, configure with:
        claude mcp add --transport http hindsight http://localhost:8888/mcp \\
-            --header "X-Bank-Id: my-bank" --header "Authorization: Bearer <token>"
+            --header "X-Bank-Id: my-bank"
    """

-    def __init__(
-        self,
-        app,
-        memory: MemoryEngine,
-        prefix: str = "/mcp",
-        multi_bank_app=None,
-        single_bank_app=None,
-        multi_bank_server=None,
-        single_bank_server=None,
-    ):
+    def __init__(self, app, memory: MemoryEngine):
        self.app = app
-        self.prefix = prefix
        self.memory = memory
-        self.tenant_extension = memory._tenant_extension
-
-        if multi_bank_app and single_bank_app:
-            # Pre-created servers (used when called via add_middleware from create_app)
-            self.multi_bank_app = multi_bank_app
-            self.single_bank_app = single_bank_app
-            self.multi_bank_server = multi_bank_server
-            self.single_bank_server = single_bank_server
-        else:
-            # Create servers internally (for direct construction / tests)
-            self.multi_bank_server = create_mcp_server(memory, multi_bank=True)
-            self.multi_bank_app = self.multi_bank_server.http_app(path="/")
-            self.single_bank_server = create_mcp_server(memory, multi_bank=False)
-            self.single_bank_app = self.single_bank_server.http_app(path="/")
+        self.mcp_server = create_mcp_server(memory)
+        self.mcp_app = self.mcp_server.http_app(path="/")
+        # Expose the lifespan for the parent app to chain
+        self.lifespan = self.mcp_app.lifespan_handler if hasattr(self.mcp_app, "lifespan_handler") else None

    def _get_header(self, scope: dict, name: str) -> str | None:
        """Extract a header value from ASGI scope."""
@@ -225,113 +95,54 @@ class MCPMiddleware:

    async def __call__(self, scope, receive, send):
        if scope["type"] != "http":
-            await self.app(scope, receive, send)
+            await self.mcp_app(scope, receive, send)
            return

        path = scope.get("path", "")

-        # Check if this is an MCP request (matches prefix)
-        if not (path == self.prefix or path.startswith(self.prefix + "/")):
-            # Not an MCP request — pass through to the inner app
-            await self.app(scope, receive, send)
-            return
+        # Strip any mount prefix (e.g., /mcp) that FastAPI might not have stripped
+        root_path = scope.get("root_path", "")
+        if root_path and path.startswith(root_path):
+            path = path[len(root_path) :] or "/"

-        # Strip prefix from path
-        path = path[len(self.prefix) :] or "/"
+        # Also handle case where mount path wasn't stripped (e.g., /mcp/...)
+        if path.startswith("/mcp/"):
+            path = path[4:]  # Remove /mcp prefix
+        elif path == "/mcp":
+            path = "/"

-        # Extract auth token from header (for tenant auth propagation)
-        auth_header = self._get_header(scope, "Authorization")
-        auth_token: str | None = None
-        if auth_header:
-            # Support both "Bearer <token>" and direct token
-            auth_token = auth_header[7:].strip() if auth_header.startswith("Bearer ") else auth_header.strip()
+        # Try to get bank_id from header first (for Claude Code compatibility)
+        bank_id = self._get_header(scope, "X-Bank-Id")

-        # Authenticate: check legacy MCP_AUTH_TOKEN first, then TenantExtension
-        tenant_context = None
-        auth_tenant_id: str | None = None
-        auth_api_key_id: str | None = None
-        if MCP_AUTH_TOKEN:
-            # Legacy authentication mode - validate against static token
-            if not auth_token:
-                await self._send_error(send, 401, "Authorization header required")
-                return
-            if auth_token != MCP_AUTH_TOKEN:
-                await self._send_error(send, 401, "Invalid authentication token")
-                return
-            # Legacy mode doesn't use tenant schemas
-            tenant_context = None
-        else:
-            # Use TenantExtension.authenticate_mcp() for auth
-            try:
-                auth_context = RequestContext(api_key=auth_token)
-                tenant_context = await self.tenant_extension.authenticate_mcp(auth_context)
-                # Capture tenant_id and api_key_id set by authenticate() for usage metering
-                auth_tenant_id = auth_context.tenant_id
-                auth_api_key_id = auth_context.api_key_id
-            except AuthenticationError as e:
-                await self._send_error(send, 401, str(e))
-                return
+        # MCP endpoint paths that should not be treated as bank_ids
+        MCP_ENDPOINTS = {"sse", "messages"}

-        # Set schema from tenant context so downstream DB queries use the correct schema
-        schema_token = (
-            _current_schema.set(tenant_context.schema_name) if tenant_context and tenant_context.schema_name else None
-        )
-
-        # Resolve bank_id: path takes priority over header.
-        # Path = user's explicit connection endpoint (e.g., /mcp/my-bank/).
-        # X-Bank-Id header = per-request override for multi-bank mode only.
-        bank_id = None
-        bank_id_from_path = False
+        # If no header, try to extract from path: /{bank_id}/...
        new_path = path
-
-        # First, try to extract from path: /{bank_id}/...
-        if path.startswith("/") and len(path) > 1:
+        if not bank_id and path.startswith("/") and len(path) > 1:
            parts = path[1:].split("/", 1)
-            if parts[0]:
+            # Don't treat MCP endpoints as bank_ids
+            if parts[0] and parts[0] not in MCP_ENDPOINTS:
+                # First segment looks like a bank_id
                bank_id = parts[0]
-                bank_id_from_path = True
                new_path = "/" + parts[1] if len(parts) > 1 else "/"

-        # If no path-based bank_id, try X-Bank-Id header (multi-bank mode)
-        if not bank_id:
-            bank_id = self._get_header(scope, "X-Bank-Id")
-
        # Fall back to default bank_id
        if not bank_id:
            bank_id = DEFAULT_BANK_ID
            logger.debug(f"Using default bank_id: {bank_id}")

-        # Select the appropriate MCP app based on how bank_id was provided:
-        # - Path-based bank_id → single-bank app (no bank_id param, scoped tools)
-        # - Header/env bank_id → multi-bank app (bank_id param, all tools)
-        target_app = self.single_bank_app if bank_id_from_path else self.multi_bank_app
-
-        # Set bank_id, api_key, tenant_id, and api_key_id context
-        bank_id_token = _current_bank_id.set(bank_id)
-        # Store the auth token for tenant extension to validate
-        api_key_token = _current_api_key.set(auth_token) if auth_token else None
-        # Store tenant_id and api_key_id from authentication for usage metering
-        tenant_id_token = _current_tenant_id.set(auth_tenant_id) if auth_tenant_id else None
-        api_key_id_token = _current_api_key_id.set(auth_api_key_id) if auth_api_key_id else None
+        # Set bank_id context
+        token = _current_bank_id.set(bank_id)
        try:
            new_scope = scope.copy()
            new_scope["path"] = new_path
            # Clear root_path since we're passing directly to the app
            new_scope["root_path"] = ""

-            # Wrap send to rewrite the SSE endpoint URL to include bank_id if using path-based routing.
-            # Only rewrite SSE (text/event-stream) responses to avoid corrupting tool results
-            # that might contain the literal string "data: /messages".
-            is_sse_response = False
-
+            # Wrap send to rewrite the SSE endpoint URL to include bank_id if using path-based routing
            async def send_wrapper(message):
-                nonlocal is_sse_response
-                if message["type"] == "http.response.start":
-                    for header_name, header_value in message.get("headers", []):
-                        if header_name == b"content-type" and b"text/event-stream" in header_value:
-                            is_sse_response = True
-                            break
-                if message["type"] == "http.response.body" and bank_id_from_path and is_sse_response:
+                if message["type"] == "http.response.body":
                    body = message.get("body", b"")
                    if body and b"/messages" in body:
                        # Rewrite /messages to /{bank_id}/messages in SSE endpoint event
@@ -339,17 +150,9 @@ class MCPMiddleware:
                        message = {**message, "body": body}
                await send(message)

-            await target_app(new_scope, receive, send_wrapper)
+            await self.mcp_app(new_scope, receive, send_wrapper)
        finally:
-            _current_bank_id.reset(bank_id_token)
-            if api_key_token is not None:
-                _current_api_key.reset(api_key_token)
-            if tenant_id_token is not None:
-                _current_tenant_id.reset(tenant_id_token)
-            if api_key_id_token is not None:
-                _current_api_key_id.reset(api_key_id_token)
-            if schema_token is not None:
-                _current_schema.reset(schema_token)
+            _current_bank_id.reset(token)

    async def _send_error(self, send, status: int, message: str):
        """Send an error response."""
@@ -369,19 +172,19 @@ class MCPMiddleware:
        )


-def create_mcp_servers(memory: MemoryEngine):
-    """Create multi-bank and single-bank MCP servers and their Starlette apps.
+def create_mcp_app(memory: MemoryEngine):
+    """
+    Create an ASGI app that handles MCP requests.

-    Returns the servers and apps separately so lifespans can be chained before
-    the middleware wraps the main app.
+    Bank ID can be provided via:
+    1. X-Bank-Id header: claude mcp add --transport http hindsight http://localhost:8888/mcp --header "X-Bank-Id: my-bank"
+    2. URL path: /mcp/{bank_id}/
+    3. Environment variable HINDSIGHT_MCP_BANK_ID (fallback, default: "default")
+
+    Args:
+        memory: MemoryEngine instance

    Returns:
-        Tuple of (multi_bank_server, single_bank_server, multi_bank_app, single_bank_app)
+        ASGI application
    """
-    multi_bank_server = create_mcp_server(memory, multi_bank=True)
-    multi_bank_app = multi_bank_server.http_app(path="/")
-
-    single_bank_server = create_mcp_server(memory, multi_bank=False)
-    single_bank_app = single_bank_server.http_app(path="/")
-
-    return multi_bank_server, single_bank_server, multi_bank_app, single_bank_app
+    return MCPMiddleware(None, memory)
@@ -4,8 +4,6 @@ Banner display for Hindsight API startup.
 Shows the logo and tagline with gradient colors.
 """

-from .utils import mask_network_location
-
 # Gradient colors: #0074d9 -> #009296
 GRADIENT_START = (0, 116, 217)  # #0074d9
 GRADIENT_END = (0, 146, 150)  # #009296
@@ -85,14 +83,11 @@ def print_startup_info(
    embeddings_provider: str,
    reranker_provider: str,
    mcp_enabled: bool = False,
-    version: str | None = None,
 ):
    """Print styled startup information."""
    print(color_start("Starting Hindsight API..."))
-    if version:
-        print(f"  {dim('Version:')} {color(f'v{version}', 0.1)}")
    print(f"  {dim('URL:')} {color(f'http://{host}:{port}', 0.2)}")
-    print(f"  {dim('Database:')} {color(mask_network_location(database_url), 0.4)}")
+    print(f"  {dim('Database:')} {color(database_url, 0.4)}")
    print(f"  {dim('LLM:')} {color(f'{llm_provider} / {llm_model}', 0.6)}")
    print(f"  {dim('Embeddings:')} {color(embeddings_provider, 0.8)}")
    print(f"  {dim('Reranker:')} {color(reranker_provider, 1.0)}")
@@ -8,9 +8,8 @@ import json
 import logging
 import os
 import sys
-from dataclasses import dataclass, field, fields
+from dataclasses import dataclass
 from datetime import datetime, timezone
-from typing import Any

 from dotenv import find_dotenv, load_dotenv

@@ -19,114 +18,13 @@ load_dotenv(find_dotenv(usecwd=True), override=True)

 logger = logging.getLogger(__name__)

-
-class ConfigFieldAccessError(AttributeError):
-    """Raised when trying to access a bank-configurable field from global config."""
-
-    pass
-
-
-class StaticConfigProxy:
-    """
-    Proxy that wraps HindsightConfig and only allows access to static (non-configurable) fields.
-
-    Raises ConfigFieldAccessError when trying to access configurable fields that vary per-bank.
-    Forces developers to use get_resolved_config(bank_id, context) for bank-specific settings.
-    """
-
-    def __init__(self, config: "HindsightConfig"):
-        object.__setattr__(self, "_config", config)
-        object.__setattr__(self, "_configurable_fields", HindsightConfig.get_configurable_fields())
-
-    def __getattribute__(self, name: str):
-        if name.startswith("_"):
-            return object.__getattribute__(self, name)
-
-        configurable_fields = object.__getattribute__(self, "_configurable_fields")
-        if name in configurable_fields:
-            raise ConfigFieldAccessError(
-                f"Field '{name}' is bank-configurable and cannot be accessed from global config. "
-                f"Use ConfigResolver.resolve_full_config(bank_id, context) to get bank-specific config. "
-                f"This prevents accidentally using global defaults when bank-specific overrides exist."
-            )
-
-        config = object.__getattribute__(self, "_config")
-        return getattr(config, name)
-
-    def __setattr__(self, name: str, value):
-        raise AttributeError("Config is read-only. Modifications must go through ConfigResolver.")
-
-
-# Configuration field markers for hierarchical configuration
-def hierarchical(default_value):
-    """
-    Mark a config field as hierarchical (can be overridden per-tenant/bank).
-
-    Hierarchical fields can be customized at the tenant or bank level via database
-    configuration. Examples: LLM settings, retention parameters, retrieval settings.
-    """
-    return field(default=default_value, metadata={"hierarchical": True})
-
-
-def static(default_value):
-    """
-    Mark a config field as static (server-level only, cannot be overridden).
-
-    Static fields are infrastructure-level settings that affect the entire server
-    and cannot vary per tenant or bank. Examples: database URL, API port, worker settings.
-    """
-    return field(default=default_value, metadata={"hierarchical": False})
-
-
-# Configuration key normalization utilities
-def normalize_config_key(key: str) -> str:
-    """
-    Convert environment variable format to Python field name format.
-
-    Examples:
-        HINDSIGHT_API_LLM_PROVIDER -> llm_provider
-        LLM_MODEL -> llm_model
-        llm_model -> llm_model (already normalized)
-
-    Args:
-        key: Environment variable name or Python field name
-
-    Returns:
-        Normalized Python field name (lowercase snake_case)
-    """
-    if key.startswith("HINDSIGHT_API_"):
-        key = key[len("HINDSIGHT_API_") :]
-    return key.lower()
-
-
-def normalize_config_dict(config: dict[str, Any]) -> dict[str, Any]:
-    """
-    Normalize all keys in a config dict to Python field names.
-
-    Allows users to provide config overrides in either format:
-    - Python field format: {"llm_provider": "openai"}
-    - Env var format: {"HINDSIGHT_API_LLM_PROVIDER": "openai"}
-
-    Args:
-        config: Dict with env var or Python field names as keys
-
-    Returns:
-        Dict with all keys normalized to Python field names
-    """
-    return {normalize_config_key(k): v for k, v in config.items()}
-
-
 # Environment variable names
 ENV_DATABASE_URL = "HINDSIGHT_API_DATABASE_URL"
-ENV_DATABASE_SCHEMA = "HINDSIGHT_API_DATABASE_SCHEMA"
 ENV_LLM_PROVIDER = "HINDSIGHT_API_LLM_PROVIDER"
 ENV_LLM_API_KEY = "HINDSIGHT_API_LLM_API_KEY"
 ENV_LLM_MODEL = "HINDSIGHT_API_LLM_MODEL"
 ENV_LLM_BASE_URL = "HINDSIGHT_API_LLM_BASE_URL"
 ENV_LLM_MAX_CONCURRENT = "HINDSIGHT_API_LLM_MAX_CONCURRENT"
-ENV_LLM_MAX_RETRIES = "HINDSIGHT_API_LLM_MAX_RETRIES"
-ENV_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_LLM_INITIAL_BACKOFF"
-ENV_LLM_MAX_BACKOFF = "HINDSIGHT_API_LLM_MAX_BACKOFF"
 ENV_LLM_TIMEOUT = "HINDSIGHT_API_LLM_TIMEOUT"
 ENV_LLM_GROQ_SERVICE_TIER = "HINDSIGHT_API_LLM_GROQ_SERVICE_TIER"

@@ -135,77 +33,39 @@ ENV_RETAIN_LLM_PROVIDER = "HINDSIGHT_API_RETAIN_LLM_PROVIDER"
 ENV_RETAIN_LLM_API_KEY = "HINDSIGHT_API_RETAIN_LLM_API_KEY"
 ENV_RETAIN_LLM_MODEL = "HINDSIGHT_API_RETAIN_LLM_MODEL"
 ENV_RETAIN_LLM_BASE_URL = "HINDSIGHT_API_RETAIN_LLM_BASE_URL"
-ENV_RETAIN_LLM_MAX_CONCURRENT = "HINDSIGHT_API_RETAIN_LLM_MAX_CONCURRENT"
-ENV_RETAIN_LLM_MAX_RETRIES = "HINDSIGHT_API_RETAIN_LLM_MAX_RETRIES"
-ENV_RETAIN_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_RETAIN_LLM_INITIAL_BACKOFF"
-ENV_RETAIN_LLM_MAX_BACKOFF = "HINDSIGHT_API_RETAIN_LLM_MAX_BACKOFF"
-ENV_RETAIN_LLM_TIMEOUT = "HINDSIGHT_API_RETAIN_LLM_TIMEOUT"

 ENV_REFLECT_LLM_PROVIDER = "HINDSIGHT_API_REFLECT_LLM_PROVIDER"
 ENV_REFLECT_LLM_API_KEY = "HINDSIGHT_API_REFLECT_LLM_API_KEY"
 ENV_REFLECT_LLM_MODEL = "HINDSIGHT_API_REFLECT_LLM_MODEL"
 ENV_REFLECT_LLM_BASE_URL = "HINDSIGHT_API_REFLECT_LLM_BASE_URL"
-ENV_REFLECT_LLM_MAX_CONCURRENT = "HINDSIGHT_API_REFLECT_LLM_MAX_CONCURRENT"
-ENV_REFLECT_LLM_MAX_RETRIES = "HINDSIGHT_API_REFLECT_LLM_MAX_RETRIES"
-ENV_REFLECT_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_REFLECT_LLM_INITIAL_BACKOFF"
-ENV_REFLECT_LLM_MAX_BACKOFF = "HINDSIGHT_API_REFLECT_LLM_MAX_BACKOFF"
-ENV_REFLECT_LLM_TIMEOUT = "HINDSIGHT_API_REFLECT_LLM_TIMEOUT"

 ENV_CONSOLIDATION_LLM_PROVIDER = "HINDSIGHT_API_CONSOLIDATION_LLM_PROVIDER"
 ENV_CONSOLIDATION_LLM_API_KEY = "HINDSIGHT_API_CONSOLIDATION_LLM_API_KEY"
 ENV_CONSOLIDATION_LLM_MODEL = "HINDSIGHT_API_CONSOLIDATION_LLM_MODEL"
 ENV_CONSOLIDATION_LLM_BASE_URL = "HINDSIGHT_API_CONSOLIDATION_LLM_BASE_URL"
-ENV_CONSOLIDATION_LLM_MAX_CONCURRENT = "HINDSIGHT_API_CONSOLIDATION_LLM_MAX_CONCURRENT"
-ENV_CONSOLIDATION_LLM_MAX_RETRIES = "HINDSIGHT_API_CONSOLIDATION_LLM_MAX_RETRIES"
-ENV_CONSOLIDATION_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_CONSOLIDATION_LLM_INITIAL_BACKOFF"
-ENV_CONSOLIDATION_LLM_MAX_BACKOFF = "HINDSIGHT_API_CONSOLIDATION_LLM_MAX_BACKOFF"
-ENV_CONSOLIDATION_LLM_TIMEOUT = "HINDSIGHT_API_CONSOLIDATION_LLM_TIMEOUT"

 ENV_EMBEDDINGS_PROVIDER = "HINDSIGHT_API_EMBEDDINGS_PROVIDER"
 ENV_EMBEDDINGS_LOCAL_MODEL = "HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL"
-ENV_EMBEDDINGS_LOCAL_FORCE_CPU = "HINDSIGHT_API_EMBEDDINGS_LOCAL_FORCE_CPU"
-ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE = "HINDSIGHT_API_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE"
 ENV_EMBEDDINGS_TEI_URL = "HINDSIGHT_API_EMBEDDINGS_TEI_URL"
 ENV_EMBEDDINGS_OPENAI_API_KEY = "HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY"
 ENV_EMBEDDINGS_OPENAI_MODEL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL"
 ENV_EMBEDDINGS_OPENAI_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_BASE_URL"

-# Cohere configuration (separate for embeddings and reranker)
-ENV_EMBEDDINGS_COHERE_API_KEY = "HINDSIGHT_API_EMBEDDINGS_COHERE_API_KEY"
+ENV_COHERE_API_KEY = "HINDSIGHT_API_COHERE_API_KEY"
 ENV_EMBEDDINGS_COHERE_MODEL = "HINDSIGHT_API_EMBEDDINGS_COHERE_MODEL"
 ENV_EMBEDDINGS_COHERE_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_COHERE_BASE_URL"
-ENV_RERANKER_COHERE_API_KEY = "HINDSIGHT_API_RERANKER_COHERE_API_KEY"
 ENV_RERANKER_COHERE_MODEL = "HINDSIGHT_API_RERANKER_COHERE_MODEL"
 ENV_RERANKER_COHERE_BASE_URL = "HINDSIGHT_API_RERANKER_COHERE_BASE_URL"

-# Deprecated: Legacy shared Cohere API key (for backward compatibility)
-ENV_COHERE_API_KEY = "HINDSIGHT_API_COHERE_API_KEY"
-
-# LiteLLM configuration (separate for embeddings and reranker)
-ENV_EMBEDDINGS_LITELLM_API_BASE = "HINDSIGHT_API_EMBEDDINGS_LITELLM_API_BASE"
-ENV_EMBEDDINGS_LITELLM_API_KEY = "HINDSIGHT_API_EMBEDDINGS_LITELLM_API_KEY"
-ENV_EMBEDDINGS_LITELLM_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_MODEL"
-ENV_RERANKER_LITELLM_API_BASE = "HINDSIGHT_API_RERANKER_LITELLM_API_BASE"
-ENV_RERANKER_LITELLM_API_KEY = "HINDSIGHT_API_RERANKER_LITELLM_API_KEY"
-ENV_RERANKER_LITELLM_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_MODEL"
-
-# LiteLLM SDK configuration (direct API access, no proxy needed)
-ENV_EMBEDDINGS_LITELLM_SDK_API_KEY = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_API_KEY"
-ENV_EMBEDDINGS_LITELLM_SDK_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_MODEL"
-ENV_EMBEDDINGS_LITELLM_SDK_API_BASE = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_API_BASE"
-ENV_RERANKER_LITELLM_SDK_API_KEY = "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY"
-ENV_RERANKER_LITELLM_SDK_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_SDK_MODEL"
-ENV_RERANKER_LITELLM_SDK_API_BASE = "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_BASE"
-
-# Deprecated: Legacy shared LiteLLM config (for backward compatibility)
+# LiteLLM gateway configuration (for embeddings and reranker via LiteLLM proxy)
 ENV_LITELLM_API_BASE = "HINDSIGHT_API_LITELLM_API_BASE"
 ENV_LITELLM_API_KEY = "HINDSIGHT_API_LITELLM_API_KEY"
+ENV_EMBEDDINGS_LITELLM_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_MODEL"
+ENV_RERANKER_LITELLM_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_MODEL"

 ENV_RERANKER_PROVIDER = "HINDSIGHT_API_RERANKER_PROVIDER"
 ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
-ENV_RERANKER_LOCAL_FORCE_CPU = "HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU"
 ENV_RERANKER_LOCAL_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT"
-ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE = "HINDSIGHT_API_RERANKER_LOCAL_TRUST_REMOTE_CODE"
 ENV_RERANKER_TEI_URL = "HINDSIGHT_API_RERANKER_TEI_URL"
 ENV_RERANKER_TEI_BATCH_SIZE = "HINDSIGHT_API_RERANKER_TEI_BATCH_SIZE"
 ENV_RERANKER_TEI_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_TEI_MAX_CONCURRENT"
@@ -213,17 +73,12 @@ ENV_RERANKER_MAX_CANDIDATES = "HINDSIGHT_API_RERANKER_MAX_CANDIDATES"
 ENV_RERANKER_FLASHRANK_MODEL = "HINDSIGHT_API_RERANKER_FLASHRANK_MODEL"
 ENV_RERANKER_FLASHRANK_CACHE_DIR = "HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR"

-ENV_VECTOR_EXTENSION = "HINDSIGHT_API_VECTOR_EXTENSION"
-ENV_TEXT_SEARCH_EXTENSION = "HINDSIGHT_API_TEXT_SEARCH_EXTENSION"
-
 ENV_HOST = "HINDSIGHT_API_HOST"
 ENV_PORT = "HINDSIGHT_API_PORT"
-ENV_BASE_PATH = "HINDSIGHT_API_BASE_PATH"
 ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
 ENV_LOG_FORMAT = "HINDSIGHT_API_LOG_FORMAT"
 ENV_WORKERS = "HINDSIGHT_API_WORKERS"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
-ENV_ENABLE_BANK_CONFIG_API = "HINDSIGHT_API_ENABLE_BANK_CONFIG_API"
 ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
 ENV_MPFP_TOP_K_NEIGHBORS = "HINDSIGHT_API_MPFP_TOP_K_NEIGHBORS"
 ENV_RECALL_MAX_CONCURRENT = "HINDSIGHT_API_RECALL_MAX_CONCURRENT"
@@ -232,30 +87,17 @@ ENV_MCP_LOCAL_BANK_ID = "HINDSIGHT_API_MCP_LOCAL_BANK_ID"
 ENV_MCP_INSTRUCTIONS = "HINDSIGHT_API_MCP_INSTRUCTIONS"
 ENV_MENTAL_MODEL_REFRESH_CONCURRENCY = "HINDSIGHT_API_MENTAL_MODEL_REFRESH_CONCURRENCY"

-# OpenTelemetry tracing configuration
-ENV_OTEL_TRACES_ENABLED = "HINDSIGHT_API_OTEL_TRACES_ENABLED"
-ENV_OTEL_EXPORTER_OTLP_ENDPOINT = "HINDSIGHT_API_OTEL_EXPORTER_OTLP_ENDPOINT"
-ENV_OTEL_EXPORTER_OTLP_HEADERS = "HINDSIGHT_API_OTEL_EXPORTER_OTLP_HEADERS"
-ENV_OTEL_SERVICE_NAME = "HINDSIGHT_API_OTEL_SERVICE_NAME"
-ENV_OTEL_DEPLOYMENT_ENVIRONMENT = "HINDSIGHT_API_OTEL_DEPLOYMENT_ENVIRONMENT"
-
-# Vertex AI configuration
-ENV_LLM_VERTEXAI_PROJECT_ID = "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID"
-ENV_LLM_VERTEXAI_REGION = "HINDSIGHT_API_LLM_VERTEXAI_REGION"
-ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY = "HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY"
-
 # Retain settings
 ENV_RETAIN_MAX_COMPLETION_TOKENS = "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"
 ENV_RETAIN_CHUNK_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_SIZE"
 ENV_RETAIN_EXTRACT_CAUSAL_LINKS = "HINDSIGHT_API_RETAIN_EXTRACT_CAUSAL_LINKS"
 ENV_RETAIN_EXTRACTION_MODE = "HINDSIGHT_API_RETAIN_EXTRACTION_MODE"
 ENV_RETAIN_CUSTOM_INSTRUCTIONS = "HINDSIGHT_API_RETAIN_CUSTOM_INSTRUCTIONS"
-ENV_RETAIN_BATCH_TOKENS = "HINDSIGHT_API_RETAIN_BATCH_TOKENS"
+ENV_RETAIN_OBSERVATIONS_ASYNC = "HINDSIGHT_API_RETAIN_OBSERVATIONS_ASYNC"

 # Observations settings (consolidated knowledge from facts)
 ENV_ENABLE_OBSERVATIONS = "HINDSIGHT_API_ENABLE_OBSERVATIONS"
 ENV_CONSOLIDATION_BATCH_SIZE = "HINDSIGHT_API_CONSOLIDATION_BATCH_SIZE"
-ENV_CONSOLIDATION_MAX_TOKENS = "HINDSIGHT_API_CONSOLIDATION_MAX_TOKENS"

 # Optimization flags
 ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
@@ -275,57 +117,27 @@ ENV_WORKER_ENABLED = "HINDSIGHT_API_WORKER_ENABLED"
 ENV_WORKER_ID = "HINDSIGHT_API_WORKER_ID"
 ENV_WORKER_POLL_INTERVAL_MS = "HINDSIGHT_API_WORKER_POLL_INTERVAL_MS"
 ENV_WORKER_MAX_RETRIES = "HINDSIGHT_API_WORKER_MAX_RETRIES"
+ENV_WORKER_BATCH_SIZE = "HINDSIGHT_API_WORKER_BATCH_SIZE"
 ENV_WORKER_HTTP_PORT = "HINDSIGHT_API_WORKER_HTTP_PORT"
-ENV_WORKER_MAX_SLOTS = "HINDSIGHT_API_WORKER_MAX_SLOTS"
-ENV_WORKER_CONSOLIDATION_MAX_SLOTS = "HINDSIGHT_API_WORKER_CONSOLIDATION_MAX_SLOTS"

 # Reflect agent settings
 ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"

 # Default values
 DEFAULT_DATABASE_URL = "pg0"
-DEFAULT_DATABASE_SCHEMA = "public"
 DEFAULT_LLM_PROVIDER = "openai"
-
-# Provider-specific default models
-PROVIDER_DEFAULT_MODELS = {
-    "openai": "o3-mini",
-    "anthropic": "claude-haiku-4-5-20251001",
-    "gemini": "gemini-2.5-flash",
-    "groq": "openai/gpt-oss-120b",
-    "ollama": "gemma3:12b",
-    "lmstudio": "local-model",
-    "vertexai": "gemini-2.0-flash-001",
-    "openai-codex": "gpt-5.2-codex",
-    "claude-code": "claude-sonnet-4-5-20250929",
-    "mock": "mock-model",
-}
-DEFAULT_LLM_MODEL = "o3-mini"  # Fallback if provider not in table
+DEFAULT_LLM_MODEL = "gpt-5-mini"
 DEFAULT_LLM_MAX_CONCURRENT = 32
-DEFAULT_LLM_MAX_RETRIES = 10  # Max retry attempts for LLM API calls
-DEFAULT_LLM_INITIAL_BACKOFF = 1.0  # Initial backoff in seconds for retry exponential backoff
-DEFAULT_LLM_MAX_BACKOFF = 60.0  # Max backoff cap in seconds for retry exponential backoff
 DEFAULT_LLM_TIMEOUT = 120.0  # seconds

-# Vertex AI defaults
-DEFAULT_LLM_VERTEXAI_PROJECT_ID = None  # Required for Vertex AI
-DEFAULT_LLM_VERTEXAI_REGION = "us-central1"
-DEFAULT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY = None  # Optional, uses ADC if not set
-
 DEFAULT_EMBEDDINGS_PROVIDER = "local"
 DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
-DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU = False  # Force CPU mode for local embeddings (avoids MPS/XPC issues on macOS)
-DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE = False  # Security: disabled by default, required for some models
 DEFAULT_EMBEDDINGS_OPENAI_MODEL = "text-embedding-3-small"
 DEFAULT_EMBEDDING_DIMENSION = 384

 DEFAULT_RERANKER_PROVIDER = "local"
 DEFAULT_RERANKER_LOCAL_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
-DEFAULT_RERANKER_LOCAL_FORCE_CPU = False  # Force CPU mode for local reranker (avoids MPS/XPC issues on macOS)
 DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4  # Limit concurrent CPU-bound reranking to prevent thrashing
-DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE = (
-    False  # Security: disabled by default, required for some models like jina-reranker-v2
-)
 DEFAULT_RERANKER_TEI_BATCH_SIZE = 128
 DEFAULT_RERANKER_TEI_MAX_CONCURRENT = 8
 DEFAULT_RERANKER_MAX_CANDIDATES = 300
@@ -335,29 +147,17 @@ DEFAULT_RERANKER_FLASHRANK_CACHE_DIR = None  # Use default cache directory
 DEFAULT_EMBEDDINGS_COHERE_MODEL = "embed-english-v3.0"
 DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"

-# Vector extension (pgvector vs vchord)
-DEFAULT_VECTOR_EXTENSION = "pgvector"  # Options: "pgvector", "vchord"
-
-# Text search extension (native PostgreSQL, vchord BM25, or Timescale pg_textsearch)
-DEFAULT_TEXT_SEARCH_EXTENSION = "native"  # Options: "native", "vchord", "pg_textsearch"
-
 # LiteLLM defaults
 DEFAULT_LITELLM_API_BASE = "http://localhost:4000"
 DEFAULT_EMBEDDINGS_LITELLM_MODEL = "text-embedding-3-small"
 DEFAULT_RERANKER_LITELLM_MODEL = "cohere/rerank-english-v3.0"

-# LiteLLM SDK defaults
-DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL = "cohere/embed-english-v3.0"
-DEFAULT_RERANKER_LITELLM_SDK_MODEL = "cohere/rerank-english-v3.0"
-
 DEFAULT_HOST = "0.0.0.0"
 DEFAULT_PORT = 8888
-DEFAULT_BASE_PATH = ""  # Empty string = root path
 DEFAULT_LOG_LEVEL = "info"
 DEFAULT_LOG_FORMAT = "text"  # Options: "text", "json"
 DEFAULT_WORKERS = 1
 DEFAULT_MCP_ENABLED = True
-DEFAULT_ENABLE_BANK_CONFIG_API = False  # Disabled by default for security
 DEFAULT_GRAPH_RETRIEVER = "link_expansion"  # Options: "link_expansion", "mpfp", "bfs"
 DEFAULT_MPFP_TOP_K_NEIGHBORS = 20  # Fan-out limit per node in MPFP graph traversal
 DEFAULT_RECALL_MAX_CONCURRENT = 32  # Max concurrent recall operations per worker
@@ -372,12 +172,11 @@ DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS = True  # Extract causal links between facts
 DEFAULT_RETAIN_EXTRACTION_MODE = "concise"  # Extraction mode: "concise", "verbose", or "custom"
 RETAIN_EXTRACTION_MODES = ("concise", "verbose", "custom")  # Allowed extraction modes
 DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS = None  # Custom extraction guidelines (only used when mode="custom")
-DEFAULT_RETAIN_BATCH_TOKENS = 10_000  # ~40KB of text  # Max chars per sub-batch for async retain auto-splitting
+DEFAULT_RETAIN_OBSERVATIONS_ASYNC = False  # Run observation generation async (after retain completes)

 # Observations defaults (consolidated knowledge from facts)
 DEFAULT_ENABLE_OBSERVATIONS = True  # Observations enabled by default
 DEFAULT_CONSOLIDATION_BATCH_SIZE = 50  # Memories to load per batch (internal memory optimization)
-DEFAULT_CONSOLIDATION_MAX_TOKENS = 1024  # Max tokens for recall when finding related observations

 # Database migrations
 DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
@@ -393,18 +192,12 @@ DEFAULT_WORKER_ENABLED = True  # API runs worker by default (standalone mode)
 DEFAULT_WORKER_ID = None  # Will use hostname if not specified
 DEFAULT_WORKER_POLL_INTERVAL_MS = 500  # Poll database every 500ms
 DEFAULT_WORKER_MAX_RETRIES = 3  # Max retries before marking task failed
+DEFAULT_WORKER_BATCH_SIZE = 10  # Tasks to claim per poll cycle
 DEFAULT_WORKER_HTTP_PORT = 8889  # HTTP port for worker metrics/health
-DEFAULT_WORKER_MAX_SLOTS = 10  # Total concurrent tasks per worker
-DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS = 2  # Max concurrent consolidation tasks per worker

 # Reflect agent settings
 DEFAULT_REFLECT_MAX_ITERATIONS = 10  # Max tool call iterations before forcing response

-# OpenTelemetry tracing configuration
-DEFAULT_OTEL_TRACES_ENABLED = False  # Disabled by default for backward compatibility
-DEFAULT_OTEL_SERVICE_NAME = "hindsight-api"
-DEFAULT_OTEL_DEPLOYMENT_ENVIRONMENT = "development"
-
 # Default MCP tool descriptions (can be customized via env vars)
 DEFAULT_MCP_RETAIN_DESCRIPTION = """Store important information to long-term memory.

@@ -471,20 +264,12 @@ def _validate_extraction_mode(mode: str) -> str:
    return mode_lower


-def _get_default_model_for_provider(provider: str) -> str:
-    """Get the default model for a given provider."""
-    return PROVIDER_DEFAULT_MODELS.get(provider.lower(), DEFAULT_LLM_MODEL)
-
-
@dataclass
 class HindsightConfig:
    """Configuration container for Hindsight API."""

    # Database
    database_url: str
-    database_schema: str
-    vector_extension: str  # "pgvector" or "vchord"
-    text_search_extension: str  # "native" or "vchord"

    # LLM (default, used as fallback for per-operation config)
    llm_provider: str
@@ -492,92 +277,46 @@ class HindsightConfig:
    llm_model: str
    llm_base_url: str | None
    llm_max_concurrent: int
-    llm_max_retries: int
-    llm_initial_backoff: float
-    llm_max_backoff: float
    llm_timeout: float

-    # Vertex AI configuration
-    llm_vertexai_project_id: str | None
-    llm_vertexai_region: str
-    llm_vertexai_service_account_key: str | None
-
    # Per-operation LLM configuration (None = use default LLM config)
    retain_llm_provider: str | None
    retain_llm_api_key: str | None
    retain_llm_model: str | None
    retain_llm_base_url: str | None
-    retain_llm_max_concurrent: int | None
-    retain_llm_max_retries: int | None
-    retain_llm_initial_backoff: float | None
-    retain_llm_max_backoff: float | None
-    retain_llm_timeout: float | None

    reflect_llm_provider: str | None
    reflect_llm_api_key: str | None
    reflect_llm_model: str | None
    reflect_llm_base_url: str | None
-    reflect_llm_max_concurrent: int | None
-    reflect_llm_max_retries: int | None
-    reflect_llm_initial_backoff: float | None
-    reflect_llm_max_backoff: float | None
-    reflect_llm_timeout: float | None

    consolidation_llm_provider: str | None
    consolidation_llm_api_key: str | None
    consolidation_llm_model: str | None
    consolidation_llm_base_url: str | None
-    consolidation_llm_max_concurrent: int | None
-    consolidation_llm_max_retries: int | None
-    consolidation_llm_initial_backoff: float | None
-    consolidation_llm_max_backoff: float | None
-    consolidation_llm_timeout: float | None

    # Embeddings
    embeddings_provider: str
    embeddings_local_model: str
-    embeddings_local_force_cpu: bool
-    embeddings_local_trust_remote_code: bool
    embeddings_tei_url: str | None
    embeddings_openai_base_url: str | None
-    embeddings_cohere_api_key: str | None
-    embeddings_cohere_model: str
    embeddings_cohere_base_url: str | None
-    embeddings_litellm_api_base: str
-    embeddings_litellm_api_key: str | None
-    embeddings_litellm_model: str
-    embeddings_litellm_sdk_api_key: str | None
-    embeddings_litellm_sdk_model: str
-    embeddings_litellm_sdk_api_base: str | None

    # Reranker
    reranker_provider: str
    reranker_local_model: str
-    reranker_local_force_cpu: bool
-    reranker_local_max_concurrent: int
-    reranker_local_trust_remote_code: bool
    reranker_tei_url: str | None
    reranker_tei_batch_size: int
    reranker_tei_max_concurrent: int
    reranker_max_candidates: int
-    reranker_cohere_api_key: str | None
-    reranker_cohere_model: str
    reranker_cohere_base_url: str | None
-    reranker_litellm_api_base: str
-    reranker_litellm_api_key: str | None
-    reranker_litellm_model: str
-    reranker_litellm_sdk_api_key: str | None
-    reranker_litellm_sdk_model: str
-    reranker_litellm_sdk_api_base: str | None

    # Server
    host: str
    port: int
-    base_path: str
    log_level: str
    log_format: str
    mcp_enabled: bool
-    enable_bank_config_api: bool

    # Recall
    graph_retriever: str
@@ -592,12 +331,11 @@ class HindsightConfig:
    retain_extract_causal_links: bool
    retain_extraction_mode: str
    retain_custom_instructions: str | None
-    retain_batch_tokens: int
+    retain_observations_async: bool

    # Observations settings (consolidated knowledge from facts)
    enable_observations: bool
    consolidation_batch_size: int
-    consolidation_max_tokens: int

    # Optimization flags
    skip_llm_verification: bool
@@ -617,305 +355,60 @@ class HindsightConfig:
    worker_id: str | None
    worker_poll_interval_ms: int
    worker_max_retries: int
+    worker_batch_size: int
    worker_http_port: int
-    worker_max_slots: int
-    worker_consolidation_max_slots: int

    # Reflect agent settings
    reflect_max_iterations: int

-    # OpenTelemetry tracing configuration
-    otel_traces_enabled: bool
-    otel_exporter_otlp_endpoint: str | None
-    otel_exporter_otlp_headers: str | None
-    otel_service_name: str
-    otel_deployment_environment: str
-
-    # Class-level sets for configuration categorization
-
-    # CREDENTIAL_FIELDS: Never exposed via API, never configurable per-tenant/bank
-    _CREDENTIAL_FIELDS = {
-        # API Keys
-        "llm_api_key",
-        "retain_llm_api_key",
-        "reflect_llm_api_key",
-        "consolidation_llm_api_key",
-        # Base URLs (could expose infrastructure)
-        "llm_base_url",
-        "retain_llm_base_url",
-        "reflect_llm_base_url",
-        "consolidation_llm_base_url",
-        "embeddings_tei_base_url",
-        "reranker_tei_base_url",
-        "reranker_cohere_base_url",
-        # Service Account Keys
-        "llm_vertexai_service_account_key",
-    }
-
-    # CONFIGURABLE_FIELDS: Safe behavioral settings that can be customized per-tenant/bank
-    # These fields are manually tagged as safe to expose and modify.
-    # Excludes credentials, infrastructure config, provider/model selection, and performance tuning.
-    _CONFIGURABLE_FIELDS = {
-        # Retention settings (behavioral)
-        "retain_chunk_size",
-        "retain_extraction_mode",
-        "retain_custom_instructions",
-        # Consolidation settings
-        "enable_observations",
-    }
-
-    @classmethod
-    def get_configurable_fields(cls) -> set[str]:
-        """
-        Get set of field names that are configurable per-tenant/bank via API.
-
-        Configurable fields are manually tagged behavioral settings that are safe
-        to expose and modify (e.g., retain_chunk_size, custom_instructions).
-        Excludes credentials, infrastructure config, and provider/model selection.
-
-        Returns:
-            Set of configurable field names
-        """
-        return cls._CONFIGURABLE_FIELDS.copy()
-
-    @classmethod
-    def get_credential_fields(cls) -> set[str]:
-        """
-        Get set of field names that are credentials (NEVER exposed via API).
-
-        Credential fields include API keys, base URLs, and service account keys.
-        These must never be returned in API responses or accepted in updates.
-
-        Returns:
-            Set of credential field names
-        """
-        return cls._CREDENTIAL_FIELDS.copy()
-
-    @classmethod
-    def get_hierarchical_fields(cls) -> set[str]:
-        """
-        DEPRECATED: Use get_configurable_fields() instead.
-
-        Kept for backward compatibility during migration.
-        """
-        return cls.get_configurable_fields()
-
-    @classmethod
-    def get_static_fields(cls) -> set[str]:
-        """
-        Get set of field names that are static (server-level only).
-
-        Static fields are infrastructure-level settings that cannot vary
-        per tenant or bank. These include database config, API port, worker settings, etc.
-        Also includes credential fields which are never configurable.
-
-        Returns:
-            Set of static field names
-        """
-        # Get all field names from dataclass
-        all_fields = {f.name for f in fields(cls)}
-        # Static fields = all fields - configurable fields
-        return all_fields - cls._CONFIGURABLE_FIELDS
-
-    def validate(self) -> None:
-        """Validate configuration values and raise errors for invalid combinations."""
-        # Validate vector_extension
-        valid_extensions = ("pgvector", "vchord")
-        if self.vector_extension not in valid_extensions:
-            raise ValueError(
-                f"Invalid vector_extension: {self.vector_extension}. Must be one of: {', '.join(valid_extensions)}"
-            )
-
-        # Validate text_search_extension
-        valid_text_search = ("native", "vchord", "pg_textsearch")
-        if self.text_search_extension not in valid_text_search:
-            raise ValueError(
-                f"Invalid text_search_extension: {self.text_search_extension}. Must be one of: {', '.join(valid_text_search)}"
-            )
-
-        # RETAIN_MAX_COMPLETION_TOKENS must be greater than RETAIN_CHUNK_SIZE
-        # to ensure the LLM has enough output capacity to extract facts from chunks
-        if self.retain_max_completion_tokens <= self.retain_chunk_size:
-            raise ValueError(
-                f"Invalid configuration: HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS "
-                f"({self.retain_max_completion_tokens}) must be greater than "
-                f"HINDSIGHT_API_RETAIN_CHUNK_SIZE ({self.retain_chunk_size}). "
-                f"\n\nYou have two options to fix this:"
-                f"\n  1. Increase HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS to a value > {self.retain_chunk_size}"
-                f"\n  2. Use a model that supports at least {self.retain_max_completion_tokens} output tokens"
-                f"\n     (current model: {self.retain_llm_model or self.llm_model}, "
-                f"provider: {self.retain_llm_provider or self.llm_provider})"
-            )
-
    @classmethod
    def from_env(cls) -> "HindsightConfig":
        """Create configuration from environment variables."""
-        # Get provider first to determine default model
-        llm_provider = os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER)
-        llm_model = os.getenv(ENV_LLM_MODEL) or _get_default_model_for_provider(llm_provider)
-
-        config = cls(
+        return cls(
            # Database
            database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
-            database_schema=os.getenv(ENV_DATABASE_SCHEMA, DEFAULT_DATABASE_SCHEMA),
-            vector_extension=os.getenv(ENV_VECTOR_EXTENSION, DEFAULT_VECTOR_EXTENSION).lower(),
-            text_search_extension=os.getenv(ENV_TEXT_SEARCH_EXTENSION, DEFAULT_TEXT_SEARCH_EXTENSION).lower(),
            # LLM
-            llm_provider=llm_provider,
+            llm_provider=os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER),
            llm_api_key=os.getenv(ENV_LLM_API_KEY),
-            llm_model=llm_model,
+            llm_model=os.getenv(ENV_LLM_MODEL, DEFAULT_LLM_MODEL),
            llm_base_url=os.getenv(ENV_LLM_BASE_URL) or None,
            llm_max_concurrent=int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_CONCURRENT))),
-            llm_max_retries=int(os.getenv(ENV_LLM_MAX_RETRIES, str(DEFAULT_LLM_MAX_RETRIES))),
-            llm_initial_backoff=float(os.getenv(ENV_LLM_INITIAL_BACKOFF, str(DEFAULT_LLM_INITIAL_BACKOFF))),
-            llm_max_backoff=float(os.getenv(ENV_LLM_MAX_BACKOFF, str(DEFAULT_LLM_MAX_BACKOFF))),
            llm_timeout=float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT))),
-            # Vertex AI
-            llm_vertexai_project_id=os.getenv(ENV_LLM_VERTEXAI_PROJECT_ID) or DEFAULT_LLM_VERTEXAI_PROJECT_ID,
-            llm_vertexai_region=os.getenv(ENV_LLM_VERTEXAI_REGION, DEFAULT_LLM_VERTEXAI_REGION),
-            llm_vertexai_service_account_key=os.getenv(ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY)
-            or DEFAULT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY,
            # Per-operation LLM config (None = use default)
            retain_llm_provider=os.getenv(ENV_RETAIN_LLM_PROVIDER) or None,
            retain_llm_api_key=os.getenv(ENV_RETAIN_LLM_API_KEY) or None,
-            retain_llm_model=os.getenv(ENV_RETAIN_LLM_MODEL)
-            or (
-                _get_default_model_for_provider(os.getenv(ENV_RETAIN_LLM_PROVIDER))
-                if os.getenv(ENV_RETAIN_LLM_PROVIDER)
-                else None
-            ),
+            retain_llm_model=os.getenv(ENV_RETAIN_LLM_MODEL) or None,
            retain_llm_base_url=os.getenv(ENV_RETAIN_LLM_BASE_URL) or None,
-            retain_llm_max_concurrent=int(os.getenv(ENV_RETAIN_LLM_MAX_CONCURRENT))
-            if os.getenv(ENV_RETAIN_LLM_MAX_CONCURRENT)
-            else None,
-            retain_llm_max_retries=int(os.getenv(ENV_RETAIN_LLM_MAX_RETRIES))
-            if os.getenv(ENV_RETAIN_LLM_MAX_RETRIES)
-            else None,
-            retain_llm_initial_backoff=float(os.getenv(ENV_RETAIN_LLM_INITIAL_BACKOFF))
-            if os.getenv(ENV_RETAIN_LLM_INITIAL_BACKOFF)
-            else None,
-            retain_llm_max_backoff=float(os.getenv(ENV_RETAIN_LLM_MAX_BACKOFF))
-            if os.getenv(ENV_RETAIN_LLM_MAX_BACKOFF)
-            else None,
-            retain_llm_timeout=float(os.getenv(ENV_RETAIN_LLM_TIMEOUT)) if os.getenv(ENV_RETAIN_LLM_TIMEOUT) else None,
            reflect_llm_provider=os.getenv(ENV_REFLECT_LLM_PROVIDER) or None,
            reflect_llm_api_key=os.getenv(ENV_REFLECT_LLM_API_KEY) or None,
-            reflect_llm_model=os.getenv(ENV_REFLECT_LLM_MODEL)
-            or (
-                _get_default_model_for_provider(os.getenv(ENV_REFLECT_LLM_PROVIDER))
-                if os.getenv(ENV_REFLECT_LLM_PROVIDER)
-                else None
-            ),
+            reflect_llm_model=os.getenv(ENV_REFLECT_LLM_MODEL) or None,
            reflect_llm_base_url=os.getenv(ENV_REFLECT_LLM_BASE_URL) or None,
-            reflect_llm_max_concurrent=int(os.getenv(ENV_REFLECT_LLM_MAX_CONCURRENT))
-            if os.getenv(ENV_REFLECT_LLM_MAX_CONCURRENT)
-            else None,
-            reflect_llm_max_retries=int(os.getenv(ENV_REFLECT_LLM_MAX_RETRIES))
-            if os.getenv(ENV_REFLECT_LLM_MAX_RETRIES)
-            else None,
-            reflect_llm_initial_backoff=float(os.getenv(ENV_REFLECT_LLM_INITIAL_BACKOFF))
-            if os.getenv(ENV_REFLECT_LLM_INITIAL_BACKOFF)
-            else None,
-            reflect_llm_max_backoff=float(os.getenv(ENV_REFLECT_LLM_MAX_BACKOFF))
-            if os.getenv(ENV_REFLECT_LLM_MAX_BACKOFF)
-            else None,
-            reflect_llm_timeout=float(os.getenv(ENV_REFLECT_LLM_TIMEOUT))
-            if os.getenv(ENV_REFLECT_LLM_TIMEOUT)
-            else None,
            consolidation_llm_provider=os.getenv(ENV_CONSOLIDATION_LLM_PROVIDER) or None,
            consolidation_llm_api_key=os.getenv(ENV_CONSOLIDATION_LLM_API_KEY) or None,
-            consolidation_llm_model=os.getenv(ENV_CONSOLIDATION_LLM_MODEL)
-            or (
-                _get_default_model_for_provider(os.getenv(ENV_CONSOLIDATION_LLM_PROVIDER))
-                if os.getenv(ENV_CONSOLIDATION_LLM_PROVIDER)
-                else None
-            ),
+            consolidation_llm_model=os.getenv(ENV_CONSOLIDATION_LLM_MODEL) or None,
            consolidation_llm_base_url=os.getenv(ENV_CONSOLIDATION_LLM_BASE_URL) or None,
-            consolidation_llm_max_concurrent=int(os.getenv(ENV_CONSOLIDATION_LLM_MAX_CONCURRENT))
-            if os.getenv(ENV_CONSOLIDATION_LLM_MAX_CONCURRENT)
-            else None,
-            consolidation_llm_max_retries=int(os.getenv(ENV_CONSOLIDATION_LLM_MAX_RETRIES))
-            if os.getenv(ENV_CONSOLIDATION_LLM_MAX_RETRIES)
-            else None,
-            consolidation_llm_initial_backoff=float(os.getenv(ENV_CONSOLIDATION_LLM_INITIAL_BACKOFF))
-            if os.getenv(ENV_CONSOLIDATION_LLM_INITIAL_BACKOFF)
-            else None,
-            consolidation_llm_max_backoff=float(os.getenv(ENV_CONSOLIDATION_LLM_MAX_BACKOFF))
-            if os.getenv(ENV_CONSOLIDATION_LLM_MAX_BACKOFF)
-            else None,
-            consolidation_llm_timeout=float(os.getenv(ENV_CONSOLIDATION_LLM_TIMEOUT))
-            if os.getenv(ENV_CONSOLIDATION_LLM_TIMEOUT)
-            else None,
            # Embeddings
            embeddings_provider=os.getenv(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER),
            embeddings_local_model=os.getenv(ENV_EMBEDDINGS_LOCAL_MODEL, DEFAULT_EMBEDDINGS_LOCAL_MODEL),
-            embeddings_local_force_cpu=os.getenv(
-                ENV_EMBEDDINGS_LOCAL_FORCE_CPU, str(DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU)
-            ).lower()
-            in ("true", "1"),
-            embeddings_local_trust_remote_code=os.getenv(
-                ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE, str(DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE)
-            ).lower()
-            in ("true", "1"),
            embeddings_tei_url=os.getenv(ENV_EMBEDDINGS_TEI_URL),
            embeddings_openai_base_url=os.getenv(ENV_EMBEDDINGS_OPENAI_BASE_URL) or None,
-            # Cohere embeddings (with backward-compatible fallback to shared API key)
-            embeddings_cohere_api_key=os.getenv(ENV_EMBEDDINGS_COHERE_API_KEY) or os.getenv(ENV_COHERE_API_KEY),
-            embeddings_cohere_model=os.getenv(ENV_EMBEDDINGS_COHERE_MODEL, DEFAULT_EMBEDDINGS_COHERE_MODEL),
            embeddings_cohere_base_url=os.getenv(ENV_EMBEDDINGS_COHERE_BASE_URL) or None,
-            # LiteLLM embeddings (with backward-compatible fallback to shared config)
-            embeddings_litellm_api_base=os.getenv(ENV_EMBEDDINGS_LITELLM_API_BASE)
-            or os.getenv(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE),
-            embeddings_litellm_api_key=os.getenv(ENV_EMBEDDINGS_LITELLM_API_KEY) or os.getenv(ENV_LITELLM_API_KEY),
-            embeddings_litellm_model=os.getenv(ENV_EMBEDDINGS_LITELLM_MODEL, DEFAULT_EMBEDDINGS_LITELLM_MODEL),
-            # LiteLLM SDK embeddings (direct API access)
-            embeddings_litellm_sdk_api_key=os.getenv(ENV_EMBEDDINGS_LITELLM_SDK_API_KEY),
-            embeddings_litellm_sdk_model=os.getenv(
-                ENV_EMBEDDINGS_LITELLM_SDK_MODEL, DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL
-            ),
-            embeddings_litellm_sdk_api_base=os.getenv(ENV_EMBEDDINGS_LITELLM_SDK_API_BASE) or None,
            # Reranker
            reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
            reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
-            reranker_local_force_cpu=os.getenv(
-                ENV_RERANKER_LOCAL_FORCE_CPU, str(DEFAULT_RERANKER_LOCAL_FORCE_CPU)
-            ).lower()
-            in ("true", "1"),
-            reranker_local_max_concurrent=int(
-                os.getenv(ENV_RERANKER_LOCAL_MAX_CONCURRENT, str(DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT))
-            ),
-            reranker_local_trust_remote_code=os.getenv(
-                ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE, str(DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE)
-            ).lower()
-            in ("true", "1"),
            reranker_tei_url=os.getenv(ENV_RERANKER_TEI_URL),
            reranker_tei_batch_size=int(os.getenv(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE))),
            reranker_tei_max_concurrent=int(
                os.getenv(ENV_RERANKER_TEI_MAX_CONCURRENT, str(DEFAULT_RERANKER_TEI_MAX_CONCURRENT))
            ),
            reranker_max_candidates=int(os.getenv(ENV_RERANKER_MAX_CANDIDATES, str(DEFAULT_RERANKER_MAX_CANDIDATES))),
-            # Cohere reranker (with backward-compatible fallback to shared API key)
-            reranker_cohere_api_key=os.getenv(ENV_RERANKER_COHERE_API_KEY) or os.getenv(ENV_COHERE_API_KEY),
-            reranker_cohere_model=os.getenv(ENV_RERANKER_COHERE_MODEL, DEFAULT_RERANKER_COHERE_MODEL),
            reranker_cohere_base_url=os.getenv(ENV_RERANKER_COHERE_BASE_URL) or None,
-            # LiteLLM reranker (with backward-compatible fallback to shared config)
-            reranker_litellm_api_base=os.getenv(ENV_RERANKER_LITELLM_API_BASE)
-            or os.getenv(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE),
-            reranker_litellm_api_key=os.getenv(ENV_RERANKER_LITELLM_API_KEY) or os.getenv(ENV_LITELLM_API_KEY),
-            reranker_litellm_model=os.getenv(ENV_RERANKER_LITELLM_MODEL, DEFAULT_RERANKER_LITELLM_MODEL),
-            # LiteLLM SDK reranker (direct API access)
-            reranker_litellm_sdk_api_key=os.getenv(ENV_RERANKER_LITELLM_SDK_API_KEY),
-            reranker_litellm_sdk_model=os.getenv(ENV_RERANKER_LITELLM_SDK_MODEL, DEFAULT_RERANKER_LITELLM_SDK_MODEL),
-            reranker_litellm_sdk_api_base=os.getenv(ENV_RERANKER_LITELLM_SDK_API_BASE) or None,
            # Server
            host=os.getenv(ENV_HOST, DEFAULT_HOST),
            port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
-            base_path=os.getenv(ENV_BASE_PATH, DEFAULT_BASE_PATH),
            log_level=os.getenv(ENV_LOG_LEVEL, DEFAULT_LOG_LEVEL),
            log_format=os.getenv(ENV_LOG_FORMAT, DEFAULT_LOG_FORMAT).lower(),
            mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).lower() == "true",
-            enable_bank_config_api=os.getenv(ENV_ENABLE_BANK_CONFIG_API, str(DEFAULT_ENABLE_BANK_CONFIG_API)).lower()
-            == "true",
            # Recall
            graph_retriever=os.getenv(ENV_GRAPH_RETRIEVER, DEFAULT_GRAPH_RETRIEVER),
            mpfp_top_k_neighbors=int(os.getenv(ENV_MPFP_TOP_K_NEIGHBORS, str(DEFAULT_MPFP_TOP_K_NEIGHBORS))),
@@ -942,15 +435,15 @@ class HindsightConfig:
                os.getenv(ENV_RETAIN_EXTRACTION_MODE, DEFAULT_RETAIN_EXTRACTION_MODE)
            ),
            retain_custom_instructions=os.getenv(ENV_RETAIN_CUSTOM_INSTRUCTIONS) or DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS,
-            retain_batch_tokens=int(os.getenv(ENV_RETAIN_BATCH_TOKENS, str(DEFAULT_RETAIN_BATCH_TOKENS))),
+            retain_observations_async=os.getenv(
+                ENV_RETAIN_OBSERVATIONS_ASYNC, str(DEFAULT_RETAIN_OBSERVATIONS_ASYNC)
+            ).lower()
+            == "true",
            # Observations settings (consolidated knowledge from facts)
            enable_observations=os.getenv(ENV_ENABLE_OBSERVATIONS, str(DEFAULT_ENABLE_OBSERVATIONS)).lower() == "true",
            consolidation_batch_size=int(
                os.getenv(ENV_CONSOLIDATION_BATCH_SIZE, str(DEFAULT_CONSOLIDATION_BATCH_SIZE))
            ),
-            consolidation_max_tokens=int(
-                os.getenv(ENV_CONSOLIDATION_MAX_TOKENS, str(DEFAULT_CONSOLIDATION_MAX_TOKENS))
-            ),
            # Database migrations
            run_migrations_on_startup=os.getenv(ENV_RUN_MIGRATIONS_ON_STARTUP, "true").lower() == "true",
            # Database connection pool
@@ -963,23 +456,11 @@ class HindsightConfig:
            worker_id=os.getenv(ENV_WORKER_ID) or DEFAULT_WORKER_ID,
            worker_poll_interval_ms=int(os.getenv(ENV_WORKER_POLL_INTERVAL_MS, str(DEFAULT_WORKER_POLL_INTERVAL_MS))),
            worker_max_retries=int(os.getenv(ENV_WORKER_MAX_RETRIES, str(DEFAULT_WORKER_MAX_RETRIES))),
+            worker_batch_size=int(os.getenv(ENV_WORKER_BATCH_SIZE, str(DEFAULT_WORKER_BATCH_SIZE))),
            worker_http_port=int(os.getenv(ENV_WORKER_HTTP_PORT, str(DEFAULT_WORKER_HTTP_PORT))),
-            worker_max_slots=int(os.getenv(ENV_WORKER_MAX_SLOTS, str(DEFAULT_WORKER_MAX_SLOTS))),
-            worker_consolidation_max_slots=int(
-                os.getenv(ENV_WORKER_CONSOLIDATION_MAX_SLOTS, str(DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS))
-            ),
            # Reflect agent settings
            reflect_max_iterations=int(os.getenv(ENV_REFLECT_MAX_ITERATIONS, str(DEFAULT_REFLECT_MAX_ITERATIONS))),
-            # OpenTelemetry tracing configuration
-            otel_traces_enabled=os.getenv(ENV_OTEL_TRACES_ENABLED, str(DEFAULT_OTEL_TRACES_ENABLED)).lower()
-            in ("true", "1", "yes"),
-            otel_exporter_otlp_endpoint=os.getenv(ENV_OTEL_EXPORTER_OTLP_ENDPOINT) or None,
-            otel_exporter_otlp_headers=os.getenv(ENV_OTEL_EXPORTER_OTLP_HEADERS) or None,
-            otel_service_name=os.getenv(ENV_OTEL_SERVICE_NAME, DEFAULT_OTEL_SERVICE_NAME),
-            otel_deployment_environment=os.getenv(ENV_OTEL_DEPLOYMENT_ENVIRONMENT, DEFAULT_OTEL_DEPLOYMENT_ENVIRONMENT),
        )
-        config.validate()
-        return config

    def get_llm_base_url(self) -> str:
        """Get the LLM base URL, with provider-specific defaults."""
@@ -1034,7 +515,7 @@ class HindsightConfig:

    def log_config(self) -> None:
        """Log the current configuration (without sensitive values)."""
-        logger.info(f"Database: {self.database_url} (schema: {self.database_schema})")
+        logger.info(f"Database: {self.database_url}")
        logger.info(f"LLM: provider={self.llm_provider}, model={self.llm_model}")
        if self.retain_llm_provider or self.retain_llm_model:
            retain_provider = self.retain_llm_provider or self.llm_provider
@@ -1057,35 +538,8 @@ class HindsightConfig:
 _config_cache: HindsightConfig | None = None


