Fix example scripts: remove non-existent API attributes

- recall.py: remove .weight, fix entities iteration (dict not list) - retain.mjs: remove result.async check
Fix opinions.py: use actual API attributes instead of non-existent ones
2025-12-17 12:14:43 -07:00 · 2025-12-17 11:40:55 -07:00 · 2025-12-17 11:18:09 -07:00 · 2025-12-17 11:11:46 -07:00 · 2025-12-17 10:48:38 -07:00 · 2025-12-17 10:40:10 -07:00
379 changed files with 24654 additions and 34235 deletions
@@ -2,23 +2,11 @@
 # Copy this file to .env and fill in your values

 # LLM Configuration (Required)
-# 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
 HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1

-# Example: Anthropic Claude configuration
-# HINDSIGHT_API_LLM_PROVIDER=anthropic
-# HINDSIGHT_API_LLM_API_KEY=your-anthropic-api-key
-# HINDSIGHT_API_LLM_MODEL=claude-sonnet-4-20250514
-
-# Example: LM Studio local configuration (Qwen 2.5 32B recommended)
-# HINDSIGHT_API_LLM_PROVIDER=lmstudio
-# HINDSIGHT_API_LLM_API_KEY=lmstudio
-# HINDSIGHT_API_LLM_BASE_URL=http://localhost:1234/v1
-# HINDSIGHT_API_LLM_MODEL=qwen2.5-32b-instruct
-
 # API Configuration (Optional)
 HINDSIGHT_API_HOST=0.0.0.0
 HINDSIGHT_API_PORT=8888
@@ -1,71 +0,0 @@
-name: Bug Report
-description: Report a bug or unexpected behavior
-labels: ["bug", "triage"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to report a bug! Please fill out the sections below.
-
-  - type: textarea
-    id: description
-    attributes:
-      label: Bug Description
-      description: A clear and concise description of the bug
-      placeholder: What happened?
-    validations:
-      required: true
-
-  - type: textarea
-    id: reproduction
-    attributes:
-      label: Steps to Reproduce
-      description: Steps to reproduce the behavior
-      placeholder: |
-        1. Configure '...'
-        2. Call '...'
-        3. See error
-    validations:
-      required: true
-
-  - type: textarea
-    id: expected
-    attributes:
-      label: Expected Behavior
-      description: What did you expect to happen?
-    validations:
-      required: true
-
-  - type: textarea
-    id: actual
-    attributes:
-      label: Actual Behavior
-      description: What actually happened?
-    validations:
-      required: true
-
-  - type: input
-    id: version
-    attributes:
-      label: Version
-      description: What version are you using?
-      placeholder: e.g., 0.1.0 or commit hash
-    validations:
-      required: false
-
-  - type: dropdown
-    id: llm-provider
-    attributes:
-      label: LLM Provider
-      description: Which LLM provider are you using?
-      options:
-        - OpenAI
-        - Anthropic
-        - Gemini
-        - Groq
-        - Ollama
-        - LM Studio
-        - Other
-    validations:
-      required: false
-
@@ -1,8 +0,0 @@
-blank_issues_enabled: false
-contact_links:
-  - name: Questions & Help
-    url: https://github.com/vectorize-io/hindsight/discussions/categories/q-a
-    about: Please ask questions and get help in Discussions instead of opening an issue.
-  - name: Ideas & Feedback
-    url: https://github.com/vectorize-io/hindsight/discussions/categories/ideas
-    about: Share ideas or give feedback in Discussions.
@@ -1,82 +0,0 @@
-name: Feature Request
-description: Suggest a new feature or enhancement
-labels: ["enhancement", "triage"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for suggesting a feature! Please describe what you'd like to see added.
-
-  - type: textarea
-    id: use-case
-    attributes:
-      label: Use Case
-      description: Describe your specific use case. What are you building? What's your goal?
-      placeholder: |
-        I'm building an AI agent that needs to...
-        My application handles...
-    validations:
-      required: true
-
-  - type: textarea
-    id: problem
-    attributes:
-      label: Problem Statement
-      description: What problem are you facing? What's missing or difficult today?
-      placeholder: Currently I have to... which causes...
-    validations:
-      required: true
-
-  - type: textarea
-    id: benefit
-    attributes:
-      label: How This Feature Would Help
-      description: Explain how this feature would improve your workflow or solve your problem
-      placeholder: With this feature, I would be able to...
-    validations:
-      required: true
-
-  - type: textarea
-    id: solution
-    attributes:
-      label: Proposed Solution
-      description: Describe your ideal solution (optional - we may have ideas too!)
-      placeholder: It would be great if Hindsight could...
-    validations:
-      required: false
-
-  - type: textarea
-    id: alternatives
-    attributes:
-      label: Alternatives Considered
-      description: Have you considered any alternative solutions or workarounds?
-    validations:
-      required: false
-
-  - type: dropdown
-    id: priority
-    attributes:
-      label: Priority
-      description: How important is this feature to you?
-      options:
-        - Nice to have
-        - Important - affects my workflow
-        - Critical - blocking my use case
-    validations:
-      required: true
-
-  - type: textarea
-    id: additional
-    attributes:
-      label: Additional Context
-      description: Any other context, mockups, or examples?
-    validations:
-      required: false
-
-  - type: checkboxes
-    id: checklist
-    attributes:
-      label: Checklist
-      options:
-        - label: I would be willing to contribute this feature
-          required: false
@@ -42,10 +42,6 @@ jobs:
      working-directory: ./hindsight-integrations/litellm
      run: uv build --out-dir dist