-def get_config() -> StaticConfigProxy:
-    """
-    Get global configuration with ONLY static (non-configurable) fields accessible.
-
-    This returns a proxy that prevents access to bank-configurable fields
-    (like enable_observations, retain_chunk_size, etc.).
-
-    For bank-specific configuration, use:
-        config_resolver.resolve_full_config(bank_id, context)
-
-    This design prevents accidentally using global defaults when bank-specific
-    overrides exist.
-
-    Returns:
-        StaticConfigProxy that only exposes static infrastructure fields
-
-    Raises:
-        ConfigFieldAccessError: If you try to access a bank-configurable field
-    """
-    return StaticConfigProxy(_get_raw_config())
-
-
-def _get_raw_config() -> HindsightConfig:
-    """
-    Get raw config (internal use only).
-
-    INTERNAL USE ONLY. Do not use this directly in application code.
-    Use get_config() for static fields or ConfigResolver.resolve_full_config() for bank-specific config.
-    """
+def get_config() -> HindsightConfig:
+    """Get the cached configuration, loading from environment on first call."""
    global _config_cache
    if _config_cache is None:
        _config_cache = HindsightConfig.from_env()
@@ -1,274 +0,0 @@
-"""
-Configuration resolution with hierarchical overrides.
-
-Resolves config values through the hierarchy:
-  Global (env vars) → Tenant config (via extension) → Bank config (database)
-
-Config values are resolved on every request to ensure consistency across
-multiple API servers.
-"""
-
-import json
-import logging
-from dataclasses import asdict
-from typing import Any
-
-import asyncpg
-
-from hindsight_api.config import HindsightConfig, _get_raw_config, normalize_config_dict
-from hindsight_api.extensions.tenant import TenantExtension
-from hindsight_api.models import RequestContext
-
-logger = logging.getLogger(__name__)
-
-
-class ConfigResolver:
-    """Resolves hierarchical configuration with tenant/bank overrides."""
-
-    def __init__(self, pool: asyncpg.Pool, tenant_extension: TenantExtension | None = None):
-        """
-        Initialize config resolver.
-
-        Args:
-            pool: Database connection pool
-            tenant_extension: Optional tenant extension for tenant-level config and permissions
-        """
-        self.pool = pool
-        self.tenant_extension = tenant_extension
-        self._global_config = _get_raw_config()
-        self._configurable_fields = HindsightConfig.get_configurable_fields()
-        self._credential_fields = HindsightConfig.get_credential_fields()
-
-    async def resolve_full_config(self, bank_id: str, context: RequestContext | None = None) -> HindsightConfig:
-        """
-        Resolve full HindsightConfig for a bank with hierarchical overrides applied.
-
-        This is for INTERNAL USE ONLY. Returns the complete config object with all fields
-        including credentials and static fields. Use get_bank_config() for API responses.
-
-        Resolution order:
-        1. Global config (from environment variables)
-        2. Tenant config overrides (from TenantExtension.get_tenant_config())
-        3. Bank config overrides (from banks.config JSONB)
-
-        Args:
-            bank_id: Bank identifier
-            context: Request context for tenant config resolution
-
-        Returns:
-            Complete HindsightConfig with hierarchical overrides applied
-        """
-        # Start with global config (all fields)
-        config_dict = asdict(self._global_config)
-
-        # Load tenant config overrides (if tenant extension available)
-        if self.tenant_extension and context:
-            try:
-                tenant_overrides = await self.tenant_extension.get_tenant_config(context)
-                if tenant_overrides:
-                    # Normalize keys and filter to configurable fields only
-                    normalized_tenant = normalize_config_dict(tenant_overrides)
-                    configurable_tenant = {k: v for k, v in normalized_tenant.items() if k in self._configurable_fields}
-                    config_dict.update(configurable_tenant)
-                    logger.debug(
-                        f"Applied tenant config overrides for bank {bank_id}: {list(configurable_tenant.keys())}"
-                    )
-            except Exception as e:
-                logger.warning(f"Failed to load tenant config for bank {bank_id}: {e}")
-
-        # Load bank config overrides
-        bank_overrides = await self._load_bank_config(bank_id)
-        if bank_overrides:
-            config_dict.update(bank_overrides)
-            logger.debug(f"Applied bank config overrides for bank {bank_id}: {list(bank_overrides.keys())}")
-
-        # Return full config object (dataclass doesn't have __init__ that accepts kwargs, so we update the object)
-        # Create a new config instance by copying the global config and updating fields
-        resolved_config = HindsightConfig(**config_dict)
-        return resolved_config
-
-    async def get_bank_config(self, bank_id: str, context: RequestContext | None = None) -> dict[str, Any]:
-        """
-        Get fully resolved config for a bank (filtered by permissions).
-
-        Resolution order:
-        1. Global config (from environment variables)
-        2. Tenant config overrides (from TenantExtension.get_tenant_config())
-        3. Bank config overrides (from banks.config JSONB)
-
-        Note: Config is resolved on every call (not cached) to ensure consistency
-        across multiple API servers.
-
-        SECURITY:
-        - Only returns configurable fields (excludes static/infrastructure fields)
-        - Filters out ALL credential fields (API keys, base URLs, etc.)
-        - Further filtered by tenant/bank permissions if extension provides them
-
-        Args:
-            bank_id: Bank identifier
-            context: Request context for tenant config resolution and permissions
-
-        Returns:
-            Dict of allowed configurable fields only (never includes credentials or static fields)
-        """
-        # Resolve full config with all hierarchical overrides
-        resolved_config = await self.resolve_full_config(bank_id, context)
-        config_dict = asdict(resolved_config)
-
-        # SECURITY: Filter to only configurable fields (exclude static/infrastructure)
-        filtered = {k: v for k, v in config_dict.items() if k in self._configurable_fields}
-
-        # SECURITY: Remove ALL credential fields (API keys, base URLs, etc.)
-        filtered = {k: v for k, v in filtered.items() if k not in self._credential_fields}
-
-        # PERMISSIONS: Further filter based on tenant/bank permissions
-        if self.tenant_extension and context:
-            try:
-                allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
-                if allowed_fields is not None:  # None means "allow all"
-                    filtered = {k: v for k, v in filtered.items() if k in allowed_fields}
-                    logger.debug(
-                        f"Applied permission filter for bank {bank_id}: allowed={len(allowed_fields)} fields, "
-                        f"returned={len(filtered)} fields"
-                    )
-            except Exception as e:
-                logger.warning(f"Failed to load permissions for bank {bank_id}: {e}")
-
-        return filtered
-
-    async def _load_bank_config(self, bank_id: str) -> dict[str, Any]:
-        """
-        Load bank config overrides from banks.config JSONB column.
-
-        Args:
-            bank_id: Bank identifier
-
-        Returns:
-            Dict of config overrides (only configurable fields, normalized keys)
-        """
-        try:
-            async with self.pool.acquire() as conn:
-                row = await conn.fetchrow(
-                    """
-                    SELECT config FROM banks WHERE bank_id = $1
-                    """,
-                    bank_id,
-                )
-
-                if row and row["config"]:
-                    config_data = row["config"]
-
-                    # Handle case where JSONB is returned as JSON string
-                    if isinstance(config_data, str):
-                        config_data = json.loads(config_data)
-
-                    # Normalize keys (handle both env var format and Python field format)
-                    normalized = normalize_config_dict(config_data)
-
-                    # Only return overrides for configurable fields
-                    return {k: v for k, v in normalized.items() if k in self._configurable_fields}
-        except Exception as e:
-            logger.error(f"Failed to load bank config for {bank_id}: {e}")
-
-        return {}
-
-    async def update_bank_config(
-        self, bank_id: str, updates: dict[str, Any], context: RequestContext | None = None
-    ) -> None:
-        """
-        Update bank configuration overrides (with permission checking).
-
-        Args:
-            bank_id: Bank identifier
-            updates: Dict of config field names to new values.
-                    Keys can be in env var format (HINDSIGHT_API_LLM_PROVIDER)
-                    or Python field format (llm_provider).
-                    Only configurable fields are allowed.
-            context: Request context for permission checking
-
-        Raises:
-            ValueError: If attempting to override invalid/disallowed fields
-        """
-        # Normalize keys
-        normalized_updates = normalize_config_dict(updates)
-
-        # SECURITY: Reject credential fields explicitly
-        credential_attempts = set(normalized_updates.keys()) & self._credential_fields
-        if credential_attempts:
-            raise ValueError(
-                f"Cannot set credential fields via API: {sorted(credential_attempts)}. "
-                f"Credentials (API keys, base URLs) must be set at server level only."
-            )
-
-        # Validate all fields are configurable
-        invalid_fields = set(normalized_updates.keys()) - self._configurable_fields
-        if invalid_fields:
-            static_fields = HindsightConfig.get_static_fields()
-            invalid_static = invalid_fields & static_fields
-            if invalid_static:
-                raise ValueError(
-                    f"Cannot override static (server-level) fields: {sorted(invalid_static)}. "
-                    f"Only configurable fields can be overridden per-bank. "
-                    f"Configurable fields include: {sorted(list(self._configurable_fields)[:10])}... "
-                    f"(total: {len(self._configurable_fields)} fields)"
-                )
-            else:
-                raise ValueError(
-                    f"Unknown configuration fields: {sorted(invalid_fields)}. "
-                    f"Valid configurable fields: {sorted(list(self._configurable_fields)[:10])}..."
-                )
-
-        # PERMISSIONS: Check tenant/bank permissions
-        if self.tenant_extension and context:
-            try:
-                allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
-                if allowed_fields is not None:  # None means "allow all"
-                    disallowed = set(normalized_updates.keys()) - allowed_fields
-                    if disallowed:
-                        raise ValueError(
-                            f"Not allowed to modify fields: {sorted(disallowed)}. "
-                            f"Your permissions allow: {sorted(list(allowed_fields)[:10])}..."
-                            if allowed_fields
-                            else "Not allowed to modify fields: {sorted(disallowed)}. "
-                            "Your permissions do not allow any config modifications."
-                        )
-            except ValueError:
-                raise  # Re-raise permission errors
-            except Exception as e:
-                logger.warning(f"Failed to check permissions for bank {bank_id}: {e}")
-                # Continue without permission check (fail open for backward compatibility)
-
-        # Merge with existing config (JSONB || operator)
-        async with self.pool.acquire() as conn:
-            await conn.execute(
-                """
-                UPDATE banks
-                SET config = config || $1::jsonb,
-                    updated_at = now()
-                WHERE bank_id = $2
-                """,
-                json.dumps(normalized_updates),
-                bank_id,
-            )
-
-        logger.info(f"Updated bank config for {bank_id}: {list(normalized_updates.keys())}")
-
-    async def reset_bank_config(self, bank_id: str) -> None:
-        """
-        Reset bank configuration to defaults (remove all overrides).
-
-        Args:
-            bank_id: Bank identifier
-        """
-        async with self.pool.acquire() as conn:
-            await conn.execute(
-                """
-                UPDATE banks
-                SET config = '{}'::jsonb,
-                    updated_at = now()
-                WHERE bank_id = $1
-                """,
-                bank_id,
-            )
-
-        logger.info(f"Reset bank config for {bank_id} to defaults")
@@ -1,10 +1,11 @@
 """
 Daemon mode support for Hindsight API.

-Provides idle timeout for running as a background daemon.
+Provides idle timeout and lockfile management for running as a background daemon.
 """

 import asyncio
+import fcntl
 import logging
 import os
 import sys
@@ -14,11 +15,10 @@ from pathlib import Path
 logger = logging.getLogger(__name__)

 # Default daemon configuration
-DEFAULT_DAEMON_PORT = 8888
+DEFAULT_DAEMON_PORT = 8889
 DEFAULT_IDLE_TIMEOUT = 0  # 0 = no auto-exit (hindsight-embed passes its own timeout)
-
-# Allow override via environment variable for profile-specific logs
-DAEMON_LOG_PATH = Path(os.getenv("HINDSIGHT_API_DAEMON_LOG", str(Path.home() / ".hindsight" / "daemon.log")))
+LOCKFILE_PATH = Path.home() / ".hindsight" / "daemon.lock"
+DAEMON_LOG_PATH = Path.home() / ".hindsight" / "daemon.log"


 class IdleTimeoutMiddleware:
@@ -52,10 +52,82 @@ class IdleTimeoutMiddleware:
                logger.info(f"Idle timeout reached ({self.idle_timeout}s), shutting down daemon")
                # Give a moment for any in-flight requests
                await asyncio.sleep(1)
-                # Send SIGTERM to ourselves to trigger graceful shutdown
-                import signal
+                os._exit(0)

-                os.kill(os.getpid(), signal.SIGTERM)
+
+class DaemonLock:
+    """
+    File-based lock to prevent multiple daemon instances.
+
+    Uses fcntl.flock for atomic locking on Unix systems.
+    """
+
+    def __init__(self, lockfile: Path = LOCKFILE_PATH):
+        self.lockfile = lockfile
+        self._fd = None
+
+    def acquire(self) -> bool:
+        """
+        Try to acquire the daemon lock.
+
+        Returns True if lock acquired, False if another daemon is running.
+        """
+        self.lockfile.parent.mkdir(parents=True, exist_ok=True)
+
+        try:
+            self._fd = open(self.lockfile, "w")
+            fcntl.flock(self._fd.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+            # Write PID for debugging
+            self._fd.write(str(os.getpid()))
+            self._fd.flush()
+            return True
+        except (IOError, OSError):
+            # Lock is held by another process
+            if self._fd:
+                self._fd.close()
+                self._fd = None
+            return False
+
+    def release(self):
+        """Release the daemon lock."""
+        if self._fd:
+            try:
+                fcntl.flock(self._fd.fileno(), fcntl.LOCK_UN)
+                self._fd.close()
+            except Exception:
+                pass
+            finally:
+                self._fd = None
+                # Remove lockfile
+                try:
+                    self.lockfile.unlink()
+                except Exception:
+                    pass
+
+    def is_locked(self) -> bool:
+        """Check if the lock is held by another process."""
+        if not self.lockfile.exists():
+            return False
+
+        try:
+            fd = open(self.lockfile, "r")
+            fcntl.flock(fd.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+            # We got the lock, so no one else has it
+            fcntl.flock(fd.fileno(), fcntl.LOCK_UN)
+            fd.close()
+            return False
+        except (IOError, OSError):
+            return True
+
+    def get_pid(self) -> int | None:
+        """Get the PID of the daemon holding the lock."""
+        if not self.lockfile.exists():
+            return None
+        try:
+            with open(self.lockfile, "r") as f:
+                return int(f.read().strip())
+        except (ValueError, IOError):
+            return None


 def daemonize():
@@ -64,21 +136,16 @@ def daemonize():

    Uses double-fork technique to properly detach from terminal.
    """
-    # First fork - detach from parent
-    try:
-        pid = os.fork()
-        if pid > 0:
-            sys.exit(0)
-    except OSError as e:
-        sys.stderr.write(f"fork #1 failed: {e}\n")
-        sys.exit(1)
+    # First fork
+    pid = os.fork()
+    if pid > 0:
+        # Parent exits
+        sys.exit(0)

-    # Decouple from parent environment
-    os.chdir("/")
+    # Create new session
    os.setsid()
-    os.umask(0)

-    # Second fork - prevent zombie
+    # Second fork to prevent zombie processes
    pid = os.fork()
    if pid > 0:
        sys.exit(0)
@@ -111,3 +178,27 @@ def check_daemon_running(port: int = DEFAULT_DAEMON_PORT) -> bool:
        return result == 0
    except Exception:
        return False
+
+
+def stop_daemon(port: int = DEFAULT_DAEMON_PORT) -> bool:
+    """Stop a running daemon by sending SIGTERM to the process."""
+    lock = DaemonLock()
+    pid = lock.get_pid()
+
+    if pid is None:
+        return False
+
+    try:
+        import signal
+
+        os.kill(pid, signal.SIGTERM)
+        # Wait for process to exit
+        for _ in range(50):  # Wait up to 5 seconds
+            time.sleep(0.1)
+            try:
+                os.kill(pid, 0)  # Check if process exists
+            except OSError:
+                return True  # Process exited
+        return False
+    except OSError:
+        return False
@@ -18,7 +18,6 @@ import uuid
 from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any

-from ...config import get_config
 from ..memory_engine import fq_table
 from ..retain import embedding_utils
 from .prompts import (
@@ -83,8 +82,9 @@ async def run_consolidation_job(
    Returns:
        Dict with consolidation results
    """
-    # Resolve bank-specific config with hierarchical overrides
-    config = await memory_engine._config_resolver.resolve_full_config(bank_id, request_context)
+    from ...config import get_config
+
+    config = get_config()
    perf = ConsolidationPerfLog(bank_id)
    max_memories_per_batch = config.consolidation_batch_size

@@ -143,18 +143,11 @@ async def run_consolidation_job(
        "skipped": 0,
    }

-    # Track all unique tags from consolidated memories for mental model refresh filtering
-    consolidated_tags: set[str] = set()
-
    batch_num = 0
-    last_progress_timings = {}  # Track timings at last progress log
    while True:
        batch_num += 1
        batch_start = time.time()

-        # Snapshot timings at batch start for per-batch calculation
-        batch_start_timings = perf.timings.copy()
-
        # Fetch next batch of unconsolidated memories
        async with pool.acquire() as conn:
            t0 = time.time()
@@ -179,11 +172,6 @@ async def run_consolidation_job(
        for memory in memories:
            mem_start = time.time()

-            # Track tags from this memory for mental model refresh filtering
-            memory_tags = memory.get("tags") or []
-            if memory_tags:
-                consolidated_tags.update(memory_tags)
-
            # Process the memory (uses its own connection internally)
            async with pool.acquire() as conn:
                result = await _process_memory(
@@ -229,44 +217,19 @@ async def run_consolidation_job(
            elif action == "skipped":
                stats["skipped"] += 1

-            # Log progress periodically with timing breakdown
+            # Log progress periodically
            if stats["memories_processed"] % 10 == 0:
-                # Calculate timing deltas since last progress log
-                timing_parts = []
-                for key in ["recall", "llm", "embedding", "db_write"]:
-                    if key in perf.timings:
-                        delta = perf.timings[key] - last_progress_timings.get(key, 0)
-                        timing_parts.append(f"{key}={delta:.2f}s")
-
-                timing_str = f" | {', '.join(timing_parts)}" if timing_parts else ""
                logger.info(
                    f"[CONSOLIDATION] bank={bank_id} progress: "
-                    f"{stats['memories_processed']}/{total_count} memories processed{timing_str}"
+                    f"{stats['memories_processed']}/{total_count} memories processed"
                )

-                # Update last progress snapshot
-                last_progress_timings = perf.timings.copy()
-
        batch_time = time.time() - batch_start
        perf.log(
            f"[2] Batch {batch_num}: {len(memories)} memories in {batch_time:.3f}s "
            f"(avg {batch_time / len(memories):.3f}s/memory)"
        )

-        # Log timing breakdown after each batch (delta from batch start)
-        timing_parts = []
-        for key in ["recall", "llm", "embedding", "db_write"]:
-            if key in perf.timings:
-                delta = perf.timings[key] - batch_start_timings.get(key, 0)
-                timing_parts.append(f"{key}={delta:.3f}s")
-
-        if timing_parts:
-            avg_per_memory = batch_time / len(memories) if memories else 0
-            logger.info(
-                f"[CONSOLIDATION] bank={bank_id} batch {batch_num}/{len(memories)} memories: "
-                f"{', '.join(timing_parts)} | avg={avg_per_memory:.3f}s/memory"
-            )
-
    # Build summary
    perf.log(
        f"[3] Results: {stats['memories_processed']} memories -> "
@@ -292,12 +255,10 @@ async def run_consolidation_job(
        perf.log(f"[4] Timing breakdown: {', '.join(timing_parts)}")

    # Trigger mental model refreshes for models with refresh_after_consolidation=true
-    # SECURITY: Only refresh mental models with matching tags (or all if no tags were consolidated)
    mental_models_refreshed = await _trigger_mental_model_refreshes(
        memory_engine=memory_engine,
        bank_id=bank_id,
        request_context=request_context,
-        consolidated_tags=list(consolidated_tags) if consolidated_tags else None,
        perf=perf,
    )
    stats["mental_models_refreshed"] = mental_models_refreshed
@@ -311,20 +272,15 @@ async def _trigger_mental_model_refreshes(
    memory_engine: "MemoryEngine",
    bank_id: str,
    request_context: "RequestContext",
-    consolidated_tags: list[str] | None = None,
    perf: ConsolidationPerfLog | None = None,
 ) -> int:
    """
    Trigger refreshes for mental models with refresh_after_consolidation=true.

-    SECURITY: Only triggers refresh for mental models whose tags overlap with the
-    consolidated memory tags, preventing unnecessary refreshes across security boundaries.
-
    Args:
        memory_engine: MemoryEngine instance
        bank_id: Bank identifier
        request_context: Request context for authentication
-        consolidated_tags: Tags from memories that were consolidated (None = refresh all)
        perf: Performance logging

    Returns:
@@ -333,52 +289,22 @@ async def _trigger_mental_model_refreshes(
    pool = memory_engine._pool

    # Find mental models with refresh_after_consolidation=true
-    # SECURITY: Control which mental models get refreshed based on tags
    async with pool.acquire() as conn:
-        if consolidated_tags:
-            # Tagged memories were consolidated - refresh:
-            # 1. Mental models with overlapping tags (security boundary)
-            # 2. Untagged mental models (they're "global" and available to all contexts)
-            # DO NOT refresh mental models with different tags
-            rows = await conn.fetch(
-                f"""
-                SELECT id, name, tags
-                FROM {fq_table("mental_models")}
-                WHERE bank_id = $1
-                  AND (trigger->>'refresh_after_consolidation')::boolean = true
-                  AND (
-                    (tags IS NOT NULL AND tags != '{{}}' AND tags && $2::varchar[])
-                    OR (tags IS NULL OR tags = '{{}}')
-                  )
-                """,
-                bank_id,
-                consolidated_tags,
-            )
-        else:
-            # Untagged memories were consolidated - only refresh untagged mental models
-            # SECURITY: Tagged mental models are NOT refreshed when untagged memories are consolidated
-            rows = await conn.fetch(
-                f"""
-                SELECT id, name, tags
-                FROM {fq_table("mental_models")}
-                WHERE bank_id = $1
-                  AND (trigger->>'refresh_after_consolidation')::boolean = true
-                  AND (tags IS NULL OR tags = '{{}}')
-                """,
-                bank_id,
-            )
+        rows = await conn.fetch(
+            f"""
+            SELECT id, name
+            FROM {fq_table("mental_models")}
+            WHERE bank_id = $1
+              AND (trigger->>'refresh_after_consolidation')::boolean = true
+            """,
+            bank_id,
+        )

    if not rows:
        return 0

    if perf:
-        if consolidated_tags:
-            perf.log(
-                f"[5] Triggering refresh for {len(rows)} mental models with refresh_after_consolidation=true "
-                f"(filtered by tags: {consolidated_tags})"
-            )
-        else:
-            perf.log(f"[5] Triggering refresh for {len(rows)} mental models with refresh_after_consolidation=true")
+        perf.log(f"[5] Triggering refresh for {len(rows)} mental models with refresh_after_consolidation=true")

    # Submit refresh tasks for each mental model
    refreshed_count = 0
@@ -426,109 +352,92 @@ async def _process_memory(
    Returns:
        Dict with action summary: created/updated/merged counts
    """
-    from ...tracing import get_tracer, is_tracing_enabled
-
    fact_text = memory["text"]
    memory_id = memory["id"]
    fact_tags = memory.get("tags") or []

-    # Create parent span for this memory's consolidation
-    tracer = get_tracer()
-    if is_tracing_enabled():
-        consolidation_span = tracer.start_span("hindsight.consolidation")
-        consolidation_span.set_attribute("hindsight.memory_id", str(memory_id))
-        consolidation_span.set_attribute("hindsight.bank_id", bank_id)
-    else:
-        consolidation_span = None
+    # Find related observations using the full recall system (NO tag filtering)
+    t0 = time.time()
+    related_observations = await _find_related_observations(
+        conn=conn,
+        memory_engine=memory_engine,
+        bank_id=bank_id,
+        query=fact_text,
+        request_context=request_context,
+    )
+    if perf:
+        perf.record_timing("recall", time.time() - t0)

-    try:
-        # Find related observations using the full recall system
-        # SECURITY: Pass tags to ensure observations don't leak across security boundaries
-        t0 = time.time()
-        related_observations = await _find_related_observations(
-            conn=conn,
-            memory_engine=memory_engine,
-            bank_id=bank_id,
-            query=fact_text,
-            request_context=request_context,
-            tags=fact_tags,  # Pass source memory's tags for security
-        )
-        if perf:
-            perf.record_timing("recall", time.time() - t0)
+    # Single LLM call handles ALL cases (with or without existing observations)
+    # Note: Tags are NOT passed to LLM - they are handled algorithmically
+    t0 = time.time()
+    actions = await _consolidate_with_llm(
+        memory_engine=memory_engine,
+        fact_text=fact_text,
+        observations=related_observations,  # Can be empty list
+        mission=mission,
+    )
+    if perf:
+        perf.record_timing("llm", time.time() - t0)

-        # Single LLM call handles ALL cases (with or without existing observations)
-        # Note: Tags are NOT passed to LLM - they are handled algorithmically
-        t0 = time.time()
-        actions = await _consolidate_with_llm(
-            memory_engine=memory_engine,
-            fact_text=fact_text,
-            observations=related_observations,  # Can be empty list
-            mission=mission,
-        )
-        if perf:
-            perf.record_timing("llm", time.time() - t0)
+    if not actions:
+        # LLM returned empty array - fact is purely ephemeral, skip
+        return {"action": "skipped", "reason": "no_durable_knowledge"}

-        if not actions:
-            # LLM returned empty array - fact is purely ephemeral, skip
-            return {"action": "skipped", "reason": "no_durable_knowledge"}
+    # Execute all actions and collect results
+    results = []
+    for action in actions:
+        action_type = action.get("action")
+        if action_type == "update":
+            result = await _execute_update_action(
+                conn=conn,
+                memory_engine=memory_engine,
+                bank_id=bank_id,
+                memory_id=memory_id,
+                action=action,
+                observations=related_observations,
+                source_fact_tags=fact_tags,  # Pass source fact's tags for security
+                source_occurred_start=memory.get("occurred_start"),
+                source_occurred_end=memory.get("occurred_end"),
+                source_mentioned_at=memory.get("mentioned_at"),
+                perf=perf,
+            )
+            results.append(result)
+        elif action_type == "create":
+            result = await _execute_create_action(
+                conn=conn,
+                memory_engine=memory_engine,
+                bank_id=bank_id,
+                memory_id=memory_id,
+                action=action,
+                source_fact_tags=fact_tags,  # Pass source fact's tags for security
+                event_date=memory.get("event_date"),
+                occurred_start=memory.get("occurred_start"),
+                occurred_end=memory.get("occurred_end"),
+                mentioned_at=memory.get("mentioned_at"),
+                perf=perf,
+            )
+            results.append(result)

-        # Execute all actions and collect results
-        results = []
-        for action in actions:
-            action_type = action.get("action")
-            if action_type == "update":
-                result = await _execute_update_action(
-                    conn=conn,
-                    memory_engine=memory_engine,
-                    bank_id=bank_id,
-                    memory_id=memory_id,
-                    action=action,
-                    observations=related_observations,
-                    source_fact_tags=fact_tags,  # Pass source fact's tags for security
-                    source_occurred_start=memory.get("occurred_start"),
-                    source_occurred_end=memory.get("occurred_end"),
-                    source_mentioned_at=memory.get("mentioned_at"),
-                    perf=perf,
-                )
-                results.append(result)
-            elif action_type == "create":
-                result = await _execute_create_action(
-                    conn=conn,
-                    memory_engine=memory_engine,
-                    bank_id=bank_id,
-                    memory_id=memory_id,
-                    action=action,
-                    source_fact_tags=fact_tags,  # Pass source fact's tags for security
-                    event_date=memory.get("event_date"),
-                    occurred_start=memory.get("occurred_start"),
-                    occurred_end=memory.get("occurred_end"),
-                    mentioned_at=memory.get("mentioned_at"),
-                    perf=perf,
-                )
-                results.append(result)
+    if not results:
+        # No valid actions executed
+        return {"action": "skipped", "reason": "no_valid_actions"}

-        if not results:
-            # No valid actions executed
-            return {"action": "skipped", "reason": "no_valid_actions"}
+    # Summarize results
+    created = sum(1 for r in results if r.get("action") == "created")
+    updated = sum(1 for r in results if r.get("action") == "updated")
+    merged = sum(1 for r in results if r.get("action") == "merged")

-        # Summarize results
-        created = sum(1 for r in results if r.get("action") == "created")
-        updated = sum(1 for r in results if r.get("action") == "updated")
-        merged = sum(1 for r in results if r.get("action") == "merged")
+    if len(results) == 1:
+        return results[0]

-        if len(results) == 1:
-            return results[0]
-
-        return {
-            "action": "multiple",
-            "created": created,
-            "updated": updated,
-            "merged": merged,
-            "total_actions": len(results),
-        }
-    finally:
-        if consolidation_span:
-            consolidation_span.end()
+    return {
+        "action": "multiple",
+        "created": created,
+        "updated": updated,
+        "merged": merged,
+        "total_actions": len(results),
+    }


 async def _execute_update_action(
@@ -728,135 +637,74 @@ async def _find_related_observations(
    bank_id: str,
    query: str,
    request_context: "RequestContext",
-    tags: list[str] | None = None,
 ) -> list[dict[str, Any]]:
    """
-    Find observations related to the given query using optimized recall.
+    Find observations related to the given query using the full recall system.

-    SECURITY: Filters by tags using all_strict matching to prevent cross-tenant/cross-user
-    information leakage. Observations are only consolidated within the same tag scope.
+    IMPORTANT: We do NOT filter by tags here. Consolidation needs to see ALL
+    potentially related observations regardless of scope, so the LLM can
+    decide on tag routing (same scope update vs cross-scope create).

-    Uses max_tokens to naturally limit observations (no artificial count limit).
-    Includes source memories with dates for LLM context.
-
-    Args:
-        tags: Optional tags to filter observations (uses all_strict matching for security)
+    This leverages:
+    - Semantic search (embedding similarity)
+    - BM25 text search (keyword matching)
+    - Entity-based retrieval (shared entities)
+    - Graph traversal (connected via entity links)

    Returns:
-        List of related observations with their tags, source memories, and dates
+        List of related observations with their tags for LLM tag routing
    """
-    # Use recall to find related observations with token budget
-    # max_tokens naturally limits how many observations are returned
-    from ...config import get_config
-    from ...tracing import get_tracer, is_tracing_enabled
-
-    config = get_config()
-
-    # SECURITY: Use all_strict matching if tags provided to prevent cross-scope consolidation
-    tags_match = "all_strict" if tags else "any"
-
-    # Create span for recall operation within consolidation
-    tracer = get_tracer()
-    if is_tracing_enabled():
-        recall_span = tracer.start_span("hindsight.consolidation_recall")
-        recall_span.set_attribute("hindsight.bank_id", bank_id)
-        recall_span.set_attribute("hindsight.query", query[:100])  # Truncate for brevity
-        recall_span.set_attribute("hindsight.fact_type", "observation")
-    else:
-        recall_span = None
-
-    try:
-        recall_result = await memory_engine.recall_async(
-            bank_id=bank_id,
-            query=query,
-            max_tokens=config.consolidation_max_tokens,  # Token budget for observations (configurable)
-            fact_type=["observation"],  # Only retrieve observations
-            request_context=request_context,
-            tags=tags,  # Filter by source memory's tags
-            tags_match=tags_match,  # Use strict matching for security
-            _quiet=True,  # Suppress logging
-        )
-    finally:
-        if recall_span:
-            recall_span.end()
+    # Use recall to find related observations
+    # NO tags parameter - we want ALL observations regardless of scope
+    # Use low max_tokens since we only need observations, not memories
+    recall_result = await memory_engine.recall_async(
+        bank_id=bank_id,
+        query=query,
+        max_tokens=5000,  # Token budget for observations
+        fact_type=["observation"],  # Only retrieve observations
+        request_context=request_context,
+        _quiet=True,  # Suppress logging
+        # NO tags parameter - intentionally get ALL observations
+    )

    # If no observations returned, return empty list
+    # When fact_type=["observation"], results come back in `results` field
    if not recall_result.results:
        return []

-    # Batch fetch all observations in a single query (no artificial limit)
-    observation_ids = [uuid.UUID(obs.id) for obs in recall_result.results]
-
-    rows = await conn.fetch(
-        f"""
-        SELECT id, text, proof_count, history, tags, source_memory_ids, created_at, updated_at,
-               occurred_start, occurred_end, mentioned_at
-        FROM {fq_table("memory_units")}
-        WHERE id = ANY($1) AND bank_id = $2 AND fact_type = 'observation'
-        """,
-        observation_ids,
-        bank_id,
-    )
-
-    # Build results list preserving recall order
-    id_to_row = {row["id"]: row for row in rows}
+    # Trust recall's relevance filtering - fetch full data for each observation
    results = []
-
    for obs in recall_result.results:
-        obs_id = uuid.UUID(obs.id)
-        if obs_id not in id_to_row:
-            continue
-
-        row = id_to_row[obs_id]
-        history = row["history"]
-        if isinstance(history, str):
-            history = json.loads(history)
-        elif history is None:
-            history = []
-
-        # Fetch source memories to include their text and dates
-        source_memory_ids = row["source_memory_ids"] or []
-        source_memories = []
-
-        if source_memory_ids:
-            source_rows = await conn.fetch(
-                f"""
-                SELECT text, occurred_start, occurred_end, mentioned_at, event_date
-                FROM {fq_table("memory_units")}
-                WHERE id = ANY($1) AND bank_id = $2
-                ORDER BY created_at ASC
-                LIMIT 5
-                """,
-                source_memory_ids[:5],  # Limit to first 5 source memories for token efficiency
-                bank_id,
-            )
-
-            for src_row in source_rows:
-                source_memories.append(
-                    {
-                        "text": src_row["text"],
-                        "occurred_start": src_row["occurred_start"],
-                        "occurred_end": src_row["occurred_end"],
-                        "mentioned_at": src_row["mentioned_at"],
-                        "event_date": src_row["event_date"],
-                    }
-                )
-
-        results.append(
-            {
-                "id": row["id"],
-                "text": row["text"],
-                "proof_count": row["proof_count"] or 1,
-                "tags": row["tags"] or [],
-                "source_memories": source_memories,
-                "occurred_start": row["occurred_start"],
-                "occurred_end": row["occurred_end"],
-                "mentioned_at": row["mentioned_at"],
-                "created_at": row["created_at"],
-                "updated_at": row["updated_at"],
-            }
+        # Fetch full observation data from DB to get history, source_memory_ids, tags
+        row = await conn.fetchrow(
+            f"""
+            SELECT id, text, proof_count, history, tags, source_memory_ids, created_at, updated_at
+            FROM {fq_table("memory_units")}
+            WHERE id = $1 AND bank_id = $2 AND fact_type = 'observation'
+            """,
+            uuid.UUID(obs.id),
+            bank_id,
        )

+        if row:
+            history = row["history"]
+            if isinstance(history, str):
+                history = json.loads(history)
+            elif history is None:
+                history = []
+
+            results.append(
+                {
+                    "id": row["id"],
+                    "text": row["text"],
+                    "proof_count": row["proof_count"] or 1,
+                    "history": history,
+                    "tags": row["tags"] or [],  # Include tags for LLM tag routing
+                    "source_memory_ids": row["source_memory_ids"] or [],
+                    "similarity": 1.0,  # Retrieved via recall so assumed relevant
+                }
+            )
+
    return results


@@ -884,43 +732,14 @@ async def _consolidate_with_llm(
        - {"action": "create", "text": "...", "reason": "..."}
        - [] if fact is purely ephemeral (no durable knowledge)
    """
-    # Format observations as JSON with source memories and dates
+    # Format observations WITH their tags (or "None" if empty)
    if observations:
-        obs_list = []
-        for obs in observations:
-            obs_data = {
-                "id": str(obs["id"]),
-                "text": obs["text"],
-                "proof_count": obs["proof_count"],
-                "tags": obs["tags"],
-                "created_at": obs["created_at"].isoformat() if obs.get("created_at") else None,
-                "updated_at": obs["updated_at"].isoformat() if obs.get("updated_at") else None,
-            }
-
-            # Include temporal info if available
-            if obs.get("occurred_start"):
-                obs_data["occurred_start"] = obs["occurred_start"].isoformat()
-            if obs.get("occurred_end"):
-                obs_data["occurred_end"] = obs["occurred_end"].isoformat()
-            if obs.get("mentioned_at"):
-                obs_data["mentioned_at"] = obs["mentioned_at"].isoformat()
-
-            # Include source memories (up to 3 for brevity)
-            if obs.get("source_memories"):
-                obs_data["source_memories"] = [
-                    {
-                        "text": sm["text"],
-                        "event_date": sm["event_date"].isoformat() if sm.get("event_date") else None,
-                        "occurred_start": sm["occurred_start"].isoformat() if sm.get("occurred_start") else None,
-                    }
-                    for sm in obs["source_memories"][:3]  # Limit to 3 for token efficiency
-                ]
-
-            obs_list.append(obs_data)
-
-        observations_text = json.dumps(obs_list, indent=2)
+        observations_text = "\n".join(
+            f'- ID: {obs["id"]}, Tags: {json.dumps(obs["tags"])}, Text: "{obs["text"]}" (proof_count: {obs["proof_count"]})'
+            for obs in observations
+        )
    else:
-        observations_text = "[]"
+        observations_text = "None (this is a new topic - create if fact contains durable knowledge)"

    # Only include mission section if mission is set and not the default
    mission_section = ""
@@ -950,14 +769,7 @@ Focus on DURABLE knowledge that serves this mission, not ephemeral state.
        )
        # Parse JSON response - should be an array
        if isinstance(result, str):
-            # Strip markdown code fences (some models wrap JSON in ```json ... ```)
-            clean = result.strip()
-            if clean.startswith("```"):
-                clean = clean.split("\n", 1)[1] if "\n" in clean else clean[3:]
-                if clean.endswith("```"):
-                    clean = clean[:-3]
-                clean = clean.strip()
-            result = json.loads(clean)
+            result = json.loads(result)
        # Ensure result is a list
        if isinstance(result, list):
            return result
@@ -1016,34 +828,15 @@ async def _create_observation_directly(

    t0 = time.time()
    observation_id = uuid.uuid4()
-
-    # Query varies based on text search backend
-    config = get_config()
-    if config.text_search_extension == "vchord":
-        # VectorChord: manually tokenize and insert search_vector
-        query = f"""
-            INSERT INTO {fq_table("memory_units")} (
-                id, bank_id, text, fact_type, embedding, proof_count, source_memory_ids, history,
-                tags, event_date, occurred_start, occurred_end, mentioned_at, search_vector
-            )
-            VALUES ($1, $2, $3, 'observation', $4::vector, 1, $5, '[]'::jsonb, $6, $7, $8, $9, $10,
-                    tokenize($3, 'llmlingua2')::bm25_catalog.bm25vector)
-            RETURNING id
-        """
-    else:  # native or pg_textsearch
-        # Native PostgreSQL: search_vector is GENERATED ALWAYS, don't include it
-        # pg_textsearch: indexes operate on base columns directly, don't populate search_vector
-        query = f"""
-            INSERT INTO {fq_table("memory_units")} (
-                id, bank_id, text, fact_type, embedding, proof_count, source_memory_ids, history,
-                tags, event_date, occurred_start, occurred_end, mentioned_at
-            )
-            VALUES ($1, $2, $3, 'observation', $4::vector, 1, $5, '[]'::jsonb, $6, $7, $8, $9, $10)
-            RETURNING id
-        """
-
    row = await conn.fetchrow(
-        query,
+        f"""
+        INSERT INTO {fq_table("memory_units")} (
+            id, bank_id, text, fact_type, embedding, proof_count, source_memory_ids, history,
+            tags, event_date, occurred_start, occurred_end, mentioned_at
+        )
+        VALUES ($1, $2, $3, 'observation', $4::vector, 1, $5, '[]'::jsonb, $6, $7, $8, $9, $10)
+        RETURNING id
+        """,
        observation_id,
        bank_id,
        observation_text,
@@ -2,7 +2,7 @@

 CONSOLIDATION_SYSTEM_PROMPT = """You are a memory consolidation system. Your job is to convert facts into durable knowledge (observations) and merge with existing knowledge when appropriate.

-You must output ONLY valid JSON with no markdown code blocks or additional text. However, the "text" field within each observation should use markdown formatting (headers, lists, bold, etc.) for clarity and readability.
+You must output ONLY valid JSON with no markdown formatting, no code blocks, and no additional text.

 ## EXTRACT DURABLE KNOWLEDGE, NOT EPHEMERAL STATE
 Facts often describe events or actions. Extract the DURABLE KNOWLEDGE implied by the fact, not the transient state.
@@ -32,16 +32,13 @@ BAD examples:

 ## MERGE RULES (when comparing to existing observations):
 1. REDUNDANT: Same information worded differently → update existing
-2. CONTRADICTION: Opposite information about same topic → update with temporal markers showing change
-   Example: "Alex used to love pizza but now hates it" OR "Alex's pizza preference changed from love to hate"
-3. UPDATE: New state replacing old state → update showing the transition with "used to", "now", "changed from X to Y"
+2. CONTRADICTION: Opposite information about same topic → update with history (e.g., "used to X, now Y")
+3. UPDATE: New state replacing old state → update with history

 ## CRITICAL RULES:
 - NEVER merge facts about DIFFERENT people
 - NEVER merge unrelated topics (food preferences vs work vs hobbies)
- When merging contradictions, the "text" field MUST capture BOTH states with temporal markers:
-  * Use "used to X, now Y" OR "changed from X to Y" OR "X but now Y"
-  * DO NOT just state the new fact - you MUST show the change
+- When merging contradictions, capture the CHANGE (before → after)
 - Keep observations focused on ONE specific topic per person
 - The "text" field MUST contain durable knowledge, not ephemeral state
 - Do NOT include "tags" in output - tags are handled automatically"""
@@ -50,36 +47,23 @@ CONSOLIDATION_USER_PROMPT = """Analyze this new fact and consolidate into knowle
 {mission_section}
 NEW FACT: {fact_text}

-EXISTING OBSERVATIONS (JSON array with source memories and dates):
+EXISTING OBSERVATIONS:
 {observations_text}

-Each observation includes:
- id: unique identifier for updating
- text: the observation content
- proof_count: number of supporting memories
- tags: visibility scope (handled automatically)
- created_at/updated_at: when observation was created/modified
- occurred_start/occurred_end: temporal range of source facts
- source_memories: array of supporting facts with their text and dates
-
 Instructions:
-1. Extract DURABLE KNOWLEDGE from the new fact (not ephemeral state)
-2. Review source_memories in existing observations to understand evidence
-3. Check dates to detect contradictions or updates
-4. Compare with observations:
-   - Same topic → UPDATE with learning_id
-   - New topic → CREATE new observation
-   - Purely ephemeral → return []
+1. First, extract the DURABLE KNOWLEDGE from the fact (not ephemeral state like "user is at X")
+2. Then compare with existing observations:
+   - If an observation covers the same topic: UPDATE it with the new knowledge
+   - If no observation covers the topic: CREATE a new one

-Output JSON array of actions (the "text" field should use markdown formatting for structure):
+Output JSON array of actions (ALWAYS an array, even for single action):
 [
-  {{"action": "update", "learning_id": "uuid-from-observations", "text": "## Updated Knowledge\n\n**Key point**: details here\n\n- Supporting detail 1\n- Supporting detail 2", "reason": "..."}},
-  {{"action": "create", "text": "## New Durable Knowledge\n\nDescription with **emphasis** and proper structure", "reason": "..."}}
+  {{"action": "update", "learning_id": "uuid", "text": "updated durable knowledge", "reason": "..."}},
+  {{"action": "create", "text": "new durable knowledge", "reason": "..."}}
 ]

-Return [] if fact contains no durable knowledge.
+If NO consolidation is needed (fact is purely ephemeral with no durable knowledge):
+[]

-IMPORTANT: Format the "text" field with markdown for better readability:
- Use headers, lists, bold/italic, tables where appropriate
- CRITICAL: Add blank lines before and after block elements (tables, code blocks, lists)
- Ensure proper spacing for markdown to render correctly"""
+If no observations exist and fact contains durable knowledge:
+[{{"action": "create", "text": "durable knowledge text", "reason": "new topic"}}]"""
@@ -9,7 +9,6 @@ Configuration via environment variables - see hindsight_api.config for all env v
 import asyncio
 import logging
 import os
-import warnings
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor

@@ -21,23 +20,21 @@ from ..config import (
    DEFAULT_RERANKER_FLASHRANK_CACHE_DIR,
    DEFAULT_RERANKER_FLASHRANK_MODEL,
    DEFAULT_RERANKER_LITELLM_MODEL,
-    DEFAULT_RERANKER_LITELLM_SDK_MODEL,
-    DEFAULT_RERANKER_LOCAL_FORCE_CPU,
    DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT,
    DEFAULT_RERANKER_LOCAL_MODEL,
-    DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE,
    DEFAULT_RERANKER_PROVIDER,
    DEFAULT_RERANKER_TEI_BATCH_SIZE,
    DEFAULT_RERANKER_TEI_MAX_CONCURRENT,
-    ENV_RERANKER_COHERE_API_KEY,
+    ENV_COHERE_API_KEY,
+    ENV_LITELLM_API_BASE,
+    ENV_LITELLM_API_KEY,
+    ENV_RERANKER_COHERE_BASE_URL,
    ENV_RERANKER_COHERE_MODEL,
    ENV_RERANKER_FLASHRANK_CACHE_DIR,
    ENV_RERANKER_FLASHRANK_MODEL,
-    ENV_RERANKER_LITELLM_SDK_API_KEY,
-    ENV_RERANKER_LOCAL_FORCE_CPU,
+    ENV_RERANKER_LITELLM_MODEL,
    ENV_RERANKER_LOCAL_MAX_CONCURRENT,
    ENV_RERANKER_LOCAL_MODEL,
-    ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE,
    ENV_RERANKER_PROVIDER,
    ENV_RERANKER_TEI_BATCH_SIZE,
    ENV_RERANKER_TEI_MAX_CONCURRENT,
@@ -102,13 +99,7 @@ class LocalSTCrossEncoder(CrossEncoderModel):
    _executor: ThreadPoolExecutor | None = None
    _max_concurrent: int = 4  # Limit concurrent CPU-bound reranking calls

-    def __init__(
-        self,
-        model_name: str | None = None,
-        max_concurrent: int = 4,
-        force_cpu: bool = False,
-        trust_remote_code: bool = False,
-    ):
+    def __init__(self, model_name: str | None = None, max_concurrent: int = 4):
        """
        Initialize local SentenceTransformers cross-encoder.

@@ -117,15 +108,8 @@ class LocalSTCrossEncoder(CrossEncoderModel):
                       Default: cross-encoder/ms-marco-MiniLM-L-6-v2
            max_concurrent: Maximum concurrent reranking calls (default: 2).
                           Higher values may cause CPU thrashing under load.
-            force_cpu: Force CPU mode (avoids MPS/XPC issues on macOS in daemon mode).
-                      Default: False
-            trust_remote_code: Allow loading models with custom code (security risk).
-                              Required for some models like jina-reranker-v2-base-multilingual.
-                              Default: False (disabled for security)
        """
        self.model_name = model_name or DEFAULT_RERANKER_LOCAL_MODEL
-        self.force_cpu = force_cpu
-        self.trust_remote_code = trust_remote_code
        self._model = None
        LocalSTCrossEncoder._max_concurrent = max_concurrent

@@ -155,47 +139,19 @@ class LocalSTCrossEncoder(CrossEncoderModel):
        # after loading, which conflicts with accelerate's device_map handling.
        import torch

-        # Force CPU mode if configured (used in daemon mode to avoid MPS/XPC issues on macOS)
-        if self.force_cpu:
-            device = "cpu"
-            logger.info("Reranker: forcing CPU mode (HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU=1)")
+        # Check for GPU (CUDA) or Apple Silicon (MPS)
+        has_gpu = torch.cuda.is_available() or (hasattr(torch.backends, "mps") and torch.backends.mps.is_available())
+
+        if has_gpu:
+            device = None  # Let sentence-transformers auto-detect GPU/MPS
        else:
-            # Check for GPU (CUDA) or Apple Silicon (MPS)
-            # Wrap in try-except to gracefully handle any device detection issues
-            # (e.g., in CI environments or when PyTorch is built without GPU support)
-            device = "cpu"  # Default to CPU
-            try:
-                has_gpu = torch.cuda.is_available() or (
-                    hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-                )
-                if has_gpu:
-                    device = None  # Let sentence-transformers auto-detect GPU/MPS
-            except Exception as e:
-                logger.warning(f"Failed to detect GPU/MPS, falling back to CPU: {e}")
+            device = "cpu"

-        # Suppress verbose transformers warnings during model loading
-        # This suppresses the "UNEXPECTED" warnings from CrossEncoder which are harmless
-        # but look alarming to users (e.g., "embeddings.position_ids | UNEXPECTED")
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=UserWarning)
-            warnings.filterwarnings("ignore", message=".*was not found in model state dict.*")
-            warnings.filterwarnings("ignore", message=".*UNEXPECTED.*")
-
-            # Also suppress transformers library logging temporarily
-            transformers_logger = logging.getLogger("transformers")
-            original_level = transformers_logger.level
-            transformers_logger.setLevel(logging.ERROR)
-
-            try:
-                self._model = CrossEncoder(
-                    self.model_name,
-                    device=device,
-                    model_kwargs={"low_cpu_mem_usage": False},
-                    trust_remote_code=self.trust_remote_code,
-                )
-            finally:
-                # Restore original logging level
-                transformers_logger.setLevel(original_level)
+        self._model = CrossEncoder(
+            self.model_name,
+            device=device,
+            model_kwargs={"low_cpu_mem_usage": False},
+        )

        # Initialize shared executor (limited workers naturally limits concurrency)
        if LocalSTCrossEncoder._executor is None:
@@ -207,16 +163,101 @@ class LocalSTCrossEncoder(CrossEncoderModel):
        else:
            logger.info("Reranker: local provider initialized (using existing executor)")

-    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
-        """Synchronous prediction wrapper for thread pool execution."""
-        scores = self._model.predict(pairs, show_progress_bar=False)
-        return scores.tolist() if hasattr(scores, "tolist") else list(scores)
+    def _is_xpc_error(self, error: Exception) -> bool:
+        """
+        Check if an error is an XPC connection error (macOS daemon issue).
+
+        On macOS, long-running daemons can lose XPC connections to system services
+        when the process is idle for extended periods.
+        """
+        error_str = str(error).lower()
+        return "xpc_error_connection_invalid" in error_str or "xpc error" in error_str
+
+    def _reinitialize_model_sync(self) -> None:
+        """
+        Clear and reinitialize the cross-encoder model synchronously.
+
+        This is used to recover from XPC errors on macOS where the
+        PyTorch/MPS backend loses its connection to system services.
+        """
+        logger.warning(f"Reinitializing reranker model {self.model_name} due to backend error")
+
+        # Clear existing model
+        self._model = None
+
+        # Force garbage collection to free resources
+        import gc
+
+        import torch
+
+        gc.collect()
+
+        # If using CUDA/MPS, clear the cache
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            try:
+                torch.mps.empty_cache()
+            except AttributeError:
+                pass  # Method might not exist in all PyTorch versions
+
+        # Reinitialize the model
+        try:
+            from sentence_transformers import CrossEncoder
+        except ImportError:
+            raise ImportError(
+                "sentence-transformers is required for LocalSTCrossEncoder. "
+                "Install it with: pip install sentence-transformers"
+            )
+
+        # Determine device based on hardware availability
+        has_gpu = torch.cuda.is_available() or (hasattr(torch.backends, "mps") and torch.backends.mps.is_available())
+
+        if has_gpu:
+            device = None  # Let sentence-transformers auto-detect GPU/MPS
+        else:
+            device = "cpu"
+
+        self._model = CrossEncoder(
+            self.model_name,
+            device=device,
+            model_kwargs={"low_cpu_mem_usage": False},
+        )
+
+        logger.info("Reranker: local provider reinitialized successfully")
+
+    def _predict_with_recovery(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Predict with automatic recovery from XPC errors.
+
+        This runs synchronously in the thread pool.
+        """
+        max_retries = 1
+        for attempt in range(max_retries + 1):
+            try:
+                scores = self._model.predict(pairs, show_progress_bar=False)
+                return scores.tolist() if hasattr(scores, "tolist") else list(scores)
+            except Exception as e:
+                # Check if this is an XPC error (macOS daemon issue)
+                if self._is_xpc_error(e) and attempt < max_retries:
+                    logger.warning(f"XPC error detected in reranker (attempt {attempt + 1}): {e}")
+                    try:
+                        self._reinitialize_model_sync()
+                        logger.info("Reranker reinitialized successfully, retrying prediction")
+                        continue
+                    except Exception as reinit_error:
+                        logger.error(f"Failed to reinitialize reranker: {reinit_error}")
+                        raise Exception(f"Failed to recover from XPC error: {str(e)}")
+                else:
+                    # Not an XPC error or out of retries
+                    raise

    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
        """
        Score query-document pairs for relevance.

        Uses a dedicated thread pool with limited workers to prevent CPU thrashing.
+        Automatically recovers from XPC errors on macOS by reinitializing the model.

        Args:
            pairs: List of (query, document) tuples to score
@@ -231,7 +272,7 @@ class LocalSTCrossEncoder(CrossEncoderModel):
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(
            LocalSTCrossEncoder._executor,
-            self._predict_sync,
+            self._predict_with_recovery,
            pairs,
        )

@@ -643,7 +684,7 @@ class FlashRankCrossEncoder(CrossEncoderModel):
            return

        try:
-            from flashrank import Ranker
+            from flashrank import Ranker  # type: ignore[import-untyped]
        except ImportError:
            raise ImportError("flashrank is required for FlashRankCrossEncoder. Install it with: pip install flashrank")

@@ -670,7 +711,7 @@ class FlashRankCrossEncoder(CrossEncoderModel):

    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
        """Synchronous predict - processes each query group."""
-        from flashrank import RerankRequest
+        from flashrank import RerankRequest  # type: ignore[import-untyped]

        if not pairs:
            return []
@@ -830,189 +871,50 @@ class LiteLLMCrossEncoder(CrossEncoderModel):
        return all_scores


-class LiteLLMSDKCrossEncoder(CrossEncoderModel):
-    """
-    LiteLLM SDK cross-encoder for direct API integration.
-
-    Supports reranking via LiteLLM SDK without requiring a proxy server.
-    Supported providers: Cohere, DeepInfra, Together AI, HuggingFace, Jina AI, Voyage AI, AWS Bedrock.
-
-    Example model names:
-    - cohere/rerank-english-v3.0
-    - deepinfra/Qwen3-reranker-8B
-    - together_ai/Salesforce/Llama-Rank-V1
-    - huggingface/BAAI/bge-reranker-v2-m3
-    """
-
-    def __init__(
-        self,
-        api_key: str,
-        model: str = DEFAULT_RERANKER_LITELLM_SDK_MODEL,
-        api_base: str | None = None,
-        timeout: float = 60.0,
-    ):
-        """
-        Initialize LiteLLM SDK cross-encoder client.
-
-        Args:
-            api_key: API key for the reranking provider
-            model: Model name with provider prefix (e.g., "deepinfra/Qwen3-reranker-8B")
-            api_base: Custom base URL for API (optional)
-            timeout: Request timeout in seconds (default: 60.0)
-        """
-        self.api_key = api_key
-        self.model = model
-        self.api_base = api_base
-        self.timeout = timeout
-        self._initialized = False
-        self._litellm = None  # Will be set during initialization
-
-    @property
-    def provider_name(self) -> str:
-        return "litellm-sdk"
-
-    async def initialize(self) -> None:
-        """Initialize the LiteLLM SDK client."""
-        if self._initialized:
-            return
-
-        try:
-            import litellm
-
-            self._litellm = litellm  # Store reference
-        except ImportError:
-            raise ImportError("litellm is required for LiteLLMSDKCrossEncoder. Install it with: pip install litellm")
-
-        api_base_msg = f" at {self.api_base}" if self.api_base else ""
-        logger.info(f"Reranker: initializing LiteLLM SDK provider with model {self.model}{api_base_msg}")
-
-        self._initialized = True
-        logger.info("Reranker: LiteLLM SDK provider initialized")
-
-    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
-        """
-        Score query-document pairs using the LiteLLM SDK.
-
-        Args:
-            pairs: List of (query, document) tuples to score
-
-        Returns:
-            List of relevance scores
-        """
-        if not self._initialized:
-            raise RuntimeError("Reranker not initialized. Call initialize() first.")
-
-        if not pairs:
-            return []
-
-        # Group pairs by query for efficient batching
-        # LiteLLM rerank expects one query with multiple documents
-        query_groups: dict[str, list[tuple[int, str]]] = {}
-        for idx, (query, text) in enumerate(pairs):
-            if query not in query_groups:
-                query_groups[query] = []
-            query_groups[query].append((idx, text))
-
-        all_scores = [0.0] * len(pairs)
-
-        for query, indexed_texts in query_groups.items():
-            texts = [text for _, text in indexed_texts]
-            indices = [idx for idx, _ in indexed_texts]
-
-            # Build kwargs for rerank call
-            rerank_kwargs = {
-                "model": self.model,
-                "query": query,
-                "documents": texts,
-                "api_key": self.api_key,
-            }
-            if self.api_base:
-                rerank_kwargs["api_base"] = self.api_base
-
-            response = await self._litellm.arerank(**rerank_kwargs)
-
-            # Map scores back to original positions
-            # Response format: RerankResponse with results list
-            # Each result is a TypedDict with "index" and "relevance_score"
-            if hasattr(response, "results") and response.results:
-                for result in response.results:
-                    # Results are TypedDicts, use dict-style access
-                    original_idx = result["index"]
-                    score = result.get("relevance_score", result.get("score", 0.0))
-                    all_scores[indices[original_idx]] = score
-            elif isinstance(response, list):
-                # Direct list of scores (unlikely but defensive)
-                for i, score in enumerate(response):
-                    all_scores[indices[i]] = score
-            else:
-                logger.warning(f"Unexpected response format from LiteLLM rerank: {type(response)}")
-
-        return all_scores
-
-
 def create_cross_encoder_from_env() -> CrossEncoderModel:
    """
-    Create a CrossEncoderModel instance based on configuration.
+    Create a CrossEncoderModel instance based on environment variables.

-    Reads configuration via get_config() to ensure consistency across the codebase.
+    See hindsight_api.config for environment variable names and defaults.

    Returns:
        Configured CrossEncoderModel instance
    """
-    from ..config import get_config
-
-    config = get_config()
-    provider = config.reranker_provider.lower()
+    provider = os.environ.get(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER).lower()

    if provider == "tei":
-        url = config.reranker_tei_url
+        url = os.environ.get(ENV_RERANKER_TEI_URL)
        if not url:
            raise ValueError(f"{ENV_RERANKER_TEI_URL} is required when {ENV_RERANKER_PROVIDER} is 'tei'")
-        return RemoteTEICrossEncoder(
-            base_url=url,
-            batch_size=config.reranker_tei_batch_size,
-            max_concurrent=config.reranker_tei_max_concurrent,
-        )
+        batch_size = int(os.environ.get(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE)))
+        max_concurrent = int(os.environ.get(ENV_RERANKER_TEI_MAX_CONCURRENT, str(DEFAULT_RERANKER_TEI_MAX_CONCURRENT)))
+        return RemoteTEICrossEncoder(base_url=url, batch_size=batch_size, max_concurrent=max_concurrent)
    elif provider == "local":
-        return LocalSTCrossEncoder(
-            model_name=config.reranker_local_model,
-            max_concurrent=config.reranker_local_max_concurrent,
-            force_cpu=config.reranker_local_force_cpu,
-            trust_remote_code=config.reranker_local_trust_remote_code,
+        model = os.environ.get(ENV_RERANKER_LOCAL_MODEL)
+        model_name = model or DEFAULT_RERANKER_LOCAL_MODEL
+        max_concurrent = int(
+            os.environ.get(ENV_RERANKER_LOCAL_MAX_CONCURRENT, str(DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT))
        )
+        return LocalSTCrossEncoder(model_name=model_name, max_concurrent=max_concurrent)
    elif provider == "cohere":
-        api_key = config.reranker_cohere_api_key
+        api_key = os.environ.get(ENV_COHERE_API_KEY)
        if not api_key:
-            raise ValueError(f"{ENV_RERANKER_COHERE_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'cohere'")
-        return CohereCrossEncoder(
-            api_key=api_key,
-            model=config.reranker_cohere_model,
-            base_url=config.reranker_cohere_base_url,
-        )
+            raise ValueError(f"{ENV_COHERE_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'cohere'")
+        model = os.environ.get(ENV_RERANKER_COHERE_MODEL, DEFAULT_RERANKER_COHERE_MODEL)
+        base_url = os.environ.get(ENV_RERANKER_COHERE_BASE_URL) or None
+        return CohereCrossEncoder(api_key=api_key, model=model, base_url=base_url)
    elif provider == "flashrank":
        model = os.environ.get(ENV_RERANKER_FLASHRANK_MODEL, DEFAULT_RERANKER_FLASHRANK_MODEL)
        cache_dir = os.environ.get(ENV_RERANKER_FLASHRANK_CACHE_DIR, DEFAULT_RERANKER_FLASHRANK_CACHE_DIR)
        return FlashRankCrossEncoder(model_name=model, cache_dir=cache_dir)
    elif provider == "litellm":
-        return LiteLLMCrossEncoder(
-            api_base=config.reranker_litellm_api_base,
-            api_key=config.reranker_litellm_api_key,
-            model=config.reranker_litellm_model,
-        )
-    elif provider == "litellm-sdk":
-        api_key = config.reranker_litellm_sdk_api_key
-        if not api_key:
-            raise ValueError(
-                f"{ENV_RERANKER_LITELLM_SDK_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'litellm-sdk'"
-            )
-        return LiteLLMSDKCrossEncoder(
-            api_key=api_key,
-            model=config.reranker_litellm_sdk_model,
-            api_base=config.reranker_litellm_sdk_api_base,
-        )
+        api_base = os.environ.get(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE)
+        api_key = os.environ.get(ENV_LITELLM_API_KEY)
+        model = os.environ.get(ENV_RERANKER_LITELLM_MODEL, DEFAULT_RERANKER_LITELLM_MODEL)
+        return LiteLLMCrossEncoder(api_base=api_base, api_key=api_key, model=model)
    elif provider == "rrf":
        return RRFPassthroughCrossEncoder()
    else:
        raise ValueError(
-            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'litellm', 'rrf'"
        )
@@ -11,7 +11,6 @@ Configuration via environment variables - see hindsight_api.config for all env v

 import logging
 import os
-import warnings
 from abc import ABC, abstractmethod

 import httpx
@@ -19,23 +18,22 @@ import httpx
 from ..config import (
    DEFAULT_EMBEDDINGS_COHERE_MODEL,
    DEFAULT_EMBEDDINGS_LITELLM_MODEL,
-    DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL,
-    DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU,
    DEFAULT_EMBEDDINGS_LOCAL_MODEL,
-    DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE,
    DEFAULT_EMBEDDINGS_OPENAI_MODEL,
    DEFAULT_EMBEDDINGS_PROVIDER,
    DEFAULT_LITELLM_API_BASE,
-    ENV_EMBEDDINGS_COHERE_API_KEY,
-    ENV_EMBEDDINGS_LITELLM_SDK_API_KEY,
-    ENV_EMBEDDINGS_LOCAL_FORCE_CPU,
+    ENV_COHERE_API_KEY,
+    ENV_EMBEDDINGS_COHERE_BASE_URL,
+    ENV_EMBEDDINGS_COHERE_MODEL,
+    ENV_EMBEDDINGS_LITELLM_MODEL,
    ENV_EMBEDDINGS_LOCAL_MODEL,
-    ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE,
    ENV_EMBEDDINGS_OPENAI_API_KEY,
    ENV_EMBEDDINGS_OPENAI_BASE_URL,
    ENV_EMBEDDINGS_OPENAI_MODEL,
    ENV_EMBEDDINGS_PROVIDER,
    ENV_EMBEDDINGS_TEI_URL,
+    ENV_LITELLM_API_BASE,
+    ENV_LITELLM_API_KEY,
    ENV_LLM_API_KEY,
 )

@@ -94,22 +92,15 @@ class LocalSTEmbeddings(Embeddings):
    The embedding dimension is auto-detected from the model.
    """

-    def __init__(self, model_name: str | None = None, force_cpu: bool = False, trust_remote_code: bool = False):
+    def __init__(self, model_name: str | None = None):
        """
        Initialize local SentenceTransformers embeddings.

        Args:
            model_name: Name of the SentenceTransformer model to use.
                       Default: BAAI/bge-small-en-v1.5
-            force_cpu: Force CPU mode (avoids MPS/XPC issues on macOS in daemon mode).
-                      Default: False
-            trust_remote_code: Allow loading models with custom code (security risk).
-                              Required for some models with custom architectures.
-                              Default: False (disabled for security)
        """
        self.model_name = model_name or DEFAULT_EMBEDDINGS_LOCAL_MODEL
-        self.force_cpu = force_cpu
-        self.trust_remote_code = trust_remote_code
        self._model = None
        self._dimension: int | None = None

@@ -143,55 +134,92 @@ class LocalSTEmbeddings(Embeddings):
        # which can cause issues when accelerate is installed but no GPU is available.
        import torch

-        # Force CPU mode if configured (used in daemon mode to avoid MPS/XPC issues on macOS)
-        if self.force_cpu:
-            device = "cpu"
-            logger.info("Embeddings: forcing CPU mode")
+        # Check for GPU (CUDA) or Apple Silicon (MPS)
+        has_gpu = torch.cuda.is_available() or (hasattr(torch.backends, "mps") and torch.backends.mps.is_available())
+
+        if has_gpu:
+            device = None  # Let sentence-transformers auto-detect GPU/MPS
        else:
-            # Check for GPU (CUDA) or Apple Silicon (MPS)
-            # Wrap in try-except to gracefully handle any device detection issues
-            # (e.g., in CI environments or when PyTorch is built without GPU support)
-            device = "cpu"  # Default to CPU
-            try:
-                has_gpu = torch.cuda.is_available() or (
-                    hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-                )
-                if has_gpu:
-                    device = None  # Let sentence-transformers auto-detect GPU/MPS
-            except Exception as e:
-                logger.warning(f"Failed to detect GPU/MPS, falling back to CPU: {e}")
+            device = "cpu"

-        # Suppress verbose transformers warnings during model loading
-        # This suppresses the "UNEXPECTED" warnings from BertModel which are harmless
-        # but look alarming to users (e.g., "embeddings.position_ids | UNEXPECTED")
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=UserWarning)
-            warnings.filterwarnings("ignore", message=".*was not found in model state dict.*")
-            warnings.filterwarnings("ignore", message=".*UNEXPECTED.*")
-
-            # Also suppress transformers library logging temporarily
-            transformers_logger = logging.getLogger("transformers")
-            original_level = transformers_logger.level
-            transformers_logger.setLevel(logging.ERROR)
-
-            try:
-                self._model = SentenceTransformer(
-                    self.model_name,
-                    device=device,
-                    model_kwargs={"low_cpu_mem_usage": False},
-                    trust_remote_code=self.trust_remote_code,
-                )
-            finally:
-                # Restore original logging level
-                transformers_logger.setLevel(original_level)
+        self._model = SentenceTransformer(
+            self.model_name,
+            device=device,
+            model_kwargs={"low_cpu_mem_usage": False},
+        )

        self._dimension = self._model.get_sentence_embedding_dimension()
        logger.info(f"Embeddings: local provider initialized (dim: {self._dimension})")

+    def _is_xpc_error(self, error: Exception) -> bool:
+        """
+        Check if an error is an XPC connection error (macOS daemon issue).
+
+        On macOS, long-running daemons can lose XPC connections to system services
+        when the process is idle for extended periods.
+        """
+        error_str = str(error).lower()
+        return "xpc_error_connection_invalid" in error_str or "xpc error" in error_str
+
+    def _reinitialize_model_sync(self) -> None:
+        """
+        Clear and reinitialize the embedding model synchronously.
+
+        This is used to recover from XPC errors on macOS where the
+        PyTorch/MPS backend loses its connection to system services.
+        """
+        logger.warning(f"Reinitializing embedding model {self.model_name} due to backend error")
+
+        # Clear existing model
+        self._model = None
+
+        # Force garbage collection to free resources
+        import gc
+
+        import torch
+
+        gc.collect()
+
+        # If using CUDA/MPS, clear the cache
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            try:
+                torch.mps.empty_cache()
+            except AttributeError:
+                pass  # Method might not exist in all PyTorch versions
+
+        # Reinitialize the model (inline version of initialize() but synchronous)
+        try:
+            from sentence_transformers import SentenceTransformer
+        except ImportError:
+            raise ImportError(
+                "sentence-transformers is required for LocalSTEmbeddings. "
+                "Install it with: pip install sentence-transformers"
+            )
+
+        # Determine device based on hardware availability
+        has_gpu = torch.cuda.is_available() or (hasattr(torch.backends, "mps") and torch.backends.mps.is_available())
+
+        if has_gpu:
+            device = None  # Let sentence-transformers auto-detect GPU/MPS
+        else:
+            device = "cpu"
+
+        self._model = SentenceTransformer(
+            self.model_name,
+            device=device,
+            model_kwargs={"low_cpu_mem_usage": False},
+        )
+
+        logger.info("Embeddings: local provider reinitialized successfully")
+
    def encode(self, texts: list[str]) -> list[list[float]]:
        """
        Generate embeddings for a list of texts.

+        Automatically recovers from XPC errors on macOS by reinitializing the model.
+
        Args:
            texts: List of text strings to encode

@@ -201,8 +229,26 @@ class LocalSTEmbeddings(Embeddings):
        if self._model is None:
            raise RuntimeError("Embeddings not initialized. Call initialize() first.")

-        embeddings = self._model.encode(texts, convert_to_numpy=True, show_progress_bar=False)
-        return [emb.tolist() for emb in embeddings]
+        # Try encoding with automatic recovery from XPC errors
+        max_retries = 1
+        for attempt in range(max_retries + 1):
+            try:
+                embeddings = self._model.encode(texts, convert_to_numpy=True, show_progress_bar=False)
+                return [emb.tolist() for emb in embeddings]
+            except Exception as e:
+                # Check if this is an XPC error (macOS daemon issue)
+                if self._is_xpc_error(e) and attempt < max_retries:
+                    logger.warning(f"XPC error detected in embedding generation (attempt {attempt + 1}): {e}")
+                    try:
+                        self._reinitialize_model_sync()
+                        logger.info("Model reinitialized successfully, retrying embedding generation")
+                        continue
+                    except Exception as reinit_error:
+                        logger.error(f"Failed to reinitialize model: {reinit_error}")
+                        raise Exception(f"Failed to recover from XPC error: {str(e)}")
+                else:
+                    # Not an XPC error or out of retries
+                    raise


 class RemoteTEIEmbeddings(Embeddings):
@@ -567,7 +613,7 @@ class CohereEmbeddings(Embeddings):
                model=self.model,
                input_type=self.input_type,
            )
-            if response.embeddings and isinstance(response.embeddings, list):
+            if response.embeddings:
                self._dimension = len(response.embeddings[0])

        logger.info(f"Embeddings: Cohere provider initialized (model: {self.model}, dim: {self._dimension})")
@@ -722,173 +768,26 @@ class LiteLLMEmbeddings(Embeddings):
        return all_embeddings


-class LiteLLMSDKEmbeddings(Embeddings):
-    """
-    LiteLLM SDK embeddings for direct API integration.
-
-    Supports embeddings via LiteLLM SDK without requiring a proxy server.
-    Supported providers: Cohere, OpenAI, Azure OpenAI, HuggingFace, Voyage AI, Together AI, etc.
-
-    Example model names:
-    - cohere/embed-english-v3.0
-    - openai/text-embedding-3-small
-    - together_ai/togethercomputer/m2-bert-80M-8k-retrieval
-    - voyage/voyage-2
-    """
-
-    def __init__(
-        self,
-        api_key: str,
-        model: str = DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL,
-        api_base: str | None = None,
-        batch_size: int = 100,
-        timeout: float = 60.0,
-    ):
-        """
-        Initialize LiteLLM SDK embeddings client.
-
-        Args:
-            api_key: API key for the embedding provider
-            model: Model name with provider prefix (e.g., "cohere/embed-english-v3.0")
-            api_base: Custom base URL for API (optional)
-            batch_size: Maximum batch size for embedding requests (default: 100)
-            timeout: Request timeout in seconds (default: 60.0)
-        """
-        self.api_key = api_key
-        self.model = model
-        self.api_base = api_base
-        self.batch_size = batch_size
-        self.timeout = timeout
-        self._litellm = None  # Will be set during initialization
-        self._dimension: int | None = None
-
-    @property
-    def provider_name(self) -> str:
-        return "litellm-sdk"
-
-    @property
-    def dimension(self) -> int:
-        if self._dimension is None:
-            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
-        return self._dimension
-
-    async def initialize(self) -> None:
-        """Initialize the LiteLLM SDK client and detect dimension."""
-        if self._litellm is not None:
-            return
-
-        try:
-            import litellm
-
-            self._litellm = litellm  # Store reference
-        except ImportError:
-            raise ImportError("litellm is required for LiteLLMSDKEmbeddings. Install it with: pip install litellm")
-
-        api_base_msg = f" at {self.api_base}" if self.api_base else ""
-        logger.info(f"Embeddings: initializing LiteLLM SDK provider with model {self.model}{api_base_msg}")
-
-        # Do a test embedding to detect dimension
-        try:
-            # Build kwargs for embedding call
-            embed_kwargs = {
-                "model": self.model,
-                "input": ["test"],
-                "api_key": self.api_key,
-            }
-            if self.api_base:
-                embed_kwargs["api_base"] = self.api_base
-
-            # Use async embedding method (standard in litellm)
-            response = await self._litellm.aembedding(**embed_kwargs)
-
-            # Extract dimension from response
-            if response.data and len(response.data) > 0:
-                self._dimension = len(response.data[0]["embedding"])
-            else:
-                raise RuntimeError(f"Unable to detect embedding dimension for model {self.model}")
-
-        except Exception as e:
-            raise RuntimeError(f"Failed to initialize LiteLLM SDK embeddings: {e}")
-
-        logger.info(f"Embeddings: LiteLLM SDK provider initialized (model: {self.model}, dim: {self._dimension})")
-
-    def encode(self, texts: list[str]) -> list[list[float]]:
-        """
-        Generate embeddings using the LiteLLM SDK.
-
-        Args:
-            texts: List of text strings to encode
-
-        Returns:
-            List of embedding vectors (one per input text)
-        """
-        if self._litellm is None:
-            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
-
-        if not texts:
-            return []
-
-        all_embeddings = []
-
-        # Process in batches
-        for i in range(0, len(texts), self.batch_size):
-            batch = texts[i : i + self.batch_size]
-
-            try:
-                # Build kwargs for embedding call
-                embed_kwargs = {
-                    "model": self.model,
-                    "input": batch,
-                    "api_key": self.api_key,
-                }
-                if self.api_base:
-                    embed_kwargs["api_base"] = self.api_base
-
-                # Use sync embedding (litellm doesn't have async in thread-safe way)
-                response = self._litellm.embedding(**embed_kwargs)
-
-                # Extract embeddings from response
-                # Sort by index to ensure correct order
-                batch_embeddings = sorted(response.data, key=lambda x: x.get("index", 0))
-                all_embeddings.extend([e["embedding"] for e in batch_embeddings])
-
-            except Exception as e:
-                import traceback
-
-                logger.error(
-                    f"Error in LiteLLM embedding for batch starting at index {i}: {e}\n"
-                    f"Traceback: {traceback.format_exc()}"
-                )
-                raise
-
-        return all_embeddings
-
-
 def create_embeddings_from_env() -> Embeddings:
    """
-    Create an Embeddings instance based on configuration.
+    Create an Embeddings instance based on environment variables.

-    Reads configuration via get_config() to ensure consistency across the codebase.
+    See hindsight_api.config for environment variable names and defaults.

    Returns:
        Configured Embeddings instance
    """
-    from ..config import get_config
-
-    config = get_config()
-    provider = config.embeddings_provider.lower()
+    provider = os.environ.get(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER).lower()

    if provider == "tei":
-        url = config.embeddings_tei_url
+        url = os.environ.get(ENV_EMBEDDINGS_TEI_URL)
        if not url:
            raise ValueError(f"{ENV_EMBEDDINGS_TEI_URL} is required when {ENV_EMBEDDINGS_PROVIDER} is 'tei'")
        return RemoteTEIEmbeddings(base_url=url)
    elif provider == "local":
-        return LocalSTEmbeddings(
-            model_name=config.embeddings_local_model,
-            force_cpu=config.embeddings_local_force_cpu,
-            trust_remote_code=config.embeddings_local_trust_remote_code,
-        )
+        model = os.environ.get(ENV_EMBEDDINGS_LOCAL_MODEL)
+        model_name = model or DEFAULT_EMBEDDINGS_LOCAL_MODEL
+        return LocalSTEmbeddings(model_name=model_name)
    elif provider == "openai":
        # Use dedicated embeddings API key, or fall back to LLM API key
        api_key = os.environ.get(ENV_EMBEDDINGS_OPENAI_API_KEY) or os.environ.get(ENV_LLM_API_KEY)
@@ -901,33 +800,18 @@ def create_embeddings_from_env() -> Embeddings:
        base_url = os.environ.get(ENV_EMBEDDINGS_OPENAI_BASE_URL) or None
        return OpenAIEmbeddings(api_key=api_key, model=model, base_url=base_url)
    elif provider == "cohere":
-        api_key = config.embeddings_cohere_api_key
+        api_key = os.environ.get(ENV_COHERE_API_KEY)
        if not api_key:
-            raise ValueError(f"{ENV_EMBEDDINGS_COHERE_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'cohere'")
-        return CohereEmbeddings(
-            api_key=api_key,
-            model=config.embeddings_cohere_model,
-            base_url=config.embeddings_cohere_base_url,
-        )
+            raise ValueError(f"{ENV_COHERE_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'cohere'")
+        model = os.environ.get(ENV_EMBEDDINGS_COHERE_MODEL, DEFAULT_EMBEDDINGS_COHERE_MODEL)
+        base_url = os.environ.get(ENV_EMBEDDINGS_COHERE_BASE_URL) or None
+        return CohereEmbeddings(api_key=api_key, model=model, base_url=base_url)
    elif provider == "litellm":
-        return LiteLLMEmbeddings(
-            api_base=config.embeddings_litellm_api_base,
-            api_key=config.embeddings_litellm_api_key,
-            model=config.embeddings_litellm_model,
-        )
-    elif provider == "litellm-sdk":
-        api_key = config.embeddings_litellm_sdk_api_key
-        if not api_key:
-            raise ValueError(
-                f"{ENV_EMBEDDINGS_LITELLM_SDK_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'litellm-sdk'"
-            )
-        return LiteLLMSDKEmbeddings(
-            api_key=api_key,
-            model=config.embeddings_litellm_sdk_model,
-            api_base=config.embeddings_litellm_sdk_api_base,
-        )
+        api_base = os.environ.get(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE)
+        api_key = os.environ.get(ENV_LITELLM_API_KEY)
+        model = os.environ.get(ENV_EMBEDDINGS_LITELLM_MODEL, DEFAULT_EMBEDDINGS_LITELLM_MODEL)
+        return LiteLLMEmbeddings(api_base=api_base, api_key=api_key, model=model)
    else:
        raise ValueError(
-            f"Unknown embeddings provider: {provider}. "
-            f"Supported: 'local', 'tei', 'openai', 'cohere', 'litellm', 'litellm-sdk'"
+            f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei', 'openai', 'cohere', 'litellm'"
        )
@@ -48,7 +48,6 @@ class MemoryEngineInterface(ABC):
        contents: list[dict[str, Any]],
        *,
        request_context: "RequestContext",
-        document_tags: list[str] | None = None,
    ) -> dict[str, Any]:
        """
        Retain a batch of memory items.
@@ -56,9 +55,8 @@ class MemoryEngineInterface(ABC):
        Args:
            bank_id: The memory bank ID.
            contents: List of content dicts with 'content', optional 'event_date',
-                     'context', 'metadata', 'document_id', and per-item 'tags'.
+                     'context', 'metadata', 'document_id'.
            request_context: Request context for authentication.
-            document_tags: Optional tags applied to all items in the batch.

        Returns:
            Dict with processing results.
@@ -444,6 +442,49 @@ class MemoryEngineInterface(ABC):
        """
        ...

+    @abstractmethod
+    async def get_entity_observations(
+        self,
+        bank_id: str,
+        entity_id: str,
+        *,
+        limit: int = 10,
+        request_context: "RequestContext",
+    ) -> list[Any]:
+        """
+        Get observations for an entity.
+
+        Args:
+            bank_id: The memory bank ID.
+            entity_id: The entity ID.
+            limit: Maximum observations.
+            request_context: Request context for authentication.
+
+        Returns:
+            List of EntityObservation objects.
+        """
+        ...
+
+    @abstractmethod
+    async def regenerate_entity_observations(
+        self,
+        bank_id: str,
+        entity_id: str,
+        entity_name: str,
+        *,
+        request_context: "RequestContext",
+    ) -> None:
+        """
+        Regenerate observations for an entity.
+
+        Args:
+            bank_id: The memory bank ID.
+            entity_id: The entity ID.
+            entity_name: The entity's canonical name.
+            request_context: Request context for authentication.
+        """
+        ...
+
    # =========================================================================
    # Statistics & Operations
    # =========================================================================
@@ -563,7 +604,6 @@ class MemoryEngineInterface(ABC):
        contents: list[dict[str, Any]],
        *,
        request_context: "RequestContext",
-        document_tags: list[str] | None = None,
    ) -> dict[str, Any]:
        """
        Submit a batch retain operation to run asynchronously.
@@ -572,7 +612,6 @@ class MemoryEngineInterface(ABC):
            bank_id: The memory bank ID.
            contents: List of content dicts to retain.
            request_context: Request context for authentication.
-            document_tags: Optional tags applied to all items in the async batch.

        Returns:
            Dict with operation_id and items_count.
@@ -1,146 +0,0 @@
-"""
-Abstract interface for LLM providers.
-
-This module defines the interface that all LLM providers must implement,
-enabling support for multiple LLM backends (OpenAI, Anthropic, Gemini, Codex, etc.)
-"""
-
-from abc import ABC, abstractmethod
-from typing import Any
-
-from .response_models import LLMToolCallResult, TokenUsage
-
-
-class LLMInterface(ABC):
-    """
-    Abstract interface for LLM providers.
-
-    All LLM provider implementations must inherit from this class and implement
-    the required methods.
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """
-        Initialize LLM provider.
-
-        Args:
-            provider: Provider name (e.g., "openai", "codex", "anthropic", "gemini").
-            api_key: API key or authentication token.
-            base_url: Base URL for the API.
-            model: Model name.
-            reasoning_effort: Reasoning effort level for supported providers.
-            **kwargs: Additional provider-specific parameters.
-        """
-        self.provider = provider.lower()
-        self.api_key = api_key
-        self.base_url = base_url
-        self.model = model
-        self.reasoning_effort = reasoning_effort
-
-    @abstractmethod
-    async def verify_connection(self) -> None:
-        """
-        Verify that the LLM provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        pass
-
-    @abstractmethod
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make an LLM API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (OpenAI only).
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with token counts.
-
-        Raises:
-            OutputTooLongError: If output exceeds token limits.
-            Exception: Re-raises API errors after retries exhausted.
-        """
-        pass
-
-    @abstractmethod
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make an LLM API call with tool/function calling support.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        pass
-
-    @abstractmethod
-    async def cleanup(self) -> None:
-        """Clean up resources (close connections, etc.)."""
-        pass
-
-
-class OutputTooLongError(Exception):
-    """
-    Bridge exception raised when LLM output exceeds token limits.
-
-    This wraps provider-specific errors (e.g., OpenAI's LengthFinishReasonError)
-    to allow callers to handle output length issues without depending on
-    provider-specific implementations.
-    """
-
-    pass
@@ -1,69 +0,0 @@
-"""
-Typed metadata models for async operations.
-
-These dataclasses define the structure of result_metadata for different operation types.
-The metadata is exposed in the API for debugging purposes and may change without notice.
-"""
-
-from dataclasses import asdict, dataclass
-from typing import Any
-
-
-@dataclass
-class BatchRetainParentMetadata:
-    """Metadata for parent batch_retain operations (when split into sub-batches)."""
-
-    items_count: int
-    total_tokens: int
-    num_sub_batches: int
-    is_parent: bool = True
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dict for JSON serialization."""
-        return asdict(self)
-
-
-@dataclass
-class BatchRetainChildMetadata:
-    """Metadata for child batch_retain operations (individual sub-batches)."""
-
-    items_count: int
-    parent_operation_id: str
-    sub_batch_index: int
-    total_sub_batches: int
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dict for JSON serialization."""
-        return asdict(self)
-
-
-@dataclass
-class RetainMetadata:
-    """Metadata for regular retain operations (non-batched, deprecated async path)."""
-
-    items_count: int
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dict for JSON serialization."""
-        return asdict(self)
-
-
-@dataclass
-class ConsolidationMetadata:
-    """Metadata for consolidation operations."""
-
-    # Currently empty, but structure for future fields
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dict for JSON serialization."""
-        return asdict(self)
-
-
-@dataclass
-class RefreshMentalModelMetadata:
-    """Metadata for mental model refresh operations."""
-
-    mental_model_id: str
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dict for JSON serialization."""
-        return asdict(self)
@@ -1,14 +0,0 @@
-"""
-LLM provider implementations.
-
-This package contains concrete implementations of the LLMInterface for various providers.
-"""
-
-from .anthropic_llm import AnthropicLLM
-from .claude_code_llm import ClaudeCodeLLM
-from .codex_llm import CodexLLM
-from .gemini_llm import GeminiLLM
-from .mock_llm import MockLLM
-from .openai_compatible_llm import OpenAICompatibleLLM
-
-__all__ = ["AnthropicLLM", "ClaudeCodeLLM", "CodexLLM", "GeminiLLM", "MockLLM", "OpenAICompatibleLLM"]
@@ -1,477 +0,0 @@
-"""
-Anthropic LLM provider using the Anthropic Python SDK.
-
-This provider enables using Claude models from Anthropic with support for:
- Structured JSON output
- Tool/function calling with proper format conversion
- Extended thinking mode
- Retry logic with exponential backoff
-"""
-
-import asyncio
-import json
-import logging
-import time
-from typing import Any
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-
-class AnthropicLLM(LLMInterface):
-    """
-    LLM provider using Anthropic's Claude models.
-
-    Supports structured output, tool calling, and extended thinking mode.
-    Handles format conversion between OpenAI-style messages and Anthropic's format.
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        timeout: float = 300.0,
-        **kwargs: Any,
-    ):
-        """
-        Initialize Anthropic LLM provider.
-
-        Args:
-            provider: Provider name (should be "anthropic").
-            api_key: Anthropic API key.
-            base_url: Base URL for the API (optional, uses Anthropic default if empty).
-            model: Model name (e.g., "claude-sonnet-4-20250514").
-            reasoning_effort: Reasoning effort level (not used by Anthropic).
-            timeout: Request timeout in seconds.
-            **kwargs: Additional provider-specific parameters.
-        """
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        if not self.api_key:
-            raise ValueError("API key is required for Anthropic provider")
-
-        # Import and initialize Anthropic client
-        try:
-            from anthropic import AsyncAnthropic
-
-            client_kwargs: dict[str, Any] = {"api_key": self.api_key}
-            if self.base_url:
-                client_kwargs["base_url"] = self.base_url
-            if timeout:
-                client_kwargs["timeout"] = timeout
-
-            self._client = AsyncAnthropic(**client_kwargs)
-            logger.info(f"Anthropic client initialized for model: {self.model}")
-        except ImportError as e:
-            raise RuntimeError("Anthropic SDK not installed. Run: uv add anthropic or pip install anthropic") from e
-
-    async def verify_connection(self) -> None:
-        """
-        Verify that the Anthropic provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            test_messages = [{"role": "user", "content": "test"}]
-            await self.call(
-                messages=test_messages,
-                max_completion_tokens=10,
-                temperature=0.0,
-                scope="verification",
-                max_retries=0,
-            )
-            logger.info("Anthropic connection verified successfully")
-        except Exception as e:
-            logger.error(f"Anthropic connection verification failed: {e}")
-            raise RuntimeError(f"Failed to verify Anthropic connection: {e}") from e
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make an LLM API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (not supported by Anthropic).
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with token counts.
-
-        Raises:
-            OutputTooLongError: If output exceeds token limits.
-            Exception: Re-raises API errors after retries exhausted.
-        """
-        from anthropic import APIConnectionError, APIStatusError, RateLimitError
-
-        start_time = time.time()
-
-        # Convert OpenAI-style messages to Anthropic format
-        system_prompt = None
-        anthropic_messages = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                if system_prompt:
-                    system_prompt += "\n\n" + content
-                else:
-                    system_prompt = content
-            else:
-                anthropic_messages.append({"role": role, "content": content})
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-            if system_prompt:
-                system_prompt += schema_msg
-            else:
-                system_prompt = schema_msg
-
-        # Prepare parameters
-        call_params: dict[str, Any] = {
-            "model": self.model,
-            "messages": anthropic_messages,
-            "max_tokens": max_completion_tokens if max_completion_tokens is not None else 4096,
-        }
-
-        if system_prompt:
-            call_params["system"] = system_prompt
-
-        if temperature is not None:
-            call_params["temperature"] = temperature
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.messages.create(**call_params)
-
-                # Anthropic response content is a list of blocks
-                content = ""
-                for block in response.content:
-                    if block.type == "text":
-                        content += block.text
-
-                if response_format is not None:
-                    # Models may wrap JSON in markdown code blocks
-                    clean_content = content
-                    if "```json" in content:
-                        clean_content = content.split("```json")[1].split("```")[0].strip()
-                    elif "```" in content:
-                        clean_content = content.split("```")[1].split("```")[0].strip()
-
-                    try:
-                        json_data = json.loads(clean_content)
-                    except json.JSONDecodeError:
-                        # Fallback to parsing raw content if markdown stripping failed
-                        json_data = json.loads(content)
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = content
-
-                # Record metrics and log slow calls
-                duration = time.time() - start_time
-                input_tokens = response.usage.input_tokens or 0 if response.usage else 0
-                output_tokens = response.usage.output_tokens or 0 if response.usage else 0
-                total_tokens = input_tokens + output_tokens
-
-                # Record LLM metrics
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Record trace span
-                from hindsight_api.tracing import _serialize_for_span, get_span_recorder
-
-                finish_reason = response.stop_reason if hasattr(response, "stop_reason") else None
-                span_recorder = get_span_recorder()
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=_serialize_for_span(result),
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    duration=duration,
-                    finish_reason=finish_reason,
-                    error=None,
-                )
-
-                # Log slow calls
-                if duration > 10.0:
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
-                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
-                        f"time={duration:.3f}s"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        total_tokens=total_tokens,
-                    )
-                    return result, token_usage
-                return result
-
-            except json.JSONDecodeError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    logger.warning("Anthropic returned invalid JSON, retrying...")
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Anthropic returned invalid JSON after {max_retries + 1} attempts")
-                    raise
-
-            except (APIConnectionError, RateLimitError, APIStatusError) as e:
-                # Fast fail on 401/403
-                if isinstance(e, APIStatusError) and e.status_code in (401, 403):
-                    logger.error(f"Anthropic auth error (HTTP {e.status_code}), not retrying: {str(e)}")
-                    raise
-
-                last_exception = e
-                if attempt < max_retries:
-                    # Check if it's a rate limit or server error
-                    should_retry = isinstance(e, (APIConnectionError, RateLimitError)) or (
-                        isinstance(e, APIStatusError) and e.status_code >= 500
-                    )
-
-                    if should_retry:
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
-                        await asyncio.sleep(backoff + jitter)
-                        continue
-
-                logger.error(f"Anthropic API error after {max_retries + 1} attempts: {str(e)}")
-                raise
-
-            except Exception as e:
-                logger.error(f"Unexpected error during Anthropic call: {type(e).__name__}: {str(e)}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Anthropic call failed after all retries")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make an LLM API call with tool/function calling support.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        from anthropic import APIConnectionError, APIStatusError
-
-        start_time = time.time()
-
-        # Convert OpenAI tool format to Anthropic format
-        anthropic_tools = []
-        for tool in tools:
-            func = tool.get("function", {})
-            anthropic_tools.append(
-                {
-                    "name": func.get("name", ""),
-                    "description": func.get("description", ""),
-                    "input_schema": func.get("parameters", {"type": "object", "properties": {}}),
-                }
-            )
-
-        # Convert messages - handle tool results
-        system_prompt = None
-        anthropic_messages = []
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_prompt = (system_prompt + "\n\n" + content) if system_prompt else content
-            elif role == "tool":
-                # Anthropic uses tool_result blocks
-                anthropic_messages.append(
-                    {
-                        "role": "user",
-                        "content": [
-                            {"type": "tool_result", "tool_use_id": msg.get("tool_call_id", ""), "content": content}
-                        ],
-                    }
-                )
-            elif role == "assistant" and msg.get("tool_calls"):
-                # Convert assistant tool calls
-                tool_use_blocks = []
-                for tc in msg["tool_calls"]:
-                    tool_use_blocks.append(
-                        {
-                            "type": "tool_use",
-                            "id": tc.get("id", ""),
-                            "name": tc.get("function", {}).get("name", ""),
-                            "input": json.loads(tc.get("function", {}).get("arguments", "{}")),
-                        }
-                    )
-                anthropic_messages.append({"role": "assistant", "content": tool_use_blocks})
-            else:
-                anthropic_messages.append({"role": role, "content": content})
-
-        call_params: dict[str, Any] = {
-            "model": self.model,
-            "messages": anthropic_messages,
-            "tools": anthropic_tools,
-            "max_tokens": max_completion_tokens or 4096,
-        }
-        if system_prompt:
-            call_params["system"] = system_prompt
-
-        if temperature is not None:
-            call_params["temperature"] = temperature
-
-        last_exception = None
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.messages.create(**call_params)
-
-                # Extract content and tool calls
-                content_parts = []
-                tool_calls: list[LLMToolCall] = []
-
-                for block in response.content:
-                    if block.type == "text":
-                        content_parts.append(block.text)
-                    elif block.type == "tool_use":
-                        tool_calls.append(LLMToolCall(id=block.id, name=block.name, arguments=block.input or {}))
-
-                content = "".join(content_parts) if content_parts else None
-                finish_reason = "tool_calls" if tool_calls else "stop"
-
-                # Extract token usage
-                input_tokens = response.usage.input_tokens or 0
-                output_tokens = response.usage.output_tokens or 0
-
-                # Record metrics
-                metrics = get_metrics_collector()
-                duration = time.time() - start_time
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Record OpenTelemetry span
-                from hindsight_api.tracing import get_span_recorder
-
-                span_recorder = get_span_recorder()
-                # Convert LLMToolCall objects to dicts for span recording
-                tool_calls_dict = (
-                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
-                    if tool_calls
-                    else None
-                )
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=content,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    duration=duration,
-                    finish_reason=finish_reason,
-                    error=None,
-                    tool_calls=tool_calls_dict,
-                )
-
-                return LLMToolCallResult(
-                    content=content,
-                    tool_calls=tool_calls,
-                    finish_reason=finish_reason,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                )
-
-            except (APIConnectionError, APIStatusError) as e:
-                if isinstance(e, APIStatusError) and e.status_code in (401, 403):
-                    raise
-                last_exception = e
-                if attempt < max_retries:
-                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
-                    continue
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Anthropic tool call failed")
-
-    async def cleanup(self) -> None:
-        """Clean up resources (close Anthropic client connections)."""
-        if hasattr(self, "_client") and self._client:
-            await self._client.close()
@@ -1,510 +0,0 @@
-"""
-Claude Code LLM provider using Claude Agent SDK.
-
-This provider enables using Claude Pro/Max subscriptions for API calls
-via the Claude CLI authentication. It uses the Claude Agent SDK which
-automatically handles authentication via `claude auth login` credentials.
-"""
-
-import asyncio
-import json
-import logging
-import time
-from typing import Any
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-
-class ClaudeCodeLLM(LLMInterface):
-    """
-    LLM provider using Claude Code authentication.
-
-    Authenticates using Claude Pro/Max credentials via `claude auth login`
-    and makes API calls through the Claude Agent SDK.
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,  # Will be ignored, uses CLI auth
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """Initialize Claude Code LLM provider."""
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        # Verify Claude Agent SDK is available
-        try:
-            self._verify_claude_code_available()
-            logger.info("Claude Code: Using Claude Agent SDK (authentication via claude auth login)")
-        except Exception as e:
-            raise RuntimeError(
-                f"Failed to initialize Claude Code provider: {e}\n\n"
-                "To set up Claude Code authentication:\n"
-                "1. Install Claude Code CLI: npm install -g @anthropics/claude-code\n"
-                "2. Login with your Pro/Max plan: claude auth login\n"
-                "3. Verify authentication: claude --version\n\n"
-                "Or use a different provider (anthropic, openai, gemini) with API keys."
-            ) from e
-
-        # Metrics collector is imported at module level
-
-    def _verify_claude_code_available(self) -> None:
-        """
-        Verify that Claude Agent SDK can be imported and is properly configured.
-
-        Raises:
-            ImportError: If Claude Agent SDK is not installed.
-            RuntimeError: If Claude Code is not authenticated.
-        """
-        try:
-            # Import Claude Agent SDK
-            # Reduce Claude Agent SDK logging verbosity
-            import logging as sdk_logging
-
-            from claude_agent_sdk import query  # noqa: F401
-
-            sdk_logging.getLogger("claude_agent_sdk").setLevel(sdk_logging.WARNING)
-            sdk_logging.getLogger("claude_agent_sdk._internal").setLevel(sdk_logging.WARNING)
-
-            logger.debug("Claude Agent SDK imported successfully")
-        except ImportError as e:
-            raise ImportError(
-                "Claude Agent SDK not installed. Run: uv add claude-agent-sdk or pip install claude-agent-sdk"
-            ) from e
-
-        # SDK will automatically check for authentication when first used
-        # No need to verify here - let it fail gracefully on first call with helpful error
-
-    async def verify_connection(self) -> None:
-        """
-        Verify that the Claude Code provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            test_messages = [{"role": "user", "content": "test"}]
-            await self.call(
-                messages=test_messages,
-                max_completion_tokens=10,
-                temperature=0.0,
-                scope="verification",
-                max_retries=0,
-            )
-            logger.info("Claude Code connection verified successfully")
-        except Exception as e:
-            logger.error(f"Claude Code connection verification failed: {e}")
-            raise RuntimeError(f"Failed to verify Claude Code connection: {e}") from e
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make an LLM API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response (ignored by Claude Agent SDK).
-            temperature: Sampling temperature (ignored by Claude Agent SDK).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (not supported).
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with estimated token counts.
-
-        Raises:
-            OutputTooLongError: If output exceeds token limits (not supported by Claude Agent SDK).
-            Exception: Re-raises API errors after retries exhausted.
-        """
-        from claude_agent_sdk import AssistantMessage, ClaudeAgentOptions, TextBlock, query
-
-        start_time = time.time()
-
-        # Build system prompt
-        system_prompt = ""
-        user_content = ""
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_prompt += ("\n\n" + content) if system_prompt else content
-            elif role == "user":
-                user_content += ("\n\n" + content) if user_content else content
-            elif role == "assistant":
-                # Claude Agent SDK doesn't support multi-turn easily in query()
-                # For now, prepend assistant messages to user content
-                user_content += f"\n\n[Previous assistant response: {content}]"
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_instruction = (
-                f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}\n\n"
-                "Respond with ONLY the JSON, no markdown formatting."
-            )
-            user_content += schema_instruction
-
-        # Configure SDK options
-        options = ClaudeAgentOptions(
-            system_prompt=system_prompt if system_prompt else None,
-            max_turns=1,  # Single-turn for API-style interactions
-            allowed_tools=[],  # Disable tools for standard LLM calls
-        )
-
-        # Call Claude Agent SDK
-        last_exception = None
-        for attempt in range(max_retries + 1):
-            try:
-                # Collect streaming response
-                full_text = ""
-
-                async for message in query(prompt=user_content, options=options):
-                    if isinstance(message, AssistantMessage):
-                        for block in message.content:
-                            if isinstance(block, TextBlock):
-                                full_text += block.text
-
-                # Handle structured output
-                if response_format is not None:
-                    # Models may wrap JSON in markdown
-                    clean_text = full_text
-                    if "```json" in full_text:
-                        clean_text = full_text.split("```json")[1].split("```")[0].strip()
-                    elif "```" in full_text:
-                        clean_text = full_text.split("```")[1].split("```")[0].strip()
-
-                    try:
-                        json_data = json.loads(clean_text)
-                    except json.JSONDecodeError as e:
-                        logger.warning(f"Claude Code JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            last_exception = e
-                            continue
-                        raise
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = full_text
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-
-                # Estimate token usage (Claude Agent SDK doesn't report exact counts)
-                # Use character count / 4 as rough estimate (1 token ≈ 4 characters)
-                estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
-                estimated_output = len(full_text) // 4
-
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=estimated_input,
-                    output_tokens=estimated_output,
-                    success=True,
-                )
-
-                # Record trace span
-                from hindsight_api.tracing import get_span_recorder
-
-                span_recorder = get_span_recorder()
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=result if isinstance(result, str) else json.dumps(result),
-                    input_tokens=estimated_input,
-                    output_tokens=estimated_output,
-                    duration=duration,
-                    finish_reason=None,
-                    error=None,
-                )
-
-                # Log slow calls
-                if duration > 10.0:
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, time={duration:.3f}s"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=estimated_input,
-                        output_tokens=estimated_output,
-                        total_tokens=estimated_input + estimated_output,
-                    )
-                    return result, token_usage
-
-                return result
-
-            except Exception as e:
-                last_exception = e
-
-                # Check for authentication errors
-                error_str = str(e).lower()
-                if "auth" in error_str or "login" in error_str or "credential" in error_str:
-                    logger.error(f"Claude Code authentication error: {e}")
-                    raise RuntimeError(
-                        f"Claude Code authentication failed: {e}\n\n"
-                        "Run 'claude auth login' to authenticate with Claude Pro/Max."
-                    ) from e
-
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    logger.warning(f"Claude Code error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Claude Code error after {max_retries + 1} attempts: {e}")
-                    raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Claude Code call failed after all retries")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make an LLM API call with tool/function calling support using Claude Agent SDK.
-
-        This implementation uses ClaudeSDKClient (not query()) because custom tools via
-        SDK MCP servers are only supported with the client. Tools are converted from OpenAI
-        format to SDK MCP tools, and tool names are formatted as mcp__hindsight_tools__{name}.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response (not used by Claude Agent SDK).
-            temperature: Sampling temperature (not used by Claude Agent SDK).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools (not used by Claude Agent SDK).
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        from claude_agent_sdk import (
-            AssistantMessage,
-            ClaudeAgentOptions,
-            ClaudeSDKClient,
-            SdkMcpTool,
-            TextBlock,
-            ToolUseBlock,
-            create_sdk_mcp_server,
-        )
-
-        start_time = time.time()
-
-        # Convert OpenAI tool format to Claude Agent SDK SdkMcpTool format
-        sdk_tools: list[SdkMcpTool] = []
-        tool_names: list[str] = []
-
-        for tool in tools:
-            func = tool.get("function", {})
-            tool_name = func.get("name", "")
-            tool_description = func.get("description", "")
-            parameters = func.get("parameters", {})
-
-            # Create a handler with proper closure to avoid transport issues
-            def make_handler(name: str):
-                async def handler(args: dict[str, Any]) -> dict[str, Any]:
-                    # Return immediately with success - tool execution happens externally
-                    return {
-                        "content": [
-                            {
-                                "type": "text",
-                                "text": f"[Tool {name} called successfully]",
-                            }
-                        ]
-                    }
-
-                return handler
-
-            sdk_tools.append(
-                SdkMcpTool(
-                    name=tool_name,
-                    description=tool_description,
-                    input_schema=parameters,
-                    handler=make_handler(tool_name),
-                )
-            )
-            tool_names.append(tool_name)
-
-        # Create an MCP server with the tools
-        mcp_server = create_sdk_mcp_server(
-            name="hindsight_tools",
-            version="1.0.0",
-            tools=sdk_tools if sdk_tools else None,
-        )
-
-        # Build system prompt and user content from messages
-        system_prompt = ""
-        user_content = ""
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_prompt += ("\n\n" + content) if system_prompt else content
-            elif role == "user":
-                user_content += ("\n\n" + content) if user_content else content
-            elif role == "assistant":
-                # Include previous assistant messages as context
-                user_content += f"\n\n[Previous assistant response: {content}]"
-            elif role == "tool":
-                # Tool results are already in tool_results_map, append to user context
-                tool_call_id = msg.get("tool_call_id", "")
-                user_content += f"\n\n[Tool result for {tool_call_id}: {content}]"
-
-        # Format tool names for SDK MCP servers: mcp__{server_name}__{tool_name}
-        # This is required by the Claude Agent SDK for MCP server tools
-        allowed_tool_names = [f"mcp__hindsight_tools__{name}" for name in tool_names]
-
-        # Configure SDK options with MCP server
-        options = ClaudeAgentOptions(
-            system_prompt=system_prompt if system_prompt else None,
-            max_turns=1,  # Single-turn for API-style interactions
-            mcp_servers={"hindsight_tools": mcp_server} if sdk_tools else {},
-            allowed_tools=allowed_tool_names if allowed_tool_names else [],
-        )
-
-        # Call Claude Agent SDK with retry logic
-        last_exception = None
-        for attempt in range(max_retries + 1):
-            try:
-                full_text = ""
-                tool_calls: list[LLMToolCall] = []
-
-                # Use ClaudeSDKClient for tool calling support
-                # Note: query() does NOT support custom tools, only ClaudeSDKClient does
-                async with ClaudeSDKClient(options=options) as client:
-                    # Send the query
-                    await client.query(user_content)
-
-                    # Receive response
-                    async for message in client.receive_response():
-                        if isinstance(message, AssistantMessage):
-                            for block in message.content:
-                                if isinstance(block, TextBlock):
-                                    full_text += block.text
-                                elif isinstance(block, ToolUseBlock):
-                                    # SDK returns tool names with MCP prefix (mcp__hindsight_tools__{name})
-                                    # Strip the prefix to return original tool name expected by caller
-                                    tool_name = block.name
-                                    if tool_name.startswith("mcp__hindsight_tools__"):
-                                        tool_name = tool_name.replace("mcp__hindsight_tools__", "", 1)
-
-                                    tool_calls.append(
-                                        LLMToolCall(
-                                            id=block.id,
-                                            name=tool_name,
-                                            arguments=block.input,
-                                        )
-                                    )
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-
-                # Estimate token usage (Claude Agent SDK doesn't report exact counts)
-                estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
-                estimated_output = len(full_text) // 4
-
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=estimated_input,
-                    output_tokens=estimated_output,
-                    success=True,
-                )
-
-                # Log slow calls
-                if duration > 10.0:
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, time={duration:.3f}s"
-                    )
-
-                return LLMToolCallResult(
-                    content=full_text if full_text else None,
-                    tool_calls=tool_calls,
-                    finish_reason="tool_calls" if tool_calls else "stop",
-                    input_tokens=estimated_input,
-                    output_tokens=estimated_output,
-                )
-
-            except Exception as e:
-                last_exception = e
-
-                # Check for authentication errors
-                error_str = str(e).lower()
-                if "auth" in error_str or "login" in error_str or "credential" in error_str:
-                    logger.error(f"Claude Code authentication error: {e}")
-                    raise RuntimeError(
-                        f"Claude Code authentication failed: {e}\n\n"
-                        "Run 'claude auth login' to authenticate with Claude Pro/Max."
-                    ) from e
-
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    logger.warning(f"Claude Code tool call error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Claude Code tool call error after {max_retries + 1} attempts: {e}")
-                    raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Claude Code tool call failed after all retries")
-
-    async def cleanup(self) -> None:
-        """Clean up resources (no HTTP client to close for Claude Agent SDK)."""
-        pass
@@ -1,621 +0,0 @@
-"""
-OpenAI Codex LLM provider using ChatGPT Plus/Pro OAuth authentication.
-
-This provider enables using ChatGPT Plus/Pro subscriptions for API calls
-without separate OpenAI Platform API credits. It uses OAuth tokens from
-~/.codex/auth.json and communicates with the ChatGPT backend API.
-"""
-
-import asyncio
-import json
-import logging
-import os
-import time
-import uuid
-from pathlib import Path
-from typing import Any
-
-import httpx
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-
-class CodexLLM(LLMInterface):
-    """
-    LLM provider using OpenAI Codex OAuth authentication.
-
-    Authenticates using ChatGPT Plus/Pro credentials stored in ~/.codex/auth.json
-    and makes API calls to chatgpt.com/backend-api/codex/responses.
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,  # Will be ignored, reads from ~/.codex/auth.json
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """Initialize Codex LLM provider."""
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        # Load Codex OAuth credentials
-        try:
-            self.access_token, self.account_id = self._load_codex_auth()
-            logger.info(f"Loaded Codex OAuth credentials for account: {self.account_id}")
-        except Exception as e:
-            raise RuntimeError(
-                f"Failed to load Codex OAuth credentials from ~/.codex/auth.json: {e}\n\n"
-                "To set up Codex authentication:\n"
-                "1. Install Codex CLI: npm install -g @openai/codex\n"
-                "2. Login: codex auth login\n"
-                "3. Verify: ls ~/.codex/auth.json\n\n"
-                "Or use a different provider (openai, anthropic, gemini) with API keys."
-            ) from e
-
-        # Use ChatGPT backend API endpoint
-        if not self.base_url:
-            self.base_url = "https://chatgpt.com/backend-api"
-
-        # Normalize model name (strip openai/ prefix if present)
-        if self.model.startswith("openai/"):
-            self.model = self.model[len("openai/") :]
-
-        # Map reasoning effort to Codex reasoning summary format
-        # Codex supports: "auto", "concise", "detailed"
-        self.reasoning_summary = self._map_reasoning_effort(reasoning_effort)
-
-        # HTTP client for SSE streaming
-        self._client = httpx.AsyncClient(timeout=120.0)
-
-    def _load_codex_auth(self) -> tuple[str, str]:
-        """
-        Load OAuth credentials from ~/.codex/auth.json.
-
-        Returns:
-            Tuple of (access_token, account_id).
-
-        Raises:
-            FileNotFoundError: If auth file doesn't exist.
-            ValueError: If auth file is invalid.
-        """
-        auth_file = Path.home() / ".codex" / "auth.json"
-
-        if not auth_file.exists():
-            raise FileNotFoundError(
-                f"Codex auth file not found: {auth_file}\nRun 'codex auth login' to authenticate with ChatGPT Plus/Pro."
-            )
-
-        with open(auth_file) as f:
-            data = json.load(f)
-
-        # Validate auth structure
-        auth_mode = data.get("auth_mode")
-        if auth_mode != "chatgpt":
-            raise ValueError(f"Expected auth_mode='chatgpt', got: {auth_mode}")
-
-        tokens = data.get("tokens", {})
-        access_token = tokens.get("access_token")
-        account_id = tokens.get("account_id")
-
-        if not access_token:
-            raise ValueError("No access_token found in Codex auth file. Run 'codex auth login' again.")
-
-        return access_token, account_id
-
-    def _map_reasoning_effort(self, effort: str) -> str:
-        """
-        Map standard reasoning effort to Codex reasoning summary format.
-
-        Args:
-            effort: Standard effort level ("low", "medium", "high", "xhigh").
-
-        Returns:
-            Codex reasoning summary: "concise", "detailed", or "auto".
-        """
-        mapping = {
-            "low": "concise",
-            "medium": "auto",
-            "high": "detailed",
-            "xhigh": "detailed",
-        }
-        return mapping.get(effort.lower(), "auto")
-
-    async def verify_connection(self) -> None:
-        """Verify Codex connection by making a simple test call."""
-        try:
-            logger.info(f"Verifying Codex LLM: model={self.model}, account={self.account_id}...")
-            await self.call(
-                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=10,
-                max_retries=2,
-                initial_backoff=0.5,
-                max_backoff=2.0,
-                scope="verification",
-            )
-            logger.info(f"Codex LLM verified: {self.model}")
-        except Exception as e:
-            raise RuntimeError(f"Codex LLM connection verification failed for {self.model}: {e}") from e
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """Make API call to Codex backend with SSE streaming."""
-        start_time = time.time()
-
-        # Prepare system instructions
-        system_instruction = ""
-        user_messages = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_instruction += ("\n\n" + content) if system_instruction else content
-            else:
-                user_messages.append(msg)
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-            system_instruction += schema_msg
-
-        # gpt-5.2-codex only supports "detailed" reasoning summary
-        reasoning_summary = "detailed" if "5.2" in self.model else self.reasoning_summary
-
-        # Build Codex request payload
-        payload = {
-            "model": self.model,
-            "instructions": system_instruction,
-            "input": [
-                {
-                    "type": "message",
-                    "role": msg.get("role", "user"),
-                    "content": msg.get("content", ""),
-                }
-                for msg in user_messages
-            ],
-            "tools": [],
-            "tool_choice": "auto",
-            "parallel_tool_calls": True,
-            "reasoning": {"summary": reasoning_summary},
-            "store": False,  # Codex uses stateless mode
-            "stream": True,  # SSE streaming
-            "include": ["reasoning.encrypted_content"],
-            "prompt_cache_key": str(uuid.uuid4()),
-        }
-
-        headers = {
-            "Authorization": f"Bearer {self.access_token}",
-            "Content-Type": "application/json",
-            "OpenAI-Account-ID": self.account_id,
-            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
-            "Origin": "https://chatgpt.com",
-        }
-
-        url = f"{self.base_url}/codex/responses"
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.post(url, json=payload, headers=headers, timeout=120.0)
-                response.raise_for_status()
-
-                # Parse SSE stream
-                content = await self._parse_sse_stream(response)
-
-                # Handle structured output
-                if response_format is not None:
-                    # Models may wrap JSON in markdown
-                    clean_content = content
-                    if "```json" in content:
-                        clean_content = content.split("```json")[1].split("```")[0].strip()
-                    elif "```" in content:
-                        clean_content = content.split("```")[1].split("```")[0].strip()
-
-                    try:
-                        json_data = json.loads(clean_content)
-                    except json.JSONDecodeError as e:
-                        logger.warning(f"Codex JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            last_exception = e
-                            continue
-                        raise
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = content
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=0,  # Codex doesn't report token counts in SSE
-                    output_tokens=0,
-                    success=True,
-                )
-
-                # Record trace span
-                from hindsight_api.tracing import get_span_recorder
-
-                # Estimate tokens for tracing
-                estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
-                estimated_output = len(content) // 4
-                span_recorder = get_span_recorder()
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=result if isinstance(result, str) else json.dumps(result),
-                    input_tokens=estimated_input,
-                    output_tokens=estimated_output,
-                    duration=duration,
-                    finish_reason=None,
-                    error=None,
-                )
-
-                if return_usage:
-                    # Codex doesn't provide token counts, estimate based on content
-                    estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
-                    estimated_output = len(content) // 4
-                    token_usage = TokenUsage(
-                        input_tokens=estimated_input,
-                        output_tokens=estimated_output,
-                        total_tokens=estimated_input + estimated_output,
-                    )
-                    return result, token_usage
-
-                return result
-
-            except httpx.HTTPStatusError as e:
-                last_exception = e
-                status_code = e.response.status_code
-
-                # Fast fail on auth errors
-                if status_code in (401, 403):
-                    logger.error(f"Codex auth error (HTTP {status_code}): {e.response.text[:200]}")
-                    raise RuntimeError(
-                        "Codex authentication failed. Your OAuth token may have expired.\n"
-                        "Run 'codex auth login' to re-authenticate."
-                    ) from e
-
-                # Log the actual error message from the API
-                error_detail = e.response.text[:500] if hasattr(e.response, "text") else str(e)
-
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    logger.warning(
-                        f"Codex HTTP error {status_code} (attempt {attempt + 1}/{max_retries + 1}): {error_detail}"
-                    )
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(
-                        f"Codex HTTP error after {max_retries + 1} attempts: Status {status_code}, Detail: {error_detail}"
-                    )
-                    raise
-
-            except httpx.RequestError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    logger.warning(f"Codex connection error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Codex connection error after {max_retries + 1} attempts: {e}")
-                    raise
-
-            except Exception as e:
-                logger.error(f"Unexpected Codex error: {type(e).__name__}: {e}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Codex call failed after all retries")
-
-    async def _parse_sse_stream(self, response: httpx.Response) -> str:
-        """
-        Parse Server-Sent Events (SSE) stream from Codex API.
-
-        Args:
-            response: HTTP response with SSE stream.
-
-        Returns:
-            Extracted text content from stream.
-        """
-        full_text = ""
-        event_type = None
-
-        async for line in response.aiter_lines():
-            if not line:
-                continue
-
-            # Track event type
-            if line.startswith("event: "):
-                event_type = line[7:]
-
-            # Parse data
-            elif line.startswith("data: "):
-                data_str = line[6:]
-                if data_str == "[DONE]":
-                    break
-
-                try:
-                    data = json.loads(data_str)
-
-                    # Extract content based on event type
-                    if event_type == "response.text.delta" and "delta" in data:
-                        full_text += data["delta"]
-                    elif event_type == "response.content_part.delta" and "delta" in data:
-                        full_text += data["delta"]
-                    # Check for item content
-                    elif "item" in data:
-                        item = data["item"]
-                        if "content" in item:
-                            content = item["content"]
-                            if isinstance(content, list):
-                                for part in content:
-                                    if isinstance(part, dict) and "text" in part:
-                                        full_text += part["text"]
-                            elif isinstance(content, str):
-                                full_text += content
-
-                except json.JSONDecodeError:
-                    # Skip malformed JSON events
-                    pass
-
-        return full_text
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make API call with tool calling support.
-
-        Parses Codex SSE stream to extract tool calls from response.output_item.done events.
-        Tools are converted from OpenAI format to Codex format (flat structure at top level).
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature.
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        start_time = time.time()
-
-        # Prepare system instructions
-        system_instruction = ""
-        user_messages = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_instruction += ("\n\n" + content) if system_instruction else content
-            elif role == "tool":
-                # Handle tool results
-                user_messages.append(
-                    {
-                        "type": "message",
-                        "role": "user",
-                        "content": f"Tool result: {content}",
-                    }
-                )
-            else:
-                user_messages.append(
-                    {
-                        "type": "message",
-                        "role": role,
-                        "content": content,
-                    }
-                )
-
-        # Convert tools to Codex format
-        # Codex expects tools with type and name/description/parameters at top level
-        codex_tools = []
-        for tool in tools:
-            func = tool.get("function", {})
-            codex_tools.append(
-                {
-                    "type": "function",
-                    "name": func.get("name", ""),
-                    "description": func.get("description", ""),
-                    "parameters": func.get("parameters", {}),
-                }
-            )
-
-        # gpt-5.2-codex only supports "detailed" reasoning summary
-        reasoning_summary = "detailed" if "5.2" in self.model else self.reasoning_summary
-
-        payload = {
-            "model": self.model,
-            "instructions": system_instruction,
-            "input": user_messages,
-            "tools": codex_tools,
-            "tool_choice": tool_choice,
-            "parallel_tool_calls": True,
-            "reasoning": {"summary": reasoning_summary},
-            "store": False,
-            "stream": True,
-            "include": ["reasoning.encrypted_content"],
-            "prompt_cache_key": str(uuid.uuid4()),
-        }
-
-        headers = {
-            "Authorization": f"Bearer {self.access_token}",
-            "Content-Type": "application/json",
-            "OpenAI-Account-ID": self.account_id,
-            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
-            "Origin": "https://chatgpt.com",
-        }
-
-        url = f"{self.base_url}/codex/responses"
-
-        # Debug logging for troubleshooting
-        logger.debug(f"Codex tool call request: url={url}, model={payload['model']}, tools={len(codex_tools)}")
-
-        try:
-            response = await self._client.post(url, json=payload, headers=headers, timeout=120.0)
-
-            # Log response details on error
-            if response.status_code != 200:
-                logger.error(f"Codex API error {response.status_code}: {response.text[:500]}")
-
-            response.raise_for_status()
-
-            # Parse SSE for tool calls and content
-            content, tool_calls = await self._parse_sse_tool_stream(response)
-
-            duration = time.time() - start_time
-            metrics = get_metrics_collector()
-            metrics.record_llm_call(
-                provider=self.provider,
-                model=self.model,
-                scope=scope,
-                duration=duration,
-                input_tokens=0,
-                output_tokens=0,
-                success=True,
-            )
-
-            # Record OpenTelemetry span
-            from hindsight_api.tracing import get_span_recorder
-
-            span_recorder = get_span_recorder()
-            # Convert LLMToolCall objects to dicts for span recording
-            tool_calls_dict = (
-                [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls] if tool_calls else None
-            )
-            span_recorder.record_llm_call(
-                provider=self.provider,
-                model=self.model,
-                scope=scope,
-                messages=messages,
-                response_content=content,
-                input_tokens=0,  # Codex doesn't provide token counts
-                output_tokens=0,
-                duration=duration,
-                finish_reason="tool_calls" if tool_calls else "stop",
-                error=None,
-                tool_calls=tool_calls_dict,
-            )
-
-            return LLMToolCallResult(
-                content=content,
-                tool_calls=tool_calls,
-                finish_reason="tool_calls" if tool_calls else "stop",
-                input_tokens=0,
-                output_tokens=0,
-            )
-
-        except Exception as e:
-            logger.error(f"Codex tool call error: {e}")
-            raise
-
-    async def _parse_sse_tool_stream(self, response: httpx.Response) -> tuple[str | None, list[LLMToolCall]]:
-        """
-        Parse SSE stream for tool calls and content.
-
-        Returns:
-            Tuple of (content, tool_calls).
-        """
-        content = ""
-        tool_calls: list[LLMToolCall] = []
-        event_type = None
-
-        async for line in response.aiter_lines():
-            if not line:
-                continue
-
-            if line.startswith("event: "):
-                event_type = line[7:]
-
-            elif line.startswith("data: "):
-                data_str = line[6:]
-                if data_str == "[DONE]":
-                    break
-
-                try:
-                    data = json.loads(data_str)
-
-                    # Extract text content
-                    if event_type == "response.text.delta" and "delta" in data:
-                        content += data["delta"]
-
-                    # Extract completed tool calls from response.output_item.done
-                    elif event_type == "response.output_item.done":
-                        item = data.get("item", {})
-                        if item.get("type") == "function_call" and item.get("status") == "completed":
-                            tool_name = item.get("name", "")
-                            arguments_str = item.get("arguments", "{}")
-                            call_id = item.get("call_id", "")
-
-                            try:
-                                arguments = json.loads(arguments_str)
-                            except json.JSONDecodeError:
-                                logger.warning(f"Failed to parse tool arguments: {arguments_str}")
-                                arguments = {}
-
-                            tool_calls.append(
-                                LLMToolCall(
-                                    id=call_id,
-                                    name=tool_name,
-                                    arguments=arguments,
-                                )
-                            )
-
-                except json.JSONDecodeError as e:
-                    logger.warning(f"Failed to parse SSE data: {e}, data_str: {data_str[:200]}")
-
-        return content if content else None, tool_calls
-
-    async def cleanup(self) -> None:
-        """Clean up HTTP client."""
-        await self._client.aclose()
@@ -1,550 +0,0 @@
-"""
-Google Gemini/VertexAI LLM provider.
-
-This provider supports both:
-1. Gemini API (api.generativeai.google.com) with API key authentication
-2. Vertex AI with service account or Application Default Credentials (ADC)
-"""
-
-import asyncio
-import json
-import logging
-import os
-import time
-from typing import Any
-
-from google import genai
-from google.genai import errors as genai_errors
-from google.genai import types as genai_types
-
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-# Vertex AI imports (optional)
-try:
-    import google.auth
-    from google.oauth2 import service_account
-
-    VERTEXAI_AVAILABLE = True
-except ImportError:
-    VERTEXAI_AVAILABLE = False
-
-
-class GeminiLLM(LLMInterface):
-    """
-    LLM provider for Google Gemini and Vertex AI.
-
-    Supports:
-    - Gemini API: provider="gemini", requires api_key
-    - Vertex AI: provider="vertexai", requires project_id and region, uses ADC or service account
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """Initialize Gemini/VertexAI LLM provider."""
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        self._client = None
-        self._is_vertexai = self.provider == "vertexai"
-
-        if self._is_vertexai:
-            self._init_vertexai(**kwargs)
-        else:
-            self._init_gemini()
-
-    def _init_gemini(self) -> None:
-        """Initialize Gemini API client."""
-        if not self.api_key:
-            raise ValueError("Gemini provider requires api_key")
-
-        self._client = genai.Client(api_key=self.api_key)
-        logger.info(f"Gemini API: model={self.model}")
-
-    def _init_vertexai(self, **kwargs: Any) -> None:
-        """Initialize Vertex AI client with project, region, and credentials."""
-        # Extract Vertex AI config from kwargs
-        project_id = kwargs.get("vertexai_project_id")
-        region = kwargs.get("vertexai_region", "us-central1")
-        service_account_key = kwargs.get("vertexai_service_account_key")
-        credentials = kwargs.get("vertexai_credentials")  # Pre-loaded credentials object
-
-        if not project_id:
-            raise ValueError(
-                "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID is required for Vertex AI provider. "
-                "Set it to your GCP project ID."
-            )
-
-        auth_method = "ADC"
-
-        # Use pre-loaded credentials if provided (passed from LLMProvider)
-        if credentials is not None:
-            auth_method = "service_account"
-        # Otherwise, load explicit service account credentials if path provided
-        elif service_account_key:
-            if not VERTEXAI_AVAILABLE:
-                raise ValueError(
-                    "Vertex AI service account auth requires 'google-auth' package. "
-                    "Install with: pip install google-auth"
-                )
-            credentials = service_account.Credentials.from_service_account_file(
-                service_account_key,
-                scopes=["https://www.googleapis.com/auth/cloud-platform"],
-            )
-            auth_method = "service_account"
-            logger.info(f"Vertex AI: Using service account key: {service_account_key}")
-
-        # Strip google/ prefix from model name — native SDK uses bare names
-        # e.g. "google/gemini-2.0-flash-lite-001" -> "gemini-2.0-flash-lite-001"
-        if self.model.startswith("google/"):
-            self.model = self.model[len("google/") :]
-
-        # Create Vertex AI client
-        client_kwargs: dict[str, Any] = {
-            "vertexai": True,
-            "project": project_id,
-            "location": region,
-        }
-        if credentials is not None:
-            client_kwargs["credentials"] = credentials
-
-        self._client = genai.Client(**client_kwargs)
-
-        logger.info(f"Vertex AI: project={project_id}, region={region}, model={self.model}, auth={auth_method}")
-
-    async def verify_connection(self) -> None:
-        """
-        Verify that the Gemini/VertexAI provider is configured correctly.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            logger.info(f"Verifying {self.provider.upper()}: model={self.model}...")
-            await self.call(
-                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=100,
-                max_retries=2,
-                initial_backoff=0.5,
-                max_backoff=2.0,
-                scope="verification",
-            )
-            logger.info(f"{self.provider.upper()} connection verified successfully")
-        except Exception as e:
-            raise RuntimeError(f"Failed to verify {self.provider.upper()} connection: {e}") from e
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make a Gemini/VertexAI API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response (not supported by Gemini).
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (not supported by Gemini).
-            return_usage: If True, return tuple (result, TokenUsage).
-
-        Returns:
-            If return_usage=False: Parsed response if response_format provided, else text.
-            If return_usage=True: Tuple of (result, TokenUsage).
-        """
-        start_time = time.time()
-
-        # Convert OpenAI-style messages to Gemini format
-        system_instruction = None
-        gemini_contents = []
-
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                if system_instruction:
-                    system_instruction += "\n\n" + content
-                else:
-                    system_instruction = content
-            elif role == "assistant":
-                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
-            else:
-                gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
-
-        # Add JSON schema instruction if response_format is provided
-        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = response_format.model_json_schema()
-            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-            if system_instruction:
-                system_instruction += schema_msg
-            else:
-                system_instruction = schema_msg
-
-        # Build generation config
-        config_kwargs: dict[str, Any] = {}
-        if system_instruction:
-            config_kwargs["system_instruction"] = system_instruction
-        if response_format is not None:
-            config_kwargs["response_mime_type"] = "application/json"
-            config_kwargs["response_schema"] = response_format
-        if temperature is not None:
-            config_kwargs["temperature"] = temperature
-
-        generation_config = genai_types.GenerateContentConfig(**config_kwargs) if config_kwargs else None
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=generation_config,
-                )
-
-                content = response.text
-
-                # Handle empty response
-                if content is None:
-                    block_reason = None
-                    if hasattr(response, "candidates") and response.candidates:
-                        candidate = response.candidates[0]
-                        if hasattr(candidate, "finish_reason"):
-                            block_reason = candidate.finish_reason
-
-                    if attempt < max_retries:
-                        logger.warning(f"Gemini returned empty response (reason: {block_reason}), retrying...")
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        raise RuntimeError(f"Gemini returned empty response after {max_retries + 1} attempts")
-
-                # Parse structured output if requested
-                if response_format is not None:
-                    json_data = json.loads(content)
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    result = content
-
-                # Extract token usage
-                input_tokens = 0
-                output_tokens = 0
-                if hasattr(response, "usage_metadata") and response.usage_metadata:
-                    usage = response.usage_metadata
-                    input_tokens = usage.prompt_token_count or 0
-                    output_tokens = usage.candidates_token_count or 0
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Record trace span
-                from hindsight_api.tracing import get_span_recorder
-
-                finish_reason = None
-                if hasattr(response, "candidates") and response.candidates:
-                    if hasattr(response.candidates[0], "finish_reason"):
-                        finish_reason = str(response.candidates[0].finish_reason)
-                span_recorder = get_span_recorder()
-                from hindsight_api.tracing import _serialize_for_span
-
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=_serialize_for_span(result),
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    duration=duration,
-                    finish_reason=finish_reason,
-                    error=None,
-                )
-
-                # Log slow calls
-                if duration > 10.0 and input_tokens > 0:
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
-                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
-                        f"time={duration:.3f}s"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        total_tokens=input_tokens + output_tokens,
-                    )
-                    return result, token_usage
-                return result
-
-            except json.JSONDecodeError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    logger.warning("Gemini returned invalid JSON, retrying...")
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Gemini returned invalid JSON after {max_retries + 1} attempts")
-                    raise
-
-            except genai_errors.APIError as e:
-                # Fast fail on auth errors - these won't recover with retries
-                if e.code in (401, 403):
-                    logger.error(f"Gemini auth error (HTTP {e.code}), not retrying: {str(e)}")
-                    raise
-
-                # Retry on retryable errors (rate limits, server errors, client errors)
-                if e.code in (400, 429, 500, 502, 503, 504) or (e.code and e.code >= 500):
-                    last_exception = e
-                    if attempt < max_retries:
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
-                        await asyncio.sleep(backoff + jitter)
-                    else:
-                        logger.error(f"Gemini API error after {max_retries + 1} attempts: {str(e)}")
-                        raise
-                else:
-                    logger.error(f"Gemini API error: {type(e).__name__}: {str(e)}")
-                    raise
-
-            except Exception as e:
-                logger.error(f"Unexpected error during Gemini call: {type(e).__name__}: {str(e)}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Gemini call failed after all retries")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make a Gemini/VertexAI API call with tool/function calling support.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens (not supported by Gemini).
-            temperature: Sampling temperature.
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools (Gemini uses "auto" only).
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        start_time = time.time()
-
-        # Convert tools to Gemini format
-        gemini_tools = []
-        for tool in tools:
-            func = tool.get("function", {})
-            gemini_tools.append(
-                genai_types.Tool(
-                    function_declarations=[
-                        genai_types.FunctionDeclaration(
-                            name=func.get("name", ""),
-                            description=func.get("description", ""),
-                            parameters=func.get("parameters"),
-                        )
-                    ]
-                )
-            )
-
-        # Convert messages
-        system_instruction = None
-        gemini_contents = []
-        for msg in messages:
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-
-            if role == "system":
-                system_instruction = (system_instruction + "\n\n" + content) if system_instruction else content
-            elif role == "tool":
-                # Gemini uses function_response
-                gemini_contents.append(
-                    genai_types.Content(
-                        role="user",
-                        parts=[
-                            genai_types.Part(
-                                function_response=genai_types.FunctionResponse(
-                                    name=msg.get("name", ""),
-                                    response={"result": content},
-                                )
-                            )
-                        ],
-                    )
-                )
-            elif role == "assistant":
-                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
-            else:
-                gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
-
-        config_kwargs: dict[str, Any] = {"tools": gemini_tools}
-        if system_instruction:
-            config_kwargs["system_instruction"] = system_instruction
-        if temperature is not None:
-            config_kwargs["temperature"] = temperature
-
-        config = genai_types.GenerateContentConfig(**config_kwargs)
-
-        last_exception = None
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=config,
-                )
-
-                # Extract content and tool calls
-                content = None
-                tool_calls: list[LLMToolCall] = []
-
-                if response.candidates and response.candidates[0].content:
-                    parts = response.candidates[0].content.parts
-                    if parts:
-                        for part in parts:
-                            if hasattr(part, "text") and part.text:
-                                content = part.text
-                            if hasattr(part, "function_call") and part.function_call:
-                                fc = part.function_call
-                                tool_calls.append(
-                                    LLMToolCall(
-                                        id=f"gemini_{len(tool_calls)}",
-                                        name=fc.name,
-                                        arguments=dict(fc.args) if fc.args else {},
-                                    )
-                                )
-
-                finish_reason = "tool_calls" if tool_calls else "stop"
-
-                # Extract token usage
-                input_tokens = 0
-                output_tokens = 0
-                if response.usage_metadata:
-                    input_tokens = response.usage_metadata.prompt_token_count or 0
-                    output_tokens = response.usage_metadata.candidates_token_count or 0
-
-                # Record metrics
-                duration = time.time() - start_time
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Record OpenTelemetry span
-                from hindsight_api.tracing import get_span_recorder
-
-                span_recorder = get_span_recorder()
-                # Convert LLMToolCall objects to dicts for span recording
-                tool_calls_dict = (
-                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
-                    if tool_calls
-                    else None
-                )
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=content,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    duration=duration,
-                    finish_reason=finish_reason,
-                    error=None,
-                    tool_calls=tool_calls_dict,
-                )
-
-                return LLMToolCallResult(
-                    content=content,
-                    tool_calls=tool_calls,
-                    finish_reason=finish_reason,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                )
-
-            except genai_errors.APIError as e:
-                # Fast fail on auth errors
-                if e.code in (401, 403):
-                    logger.error(f"Gemini auth error (HTTP {e.code}), not retrying: {str(e)}")
-                    raise
-
-                # Retry on retryable errors
-                last_exception = e
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                raise
-
-            except Exception as e:
-                logger.error(f"Unexpected error during Gemini tool call: {type(e).__name__}: {str(e)}")
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Gemini tool call failed")
-
-    async def cleanup(self) -> None:
-        """Clean up resources (close connections, etc.)."""
-        # Gemini client doesn't require explicit cleanup
-        pass
@@ -1,301 +0,0 @@
-"""
-Mock LLM provider for testing.
-
-This provider allows tests to record LLM calls and return configurable mock responses
-without making actual API calls to external LLM services.
-"""
-
-import logging
-from typing import Any
-
-from ..llm_interface import LLMInterface
-from ..response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-
-logger = logging.getLogger(__name__)
-
-
-class MockLLM(LLMInterface):
-    """
-    Mock LLM provider for testing.
-
-    This provider records all calls and returns configurable mock responses,
-    enabling tests to verify LLM interactions without making real API calls.
-
-    Example:
-        # Create mock provider
-        mock_llm = MockLLM(provider="mock", api_key="", base_url="", model="mock-model")
-
-        # Set mock response
-        mock_llm.set_mock_response({"answer": "test"})
-
-        # Make calls
-        result = await mock_llm.call(
-            messages=[{"role": "user", "content": "test"}],
-            response_format=MyResponseModel
-        )
-
-        # Verify calls
-        calls = mock_llm.get_mock_calls()
-        assert len(calls) == 1
-        assert calls[0]["scope"] == "memory"
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        **kwargs: Any,
-    ):
-        """
-        Initialize mock LLM provider.
-
-        Args:
-            provider: Provider name (should be "mock").
-            api_key: Not used for mock provider.
-            base_url: Not used for mock provider.
-            model: Model name for tracking.
-            reasoning_effort: Not used for mock provider.
-            **kwargs: Additional parameters (not used).
-        """
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        # Storage for test verification
-        self._mock_calls: list[dict] = []
-        self._mock_response: Any = None
-        self._mock_exception: Exception | None = None
-
-    async def verify_connection(self) -> None:
-        """
-        Verify mock provider (always succeeds).
-
-        Mock provider doesn't need connection verification since it doesn't
-        make real API calls.
-        """
-        logger.debug("Mock LLM: connection verification (always succeeds)")
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make a mock LLM API call.
-
-        Records the call for test verification and returns the configured mock response.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Not used in mock.
-            temperature: Not used in mock.
-            scope: Scope identifier for tracking.
-            max_retries: Not used in mock.
-            initial_backoff: Not used in mock.
-            max_backoff: Not used in mock.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Not used in mock.
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with mock token counts.
-        """
-        # Record the call for test verification
-        call_record = {
-            "provider": self.provider,
-            "model": self.model,
-            "messages": messages,
-            "response_format": response_format.__name__
-            if response_format and hasattr(response_format, "__name__")
-            else str(response_format),
-            "scope": scope,
-        }
-        self._mock_calls.append(call_record)
-        logger.debug(f"Mock LLM call recorded: scope={scope}, model={self.model}")
-
-        # Raise mock exception if configured
-        if self._mock_exception is not None:
-            raise self._mock_exception
-
-        # Record trace span (minimal for mock provider)
-        from hindsight_api.tracing import get_span_recorder
-
-        span_recorder = get_span_recorder()
-        span_recorder.record_llm_call(
-            provider=self.provider,
-            model=self.model,
-            scope=scope,
-            messages=messages,
-            response_content="mock response",
-            input_tokens=10,
-            output_tokens=5,
-            duration=0.001,  # Mock calls are instant
-            finish_reason="stop",
-            error=None,
-        )
-
-        # Return mock response
-        if self._mock_response is not None:
-            result = self._mock_response
-        elif response_format is not None:
-            # Try to create a minimal valid instance of the response format
-            try:
-                # For Pydantic models, try to create with minimal valid data
-                result = {"mock": True}
-            except Exception:
-                result = {"mock": True}
-        else:
-            result = "mock response"
-
-        if return_usage:
-            token_usage = TokenUsage(input_tokens=10, output_tokens=5, total_tokens=15)
-            return result, token_usage
-        return result
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make a mock LLM API call with tool/function calling support.
-
-        Records the call for test verification and returns the configured mock response.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Not used in mock.
-            temperature: Not used in mock.
-            scope: Scope identifier for tracking.
-            max_retries: Not used in mock.
-            initial_backoff: Not used in mock.
-            max_backoff: Not used in mock.
-            tool_choice: Not used in mock.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        # Record the call for test verification
-        call_record = {
-            "provider": self.provider,
-            "model": self.model,
-            "messages": messages,
-            "tools": [t.get("function", {}).get("name") for t in tools],
-            "scope": scope,
-        }
-        self._mock_calls.append(call_record)
-
-        # Raise mock exception if configured
-        if self._mock_exception is not None:
-            raise self._mock_exception
-
-        # Record OpenTelemetry span
-        from hindsight_api.tracing import get_span_recorder
-
-        span_recorder = get_span_recorder()
-
-        if self._mock_response is not None:
-            if isinstance(self._mock_response, LLMToolCallResult):
-                result = self._mock_response
-            elif isinstance(self._mock_response, list):
-                # Allow setting just tool calls as a list
-                result = LLMToolCallResult(
-                    tool_calls=[
-                        LLMToolCall(id=f"mock_{i}", name=tc["name"], arguments=tc.get("arguments", {}))
-                        for i, tc in enumerate(self._mock_response)
-                    ],
-                    finish_reason="tool_calls",
-                )
-            else:
-                result = LLMToolCallResult(content="mock response", finish_reason="stop")
-        else:
-            result = LLMToolCallResult(content="mock response", finish_reason="stop")
-
-        # Record span with mock values
-        # Convert LLMToolCall objects to dicts for span recording
-        tool_calls_dict = (
-            [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in result.tool_calls]
-            if result.tool_calls
-            else None
-        )
-        span_recorder.record_llm_call(
-            provider=self.provider,
-            model=self.model,
-            scope=scope,
-            messages=messages,
-            response_content=result.content,
-            input_tokens=10,  # Mock value
-            output_tokens=5,  # Mock value
-            duration=0.1,  # Mock value
-            finish_reason=result.finish_reason,
-            error=None,
-            tool_calls=tool_calls_dict,
-        )
-
-        return result
-
-    async def cleanup(self) -> None:
-        """Clean up resources (no-op for mock provider)."""
-        pass
-
-    def set_mock_response(self, response: Any) -> None:
-        """
-        Set the response to return from mock calls.
-
-        Args:
-            response: The response to return. Can be:
-                - A dict/Pydantic model for regular calls
-                - An LLMToolCallResult for tool calls
-                - A list of tool call dicts for tool calls
-                - Any other value to return as-is
-        """
-        self._mock_response = response
-
-    def set_mock_exception(self, exception: Exception) -> None:
-        """
-        Set an exception to raise from mock calls.
-
-        Args:
-            exception: The exception to raise on the next call.
-                      After raising, the exception is cleared.
-        """
-        self._mock_exception = exception
-
-    def get_mock_calls(self) -> list[dict]:
-        """
-        Get the list of recorded mock calls.
-
-        Returns:
-            List of call records, each containing:
-                - provider: Provider name
-                - model: Model name
-                - messages: Messages sent
-                - response_format/tools: Format or tools used
-                - scope: Call scope
-        """
-        return self._mock_calls
-
-    def clear_mock_calls(self) -> None:
-        """Clear the recorded mock calls and any set exception."""
-        self._mock_calls = []
-        self._mock_exception = None
@@ -1,788 +0,0 @@
-"""
-OpenAI-compatible LLM provider supporting OpenAI, Groq, Ollama, and LMStudio.
-
-This provider handles all OpenAI API-compatible models including:
- OpenAI: GPT-4, GPT-4o, GPT-5, o1, o3 (reasoning models)
- Groq: Fast inference with seed control and service tiers
- Ollama: Local models with native streaming API support
- LMStudio: Local models with OpenAI-compatible API
-
-Features:
- Reasoning models with extended thinking (o1, o3, GPT-5 families)
- Strict JSON schema enforcement (OpenAI)
- Provider-specific parameters (Groq seed, service tier)
- Native Ollama streaming for better structured output
- Automatic token limit handling per model family
-"""
-
-import asyncio
-import json
-import logging
-import os
-import re
-import time
-from typing import Any
-
-import httpx
-from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinishReasonError
-
-from hindsight_api.config import DEFAULT_LLM_TIMEOUT, ENV_LLM_TIMEOUT
-from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
-from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.metrics import get_metrics_collector
-
-logger = logging.getLogger(__name__)
-
-# Seed applied to every Groq request for deterministic behavior
-DEFAULT_LLM_SEED = 4242
-
-
-class OpenAICompatibleLLM(LLMInterface):
-    """
-    LLM provider for OpenAI-compatible APIs.
-
-    Supports:
-    - OpenAI: Standard models (GPT-4, GPT-4o) and reasoning models (o1, o3, GPT-5)
-    - Groq: Fast inference with seed control and service tiers
-    - Ollama: Local models with native streaming API for better structured output
-    - LMStudio: Local models with OpenAI-compatible API
-    """
-
-    def __init__(
-        self,
-        provider: str,
-        api_key: str,
-        base_url: str,
-        model: str,
-        reasoning_effort: str = "low",
-        timeout: float | None = None,
-        groq_service_tier: str | None = None,
-        **kwargs: Any,
-    ):
-        """
-        Initialize OpenAI-compatible LLM provider.
-
-        Args:
-            provider: Provider name ("openai", "groq", "ollama", "lmstudio").
-            api_key: API key (optional for ollama/lmstudio).
-            base_url: Base URL for the API (uses defaults for groq/ollama/lmstudio if empty).
-            model: Model name.
-            reasoning_effort: Reasoning effort level for supported models ("low", "medium", "high").
-            timeout: Request timeout in seconds (uses env var or 300s default).
-            groq_service_tier: Groq service tier ("on_demand", "flex", "auto").
-            **kwargs: Additional provider-specific parameters.
-        """
-        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
-
-        # Validate provider
-        valid_providers = ["openai", "groq", "ollama", "lmstudio"]
-        if self.provider not in valid_providers:
-            raise ValueError(f"OpenAICompatibleLLM only supports: {', '.join(valid_providers)}. Got: {self.provider}")
-
-        # Set default base URLs
-        if not self.base_url:
-            if self.provider == "groq":
-                self.base_url = "https://api.groq.com/openai/v1"
-            elif self.provider == "ollama":
-                self.base_url = "http://localhost:11434/v1"
-            elif self.provider == "lmstudio":
-                self.base_url = "http://localhost:1234/v1"
-
-        # For ollama/lmstudio, use dummy key if not provided
-        if self.provider in ("ollama", "lmstudio") and not self.api_key:
-            self.api_key = "local"
-
-        # Validate API key for cloud providers
-        if self.provider in ("openai", "groq") and not self.api_key:
-            raise ValueError(f"API key is required for {self.provider}")
-
-        # Groq service tier configuration
-        self.groq_service_tier = groq_service_tier or os.getenv("HINDSIGHT_API_LLM_GROQ_SERVICE_TIER", "auto")
-
-        # Get timeout config
-        self.timeout = timeout or float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))
-
-        # Create OpenAI client
-        client_kwargs: dict[str, Any] = {"api_key": self.api_key, "max_retries": 0}
-        if self.base_url:
-            client_kwargs["base_url"] = self.base_url
-        if self.timeout:
-            client_kwargs["timeout"] = self.timeout
-
-        self._client = AsyncOpenAI(**client_kwargs)
-        logger.info(
-            f"OpenAI-compatible client initialized: provider={self.provider}, model={self.model}, "
-            f"base_url={self.base_url or 'default'}"
-        )
-
-    async def verify_connection(self) -> None:
-        """
-        Verify that the provider is configured correctly by making a simple test call.
-
-        Raises:
-            RuntimeError: If the connection test fails.
-        """
-        try:
-            logger.info(f"Verifying connection: {self.provider}/{self.model}")
-            await self.call(
-                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=100,
-                max_retries=2,
-                initial_backoff=0.5,
-                max_backoff=2.0,
-                scope="verification",
-            )
-            logger.info(f"Connection verified: {self.provider}/{self.model}")
-        except Exception as e:
-            raise RuntimeError(f"Connection verification failed for {self.provider}/{self.model}: {e}") from e
-
-    def _supports_reasoning_model(self) -> bool:
-        """Check if the current model is a reasoning model (o1, o3, GPT-5, DeepSeek)."""
-        model_lower = self.model.lower()
-        return any(x in model_lower for x in ["gpt-5", "o1", "o3", "deepseek"])
-
-    def _get_max_reasoning_tokens(self) -> int | None:
-        """Get max reasoning tokens for reasoning models."""
-        model_lower = self.model.lower()
-
-        # GPT-4 and GPT-4.1 models have different caps
-        if any(x in model_lower for x in ["gpt-4.1", "gpt-4-"]):
-            return 32000
-        elif "gpt-4o" in model_lower:
-            return 16384
-
-        return None
-
-    async def call(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None = None,
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "memory",
-        max_retries: int = 10,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 60.0,
-        skip_validation: bool = False,
-        strict_schema: bool = False,
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Make an LLM API call with retry logic.
-
-        Args:
-            messages: List of message dicts with 'role' and 'content'.
-            response_format: Optional Pydantic model for structured output.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            skip_validation: Return raw JSON without Pydantic validation.
-            strict_schema: Use strict JSON schema enforcement (OpenAI only).
-            return_usage: If True, return tuple (result, TokenUsage) instead of just result.
-
-        Returns:
-            If return_usage=False: Parsed response if response_format is provided, otherwise text content.
-            If return_usage=True: Tuple of (result, TokenUsage) with token counts.
-
-        Raises:
-            OutputTooLongError: If output exceeds token limits.
-            Exception: Re-raises API errors after retries exhausted.
-        """
-        # Handle Ollama with native API for structured output (better schema enforcement)
-        if self.provider == "ollama" and response_format is not None:
-            return await self._call_ollama_native(
-                messages=messages,
-                response_format=response_format,
-                max_completion_tokens=max_completion_tokens,
-                temperature=temperature,
-                max_retries=max_retries,
-                initial_backoff=initial_backoff,
-                max_backoff=max_backoff,
-                skip_validation=skip_validation,
-                scope=scope,
-                return_usage=return_usage,
-            )
-
-        start_time = time.time()
-
-        # Build call parameters
-        call_params: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-        }
-
-        # Check if model supports reasoning parameter
-        is_reasoning_model = self._supports_reasoning_model()
-
-        # Apply model-specific token limits
-        if max_completion_tokens is not None:
-            max_tokens_cap = self._get_max_reasoning_tokens()
-            if max_tokens_cap and max_completion_tokens > max_tokens_cap:
-                max_completion_tokens = max_tokens_cap
-            # For reasoning models, enforce minimum to ensure space for reasoning + output
-            if is_reasoning_model and max_completion_tokens < 16000:
-                max_completion_tokens = 16000
-            call_params["max_completion_tokens"] = max_completion_tokens
-
-        # Temperature - reasoning models don't support custom temperature
-        if temperature is not None and not is_reasoning_model:
-            call_params["temperature"] = temperature
-
-        # Set reasoning_effort for reasoning models
-        if is_reasoning_model:
-            call_params["reasoning_effort"] = self.reasoning_effort
-
-        # Provider-specific parameters
-        if self.provider == "groq":
-            call_params["seed"] = DEFAULT_LLM_SEED
-            extra_body: dict[str, Any] = {}
-            # Add service_tier if configured
-            if self.groq_service_tier:
-                extra_body["service_tier"] = self.groq_service_tier
-            # Add reasoning parameters for reasoning models
-            if is_reasoning_model:
-                extra_body["include_reasoning"] = False
-            if extra_body:
-                call_params["extra_body"] = extra_body
-
-        # Prepare response format ONCE before retry loop
-        if response_format is not None:
-            schema = None
-            if hasattr(response_format, "model_json_schema"):
-                schema = response_format.model_json_schema()
-
-            if strict_schema and schema is not None:
-                # Use OpenAI's strict JSON schema enforcement
-                call_params["response_format"] = {
-                    "type": "json_schema",
-                    "json_schema": {
-                        "name": "response",
-                        "strict": True,
-                        "schema": schema,
-                    },
-                }
-            else:
-                # Soft enforcement: add schema to prompt and use json_object mode
-                if schema is not None:
-                    schema_msg = (
-                        f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
-                    )
-
-                    if call_params["messages"] and call_params["messages"][0].get("role") == "system":
-                        first_msg = call_params["messages"][0]
-                        if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
-                            first_msg["content"] += schema_msg
-                    elif call_params["messages"]:
-                        first_msg = call_params["messages"][0]
-                        if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
-                            first_msg["content"] = schema_msg + "\n\n" + first_msg["content"]
-                if self.provider not in ("lmstudio", "ollama"):
-                    # LM Studio and Ollama don't support json_object response format reliably
-                    call_params["response_format"] = {"type": "json_object"}
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                if response_format is not None:
-                    response = await self._client.chat.completions.create(**call_params)
-
-                    content = response.choices[0].message.content
-
-                    # Strip reasoning model thinking tags
-                    # Supports: <think>, <thinking>, <reasoning>, |startthink|/|endthink|
-                    if content:
-                        original_len = len(content)
-                        content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL)
-                        content = re.sub(r"<thinking>.*?</thinking>", "", content, flags=re.DOTALL)
-                        content = re.sub(r"<reasoning>.*?</reasoning>", "", content, flags=re.DOTALL)
-                        content = re.sub(r"\|startthink\|.*?\|endthink\|", "", content, flags=re.DOTALL)
-                        content = content.strip()
-                        if len(content) < original_len:
-                            logger.debug(f"Stripped {original_len - len(content)} chars of reasoning tokens")
-
-                    # For local models, they may wrap JSON in markdown code blocks
-                    if self.provider in ("lmstudio", "ollama"):
-                        clean_content = content
-                        if "```json" in content:
-                            clean_content = content.split("```json")[1].split("```")[0].strip()
-                        elif "```" in content:
-                            clean_content = content.split("```")[1].split("```")[0].strip()
-                        try:
-                            json_data = json.loads(clean_content)
-                        except json.JSONDecodeError:
-                            # Fallback to parsing raw content
-                            json_data = json.loads(content)
-                    else:
-                        # Log raw LLM response for debugging JSON parse issues
-                        try:
-                            json_data = json.loads(content)
-                        except json.JSONDecodeError as json_err:
-                            # Truncate content for logging
-                            content_preview = content[:500] if content else "<empty>"
-                            if content and len(content) > 700:
-                                content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
-                            logger.warning(
-                                f"JSON parse error from LLM response (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
-                                f"  Model: {self.provider}/{self.model}\n"
-                                f"  Content length: {len(content) if content else 0} chars\n"
-                                f"  Content preview: {content_preview!r}\n"
-                                f"  Finish reason: {response.choices[0].finish_reason if response.choices else 'unknown'}"
-                            )
-                            # Retry on JSON parse errors
-                            if attempt < max_retries:
-                                backoff = min(initial_backoff * (2**attempt), max_backoff)
-                                await asyncio.sleep(backoff)
-                                last_exception = json_err
-                                continue
-                            else:
-                                logger.error(f"JSON parse error after {max_retries + 1} attempts, giving up")
-                                raise
-
-                    if skip_validation:
-                        result = json_data
-                    else:
-                        result = response_format.model_validate(json_data)
-                else:
-                    response = await self._client.chat.completions.create(**call_params)
-                    result = response.choices[0].message.content
-
-                # Record token usage metrics
-                duration = time.time() - start_time
-                usage = response.usage
-                input_tokens = usage.prompt_tokens or 0 if usage else 0
-                output_tokens = usage.completion_tokens or 0 if usage else 0
-                total_tokens = usage.total_tokens or 0 if usage else 0
-
-                # Record LLM metrics
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Record trace span
-                from hindsight_api.tracing import _serialize_for_span, get_span_recorder
-
-                finish_reason = response.choices[0].finish_reason if response.choices else None
-                span_recorder = get_span_recorder()
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=_serialize_for_span(result),
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    duration=duration,
-                    finish_reason=finish_reason,
-                    error=None,
-                )
-
-                # Log slow calls
-                if duration > 10.0 and usage:
-                    ratio = max(1, output_tokens) / max(1, input_tokens)
-                    cached_tokens = 0
-                    if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details:
-                        cached_tokens = getattr(usage.prompt_tokens_details, "cached_tokens", 0) or 0
-                    cache_info = f", cached_tokens={cached_tokens}" if cached_tokens > 0 else ""
-                    logger.info(
-                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
-                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
-                        f"total_tokens={total_tokens}{cache_info}, time={duration:.3f}s, ratio out/in={ratio:.2f}"
-                    )
-
-                if return_usage:
-                    token_usage = TokenUsage(
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        total_tokens=total_tokens,
-                    )
-                    return result, token_usage
-                return result
-
-            except LengthFinishReasonError as e:
-                logger.warning(f"LLM output exceeded token limits: {str(e)}")
-                raise OutputTooLongError(
-                    "LLM output exceeded token limits. Input may need to be split into smaller chunks."
-                ) from e
-
-            except APIConnectionError as e:
-                last_exception = e
-                status_code = getattr(e, "status_code", None) or getattr(
-                    getattr(e, "response", None), "status_code", None
-                )
-                logger.warning(f"APIConnectionError (HTTP {status_code}), attempt {attempt + 1}: {str(e)[:200]}")
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    await asyncio.sleep(backoff)
-                    continue
-                else:
-                    logger.error(f"Connection error after {max_retries + 1} attempts: {str(e)}")
-                    raise
-
-            except APIStatusError as e:
-                # Fast fail only on 401 (unauthorized) and 403 (forbidden)
-                if e.status_code in (401, 403):
-                    logger.error(f"Auth error (HTTP {e.status_code}), not retrying: {str(e)}")
-                    raise
-
-                # Handle tool_use_failed error - model outputted in tool call format
-                if e.status_code == 400 and response_format is not None:
-                    try:
-                        error_body = e.body if hasattr(e, "body") else {}
-                        if isinstance(error_body, dict):
-                            error_info: dict[str, Any] = error_body.get("error") or {}
-                            if error_info.get("code") == "tool_use_failed":
-                                failed_gen = error_info.get("failed_generation", "")
-                                if failed_gen:
-                                    # Parse tool call format and convert to expected format
-                                    tool_call = json.loads(failed_gen)
-                                    tool_name = tool_call.get("name", "")
-                                    tool_args = tool_call.get("arguments", {})
-                                    converted = {"actions": [{"tool": tool_name, **tool_args}]}
-                                    if skip_validation:
-                                        result = converted
-                                    else:
-                                        result = response_format.model_validate(converted)
-
-                                    # Record metrics
-                                    duration = time.time() - start_time
-                                    metrics = get_metrics_collector()
-                                    metrics.record_llm_call(
-                                        provider=self.provider,
-                                        model=self.model,
-                                        scope=scope,
-                                        duration=duration,
-                                        input_tokens=0,
-                                        output_tokens=0,
-                                        success=True,
-                                    )
-                                    if return_usage:
-                                        return result, TokenUsage(input_tokens=0, output_tokens=0, total_tokens=0)
-                                    return result
-                    except (json.JSONDecodeError, KeyError, TypeError):
-                        pass  # Failed to parse tool_use_failed, continue with normal retry
-
-                last_exception = e
-                if attempt < max_retries:
-                    backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
-                    sleep_time = backoff + jitter
-                    await asyncio.sleep(sleep_time)
-                else:
-                    logger.error(f"API error after {max_retries + 1} attempts: {str(e)}")
-                    raise
-
-            except Exception:
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("LLM call failed after all retries with no exception captured")
-
-    async def call_with_tools(
-        self,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]],
-        max_completion_tokens: int | None = None,
-        temperature: float | None = None,
-        scope: str = "tools",
-        max_retries: int = 5,
-        initial_backoff: float = 1.0,
-        max_backoff: float = 30.0,
-        tool_choice: str | dict[str, Any] = "auto",
-    ) -> LLMToolCallResult:
-        """
-        Make an LLM API call with tool/function calling support.
-
-        Args:
-            messages: List of message dicts. Can include tool results with role='tool'.
-            tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature (0.0-2.0).
-            scope: Scope identifier for tracking.
-            max_retries: Maximum retry attempts.
-            initial_backoff: Initial backoff time in seconds.
-            max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
-
-        Returns:
-            LLMToolCallResult with content and/or tool_calls.
-        """
-        start_time = time.time()
-
-        # Build call parameters
-        call_params: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-            "tools": tools,
-            "tool_choice": tool_choice,
-        }
-
-        if max_completion_tokens is not None:
-            call_params["max_completion_tokens"] = max_completion_tokens
-        if temperature is not None:
-            call_params["temperature"] = temperature
-
-        # Provider-specific parameters
-        if self.provider == "groq":
-            call_params["seed"] = DEFAULT_LLM_SEED
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._client.chat.completions.create(**call_params)
-
-                message = response.choices[0].message
-                finish_reason = response.choices[0].finish_reason
-
-                # Extract tool calls if present
-                tool_calls: list[LLMToolCall] = []
-                if message.tool_calls:
-                    for tc in message.tool_calls:
-                        try:
-                            args = json.loads(tc.function.arguments) if tc.function.arguments else {}
-                        except json.JSONDecodeError:
-                            args = {"_raw": tc.function.arguments}
-                        tool_calls.append(LLMToolCall(id=tc.id, name=tc.function.name, arguments=args))
-
-                content = message.content
-
-                # Record metrics
-                duration = time.time() - start_time
-                usage = response.usage
-                input_tokens = usage.prompt_tokens or 0 if usage else 0
-                output_tokens = usage.completion_tokens or 0 if usage else 0
-
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Record OpenTelemetry span
-                from hindsight_api.tracing import get_span_recorder
-
-                span_recorder = get_span_recorder()
-                # Convert LLMToolCall objects to dicts for span recording
-                tool_calls_dict = (
-                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
-                    if tool_calls
-                    else None
-                )
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=content,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    duration=duration,
-                    finish_reason=finish_reason,
-                    error=None,
-                    tool_calls=tool_calls_dict,
-                )
-
-                return LLMToolCallResult(
-                    content=content,
-                    tool_calls=tool_calls,
-                    finish_reason=finish_reason,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                )
-
-            except APIConnectionError as e:
-                last_exception = e
-                if attempt < max_retries:
-                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
-                    continue
-                raise
-
-            except APIStatusError as e:
-                if e.status_code in (401, 403):
-                    raise
-                last_exception = e
-                if attempt < max_retries:
-                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
-                    continue
-                raise
-
-            except Exception:
-                raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Tool call failed after all retries")
-
-    async def _call_ollama_native(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any,
-        max_completion_tokens: int | None,
-        temperature: float | None,
-        max_retries: int,
-        initial_backoff: float,
-        max_backoff: float,
-        skip_validation: bool,
-        scope: str = "memory",
-        return_usage: bool = False,
-    ) -> Any:
-        """
-        Call Ollama using native API with JSON schema enforcement.
-
-        Ollama's native API supports passing a full JSON schema in the 'format' parameter,
-        which provides better structured output control than the OpenAI-compatible API.
-        """
-        start_time = time.time()
-
-        # Get the JSON schema from the Pydantic model
-        schema = response_format.model_json_schema() if hasattr(response_format, "model_json_schema") else None
-
-        # Build the base URL for Ollama's native API
-        # Default OpenAI-compatible URL is http://localhost:11434/v1
-        # Native API is at http://localhost:11434/api/chat
-        base_url = self.base_url or "http://localhost:11434/v1"
-        if base_url.endswith("/v1"):
-            native_url = base_url[:-3] + "/api/chat"
-        else:
-            native_url = base_url.rstrip("/") + "/api/chat"
-
-        # Build request payload
-        payload: dict[str, Any] = {
-            "model": self.model,
-            "messages": messages,
-            "stream": False,
-        }
-
-        # Add schema as format parameter for structured output
-        if schema:
-            payload["format"] = schema
-
-        # Add optional parameters with optimized defaults for Ollama
-        options: dict[str, Any] = {
-            "num_ctx": 16384,  # 16k context window for larger prompts
-            "num_batch": 512,  # Optimal batch size for prompt processing
-        }
-        if max_completion_tokens:
-            options["num_predict"] = max_completion_tokens
-        if temperature is not None:
-            options["temperature"] = temperature
-        payload["options"] = options
-
-        last_exception = None
-
-        async with httpx.AsyncClient(timeout=300.0) as client:
-            for attempt in range(max_retries + 1):
-                try:
-                    response = await client.post(native_url, json=payload)
-                    response.raise_for_status()
-
-                    result = response.json()
-                    content = result.get("message", {}).get("content", "")
-
-                    # Parse JSON response
-                    try:
-                        json_data = json.loads(content)
-                    except json.JSONDecodeError as json_err:
-                        content_preview = content[:500] if content else "<empty>"
-                        if content and len(content) > 700:
-                            content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
-                        logger.warning(
-                            f"Ollama JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
-                            f"  Model: ollama/{self.model}\n"
-                            f"  Content length: {len(content) if content else 0} chars\n"
-                            f"  Content preview: {content_preview!r}"
-                        )
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            last_exception = json_err
-                            continue
-                        else:
-                            raise
-
-                    # Extract token usage from Ollama response
-                    duration = time.time() - start_time
-                    input_tokens = result.get("prompt_eval_count", 0) or 0
-                    output_tokens = result.get("eval_count", 0) or 0
-                    total_tokens = input_tokens + output_tokens
-
-                    # Record LLM metrics
-                    metrics = get_metrics_collector()
-                    metrics.record_llm_call(
-                        provider=self.provider,
-                        model=self.model,
-                        scope=scope,
-                        duration=duration,
-                        input_tokens=input_tokens,
-                        output_tokens=output_tokens,
-                        success=True,
-                    )
-
-                    # Validate against Pydantic model or return raw JSON
-                    if skip_validation:
-                        validated_result = json_data
-                    else:
-                        validated_result = response_format.model_validate(json_data)
-
-                    if return_usage:
-                        token_usage = TokenUsage(
-                            input_tokens=input_tokens,
-                            output_tokens=output_tokens,
-                            total_tokens=total_tokens,
-                        )
-                        return validated_result, token_usage
-                    return validated_result
-
-                except httpx.HTTPStatusError as e:
-                    last_exception = e
-                    if attempt < max_retries:
-                        logger.warning(
-                            f"Ollama HTTP error (attempt {attempt + 1}/{max_retries + 1}): {e.response.status_code}"
-                        )
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        logger.error(f"Ollama HTTP error after {max_retries + 1} attempts: {e}")
-                        raise
-
-                except httpx.RequestError as e:
-                    last_exception = e
-                    if attempt < max_retries:
-                        logger.warning(f"Ollama connection error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                        backoff = min(initial_backoff * (2**attempt), max_backoff)
-                        await asyncio.sleep(backoff)
-                        continue
-                    else:
-                        logger.error(f"Ollama connection error after {max_retries + 1} attempts: {e}")
-                        raise
-
-                except Exception as e:
-                    logger.error(f"Unexpected error during Ollama call: {type(e).__name__}: {e}")
-                    raise
-
-        if last_exception:
-            raise last_exception
-        raise RuntimeError("Ollama call failed after all retries")
-
-    async def cleanup(self) -> None:
-        """Clean up resources (close OpenAI client connections)."""
-        if hasattr(self, "_client") and self._client:
-            await self._client.close()
@@ -58,7 +58,6 @@ def _normalize_tool_name(name: str) -> str:
    - 'functions.done' (OpenAI-style prefix)
    - 'call=functions.done' (some models)
    - 'call=done' (some models)
-    - 'done<|channel|>commentary' (malformed special tokens appended)

    Returns the normalized tool name (e.g., 'done', 'recall', etc.)
    """
@@ -70,11 +69,6 @@ def _normalize_tool_name(name: str) -> str:
    if name.startswith("functions."):
        name = name[len("functions.") :]

-    # Handle malformed special tokens appended to tool name
-    # e.g., 'done<|channel|>commentary' -> 'done'
-    if "<|" in name:
-        name = name.split("<|")[0]
-
    return name


@@ -402,7 +396,7 @@ async def run_reflect_agent(
                    {"role": "system", "content": FINAL_SYSTEM_PROMPT},
                    {"role": "user", "content": prompt},
                ],
-                scope="reflect",
+                scope="reflect_agent_final",
                max_completion_tokens=max_tokens,
                return_usage=True,
            )
@@ -447,7 +441,7 @@ async def run_reflect_agent(
            result = await llm_config.call_with_tools(
                messages=messages,
                tools=tools,
-                scope="reflect_tool_call",
+                scope="reflect_agent",
                tool_choice="required" if iteration == 0 else "auto",  # Force tool use on first iteration
            )
            llm_duration = int((time.time() - llm_start) * 1000)
@@ -479,7 +473,7 @@ async def run_reflect_agent(
                    {"role": "system", "content": FINAL_SYSTEM_PROMPT},
                    {"role": "user", "content": prompt},
                ],
-                scope="reflect",
+                scope="reflect_agent_final",
                max_completion_tokens=max_tokens,
                return_usage=True,
            )
@@ -550,7 +544,7 @@ async def run_reflect_agent(
                    {"role": "system", "content": FINAL_SYSTEM_PROMPT},
                    {"role": "user", "content": prompt},
                ],
-                scope="reflect",
+                scope="reflect_agent_final",
                max_completion_tokens=max_tokens,
                return_usage=True,
            )
@@ -617,30 +611,23 @@ async def run_reflect_agent(
                )
                continue

-            # Process done tool - wrap with tool call span
-            from hindsight_api.tracing import get_tracer
-
-            tracer = get_tracer()
-            span_name = "hindsight.reflect_tool_call"
-            with tracer.start_as_current_span(span_name) as span:
-                span.set_attribute("hindsight.scope", "reflect_tool_call")
-                span.set_attribute("hindsight.operation", "reflect_tool_call")
-                return await _process_done_tool(
-                    done_call,
-                    available_memory_ids,
-                    available_mental_model_ids,
-                    available_observation_ids,
-                    iteration + 1,
-                    total_tools_called,
-                    tool_trace,
-                    _get_llm_trace(),
-                    _get_usage(),
-                    _log_completion,
-                    reflect_id,
-                    directives_applied=directives_applied,
-                    llm_config=llm_config,
-                    response_schema=response_schema,
-                )
+            # Process done tool
+            return await _process_done_tool(
+                done_call,
+                available_memory_ids,
+                available_mental_model_ids,
+                available_observation_ids,
+                iteration + 1,
+                total_tools_called,
+                tool_trace,
+                _get_llm_trace(),
+                _get_usage(),
+                _log_completion,
+                reflect_id,
+                directives_applied=directives_applied,
+                llm_config=llm_config,
+                response_schema=response_schema,
+            )

        # Execute other tools in parallel (exclude done tool in all its format variants)
        other_tools = [tc for tc in result.tool_calls if not _is_done_tool(tc.name)]
@@ -849,67 +836,17 @@ async def _execute_tool_with_timing(
    expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
 ) -> tuple[dict[str, Any], int]:
    """Execute a tool call and return result with timing."""
-    from hindsight_api.tracing import get_tracer
-
-    start_time = time.time()
-
-    # Create span for tool execution
-    tracer = get_tracer()
-    # Normalize tool name for span
-    normalized_name = _normalize_tool_name(tc.name)
-    span_name = f"hindsight.reflect_tool_exec.{normalized_name}"
-
-    # Calculate timestamps
-    start_time_ns = time.time_ns()
-
-    with tracer.start_as_current_span(
-        span_name,
-        start_time=start_time_ns,
-        end_on_exit=False,
-    ) as span:
-        # Set attributes
-        span.set_attribute("hindsight.tool.name", normalized_name)
-        span.set_attribute("hindsight.tool.id", tc.id)
-        span.set_attribute("hindsight.tool.arguments", json.dumps(tc.arguments))
-
-        try:
-            result = await _execute_tool(
-                tc.name,
-                tc.arguments,
-                search_mental_models_fn,
-                search_observations_fn,
-                recall_fn,
-                expand_fn,
-            )
-
-            # Set success attributes
-            if isinstance(result, dict) and "error" in result:
-                from opentelemetry.trace import Status, StatusCode
-
-                span.set_status(Status(StatusCode.ERROR, result["error"]))
-            else:
-                from opentelemetry.trace import Status, StatusCode
-
-                span.set_status(Status(StatusCode.OK))
-
-            duration_ms = int((time.time() - start_time) * 1000)
-            span.set_attribute("hindsight.tool.duration_ms", duration_ms)
-
-            # End span with correct timestamp
-            end_time_ns = time.time_ns()
-            span.end(end_time=end_time_ns)
-
-            return result, duration_ms
-        except Exception as e:
-            from opentelemetry.trace import Status, StatusCode
-
-            span.set_status(Status(StatusCode.ERROR, str(e)))
-            span.record_exception(e)
-            duration_ms = int((time.time() - start_time) * 1000)
-            span.set_attribute("hindsight.tool.duration_ms", duration_ms)
-            end_time_ns = time.time_ns()
-            span.end(end_time=end_time_ns)
-            raise
+    start = time.time()
+    result = await _execute_tool(
+        tc.name,
+        tc.arguments,
+        search_mental_models_fn,
+        search_observations_fn,
+        recall_fn,
+        expand_fn,
+    )
+    duration_ms = int((time.time() - start) * 1000)
+    return result, duration_ms


 async def _execute_tool(
@@ -928,21 +865,21 @@ async def _execute_tool(
        query = args.get("query")
        if not query:
            return {"error": "search_mental_models requires a query parameter"}
-        max_results = int(args.get("max_results") or 5)
+        max_results = args.get("max_results") or 5
        return await search_mental_models_fn(query, max_results)

    elif tool_name == "search_observations":
        query = args.get("query")
        if not query:
            return {"error": "search_observations requires a query parameter"}
-        max_tokens = max(int(args.get("max_tokens") or 5000), 1000)  # Default 5000, min 1000
+        max_tokens = max(args.get("max_tokens") or 5000, 1000)  # Default 5000, min 1000
        return await search_observations_fn(query, max_tokens)

    elif tool_name == "recall":
        query = args.get("query")
        if not query:
            return {"error": "recall requires a query parameter"}
-        max_tokens = max(int(args.get("max_tokens") or 2048), 1000)  # Default 2048, min 1000
+        max_tokens = max(args.get("max_tokens") or 2048, 1000)  # Default 2048, min 1000
        return await recall_fn(query, max_tokens)

    elif tool_name == "expand":
@@ -961,18 +898,18 @@ def _summarize_input(tool_name: str, args: dict[str, Any]) -> str:
    if tool_name == "search_mental_models":
        query = args.get("query", "")
        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
-        max_results = int(args.get("max_results") or 5)
+        max_results = args.get("max_results") or 5
        return f"(query={query_preview}, max_results={max_results})"
    elif tool_name == "search_observations":
        query = args.get("query", "")
        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
-        max_tokens = max(int(args.get("max_tokens") or 5000), 1000)
+        max_tokens = max(args.get("max_tokens") or 5000, 1000)
        return f"(query={query_preview}, max_tokens={max_tokens})"
    elif tool_name == "recall":
        query = args.get("query", "")
        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
        # Show actual value used (default 2048, min 1000)
-        max_tokens = max(int(args.get("max_tokens") or 2048), 1000)
+        max_tokens = max(args.get("max_tokens") or 2048, 1000)
        return f"(query={query_preview}, max_tokens={max_tokens})"
    elif tool_name == "expand":
        memory_ids = args.get("memory_ids", [])
@@ -31,7 +31,7 @@ class ReflectAction(BaseModel):
        default=None, description="Observation sections for done action (when output_mode=observations)"
    )
    # Plain text answer fields (for output_mode=answer)
-    answer: str | None = Field(default=None, description="Well-formatted markdown answer for done action")
+    answer: str | None = Field(default=None, description="Plain text answer for done action (no markdown)")
    answer_memory_ids: list[str] | None = Field(
        default=None, description="Memory IDs supporting the answer", alias="memory_ids"
    )
@@ -148,15 +148,7 @@ def build_system_prompt_for_tools(

    parts = []

-    # Anti-hallucination rule at the very top
-    parts.extend(
-        [
-            "CRITICAL: You MUST ONLY use information from retrieved tool results. NEVER make up names, people, events, or entities.",
-            "",
-        ]
-    )
-
-    # Inject directives after anti-hallucination rule
+    # Inject directives at the VERY START for maximum prominence
    if directives:
        parts.append(build_directives_section(directives))

@@ -170,7 +162,7 @@ def build_system_prompt_for_tools(
    parts.extend(
        [
            "## CRITICAL RULES",
-            "- ONLY use information from tool results - no external knowledge or guessing",
+            "- You must NEVER fabricate information that has no basis in retrieved data",
            "- You SHOULD synthesize, infer, and reason from the retrieved memories",
            "- You MUST search before saying you don't have information",
            "",
@@ -308,11 +300,9 @@ def build_system_prompt_for_tools(
    parts.extend(
        [
            "",
-            "## Output Format: Well-Formatted Markdown Answer",
-            "Call done() with a well-formatted markdown 'answer' field.",
-            "- USE markdown formatting for structure (headers, lists, bold, italic, code blocks, tables, etc.)",
-            "- CRITICAL: Add blank lines before and after block elements (tables, code blocks, lists)",
-            "- Format for clarity and readability with proper spacing and hierarchy",
+            "## Output Format: Plain Text Answer",
+            "Call done() with a plain text 'answer' field.",
+            "- Do NOT use markdown formatting",
            "- NEVER include memory IDs, UUIDs, or 'Memory references' in the answer text",
            "- Put IDs ONLY in the memory_ids/mental_model_ids/observation_ids arrays, not in the answer",
        ]
@@ -473,41 +463,21 @@ def build_final_prompt(
    parts.append(
        "\n## Instructions\n"
        "Provide a thoughtful answer by synthesizing and reasoning from the retrieved data above. "
-        "You can make reasonable inferences from the memories, but don't completely fabricate information. "
+        "You can make reasonable inferences from the memories, but don't completely fabricate information."
        "If the exact answer isn't stated, use what IS stated to give the best possible answer. "
-        "Only say 'I don't have information' if the retrieved data is truly unrelated to the question.\n\n"
-        "IMPORTANT: Output ONLY the final answer. Do NOT include meta-commentary like "
-        '"I\'ll search..." or "Let me analyze...". Do NOT explain your reasoning process. '
-        "Just provide the direct synthesized answer."
+        "Only say 'I don't have information' if the retrieved data is truly unrelated to the question."
    )

    return "\n".join(parts)


-FINAL_SYSTEM_PROMPT = """CRITICAL: You MUST ONLY use information from retrieved tool results. NEVER make up names, people, events, or entities.
-
-You are a thoughtful assistant that synthesizes answers from retrieved memories.
+FINAL_SYSTEM_PROMPT = """You are a thoughtful assistant that synthesizes answers from retrieved memories.

 Your approach:
 - Reason over the retrieved memories to answer the question
 - Make reasonable inferences when the exact answer isn't explicitly stated
 - Connect related memories to form a complete picture
 - Be helpful - if you have related information, use it to give the best possible answer
- ONLY use information from tool results - no external knowledge or guessing

 Only say "I don't have information" if the retrieved data is truly unrelated to the question.
-
-FORMATTING: Use proper markdown formatting in your answer:
- Headers (##, ###) for sections
- Lists (bullet or numbered) for enumerations
- Bold/italic for emphasis
- Tables with proper syntax (ensure blank line before and after)
- Code blocks where appropriate
- CRITICAL: Always add blank lines before and after block elements (tables, code blocks, lists)
- Proper spacing between sections
-
-CRITICAL: Output ONLY the final synthesized answer. Do NOT include:
- Meta-commentary about what you're doing ("I'll search...", "Let me analyze...")
- Explanations of your reasoning process
- Descriptions of your approach
-Just provide the direct answer with proper markdown formatting."""
+Do NOT fabricate information that has no basis in the retrieved data."""
@@ -54,21 +54,22 @@ async def tool_search_mental_models(
        Dict with matching mental models including content and freshness info
    """
    from ..memory_engine import fq_table
-    from ..search.tags import build_tags_where_clause

    # Build filters dynamically
    filters = ""
    params: list[Any] = [bank_id, str(query_embedding), max_results]
    next_param = 4

-    # Use the centralized tag filtering logic
    if tags:
-        tag_clause, tag_params, next_param = build_tags_where_clause(tags, param_offset=next_param, match=tags_match)
-        filters += f" {tag_clause}"
-        params.extend(tag_params)
+        if tags_match == "all":
+            filters += f" AND tags @> ${next_param}::varchar[]"
+        else:
+            filters += f" AND (tags && ${next_param}::varchar[] OR tags IS NULL OR tags = '{{}}')"
+        params.append(tags)
+        next_param += 1

    if exclude_ids:
-        filters += f" AND id != ALL(${next_param}::text[])"
+        filters += f" AND id != ALL(${next_param}::uuid[])"
        params.append(exclude_ids)
        next_param += 1

@@ -139,7 +139,7 @@ TOOL_DONE_ANSWER = {
            "properties": {
                "answer": {
                    "type": "string",
-                    "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                    "description": "Your response as plain text. Do NOT use markdown formatting. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
                },
                "memory_ids": {
                    "type": "array",
@@ -190,7 +190,7 @@ def _build_done_tool_with_directives(directive_rules: list[str]) -> dict:
                "properties": {
                    "answer": {
                        "type": "string",
-                        "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                        "description": "Your response as plain text. Do NOT use markdown formatting. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
                    },
                    "memory_ids": {
                        "type": "array",
@@ -263,6 +263,7 @@ class ReflectResult(BaseModel):
                        }
                    ],
                },
+                "new_opinions": ["Machine learning has great potential in healthcare"],
                "structured_output": {"summary": "ML in healthcare", "confidence": 0.9},
                "usage": {"input_tokens": 1500, "output_tokens": 500, "total_tokens": 2000},
            }
@@ -271,8 +272,9 @@ class ReflectResult(BaseModel):

    text: str = Field(description="The formulated answer text")
    based_on: dict[str, Any] = Field(
-        description="Facts used to formulate the answer, organized by type (world, experience, mental_models, directives)"
+        description="Facts used to formulate the answer, organized by type (world, experience, opinion, mental_models, directives)"
    )
+    new_opinions: list[str] = Field(default_factory=list, description="List of newly formed opinions during reflection")
    structured_output: dict[str, Any] | None = Field(
        default=None,
        description="Structured output parsed according to the provided response schema. Only present when response_schema was provided.",
@@ -295,6 +297,24 @@ class ReflectResult(BaseModel):
    )


+class Opinion(BaseModel):
+    """
+    An opinion with confidence score.
+
+    Opinions represent the bank's formed perspectives on topics,
+    with a confidence level indicating strength of belief.
+    """
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {"text": "Machine learning has great potential in healthcare", "confidence": 0.85}
+        }
+    )
+
+    text: str = Field(description="The opinion text")
+    confidence: float = Field(description="Confidence score between 0.0 and 1.0")
+
+
 class EntityObservation(BaseModel):
    """
    An observation about an entity.
@@ -57,25 +57,21 @@ def _infer_temporal_date(fact_text: str, event_date: datetime) -> str | None:
    return None


-def _sanitize_text(text: str | None) -> str | None:
+def _sanitize_text(text: str) -> str:
    """
-    Sanitize text by removing characters that break downstream systems.
+    Sanitize text by removing invalid Unicode surrogate characters.

-    Removes:
-    - Null bytes (\\x00): Invalid in PostgreSQL UTF-8 encoding
-    - Unicode surrogates (U+D800-U+DFFF): Invalid in UTF-8, break LLM APIs
+    Surrogate characters (U+D800 to U+DFFF) are used in UTF-16 encoding
+    but cannot be encoded in UTF-8. They can appear in Python strings
+    from improperly decoded data (e.g., from JavaScript or broken files).

-    Surrogate characters are used in UTF-16 encoding but cannot be encoded
-    in UTF-8. They can appear in Python strings from improperly decoded data
-    (e.g., from JavaScript or broken files). Null bytes commonly appear in
-    OCR output, PDF extraction, or copy-paste from binary sources.
+    This function removes unpaired surrogates to prevent UnicodeEncodeError
+    when the text is sent to the LLM API.
    """
-    if text is None:
-        return None
    if not text:
        return text
-    # Remove null bytes and surrogate characters
-    text = text.replace("\x00", "")
+    # Remove surrogate characters (U+D800 to U+DFFF) using regex
+    # These are invalid in UTF-8 and cause encoding errors
    return re.sub(r"[\ud800-\udfff]", "", text)


@@ -542,12 +538,7 @@ Output: ONLY 2 facts (skip coffee preference - too trivial):
 QUALITY OVER QUANTITY
 ══════════════════════════════════════════════════════════════════════════

-Ask: "Would this be useful to recall in 6 months?" If no, skip it.
-
-IMPORTANT: Sensory/emotional details and observations that provide meaningful context
-about experiences ARE important to remember, even if they seem small (e.g., how food
-tasted, how someone looked, how loud music was). Extract these if they characterize
-an experience or person."""
+Ask: "Would this be useful to recall in 6 months?" If no, skip it."""

 # Assembled concise prompt (backward compatible - exact same output as before)
 CONCISE_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
@@ -646,7 +637,6 @@ For EVENTS (fact_kind="event") - MUST SET BOTH occurred_start AND occurred_end:
 - Convert relative dates → absolute using Event Date as reference
 - If Event Date is "Saturday, March 15, 2020", then "yesterday" = Friday, March 14, 2020
 - Dates mentioned in text (e.g., "in March 2020") should use THAT year, not current year
- CRITICAL: If the content mentions an absolute date (e.g., "March 15, 2024", "2024-03-15"), you MUST extract it and set occurred_start in ISO format
 - Always include the day name (Monday, Tuesday, etc.) in the 'when' field
 - Set occurred_start AND occurred_end to WHEN IT HAPPENED (not when mentioned)
 - For single-day/point events: set occurred_end = occurred_start (same timestamp)
@@ -702,8 +692,8 @@ async def _extract_facts_from_chunk(
    event_date: datetime,
    context: str,
    llm_config: "LLMConfig",
-    config,
    agent_name: str = None,
+    extract_opinions: bool = False,
 ) -> tuple[list[dict[str, str]], TokenUsage]:
    """
    Extract facts from a single chunk (internal helper for parallel processing).
@@ -717,11 +707,20 @@ async def _extract_facts_from_chunk(

    logger = logging.getLogger(__name__)

-    # Determine which fact types to extract
+    memory_bank_context = f"\n- Your name: {agent_name}" if agent_name and extract_opinions else ""
+
+    # Determine which fact types to extract based on the flag
    # Note: We use "assistant" in the prompt but convert to "bank" for storage
-    fact_types_instruction = "Extract ONLY 'world' and 'assistant' type facts."
+    if extract_opinions:
+        # Opinion extraction uses a separate prompt (not this one)
+        fact_types_instruction = "Extract ONLY 'opinion' type facts (formed opinions, beliefs, and perspectives). DO NOT extract 'world' or 'assistant' facts."
+    else:
+        fact_types_instruction = (
+            "Extract ONLY 'world' and 'assistant' type facts. DO NOT extract opinions - those are extracted separately."
+        )

    # Check config for extraction mode and causal link extraction
+    config = get_config()
    extraction_mode = config.retain_extraction_mode
    extract_causal_links = config.retain_extract_causal_links

@@ -769,12 +768,9 @@ async def _extract_facts_from_chunk(

    # Build user message with metadata and chunk content in a clear format
    # Format event_date with day of week for better temporal reasoning
-    # Handle both datetime objects and ISO string formats (from deserialized async tasks)
-    from .orchestrator import parse_datetime_flexible
-
-    event_date = parse_datetime_flexible(event_date)
    event_date_formatted = event_date.strftime("%A, %B %d, %Y")  # e.g., "Monday, June 10, 2024"
    user_message = f"""Extract facts from the following text chunk.
+{memory_bank_context}

 Chunk: {chunk_index + 1}/{total_chunks}
 Event Date: {event_date_formatted} ({event_date.isoformat()})
@@ -786,28 +782,12 @@ Text:
    usage = TokenUsage()  # Track cumulative usage across retries
    for attempt in range(max_retries):
        try:
-            # Use retain-specific overrides if set, otherwise fall back to global LLM config
-            max_retries = (
-                config.retain_llm_max_retries if config.retain_llm_max_retries is not None else config.llm_max_retries
-            )
-            initial_backoff = (
-                config.retain_llm_initial_backoff
-                if config.retain_llm_initial_backoff is not None
-                else config.llm_initial_backoff
-            )
-            max_backoff = (
-                config.retain_llm_max_backoff if config.retain_llm_max_backoff is not None else config.llm_max_backoff
-            )
-
            extraction_response_json, call_usage = await llm_config.call(
                messages=[{"role": "system", "content": prompt}, {"role": "user", "content": user_message}],
                response_format=response_schema,
-                scope="retain_extract_facts",
+                scope="memory_extract_facts",
                temperature=0.1,
                max_completion_tokens=config.retain_max_completion_tokens,
-                max_retries=max_retries,
-                initial_backoff=initial_backoff,
-                max_backoff=max_backoff,
                skip_validation=True,  # Get raw JSON, we'll validate leniently
                return_usage=True,
            )
@@ -1011,29 +991,6 @@ Text:

        except BadRequestError as e:
            last_error = e
-            error_str = str(e).lower()
-
-            # Check if error is related to max_tokens/completion_tokens not being supported
-            if any(
-                keyword in error_str
-                for keyword in [
-                    "max_tokens",
-                    "max_completion_tokens",
-                    "maximum context",
-                    "token limit",
-                    "context length",
-                ]
-            ):
-                # Provide helpful error message with configuration suggestions
-                raise ValueError(
-                    f"Model does not support the required output token limit.\n\n"
-                    f"The model '{llm_config.model}' (provider: {llm_config.provider}) failed with: {e}\n\n"
-                    f"You have two options to fix this:\n"
-                    f"  1. Use a different model that supports at least {config.retain_max_completion_tokens} output tokens\n"
-                    f"  2. Decrease HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS to a value your model supports\n"
-                    f"     (current value: {config.retain_max_completion_tokens}, must be > RETAIN_CHUNK_SIZE={config.retain_chunk_size})"
-                ) from e
-
            if "json_validate_failed" in str(e):
                logger.warning(
                    f"          [1.3.{chunk_index + 1}] Attempt {attempt + 1}/{max_retries} failed with JSON validation error: {e}"
@@ -1055,8 +1012,8 @@ async def _extract_facts_with_auto_split(
    event_date: datetime,
    context: str,
    llm_config: LLMConfig,
-    config,
    agent_name: str = None,
+    extract_opinions: bool = False,
 ) -> tuple[list[dict[str, str]], TokenUsage]:
    """
    Extract facts from a chunk with automatic splitting if output exceeds token limits.
@@ -1071,8 +1028,8 @@ async def _extract_facts_with_auto_split(
        event_date: Reference date for temporal information
        context: Context about the conversation/document
        llm_config: LLM configuration to use
-        config: Resolved HindsightConfig for this bank
        agent_name: Optional agent name (memory owner)
+        extract_opinions: If True, extract ONLY opinions. If False, extract world and agent facts (no opinions)

    Returns:
        Tuple of (facts list, token usage) extracted from the chunk (possibly from sub-chunks)
@@ -1090,8 +1047,8 @@ async def _extract_facts_with_auto_split(
            event_date=event_date,
            context=context,
            llm_config=llm_config,
-            config=config,
            agent_name=agent_name,
+            extract_opinions=extract_opinions,
        )
    except OutputTooLongError:
        # Output exceeded token limits - split the chunk in half and retry
@@ -1135,8 +1092,8 @@ async def _extract_facts_with_auto_split(
                event_date=event_date,
                context=context,
                llm_config=llm_config,
-                config=config,
                agent_name=agent_name,
+                extract_opinions=extract_opinions,
            ),
            _extract_facts_with_auto_split(
                chunk=second_half,
@@ -1145,8 +1102,8 @@ async def _extract_facts_with_auto_split(
                event_date=event_date,
                context=context,
                llm_config=llm_config,
-                config=config,
                agent_name=agent_name,
+                extract_opinions=extract_opinions,
            ),
        ]

@@ -1169,8 +1126,8 @@ async def extract_facts_from_text(
    event_date: datetime,
    llm_config: LLMConfig,
    agent_name: str,
-    config,
    context: str = "",
+    extract_opinions: bool = False,
 ) -> tuple[list[Fact], list[tuple[str, int]], TokenUsage]:
    """
    Extract semantic facts from conversational or narrative text using LLM.
@@ -1184,10 +1141,10 @@ async def extract_facts_from_text(
    Args:
        text: Input text (conversation, article, etc.)
        event_date: Reference date for resolving relative times
+        context: Context about the conversation/document
        llm_config: LLM configuration to use
        agent_name: Agent name (memory owner)
-        config: Resolved HindsightConfig for this bank
-        context: Context about the conversation/document
+        extract_opinions: If True, extract ONLY opinions. If False, extract world and bank facts (no opinions)

    Returns:
        Tuple of (facts, chunks, usage) where:
@@ -1195,6 +1152,7 @@ async def extract_facts_from_text(
        - chunks: List of tuples (chunk_text, fact_count) for each chunk
        - usage: Aggregated token usage across all LLM calls
    """
+    config = get_config()
    chunks = chunk_text(text, max_chars=config.retain_chunk_size)

    # Log chunk count before starting LLM requests
@@ -1213,8 +1171,8 @@ async def extract_facts_from_text(
            event_date=event_date,
            context=context,
            llm_config=llm_config,
-            config=config,
            agent_name=agent_name,
+            extract_opinions=extract_opinions,
        )
        for i, chunk in enumerate(chunks)
    ]
@@ -1246,7 +1204,7 @@ SECONDS_PER_FACT = 10


 async def extract_facts_from_contents(
-    contents: list[RetainContent], llm_config, agent_name: str, config
+    contents: list[RetainContent], llm_config, agent_name: str, extract_opinions: bool = False
 ) -> tuple[list[ExtractedFactType], list[ChunkMetadata], TokenUsage]:
    """
    Extract facts from multiple content items in parallel.
@@ -1261,7 +1219,7 @@ async def extract_facts_from_contents(
        contents: List of RetainContent objects to process
        llm_config: LLM configuration for fact extraction
        agent_name: Name of the agent (for agent-related fact detection)
-        config: Resolved HindsightConfig for this bank
+        extract_opinions: If True, extract only opinions; otherwise world/bank facts

    Returns:
        Tuple of (extracted_facts, chunks_metadata, usage)
@@ -1280,7 +1238,7 @@ async def extract_facts_from_contents(
            context=item.context,
            llm_config=llm_config,
            agent_name=agent_name,
-            config=config,
+            extract_opinions=extract_opinions,
        )
        fact_extraction_tasks.append(task)

@@ -1385,26 +1343,31 @@ def _convert_causal_relations(relations_from_llm, fact_start_idx: int) -> list[C

 def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainContent]) -> None:
    """
-    Add time offsets to preserve fact ordering across all contents.
+    Add time offsets to preserve fact ordering within each content.

-    This allows retrieval to distinguish between facts from different documents/conversations
-    even when they have the same base event_date, and also between facts within the same
-    conversation.
-
-    Uses absolute position across all facts to ensure unique timestamps.
+    This allows retrieval to distinguish between facts that happened earlier vs later
+    in the same conversation, even when the base event_date is the same.

    Modifies facts in place.
    """
-    from .orchestrator import parse_datetime_flexible
+    # Group facts by content_index
+    current_content_idx = 0
+    content_fact_start = 0

    for i, fact in enumerate(facts):
-        # Use absolute position across all facts to ensure uniqueness across different contents
-        offset = timedelta(seconds=i * SECONDS_PER_FACT)
+        if fact.content_index != current_content_idx:
+            # Moved to next content
+            current_content_idx = fact.content_index
+            content_fact_start = i

-        # Apply offset to all temporal fields (handle both datetime objects and ISO strings)
+        # Calculate position within this content
+        fact_position = i - content_fact_start
+        offset = timedelta(seconds=fact_position * SECONDS_PER_FACT)
+
+        # Apply offset to all temporal fields
        if fact.occurred_start:
-            fact.occurred_start = parse_datetime_flexible(fact.occurred_start) + offset
+            fact.occurred_start = fact.occurred_start + offset
        if fact.occurred_end:
-            fact.occurred_end = parse_datetime_flexible(fact.occurred_end) + offset
+            fact.occurred_end = fact.occurred_end + offset
        if fact.mentioned_at:
-            fact.mentioned_at = parse_datetime_flexible(fact.mentioned_at) + offset
+            fact.mentioned_at = fact.mentioned_at + offset
@@ -7,9 +7,7 @@ Handles insertion of facts into the database.
 import json
 import logging

-from ...config import get_config
 from ..memory_engine import fq_table
-from .fact_extraction import _sanitize_text
 from .types import ProcessedFact

 logger = logging.getLogger(__name__)
@@ -49,7 +47,7 @@ async def insert_facts_batch(
    tags_list = []

    for fact in facts:
-        fact_texts.append(_sanitize_text(fact.fact_text))
+        fact_texts.append(fact.fact_text)
        # Convert embedding to string for asyncpg vector type
        embeddings.append(str(fact.embedding))
        # event_date: Use occurred_start if available, otherwise use mentioned_at
@@ -58,7 +56,7 @@ async def insert_facts_batch(
        occurred_starts.append(fact.occurred_start)
        occurred_ends.append(fact.occurred_end)
        mentioned_ats.append(fact.mentioned_at)
-        contexts.append(_sanitize_text(fact.context))
+        contexts.append(fact.context)
        fact_types.append(fact.fact_type)
        # confidence_score is only for opinion facts
        confidence_scores.append(1.0 if fact.fact_type == "opinion" else None)
@@ -71,59 +69,28 @@ async def insert_facts_batch(

    # Batch insert all facts
    # Note: tags are passed as JSON strings and converted back to varchar[] via jsonb_array_elements_text + array_agg
-    # Query varies based on text search backend
-    config = get_config()
-    if config.text_search_extension == "vchord":
-        # VectorChord: manually tokenize and insert search_vector
-        query = f"""
-            WITH input_data AS (
-                SELECT * FROM unnest(
-                    $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
-                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
-            )
-            INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags, search_vector)
-            SELECT
-                $1,
-                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                context, fact_type, confidence_score, metadata, chunk_id, document_id,
-                COALESCE(
-                    (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
-                    '{{}}'::varchar[]
-                ),
-                tokenize(COALESCE(text, '') || ' ' || COALESCE(context, ''), 'llmlingua2')::bm25_catalog.bm25vector
-            FROM input_data
-            RETURNING id
-        """
-    else:  # native or pg_textsearch
-        # Native PostgreSQL: search_vector is GENERATED ALWAYS, don't include it
-        # pg_textsearch: indexes operate on base columns directly, don't populate search_vector
-        query = f"""
-            WITH input_data AS (
-                SELECT * FROM unnest(
-                    $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
-                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
-            )
-            INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags)
-            SELECT
-                $1,
-                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                context, fact_type, confidence_score, metadata, chunk_id, document_id,
-                COALESCE(
-                    (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
-                    '{{}}'::varchar[]
-                )
-            FROM input_data
-            RETURNING id
-        """
-
    results = await conn.fetch(
-        query,
+        f"""
+        WITH input_data AS (
+            SELECT * FROM unnest(
+                $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
+            ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                   context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
+        )
+        INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                 context, fact_type, confidence_score, metadata, chunk_id, document_id, tags)
+        SELECT
+            $1,
+            text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+            context, fact_type, confidence_score, metadata, chunk_id, document_id,
+            COALESCE(
+                (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
+                '{{}}'::varchar[]
+            )
+        FROM input_data
+        RETURNING id
+        """,
        bank_id,
        fact_texts,
        embeddings,
@@ -190,8 +157,7 @@ async def handle_document_tracking(
    """
    import hashlib

-    # Sanitize and calculate content hash
-    combined_content = _sanitize_text(combined_content) or ""
+    # Calculate content hash
    content_hash = hashlib.sha256(combined_content.encode()).hexdigest()

    # Always delete old document first if it exists (cascades to units and links)
@@ -8,7 +8,6 @@ import logging
 import time
 import uuid
 from datetime import UTC, datetime
-from typing import Any

 from ..db_utils import acquire_with_retry
 from . import bank_utils
@@ -19,39 +18,6 @@ def utcnow():
    return datetime.now(UTC)


-def parse_datetime_flexible(value: Any) -> datetime:
-    """
-    Parse a datetime value that could be either a datetime object or an ISO string.
-
-    This handles datetime values from both direct Python calls and deserialized JSON
-    (where datetime objects are serialized as ISO strings).
-
-    Args:
-        value: Either a datetime object or an ISO format string
-
-    Returns:
-        datetime object (timezone-aware)
-
-    Raises:
-        TypeError: If value is neither datetime nor string
-        ValueError: If string is not a valid ISO datetime
-    """
-    if isinstance(value, datetime):
-        # Ensure timezone-aware
-        if value.tzinfo is None:
-            return value.replace(tzinfo=UTC)
-        return value
-    elif isinstance(value, str):
-        # Parse ISO format string (handles both 'Z' and '+00:00' timezone formats)
-        dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
-        # Ensure timezone-aware
-        if dt.tzinfo is None:
-            return dt.replace(tzinfo=UTC)
-        return dt
-    else:
-        raise TypeError(f"Expected datetime or string, got {type(value).__name__}")
-
-
 from ..response_models import TokenUsage
 from . import (
    chunk_storage,
@@ -76,7 +42,6 @@ async def retain_batch(
    duplicate_checker_fn,
    bank_id: str,
    contents_dicts: list[RetainContentDict],
-    config,
    document_id: str | None = None,
    is_first_batch: bool = True,
    fact_type_override: str | None = None,
@@ -95,7 +60,6 @@ async def retain_batch(
        duplicate_checker_fn: Function to check for duplicate facts
        bank_id: Bank identifier
        contents_dicts: List of content dictionaries
-        config: Resolved HindsightConfig for this bank
        document_id: Optional document ID
        is_first_batch: Whether this is the first batch
        fact_type_override: Override fact type for all facts
@@ -125,18 +89,10 @@ async def retain_batch(
        # Merge item-level tags with document-level tags
        item_tags = item.get("tags", []) or []
        merged_tags = list(set(item_tags + (document_tags or [])))
-
-        # Handle event_date: parse flexibly (handles both datetime objects and ISO strings)
-        event_date_value = item.get("event_date")
-        if event_date_value:
-            event_date_value = parse_datetime_flexible(event_date_value)
-        else:
-            event_date_value = utcnow()
-
        content = RetainContent(
            content=item["content"],
            context=item.get("context", ""),
-            event_date=event_date_value,
+            event_date=item.get("event_date") or utcnow(),
            metadata=item.get("metadata", {}),
            entities=item.get("entities", []),
            tags=merged_tags,
@@ -145,9 +101,10 @@ async def retain_batch(

    # Step 1: Extract facts from all contents
    step_start = time.time()
+    extract_opinions = fact_type_override == "opinion"

    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(
-        contents, llm_config, agent_name, config
+        contents, llm_config, agent_name, extract_opinions
    )
    log_buffer.append(
        f"[1] Extract facts: {len(extracted_facts)} facts, {len(chunks)} chunks from {len(contents)} contents in {time.time() - step_start:.3f}s"
@@ -162,13 +119,6 @@ async def retain_batch(
                # Handle document tracking even with no facts
                if document_id:
                    combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
-                    # Collect tags from all content items and merge with document_tags
-                    all_tags = set(document_tags or [])
-                    for item in contents_dicts:
-                        item_tags = item.get("tags", []) or []
-                        all_tags.update(item_tags)
-                    merged_tags = list(all_tags)
-
                    retain_params = {}
                    if contents_dicts:
                        first_item = contents_dicts[0]
@@ -183,7 +133,7 @@ async def retain_batch(
                        if first_item.get("metadata"):
                            retain_params["metadata"] = first_item["metadata"]
                    await fact_storage.handle_document_tracking(
-                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
+                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params, document_tags
                    )
                else:
                    # Check for per-item document_ids
@@ -197,13 +147,6 @@ async def retain_batch(

                    for doc_id, doc_contents in contents_by_doc.items():
                        combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
-                        # Collect tags from all content items for this document and merge with document_tags
-                        all_tags = set(document_tags or [])
-                        for _, item in doc_contents:
-                            item_tags = item.get("tags", []) or []
-                            all_tags.update(item_tags)
-                        merged_tags = list(all_tags)
-
                        retain_params = {}
                        if doc_contents:
                            first_item = doc_contents[0][1]
@@ -218,7 +161,7 @@ async def retain_batch(
                            if first_item.get("metadata"):
                                retain_params["metadata"] = first_item["metadata"]
                        await fact_storage.handle_document_tracking(
-                            conn, bank_id, doc_id, combined_content, is_first_batch, retain_params, merged_tags
+                            conn, bank_id, doc_id, combined_content, is_first_batch, retain_params, document_tags
                        )

        total_time = time.time() - start_time
@@ -270,13 +213,6 @@ async def retain_batch(
                # Legacy: single document_id parameter
                combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
                retain_params = {}
-                # Collect tags from all content items and merge with document_tags
-                all_tags = set(document_tags or [])
-                for item in contents_dicts:
-                    item_tags = item.get("tags", []) or []
-                    all_tags.update(item_tags)
-                merged_tags = list(all_tags)
-
                if contents_dicts:
                    first_item = contents_dicts[0]
                    if first_item.get("context"):
@@ -291,7 +227,7 @@ async def retain_batch(
                        retain_params["metadata"] = first_item["metadata"]

                await fact_storage.handle_document_tracking(
-                    conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
+                    conn, bank_id, document_id, combined_content, is_first_batch, retain_params, document_tags
                )
                document_ids_added.append(document_id)
                doc_id_mapping[None] = document_id  # For backwards compatibility
@@ -319,13 +255,6 @@ async def retain_batch(
                            # Combine content for this document
                            combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])

-                            # Collect tags from all content items for this document and merge with document_tags
-                            all_tags = set(document_tags or [])
-                            for _, item in doc_contents:
-                                item_tags = item.get("tags", []) or []
-                                all_tags.update(item_tags)
-                            merged_tags = list(all_tags)
-
                            # Extract retain params from first content item
                            retain_params = {}
                            if doc_contents:
@@ -348,7 +277,7 @@ async def retain_batch(
                                combined_content,
                                is_first_batch,
                                retain_params,
-                                merged_tags,
+                                document_tags,
                            )
                            document_ids_added.append(actual_doc_id)

@@ -13,10 +13,12 @@ from .reranking import CrossEncoderReranker
 from .retrieval import (
    ParallelRetrievalResult,
    get_default_graph_retriever,
+    retrieve_parallel,
    set_default_graph_retriever,
 )

 __all__ = [
+    "retrieve_parallel",
    "get_default_graph_retriever",
    "set_default_graph_retriever",
    "ParallelRetrievalResult",
@@ -155,6 +155,7 @@ class LinkExpansionRetriever(GraphRetriever):
                all_seeds.extend(temporal_seeds)

            if not all_seeds:
+                logger.info("[LinkExpansion] No seeds found, returning empty results")
                return [], timings

            seed_ids = list({s.id for s in all_seeds})
@@ -85,6 +85,116 @@ def set_default_graph_retriever(retriever: GraphRetriever) -> None:
    _default_graph_retriever = retriever


+async def retrieve_semantic(
+    conn,
+    query_emb_str: str,
+    bank_id: str,
+    fact_type: str,
+    limit: int,
+    tags: list[str] | None = None,
+) -> list[RetrievalResult]:
+    """
+    Semantic retrieval via vector similarity.
+
+    Args:
+        conn: Database connection
+        query_emb_str: Query embedding as string
+        agent_id: bank ID
+        fact_type: Fact type to filter
+        limit: Maximum results to return
+        tags: Optional list of tags for visibility filtering (OR matching)
+
+    Returns:
+        List of RetrievalResult objects
+    """
+    from .tags import TagsMatch, build_tags_where_clause_simple
+
+    tags_clause = build_tags_where_clause_simple(tags, 5)
+    params = [query_emb_str, bank_id, fact_type, limit]
+    if tags:
+        params.append(tags)
+
+    results = await conn.fetch(
+        f"""
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+               1 - (embedding <=> $1::vector) AS similarity
+        FROM {fq_table("memory_units")}
+        WHERE bank_id = $2
+          AND embedding IS NOT NULL
+          AND fact_type = $3
+          AND (1 - (embedding <=> $1::vector)) >= 0.3
+          {tags_clause}
+        ORDER BY embedding <=> $1::vector
+        LIMIT $4
+        """,
+        *params,
+    )
+    return [RetrievalResult.from_db_row(dict(r)) for r in results]
+
+
+async def retrieve_bm25(
+    conn,
+    query_text: str,
+    bank_id: str,
+    fact_type: str,
+    limit: int,
+    tags: list[str] | None = None,
+) -> list[RetrievalResult]:
+    """
+    BM25 keyword retrieval via full-text search.
+
+    Args:
+        conn: Database connection
+        query_text: Query text
+        agent_id: bank ID
+        fact_type: Fact type to filter
+        limit: Maximum results to return
+        tags: Optional list of tags for visibility filtering (OR matching)
+
+    Returns:
+        List of RetrievalResult objects
+    """
+    import re
+
+    from .tags import TagsMatch, build_tags_where_clause_simple
+
+    # Sanitize query text: remove special characters that have meaning in tsquery
+    # Keep only alphanumeric characters and spaces
+    sanitized_text = re.sub(r"[^\w\s]", " ", query_text.lower())
+
+    # Split and filter empty strings
+    tokens = [token for token in sanitized_text.split() if token]
+
+    if not tokens:
+        # If no valid tokens, return empty results
+        return []
+
+    # Convert query to tsquery using OR for more flexible matching
+    # This prevents empty results when some terms are missing
+    query_tsquery = " | ".join(tokens)
+
+    tags_clause = build_tags_where_clause_simple(tags, 5)
+    params = [query_tsquery, bank_id, fact_type, limit]
+    if tags:
+        params.append(tags)
+
+    results = await conn.fetch(
+        f"""
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+               ts_rank_cd(search_vector, to_tsquery('english', $1)) AS bm25_score
+        FROM {fq_table("memory_units")}
+        WHERE bank_id = $2
+          AND fact_type = $3
+          AND search_vector @@ to_tsquery('english', $1)
+          {tags_clause}
+        ORDER BY bm25_score DESC
+        LIMIT $4
+        """,
+        *params,
+    )
+    return [RetrievalResult.from_db_row(dict(r)) for r in results]
+
+
 async def retrieve_semantic_bm25_combined(
    conn,
    query_emb_str: str,
@@ -158,41 +268,18 @@ async def retrieve_semantic_bm25_combined(
                result_dict[ft][0].append(RetrievalResult.from_db_row(row))
        return result_dict

-    # Build BM25 query based on text search backend
-    config = get_config()
+    query_tsquery = " | ".join(tokens)

    # Build tags clause - param 6 if tags provided
    tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
-
-    # Build backend-specific BM25 parts
-    if config.text_search_extension == "vchord":
-        # VectorChord BM25: use <&> operator with to_bm25query and tokenize
-        # Note: VectorChord scores are negative (higher = better, so -1 > -10)
-        bm25_score_expr = "search_vector <&> to_bm25query('idx_memory_units_text_search', tokenize($5, 'llmlingua2'))"
-        bm25_order_by = f"{bm25_score_expr} DESC"
-        bm25_where_filter = ""  # No additional WHERE filter for vchord
-        params = [query_emb_str, bank_id, fact_types, limit, query_text]  # Pass raw query_text for tokenization
-    elif config.text_search_extension == "pg_textsearch":
-        # Timescale pg_textsearch: use <@> operator with to_bm25query
-        # Note: pg_textsearch scores are negative (lower/more negative = better, so -10 > -1)
-        # We negate the score to maintain API consistency (higher = better)
-        bm25_score_expr = "-(text <@> to_bm25query($5, 'idx_memory_units_text_search'))"
-        bm25_order_by = "text <@> to_bm25query($5, 'idx_memory_units_text_search') ASC"
-        bm25_where_filter = ""  # No additional WHERE filter for pg_textsearch
-        params = [query_emb_str, bank_id, fact_types, limit, query_text]
-    else:  # native
-        # Native PostgreSQL: use ts_rank_cd with to_tsquery
-        query_tsquery = " | ".join(tokens)
-        bm25_score_expr = "ts_rank_cd(search_vector, to_tsquery('english', $5))"
-        bm25_order_by = f"{bm25_score_expr} DESC"
-        bm25_where_filter = "AND search_vector @@ to_tsquery('english', $5)"
-        params = [query_emb_str, bank_id, fact_types, limit, query_tsquery]
-
+    params = [query_emb_str, bank_id, fact_types, limit, query_tsquery]
    if tags:
        params.append(tags)

-    # Single query template with backend-specific parts injected
-    query = f"""
+    # Combined CTE query for both semantic and BM25 across all fact types
+    # Uses window functions to limit per fact_type per method
+    results = await conn.fetch(
+        f"""
        WITH semantic_ranked AS (
            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
                   1 - (embedding <=> $1::vector) AS similarity,
@@ -209,13 +296,13 @@ async def retrieve_semantic_bm25_combined(
        bm25_ranked AS (
            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
                   NULL::float AS similarity,
-                   {bm25_score_expr} AS bm25_score,
+                   ts_rank_cd(search_vector, to_tsquery('english', $5)) AS bm25_score,
                   'bm25' AS source,
-                   ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY {bm25_order_by}) AS rn
+                   ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY ts_rank_cd(search_vector, to_tsquery('english', $5)) DESC) AS rn
            FROM {fq_table("memory_units")}
            WHERE bank_id = $2
              AND fact_type = ANY($3)
-              {bm25_where_filter}
+              AND search_vector @@ to_tsquery('english', $5)
              {tags_clause}
        ),
        semantic AS (
@@ -231,11 +318,9 @@ async def retrieve_semantic_bm25_combined(
        SELECT * FROM semantic
        UNION ALL
        SELECT * FROM bm25
-    """
-
-    # Combined CTE query for both semantic and BM25 across all fact types
-    # Uses window functions to limit per fact_type per method
-    results = await conn.fetch(query, *params)
+        """,
+        *params,
+    )

    # Group results by fact_type and source
    result_dict: dict[str, tuple[list[RetrievalResult], list[RetrievalResult]]] = {ft: ([], []) for ft in fact_types}
@@ -476,6 +561,623 @@ async def retrieve_temporal_combined(
    return results_by_ft


+async def retrieve_temporal(
+    conn,
+    query_emb_str: str,
+    bank_id: str,
+    fact_type: str,
+    start_date: datetime,
+    end_date: datetime,
+    budget: int,
+    semantic_threshold: float = 0.1,
+    tags: list[str] | None = None,
+) -> list[RetrievalResult]:
+    """
+    Temporal retrieval with spreading activation.
+
+    Strategy:
+    1. Find entry points (facts in date range with semantic relevance)
+    2. Spread through temporal links to related facts
+    3. Score by temporal proximity + semantic similarity + link weight
+
+    Args:
+        conn: Database connection
+        query_emb_str: Query embedding as string
+        agent_id: bank ID
+        fact_type: Fact type to filter
+        start_date: Start of time range
+        end_date: End of time range
+        budget: Node budget for spreading
+        semantic_threshold: Minimum semantic similarity to include
+        tags: Optional list of tags for visibility filtering (OR matching)
+
+    Returns:
+        List of RetrievalResult objects with temporal scores
+    """
+
+    # Ensure start_date and end_date are timezone-aware (UTC) to match database datetimes
+    if start_date.tzinfo is None:
+        start_date = start_date.replace(tzinfo=UTC)
+    if end_date.tzinfo is None:
+        end_date = end_date.replace(tzinfo=UTC)
+
+    from .tags import TagsMatch, build_tags_where_clause_simple
+
+    tags_clause = build_tags_where_clause_simple(tags, 7)
+    params = [query_emb_str, bank_id, fact_type, start_date, end_date, semantic_threshold]
+    if tags:
+        params.append(tags)
+
+    entry_points = await conn.fetch(
+        f"""
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+               1 - (embedding <=> $1::vector) AS similarity
+        FROM {fq_table("memory_units")}
+        WHERE bank_id = $2
+          AND fact_type = $3
+          AND embedding IS NOT NULL
+          AND (
+              -- Match if occurred range overlaps with query range
+              (occurred_start IS NOT NULL AND occurred_end IS NOT NULL
+               AND occurred_start <= $5 AND occurred_end >= $4)
+              OR
+              -- Match if mentioned_at falls within query range
+              (mentioned_at IS NOT NULL AND mentioned_at BETWEEN $4 AND $5)
+              OR
+              -- Match if any occurred date is set and overlaps (even if only start or end is set)
+              (occurred_start IS NOT NULL AND occurred_start BETWEEN $4 AND $5)
+              OR
+              (occurred_end IS NOT NULL AND occurred_end BETWEEN $4 AND $5)
+          )
+          AND (1 - (embedding <=> $1::vector)) >= $6
+          {tags_clause}
+        ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC, (embedding <=> $1::vector) ASC
+        LIMIT 10
+        """,
+        *params,
+    )
+
+    if not entry_points:
+        return []
+
+    # Calculate temporal scores for entry points
+    total_days = (end_date - start_date).total_seconds() / 86400
+    mid_date = start_date + (end_date - start_date) / 2  # Calculate once for all comparisons
+    results = []
+    visited = set()
+
+    for ep in entry_points:
+        unit_id = str(ep["id"])
+        visited.add(unit_id)
+
+        # Calculate temporal proximity using the most relevant date
+        # Priority: occurred_start/end (event time) > mentioned_at (mention time)
+        best_date = None
+        if ep["occurred_start"] is not None and ep["occurred_end"] is not None:
+            # Use midpoint of occurred range
+            best_date = ep["occurred_start"] + (ep["occurred_end"] - ep["occurred_start"]) / 2
+        elif ep["occurred_start"] is not None:
+            best_date = ep["occurred_start"]
+        elif ep["occurred_end"] is not None:
+            best_date = ep["occurred_end"]
+        elif ep["mentioned_at"] is not None:
+            best_date = ep["mentioned_at"]
+
+        # Temporal proximity score (closer to range center = higher score)
+        if best_date:
+            days_from_mid = abs((best_date - mid_date).total_seconds() / 86400)
+            temporal_proximity = 1.0 - min(days_from_mid / (total_days / 2), 1.0) if total_days > 0 else 1.0
+        else:
+            temporal_proximity = 0.5  # Fallback if no dates (shouldn't happen due to WHERE clause)
+
+        # Create RetrievalResult with temporal scores
+        ep_result = RetrievalResult.from_db_row(dict(ep))
+        ep_result.temporal_score = temporal_proximity
+        ep_result.temporal_proximity = temporal_proximity
+        results.append(ep_result)
+
+    # Spread through temporal links using BATCHED neighbor fetching
+    # Map node_id -> (semantic_sim, temporal_score) for propagation
+    node_scores = {str(ep["id"]): (ep["similarity"], 1.0) for ep in entry_points}
+    frontier = list(node_scores.keys())  # Current batch of nodes to expand
+    budget_remaining = budget - len(entry_points)
+    batch_size = 20  # Process this many nodes per DB query
+
+    while frontier and budget_remaining > 0:
+        # Take a batch from frontier
+        batch_ids = frontier[:batch_size]
+        frontier = frontier[batch_size:]
+
+        # Batch fetch all neighbors for this batch of nodes
+        neighbors = await conn.fetch(
+            f"""
+            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id,
+                   ml.weight, ml.link_type, ml.from_unit_id,
+                   1 - (mu.embedding <=> $1::vector) AS similarity
+            FROM {fq_table("memory_links")} ml
+            JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
+            WHERE ml.from_unit_id = ANY($2::uuid[])
+              AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
+              AND ml.weight >= 0.1
+              AND mu.fact_type = $3
+              AND mu.embedding IS NOT NULL
+              AND (1 - (mu.embedding <=> $1::vector)) >= $4
+            ORDER BY ml.weight DESC
+            LIMIT $5
+            """,
+            query_emb_str,
+            batch_ids,
+            fact_type,
+            semantic_threshold,
+            batch_size * 10,  # Allow up to 10 neighbors per node in batch
+        )
+
+        for n in neighbors:
+            neighbor_id = str(n["id"])
+            if neighbor_id in visited:
+                continue
+
+            visited.add(neighbor_id)
+            budget_remaining -= 1
+
+            # Get parent's scores for propagation
+            parent_id = str(n["from_unit_id"])
+            _, parent_temporal_score = node_scores.get(parent_id, (0.5, 0.5))
+
+            # Calculate temporal score for neighbor using best available date
+            neighbor_best_date = None
+            if n["occurred_start"] is not None and n["occurred_end"] is not None:
+                neighbor_best_date = n["occurred_start"] + (n["occurred_end"] - n["occurred_start"]) / 2
+            elif n["occurred_start"] is not None:
+                neighbor_best_date = n["occurred_start"]
+            elif n["occurred_end"] is not None:
+                neighbor_best_date = n["occurred_end"]
+            elif n["mentioned_at"] is not None:
+                neighbor_best_date = n["mentioned_at"]
+
+            if neighbor_best_date:
+                days_from_mid = abs((neighbor_best_date - mid_date).total_seconds() / 86400)
+                neighbor_temporal_proximity = (
+                    1.0 - min(days_from_mid / (total_days / 2), 1.0) if total_days > 0 else 1.0
+                )
+            else:
+                neighbor_temporal_proximity = 0.3  # Lower score if no temporal data
+
+            # Boost causal links (same as graph retrieval)
+            link_type = n["link_type"]
+            if link_type in ("causes", "caused_by"):
+                causal_boost = 2.0
+            elif link_type in ("enables", "prevents"):
+                causal_boost = 1.5
+            else:
+                causal_boost = 1.0
+
+            # Propagate temporal score through links (decay, with causal boost)
+            propagated_temporal = parent_temporal_score * n["weight"] * causal_boost * 0.7
+
+            # Combined temporal score
+            combined_temporal = max(neighbor_temporal_proximity, propagated_temporal)
+
+            # Create RetrievalResult with temporal scores
+            neighbor_result = RetrievalResult.from_db_row(dict(n))
+            neighbor_result.temporal_score = combined_temporal
+            neighbor_result.temporal_proximity = neighbor_temporal_proximity
+            results.append(neighbor_result)
+
+            # Track scores for propagation and add to frontier
+            if budget_remaining > 0 and combined_temporal > 0.2:
+                node_scores[neighbor_id] = (n["similarity"], combined_temporal)
+                frontier.append(neighbor_id)
+
+            if budget_remaining <= 0:
+                break
+
+    return results
+
+
+async def retrieve_parallel(
+    pool,
+    query_text: str,
+    query_embedding_str: str,
+    bank_id: str,
+    fact_type: str,
+    thinking_budget: int,
+    question_date: datetime | None = None,
+    query_analyzer: Optional["QueryAnalyzer"] = None,
+    graph_retriever: GraphRetriever | None = None,
+    temporal_constraint: tuple | None = None,  # Pre-extracted temporal constraint
+    tags: list[str] | None = None,  # Visibility scope tags for filtering
+) -> ParallelRetrievalResult:
+    """
+    Run 3-way or 4-way parallel retrieval (adds temporal if detected).
+
+    Args:
+        pool: Database connection pool
+        query_text: Query text
+        query_embedding_str: Query embedding as string
+        bank_id: Bank ID
+        fact_type: Fact type to filter
+        thinking_budget: Budget for graph traversal and retrieval limits
+        question_date: Optional date when question was asked (for temporal filtering)
+        query_analyzer: Query analyzer to use (defaults to TransformerQueryAnalyzer)
+        graph_retriever: Graph retrieval strategy (defaults to configured retriever)
+        temporal_constraint: Pre-extracted temporal constraint (optional)
+        tags: Optional list of tags for visibility filtering (OR matching)
+
+    Returns:
+        ParallelRetrievalResult with semantic, bm25, graph, temporal results and timings
+    """
+    retriever = graph_retriever or get_default_graph_retriever()
+
+    # Use optimized parallel path for MPFP and LinkExpansion (runs all methods truly in parallel)
+    # BFS uses legacy path that extracts temporal constraint upfront
+    if retriever.name in ("mpfp", "link_expansion"):
+        return await _retrieve_parallel_mpfp(
+            pool,
+            query_text,
+            query_embedding_str,
+            bank_id,
+            fact_type,
+            thinking_budget,
+            temporal_constraint,
+            retriever,
+            question_date,
+            query_analyzer,
+            tags=tags,
+        )
+    else:
+        # For BFS, extract temporal constraint upfront (legacy path)
+        if temporal_constraint is None:
+            from .temporal_extraction import extract_temporal_constraint
+
+            temporal_constraint = extract_temporal_constraint(
+                query_text, reference_date=question_date, analyzer=query_analyzer
+            )
+        return await _retrieve_parallel_bfs(
+            pool,
+            query_text,
+            query_embedding_str,
+            bank_id,
+            fact_type,
+            thinking_budget,
+            temporal_constraint,
+            retriever,
+            tags=tags,
+        )
+
+
+@dataclass
+class _TimedResult:
+    """Internal result with timing."""
+
+    results: list[RetrievalResult]
+    time: float
+    conn_wait: float = 0.0  # Connection acquisition wait time
+
+
+async def _retrieve_parallel_mpfp(
+    pool,
+    query_text: str,
+    query_embedding_str: str,
+    bank_id: str,
+    fact_type: str,
+    thinking_budget: int,
+    temporal_constraint: tuple | None,
+    retriever: GraphRetriever,
+    question_date: datetime | None = None,
+    query_analyzer=None,
+    tags: list[str] | None = None,
+) -> ParallelRetrievalResult:
+    """
+    MPFP retrieval with true parallelization.
+
+    All methods run independently in parallel:
+    - Semantic: vector similarity search
+    - BM25: keyword search
+    - Graph: MPFP traversal (does its own semantic seeds internally)
+    - Temporal: date extraction (if needed) + date-range search
+
+    Temporal extraction runs IN PARALLEL with other retrievals, so even if
+    dateparser is slow, it doesn't block semantic/BM25/graph.
+    """
+    import time
+
+    async def run_semantic() -> _TimedResult:
+        """Independent semantic retrieval."""
+        start = time.time()
+        acquire_start = time.time()
+        async with acquire_with_retry(pool) as conn:
+            conn_wait = time.time() - acquire_start
+            results = await retrieve_semantic(
+                conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget, tags=tags
+            )
+        return _TimedResult(results, time.time() - start, conn_wait)
+
+    async def run_bm25() -> _TimedResult:
+        """Independent BM25 retrieval."""
+        start = time.time()
+        acquire_start = time.time()
+        async with acquire_with_retry(pool) as conn:
+            conn_wait = time.time() - acquire_start
+            results = await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget, tags=tags)
+        return _TimedResult(results, time.time() - start, conn_wait)
+
+    async def run_graph() -> tuple[list[RetrievalResult], float, MPFPTimings | None]:
+        """Independent graph retrieval - does its own semantic seeds."""
+        start = time.time()
+
+        # MPFP does its own semantic seeds via _find_semantic_seeds
+        # Note: temporal_seeds not used here to avoid dependency on temporal extraction
+        results, mpfp_timing = await retriever.retrieve(
+            pool=pool,
+            query_embedding_str=query_embedding_str,
+            bank_id=bank_id,
+            fact_type=fact_type,
+            budget=thinking_budget,
+            query_text=query_text,
+            semantic_seeds=None,  # Let MPFP find its own seeds
+            temporal_seeds=None,  # Don't wait for temporal extraction
+            tags=tags,
+        )
+        return results, time.time() - start, mpfp_timing
+
+    @dataclass
+    class _TemporalWithConstraint:
+        """Temporal results with the extracted constraint."""
+
+        results: list[RetrievalResult]
+        time: float
+        constraint: tuple | None
+        extraction_time: float  # Time spent in query analyzer (dateparser)
+        conn_wait: float = 0.0  # Connection acquisition wait time
+
+    async def run_temporal_with_extraction() -> _TemporalWithConstraint:
+        """
+        Extract temporal constraint AND run temporal retrieval.
+
+        This runs in parallel with semantic/BM25/graph, so dateparser
+        latency doesn't block other retrievals.
+        """
+        start = time.time()
+
+        # Use pre-provided constraint if available
+        tc = temporal_constraint
+        extraction_time = 0.0
+
+        # Otherwise extract from query (this is the potentially slow dateparser call)
+        if tc is None:
+            from .temporal_extraction import extract_temporal_constraint
+
+            extraction_start = time.time()
+            tc = extract_temporal_constraint(query_text, reference_date=question_date, analyzer=query_analyzer)
+            extraction_time = time.time() - extraction_start
+
+        # If no temporal constraint found, return empty (but still report extraction time)
+        if tc is None:
+            return _TemporalWithConstraint([], time.time() - start, None, extraction_time, 0.0)
+
+        # Run temporal retrieval with the extracted constraint
+        tc_start, tc_end = tc
+        acquire_start = time.time()
+        async with acquire_with_retry(pool) as conn:
+            conn_wait = time.time() - acquire_start
+            results = await retrieve_temporal(
+                conn,
+                query_embedding_str,
+                bank_id,
+                fact_type,
+                tc_start,
+                tc_end,
+                budget=thinking_budget,
+                semantic_threshold=0.1,
+            )
+        return _TemporalWithConstraint(results, time.time() - start, tc, extraction_time, conn_wait)
+
+    # Run ALL methods in parallel (including temporal extraction!)
+    semantic_result, bm25_result, graph_result, temporal_result = await asyncio.gather(
+        run_semantic(),
+        run_bm25(),
+        run_graph(),
+        run_temporal_with_extraction(),
+    )
+    graph_results, graph_time, mpfp_timing = graph_result
+
+    # Compute max connection wait across all methods (graph handles its own connections)
+    max_conn_wait = max(semantic_result.conn_wait, bm25_result.conn_wait, temporal_result.conn_wait)
+
+    return ParallelRetrievalResult(
+        semantic=semantic_result.results,
+        bm25=bm25_result.results,
+        graph=graph_results,
+        temporal=temporal_result.results if temporal_result.results else None,
+        timings={
+            "semantic": semantic_result.time,
+            "bm25": bm25_result.time,
+            "graph": graph_time,
+            "temporal": temporal_result.time,
+            "temporal_extraction": temporal_result.extraction_time,
+        },
+        temporal_constraint=temporal_result.constraint,
+        mpfp_timings=[mpfp_timing] if mpfp_timing else [],
+        max_conn_wait=max_conn_wait,
+    )
+
+
+async def _get_temporal_entry_points(
+    conn,
+    query_embedding_str: str,
+    bank_id: str,
+    fact_type: str,
+    start_date: datetime,
+    end_date: datetime,
+    limit: int = 20,
+    semantic_threshold: float = 0.1,
+) -> list[RetrievalResult]:
+    """Get temporal entry points (facts in date range with semantic relevance)."""
+
+    if start_date.tzinfo is None:
+        start_date = start_date.replace(tzinfo=UTC)
+    if end_date.tzinfo is None:
+        end_date = end_date.replace(tzinfo=UTC)
+
+    rows = await conn.fetch(
+        f"""
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at,
+               embedding, fact_type, document_id, chunk_id,
+               1 - (embedding <=> $1::vector) AS similarity
+        FROM {fq_table("memory_units")}
+        WHERE bank_id = $2
+          AND fact_type = $3
+          AND embedding IS NOT NULL
+          AND (
+              (occurred_start IS NOT NULL AND occurred_end IS NOT NULL
+               AND occurred_start <= $5 AND occurred_end >= $4)
+              OR (mentioned_at IS NOT NULL AND mentioned_at BETWEEN $4 AND $5)
+              OR (occurred_start IS NOT NULL AND occurred_start BETWEEN $4 AND $5)
+              OR (occurred_end IS NOT NULL AND occurred_end BETWEEN $4 AND $5)
+          )
+          AND (1 - (embedding <=> $1::vector)) >= $6
+        ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC,
+                 (embedding <=> $1::vector) ASC
+        LIMIT $7
+        """,
+        query_embedding_str,
+        bank_id,
+        fact_type,
+        start_date,
+        end_date,
+        semantic_threshold,
+        limit,
+    )
+
+    results = []
+    total_days = max((end_date - start_date).total_seconds() / 86400, 1)
+    mid_date = start_date + (end_date - start_date) / 2
+
+    for row in rows:
+        result = RetrievalResult.from_db_row(dict(row))
+
+        # Calculate temporal proximity score
+        best_date = None
+        if row["occurred_start"] and row["occurred_end"]:
+            best_date = row["occurred_start"] + (row["occurred_end"] - row["occurred_start"]) / 2
+        elif row["occurred_start"]:
+            best_date = row["occurred_start"]
+        elif row["occurred_end"]:
+            best_date = row["occurred_end"]
+        elif row["mentioned_at"]:
+            best_date = row["mentioned_at"]
+
+        if best_date:
+            days_from_mid = abs((best_date - mid_date).total_seconds() / 86400)
+            result.temporal_proximity = 1.0 - min(days_from_mid / (total_days / 2), 1.0)
+        else:
+            result.temporal_proximity = 0.5
+
+        result.temporal_score = result.temporal_proximity
+        results.append(result)
+
+    return results
+
+
+async def _retrieve_parallel_bfs(
+    pool,
+    query_text: str,
+    query_embedding_str: str,
+    bank_id: str,
+    fact_type: str,
+    thinking_budget: int,
+    temporal_constraint: tuple | None,
+    retriever: GraphRetriever,
+    tags: list[str] | None = None,
+) -> ParallelRetrievalResult:
+    """BFS retrieval: all methods run in parallel (original behavior)."""
+    import time
+
+    async def run_semantic() -> _TimedResult:
+        start = time.time()
+        async with acquire_with_retry(pool) as conn:
+            results = await retrieve_semantic(
+                conn, query_embedding_str, bank_id, fact_type, limit=thinking_budget, tags=tags
+            )
+        return _TimedResult(results, time.time() - start)
+
+    async def run_bm25() -> _TimedResult:
+        start = time.time()
+        async with acquire_with_retry(pool) as conn:
+            results = await retrieve_bm25(conn, query_text, bank_id, fact_type, limit=thinking_budget, tags=tags)
+        return _TimedResult(results, time.time() - start)
+
+    async def run_graph() -> _TimedResult:
+        start = time.time()
+        results, _ = await retriever.retrieve(
+            pool=pool,
+            query_embedding_str=query_embedding_str,
+            bank_id=bank_id,
+            fact_type=fact_type,
+            budget=thinking_budget,
+            query_text=query_text,
+            tags=tags,
+        )
+        return _TimedResult(results, time.time() - start)
+
+    async def run_temporal(tc_start, tc_end) -> _TimedResult:
+        start = time.time()
+        async with acquire_with_retry(pool) as conn:
+            results = await retrieve_temporal(
+                conn,
+                query_embedding_str,
+                bank_id,
+                fact_type,
+                tc_start,
+                tc_end,
+                budget=thinking_budget,
+                semantic_threshold=0.1,
+                tags=tags,
+            )
+        return _TimedResult(results, time.time() - start)
+
+    if temporal_constraint:
+        tc_start, tc_end = temporal_constraint
+        semantic_r, bm25_r, graph_r, temporal_r = await asyncio.gather(
+            run_semantic(),
+            run_bm25(),
+            run_graph(),
+            run_temporal(tc_start, tc_end),
+        )
+        return ParallelRetrievalResult(
+            semantic=semantic_r.results,
+            bm25=bm25_r.results,
+            graph=graph_r.results,
+            temporal=temporal_r.results,
+            timings={
+                "semantic": semantic_r.time,
+                "bm25": bm25_r.time,
+                "graph": graph_r.time,
+                "temporal": temporal_r.time,
+            },
+            temporal_constraint=temporal_constraint,
+        )
+    else:
+        semantic_r, bm25_r, graph_r = await asyncio.gather(
+            run_semantic(),
+            run_bm25(),
+            run_graph(),
+        )
+        return ParallelRetrievalResult(
+            semantic=semantic_r.results,
+            bm25=bm25_r.results,
+            graph=graph_r.results,
+            temporal=None,
+            timings={
+                "semantic": semantic_r.time,
+                "bm25": bm25_r.time,
+                "graph": graph_r.time,
+            },
+            temporal_constraint=None,
+        )
+
+
 async def retrieve_all_fact_types_parallel(
    pool,
    query_text: str,
@@ -188,7 +188,7 @@ def get_system_message(disposition: DispositionTraits) -> str:
        " ".join(instructions) if instructions else "Balance your disposition traits when interpreting information."
    )

-    return f"You are a person with your own thoughts, experiences, opinions, and disposition. When asked a question, respond naturally like a human would - share what you genuinely think based on what you know and have experienced. {disposition_instruction} Be direct, express your views confidently, and use 'I think', 'I believe', 'in my view', etc. Respond in plain text without markdown formatting. CRITICAL: ONLY use the facts and information provided in the prompt - do not make up names, events, or information that weren't mentioned. If you don't have enough information to answer, say so. IMPORTANT: Detect the language of the question and respond in the SAME language. Do not translate to English if the question is in another language."
+    return f"You are a person with your own thoughts, experiences, opinions, and disposition. When asked a question, respond naturally like a human would - share what you genuinely think based on what you know and have experienced. {disposition_instruction} Be direct, express your views confidently, and use 'I think', 'I believe', 'in my view', etc. Respond in plain text without markdown formatting. IMPORTANT: Detect the language of the question and respond in the SAME language. Do not translate to English if the question is in another language."


 async def reflect(
@@ -182,16 +182,7 @@ class BrokerTaskBackend(TaskBackend):
        operation_id = task_dict.get("operation_id")
        task_type = task_dict.get("type", "unknown")
        bank_id = task_dict.get("bank_id")
-
-        # Custom encoder to handle datetime objects
-        from datetime import datetime
-
-        def datetime_encoder(obj):
-            if isinstance(obj, datetime):
-                return obj.isoformat()
-            raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable")
-
-        payload_json = json.dumps(task_dict, default=datetime_encoder)
+        payload_json = json.dumps(task_dict)

        schema = self._schema_getter() if self._schema_getter else self._schema
        table = fq_table("async_operations", schema)
@@ -19,7 +19,7 @@ async def extract_facts(
    context: str = "",
    llm_config: "LLMConfig" = None,
    agent_name: str = None,
-    config=None,
+    extract_opinions: bool = False,
 ) -> tuple[list["Fact"], list[tuple[str, int]]]:
    """
    Extract semantic facts from text using LLM.
@@ -36,7 +36,7 @@ async def extract_facts(
        context: Context about the conversation/document
        llm_config: LLM configuration to use
        agent_name: Optional agent name to help identify agent-related facts
-        config: HindsightConfig to use (defaults to global config if not provided)
+        extract_opinions: If True, extract ONLY opinions. If False, extract world and agent facts (no opinions)

    Returns:
        Tuple of (facts, chunks) where:
@@ -49,19 +49,13 @@ async def extract_facts(
    if not text or not text.strip():
        return [], []

-    # Use provided config or fall back to global config
-    if config is None:
-        from ..config import _get_raw_config
-
-        config = _get_raw_config()
-
    facts, chunks, _ = await extract_facts_from_text(
        text,
        event_date,
+        context=context,
        llm_config=llm_config,
        agent_name=agent_name,
-        config=config,
-        context=context,
+        extract_opinions=extract_opinions,
    )

    if not facts:
@@ -16,20 +16,14 @@ with the system (e.g., running migrations for tenant schemas).
 """

 from hindsight_api.extensions.base import Extension
-from hindsight_api.extensions.builtin import ApiKeyTenantExtension, SupabaseTenantExtension
+from hindsight_api.extensions.builtin import ApiKeyTenantExtension
 from hindsight_api.extensions.context import DefaultExtensionContext, ExtensionContext
 from hindsight_api.extensions.http import HttpExtension
 from hindsight_api.extensions.loader import load_extension
-from hindsight_api.extensions.mcp import MCPExtension
 from hindsight_api.extensions.operation_validator import (
    # Consolidation operation
    ConsolidateContext,
    ConsolidateResult,
-    # Mental Model operations
-    MentalModelGetContext,
-    MentalModelGetResult,
-    MentalModelRefreshContext,
-    MentalModelRefreshResult,
    # Core operations
    OperationValidationError,
    OperationValidatorExtension,
@@ -58,8 +52,6 @@ __all__ = [
    "DefaultExtensionContext",
    # HTTP Extension
    "HttpExtension",
-    # MCP Extension
-    "MCPExtension",
    # Operation Validator - Core
    "OperationValidationError",
    "OperationValidatorExtension",
@@ -73,14 +65,8 @@ __all__ = [
    # Operation Validator - Consolidation
    "ConsolidateContext",
    "ConsolidateResult",
-    # Operation Validator - Mental Model
-    "MentalModelGetContext",
-    "MentalModelGetResult",
-    "MentalModelRefreshContext",
-    "MentalModelRefreshResult",
    # Tenant/Auth
    "ApiKeyTenantExtension",
-    "SupabaseTenantExtension",
    "AuthenticationError",
    "RequestContext",
    "Tenant",
@@ -6,17 +6,13 @@ They can be used directly or serve as examples for custom implementations.

 Available built-in extensions:
    - ApiKeyTenantExtension: Simple API key validation with public schema
-    - SupabaseTenantExtension: Supabase JWT validation with per-user schema isolation

 Example usage:
    HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
-    HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.supabase_tenant:SupabaseTenantExtension
 """

-from hindsight_api.extensions.builtin.supabase_tenant import SupabaseTenantExtension
 from hindsight_api.extensions.builtin.tenant import ApiKeyTenantExtension

 __all__ = [
    "ApiKeyTenantExtension",
-    "SupabaseTenantExtension",
 ]
@@ -1,433 +0,0 @@
-"""
-Supabase Tenant Extension for Hindsight
-
-Validates Supabase JWTs and maps authenticated users to isolated memory banks.
-Each user gets their own PostgreSQL schema based on their Supabase user ID.
-
-This extension enables multi-tenant memory isolation for applications using
-Supabase Auth - each authenticated user's memories are stored in a separate
-schema, ensuring complete data isolation.
-
-Features:
-    - Local JWT Verification: Validates tokens locally using JWKS public keys
-      (no network call per request)
-    - Automatic Schema Isolation: Each user gets {prefix}_{user_id} schema
-    - Zero User Management: Leverages your existing Supabase Auth setup
-    - Production Ready: Includes health checks, timeouts, key rotation handling,
-      and error handling
-    - Built-in: Ships with Hindsight, no extra installation needed
-    - Legacy Support: Falls back to /auth/v1/user endpoint for HS256 projects
-
-JWT Verification Strategy:
-    By default, JWTs are verified locally using public keys from the Supabase
-    JWKS endpoint (/auth/v1/.well-known/jwks.json). This is the Supabase-recommended
-    approach: no network call per request, fast, and secure.
-
-    If JWKS keys are unavailable (e.g., legacy HS256 projects), the extension
-    falls back to calling /auth/v1/user per request for validation. This requires
-    the service_role key to be configured.
-
-Configuration via environment variables:
-    HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.supabase_tenant:SupabaseTenantExtension
-    HINDSIGHT_API_TENANT_SUPABASE_URL=https://your-project.supabase.co
-
-    # Optional - only required for legacy HS256 projects or health checks
-    HINDSIGHT_API_TENANT_SUPABASE_SERVICE_KEY=your-service-role-key
-
-    # Optional
-    HINDSIGHT_API_TENANT_SCHEMA_PREFIX=user  # Default: "user" (creates user_<uuid> schemas)
-
-Usage:
-    Clients pass their Supabase JWT in the Authorization header:
-
-    curl -H "Authorization: Bearer <supabase_jwt>" \\
-        https://your-hindsight-server/v1/default/banks/my-bank/memories/recall
-
-Author: BrighterBalance (https://brighterbalance.app)
-License: MIT
-"""
-
-from __future__ import annotations
-
-import logging
-import re
-import time
-
-import httpx
-import jwt as pyjwt
-from jwt import PyJWK
-
-from hindsight_api.extensions.tenant import AuthenticationError, Tenant, TenantContext, TenantExtension
-from hindsight_api.models import RequestContext
-
-logger = logging.getLogger(__name__)
-
-__all__ = ["SupabaseTenantExtension"]
-
-# Minimum expected JWT length (JWTs are typically 100+ characters)
-MIN_TOKEN_LENGTH = 20
-
-# Timeout for Supabase API calls
-REQUEST_TIMEOUT_SECONDS = 10.0
-
-# JWKS cache TTL — Supabase Edge caches JWKS for 10 minutes, so we match that
-JWKS_CACHE_TTL_SECONDS = 600
-
-# Minimum interval between JWKS refreshes to avoid hammering the endpoint
-JWKS_MIN_REFRESH_INTERVAL_SECONDS = 30
-
-# Algorithms supported by Supabase Auth for asymmetric JWT signing
-SUPPORTED_ALGORITHMS = ["RS256", "ES256"]
-
-# Supabase user IDs are UUIDs — validate before using in schema names
-_UUID_RE = re.compile(r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$", re.IGNORECASE)
-
-# Schema prefix must be a valid Postgres identifier component (letters, digits, underscores)
-_SCHEMA_PREFIX_RE = re.compile(r"^[a-zA-Z_][a-zA-Z0-9_]*$")
-
-
-class SupabaseTenantExtension(TenantExtension):
-    """
-    TenantExtension that validates Supabase JWTs for multi-tenant isolation.
-
-    Each authenticated user gets their own PostgreSQL schema, ensuring complete
-    memory isolation between users. The schema name is derived from the user's
-    Supabase user ID (the ``sub`` claim in the JWT).
-
-    JWT verification uses JWKS (local, no network call per request) when
-    asymmetric keys are configured in Supabase, and falls back to the
-    ``/auth/v1/user`` endpoint for legacy HS256 projects.
-
-    Example:
-        User with ID "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
-        gets schema "user_a1b2c3d4_e5f6_7890_abcd_ef1234567890"
-    """
-
-    def __init__(self, config: dict[str, str]) -> None:
-        """
-        Initialize with configuration from environment variables.
-
-        Config keys are derived from HINDSIGHT_API_TENANT_* env vars:
-        - HINDSIGHT_API_TENANT_SUPABASE_URL -> config["supabase_url"] (required)
-        - HINDSIGHT_API_TENANT_SUPABASE_SERVICE_KEY -> config["supabase_service_key"] (optional)
-        - HINDSIGHT_API_TENANT_SCHEMA_PREFIX -> config["schema_prefix"] (optional)
-
-        Args:
-            config: Dictionary of configuration values from environment
-
-        Raises:
-            ValueError: If required configuration is missing
-        """
-        super().__init__(config)
-
-        self.supabase_url = (config.get("supabase_url") or "").rstrip("/")
-        self.supabase_service_key = config.get("supabase_service_key")
-        self.schema_prefix = config.get("schema_prefix", "user")
-
-        # Track initialized schemas to avoid redundant migrations
-        self._initialized_schemas: set[str] = set()
-
-        # Reusable HTTP client (created on startup)
-        self._http_client: httpx.AsyncClient | None = None
-
-        # JWKS state
-        self._jwks_keys: dict[str, PyJWK] = {}
-        self._jwks_last_fetched: float = 0
-        self._use_jwks: bool = False
-
-        if not self.supabase_url:
-            raise ValueError(
-                "HINDSIGHT_API_TENANT_SUPABASE_URL is required. "
-                "Set it to your Supabase project URL (e.g., https://xxx.supabase.co)"
-            )
-
-        if not _SCHEMA_PREFIX_RE.match(self.schema_prefix):
-            raise ValueError(
-                f"Invalid schema_prefix '{self.schema_prefix}'. "
-                "Must be a valid Postgres identifier (letters, digits, underscores, starting with a letter or underscore)."
-            )
-
-    # ------------------------------------------------------------------
-    # Lifecycle
-    # ------------------------------------------------------------------
-
-    async def on_startup(self) -> None:
-        """
-        Called when Hindsight starts.
-
-        Creates a reusable HTTP client, fetches JWKS for local JWT verification,
-        and optionally verifies connectivity to Supabase.
-        """
-        logger.info("Initializing Supabase tenant extension")
-        logger.info("Supabase URL: %s", self.supabase_url)
-        logger.info("Schema prefix: %s_", self.schema_prefix)
-
-        self._http_client = httpx.AsyncClient(timeout=REQUEST_TIMEOUT_SECONDS)
-
-        # Attempt to fetch JWKS for fast local JWT verification
-        await self._try_init_jwks()
-
-        # Optional health check using service key
-        if self.supabase_service_key:
-            await self._health_check()
-
-    async def on_shutdown(self) -> None:
-        """Called when Hindsight shuts down. Closes the HTTP client."""
-        logger.info("Shutting down Supabase tenant extension")
-        if self._http_client:
-            await self._http_client.aclose()
-            self._http_client = None
-
-    # ------------------------------------------------------------------
-    # JWKS management
-    # ------------------------------------------------------------------
-
-    async def _try_init_jwks(self) -> None:
-        """Fetch JWKS and decide verification mode (local JWKS vs legacy endpoint)."""
-        try:
-            await self._fetch_jwks()
-            if self._jwks_keys:
-                self._use_jwks = True
-                logger.info(
-                    "JWKS loaded — using local JWT verification with %d key(s)",
-                    len(self._jwks_keys),
-                )
-                return
-
-            # JWKS endpoint returned no keys — project likely uses legacy HS256
-            logger.warning(
-                "JWKS endpoint returned no signing keys. "
-                "Falling back to /auth/v1/user endpoint for JWT verification. "
-                "For better performance, enable asymmetric JWT signing in your "
-                "Supabase dashboard (Project Settings → Auth → JWT Algorithm)."
-            )
-        except Exception as e:
-            logger.warning(
-                "Could not fetch JWKS (%s). Falling back to /auth/v1/user endpoint for JWT verification.",
-                e,
-            )
-
-        # Legacy mode requires service key
-        if not self.supabase_service_key:
-            raise ValueError(
-                "HINDSIGHT_API_TENANT_SUPABASE_SERVICE_KEY is required when JWKS "
-                "is not available. Either enable asymmetric JWT signing in your "
-                "Supabase project or provide the service_role key."
-            )
-        self._use_jwks = False
-
-    async def _fetch_jwks(self) -> None:
-        """Fetch public signing keys from the Supabase JWKS endpoint."""
-        if self._http_client is None:
-            raise RuntimeError("HTTP client not initialized")
-
-        url = f"{self.supabase_url}/auth/v1/.well-known/jwks.json"
-        response = await self._http_client.get(url)
-        response.raise_for_status()
-
-        jwks_data = response.json()
-        keys: dict[str, PyJWK] = {}
-        for key_data in jwks_data.get("keys", []):
-            kid = key_data.get("kid")
-            if kid:
-                keys[kid] = PyJWK(key_data)
-
-        self._jwks_keys = keys
-        self._jwks_last_fetched = time.monotonic()
-
-    async def _get_signing_key(self, token: str) -> PyJWK:
-        """
-        Resolve the signing key for a token from the JWKS cache.
-
-        If the key ID (``kid``) is not in the cache, triggers one JWKS refresh
-        to handle key rotation before raising an error.
-        """
-        header = pyjwt.get_unverified_header(token)
-        kid = header.get("kid")
-        if not kid:
-            raise AuthenticationError("Token missing key ID (kid) header")
-
-        # Refresh cache if stale
-        now = time.monotonic()
-        if now - self._jwks_last_fetched > JWKS_CACHE_TTL_SECONDS:
-            logger.debug("JWKS cache expired, refreshing")
-            await self._fetch_jwks()
-
-        if kid in self._jwks_keys:
-            return self._jwks_keys[kid]
-
-        # Key not found — try one forced refresh to handle key rotation,
-        # but only if we haven't just refreshed
-        if now - self._jwks_last_fetched > JWKS_MIN_REFRESH_INTERVAL_SECONDS:
-            logger.info("Signing key %s not in cache, refreshing JWKS for possible key rotation", kid)
-            await self._fetch_jwks()
-            if kid in self._jwks_keys:
-                return self._jwks_keys[kid]
-
-        raise AuthenticationError("Unable to find signing key for token")
-
-    # ------------------------------------------------------------------
-    # Authentication
-    # ------------------------------------------------------------------
-
-    async def authenticate(self, context: RequestContext) -> TenantContext:
-        """
-        Validate a Supabase JWT and return tenant context.
-
-        Uses local JWKS verification when available (no network call per
-        request), falling back to the ``/auth/v1/user`` endpoint for legacy
-        HS256 projects.
-
-        Args:
-            context: Request context containing the API key (JWT)
-
-        Returns:
-            TenantContext with schema_name set to ``{prefix}_{user_uuid}``
-
-        Raises:
-            AuthenticationError: If token is missing, invalid, or expired
-        """
-        token = context.api_key
-
-        if not token:
-            raise AuthenticationError("Missing Authorization header. Expected: Bearer <supabase_jwt>")
-
-        if len(token) < MIN_TOKEN_LENGTH:
-            raise AuthenticationError("Invalid token format")
-
-        if self._http_client is None:
-            raise AuthenticationError("Extension not initialized")
-
-        # Verify the JWT and extract user ID
-        if self._use_jwks:
-            user_id = await self._verify_token_jwks(token)
-        else:
-            user_id = await self._verify_token_legacy(token)
-
-        # Validate user ID format before using in schema name
-        if not _UUID_RE.match(user_id):
-            raise AuthenticationError("Invalid user ID format in token")
-
-        # Build isolated schema name — hyphens to underscores for Postgres compatibility
-        safe_user_id = user_id.replace("-", "_")
-        schema_name = f"{self.schema_prefix}_{safe_user_id}"
-
-        # Initialize schema on first access
-        if schema_name not in self._initialized_schemas:
-            await self._initialize_schema(schema_name)
-
-        return TenantContext(schema_name=schema_name)
-
-    async def _verify_token_jwks(self, token: str) -> str:
-        """
-        Verify a JWT locally using cached JWKS public keys.
-
-        Validates signature, expiration, issuer, and audience. Returns the
-        user ID from the ``sub`` claim.
-
-        Raises:
-            AuthenticationError: If the token is invalid or expired.
-        """
-        try:
-            signing_key = await self._get_signing_key(token)
-            payload = pyjwt.decode(
-                token,
-                signing_key.key,
-                algorithms=SUPPORTED_ALGORITHMS,
-                audience="authenticated",
-                issuer=f"{self.supabase_url}/auth/v1",
-            )
-        except pyjwt.ExpiredSignatureError:
-            raise AuthenticationError("Token has expired")
-        except pyjwt.InvalidAudienceError:
-            raise AuthenticationError("Invalid token audience")
-        except pyjwt.InvalidIssuerError:
-            raise AuthenticationError("Invalid token issuer")
-        except pyjwt.DecodeError:
-            raise AuthenticationError("Invalid token")
-        except AuthenticationError:
-            raise
-        except Exception as e:
-            raise AuthenticationError(f"Token verification failed: {e!s}")
-
-        user_id = payload.get("sub")
-        if not user_id:
-            raise AuthenticationError("Token valid but missing subject (sub) claim")
-        return user_id
-
-    async def _verify_token_legacy(self, token: str) -> str:
-        """
-        Verify a JWT by calling the Supabase ``/auth/v1/user`` endpoint.
-
-        This is the fallback for projects using legacy HS256 JWT signing.
-        Adds a network round-trip per request.
-
-        Raises:
-            AuthenticationError: If the token is invalid or the request fails.
-        """
-        try:
-            response = await self._http_client.get(
-                f"{self.supabase_url}/auth/v1/user",
-                headers={
-                    "Authorization": f"Bearer {token}",
-                    "apikey": self.supabase_service_key,
-                },
-            )
-
-            if response.status_code == 401:
-                raise AuthenticationError("Invalid or expired token")
-
-            if response.status_code != 200:
-                raise AuthenticationError(f"Authentication failed: {response.status_code}")
-
-            user_data = response.json()
-            user_id = user_data.get("id")
-
-            if not user_id:
-                raise AuthenticationError("Token valid but no user ID found")
-
-            return user_id
-
-        except AuthenticationError:
-            raise
-        except httpx.TimeoutException:
-            raise AuthenticationError("Authentication timeout - please retry")
-        except httpx.RequestError as e:
-            raise AuthenticationError(f"Connection error: {e!s}")
-
-    # ------------------------------------------------------------------
-    # Schema management
-    # ------------------------------------------------------------------
-
-    async def _initialize_schema(self, schema_name: str) -> None:
-        """Run migrations for a new tenant schema and cache the result."""
-        logger.info("Initializing schema: %s", schema_name)
-        try:
-            await self.context.run_migration(schema_name)
-            self._initialized_schemas.add(schema_name)
-            logger.info("Schema ready: %s", schema_name)
-        except Exception as e:
-            logger.error("Schema initialization failed for %s: %s", schema_name, e)
-            raise AuthenticationError(f"Failed to initialize tenant: {e!s}")
-
-    async def list_tenants(self) -> list[Tenant]:
-        """Return all tenant schemas that have been initialized."""
-        return [Tenant(schema=schema) for schema in self._initialized_schemas]
-
-    # ------------------------------------------------------------------
-    # Health check
-    # ------------------------------------------------------------------
-
-    async def _health_check(self) -> None:
-        """Verify connectivity to Supabase using the auth health endpoint."""
-        try:
-            response = await self._http_client.get(
-                f"{self.supabase_url}/auth/v1/health",
-                headers={"apikey": self.supabase_service_key},
-            )
-            if response.status_code == 200:
-                logger.info("Supabase connection verified")
-            else:
-                logger.warning("Supabase health check returned %d", response.status_code)
-        except Exception as e:
-            logger.warning("Could not verify Supabase connection: %s", e)
@@ -1,60 +1,20 @@
 """Built-in tenant extension implementations."""

-from hindsight_api.config import get_config
 from hindsight_api.extensions.tenant import AuthenticationError, Tenant, TenantContext, TenantExtension
 from hindsight_api.models import RequestContext


-class DefaultTenantExtension(TenantExtension):
-    """
-    Default single-tenant extension with no authentication.
-
-    This is the default extension used when no tenant extension is configured.
-    It provides single-tenant behavior using the configured schema from
-    HINDSIGHT_API_DATABASE_SCHEMA (defaults to 'public').
-
-    Features:
-    - No authentication required (passes all requests)
-    - Uses configured schema from environment
-    - Perfect for single-tenant deployments without auth
-
-    Configuration:
-        HINDSIGHT_API_DATABASE_SCHEMA=your-schema (optional, defaults to 'public')
-
-    This is automatically enabled by default. To use custom authentication,
-    configure a different tenant extension:
-        HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
-    """
-
-    def __init__(self, config: dict[str, str]):
-        super().__init__(config)
-        # Cache the schema at initialization for consistency
-        # Support explicit schema override via config, otherwise use environment
-        self._schema = config.get("schema", get_config().database_schema)
-
-    async def authenticate(self, context: RequestContext) -> TenantContext:
-        """Return configured schema without any authentication."""
-        return TenantContext(schema_name=self._schema)
-
-    async def list_tenants(self) -> list[Tenant]:
-        """Return configured schema for single-tenant setup."""
-        return [Tenant(schema=self._schema)]
-
-
 class ApiKeyTenantExtension(TenantExtension):
    """
    Built-in tenant extension that validates API key against an environment variable.

    This is a simple implementation that:
    1. Validates the API key matches HINDSIGHT_API_TENANT_API_KEY
-    2. Returns the configured schema (HINDSIGHT_API_DATABASE_SCHEMA, default 'public')
-       for all authenticated requests
+    2. Returns 'public' as the schema for all authenticated requests

    Configuration:
        HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
        HINDSIGHT_API_TENANT_API_KEY=your-secret-key
-        HINDSIGHT_API_DATABASE_SCHEMA=your-schema (optional, defaults to 'public')
-        HINDSIGHT_API_TENANT_MCP_AUTH_DISABLED=true (optional, disable auth for MCP endpoints)

    For multi-tenant setups with separate schemas per tenant, implement a custom
    TenantExtension that looks up the schema based on the API key or token claims.
@@ -65,26 +25,13 @@ class ApiKeyTenantExtension(TenantExtension):
        self.expected_api_key = config.get("api_key")
        if not self.expected_api_key:
            raise ValueError("HINDSIGHT_API_TENANT_API_KEY is required when using ApiKeyTenantExtension")
-        # Allow disabling MCP auth for backwards compatibility
-        self.mcp_auth_disabled = config.get("mcp_auth_disabled", "").lower() in ("true", "1", "yes")

    async def authenticate(self, context: RequestContext) -> TenantContext:
-        """Validate API key and return configured schema context."""
+        """Validate API key and return public schema context."""
        if context.api_key != self.expected_api_key:
            raise AuthenticationError("Invalid API key")
-        return TenantContext(schema_name=get_config().database_schema)
+        return TenantContext(schema_name="public")

    async def list_tenants(self) -> list[Tenant]:
-        """Return configured schema for single-tenant setup."""
-        return [Tenant(schema=get_config().database_schema)]
-
-    async def authenticate_mcp(self, context: RequestContext) -> TenantContext:
-        """
-        Authenticate MCP requests.
-
-        If mcp_auth_disabled is set, skip authentication for backwards compatibility.
-        Otherwise, delegate to authenticate().
-        """
-        if self.mcp_auth_disabled:
-            return TenantContext(schema_name=get_config().database_schema)
-        return await self.authenticate(context)
+        """Return public schema for single-tenant setup."""
+        return [Tenant(schema="public")]
@@ -96,13 +96,7 @@ class DefaultExtensionContext(ExtensionContext):

    async def run_migration(self, schema: str) -> None:
        """Run migrations for a specific schema."""
-        from hindsight_api.config import get_config
-        from hindsight_api.migrations import (
-            ensure_embedding_dimension,
-            ensure_text_search_extension,
-            ensure_vector_extension,
-            run_migrations,
-        )
+        from hindsight_api.migrations import ensure_embedding_dimension, run_migrations

        # Prefer getting URL from memory engine (handles pg0 case where URL is set after init)
        db_url = self._database_url
@@ -113,9 +107,6 @@ class DefaultExtensionContext(ExtensionContext):

        run_migrations(db_url, schema=schema)

-        # Get config for vector extension setting
-        config = get_config()
-
        # Ensure embedding column dimension matches the model's dimension
        # This is needed because migrations create columns with default dimension
        if self._memory_engine is not None:
@@ -123,15 +114,7 @@ class DefaultExtensionContext(ExtensionContext):
            if embeddings is not None:
                dimension = getattr(embeddings, "dimension", None)
                if dimension is not None:
-                    ensure_embedding_dimension(
-                        db_url, dimension, schema=schema, vector_extension=config.vector_extension
-                    )
-
-        # Ensure vector indexes match the configured extension
-        ensure_vector_extension(db_url, vector_extension=config.vector_extension, schema=schema)
-
-        # Ensure text search columns/indexes match the configured extension
-        ensure_text_search_extension(db_url, text_search_extension=config.text_search_extension, schema=schema)
+                    ensure_embedding_dimension(db_url, dimension, schema=schema)

    def get_memory_engine(self) -> "MemoryEngineInterface":
        """Get the memory engine interface."""
@@ -1,42 +0,0 @@
-"""MCP Extension for registering additional MCP tools.
-
-This extension allows external packages (like hindsight-cloud) to register
-additional MCP tools on the Hindsight MCP server.
-
-Example:
-    HINDSIGHT_API_MCP_EXTENSION=hindsight_cloud.extensions:CloudMCPExtension
-"""
-
-import logging
-from abc import abstractmethod
-
-from fastmcp import FastMCP
-
-from hindsight_api import MemoryEngine
-from hindsight_api.extensions.base import Extension
-
-logger = logging.getLogger(__name__)
-
-
-class MCPExtension(Extension):
-    """Base class for MCP extensions that register additional tools.
-
-    Subclass this to add MCP tools in extension packages.
-
-    Example:
-        class CloudMCPExtension(MCPExtension):
-            def register_tools(self, mcp: FastMCP, memory: MemoryEngine) -> None:
-                @mcp.tool()
-                async def my_custom_tool(query: str) -> str:
-                    return "result"
-    """
-
-    @abstractmethod
-    def register_tools(self, mcp: FastMCP, memory: MemoryEngine) -> None:
-        """Register additional MCP tools.
-
-        Args:
-            mcp: FastMCP server instance to register tools on
-            memory: MemoryEngine instance for accessing memory operations
-        """
-        pass
@@ -132,10 +132,6 @@ class RetainResult:
    unit_ids: list[list[str]]  # List of unit IDs per content item
    success: bool = True
    error: str | None = None
-    # Actual LLM token usage (populated by engine when available)
-    llm_input_tokens: int | None = None
-    llm_output_tokens: int | None = None
-    llm_total_tokens: int | None = None


@dataclass
@@ -200,57 +196,6 @@ class ConsolidateResult:
    error: str | None = None


-# =============================================================================
-# Mental Model Contexts
-# =============================================================================
-
-
-@dataclass
-class MentalModelGetContext:
-    """Context for a mental model GET operation validation (pre-operation)."""
-
-    bank_id: str
-    mental_model_id: str
-    request_context: "RequestContext"
-
-
-@dataclass
-class MentalModelRefreshContext:
-    """Context for a mental model refresh/create operation validation (pre-operation)."""
-
-    bank_id: str
-    mental_model_id: str | None  # None for create (not yet assigned)
-    request_context: "RequestContext"
-
-
-@dataclass
-class MentalModelGetResult:
-    """Result context for post-mental-model-GET hook."""
-
-    bank_id: str
-    mental_model_id: str
-    request_context: "RequestContext"
-    output_tokens: int  # tokens in the returned content
-    success: bool = True
-    error: str | None = None
-
-
-@dataclass
-class MentalModelRefreshResult:
-    """Result context for post-mental-model-refresh hook."""
-
-    bank_id: str
-    mental_model_id: str
-    request_context: "RequestContext"
-    query_tokens: int  # tokens in source_query
-    output_tokens: int  # tokens in generated content
-    context_tokens: int  # tokens in context (if any)
-    facts_used: int  # facts referenced in based_on
-    mental_models_used: int  # mental models referenced in based_on
-    success: bool = True
-    error: str | None = None
-
-
 class OperationValidatorExtension(Extension, ABC):
    """
    Validates and hooks into retain/recall/reflect/consolidate operations.
@@ -457,81 +402,3 @@ class OperationValidatorExtension(Extension, ABC):
                - error: Error message (if failed)
        """
        pass
-
-    # =========================================================================
-    # Mental Model - Pre-operation validation hook (optional - override to implement)
-    # =========================================================================
-
-    async def validate_mental_model_get(self, ctx: MentalModelGetContext) -> ValidationResult:
-        """
-        Validate a mental model GET operation before execution.
-
-        Override to implement custom validation logic for mental model retrieval.
-
-        Args:
-            ctx: Context containing:
-                - bank_id: Bank identifier
-                - mental_model_id: Mental model identifier
-                - request_context: Request context with auth info
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        return ValidationResult.accept()
-
-    async def validate_mental_model_refresh(self, ctx: MentalModelRefreshContext) -> ValidationResult:
-        """
-        Validate a mental model refresh/create operation before execution.
-
-        Override to implement custom validation logic for mental model refresh.
-
-        Args:
-            ctx: Context containing:
-                - bank_id: Bank identifier
-                - mental_model_id: Mental model identifier (None for create)
-                - request_context: Request context with auth info
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        return ValidationResult.accept()
-
-    # =========================================================================
-    # Mental Model - Post-operation hooks (optional - override to implement)
-    # =========================================================================
-
-    async def on_mental_model_get_complete(self, result: MentalModelGetResult) -> None:
-        """
-        Called after a mental model GET operation completes (success or failure).
-
-        Override to implement post-operation logic such as tracking or audit logging.
-
-        Args:
-            result: Result context containing:
-                - bank_id: Bank identifier
-                - mental_model_id: Mental model identifier
-                - output_tokens: Token count of the returned content
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
-
-    async def on_mental_model_refresh_complete(self, result: MentalModelRefreshResult) -> None:
-        """
-        Called after a mental model refresh operation completes (success or failure).
-
-        Override to implement post-operation logic such as tracking or audit logging.
-
-        Args:
-            result: Result context containing:
-                - bank_id: Bank identifier
-                - mental_model_id: Mental model identifier
-                - query_tokens: Tokens in source_query
-                - output_tokens: Tokens in generated content
-                - context_tokens: Tokens in context
-                - facts_used: Number of facts referenced
-                - mental_models_used: Number of mental models referenced
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
@@ -2,7 +2,6 @@

 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Any

 from hindsight_api.extensions.base import Extension
 from hindsight_api.models import RequestContext
@@ -88,70 +87,3 @@ class TenantExtension(Extension, ABC):
            For single-tenant setups, return [Tenant(schema="public")].
        """
        ...
-
-    async def get_tenant_config(self, context: RequestContext) -> dict[str, Any]:
-        """
-        Get tenant-specific configuration overrides.
-
-        This method is called during hierarchical configuration resolution to get
-        tenant-level config overrides. The returned dict should contain Python field
-        names (lowercase snake_case) as keys, not environment variable names.
-
-        Example:
-            {"llm_model": "gpt-4", "retain_extraction_mode": "verbose"}
-
-        The default implementation returns an empty dict (no tenant-specific config).
-        Override this method in custom extensions to provide tenant-specific configuration.
-
-        Args:
-            context: The request context containing tenant information.
-
-        Returns:
-            Dict of config field names to values (only configurable fields).
-            Empty dict if no tenant-specific config.
-        """
-        return {}
-
-    async def get_allowed_config_fields(self, context: RequestContext, bank_id: str) -> set[str] | None:
-        """
-        Get set of config fields that this tenant/bank is allowed to modify.
-
-        This method controls which configurable fields can be modified via the bank config API.
-        It enables fine-grained permission control per tenant or per bank.
-
-        Examples:
-            - Return None: Allow all configurable fields (default)
-            - Return {"retain_chunk_size", "retain_custom_instructions"}: Allow only these fields
-            - Return set(): Allow no modifications (read-only)
-
-        The default implementation returns None (all configurable fields allowed).
-        Override this method in custom extensions to implement custom permission logic.
-
-        Args:
-            context: The request context containing tenant information.
-            bank_id: The bank identifier for per-bank permissions.
-
-        Returns:
-            Set of allowed field names, or None to allow all configurable fields.
-            Returned fields must be a subset of HindsightConfig.get_configurable_fields().
-        """
-        return None
-
-    async def authenticate_mcp(self, context: RequestContext) -> TenantContext:
-        """
-        Authenticate MCP requests.
-
-        By default, this calls authenticate(). Override this method to provide
-        different authentication behavior for MCP endpoints (e.g., to disable
-        auth for backwards compatibility with existing MCP servers).
-
-        Args:
-            context: The action context containing API key and other auth data.
-
-        Returns:
-            TenantContext with the schema_name for database operations.
-
-        Raises:
-            AuthenticationError: If authentication fails.
-        """
-        return await self.authenticate(context)
@@ -20,13 +20,14 @@ import warnings

 import uvicorn

-from . import MemoryEngine, __version__
+from . import MemoryEngine
 from .api import create_app
 from .banner import print_banner
-from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, _get_raw_config
+from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, get_config
 from .daemon import (
    DEFAULT_DAEMON_PORT,
    DEFAULT_IDLE_TIMEOUT,
+    DaemonLock,
    IdleTimeoutMiddleware,
    daemonize,
 )
@@ -68,7 +69,7 @@ def main():
    global _memory

    # Load configuration from environment (for CLI args defaults)
-    config = _get_raw_config()
+    config = get_config()

    parser = argparse.ArgumentParser(
        prog="hindsight-api",
@@ -135,15 +136,30 @@ def main():

    # Daemon mode handling
    if args.daemon:
-        # Use port from args (may be custom for profiles)
-        if args.port == config.port:  # No custom port specified
-            args.port = DEFAULT_DAEMON_PORT
+        # Use fixed daemon port
+        args.port = DEFAULT_DAEMON_PORT
        args.host = "127.0.0.1"  # Only bind to localhost for security

+        # Check if another daemon is already running
+        daemon_lock = DaemonLock()
+        if not daemon_lock.acquire():
+            print(f"Daemon already running (PID: {daemon_lock.get_pid()})", file=sys.stderr)
+            sys.exit(1)
+
        # Fork into background
-        # No lockfile needed - port binding prevents duplicate daemons
        daemonize()

+        # Re-acquire lock in child process
+        daemon_lock = DaemonLock()
+        if not daemon_lock.acquire():
+            sys.exit(1)
+
+        # Register cleanup to release lock
+        def release_lock():
+            daemon_lock.release()
+
+        atexit.register(release_lock)
+
    # Print banner (not in daemon mode)
    if not args.daemon:
        print()
@@ -154,88 +170,41 @@ def main():
    if args.log_level != config.log_level:
        config = HindsightConfig(
            database_url=config.database_url,
-            database_schema=config.database_schema,
-            vector_extension=config.vector_extension,
-            text_search_extension=config.text_search_extension,
            llm_provider=config.llm_provider,
            llm_api_key=config.llm_api_key,
            llm_model=config.llm_model,
            llm_base_url=config.llm_base_url,
            llm_max_concurrent=config.llm_max_concurrent,
-            llm_max_retries=config.llm_max_retries,
-            llm_initial_backoff=config.llm_initial_backoff,
-            llm_max_backoff=config.llm_max_backoff,
            llm_timeout=config.llm_timeout,
-            llm_vertexai_project_id=config.llm_vertexai_project_id,
-            llm_vertexai_region=config.llm_vertexai_region,
-            llm_vertexai_service_account_key=config.llm_vertexai_service_account_key,
            retain_llm_provider=config.retain_llm_provider,
            retain_llm_api_key=config.retain_llm_api_key,
            retain_llm_model=config.retain_llm_model,
            retain_llm_base_url=config.retain_llm_base_url,
-            retain_llm_max_concurrent=config.retain_llm_max_concurrent,
-            retain_llm_max_retries=config.retain_llm_max_retries,
-            retain_llm_initial_backoff=config.retain_llm_initial_backoff,
-            retain_llm_max_backoff=config.retain_llm_max_backoff,
-            retain_llm_timeout=config.retain_llm_timeout,
            reflect_llm_provider=config.reflect_llm_provider,
            reflect_llm_api_key=config.reflect_llm_api_key,
            reflect_llm_model=config.reflect_llm_model,
            reflect_llm_base_url=config.reflect_llm_base_url,
-            reflect_llm_max_concurrent=config.reflect_llm_max_concurrent,
-            reflect_llm_max_retries=config.reflect_llm_max_retries,
-            reflect_llm_initial_backoff=config.reflect_llm_initial_backoff,
-            reflect_llm_max_backoff=config.reflect_llm_max_backoff,
-            reflect_llm_timeout=config.reflect_llm_timeout,
            consolidation_llm_provider=config.consolidation_llm_provider,
            consolidation_llm_api_key=config.consolidation_llm_api_key,
            consolidation_llm_model=config.consolidation_llm_model,
            consolidation_llm_base_url=config.consolidation_llm_base_url,
-            consolidation_llm_max_concurrent=config.consolidation_llm_max_concurrent,
-            consolidation_llm_max_retries=config.consolidation_llm_max_retries,
-            consolidation_llm_initial_backoff=config.consolidation_llm_initial_backoff,
-            consolidation_llm_max_backoff=config.consolidation_llm_max_backoff,
-            consolidation_llm_timeout=config.consolidation_llm_timeout,
            embeddings_provider=config.embeddings_provider,
            embeddings_local_model=config.embeddings_local_model,
-            embeddings_local_force_cpu=config.embeddings_local_force_cpu,
-            embeddings_local_trust_remote_code=config.embeddings_local_trust_remote_code,
            embeddings_tei_url=config.embeddings_tei_url,
            embeddings_openai_base_url=config.embeddings_openai_base_url,
-            embeddings_cohere_api_key=config.embeddings_cohere_api_key,
-            embeddings_cohere_model=config.embeddings_cohere_model,
            embeddings_cohere_base_url=config.embeddings_cohere_base_url,
-            embeddings_litellm_api_base=config.embeddings_litellm_api_base,
-            embeddings_litellm_api_key=config.embeddings_litellm_api_key,
-            embeddings_litellm_model=config.embeddings_litellm_model,
-            embeddings_litellm_sdk_api_key=config.embeddings_litellm_sdk_api_key,
-            embeddings_litellm_sdk_model=config.embeddings_litellm_sdk_model,
-            embeddings_litellm_sdk_api_base=config.embeddings_litellm_sdk_api_base,
            reranker_provider=config.reranker_provider,
            reranker_local_model=config.reranker_local_model,
-            reranker_local_force_cpu=config.reranker_local_force_cpu,
-            reranker_local_max_concurrent=config.reranker_local_max_concurrent,
-            reranker_local_trust_remote_code=config.reranker_local_trust_remote_code,
            reranker_tei_url=config.reranker_tei_url,
            reranker_tei_batch_size=config.reranker_tei_batch_size,
            reranker_tei_max_concurrent=config.reranker_tei_max_concurrent,
            reranker_max_candidates=config.reranker_max_candidates,
-            reranker_cohere_api_key=config.reranker_cohere_api_key,
-            reranker_cohere_model=config.reranker_cohere_model,
            reranker_cohere_base_url=config.reranker_cohere_base_url,
-            reranker_litellm_api_base=config.reranker_litellm_api_base,
-            reranker_litellm_api_key=config.reranker_litellm_api_key,
-            reranker_litellm_model=config.reranker_litellm_model,
-            reranker_litellm_sdk_api_key=config.reranker_litellm_sdk_api_key,
-            reranker_litellm_sdk_model=config.reranker_litellm_sdk_model,
-            reranker_litellm_sdk_api_base=config.reranker_litellm_sdk_api_base,
            host=args.host,
            port=args.port,
-            base_path=config.base_path,
            log_level=args.log_level,
            log_format=config.log_format,
            mcp_enabled=config.mcp_enabled,
-            enable_bank_config_api=config.enable_bank_config_api,
            graph_retriever=config.graph_retriever,
            mpfp_top_k_neighbors=config.mpfp_top_k_neighbors,
            recall_max_concurrent=config.recall_max_concurrent,
@@ -245,10 +214,9 @@ def main():
            retain_extract_causal_links=config.retain_extract_causal_links,
            retain_extraction_mode=config.retain_extraction_mode,
            retain_custom_instructions=config.retain_custom_instructions,
-            retain_batch_tokens=config.retain_batch_tokens,
+            retain_observations_async=config.retain_observations_async,
            enable_observations=config.enable_observations,
            consolidation_batch_size=config.consolidation_batch_size,
-            consolidation_max_tokens=config.consolidation_max_tokens,
            skip_llm_verification=config.skip_llm_verification,
            lazy_reranker=config.lazy_reranker,
            run_migrations_on_startup=config.run_migrations_on_startup,
@@ -260,16 +228,10 @@ def main():
            worker_id=config.worker_id,
            worker_poll_interval_ms=config.worker_poll_interval_ms,
            worker_max_retries=config.worker_max_retries,
+            worker_batch_size=config.worker_batch_size,
            worker_http_port=config.worker_http_port,
-            worker_max_slots=config.worker_max_slots,
-            worker_consolidation_max_slots=config.worker_consolidation_max_slots,
            reflect_max_iterations=config.reflect_max_iterations,
            mental_model_refresh_concurrency=config.mental_model_refresh_concurrency,
-            otel_traces_enabled=config.otel_traces_enabled,
-            otel_exporter_otlp_endpoint=config.otel_exporter_otlp_endpoint,
-            otel_exporter_otlp_headers=config.otel_exporter_otlp_headers,
-            otel_service_name=config.otel_service_name,
-            otel_deployment_environment=config.otel_deployment_environment,
        )
    config.configure_logging()
    if not args.daemon:
@@ -374,13 +336,11 @@ def main():
            embeddings_provider=config.embeddings_provider,
            reranker_provider=config.reranker_provider,
            mcp_enabled=config.mcp_enabled,
-            version=__version__,
        )

    # Start idle checker in daemon mode
    if idle_middleware is not None:
        # Start the idle checker in a background thread with its own event loop
-        import logging
        import threading

        def run_idle_checker():
@@ -391,12 +351,12 @@ def main():
                loop = asyncio.new_event_loop()
                asyncio.set_event_loop(loop)
                loop.run_until_complete(idle_middleware._check_idle())
-            except Exception as e:
-                logging.error(f"Idle checker error: {e}", exc_info=True)
+            except Exception:
+                pass

        threading.Thread(target=run_idle_checker, daemon=True).start()

-    uvicorn.run(**uvicorn_config)
+    uvicorn.run(**uvicorn_config)  # type: ignore[invalid-argument-type] - dict kwargs


 if __name__ == "__main__":
@@ -32,15 +32,6 @@ class MCPToolsConfig:
    # How to resolve bank_id for operations
    bank_id_resolver: Callable[[], str | None]

-    # How to resolve API key for tenant auth (optional)
-    api_key_resolver: Callable[[], str | None] | None = None
-
-    # How to resolve tenant_id for usage metering (set by MCP middleware after auth)
-    tenant_id_resolver: Callable[[], str | None] | None = None
-
-    # How to resolve api_key_id for usage metering (set by MCP middleware after auth)
-    api_key_id_resolver: Callable[[], str | None] | None = None
-
    # Whether to include bank_id as a parameter on tools (for multi-bank support)
    include_bank_id_param: bool = False

@@ -55,18 +46,6 @@ class MCPToolsConfig:
    retain_fire_and_forget: bool = False  # If True, use asyncio.create_task pattern


-def _get_request_context(config: MCPToolsConfig) -> RequestContext:
-    """Create RequestContext with auth details from resolvers.
-
-    This enables tenant auth and usage metering to work with MCP tools by propagating
-    the authentication results from the MCP middleware to the memory engine.
-    """
-    api_key = config.api_key_resolver() if config.api_key_resolver else None
-    tenant_id = config.tenant_id_resolver() if config.tenant_id_resolver else None
-    api_key_id = config.api_key_id_resolver() if config.api_key_id_resolver else None
-    return RequestContext(api_key=api_key, tenant_id=tenant_id, api_key_id=api_key_id)
-
-
 def parse_timestamp(timestamp: str) -> datetime | None:
    """Parse an ISO format timestamp string.

@@ -127,19 +106,7 @@ def register_mcp_tools(
        memory: MemoryEngine instance
        config: Tool configuration
    """
-    tools_to_register = config.tools or {
-        "retain",
-        "recall",
-        "reflect",
-        "list_banks",
-        "create_bank",
-        "list_mental_models",
-        "get_mental_model",
-        "create_mental_model",
-        "update_mental_model",
-        "delete_mental_model",
-        "refresh_mental_model",
-    }
+    tools_to_register = config.tools or {"retain", "recall", "reflect", "list_banks", "create_bank"}

    if "retain" in tools_to_register:
        _register_retain(mcp, memory, config)
@@ -156,25 +123,6 @@ def register_mcp_tools(
    if "create_bank" in tools_to_register:
        _register_create_bank(mcp, memory, config)

-    # Mental model tools
-    if "list_mental_models" in tools_to_register:
-        _register_list_mental_models(mcp, memory, config)
-
-    if "get_mental_model" in tools_to_register:
-        _register_get_mental_model(mcp, memory, config)
-
-    if "create_mental_model" in tools_to_register:
-        _register_create_mental_model(mcp, memory, config)
-
-    if "update_mental_model" in tools_to_register:
-        _register_update_mental_model(mcp, memory, config)
-
-    if "delete_mental_model" in tools_to_register:
-        _register_delete_mental_model(mcp, memory, config)
-
-    if "refresh_mental_model" in tools_to_register:
-        _register_refresh_mental_model(mcp, memory, config)
-

 def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
    """Register the retain tool."""
@@ -207,14 +155,12 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                if error:
                    return {"status": "error", "message": error}

-                request_context = _get_request_context(config)
-
                async def _retain():
                    try:
                        await memory.retain_batch_async(
                            bank_id=target_bank,
                            contents=[content_dict],
-                            request_context=request_context,
+                            request_context=RequestContext(),
                        )
                    except Exception as e:
                        logger.error(f"Error storing memory: {e}", exc_info=True)
@@ -250,17 +196,16 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                        return f"Error: {error}"

                    contents = [content_dict]
-                    request_context = _get_request_context(config)
                    if async_processing:
                        result = await memory.submit_async_retain(
-                            bank_id=target_bank, contents=contents, request_context=request_context
+                            bank_id=target_bank, contents=contents, request_context=RequestContext()
                        )
                        return f"Memory queued for background processing (operation_id: {result.get('operation_id', 'N/A')})"
                    else:
                        await memory.retain_batch_async(
                            bank_id=target_bank,
                            contents=contents,
-                            request_context=request_context,
+                            request_context=RequestContext(),
                        )
                        return f"Memory stored successfully in bank '{target_bank}'"
                except Exception as e:
@@ -292,14 +237,12 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
            if error:
                return {"status": "error", "message": error}

-            request_context = _get_request_context(config)
-
            async def _retain():
                try:
                    await memory.retain_batch_async(
                        bank_id=target_bank,
                        contents=[content_dict],
-                        request_context=request_context,
+                        request_context=RequestContext(),
                    )
                except Exception as e:
                    logger.error(f"Error storing memory: {e}", exc_info=True)
@@ -337,7 +280,7 @@ def _register_recall(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                    fact_type=list(VALID_RECALL_FACT_TYPES),
                    budget=Budget.HIGH,
                    max_tokens=max_tokens,
-                    request_context=_get_request_context(config),
+                    request_context=RequestContext(),
                )

                return recall_result.model_dump_json(indent=2)
@@ -368,7 +311,7 @@ def _register_recall(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                    fact_type=list(VALID_RECALL_FACT_TYPES),
                    budget=Budget.HIGH,
                    max_tokens=max_tokens,
-                    request_context=_get_request_context(config),
+                    request_context=RequestContext(),
                )

                return recall_result.model_dump()
@@ -427,7 +370,7 @@ def _register_reflect(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig
                    query=query,
                    budget=budget_enum,
                    context=context,
-                    request_context=_get_request_context(config),
+                    request_context=RequestContext(),
                )

                return reflect_result.model_dump_json(indent=2)
@@ -480,7 +423,7 @@ def _register_reflect(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig
                    query=query,
                    budget=budget_enum,
                    context=context,
-                    request_context=_get_request_context(config),
+                    request_context=RequestContext(),
                )

                return reflect_result.model_dump()
@@ -504,7 +447,7 @@ def _register_list_banks(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCon
            JSON list of banks with their IDs, names, dispositions, and missions.
        """
        try:
-            banks = await memory.list_banks(request_context=_get_request_context(config))
+            banks = await memory.list_banks(request_context=RequestContext())
            return json.dumps({"banks": banks}, indent=2)
        except Exception as e:
            logger.error(f"Error listing banks: {e}", exc_info=True)
@@ -528,9 +471,8 @@ def _register_create_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCo
            mission: Optional mission describing who the agent is and what they're trying to accomplish
        """
        try:
-            request_context = _get_request_context(config)
            # get_bank_profile auto-creates bank if it doesn't exist
-            profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+            profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())

            # Update name/mission if provided
            if name is not None or mission is not None:
@@ -538,10 +480,10 @@ def _register_create_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCo
                    bank_id,
                    name=name,
                    mission=mission,
-                    request_context=request_context,
+                    request_context=RequestContext(),
                )
                # Fetch updated profile
-                profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+                profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())

            # Serialize disposition if it's a Pydantic model
            if "disposition" in profile and hasattr(profile["disposition"], "model_dump"):
@@ -550,567 +492,3 @@ def _register_create_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCo
        except Exception as e:
            logger.error(f"Error creating bank: {e}", exc_info=True)
            return f'{{"error": "{e}"}}'
-
-
-def _validate_mental_model_inputs(
-    name: str | None = None, source_query: str | None = None, max_tokens: int | None = None
-) -> str | None:
-    """Validate mental model inputs, returning an error message or None if valid."""
-    if name is not None and not name.strip():
-        return "name cannot be empty"
-    if source_query is not None and not source_query.strip():
-        return "source_query cannot be empty"
-    if max_tokens is not None and (max_tokens < 256 or max_tokens > 8192):
-        return f"max_tokens must be between 256 and 8192, got {max_tokens}"
-    return None
-
-
-# =========================================================================
-# MENTAL MODEL TOOLS
-# =========================================================================
-
-
-def _register_list_mental_models(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
-    """Register the list_mental_models tool."""
-
-    if config.include_bank_id_param:
-
-        @mcp.tool()
-        async def list_mental_models(
-            tags: list[str] | None = None,
-            bank_id: str | None = None,
-        ) -> str:
-            """
-            List mental models (pinned reflections) for a memory bank.
-
-            Mental models are living documents that stay current by periodically re-running
-            a source query through reflect. Use them to maintain up-to-date summaries,
-            preferences, or synthesized knowledge.
-
-            Args:
-                tags: Optional tags to filter by (returns models matching any tag)
-                bank_id: Optional bank to list from (defaults to session bank). Use for cross-bank operations.
-            """
-            try:
-                target_bank = bank_id or config.bank_id_resolver()
-                if target_bank is None:
-                    return '{"error": "No bank_id configured", "items": []}'
-
-                models = await memory.list_mental_models(
-                    bank_id=target_bank,
-                    tags=tags,
-                    request_context=_get_request_context(config),
-                )
-                return json.dumps({"items": models}, indent=2, default=str)
-            except Exception as e:
-                logger.error(f"Error listing mental models: {e}", exc_info=True)
-                return f'{{"error": "{e}", "items": []}}'
-
-    else:
-
-        @mcp.tool()
-        async def list_mental_models(
-            tags: list[str] | None = None,
-        ) -> dict:
-            """
-            List mental models (pinned reflections) for this memory bank.
-
-            Mental models are living documents that stay current by periodically re-running
-            a source query through reflect. Use them to maintain up-to-date summaries,
-            preferences, or synthesized knowledge.
-
-            Args:
-                tags: Optional tags to filter by (returns models matching any tag)
-            """
-            try:
-                target_bank = config.bank_id_resolver()
-                if target_bank is None:
-                    return {"error": "No bank_id configured", "items": []}
-
-                models = await memory.list_mental_models(
-                    bank_id=target_bank,
-                    tags=tags,
-                    request_context=_get_request_context(config),
-                )
-                return {"items": models}
-            except Exception as e:
-                logger.error(f"Error listing mental models: {e}", exc_info=True)
-                return {"error": str(e), "items": []}
-
-
-def _register_get_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
-    """Register the get_mental_model tool."""
-
-    if config.include_bank_id_param:
-
-        @mcp.tool()
-        async def get_mental_model(
-            mental_model_id: str,
-            bank_id: str | None = None,
-        ) -> str:
-            """
-            Get a specific mental model by ID.
-
-            Returns the full mental model including its generated content, source query,
-            and metadata. Use list_mental_models first to discover available model IDs.
-
-            Args:
-                mental_model_id: The ID of the mental model to retrieve
-                bank_id: Optional bank (defaults to session bank). Use for cross-bank operations.
-            """
-            try:
-                target_bank = bank_id or config.bank_id_resolver()
-                if target_bank is None:
-                    return '{"error": "No bank_id configured"}'
-
-                model = await memory.get_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=mental_model_id,
-                    request_context=_get_request_context(config),
-                )
-                if model is None:
-                    return json.dumps({"error": f"Mental model '{mental_model_id}' not found in bank '{target_bank}'"})
-                return json.dumps(model, indent=2, default=str)
-            except Exception as e:
-                logger.error(f"Error getting mental model: {e}", exc_info=True)
-                return f'{{"error": "{e}"}}'
-
-    else:
-
-        @mcp.tool()
-        async def get_mental_model(
-            mental_model_id: str,
-        ) -> dict:
-            """
-            Get a specific mental model by ID.
-
-            Returns the full mental model including its generated content, source query,
-            and metadata. Use list_mental_models first to discover available model IDs.
-
-            Args:
-                mental_model_id: The ID of the mental model to retrieve
-            """
-            try:
-                target_bank = config.bank_id_resolver()
-                if target_bank is None:
-                    return {"error": "No bank_id configured"}
-
-                model = await memory.get_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=mental_model_id,
-                    request_context=_get_request_context(config),
-                )
-                if model is None:
-                    return {"error": f"Mental model '{mental_model_id}' not found in bank '{target_bank}'"}
-                return model
-            except Exception as e:
-                logger.error(f"Error getting mental model: {e}", exc_info=True)
-                return {"error": str(e)}
-
-
-def _register_create_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
-    """Register the create_mental_model tool."""
-
-    if config.include_bank_id_param:
-
-        @mcp.tool()
-        async def create_mental_model(
-            name: str,
-            source_query: str,
-            mental_model_id: str | None = None,
-            tags: list[str] | None = None,
-            max_tokens: int = 2048,
-            bank_id: str | None = None,
-        ) -> str:
-            """
-            Create a new mental model (pinned reflection).
-
-            A mental model is a living document generated by running the source_query through
-            reflect. The content is auto-generated asynchronously - use the returned operation_id
-            to track progress.
-
-            EXAMPLES:
-            - name="Coding Preferences", source_query="What coding patterns and tools does the user prefer?"
-            - name="Project Goals", source_query="What are the user's current project goals and priorities?"
-            - name="Communication Style", source_query="How does the user prefer to communicate?"
-
-            Args:
-                name: Human-readable name for the mental model
-                source_query: The query to run through reflect to generate content
-                mental_model_id: Optional custom ID (alphanumeric lowercase with hyphens). Auto-generated if not provided.
-                tags: Optional tags for scoped visibility filtering
-                max_tokens: Maximum tokens for generated content (256-8192, default: 2048)
-                bank_id: Optional bank (defaults to session bank). Use for cross-bank operations.
-            """
-            try:
-                target_bank = bank_id or config.bank_id_resolver()
-                if target_bank is None:
-                    return '{"error": "No bank_id configured"}'
-
-                validation_error = _validate_mental_model_inputs(
-                    name=name, source_query=source_query, max_tokens=max_tokens
-                )
-                if validation_error:
-                    return json.dumps({"error": validation_error})
-
-                request_context = _get_request_context(config)
-
-                # Create with placeholder content
-                model = await memory.create_mental_model(
-                    bank_id=target_bank,
-                    name=name,
-                    source_query=source_query,
-                    content="Generating content...",
-                    mental_model_id=mental_model_id,
-                    tags=tags,
-                    max_tokens=max_tokens,
-                    request_context=request_context,
-                )
-
-                # Schedule async refresh to generate actual content
-                result = await memory.submit_async_refresh_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=model["id"],
-                    request_context=request_context,
-                )
-
-                return json.dumps(
-                    {
-                        "mental_model_id": model["id"],
-                        "operation_id": result["operation_id"],
-                        "status": "created",
-                        "message": f"Mental model '{name}' created. Content is being generated asynchronously.",
-                    }
-                )
-            except ValueError as e:
-                return json.dumps({"error": str(e)})
-            except Exception as e:
-                logger.error(f"Error creating mental model: {e}", exc_info=True)
-                return f'{{"error": "{e}"}}'
-
-    else:
-
-        @mcp.tool()
-        async def create_mental_model(
-            name: str,
-            source_query: str,
-            mental_model_id: str | None = None,
-            tags: list[str] | None = None,
-            max_tokens: int = 2048,
-        ) -> dict:
-            """
-            Create a new mental model (pinned reflection).
-
-            A mental model is a living document generated by running the source_query through
-            reflect. The content is auto-generated asynchronously - use the returned operation_id
-            to track progress.
-
-            EXAMPLES:
-            - name="Coding Preferences", source_query="What coding patterns and tools does the user prefer?"
-            - name="Project Goals", source_query="What are the user's current project goals and priorities?"
-            - name="Communication Style", source_query="How does the user prefer to communicate?"
-
-            Args:
-                name: Human-readable name for the mental model
-                source_query: The query to run through reflect to generate content
-                mental_model_id: Optional custom ID (alphanumeric lowercase with hyphens). Auto-generated if not provided.
-                tags: Optional tags for scoped visibility filtering
-                max_tokens: Maximum tokens for generated content (256-8192, default: 2048)
-            """
-            try:
-                target_bank = config.bank_id_resolver()
-                if target_bank is None:
-                    return {"error": "No bank_id configured"}
-
-                validation_error = _validate_mental_model_inputs(
-                    name=name, source_query=source_query, max_tokens=max_tokens
-                )
-                if validation_error:
-                    return {"error": validation_error}
-
-                request_context = _get_request_context(config)
-
-                model = await memory.create_mental_model(
-                    bank_id=target_bank,
-                    name=name,
-                    source_query=source_query,
-                    content="Generating content...",
-                    mental_model_id=mental_model_id,
-                    tags=tags,
-                    max_tokens=max_tokens,
-                    request_context=request_context,
-                )
-
-                result = await memory.submit_async_refresh_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=model["id"],
-                    request_context=request_context,
-                )
-
-                return {
-                    "mental_model_id": model["id"],
-                    "operation_id": result["operation_id"],
-                    "status": "created",
-                    "message": f"Mental model '{name}' created. Content is being generated asynchronously.",
-                }
-            except ValueError as e:
-                return {"error": str(e)}
-            except Exception as e:
-                logger.error(f"Error creating mental model: {e}", exc_info=True)
-                return {"error": str(e)}
-
-
-def _register_update_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
-    """Register the update_mental_model tool."""
-
-    if config.include_bank_id_param:
-
-        @mcp.tool()
-        async def update_mental_model(
-            mental_model_id: str,
-            name: str | None = None,
-            source_query: str | None = None,
-            max_tokens: int | None = None,
-            tags: list[str] | None = None,
-            bank_id: str | None = None,
-        ) -> str:
-            """
-            Update a mental model's metadata.
-
-            Changes the name, source query, or tags of an existing mental model.
-            To regenerate the content, use refresh_mental_model after updating the source query.
-
-            Args:
-                mental_model_id: The ID of the mental model to update
-                name: New name (leave None to keep current)
-                source_query: New source query (leave None to keep current)
-                max_tokens: New max tokens for content generation (256-8192, leave None to keep current)
-                tags: New tags (leave None to keep current)
-                bank_id: Optional bank (defaults to session bank). Use for cross-bank operations.
-            """
-            try:
-                target_bank = bank_id or config.bank_id_resolver()
-                if target_bank is None:
-                    return '{"error": "No bank_id configured"}'
-
-                validation_error = _validate_mental_model_inputs(
-                    name=name, source_query=source_query, max_tokens=max_tokens
-                )
-                if validation_error:
-                    return json.dumps({"error": validation_error})
-
-                model = await memory.update_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=mental_model_id,
-                    name=name,
-                    source_query=source_query,
-                    max_tokens=max_tokens,
-                    tags=tags,
-                    request_context=_get_request_context(config),
-                )
-                if model is None:
-                    return json.dumps({"error": f"Mental model '{mental_model_id}' not found in bank '{target_bank}'"})
-                return json.dumps(model, indent=2, default=str)
-            except Exception as e:
-                logger.error(f"Error updating mental model: {e}", exc_info=True)
-                return f'{{"error": "{e}"}}'
-
-    else:
-
-        @mcp.tool()
-        async def update_mental_model(
-            mental_model_id: str,
-            name: str | None = None,
-            source_query: str | None = None,
-            max_tokens: int | None = None,
-            tags: list[str] | None = None,
-        ) -> dict:
-            """
-            Update a mental model's metadata.
-
-            Changes the name, source query, or tags of an existing mental model.
-            To regenerate the content, use refresh_mental_model after updating the source query.
-
-            Args:
-                mental_model_id: The ID of the mental model to update
-                name: New name (leave None to keep current)
-                source_query: New source query (leave None to keep current)
-                max_tokens: New max tokens for content generation (256-8192, leave None to keep current)
-                tags: New tags (leave None to keep current)
-            """
-            try:
-                target_bank = config.bank_id_resolver()
-                if target_bank is None:
-                    return {"error": "No bank_id configured"}
-
-                validation_error = _validate_mental_model_inputs(
-                    name=name, source_query=source_query, max_tokens=max_tokens
-                )
-                if validation_error:
-                    return {"error": validation_error}
-
-                model = await memory.update_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=mental_model_id,
-                    name=name,
-                    source_query=source_query,
-                    max_tokens=max_tokens,
-                    tags=tags,
-                    request_context=_get_request_context(config),
-                )
-                if model is None:
-                    return {"error": f"Mental model '{mental_model_id}' not found in bank '{target_bank}'"}
-                return model
-            except Exception as e:
-                logger.error(f"Error updating mental model: {e}", exc_info=True)
-                return {"error": str(e)}
-
-
-def _register_delete_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
-    """Register the delete_mental_model tool."""
-
-    if config.include_bank_id_param:
-
-        @mcp.tool()
-        async def delete_mental_model(
-            mental_model_id: str,
-            bank_id: str | None = None,
-        ) -> str:
-            """
-            Delete a mental model.
-
-            Permanently removes a mental model and its generated content.
-
-            Args:
-                mental_model_id: The ID of the mental model to delete
-                bank_id: Optional bank (defaults to session bank). Use for cross-bank operations.
-            """
-            try:
-                target_bank = bank_id or config.bank_id_resolver()
-                if target_bank is None:
-                    return '{"error": "No bank_id configured"}'
-
-                deleted = await memory.delete_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=mental_model_id,
-                    request_context=_get_request_context(config),
-                )
-                if not deleted:
-                    return json.dumps({"error": f"Mental model '{mental_model_id}' not found in bank '{target_bank}'"})
-                return json.dumps({"status": "deleted", "mental_model_id": mental_model_id})
-            except Exception as e:
-                logger.error(f"Error deleting mental model: {e}", exc_info=True)
-                return f'{{"error": "{e}"}}'
-
-    else:
-
-        @mcp.tool()
-        async def delete_mental_model(
-            mental_model_id: str,
-        ) -> dict:
-            """
-            Delete a mental model.
-
-            Permanently removes a mental model and its generated content.
-
-            Args:
-                mental_model_id: The ID of the mental model to delete
-            """
-            try:
-                target_bank = config.bank_id_resolver()
-                if target_bank is None:
-                    return {"error": "No bank_id configured"}
-
-                deleted = await memory.delete_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=mental_model_id,
-                    request_context=_get_request_context(config),
-                )
-                if not deleted:
-                    return {"error": f"Mental model '{mental_model_id}' not found in bank '{target_bank}'"}
-                return {"status": "deleted", "mental_model_id": mental_model_id}
-            except Exception as e:
-                logger.error(f"Error deleting mental model: {e}", exc_info=True)
-                return {"error": str(e)}
-
-
-def _register_refresh_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
-    """Register the refresh_mental_model tool."""
-
-    if config.include_bank_id_param:
-
-        @mcp.tool()
-        async def refresh_mental_model(
-            mental_model_id: str,
-            bank_id: str | None = None,
-        ) -> str:
-            """
-            Refresh a mental model by re-running its source query.
-
-            Schedules an async task to re-run the source query through reflect and update the
-            mental model's content with fresh results. Use this after adding new memories or
-            when the mental model's content may be stale.
-
-            Args:
-                mental_model_id: The ID of the mental model to refresh
-                bank_id: Optional bank (defaults to session bank). Use for cross-bank operations.
-            """
-            try:
-                target_bank = bank_id or config.bank_id_resolver()
-                if target_bank is None:
-                    return '{"error": "No bank_id configured"}'
-
-                result = await memory.submit_async_refresh_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=mental_model_id,
-                    request_context=_get_request_context(config),
-                )
-                return json.dumps(
-                    {
-                        "operation_id": result["operation_id"],
-                        "status": "queued",
-                        "message": f"Refresh queued for mental model '{mental_model_id}'.",
-                    }
-                )
-            except ValueError as e:
-                return json.dumps({"error": str(e)})
-            except Exception as e:
-                logger.error(f"Error refreshing mental model: {e}", exc_info=True)
-                return f'{{"error": "{e}"}}'
-
-    else:
-
-        @mcp.tool()
-        async def refresh_mental_model(
-            mental_model_id: str,
-        ) -> dict:
-            """
-            Refresh a mental model by re-running its source query.
-
-            Schedules an async task to re-run the source query through reflect and update the
-            mental model's content with fresh results. Use this after adding new memories or
-            when the mental model's content may be stale.
-
-            Args:
-                mental_model_id: The ID of the mental model to refresh
-            """
-            try:
-                target_bank = config.bank_id_resolver()
-                if target_bank is None:
-                    return {"error": "No bank_id configured"}
-
-                result = await memory.submit_async_refresh_mental_model(
-                    bank_id=target_bank,
-                    mental_model_id=mental_model_id,
-                    request_context=_get_request_context(config),
-                )
-                return {
-                    "operation_id": result["operation_id"],
-                    "status": "queued",
-                    "message": f"Refresh queued for mental model '{mental_model_id}'.",
-                }
-            except ValueError as e:
-                return {"error": str(e)}
-            except Exception as e:
-                logger.error(f"Error refreshing mental model: {e}", exc_info=True)
-                return {"error": str(e)}
@@ -189,7 +189,7 @@ class MetricsCollectorBase:
        Args:
            provider: LLM provider name (openai, anthropic, gemini, groq, ollama, lmstudio)
            model: Model name
-            scope: Scope identifier (e.g., "memory", "reflect", "consolidation")
+            scope: Scope identifier (e.g., "memory", "reflect", "entity_observation")
            duration: Call duration in seconds
            input_tokens: Number of input/prompt tokens
            output_tokens: Number of output/completion tokens
@@ -321,7 +321,7 @@ class MetricsCollector(MetricsCollectorBase):
                pass

        Args:
-            operation: Operation name (retain, recall, reflect, consolidation)
+            operation: Operation name (retain, recall, reflect, entity_observation)
            bank_id: Memory bank ID
            source: Source of the operation (api, reflect, internal)
            budget: Optional budget level (low, mid, high)
@@ -371,7 +371,7 @@ class MetricsCollector(MetricsCollectorBase):
        Args:
            provider: LLM provider name (openai, anthropic, gemini, groq, ollama, lmstudio)
            model: Model name
-            scope: Scope identifier (e.g., "memory", "reflect", "consolidation")
+            scope: Scope identifier (e.g., "memory", "reflect", "entity_observation")
            duration: Call duration in seconds
            input_tokens: Number of input/prompt tokens
            output_tokens: Number of output/completion tokens
@@ -25,49 +25,12 @@ from alembic.config import Config
 from alembic.script.revision import ResolutionError
 from sqlalchemy import create_engine, text

-from .utils import mask_network_location
-
 logger = logging.getLogger(__name__)

 # Advisory lock ID for migrations (arbitrary unique number)
 MIGRATION_LOCK_ID = 123456789


-def _detect_vector_extension(conn, vector_extension: str = "pgvector") -> str:
-    """
-    Validate vector extension: 'vchord' or 'pgvector'.
-
-    Args:
-        conn: SQLAlchemy connection object
-        vector_extension: Configured extension ("pgvector" or "vchord")
-
-    Returns:
-        "vchord" or "pgvector"
-
-    Raises:
-        RuntimeError: If configured extension is not installed
-    """
-    # Verify the configured extension is installed
-    if vector_extension == "vchord":
-        vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
-        if not vchord_check:
-            raise RuntimeError(
-                "Configured vector extension 'vchord' not found. Install it with: CREATE EXTENSION vchord CASCADE;"
-            )
-        logger.debug("Using configured vector extension: vchord")
-        return "vchord"
-    elif vector_extension == "pgvector":
-        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
-        if not pgvector_check:
-            raise RuntimeError(
-                "Configured vector extension 'pgvector' not found. Install it with: CREATE EXTENSION vector;"
-            )
-        logger.debug("Using configured vector extension: pgvector")
-        return "pgvector"
-    else:
-        raise ValueError(f"Invalid vector_extension: {vector_extension}. Must be 'pgvector' or 'vchord'")
-
-
 def _get_schema_lock_id(schema: str) -> int:
    """
    Generate a unique advisory lock ID for a schema.
@@ -91,7 +54,7 @@ def _run_migrations_internal(database_url: str, script_location: str, schema: st
    """
    schema_name = schema or "public"
    logger.info(f"Running database migrations to head for schema '{schema_name}'...")
-    logger.info(f"Database URL: {mask_network_location(database_url)}")
+    logger.info(f"Database URL: {database_url}")
    logger.info(f"Script location: {script_location}")

    # Create Alembic configuration programmatically (no alembic.ini needed)
@@ -202,81 +165,6 @@ def run_migrations(
            logger.debug("Migration advisory lock acquired")

            try:
-                # Ensure pgvector extension is installed globally BEFORE schema migrations
-                # This is critical: the extension must exist database-wide before any schema
-                # migrations run, otherwise custom schemas won't have access to vector types
-                logger.debug("Checking pgvector extension availability...")
-
-                # First, check if extension already exists
-                ext_check = conn.execute(
-                    text(
-                        "SELECT extname, nspname FROM pg_extension e "
-                        "JOIN pg_namespace n ON e.extnamespace = n.oid "
-                        "WHERE extname = 'vector'"
-                    )
-                ).fetchone()
-
-                if ext_check:
-                    # Extension exists - check if in correct schema
-                    ext_schema = ext_check[1]
-                    if ext_schema == "public":
-                        logger.info("pgvector extension found in public schema - ready to use")
-                    else:
-                        # Extension in wrong schema - try to fix if we have permissions
-                        logger.warning(
-                            f"pgvector extension found in schema '{ext_schema}' instead of 'public'. "
-                            f"Attempting to relocate..."
-                        )
-                        try:
-                            conn.execute(text("DROP EXTENSION vector CASCADE"))
-                            conn.execute(text("SET search_path TO public"))
-                            conn.execute(text("CREATE EXTENSION vector"))
-                            conn.commit()
-                            logger.info("pgvector extension relocated to public schema")
-                        except Exception as e:
-                            # Failed to relocate - log but don't fail if extension exists somewhere
-                            logger.warning(
-                                f"Could not relocate pgvector extension to public schema: {e}. "
-                                f"Continuing with extension in '{ext_schema}' schema."
-                            )
-                            conn.rollback()
-                else:
-                    # Extension doesn't exist - try to install
-                    logger.info("pgvector extension not found, attempting to install...")
-                    try:
-                        conn.execute(text("SET search_path TO public"))
-                        conn.execute(text("CREATE EXTENSION vector"))
-                        conn.commit()
-                        logger.info("pgvector extension installed in public schema")
-                    except Exception as e:
-                        # Installation failed - this is only fatal if extension truly doesn't exist
-                        # Check one more time in case another process installed it
-                        conn.rollback()
-                        ext_recheck = conn.execute(
-                            text(
-                                "SELECT nspname FROM pg_extension e "
-                                "JOIN pg_namespace n ON e.extnamespace = n.oid "
-                                "WHERE extname = 'vector'"
-                            )
-                        ).fetchone()
-
-                        if ext_recheck:
-                            logger.warning(
-                                f"Could not install pgvector extension (permission denied?), "
-                                f"but extension exists in '{ext_recheck[0]}' schema. Continuing..."
-                            )
-                        else:
-                            # Extension truly doesn't exist and we can't install it
-                            logger.error(
-                                f"pgvector extension is not installed and cannot be installed: {e}. "
-                                f"Please ensure pgvector is installed by a database administrator. "
-                                f"See: https://github.com/pgvector/pgvector#installation"
-                            )
-                            raise RuntimeError(
-                                "pgvector extension is required but not installed. "
-                                "Please install it with: CREATE EXTENSION vector;"
-                            ) from e
-
                # Run migrations while holding the lock
                _run_migrations_internal(database_url, script_location, schema=schema)
            finally:
@@ -359,7 +247,6 @@ def ensure_embedding_dimension(
    database_url: str,
    required_dimension: int,
    schema: str | None = None,
-    vector_extension: str = "pgvector",
 ) -> None:
    """
    Ensure the embedding column dimension matches the model's dimension.
@@ -374,7 +261,6 @@ def ensure_embedding_dimension(
        database_url: SQLAlchemy database URL
        required_dimension: The embedding dimension required by the model
        schema: Target PostgreSQL schema name (None for public)
-        vector_extension: Configured vector extension ("pgvector" or "vchord")

    Raises:
        RuntimeError: If dimension mismatch with existing data
@@ -398,10 +284,6 @@ def ensure_embedding_dimension(
            logger.debug(f"memory_units table does not exist in schema '{schema_name}', skipping dimension check")
            return

-        # Detect which vector extension is available
-        vector_ext = _detect_vector_extension(conn, vector_extension)
-        logger.info(f"Using vector extension: {vector_ext}")
-
        # Get current column dimension from pg_attribute
        # pgvector stores dimension in atttypmod
        current_dim = conn.execute(
@@ -449,7 +331,8 @@ def ensure_embedding_dimension(
        # Table is empty, safe to alter column
        logger.info(f"Altering embedding column dimension from {current_dimension} to {required_dimension}")

-        # Drop existing vector index (works for both HNSW and vchordrq)
+        # Drop the HNSW index on embedding column if it exists
+        # Only drop indexes that use 'hnsw' and reference the 'embedding' column
        conn.execute(
            text(f"""
                DO $$
@@ -459,7 +342,7 @@ def ensure_embedding_dimension(
                        SELECT indexname FROM pg_indexes
                        WHERE schemaname = '{schema_name}'
                          AND tablename = 'memory_units'
-                          AND (indexdef LIKE '%hnsw%' OR indexdef LIKE '%vchordrq%')
+                          AND indexdef LIKE '%hnsw%'
                          AND indexdef LIKE '%embedding%'
                    LOOP
                        EXECUTE 'DROP INDEX IF EXISTS {schema_name}.' || idx_name;
@@ -474,410 +357,15 @@ def ensure_embedding_dimension(
        )
        conn.commit()

-        # Recreate index with appropriate type based on detected extension
-        if vector_ext == "vchord":
-            conn.execute(
-                text(f"""
-                    CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_vchordrq
-                    ON {schema_name}.memory_units
-                    USING vchordrq (embedding vector_l2_ops)
-                """)
-            )
-            logger.info(f"Created vchordrq index for {required_dimension}-dimensional embeddings")
-        else:  # pgvector
-            conn.execute(
-                text(f"""
-                    CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_hnsw
-                    ON {schema_name}.memory_units
-                    USING hnsw (embedding vector_cosine_ops)
-                    WITH (m = 16, ef_construction = 64)
-                """)
-            )
-            logger.info(f"Created HNSW index for {required_dimension}-dimensional embeddings")
+        # Recreate the HNSW index
+        conn.execute(
+            text(f"""
+                CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_hnsw
+                ON {schema_name}.memory_units
+                USING hnsw (embedding vector_cosine_ops)
+                WITH (m = 16, ef_construction = 64)
+            """)
+        )
        conn.commit()

        logger.info(f"Successfully changed embedding dimension to {required_dimension}")
-
-
-def ensure_vector_extension(
-    database_url: str,
-    vector_extension: str = "pgvector",
-    schema: str | None = None,
-) -> None:
-    """
-    Ensure the vector indexes match the configured vector extension.
-
-    This function checks the current vector index type in the database
-    and adjusts it if necessary:
-    - If index type matches configured extension: no action needed
-    - If they differ and tables are empty: drop old indexes, recreate with new type
-    - If they differ and tables have data: raise error with migration guidance
-
-    Args:
-        database_url: SQLAlchemy database URL
-        vector_extension: Configured vector extension ("pgvector" or "vchord")
-        schema: Target PostgreSQL schema name (None for public)
-
-    Raises:
-        RuntimeError: If extension mismatch with existing data
-    """
-    schema_name = schema or "public"
-
-    engine = create_engine(database_url)
-    with engine.connect() as conn:
-        # Detect which vector extension should be used
-        target_ext = _detect_vector_extension(conn, vector_extension)
-        logger.info(f"Target vector extension: {target_ext}")
-
-        # Tables with vector indexes to check
-        tables_to_check = [
-            ("memory_units", "idx_memory_units_embedding"),
-            ("learnings", "idx_learnings_embedding"),
-            ("pinned_reflections", "idx_pinned_reflections_embedding"),
-        ]
-
-        # Determine target index type
-        target_index_type = "vchordrq" if target_ext == "vchord" else "hnsw"
-
-        mismatched_tables = []
-        tables_with_data = []
-
-        for table_name, index_name in tables_to_check:
-            # Check if table exists
-            table_exists = conn.execute(
-                text("""
-                    SELECT EXISTS (
-                        SELECT 1 FROM information_schema.tables
-                        WHERE table_schema = :schema AND table_name = :table_name
-                    )
-                """),
-                {"schema": schema_name, "table_name": table_name},
-            ).scalar()
-
-            if not table_exists:
-                logger.debug(f"Table {table_name} does not exist in schema '{schema_name}', skipping")
-                continue
-
-            # Check current index type by querying pg_indexes
-            current_index_info = conn.execute(
-                text("""
-                    SELECT indexdef
-                    FROM pg_indexes
-                    WHERE schemaname = :schema
-                      AND tablename = :table_name
-                      AND indexname LIKE :index_pattern
-                """),
-                {"schema": schema_name, "table_name": table_name, "index_pattern": "%embedding%"},
-            ).fetchone()
-
-            if not current_index_info:
-                logger.warning(f"No embedding index found for {table_name}, will create it")
-                mismatched_tables.append((table_name, index_name, None))
-                continue
-
-            indexdef = current_index_info[0].lower()
-            if "vchordrq" in indexdef:
-                current_index_type = "vchordrq"
-            elif "hnsw" in indexdef:
-                current_index_type = "hnsw"
-            else:
-                logger.warning(f"Unknown index type for {table_name}: {indexdef}")
-                continue
-
-            # Check if index type matches target
-            if current_index_type != target_index_type:
-                logger.info(
-                    f"Index type mismatch on {table_name}: current={current_index_type}, target={target_index_type}"
-                )
-                mismatched_tables.append((table_name, index_name, current_index_type))
-
-                # Check if table has data
-                row_count = conn.execute(
-                    text(f"SELECT COUNT(*) FROM {schema_name}.{table_name} WHERE embedding IS NOT NULL")
-                ).scalar()
-
-                if row_count > 0:
-                    tables_with_data.append((table_name, row_count))
-            else:
-                logger.debug(f"Index type OK for {table_name}: {current_index_type}")
-
-        # If no mismatches, we're done
-        if not mismatched_tables:
-            logger.debug(f"All vector indexes match configured extension: {target_ext}")
-            return
-
-        # If there's data in any mismatched table, raise error
-        if tables_with_data:
-            table_list = ", ".join([f"{table}({count} rows)" for table, count in tables_with_data])
-            raise RuntimeError(
-                f"Cannot change vector extension from {current_index_type} to {target_index_type}: "
-                f"the following tables contain data: {table_list}. "
-                f"To change vector extension, you must either:\n"
-                f"  1. Re-embed all data: DELETE FROM {schema_name}.memory_units; "
-                f"DELETE FROM {schema_name}.learnings; DELETE FROM {schema_name}.pinned_reflections; then restart\n"
-                f"  2. Use the current vector extension (set HINDSIGHT_API_VECTOR_EXTENSION='{current_index_type.replace('vchordrq', 'vchord').replace('hnsw', 'pgvector')}')"
-            )
-
-        # Tables are empty, safe to recreate indexes
-        logger.info(f"Recreating vector indexes for {target_ext}")
-
-        for table_name, index_name, current_type in mismatched_tables:
-            # Drop existing index if it exists
-            if current_type:
-                logger.info(f"Dropping {current_type} index on {table_name}")
-                conn.execute(text(f"DROP INDEX IF EXISTS {schema_name}.{index_name}"))
-
-            # Create new index with appropriate type
-            if target_ext == "vchord":
-                logger.info(f"Creating vchordrq index on {table_name}")
-                conn.execute(
-                    text(f"""
-                        CREATE INDEX IF NOT EXISTS {index_name}
-                        ON {schema_name}.{table_name}
-                        USING vchordrq (embedding vector_l2_ops)
-                    """)
-                )
-            else:  # pgvector
-                logger.info(f"Creating HNSW index on {table_name}")
-                conn.execute(
-                    text(f"""
-                        CREATE INDEX IF NOT EXISTS {index_name}
-                        ON {schema_name}.{table_name}
-                        USING hnsw (embedding vector_cosine_ops)
-                        WITH (m = 16, ef_construction = 64)
-                    """)
-                )
-
-        conn.commit()
-        logger.info(f"Successfully migrated vector indexes to {target_ext}")
-
-
-def ensure_text_search_extension(
-    database_url: str,
-    text_search_extension: str = "native",
-    schema: str | None = None,
-) -> None:
-    """
-    Ensure the text search columns and indexes match the configured extension.
-
-    This function checks the current search_vector column type and index type
-    in the database and adjusts them if necessary:
-    - If they match configured extension: no action needed
-    - If they differ and tables are empty: drop old column/index, recreate with new type
-    - If they differ and tables have data: raise error with migration guidance
-
-    Args:
-        database_url: SQLAlchemy database URL
-        text_search_extension: Configured text search extension ("native" or "vchord")
-        schema: Target PostgreSQL schema name (None for public)
-
-    Raises:
-        RuntimeError: If extension mismatch with existing data
-    """
-    schema_name = schema or "public"
-
-    engine = create_engine(database_url)
-    with engine.connect() as conn:
-        # Tables with search_vector columns to check
-        tables_to_check = [
-            "memory_units",
-            "reflections",  # Renamed from pinned_reflections in p1k2l3m4n5o6 migration
-        ]
-
-        # Determine target column type and index type
-        if text_search_extension == "vchord":
-            target_column_type = "bm25vector"
-            target_index_type = "bm25"
-        elif text_search_extension == "pg_textsearch":
-            target_column_type = "text"
-            target_index_type = "bm25"
-        else:  # native
-            target_column_type = "tsvector"
-            target_index_type = "gin"
-
-        mismatched_tables = []
-        tables_with_data = []
-
-        for table_name in tables_to_check:
-            # Check if table exists
-            table_exists = conn.execute(
-                text("""
-                    SELECT EXISTS (
-                        SELECT 1 FROM information_schema.tables
-                        WHERE table_schema = :schema AND table_name = :table_name
-                    )
-                """),
-                {"schema": schema_name, "table_name": table_name},
-            ).scalar()
-
-            if not table_exists:
-                logger.debug(f"Table {table_name} does not exist in schema '{schema_name}', skipping")
-                continue
-
-            # Get current column type from information_schema
-            current_column_info = conn.execute(
-                text("""
-                    SELECT data_type, udt_name
-                    FROM information_schema.columns
-                    WHERE table_schema = :schema
-                      AND table_name = :table_name
-                      AND column_name = 'search_vector'
-                """),
-                {"schema": schema_name, "table_name": table_name},
-            ).fetchone()
-
-            if not current_column_info:
-                logger.warning(f"No search_vector column found for {table_name}, will create it")
-                mismatched_tables.append((table_name, None, None))
-                continue
-
-            # Check column type (udt_name contains the actual type: tsvector, bm25vector, etc.)
-            current_column_type = current_column_info[1]  # udt_name
-
-            # Get current index type
-            current_index_info = conn.execute(
-                text("""
-                    SELECT am.amname
-                    FROM pg_indexes pi
-                    JOIN pg_class c ON c.relname = pi.indexname
-                    JOIN pg_am am ON am.oid = c.relam
-                    WHERE pi.schemaname = :schema
-                      AND pi.tablename = :table_name
-                      AND pi.indexname LIKE '%text_search%'
-                """),
-                {"schema": schema_name, "table_name": table_name},
-            ).fetchone()
-
-            current_index_type = current_index_info[0] if current_index_info else None
-
-            # Check if column and index types match target
-            column_matches = current_column_type == target_column_type
-            index_matches = current_index_type == target_index_type if current_index_type else False
-
-            if not (column_matches and index_matches):
-                logger.info(
-                    f"Text search mismatch on {table_name}: "
-                    f"column={current_column_type} (want {target_column_type}), "
-                    f"index={current_index_type} (want {target_index_type})"
-                )
-                mismatched_tables.append((table_name, current_column_type, current_index_type))
-
-                # Check if table has data
-                row_count = conn.execute(text(f"SELECT COUNT(*) FROM {schema_name}.{table_name}")).scalar()
-
-                if row_count > 0:
-                    tables_with_data.append((table_name, row_count))
-            else:
-                logger.debug(f"Text search OK for {table_name}: {current_column_type}/{current_index_type}")
-
-        # If no mismatches, we're done
-        if not mismatched_tables:
-            logger.debug(f"All text search columns/indexes match configured extension: {text_search_extension}")
-            return
-
-        # If there's data in any mismatched table, raise error
-        if tables_with_data:
-            table_list = ", ".join([f"{table}({count} rows)" for table, count in tables_with_data])
-            # Detect current extension from column type
-            current_col_type = mismatched_tables[0][1]
-            if current_col_type == "tsvector":
-                current_ext = "native"
-            elif current_col_type == "bm25vector":
-                current_ext = "vchord"
-            elif current_col_type == "text":
-                current_ext = "pg_textsearch"
-            else:
-                current_ext = "unknown"
-            raise RuntimeError(
-                f"Cannot change text search extension from {current_ext} to {text_search_extension}: "
-                f"the following tables contain data: {table_list}. "
-                f"To change text search extension, you must either:\n"
-                f"  1. Clear all data: DELETE FROM {schema_name}.memory_units; "
-                f"DELETE FROM {schema_name}.reflections; then restart\n"
-                f"  2. Use the current text search extension (set HINDSIGHT_API_TEXT_SEARCH_EXTENSION='{current_ext}')"
-            )
-
-        # Tables are empty, safe to recreate columns/indexes
-        logger.info(f"Recreating text search columns/indexes for {text_search_extension}")
-
-        for table_name, current_col_type, current_idx_type in mismatched_tables:
-            # Drop existing index if it exists
-            if current_idx_type:
-                logger.info(f"Dropping {current_idx_type} index on {table_name}")
-                conn.execute(
-                    text(f"""
-                        DROP INDEX IF EXISTS {schema_name}.idx_{table_name.replace(".", "_")}_text_search
-                    """)
-                )
-
-            # Drop existing column if it exists
-            if current_col_type:
-                logger.info(f"Dropping {current_col_type} column on {table_name}")
-                conn.execute(text(f"ALTER TABLE {schema_name}.{table_name} DROP COLUMN IF EXISTS search_vector"))
-
-            # Create new column with appropriate type
-            if text_search_extension == "vchord":
-                logger.info(f"Creating bm25vector column on {table_name}")
-                # Note: vchord_bm25 extension creates types in bm25_catalog schema
-                conn.execute(
-                    text(f"ALTER TABLE {schema_name}.{table_name} ADD COLUMN search_vector bm25_catalog.bm25vector")
-                )
-
-                # Create BM25 index
-                logger.info(f"Creating BM25 index on {table_name}")
-                conn.execute(
-                    text(f"""
-                        CREATE INDEX idx_{table_name.replace(".", "_")}_text_search
-                        ON {schema_name}.{table_name}
-                        USING bm25 (search_vector bm25_catalog.bm25_ops)
-                    """)
-                )
-            elif text_search_extension == "pg_textsearch":
-                logger.info(f"Creating TEXT column on {table_name}")
-                # Dummy TEXT column for consistency (indexes operate on base columns)
-                conn.execute(text(f"ALTER TABLE {schema_name}.{table_name} ADD COLUMN search_vector TEXT"))
-
-                # Create BM25 index on expression
-                logger.info(f"Creating BM25 index on {table_name}")
-                # Different expression for each table
-                if table_name == "memory_units":
-                    index_expr = "(COALESCE(text, '') || ' ' || COALESCE(context, ''))"
-                else:  # reflections
-                    index_expr = "(COALESCE(name, '') || ' ' || content)"
-
-                conn.execute(
-                    text(f"""
-                        CREATE INDEX idx_{table_name.replace(".", "_")}_text_search
-                        ON {schema_name}.{table_name}
-                        USING bm25({index_expr})
-                        WITH (text_config='english')
-                    """)
-                )
-            else:  # native
-                logger.info(f"Creating tsvector column on {table_name}")
-                # Different GENERATED expression for each table
-                if table_name == "memory_units":
-                    generated_expr = "to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))"
-                else:  # reflections
-                    generated_expr = "to_tsvector('english', COALESCE(name, '') || ' ' || content)"
-
-                conn.execute(
-                    text(f"""
-                        ALTER TABLE {schema_name}.{table_name}
-                        ADD COLUMN search_vector tsvector
-                        GENERATED ALWAYS AS ({generated_expr}) STORED
-                    """)
-                )
-
-                # Create GIN index
-                logger.info(f"Creating GIN index on {table_name}")
-                conn.execute(
-                    text(f"""
-                        CREATE INDEX idx_{table_name.replace(".", "_")}_text_search
-                        ON {schema_name}.{table_name}
-                        USING gin(search_vector)
-                    """)
-                )
-
-        conn.commit()
-        logger.info(f"Successfully migrated text search to {text_search_extension}")
@@ -20,8 +20,7 @@ class RequestContext:
    api_key: str | None = None
    api_key_id: str | None = None  # UUID of the API key used for authentication
    tenant_id: str | None = None  # Tenant identifier (set by extension after auth)
-    internal: bool = False  # True for background/internal operations (skips extension auth)
-    user_initiated: bool = False  # True for async operations that originated from a user request
+    internal: bool = False  # True for background/internal operations (not user-visible)


 from pgvector.sqlalchemy import Vector
@@ -40,7 +40,7 @@ class EmbeddedPostgres:
            # Only set port if explicitly specified
            if self.port is not None:
                kwargs["port"] = self.port
-            self._pg0 = Pg0(**kwargs)
+            self._pg0 = Pg0(**kwargs)  # type: ignore[invalid-argument-type] - dict kwargs
        return self._pg0

    async def start(self, max_retries: int = 5, retry_delay: float = 4.0) -> str:
@@ -1,480 +0,0 @@
-"""
-OpenTelemetry distributed tracing instrumentation for Hindsight API.
-
-This module provides tracing for:
- LLM API calls with full prompts/completions following GenAI semantic conventions
- Token usage and model information
- Error tracking and finish reasons
-
-Tracing is conditional and disabled by default. When enabled, traces are exported
-to Langfuse (or any OTLP-compatible backend) via OTLP HTTP protocol.
-"""
-
-import json
-import logging
-import time
-from typing import Any, Optional
-
-from opentelemetry import trace
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
-from opentelemetry.sdk.resources import Resource
-from opentelemetry.sdk.trace import TracerProvider
-from opentelemetry.sdk.trace.export import BatchSpanProcessor
-from opentelemetry.trace import Status, StatusCode
-
-logger = logging.getLogger(__name__)
-
-
-def _serialize_for_span(obj: Any) -> str:
-    """Serialize an object for span recording, handling Pydantic models."""
-    if isinstance(obj, str):
-        return obj
-    if hasattr(obj, "model_dump_json"):
-        # Pydantic v2 model
-        return obj.model_dump_json()
-    if hasattr(obj, "json"):
-        # Pydantic v1 model
-        return obj.json()
-    if hasattr(obj, "model_dump"):
-        # Pydantic v2 model - convert to dict then json
-        return json.dumps(obj.model_dump())
-    if hasattr(obj, "dict"):
-        # Pydantic v1 model - convert to dict then json
-        return json.dumps(obj.dict())
-    # Fallback to json.dumps for dicts and other types
-    return json.dumps(obj)
-
-
-# No-op tracer for when tracing is disabled
-class NoOpTracer:
-    """No-op tracer that provides the same interface as OpenTelemetry Tracer but does nothing."""
-
-    def start_as_current_span(self, name: str, **kwargs):
-        """Return a no-op context manager that yields a NoOpSpan."""
-        from contextlib import contextmanager
-
-        @contextmanager
-        def noop_span_context():
-            yield NoOpSpan()
-
-        return noop_span_context()
-
-    def start_span(self, name: str, **kwargs):
-        """Return a no-op span."""
-        return NoOpSpan()
-
-
-class NoOpSpan:
-    """No-op span that provides the same interface as OpenTelemetry Span but does nothing."""
-
-    def set_attribute(self, key: str, value: Any) -> None:
-        """No-op."""
-        pass
-
-    def set_status(self, status: Any) -> None:
-        """No-op."""
-        pass
-
-    def record_exception(self, exception: Exception) -> None:
-        """No-op."""
-        pass
-
-    def add_event(self, name: str, attributes: dict | None = None) -> None:
-        """No-op."""
-        pass
-
-    def end(self, end_time: int | None = None) -> None:
-        """No-op."""
-        pass
-
-
-# Global tracer instance
-_tracer: trace.Tracer | NoOpTracer = NoOpTracer()
-_tracing_enabled: bool = False
-
-
-# GenAI semantic convention attribute names (based on v1.37 spec)
-class GenAIAttributes:
-    """GenAI semantic convention attribute names."""
-
-    # Operation and provider
-    OPERATION_NAME = "gen_ai.operation.name"
-    PROVIDER_NAME = "gen_ai.provider.name"
-
-    # Model information
-    REQUEST_MODEL = "gen_ai.request.model"
-    RESPONSE_MODEL = "gen_ai.response.model"
-
-    # Token usage
-    USAGE_INPUT_TOKENS = "gen_ai.usage.input_tokens"
-    USAGE_OUTPUT_TOKENS = "gen_ai.usage.output_tokens"
-
-    # Messages and prompts
-    SYSTEM_INSTRUCTIONS = "gen_ai.system_instructions"
-    INPUT_MESSAGES = "gen_ai.input.messages"
-    OUTPUT_MESSAGES = "gen_ai.output.messages"
-
-    # Response metadata
-    FINISH_REASONS = "gen_ai.response.finish_reasons"
-
-    # Error tracking
-    ERROR_TYPE = "error.type"
-
-
-# Provider name mapping (Hindsight internal -> GenAI semantic convention)
-PROVIDER_NAME_MAPPING = {
-    "openai": "openai",
-    "anthropic": "anthropic",
-    "gemini": "google",
-    "vertexai": "google",
-    "groq": "groq",
-    "ollama": "ollama",
-    "lmstudio": "lmstudio",
-    "openai-codex": "openai",
-    "claude-code": "anthropic",
-    "mock": "mock",
-}
-
-
-def initialize_tracing(
-    service_name: str,
-    endpoint: str,
-    headers: Optional[str] = None,
-    deployment_environment: str = "development",
-) -> None:
-    """
-    Initialize OpenTelemetry tracing with OTLP exporter.
-
-    Args:
-        service_name: Name of the service for resource attributes
-        endpoint: OTLP endpoint URL (e.g., https://cloud.langfuse.com/api/public/otel)
-        headers: Optional headers in format "key1=value1,key2=value2"
-        deployment_environment: Deployment environment (e.g., development, staging, production)
-    """
-    global _tracer, _tracing_enabled
-
-    # Create resource with service information
-    resource = Resource.create(
-        {
-            "service.name": service_name,
-            "service.version": "0.4.8",  # Could import from __version__
-            "deployment.environment.name": deployment_environment,
-        }
-    )
-
-    # Parse headers
-    headers_dict = {}
-    if headers:
-        for pair in headers.split(","):
-            if "=" in pair:
-                key, value = pair.split("=", 1)
-                headers_dict[key.strip()] = value.strip()
-
-    # Create OTLP HTTP exporter
-    # Note: Langfuse expects /v1/traces path appended to base endpoint
-    otlp_endpoint = endpoint if endpoint.endswith("/v1/traces") else f"{endpoint}/v1/traces"
-    otlp_exporter = OTLPSpanExporter(
-        endpoint=otlp_endpoint,
-        headers=headers_dict,
-    )
-
-    # Create tracer provider with batch processor
-    provider = TracerProvider(resource=resource)
-    provider.add_span_processor(BatchSpanProcessor(otlp_exporter))
-
-    # Set global tracer provider
-    trace.set_tracer_provider(provider)
-
-    # Get tracer for this application
-    _tracer = trace.get_tracer(__name__)
-    _tracing_enabled = True
-
-    logger.info(f"Tracing initialized: endpoint={otlp_endpoint}, service={service_name}")
-
-
-def get_tracer() -> trace.Tracer | NoOpTracer:
-    """
-    Get the global tracer instance.
-
-    Returns a no-op tracer if tracing is disabled, so callers don't need to check for None.
-    This improves code readability by allowing direct use without null checks.
-    """
-    return _tracer
-
-
-def create_operation_span(operation: str, bank_id: str | None = None):
-    """
-    Create a parent span for a Hindsight operation (retain, reflect, consolidation, etc.).
-
-    This creates the span hierarchy:
-    - hindsight.{operation} (parent)
-      - chat {model} (child LLM calls)
-
-    Args:
-        operation: Operation name (retain, reflect, consolidation, mental_model_refresh)
-        bank_id: Optional bank ID for context
-
-    Returns:
-        Span context manager
-    """
-    if not _tracing_enabled or _tracer is None:
-        # Return a no-op context manager
-        from contextlib import nullcontext
-
-        return nullcontext()
-
-    span_name = f"hindsight.{operation}"
-    span = _tracer.start_as_current_span(span_name)
-
-    # Add operation-specific attributes
-    if span and hasattr(span, "set_attribute"):
-        span.set_attribute("hindsight.operation", operation)
-        if bank_id:
-            span.set_attribute("hindsight.bank_id", bank_id)
-
-    return span
-
-
-def is_tracing_enabled() -> bool:
-    """Check if tracing is enabled."""
-    return _tracing_enabled
-
-
-# Maximum content length before truncation (to stay within span size limits)
-MAX_CONTENT_LENGTH = 100_000  # characters
-
-
-def _truncate_content(content: str) -> str:
-    """Truncate content if too large for span."""
-    if len(content) > MAX_CONTENT_LENGTH:
-        return content[:MAX_CONTENT_LENGTH] + f"\n\n[TRUNCATED: {len(content) - MAX_CONTENT_LENGTH} chars omitted]"
-    return content
-
-
-class LLMSpanRecorder:
-    """
-    Records OpenTelemetry spans for LLM calls following GenAI semantic conventions.
-    """
-
-    def __init__(self, tracer: trace.Tracer):
-        self.tracer = tracer
-
-    def record_llm_call(
-        self,
-        provider: str,
-        model: str,
-        scope: str,
-        messages: list[dict[str, str]],
-        response_content: Optional[str],
-        input_tokens: int,
-        output_tokens: int,
-        duration: float,
-        finish_reason: Optional[str] = None,
-        error: Optional[Exception] = None,
-        tool_calls: Optional[list[dict[str, Any]]] = None,
-    ) -> None:
-        """
-        Record a completed LLM call as a span with GenAI semantic conventions.
-
-        This creates a span AFTER the call completes, using timestamps to
-        set the correct start/end times. This approach works better with
-        the existing sync metrics recording pattern.
-
-        Args:
-            provider: Hindsight provider name
-            model: Model name
-            scope: Scope identifier (memory, reflect, consolidation, etc.)
-            messages: Input messages (chat history)
-            response_content: Response text from LLM
-            input_tokens: Input token count
-            output_tokens: Output token count
-            duration: Call duration in seconds
-            finish_reason: Reason the model stopped (stop, length, tool_calls, etc.)
-            error: Exception if call failed
-            tool_calls: List of tool calls made (for function calling)
-        """
-        try:
-            # Map provider name to GenAI semantic convention
-            genai_provider = PROVIDER_NAME_MAPPING.get(provider.lower(), provider.lower())
-
-            # Determine operation name based on scope/context
-            operation_name = "chat"  # Default for GenAI semantic conventions
-
-            # Create span name: "hindsight.{scope}" for consistency with parent spans
-            # Model info is available in span attributes (gen_ai.request.model)
-            if scope:
-                span_name = f"hindsight.{scope}"
-            else:
-                # Fallback to chat {model} if no scope provided
-                span_name = f"{operation_name} {model}"
-
-            # Calculate timestamps
-            end_time_ns = time.time_ns()
-            start_time_ns = end_time_ns - int(duration * 1_000_000_000)
-
-            # Create span with explicit timestamps
-            with self.tracer.start_as_current_span(
-                span_name,
-                start_time=start_time_ns,
-                end_on_exit=False,  # We'll set end time manually
-            ) as span:
-                # Set required attributes
-                span.set_attribute(GenAIAttributes.OPERATION_NAME, operation_name)
-                span.set_attribute(GenAIAttributes.PROVIDER_NAME, genai_provider)
-                span.set_attribute(GenAIAttributes.REQUEST_MODEL, model)
-                span.set_attribute(GenAIAttributes.RESPONSE_MODEL, model)
-                span.set_attribute(GenAIAttributes.USAGE_INPUT_TOKENS, input_tokens)
-                span.set_attribute(GenAIAttributes.USAGE_OUTPUT_TOKENS, output_tokens)
-
-                # Add custom attributes for Hindsight context
-                span.set_attribute("hindsight.scope", scope)
-                span.set_attribute("hindsight.provider.internal", provider)
-
-                # Add tool call information if present
-                if tool_calls:
-                    span.set_attribute("gen_ai.tool_calls.count", len(tool_calls))
-                    # Add tool names as comma-separated list
-                    tool_names = [tc.get("name", "") for tc in tool_calls]
-                    span.set_attribute("gen_ai.tool_calls.names", ",".join(tool_names))
-
-                # Format messages for GenAI conventions (as JSON)
-                input_messages_json = self._format_messages(messages)
-                output_messages_json = self._format_output(response_content, finish_reason)
-
-                # Extract system instructions if present
-                system_instructions = self._extract_system_instructions(messages)
-
-                # Add event with prompts/completions following v1.37 conventions
-                event_attrs = {}
-                if input_messages_json:
-                    event_attrs[GenAIAttributes.INPUT_MESSAGES] = input_messages_json
-                if output_messages_json:
-                    event_attrs[GenAIAttributes.OUTPUT_MESSAGES] = output_messages_json
-                if system_instructions:
-                    event_attrs[GenAIAttributes.SYSTEM_INSTRUCTIONS] = system_instructions
-                if finish_reason:
-                    event_attrs[GenAIAttributes.FINISH_REASONS] = json.dumps([finish_reason])
-
-                span.add_event(
-                    "gen_ai.client.inference.operation.details",
-                    attributes=event_attrs,
-                )
-
-                # Add individual tool call events with details
-                if tool_calls:
-                    for i, tc in enumerate(tool_calls):
-                        tool_event_attrs = {
-                            "tool.name": tc.get("name", ""),
-                            "tool.id": tc.get("id", ""),
-                            "tool.arguments": json.dumps(tc.get("arguments", {})),
-                        }
-                        span.add_event(f"gen_ai.tool_call.{i}", attributes=tool_event_attrs)
-
-                # Handle errors
-                if error:
-                    span.set_status(Status(StatusCode.ERROR, str(error)))
-                    span.set_attribute(GenAIAttributes.ERROR_TYPE, type(error).__name__)
-                    span.record_exception(error)
-                else:
-                    span.set_status(Status(StatusCode.OK))
-
-                # Set end time
-                span.end(end_time=end_time_ns)
-
-        except Exception as e:
-            # Don't let tracing errors break LLM calls
-            logger.error(f"Failed to record LLM span: {e}", exc_info=True)
-
-    def _format_messages(self, messages: list[dict[str, str]]) -> str:
-        """
-        Format messages into GenAI semantic convention format (JSON array).
-
-        Returns JSON string representation of message array.
-        """
-        try:
-            formatted = []
-            for msg in messages:
-                content = msg.get("content", "")
-                # Truncate if needed
-                if isinstance(content, str):
-                    content = _truncate_content(content)
-
-                formatted.append(
-                    {
-                        "role": msg.get("role", "user"),
-                        "content": content,
-                    }
-                )
-
-            return json.dumps(formatted)
-        except Exception as e:
-            logger.warning(f"Failed to format input messages: {e}")
-            return "[]"
-
-    def _format_output(
-        self,
-        content: Optional[str],
-        finish_reason: Optional[str],
-    ) -> str:
-        """Format output message into GenAI semantic convention format."""
-        try:
-            if content is None:
-                return "[]"
-
-            # Truncate if needed
-            if isinstance(content, str):
-                content = _truncate_content(content)
-
-            return json.dumps(
-                [
-                    {
-                        "role": "assistant",
-                        "content": content,
-                    }
-                ]
-            )
-        except Exception as e:
-            logger.warning(f"Failed to format output message: {e}")
-            return "[]"
-
-    def _extract_system_instructions(self, messages: list[dict[str, str]]) -> Optional[str]:
-        """Extract system instructions from messages if present."""
-        try:
-            for msg in messages:
-                if msg.get("role") == "system":
-                    content = msg.get("content", "")
-                    if isinstance(content, str):
-                        return _truncate_content(content)
-                    return str(content)
-        except Exception as e:
-            logger.warning(f"Failed to extract system instructions: {e}")
-        return None
-
-
-class NoOpLLMSpanRecorder:
-    """No-op span recorder for when tracing is disabled."""
-
-    def record_llm_call(self, **kwargs) -> None:
-        """No-op."""
-        pass
-
-
-# Global span recorder instance
-_span_recorder: Optional[LLMSpanRecorder] = None
-
-
-def get_span_recorder() -> LLMSpanRecorder | NoOpLLMSpanRecorder:
-    """Get the global span recorder (NoOp if tracing disabled)."""
-    if _span_recorder is None:
-        return NoOpLLMSpanRecorder()
-    return _span_recorder
-
-
-def create_span_recorder() -> LLMSpanRecorder:
-    """Create and set the global span recorder."""
-    global _span_recorder
-    tracer = get_tracer()
-    if tracer is None:
-        raise RuntimeError("Tracing not initialized. Call initialize_tracing() first.")
-    _span_recorder = LLMSpanRecorder(tracer)
-    return _span_recorder
@@ -1,13 +0,0 @@
-from urllib.parse import urlparse, urlunparse
-
-
-def mask_network_location(url):
-    if not url:
-        return url
-    parsed_url = urlparse(url)
-    masked_network_location = parsed_url.hostname or ""
-    if parsed_url.port:
-        masked_network_location += f":{parsed_url.port}"
-    if parsed_url.username or parsed_url.password:
-        masked_network_location = f"***:***@{masked_network_location}"
-    return urlunparse(parsed_url._replace(netloc=masked_network_location))
@@ -124,6 +124,12 @@ def main():
        default=config.worker_poll_interval_ms,
        help=f"Poll interval in milliseconds (default: {config.worker_poll_interval_ms}, env: HINDSIGHT_API_WORKER_POLL_INTERVAL_MS)",
    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=config.worker_batch_size,
+        help=f"Tasks to claim per poll (default: {config.worker_batch_size}, env: HINDSIGHT_API_WORKER_BATCH_SIZE)",
+    )
    parser.add_argument(
        "--max-retries",
        type=int,
@@ -162,9 +168,8 @@ def main():

    print(f"Starting Hindsight Worker: {args.worker_id}")
    print(f"  Poll interval: {args.poll_interval}ms")
+    print(f"  Batch size: {args.batch_size}")
    print(f"  Max retries: {args.max_retries}")
-    print(f"  Max slots: {config.worker_max_slots}")
-    print(f"  Consolidation max slots: {config.worker_consolidation_max_slots}")
    print(f"  HTTP server: {args.http_host}:{args.http_port}")
    print()

@@ -176,19 +181,7 @@ def main():
        nonlocal memory, poller
        import uvicorn

-        from ..extensions import OperationValidatorExtension, TenantExtension, load_extension
-
-        # Load tenant extension BEFORE creating MemoryEngine so it can
-        # set correct schema context during task execution. Without this,
-        # _authenticate_tenant sees no extension and resets schema to "public",
-        # causing worker writes to land in the wrong schema.
-        tenant_extension = load_extension("TENANT", TenantExtension)
-
-        # Load operation validator so workers can record usage metering
-        # for async operations (e.g. refresh_mental_model after consolidation)
-        operation_validator = load_extension("OPERATION_VALIDATOR", OperationValidatorExtension)
-        if operation_validator:
-            logger.info(f"Loaded operation validator: {operation_validator.__class__.__name__}")
+        from ..extensions import TenantExtension, load_extension

        # Initialize MemoryEngine
        # Workers use SyncTaskBackend because they execute tasks directly,
@@ -196,63 +189,43 @@ def main():
        memory = MemoryEngine(
            run_migrations=False,  # Workers don't run migrations
            task_backend=SyncTaskBackend(),
-            tenant_extension=tenant_extension,
-            operation_validator=operation_validator,
        )

        await memory.initialize()

        print(f"Database connected: {config.database_url}")

+        # Load tenant extension for dynamic schema discovery
+        tenant_extension = load_extension("TENANT", TenantExtension)
+
        if tenant_extension:
            print("Tenant extension loaded - schemas will be discovered dynamically on each poll")
        else:
-            print(f"No tenant extension configured, using schema: {config.database_schema}")
+            print("No tenant extension configured, using public schema only")

        # Create a single poller that handles all schemas dynamically
-        # Convert default schema to None for SQL compatibility (no schema prefix)
-        from hindsight_api.config import DEFAULT_DATABASE_SCHEMA
-
-        schema = None if config.database_schema == DEFAULT_DATABASE_SCHEMA else config.database_schema
        poller = WorkerPoller(
            pool=memory._pool,
            worker_id=args.worker_id,
            executor=memory.execute_task,
            poll_interval_ms=args.poll_interval,
+            batch_size=args.batch_size,
            max_retries=args.max_retries,
-            schema=schema,
            tenant_extension=tenant_extension,
-            max_slots=config.worker_max_slots,
-            consolidation_max_slots=config.worker_consolidation_max_slots,
        )

        # Create the HTTP app for metrics/health
        app = create_worker_app(poller, memory)

-        # Setup signal handlers for graceful shutdown using asyncio
+        # Setup signal handlers for graceful shutdown
        shutdown_requested = asyncio.Event()
-        force_exit = False

-        loop = asyncio.get_event_loop()
+        def signal_handler(signum, frame):
+            print(f"\nReceived signal {signum}, initiating graceful shutdown...")
+            shutdown_requested.set()

-        def signal_handler():
-            nonlocal force_exit
-            if shutdown_requested.is_set():
-                # Second signal = force exit
-                print("\nReceived second signal, forcing immediate exit...")
-                force_exit = True
-                # Restore default handler so third signal kills process
-                loop.remove_signal_handler(signal.SIGINT)
-                loop.remove_signal_handler(signal.SIGTERM)
-                sys.exit(1)
-            else:
-                print("\nReceived shutdown signal, initiating graceful shutdown...")
-                print("(Press Ctrl+C again to force immediate exit)")
-                shutdown_requested.set()
-
-        # Use asyncio's signal handlers which work properly with the event loop
-        loop.add_signal_handler(signal.SIGINT, signal_handler)
-        loop.add_signal_handler(signal.SIGTERM, signal_handler)
+        signal.signal(signal.SIGINT, signal_handler)
+        signal.signal(signal.SIGTERM, signal_handler)

        # Create uvicorn config and server
        uvicorn_config = uvicorn.Config(
@@ -271,10 +244,7 @@ def main():
        print(f"Worker started. Metrics available at http://{args.http_host}:{args.http_port}/metrics")

        # Wait for shutdown signal
-        try:
-            await shutdown_requested.wait()
-        except KeyboardInterrupt:
-            print("\nReceived interrupt, initiating graceful shutdown...")
+        await shutdown_requested.wait()

        # Graceful shutdown
        print("Shutting down HTTP server...")
@@ -57,11 +57,10 @@ class WorkerPoller:
        worker_id: str,
        executor: Callable[[dict[str, Any]], Awaitable[None]],
        poll_interval_ms: int = 500,
+        batch_size: int = 10,
        max_retries: int = 3,
        schema: str | None = None,
        tenant_extension: "TenantExtension | None" = None,
-        max_slots: int = 10,
-        consolidation_max_slots: int = 2,
    ):
        """
        Initialize the worker poller.
@@ -71,158 +70,91 @@ class WorkerPoller:
            worker_id: Unique identifier for this worker
            executor: Async function to execute tasks (typically MemoryEngine.execute_task)
            poll_interval_ms: Interval between polls when no tasks found (milliseconds)
+            batch_size: Maximum number of tasks to claim per poll cycle
            max_retries: Maximum retry attempts before marking task as failed
-            schema: Database schema for single-tenant support (deprecated, use tenant_extension)
-            tenant_extension: Extension for dynamic multi-tenant discovery. If None, creates a
-                            DefaultTenantExtension with the configured schema.
-            max_slots: Maximum concurrent tasks per worker
-            consolidation_max_slots: Maximum concurrent consolidation tasks per worker
+            schema: Database schema for single-tenant support (ignored if tenant_extension is set)
+            tenant_extension: Extension for dynamic multi-tenant discovery. If set, list_tenants()
+                            is called on each poll cycle to discover schemas dynamically.
        """
        self._pool = pool
        self._worker_id = worker_id
        self._executor = executor
        self._poll_interval_ms = poll_interval_ms
+        self._batch_size = batch_size
        self._max_retries = max_retries
        self._schema = schema
-        # Always set tenant extension (use DefaultTenantExtension if none provided)
-        if tenant_extension is None:
-            from ..extensions.builtin.tenant import DefaultTenantExtension
-
-            # Pass schema parameter to DefaultTenantExtension if explicitly provided
-            config = {"schema": schema} if schema else {}
-            tenant_extension = DefaultTenantExtension(config=config)
        self._tenant_extension = tenant_extension
-        self._max_slots = max_slots
-        self._consolidation_max_slots = consolidation_max_slots
        self._shutdown = asyncio.Event()
        self._current_tasks: set[asyncio.Task] = set()
        self._in_flight_count = 0
        self._in_flight_lock = asyncio.Lock()
        self._last_progress_log = 0.0
        self._tasks_completed_since_log = 0
-        # Track active tasks locally: operation_id -> (op_type, bank_id, schema, asyncio.Task)
-        self._active_tasks: dict[str, tuple[str, str, str | None, asyncio.Task]] = {}
-        # Track in-flight tasks by operation type
-        self._in_flight_by_type: dict[str, int] = {}
+        # Track active tasks locally: operation_id -> (op_type, bank_id, schema)
+        self._active_tasks: dict[str, tuple[str, str, str | None]] = {}

    async def _get_schemas(self) -> list[str | None]:
-        """Get list of schemas to poll. Returns [None] for default schema (no prefix)."""
-        from ..config import DEFAULT_DATABASE_SCHEMA
-
-        tenants = await self._tenant_extension.list_tenants()
-        # Convert default schema to None for SQL compatibility (no prefix), keep others as-is
-        return [t.schema if t.schema != DEFAULT_DATABASE_SCHEMA else None for t in tenants]
-
-    async def _get_available_slots(self) -> tuple[int, int]:
-        """
-        Calculate available slots for claiming tasks.
-
-        Returns:
-            (total_available, consolidation_available) tuple
-        """
-        async with self._in_flight_lock:
-            total_in_flight = self._in_flight_count
-            consolidation_in_flight = self._in_flight_by_type.get("consolidation", 0)
-
-        total_available = max(0, self._max_slots - total_in_flight)
-        consolidation_available = max(0, self._consolidation_max_slots - consolidation_in_flight)
-
-        return total_available, consolidation_available
-
-    async def wait_for_active_tasks(self, timeout: float = 10.0) -> bool:
-        """
-        Wait for all active background tasks to complete (test helper).
-
-        This is a test-only utility that allows tests to synchronize with
-        fire-and-forget background tasks without using sleep().
-
-        Args:
-            timeout: Maximum time to wait in seconds
-
-        Returns:
-            True if all tasks completed, False if timeout was reached
-        """
-        start_time = asyncio.get_event_loop().time()
-        while True:
-            async with self._in_flight_lock:
-                if self._in_flight_count == 0:
-                    return True
-
-            elapsed = asyncio.get_event_loop().time() - start_time
-            if elapsed >= timeout:
-                return False
-
-            # Short sleep to avoid busy-waiting
-            await asyncio.sleep(0.01)
+        """Get list of schemas to poll. Returns [None] for public schema."""
+        if self._tenant_extension is not None:
+            tenants = await self._tenant_extension.list_tenants()
+            # Convert "public" to None for SQL compatibility, keep others as-is
+            return [t.schema if t.schema != "public" else None for t in tenants]
+        # Single schema mode
+        return [self._schema]

    async def claim_batch(self) -> list[ClaimedTask]:
        """
-        Claim pending tasks atomically across all tenant schemas,
-        respecting slot limits (total and consolidation).
+        Claim up to batch_size pending tasks atomically across all tenant schemas.

        Uses FOR UPDATE SKIP LOCKED to ensure no conflicts with other workers.

+        For consolidation tasks specifically, skips pending tasks if there's already
+        a processing consolidation for the same bank (to avoid duplicate work).
+
+        If tenant_extension is configured, dynamically discovers schemas on each call.
+
        Returns:
            List of ClaimedTask objects containing operation_id, task_dict, and schema
        """
-        # Calculate available slots
-        total_available, consolidation_available = await self._get_available_slots()
-
-        if total_available <= 0:
-            return []
-
        schemas = await self._get_schemas()
        all_tasks: list[ClaimedTask] = []
-        remaining_total = total_available
-        remaining_consolidation = consolidation_available
+        remaining_batch = self._batch_size

        for schema in schemas:
-            if remaining_total <= 0:
+            if remaining_batch <= 0:
                break

-            tasks = await self._claim_batch_for_schema(schema, remaining_total, remaining_consolidation)
-
-            # Update remaining slots based on what was claimed
-            for task in tasks:
-                op_type = task.task_dict.get("operation_type", "unknown")
-                if op_type == "consolidation":
-                    remaining_consolidation -= 1
-
+            tasks = await self._claim_batch_for_schema(schema, remaining_batch)
            all_tasks.extend(tasks)
-            remaining_total -= len(tasks)
+            remaining_batch -= len(tasks)

        return all_tasks

-    async def _claim_batch_for_schema(
-        self, schema: str | None, limit: int, consolidation_limit: int
-    ) -> list[ClaimedTask]:
-        """Claim tasks from a specific schema respecting slot limits."""
-        try:
-            return await self._claim_batch_for_schema_inner(schema, limit, consolidation_limit)
-        except Exception as e:
-            # Format schema for logging: custom schemas in quotes, None as-is
-            schema_display = f'"{schema}"' if schema else str(schema)
-            logger.warning(f"Worker {self._worker_id} failed to claim tasks for schema {schema_display}: {e}")
-            return []
-
-    async def _claim_batch_for_schema_inner(
-        self, schema: str | None, limit: int, consolidation_limit: int
-    ) -> list[ClaimedTask]:
-        """Inner implementation for claiming tasks from a specific schema with slot limits."""
+    async def _claim_batch_for_schema(self, schema: str | None, limit: int) -> list[ClaimedTask]:
+        """Claim tasks from a specific schema."""
        table = fq_table("async_operations", schema)

        async with self._pool.acquire() as conn:
            async with conn.transaction():
-                # Strategy: Claim non-consolidation tasks first, then consolidation up to limit
-
-                # 1. Claim non-consolidation tasks (up to limit)
-                non_consolidation_rows = await conn.fetch(
+                # Select and lock pending tasks
+                # For consolidation: skip if same bank already has one processing
+                rows = await conn.fetch(
                    f"""
                    SELECT operation_id, task_payload
-                    FROM {table}
-                    WHERE status = 'pending'
-                      AND task_payload IS NOT NULL
-                      AND operation_type != 'consolidation'
+                    FROM {table} AS pending
+                    WHERE status = 'pending' AND task_payload IS NOT NULL
+                    AND (
+                        -- Non-consolidation tasks: always claimable
+                        operation_type != 'consolidation'
+                        OR
+                        -- Consolidation: only if no other consolidation processing for same bank
+                        NOT EXISTS (
+                            SELECT 1 FROM {table} AS processing
+                            WHERE processing.bank_id = pending.bank_id
+                            AND processing.operation_type = 'consolidation'
+                            AND processing.status = 'processing'
+                        )
+                    )
                    ORDER BY created_at
                    LIMIT $1
                    FOR UPDATE SKIP LOCKED
@@ -230,39 +162,11 @@ class WorkerPoller:
                    limit,
                )

-                claimed_count = len(non_consolidation_rows)
-                remaining_limit = limit - claimed_count
-
-                # 2. Claim consolidation tasks (up to consolidation_limit and remaining_limit)
-                consolidation_rows = []
-                if consolidation_limit > 0 and remaining_limit > 0:
-                    consolidation_rows = await conn.fetch(
-                        f"""
-                        SELECT operation_id, task_payload
-                        FROM {table} AS pending
-                        WHERE status = 'pending'
-                          AND task_payload IS NOT NULL
-                          AND operation_type = 'consolidation'
-                          AND NOT EXISTS (
-                              SELECT 1 FROM {table} AS processing
-                              WHERE processing.bank_id = pending.bank_id
-                                AND processing.operation_type = 'consolidation'
-                                AND processing.status = 'processing'
-                          )
-                        ORDER BY created_at
-                        LIMIT $1
-                        FOR UPDATE SKIP LOCKED
-                        """,
-                        min(consolidation_limit, remaining_limit),
-                    )
-
-                all_rows = non_consolidation_rows + consolidation_rows
-
-                if not all_rows:
+                if not rows:
                    return []

                # Claim the tasks by updating status and worker_id
-                operation_ids = [row["operation_id"] for row in all_rows]
+                operation_ids = [row["operation_id"] for row in rows]
                await conn.execute(
                    f"""
                    UPDATE {table}
@@ -280,7 +184,7 @@ class WorkerPoller:
                        task_dict=json.loads(row["task_payload"]),
                        schema=schema,
                    )
-                    for row in all_rows
+                    for row in rows
                ]

    async def _mark_completed(self, operation_id: str, schema: str | None):
@@ -346,43 +250,18 @@ class WorkerPoller:
            logger.warning(f"Task {operation_id} failed, will retry (attempt {retry_count + 1}/{self._max_retries})")

    async def execute_task(self, task: ClaimedTask):
-        """Execute a single task as a background job (fire-and-forget)."""
+        """Execute a single task and update its status."""
        task_type = task.task_dict.get("type", "unknown")
-        operation_type = task.task_dict.get("operation_type", "unknown")
        bank_id = task.task_dict.get("bank_id", "unknown")

-        # Create background task
-        bg_task = asyncio.create_task(self._execute_task_inner(task))
-
        # Track this task as active
        async with self._in_flight_lock:
-            self._active_tasks[task.operation_id] = (task_type, bank_id, task.schema, bg_task)
-            self._in_flight_count += 1
-            self._in_flight_by_type[operation_type] = self._in_flight_by_type.get(operation_type, 0) + 1
-
-        # Add cleanup callback
-        bg_task.add_done_callback(lambda _: asyncio.create_task(self._cleanup_task(task.operation_id, operation_type)))
-
-    async def _cleanup_task(self, operation_id: str, operation_type: str):
-        """Remove task from tracking after completion."""
-        async with self._in_flight_lock:
-            if operation_id in self._active_tasks:
-                self._active_tasks.pop(operation_id, None)
-                self._in_flight_count -= 1
-                count = self._in_flight_by_type.get(operation_type, 0)
-                if count > 0:
-                    self._in_flight_by_type[operation_type] = count - 1
-                    if self._in_flight_by_type[operation_type] == 0:
-                        del self._in_flight_by_type[operation_type]
-
-    async def _execute_task_inner(self, task: ClaimedTask):
-        """Inner task execution with error handling."""
-        task_type = task.task_dict.get("type", "unknown")
-        bank_id = task.task_dict.get("bank_id", "unknown")
+            self._active_tasks[task.operation_id] = (task_type, bank_id, task.schema)

        try:
            schema_info = f", schema={task.schema}" if task.schema else ""
            logger.debug(f"Executing task {task.operation_id} (type={task_type}, bank={bank_id}{schema_info})")
+            # Pass schema to executor so it can set the correct context
            if task.schema:
                task.task_dict["_schema"] = task.schema
            await self._executor(task.task_dict)
@@ -392,6 +271,10 @@ class WorkerPoller:
            error_msg = f"{type(e).__name__}: {e}\n{traceback.format_exc()}"
            logger.error(f"Task {task.operation_id} failed: {e}")
            await self._retry_or_fail(task.operation_id, error_msg, task.schema)
+        finally:
+            # Remove from active tasks
+            async with self._in_flight_lock:
+                self._active_tasks.pop(task.operation_id, None)

    async def recover_own_tasks(self) -> int:
        """
@@ -410,25 +293,20 @@ class WorkerPoller:
        total_count = 0

        for schema in schemas:
-            try:
-                table = fq_table("async_operations", schema)
+            table = fq_table("async_operations", schema)

-                result = await self._pool.execute(
-                    f"""
-                    UPDATE {table}
-                    SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
-                    WHERE status = 'processing' AND worker_id = $1
-                    """,
-                    self._worker_id,
-                )
+            result = await self._pool.execute(
+                f"""
+                UPDATE {table}
+                SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
+                WHERE status = 'processing' AND worker_id = $1
+                """,
+                self._worker_id,
+            )

-                # Parse "UPDATE N" to get count
-                count = int(result.split()[-1]) if result else 0
-                total_count += count
-            except Exception as e:
-                # Format schema for logging: custom schemas in quotes, None as-is
-                schema_display = f'"{schema}"' if schema else str(schema)
-                logger.warning(f"Worker {self._worker_id} failed to recover tasks for schema {schema_display}: {e}")
+            # Parse "UPDATE N" to get count
+            count = int(result.split()[-1]) if result else 0
+            total_count += count

        if total_count > 0:
            logger.info(f"Worker {self._worker_id} recovered {total_count} stale tasks from previous run")
@@ -436,60 +314,59 @@ class WorkerPoller:

    async def run(self):
        """
-        Main polling loop with fire-and-forget task execution.
+        Main polling loop.

-        Continuously polls for pending tasks, spawns them as background tasks,
-        and immediately continues polling (up to slot limits).
+        Continuously polls for pending tasks, claims them, and executes them
+        until shutdown is signaled.
+
+        If tenant_extension is configured, dynamically discovers schemas on each poll.
        """
+        # Recover any tasks from a previous crash before starting
        await self.recover_own_tasks()

-        logger.info(
-            f"Worker {self._worker_id} starting polling loop "
-            f"(max_slots={self._max_slots}, consolidation_max_slots={self._consolidation_max_slots})"
-        )
+        logger.info(f"Worker {self._worker_id} starting polling loop")

        while not self._shutdown.is_set():
            try:
-                # Claim a batch of tasks (respecting slot limits)
+                # Claim a batch of tasks (across all tenant schemas if configured)
                tasks = await self.claim_batch()

                if tasks:
                    # Log batch info
                    task_types: dict[str, int] = {}
                    schemas_seen: set[str | None] = set()
-                    consolidation_count = 0
                    for task in tasks:
                        t = task.task_dict.get("type", "unknown")
-                        op_type = task.task_dict.get("operation_type", "unknown")
                        task_types[t] = task_types.get(t, 0) + 1
                        schemas_seen.add(task.schema)
-                        if op_type == "consolidation":
-                            consolidation_count += 1
-
                    types_str = ", ".join(f"{k}:{v}" for k, v in task_types.items())
-                    # Display None as "default" in logs
-                    schemas_str = ", ".join(s if s else "default" for s in schemas_seen)
+                    schemas_str = ", ".join(s or "public" for s in schemas_seen)
                    logger.info(
-                        f"Worker {self._worker_id} claimed {len(tasks)} tasks "
-                        f"({consolidation_count} consolidation): {types_str} (schemas: {schemas_str})"
+                        f"Worker {self._worker_id} claimed {len(tasks)} tasks: {types_str} (schemas: {schemas_str})"
                    )

-                    # Spawn tasks as background jobs (fire-and-forget)
-                    for task in tasks:
-                        await self.execute_task(task)
+                    # Track in-flight tasks
+                    async with self._in_flight_lock:
+                        self._in_flight_count += len(tasks)

-                    # Continue immediately to claim more tasks (if slots available)
-                    continue
-
-                # No tasks claimed (either no pending tasks or slots full)
-                # Wait before polling again
-                try:
-                    await asyncio.wait_for(
-                        self._shutdown.wait(),
-                        timeout=self._poll_interval_ms / 1000,
-                    )
-                except asyncio.TimeoutError:
-                    pass  # Normal timeout, continue polling
+                    # Execute tasks concurrently
+                    try:
+                        await asyncio.gather(
+                            *[self.execute_task(task) for task in tasks],
+                            return_exceptions=True,
+                        )
+                    finally:
+                        async with self._in_flight_lock:
+                            self._in_flight_count -= len(tasks)
+                else:
+                    # No tasks found, wait before polling again
+                    try:
+                        await asyncio.wait_for(
+                            self._shutdown.wait(),
+                            timeout=self._poll_interval_ms / 1000,
+                        )
+                    except asyncio.TimeoutError:
+                        pass  # Normal timeout, continue polling

                # Log progress stats periodically
                await self._log_progress_if_due()
@@ -520,27 +397,15 @@ class WorkerPoller:
        while asyncio.get_event_loop().time() - start_time < timeout:
            async with self._in_flight_lock:
                in_flight = self._in_flight_count
-                active_task_objects = [task_info[3] for task_info in self._active_tasks.values()]

            if in_flight == 0:
                logger.info(f"Worker {self._worker_id} graceful shutdown complete")
                return

            logger.info(f"Worker {self._worker_id} waiting for {in_flight} in-flight tasks")
+            await asyncio.sleep(0.5)

-            # Wait for at least one task to complete
-            if active_task_objects:
-                done, _ = await asyncio.wait(active_task_objects, timeout=0.5, return_when=asyncio.FIRST_COMPLETED)
-            else:
-                await asyncio.sleep(0.5)
-
-        logger.warning(f"Worker {self._worker_id} shutdown timeout after {timeout}s, cancelling remaining tasks")
-
-        # Cancel remaining tasks
-        async with self._in_flight_lock:
-            for operation_id, (_, _, _, bg_task) in list(self._active_tasks.items()):
-                if not bg_task.done():
-                    bg_task.cancel()
+        logger.warning(f"Worker {self._worker_id} shutdown timeout after {timeout}s")

    async def _log_progress_if_due(self):
        """Log progress stats every PROGRESS_LOG_INTERVAL seconds."""
@@ -551,19 +416,14 @@ class WorkerPoller:
        self._last_progress_log = now

        try:
-            # Get local active tasks
+            # Get local active tasks (this worker only)
            async with self._in_flight_lock:
                in_flight = self._in_flight_count
-                in_flight_by_type = dict(self._in_flight_by_type)
-                active_tasks = dict(self._active_tasks)
+                active_tasks = dict(self._active_tasks)  # Copy to avoid holding lock

-            consolidation_count = in_flight_by_type.get("consolidation", 0)
-            available_slots = self._max_slots - in_flight
-            available_consolidation_slots = self._consolidation_max_slots - consolidation_count
-
-            # Build local processing breakdown
+            # Build local processing breakdown grouped by (op_type, bank_id)
            task_groups: dict[tuple[str, str], int] = {}
-            for op_type, bank_id, _, _ in active_tasks.values():
+            for op_type, bank_id, _ in active_tasks.values():
                key = (op_type, bank_id)
                task_groups[key] = task_groups.get(key, 0) + 1

@@ -572,7 +432,7 @@ class WorkerPoller:
            if len(processing_info) > 10:
                processing_str += f" +{len(processing_info) - 10} more"

-            # Get global stats from DB
+            # Get global stats from DB across all schemas
            schemas = await self._get_schemas()
            global_pending = 0
            all_worker_counts: dict[str, int] = {}
@@ -584,6 +444,7 @@ class WorkerPoller:
                    row = await conn.fetchrow(f"SELECT COUNT(*) as count FROM {table} WHERE status = 'pending'")
                    global_pending += row["count"] if row else 0

+                    # Get processing breakdown by worker
                    worker_rows = await conn.fetch(
                        f"""
                        SELECT worker_id, COUNT(*) as count
@@ -596,18 +457,16 @@ class WorkerPoller:
                        wid = wr["worker_id"] or "unknown"
                        all_worker_counts[wid] = all_worker_counts.get(wid, 0) + wr["count"]

+            # Format other workers' processing counts
            other_workers = []
            for wid, cnt in all_worker_counts.items():
                if wid != self._worker_id:
                    other_workers.append(f"{wid}:{cnt}")
            others_str = ", ".join(other_workers) if other_workers else "none"

-            # Display None as "default" in logs
-            schemas_str = ", ".join(s if s else "default" for s in schemas)
+            schemas_str = ", ".join(s or "public" for s in schemas)
            logger.info(
-                f"[WORKER_STATS] worker={self._worker_id} "
-                f"slots={in_flight}/{self._max_slots} (consolidation={consolidation_count}/{self._consolidation_max_slots}) | "
-                f"available={available_slots} (consolidation={available_consolidation_slots}) | "
+                f"[WORKER_STATS] worker={self._worker_id} in_flight={in_flight} | "
                f"global: pending={global_pending} (schemas: {schemas_str}) | "
                f"others: {others_str} | "
                f"my_active: {processing_str}"
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "hindsight-api"
-version = "0.4.11"
+version = "0.3.0"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -25,37 +25,31 @@ dependencies = [
    "psycopg2-binary>=2.9.11",
    "tiktoken>=0.12.0",
    "httpx>=0.27.0",
-    "PyJWT[crypto]>=2.8.0",
-    "fastmcp>=2.14.0", # CVE-2025-66416
+    "fastmcp>=2.14.0",  # CVE-2025-66416
    "pg0-embedded>=0.11.0",
    "python-dateutil>=2.8.0",
    "opentelemetry-api>=1.20.0",
    "opentelemetry-sdk>=1.20.0",
    "opentelemetry-instrumentation-fastapi>=0.41b0",
    "opentelemetry-exporter-prometheus>=0.41b0",
-    "opentelemetry-exporter-otlp-proto-http>=1.20.0",
-    "opentelemetry-semantic-conventions>=0.41b0",
    "dateparser>=1.2.2",
    "google-genai>=1.0.0",
-    "google-auth>=2.0.0",
    "anthropic>=0.40.0",
    "typer>=0.9.0",
    "cohere>=5.0.0",
    "flashrank>=0.2.0",
-    "litellm>=1.0.0",
    # Local ML models for embeddings/reranking - can be excluded in Docker with INCLUDE_LOCAL_MODELS=false
    "sentence-transformers>=3.3.0",
-    "transformers>=4.53.0", # Security fixes for ReDoS vulnerabilities
-    "torch>=2.6.0", # CVE fix for remote code execution
+    "transformers>=4.53.0",  # Security fixes for ReDoS vulnerabilities
+    "torch>=2.6.0",  # CVE fix for remote code execution
    "uvloop>=0.22.1",
    # Transitive dependency security fixes
-    "pyasn1>=0.6.2", # DoS vulnerability fix
-    "urllib3>=2.6.3", # Decompression-bomb safeguards bypass fix
-    "langchain-core>=1.2.5", # Serialization injection vulnerability fix
-    "filelock>=3.20.1", # TOCTOU race condition fix
-    "authlib>=1.6.6", # Account takeover vulnerability fix
-    "aiohttp>=3.13.3", # Multiple DoS vulnerabilities
-    "claude-agent-sdk>=0.1.27",
+    "pyasn1>=0.6.2",  # DoS vulnerability fix
+    "urllib3>=2.6.3",  # Decompression-bomb safeguards bypass fix
+    "langchain-core>=1.2.5",  # Serialization injection vulnerability fix
+    "filelock>=3.20.1",  # TOCTOU race condition fix
+    "authlib>=1.6.6",  # Account takeover vulnerability fix
+    "aiohttp>=3.13.3",  # Multiple DoS vulnerabilities
 ]

 [project.optional-dependencies]
@@ -147,11 +141,6 @@ known-third-party = ["alembic"]
 quote-style = "double"
 indent-style = "space"

-[tool.uv]
-# Allow uv to search all configured indexes for packages, not just the first one
-# This prevents dependency resolution failures when using pytorch index + PyPI
-index-strategy = "unsafe-best-match"
-
 [tool.ty]
 # Type checking configuration
 # ty is an extremely fast Python type checker from Astral (same team as ruff/uv)
@@ -220,34 +220,3 @@ async def memory(pg0_db_url, embeddings, cross_encoder, query_analyzer):
            await mem.close()
    except Exception:
        pass
-
-
-@pytest_asyncio.fixture(scope="function")
-async def memory_no_llm_verify(pg0_db_url, embeddings, cross_encoder, query_analyzer):
-    """
-    Provide a MemoryEngine instance that skips LLM connection verification.
-
-    This fixture is useful for tests that override the LLM configuration
-    after initialization (e.g., to test specific providers).
-    """
-    mem = MemoryEngine(
-        db_url=pg0_db_url,
-        memory_llm_provider="mock",  # Use mock provider as placeholder
-        memory_llm_api_key="",
-        memory_llm_model="mock",
-        embeddings=embeddings,
-        cross_encoder=cross_encoder,
-        query_analyzer=query_analyzer,
-        pool_min_size=1,
-        pool_max_size=5,
-        run_migrations=False,
-        task_backend=SyncTaskBackend(),
-        skip_llm_verification=True,  # Skip verification - will be overridden by test
-    )
-    await mem.initialize()
-    yield mem
-    try:
-        if mem._pool and not mem._pool._closing:
-            await mem.close()
-    except Exception:
-        pass
@@ -1,423 +0,0 @@
-"""Test async batch retain with smart batching and parent-child operations."""
-
-import asyncio
-import json
-import uuid
-
-import pytest
-
-from hindsight_api.extensions import RequestContext
-
-
-@pytest.mark.asyncio
-async def test_duplicate_document_ids_rejected_async(memory, request_context):
-    """Test that async retain rejects batches with duplicate document_ids."""
-    bank_id = "test_duplicate_async"
-    contents = [
-        {"content": "First item", "document_id": "doc1"},
-        {"content": "Second item", "document_id": "doc2"},
-        {"content": "Third item", "document_id": "doc1"},  # Duplicate!
-    ]
-
-    # Should raise ValueError due to duplicate document_ids
-    with pytest.raises(ValueError, match="duplicate document_ids.*doc1"):
-        await memory.submit_async_retain(
-            bank_id=bank_id,
-            contents=contents,
-            request_context=request_context,
-        )
-
-
-@pytest.mark.asyncio
-async def test_duplicate_document_ids_rejected_sync(memory, request_context):
-    """Test that sync retain also rejects batches with duplicate document_ids."""
-    bank_id = "test_duplicate_sync"
-    contents = [
-        {"content": "First item", "document_id": "doc1"},
-        {"content": "Second item", "document_id": "doc1"},  # Duplicate!
-    ]
-
-    # Should raise ValueError due to duplicate document_ids
-    with pytest.raises(ValueError, match="duplicate document_ids.*doc1"):
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=contents,
-            request_context=request_context,
-        )
-
-
-@pytest.mark.asyncio
-async def test_small_async_batch_no_splitting(memory, request_context):
-    """Test that small async batches create parent with single child (simplified code path)."""
-    bank_id = "test_small_async"
-    contents = [{"content": "Alice works at Google", "document_id": f"doc{i}"} for i in range(5)]
-
-    # Calculate total chars (should be well under threshold)
-    total_chars = sum(len(item["content"]) for item in contents)
-    assert total_chars < 10_000, "Test batch should be small"
-
-    # Submit async retain
-    result = await memory.submit_async_retain(
-        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
-    )
-
-    # Verify we got an operation_id back
-    assert "operation_id" in result
-    assert "items_count" in result
-    assert result["items_count"] == 5
-
-    operation_id = result["operation_id"]
-
-    # Wait for task to complete (SyncTaskBackend executes immediately)
-    await asyncio.sleep(0.1)
-
-    # Check operation status
-    status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=operation_id,
-        request_context=request_context,
-    )
-
-    # Should be a parent operation with single child (simplified code path)
-    assert status["status"] == "completed"
-    assert status["operation_type"] == "batch_retain"
-    assert "child_operations" in status
-    assert status["result_metadata"]["num_sub_batches"] == 1  # Single sub-batch
-    assert len(status["child_operations"]) == 1
-    assert status["child_operations"][0]["status"] == "completed"
-
-
-@pytest.mark.asyncio
-async def test_large_async_batch_auto_splits(memory, request_context):
-    """Test that large async batches automatically split into sub-batches with parent operation."""
-    from hindsight_api.engine.memory_engine import count_tokens
-
-    bank_id = "test_large_async"
-
-    # Create a large batch that exceeds the threshold (10k tokens default)
-    # Repeating "A"s gets heavily compressed by tokenizer, use varied content
-    # Use ~22k chars per item = ~5.5k tokens per item, 2 items = ~11k tokens total (exceeds 10k)
-    large_content = "The quick brown fox jumps over the lazy dog. " * 500  # ~22k chars = ~5.5k tokens
-    contents = [{"content": large_content + f" item {i}", "document_id": f"doc{i}"} for i in range(2)]
-
-    # Calculate total tokens (should exceed threshold)
-    total_tokens = sum(count_tokens(item["content"]) for item in contents)
-    assert total_tokens > 10_000, "Test batch should exceed threshold"
-
-    # Submit async retain
-    result = await memory.submit_async_retain(
-        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
-    )
-
-    # Verify we got an operation_id back
-    assert "operation_id" in result
-    assert "items_count" in result
-    assert result["items_count"] == 2
-
-    parent_operation_id = result["operation_id"]
-
-    # Wait for tasks to complete
-    await asyncio.sleep(0.5)
-
-    # Check parent operation status
-    parent_status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=parent_operation_id,
-        request_context=request_context,
-    )
-
-    # Should be a parent operation with children
-    assert parent_status["operation_type"] == "batch_retain"
-    assert "child_operations" in parent_status
-    assert "num_sub_batches" in parent_status["result_metadata"]
-    assert parent_status["result_metadata"]["num_sub_batches"] >= 2  # Should split into at least 2 batches
-    assert parent_status["result_metadata"]["items_count"] == 2
-
-    # Verify child operations
-    child_ops = parent_status["child_operations"]
-    assert len(child_ops) >= 2, "Should have at least 2 child operations"
-
-    # All children should be completed (SyncTaskBackend executes immediately)
-    for child in child_ops:
-        assert child["status"] == "completed"
-        assert child["sub_batch_index"] is not None
-        assert child["items_count"] > 0
-
-    # Parent status should be aggregated as "completed"
-    assert parent_status["status"] == "completed"
-
-
-@pytest.mark.asyncio
-async def test_parent_operation_status_aggregation_pending(memory, request_context):
-    """Test that parent operation shows 'pending' when children are pending."""
-    bank_id = "test_parent_pending"
-    pool = await memory._get_pool()
-
-    # Manually create a parent operation
-    parent_id = uuid.uuid4()
-    async with pool.acquire() as conn:
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            parent_id,
-            bank_id,
-            "batch_retain",
-            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
-            "pending",
-        )
-
-        # Create 2 child operations - one completed, one pending
-        child1_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child1_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 1,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "completed",
-        )
-
-        child2_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child2_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 2,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "pending",
-        )
-
-    # Check parent status
-    parent_status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=str(parent_id),
-        request_context=request_context,
-    )
-
-    # Parent should aggregate as "pending" since one child is still pending
-    assert parent_status["status"] == "pending"
-    assert len(parent_status["child_operations"]) == 2
-
-
-@pytest.mark.asyncio
-async def test_parent_operation_status_aggregation_failed(memory, request_context):
-    """Test that parent operation shows 'failed' when any child fails."""
-    bank_id = "test_parent_failed"
-    pool = await memory._get_pool()
-
-    # Manually create a parent operation
-    parent_id = uuid.uuid4()
-    async with pool.acquire() as conn:
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            parent_id,
-            bank_id,
-            "batch_retain",
-            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
-            "pending",
-        )
-
-        # Create 2 child operations - one completed, one failed
-        child1_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child1_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 1,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "completed",
-        )
-
-        child2_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status, error_message)
-            VALUES ($1, $2, $3, $4, $5, $6)
-            """,
-            child2_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 2,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "failed",
-            "Test error",
-        )
-
-    # Check parent status
-    parent_status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=str(parent_id),
-        request_context=request_context,
-    )
-
-    # Parent should aggregate as "failed" since one child failed
-    assert parent_status["status"] == "failed"
-    assert len(parent_status["child_operations"]) == 2
-
-    # Verify child with error is included
-    failed_child = [c for c in parent_status["child_operations"] if c["status"] == "failed"][0]
-    assert failed_child["error_message"] == "Test error"
-
-
-@pytest.mark.asyncio
-async def test_parent_operation_status_aggregation_completed(memory, request_context):
-    """Test that parent operation shows 'completed' when all children are completed."""
-    bank_id = "test_parent_completed"
-    pool = await memory._get_pool()
-
-    # Manually create a parent operation
-    parent_id = uuid.uuid4()
-    async with pool.acquire() as conn:
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            parent_id,
-            bank_id,
-            "batch_retain",
-            json.dumps({"items_count": 20, "num_sub_batches": 2, "is_parent": True}),
-            "pending",
-        )
-
-        # Create 2 child operations - both completed
-        child1_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child1_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 1,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "completed",
-        )
-
-        child2_id = uuid.uuid4()
-        await conn.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-            VALUES ($1, $2, $3, $4, $5)
-            """,
-            child2_id,
-            bank_id,
-            "retain",
-            json.dumps(
-                {
-                    "items_count": 10,
-                    "parent_operation_id": str(parent_id),
-                    "sub_batch_index": 2,
-                    "total_sub_batches": 2,
-                }
-            ),
-            "completed",
-        )
-
-    # Check parent status
-    parent_status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=str(parent_id),
-        request_context=request_context,
-    )
-
-    # Parent should aggregate as "completed" since all children are completed
-    assert parent_status["status"] == "completed"
-    assert len(parent_status["child_operations"]) == 2
-    assert all(c["status"] == "completed" for c in parent_status["child_operations"])
-
-
-@pytest.mark.asyncio
-async def test_config_retain_batch_tokens_respected(memory, request_context):
-    """Test that the retain_batch_tokens config setting is respected."""
-    from hindsight_api.config import get_config
-    from hindsight_api.engine.memory_engine import count_tokens
-
-    bank_id = "test_config_batch_tokens"
-    config = get_config()
-
-    # Check that config has the retain_batch_tokens setting
-    assert hasattr(config, "retain_batch_tokens")
-    assert config.retain_batch_tokens > 0
-
-    # Create a batch that's just under the threshold
-    # Use content that produces roughly half the token limit per item
-    content_size = config.retain_batch_tokens * 2  # chars (rough estimate: 1 token ~= 4 chars)
-    contents = [{"content": "A" * content_size, "document_id": f"doc{i}"} for i in range(2)]
-
-    total_tokens = sum(count_tokens(item["content"]) for item in contents)
-    # Should be equal to threshold (boundary case, no splitting since we use > not >=)
-    assert total_tokens <= config.retain_batch_tokens
-
-    # Submit - should NOT split
-    result = await memory.submit_async_retain(
-        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
-    )
-
-    # Wait for completion
-    await asyncio.sleep(0.1)
-
-    # Check status - should be a parent with single child (even for small batches)
-    status = await memory.get_operation_status(
-        bank_id=bank_id,
-        operation_id=result["operation_id"],
-        request_context=request_context,
-    )
-
-    # Even small batches use parent-child pattern now (simpler code path)
-    assert "child_operations" in status
-    assert status["result_metadata"]["num_sub_batches"] == 1
@@ -1,93 +0,0 @@
-"""Unit tests for async retain tag propagation."""
-
-from unittest.mock import AsyncMock, MagicMock
-
-import pytest
-
-from hindsight_api.engine.memory_engine import MemoryEngine
-from hindsight_api.models import RequestContext
-
-
-@pytest.mark.asyncio
-async def test_submit_async_retain_includes_document_tags_in_task_payload():
-    """submit_async_retain should include document_tags in queued task payload."""
-    engine = MemoryEngine.__new__(MemoryEngine)
-    engine._initialized = True
-    engine._authenticate_tenant = AsyncMock()
-    engine._submit_async_operation = AsyncMock(return_value={"operation_id": "op-1"})
-
-    # Mock the pool and connection for parent operation creation
-    mock_conn = AsyncMock()
-    mock_conn.execute = AsyncMock()
-    mock_conn.transaction = MagicMock()
-    mock_conn.transaction.return_value.__aenter__ = AsyncMock()
-    mock_conn.transaction.return_value.__aexit__ = AsyncMock()
-
-    mock_pool = AsyncMock()
-    mock_pool.acquire = AsyncMock(return_value=mock_conn)
-    mock_pool.release = AsyncMock()
-
-    engine._get_pool = AsyncMock(return_value=mock_pool)
-
-    request_context = RequestContext(tenant_id="tenant-a", api_key_id="key-a")
-    contents = [{"content": "Async retain payload test."}]
-    document_tags = ["scope:tools", "user:alice"]
-
-    result = await MemoryEngine.submit_async_retain(
-        engine,
-        bank_id="bank-1",
-        contents=contents,
-        document_tags=document_tags,
-        request_context=request_context,
-    )
-
-    # Check result structure
-    assert "operation_id" in result
-    assert "items_count" in result
-    assert result["items_count"] == 1
-
-    # Verify authentication was called
-    engine._authenticate_tenant.assert_awaited_once_with(request_context)
-
-    # Verify child operation was submitted
-    engine._submit_async_operation.assert_awaited_once()
-
-    # Verify child operation payload contains document_tags
-    kwargs = engine._submit_async_operation.await_args.kwargs
-    assert kwargs["bank_id"] == "bank-1"
-    assert kwargs["operation_type"] == "retain"
-    assert kwargs["task_type"] == "batch_retain"
-    assert kwargs["task_payload"]["contents"] == contents
-    assert kwargs["task_payload"]["document_tags"] == document_tags
-    assert kwargs["task_payload"]["_tenant_id"] == "tenant-a"
-    assert kwargs["task_payload"]["_api_key_id"] == "key-a"
-
-
-@pytest.mark.asyncio
-async def test_handle_batch_retain_forwards_document_tags_to_retain_batch_async():
-    """Worker handler should forward document_tags from task payload."""
-    engine = MemoryEngine.__new__(MemoryEngine)
-    engine._initialized = True
-    engine.retain_batch_async = AsyncMock(return_value={"items_count": 1})
-
-    task_dict = {
-        "bank_id": "bank-1",
-        "contents": [{"content": "Forward tags test."}],
-        "document_tags": ["scope:client"],
-        "_tenant_id": "tenant-a",
-        "_api_key_id": "key-a",
-    }
-
-    await MemoryEngine._handle_batch_retain(engine, task_dict)
-
-    engine.retain_batch_async.assert_awaited_once()
-    kwargs = engine.retain_batch_async.await_args.kwargs
-    assert kwargs["bank_id"] == "bank-1"
-    assert kwargs["contents"] == task_dict["contents"]
-    assert kwargs["document_tags"] == ["scope:client"]
-
-    request_context = kwargs["request_context"]
-    assert request_context.internal is True
-    assert request_context.user_initiated is True
-    assert request_context.tenant_id == "tenant-a"
-    assert request_context.api_key_id == "key-a"
@@ -1,189 +0,0 @@
-"""
-Integration test for API base path support.
-
-Tests that the API works correctly when deployed with a base path (e.g., /hindsight)
-for reverse proxy deployments.
-"""
-import os
-import pytest
-import pytest_asyncio
-import httpx
-from hindsight_api.api import create_app
-from hindsight_api.config import clear_config_cache
-
-
-@pytest_asyncio.fixture
-async def api_client_with_base_path(memory):
-    """Create an async test client for the FastAPI app with a base path."""
-    # Set base path in environment
-    base_path = "/hindsight"
-    os.environ["HINDSIGHT_API_BASE_PATH"] = base_path
-
-    # Clear config cache to force reload with new base_path
-    clear_config_cache()
-
-    # Memory is already initialized by the conftest fixture (with migrations)
-    app = create_app(memory, initialize_memory=False)
-
-    # Use base_url with base path
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(
-        transport=transport,
-        base_url=f"http://test{base_path}"
-    ) as client:
-        yield client
-
-    # Cleanup: unset base path
-    os.environ.pop("HINDSIGHT_API_BASE_PATH", None)
-    clear_config_cache()
-
-
-@pytest_asyncio.fixture
-async def api_client_without_base_path(memory):
-    """Create an async test client for the FastAPI app without a base path (root)."""
-    # Ensure no base path is set
-    os.environ.pop("HINDSIGHT_API_BASE_PATH", None)
-    clear_config_cache()
-
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
-        yield client
-
-
-@pytest.mark.asyncio
-async def test_base_path_health_endpoint(api_client_with_base_path):
-    """Test that health endpoint works with base path."""
-    # With base path set to /hindsight, health should be at /hindsight/health
-    # But since our client base_url is already http://test/hindsight, we request /health
-    response = await api_client_with_base_path.get("/health")
-    assert response.status_code == 200
-    data = response.json()
-    assert "status" in data
-    assert data["status"] in ["ok", "healthy"]  # Accept both formats
-
-
-@pytest.mark.asyncio
-async def test_base_path_banks_endpoint(api_client_with_base_path):
-    """Test that banks endpoint works with base path."""
-    response = await api_client_with_base_path.get("/v1/default/banks")
-    assert response.status_code == 200
-    data = response.json()
-    assert "banks" in data
-
-
-@pytest.mark.asyncio
-async def test_base_path_openapi_schema(api_client_with_base_path):
-    """Test that OpenAPI schema includes correct base path in servers."""
-    response = await api_client_with_base_path.get("/openapi.json")
-    assert response.status_code == 200
-    openapi_schema = response.json()
-
-    # Check that servers array includes base path
-    assert "servers" in openapi_schema
-    servers = openapi_schema["servers"]
-    assert len(servers) > 0
-    # FastAPI should set server URL to the root_path
-    assert servers[0]["url"] == "/hindsight"
-
-
-@pytest.mark.asyncio
-async def test_base_path_docs_redirect(api_client_with_base_path):
-    """Test that /docs redirects correctly with base path."""
-    # FastAPI docs endpoint should work
-    response = await api_client_with_base_path.get("/docs", follow_redirects=False)
-    # Should either return 200 (direct) or 307 (redirect to trailing slash)
-    assert response.status_code in [200, 307]
-
-
-@pytest.mark.asyncio
-async def test_base_path_metrics(api_client_with_base_path):
-    """Test that metrics endpoint works with base path."""
-    response = await api_client_with_base_path.get("/metrics")
-    assert response.status_code == 200
-    # Metrics should be in Prometheus format
-    assert "# HELP" in response.text or "# TYPE" in response.text
-
-
-@pytest.mark.asyncio
-async def test_base_path_full_workflow(api_client_with_base_path):
-    """
-    Test a full retain/recall workflow with base path.
-
-    This ensures that all memory operations work correctly when the API
-    is deployed with a base path.
-    """
-    bank_id = "test_base_path_bank"
-
-    # 1. Create/get bank
-    response = await api_client_with_base_path.get(f"/v1/default/banks/{bank_id}/profile")
-    assert response.status_code == 200
-
-    # 2. Store a memory
-    response = await api_client_with_base_path.post(
-        f"/v1/default/banks/{bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "The API supports base path deployment for reverse proxy use cases.",
-                    "context": "testing base path feature"
-                }
-            ]
-        }
-    )
-    assert response.status_code == 200
-    result = response.json()
-    assert result["success"] is True
-
-    # 3. Recall the memory
-    response = await api_client_with_base_path.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={
-            "query": "base path support"
-        }
-    )
-    assert response.status_code == 200
-    recall_result = response.json()
-    # API returns "results" not "memories"
-    assert "results" in recall_result
-    assert len(recall_result["results"]) > 0
-
-
-@pytest.mark.asyncio
-async def test_without_base_path_still_works(api_client_without_base_path):
-    """
-    Regression test: ensure default behavior (no base path) still works.
-
-    This test verifies that when HINDSIGHT_API_BASE_PATH is not set,
-    the API works at the root path as before.
-    """
-    # Health check at root
-    response = await api_client_without_base_path.get("/health")
-    assert response.status_code == 200
-
-    # Banks endpoint at root
-    response = await api_client_without_base_path.get("/v1/default/banks")
-    assert response.status_code == 200
-
-    # OpenAPI schema should have empty or "/" server path
-    response = await api_client_without_base_path.get("/openapi.json")
-    assert response.status_code == 200
-    openapi_schema = response.json()
-    servers = openapi_schema.get("servers", [])
-    if servers:
-        # Server URL should be empty string (root) or "/"
-        assert servers[0]["url"] in ["", "/"]
-
-
-@pytest.mark.skip(reason="MCP endpoint routing with base path needs investigation")
-@pytest.mark.asyncio
-async def test_base_path_mcp_endpoint(api_client_with_base_path):
-    """Test that MCP endpoint is accessible with base path."""
-    bank_id = "test_mcp_bank"
-
-    # MCP endpoint should be mounted at /mcp/{bank_id}/
-    # The MCP server uses a different protocol, so just check the root exists
-    response = await api_client_with_base_path.get(f"/mcp/{bank_id}/")
-    # MCP may return various status codes, but should not be 404 (not found)
-    # Accept 405 (method not allowed), 400 (bad request), etc.
-    assert response.status_code != 404, "MCP endpoint should exist"
@@ -12,7 +12,6 @@ from datetime import datetime
 import pytest

 from hindsight_api import LLMConfig
-from hindsight_api.config import _get_raw_config
 from hindsight_api.engine.retain.fact_extraction import extract_facts_from_text


@@ -45,7 +44,6 @@ class TestCausalRelationsValidation:
            context=context,
            llm_config=llm_config,
            agent_name="TestUser",
-            config=_get_raw_config(),
        )

        assert len(facts) > 0, "Should extract at least one fact"
@@ -90,7 +88,6 @@ class TestCausalRelationsValidation:
            context=context,
            llm_config=llm_config,
            agent_name="TestUser",
-            config=_get_raw_config(),
        )

        assert len(facts) > 0, "Should extract at least one fact"
@@ -127,7 +124,6 @@ class TestCausalRelationsValidation:
            context=context,
            llm_config=llm_config,
            agent_name="TestUser",
-            config=_get_raw_config(),
        )

        assert len(facts) > 0, "Should extract facts about the causal chain"
@@ -177,7 +173,6 @@ class TestCausalRelationsValidation:
            context=context,
            llm_config=llm_config,
            agent_name="TestUser",
-            config=_get_raw_config(),
        )

        assert len(facts) > 0, "Should extract facts"
@@ -214,7 +209,6 @@ class TestCausalRelationsValidation:
            context=context,
            llm_config=llm_config,
            agent_name="TestUser",
-            config=_get_raw_config(),
        )

        # Verify relation types are all backward-looking
@@ -10,7 +10,6 @@ from datetime import datetime
 import pytest

 from hindsight_api import LLMConfig
-from hindsight_api.config import _get_raw_config
 from hindsight_api.engine.retain.fact_extraction import extract_facts_from_text


@@ -38,8 +37,7 @@ After searching for weeks, I finally found a cheaper apartment in Brooklyn.
        llm_config = LLMConfig.for_memory()

        facts, _, _ = await extract_facts_from_text(
-            text=text, event_date=datetime(2024, 3, 15), context=context, llm_config=llm_config, agent_name="TestUser",
-            config=_get_raw_config(),
+            text=text, event_date=datetime(2024, 3, 15), context=context, llm_config=llm_config, agent_name="TestUser"
        )

        assert len(facts) >= 3, f"Should extract at least 3 facts from the causal chain. Got {len(facts)}"
@@ -108,8 +106,7 @@ The renovation took three months and cost $15,000.
        llm_config = LLMConfig.for_memory()

        facts, _, _ = await extract_facts_from_text(
-            text=text, event_date=datetime(2024, 6, 1), context=context, llm_config=llm_config, agent_name="TestUser",
-            config=_get_raw_config(),
+            text=text, event_date=datetime(2024, 6, 1), context=context, llm_config=llm_config, agent_name="TestUser"
        )

        assert len(facts) >= 4, f"Should extract at least 4 facts. Got {len(facts)}"
@@ -139,8 +136,7 @@ Machine learning fascinated me so much that I changed my career to data science.
        llm_config = LLMConfig.for_memory()

        facts, _, _ = await extract_facts_from_text(
-            text=text, event_date=datetime(2024, 1, 1), context=context, llm_config=llm_config, agent_name="TestUser",
-            config=_get_raw_config(),
+            text=text, event_date=datetime(2024, 1, 1), context=context, llm_config=llm_config, agent_name="TestUser"
        )

        # Check no fact references itself
@@ -167,8 +163,7 @@ The new role enabled me to lead a team of engineers.
        llm_config = LLMConfig.for_memory()

        facts, _, _ = await extract_facts_from_text(
-            text=text, event_date=datetime(2024, 2, 15), context=context, llm_config=llm_config, agent_name="TestUser",
-            config=_get_raw_config(),
+            text=text, event_date=datetime(2024, 2, 15), context=context, llm_config=llm_config, agent_name="TestUser"
        )

        # Validate all indices (must reference PREVIOUS facts only)
@@ -195,8 +190,7 @@ Reduced spending somewhat affected local businesses.
        llm_config = LLMConfig.for_memory()

        facts, _, _ = await extract_facts_from_text(
-            text=text, event_date=datetime(2024, 4, 1), context=context, llm_config=llm_config, agent_name="TestUser",
-            config=_get_raw_config(),
+            text=text, event_date=datetime(2024, 4, 1), context=context, llm_config=llm_config, agent_name="TestUser"
        )

        for i, fact in enumerate(facts):
@@ -1,109 +0,0 @@
-"""
-Tests for configuration validation.
-
-Verifies that config validation catches invalid parameter combinations.
-"""
-
-import os
-
-import pytest
-
-
-@pytest.fixture(autouse=True)
-def setup_test_env():
-    """Set up environment for each test, restoring original values after."""
-    from hindsight_api.config import clear_config_cache
-
-    # Save original environment values
-    env_vars_to_save = [
-        "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS",
-        "HINDSIGHT_API_RETAIN_CHUNK_SIZE",
-        "HINDSIGHT_API_LLM_PROVIDER",
-        "HINDSIGHT_API_LLM_MODEL",
-    ]
-
-    # Save original values
-    original_values = {}
-    for key in env_vars_to_save:
-        original_values[key] = os.environ.get(key)
-
-    clear_config_cache()
-
-    yield
-
-    # Restore original environment
-    for key, original_value in original_values.items():
-        if original_value is None:
-            os.environ.pop(key, None)
-        else:
-            os.environ[key] = original_value
-
-    clear_config_cache()
-
-
-def test_retain_max_completion_tokens_must_be_greater_than_chunk_size():
-    """Test that RETAIN_MAX_COMPLETION_TOKENS > RETAIN_CHUNK_SIZE validation works."""
-    from hindsight_api.config import HindsightConfig
-
-    # Set invalid config: max_completion_tokens <= chunk_size
-    os.environ["HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"] = "1000"
-    os.environ["HINDSIGHT_API_RETAIN_CHUNK_SIZE"] = "2000"
-    os.environ["HINDSIGHT_API_LLM_PROVIDER"] = "mock"
-
-    # Should raise ValueError with helpful message
-    with pytest.raises(ValueError) as exc_info:
-        HindsightConfig.from_env()
-
-    error_message = str(exc_info.value)
-
-    # Verify error message contains helpful information
-    assert "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS" in error_message
-    assert "1000" in error_message
-    assert "HINDSIGHT_API_RETAIN_CHUNK_SIZE" in error_message
-    assert "2000" in error_message
-    assert "must be greater than" in error_message
-    assert "You have two options to fix this:" in error_message
-    assert "Increase HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS" in error_message
-    assert "Use a model that supports" in error_message
-
-
-def test_retain_max_completion_tokens_equal_to_chunk_size_fails():
-    """Test that RETAIN_MAX_COMPLETION_TOKENS == RETAIN_CHUNK_SIZE also fails."""
-    from hindsight_api.config import HindsightConfig
-
-    # Set invalid config: max_completion_tokens == chunk_size
-    os.environ["HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"] = "3000"
-    os.environ["HINDSIGHT_API_RETAIN_CHUNK_SIZE"] = "3000"
-    os.environ["HINDSIGHT_API_LLM_PROVIDER"] = "mock"
-
-    # Should raise ValueError
-    with pytest.raises(ValueError) as exc_info:
-        HindsightConfig.from_env()
-
-    error_message = str(exc_info.value)
-    assert "must be greater than" in error_message
-
-
-def test_valid_retain_config_succeeds():
-    """Test that valid config with max_completion_tokens > chunk_size works."""
-    from hindsight_api.config import HindsightConfig
-
-    # Set valid config: max_completion_tokens > chunk_size
-    os.environ["HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"] = "64000"
-    os.environ["HINDSIGHT_API_RETAIN_CHUNK_SIZE"] = "3000"
-    os.environ["HINDSIGHT_API_LLM_PROVIDER"] = "mock"
-
-    # Should not raise
-    config = HindsightConfig.from_env()
-    assert config.retain_max_completion_tokens == 64000
-    assert config.retain_chunk_size == 3000
-
-
-# Note: The BadRequestError wrapping is implemented in fact_extraction.py
-# but requires a complex integration test setup. The functionality is
-# straightforward: when a BadRequestError containing keywords like
-# "max_tokens", "max_completion_tokens", or "maximum context" is caught,
-# it's wrapped in a ValueError with helpful guidance.
-#
-# The config validation tests above ensure users get early feedback
-# about invalid configurations before runtime errors occur.
@@ -21,9 +21,9 @@ from hindsight_api.engine.reflect.tools import (
@pytest.fixture(autouse=True)
 def enable_observations():
    """Enable observations for all tests in this module."""
-    from hindsight_api.config import _get_raw_config
+    from hindsight_api.config import get_config

-    config = _get_raw_config()
+    config = get_config()
    original_value = config.enable_observations
    config.enable_observations = True
    yield
@@ -346,11 +346,11 @@ class TestConsolidationIntegration:
        or when one directly updates another (e.g., location change).

        Given:
-        - "Alex lives in Italy"
-        - "Alex moved to the US recently" (updates the living location)
+        - "Nicolò lives in Italy"
+        - "Nicolò moved to the US recently" (updates the living location)

        The second fact should UPDATE the first, not create a separate observation.
-        But unrelated facts like "Alex works at Vectorize" should stay separate.
+        But unrelated facts like "Nicolò works at Vectorize" should stay separate.
        """
        bank_id = f"test-consolidation-merge-{uuid.uuid4().hex[:8]}"

@@ -360,14 +360,14 @@ class TestConsolidationIntegration:
        # Retain a memory about living location
        await memory.retain_async(
            bank_id=bank_id,
-            content="Alex lives in Italy.",
+            content="Nicolò lives in Italy.",
            request_context=request_context,
        )

        # Retain an unrelated memory (different topic - should NOT merge)
        await memory.retain_async(
            bank_id=bank_id,
-            content="Alex works at Vectorize as an engineer.",
+            content="Nicolò works at Vectorize as an engineer.",
            request_context=request_context,
        )

@@ -384,7 +384,7 @@ class TestConsolidationIntegration:
        # Add a memory that UPDATES the living location (should merge with first)
        await memory.retain_async(
            bank_id=bank_id,
-            content="Alex recently moved to the United States.",
+            content="Nicolò recently moved to the United States.",
            request_context=request_context,
        )

@@ -485,9 +485,9 @@ class TestConsolidationIntegration:
        they should be merged into ONE observation that captures the change.

        Example:
-        - "Alex loves pizza"
-        - "Alex hates pizza"
-        → Should become: "Alex used to love pizza but now hates it" (or similar)
+        - "Nicolò loves pizza"
+        - "Nicolò hates pizza"
+        → Should become: "Nicolò used to love pizza but now hates it" (or similar)
        """
        bank_id = f"test-consolidation-contradict-{uuid.uuid4().hex[:8]}"

@@ -497,7 +497,7 @@ class TestConsolidationIntegration:
        # Add initial fact
        await memory.retain_async(
            bank_id=bank_id,
-            content="Alex loves pizza.",
+            content="Nicolò loves pizza.",
            request_context=request_context,
        )

@@ -515,7 +515,7 @@ class TestConsolidationIntegration:
        # Add contradicting fact (same person, same topic, opposite sentiment)
        await memory.retain_async(
            bank_id=bank_id,
-            content="Alex hates pizza.",
+            content="Nicolò hates pizza.",
            request_context=request_context,
        )

@@ -563,26 +563,25 @@ class TestConsolidationDisabled:
        self, memory: MemoryEngine, request_context
    ):
        """Test that consolidation returns disabled status when enable_observations is False."""
+        from unittest.mock import patch
+
        bank_id = f"test-consolidation-disabled-{uuid.uuid4().hex[:8]}"

        # Create the bank
        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)

-        # Disable observations for this bank via bank config
-        await memory._config_resolver.update_bank_config(
-            bank_id=bank_id,
-            updates={"enable_observations": False},
-            context=request_context,
-        )
+        # Disable observations via config
+        with patch("hindsight_api.config.get_config") as mock_config:
+            mock_config.return_value.enable_observations = False

-        result = await run_consolidation_job(
-            memory_engine=memory,
-            bank_id=bank_id,
-            request_context=request_context,
-        )
+            result = await run_consolidation_job(
+                memory_engine=memory,
+                bank_id=bank_id,
+                request_context=request_context,
+            )

-        assert result["status"] == "disabled"
-        assert result["bank_id"] == bank_id
+            assert result["status"] == "disabled"
+            assert result["bank_id"] == bank_id

        # Cleanup
        await memory.delete_bank(bank_id, request_context=request_context)
@@ -1898,93 +1897,3 @@ class TestMentalModelRefreshAfterConsolidation:

        # Cleanup
        await memory.delete_bank(bank_id, request_context=request_context)
-
-    @pytest.mark.asyncio
-    async def test_graph_endpoint_observations_inherit_links_and_entities(
-        self, memory: MemoryEngine, request_context
-    ):
-        """Test that graph endpoint shows links and entities for observations filtered by type.
-
-        When filtering graph by type=observation:
-        - Observations should inherit links from their source memories
-        - Observations should show entities inherited from source memories
-        - Even when source memories are not visible, their links should be copied to observations
-        """
-        bank_id = f"test-graph-obs-{uuid.uuid4().hex[:8]}"
-
-        # Create the bank
-        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
-
-        # Retain content that will create world facts with shared entities
-        # This should create facts that are linked by shared entities
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice works at Google as a software engineer.",
-            request_context=request_context,
-        )
-
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Bob also works at Google in the sales department.",
-            request_context=request_context,
-        )
-
-        # Wait for consolidation to create observations
-        import asyncio
-
-        await asyncio.sleep(2)
-
-        # Get graph data filtered by observation type only
-        graph_data = await memory.get_graph_data(
-            bank_id=bank_id,
-            fact_type="observation",
-            limit=1000,
-            request_context=request_context,
-        )
-
-        # Should have observations
-        assert graph_data["total_units"] > 0, "Should have observations"
-        assert len(graph_data["nodes"]) > 0, "Should have observation nodes"
-
-        # Verify all nodes are observations
-        for row in graph_data["table_rows"]:
-            assert row["fact_type"] == "observation", f"All nodes should be observations, got {row['fact_type']}"
-
-        # Should have edges (inherited from source memories)
-        # Even though we're only showing observations, they should inherit links from their sources
-        assert len(graph_data["edges"]) > 0, (
-            "Observations should have edges inherited from source memories. "
-            f"Found {len(graph_data['edges'])} edges"
-        )
-
-        # Should have entities (inherited from source memories)
-        observations_with_entities = [
-            row for row in graph_data["table_rows"] if row["entities"] and row["entities"] != "None"
-        ]
-        assert len(observations_with_entities) > 0, (
-            "Observations should inherit entities from source memories. "
-            f"Found {len(observations_with_entities)} observations with entities"
-        )
-
-        # Verify entities contain expected values
-        all_entities = " ".join([row["entities"] for row in graph_data["table_rows"]])
-        assert "Alice" in all_entities or "Bob" in all_entities or "Google" in all_entities, (
-            f"Expected to find Alice, Bob, or Google in entities, got: {all_entities}"
-        )
-
-        # Verify edge types are valid
-        valid_link_types = {"semantic", "temporal", "entity"}
-        for edge in graph_data["edges"]:
-            link_type = edge["data"]["linkType"]
-            assert link_type in valid_link_types, f"Invalid link type: {link_type}"
-
-        # Verify all edges connect visible observation nodes
-        visible_node_ids = {row["id"] for row in graph_data["table_rows"]}
-        for edge in graph_data["edges"]:
-            source_id = edge["data"]["source"]
-            target_id = edge["data"]["target"]
-            assert source_id in visible_node_ids, f"Edge source {source_id[:8]} not in visible nodes"
-            assert target_id in visible_node_ids, f"Edge target {target_id[:8]} not in visible nodes"
-
-        # Cleanup
-        await memory.delete_bank(bank_id, request_context=request_context)
@@ -0,0 +1,148 @@
+"""
+Tests for XPC error recovery in LocalSTCrossEncoder.
+
+This tests the automatic reinitialization of the cross-encoder model when
+XPC connection errors occur on macOS (common in long-running daemon processes).
+"""
+
+import asyncio
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder
+
+
+class TestCrossEncoderXPCErrorRecovery:
+    """Tests for XPC error detection and recovery in LocalSTCrossEncoder."""
+
+    @pytest.fixture
+    def cross_encoder(self):
+        """Create a LocalSTCrossEncoder instance."""
+        return LocalSTCrossEncoder(model_name="cross-encoder/ms-marco-TinyBERT-L-2-v2")
+
+    def test_is_xpc_error_detection(self, cross_encoder):
+        """Test that XPC errors are correctly detected."""
+        # Test various XPC error message formats
+        xpc_error = Exception("Compiler encountered XPC_ERROR_CONNECTION_INVALID (is the OS shutting down?)")
+        assert cross_encoder._is_xpc_error(xpc_error)
+
+        xpc_error2 = Exception("XPC error occurred")
+        assert cross_encoder._is_xpc_error(xpc_error2)
+
+        # Test that non-XPC errors are not detected
+        normal_error = Exception("Some other error")
+        assert not cross_encoder._is_xpc_error(normal_error)
+
+    @pytest.mark.asyncio
+    async def test_predict_with_xpc_recovery(self, cross_encoder):
+        """Test that predict() recovers from XPC errors by reinitializing."""
+        # Initialize the cross-encoder
+        await cross_encoder.initialize()
+
+        # Track calls to reinitialize
+        reinit_called = False
+        original_reinit = cross_encoder._reinitialize_model_sync
+
+        def track_reinit():
+            nonlocal reinit_called
+            reinit_called = True
+            original_reinit()
+
+        # Track predict attempts
+        predict_attempts = []
+        original_predict = cross_encoder._model.predict
+
+        def mock_predict(*args, **kwargs):
+            predict_attempts.append(1)
+            # Only fail on first attempt
+            if len(predict_attempts) == 1:
+                raise RuntimeError("Compiler encountered XPC_ERROR_CONNECTION_INVALID (is the OS shutting down?)")
+            else:
+                # After reinit: succeed
+                return original_predict(*args, **kwargs)
+
+        # Mock the initial predict to fail, reinit happens, then new model succeeds
+        with patch.object(cross_encoder, "_reinitialize_model_sync", side_effect=track_reinit):
+            with patch.object(cross_encoder._model, "predict", side_effect=mock_predict):
+                # This should trigger XPC error on first attempt, then recover and succeed
+                result = await cross_encoder.predict([("query", "document")])
+
+                # Verify we got a result
+                assert result is not None
+                assert len(result) == 1
+                assert isinstance(result[0], float)
+                assert reinit_called  # Should have reinitialized
+                assert len(predict_attempts) >= 1  # At least one attempt was made
+
+    @pytest.mark.asyncio
+    async def test_predict_fails_on_non_xpc_error(self, cross_encoder):
+        """Test that predict() does not retry for non-XPC errors."""
+        # Initialize the cross-encoder
+        await cross_encoder.initialize()
+
+        # Create a mock that raises a non-XPC error
+        def mock_predict(*args, **kwargs):
+            raise RuntimeError("Some other error")
+
+        # Patch the model's predict method
+        with patch.object(cross_encoder._model, "predict", side_effect=mock_predict):
+            # This should fail without retry
+            with pytest.raises(RuntimeError) as exc_info:
+                await cross_encoder.predict([("query", "document")])
+
+            assert "Some other error" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    async def test_reinitialize_clears_model(self, cross_encoder):
+        """Test that _reinitialize_model_sync properly clears and reinits the model."""
+        # Initialize the cross-encoder
+        await cross_encoder.initialize()
+
+        original_model = cross_encoder._model
+        assert original_model is not None
+
+        # Reinitialize
+        cross_encoder._reinitialize_model_sync()
+
+        # Model should be reinitialized (new instance)
+        assert cross_encoder._model is not None
+        assert cross_encoder._model is not original_model
+
+        # Should still work
+        result = await cross_encoder.predict([("test query", "test document")])
+        assert len(result) == 1
+        assert isinstance(result[0], float)
+
+    @pytest.mark.asyncio
+    async def test_xpc_recovery_exhausts_retries(self, cross_encoder):
+        """Test that XPC recovery gives up after max retries."""
+        # Initialize the cross-encoder
+        await cross_encoder.initialize()
+
+        # Track reinit calls
+        reinit_count = 0
+        original_reinit = cross_encoder._reinitialize_model_sync
+
+        def track_and_fail_reinit():
+            nonlocal reinit_count
+            reinit_count += 1
+            # Call original reinit, but the new model will also be mocked to fail
+            original_reinit()
+            # After reinit, patch the new model too
+            cross_encoder._model.predict = MagicMock(
+                side_effect=RuntimeError("Compiler encountered XPC_ERROR_CONNECTION_INVALID")
+            )
+
+        # Mock that always raises XPC error
+        cross_encoder._model.predict = MagicMock(
+            side_effect=RuntimeError("Compiler encountered XPC_ERROR_CONNECTION_INVALID")
+        )
+
+        with patch.object(cross_encoder, "_reinitialize_model_sync", side_effect=track_and_fail_reinit):
+            # Should try once, reinitialize, try again, and fail
+            with pytest.raises(Exception) as exc_info:
+                await cross_encoder.predict([("query", "document")])
+
+            assert "XPC_ERROR_CONNECTION_INVALID" in str(exc_info.value) or "Failed to recover" in str(exc_info.value)
+            assert reinit_count == 1  # Should have tried to reinitialize once
@@ -0,0 +1,148 @@
+"""
+Tests for XPC error recovery in LocalSTEmbeddings.
+
+This tests the automatic reinitialization of the embedding model when
+XPC connection errors occur on macOS (common in long-running daemon processes).
+"""
+
+import asyncio
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from hindsight_api.engine.embeddings import LocalSTEmbeddings
+
+
+class TestXPCErrorRecovery:
+    """Tests for XPC error detection and recovery in LocalSTEmbeddings."""
+
+    @pytest.fixture
+    def embeddings(self):
+        """Create a LocalSTEmbeddings instance."""
+        return LocalSTEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+    def test_is_xpc_error_detection(self, embeddings):
+        """Test that XPC errors are correctly detected."""
+        # Test various XPC error message formats
+        xpc_error = Exception("Compiler encountered XPC_ERROR_CONNECTION_INVALID (is the OS shutting down?)")
+        assert embeddings._is_xpc_error(xpc_error)
+
+        xpc_error2 = Exception("XPC error occurred")
+        assert embeddings._is_xpc_error(xpc_error2)
+
+        # Test that non-XPC errors are not detected
+        normal_error = Exception("Some other error")
+        assert not embeddings._is_xpc_error(normal_error)
+
+    @pytest.mark.asyncio
+    async def test_encode_with_xpc_recovery(self, embeddings):
+        """Test that encode() recovers from XPC errors by reinitializing."""
+        # Initialize the embeddings
+        await embeddings.initialize()
+
+        # Track calls to reinitialize
+        reinit_called = False
+        original_reinit = embeddings._reinitialize_model_sync
+
+        def track_reinit():
+            nonlocal reinit_called
+            reinit_called = True
+            original_reinit()
+
+        # Track encode attempts
+        encode_attempts = []
+        original_encode = embeddings._model.encode
+
+        def mock_encode(*args, **kwargs):
+            encode_attempts.append(1)
+            # Only fail on first attempt
+            if len(encode_attempts) == 1:
+                raise RuntimeError("Compiler encountered XPC_ERROR_CONNECTION_INVALID (is the OS shutting down?)")
+            else:
+                # After reinit: succeed
+                return original_encode(*args, **kwargs)
+
+        # Mock the initial encode to fail, reinit happens, then new model succeeds
+        with patch.object(embeddings, "_reinitialize_model_sync", side_effect=track_reinit):
+            with patch.object(embeddings._model, "encode", side_effect=mock_encode):
+                # This should trigger XPC error on first attempt, then recover and succeed
+                result = embeddings.encode(["test text"])
+
+                # Verify we got a result
+                assert result is not None
+                assert len(result) == 1
+                assert len(result[0]) > 0  # Should have embedding vector
+                assert reinit_called  # Should have reinitialized
+                assert len(encode_attempts) >= 1  # At least one attempt was made
+
+    @pytest.mark.asyncio
+    async def test_encode_fails_on_non_xpc_error(self, embeddings):
+        """Test that encode() does not retry for non-XPC errors."""
+        # Initialize the embeddings
+        await embeddings.initialize()
+
+        # Create a mock that raises a non-XPC error
+        def mock_encode(*args, **kwargs):
+            raise RuntimeError("Some other error")
+
+        # Patch the model's encode method
+        with patch.object(embeddings._model, "encode", side_effect=mock_encode):
+            # This should fail without retry
+            with pytest.raises(RuntimeError) as exc_info:
+                embeddings.encode(["test text"])
+
+            assert "Some other error" in str(exc_info.value)
+
+    @pytest.mark.asyncio
+    async def test_reinitialize_clears_model(self, embeddings):
+        """Test that _reinitialize_model_sync properly clears and reinits the model."""
+        # Initialize the embeddings
+        await embeddings.initialize()
+
+        original_model = embeddings._model
+        assert original_model is not None
+
+        # Reinitialize
+        embeddings._reinitialize_model_sync()
+
+        # Model should be reinitialized (new instance)
+        assert embeddings._model is not None
+        assert embeddings._model is not original_model
+
+        # Should still work
+        result = embeddings.encode(["test"])
+        assert len(result) == 1
+        assert len(result[0]) > 0
+
+    @pytest.mark.asyncio
+    async def test_xpc_recovery_exhausts_retries(self, embeddings):
+        """Test that XPC recovery gives up after max retries."""
+        # Initialize the embeddings
+        await embeddings.initialize()
+
+        # Track reinit calls
+        reinit_count = 0
+        original_reinit = embeddings._reinitialize_model_sync
+
+        def track_and_fail_reinit():
+            nonlocal reinit_count
+            reinit_count += 1
+            # Call original reinit, but the new model will also be mocked to fail
+            original_reinit()
+            # After reinit, patch the new model too
+            embeddings._model.encode = MagicMock(
+                side_effect=RuntimeError("Compiler encountered XPC_ERROR_CONNECTION_INVALID")
+            )
+
+        # Mock that always raises XPC error
+        embeddings._model.encode = MagicMock(
+            side_effect=RuntimeError("Compiler encountered XPC_ERROR_CONNECTION_INVALID")
+        )
+
+        with patch.object(embeddings, "_reinitialize_model_sync", side_effect=track_and_fail_reinit):
+            # Should try once, reinitialize, try again, and fail
+            with pytest.raises(RuntimeError) as exc_info:
+                embeddings.encode(["test"])
+
+            assert "XPC_ERROR_CONNECTION_INVALID" in str(exc_info.value)
+            assert reinit_count == 1  # Should have tried to reinitialize once
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Nicolò Boschi	00d46c3a73	other fix	2026-01-28 14:43:35 +01:00
Nicolò Boschi	320712f998	fix(embed): daemon process XPC connection crash on macos	2026-01-28 14:34:42 +01:00