-    - name: Build hindsight-embed
-      working-directory: ./hindsight-embed
-      run: uv build --out-dir dist
-
    # Publish in order (client and api first, then hindsight-all which depends on them)
    - name: Publish hindsight-client to PyPI
      uses: pypa/gh-action-pypi-publish@release/v1
@@ -71,12 +67,6 @@ jobs:
        packages-dir: ./hindsight-integrations/litellm/dist
        skip-existing: true

-    - name: Publish hindsight-embed to PyPI
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        packages-dir: ./hindsight-embed/dist
-        skip-existing: true
-
    # Upload artifacts for GitHub release
    - name: Upload artifacts
      uses: actions/upload-artifact@v4
@@ -87,7 +77,6 @@ jobs:
          hindsight-api/dist/*
          hindsight/dist/*
          hindsight-integrations/litellm/dist/*
-          hindsight-embed/dist/*
        retention-days: 1

  release-typescript-client:
@@ -113,18 +102,7 @@ jobs:

    - name: Publish to npm
      working-directory: ./hindsight-clients/typescript
-      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
+      run: npm publish --access public
      env:
        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}

@@ -139,65 +117,6 @@ jobs:
        path: hindsight-clients/typescript/*.tgz
        retention-days: 1

-  release-control-plane:
-    runs-on: ubuntu-latest
-    environment: npm
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        registry-url: 'https://registry.npmjs.org'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json
-
-    - name: Install dependencies
-      run: npm ci
-
-    - name: Build TypeScript client (dependency)
-      run: npm run build --workspace=hindsight-clients/typescript
-
-    - name: Fix platform-specific native modules
-      run: |
-        # npm ci installs from lockfile which may have wrong platform binaries
-        # Delete hoisted native modules and reinstall for current platform
-        rm -rf node_modules/lightningcss node_modules/@tailwindcss
-        npm install lightningcss @tailwindcss/postcss @tailwindcss/node
-
-    - name: Build
-      run: npm run build --workspace=hindsight-control-plane
-
-    - name: Publish to npm
-      working-directory: ./hindsight-control-plane
-      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-control-plane
-      run: npm pack
-
-    - name: Upload artifacts
-      uses: actions/upload-artifact@v4
-      with:
-        name: control-plane
-        path: hindsight-control-plane/*.tgz
-        retention-days: 1
-
  release-rust-cli:
    runs-on: ${{ matrix.os }}
    strategy:
@@ -262,7 +181,7 @@ jobs:
    - name: Free Disk Space
      uses: jlumbroso/free-disk-space@main
      with:
-        tool-cache: true
+        tool-cache: false
        android: true
        dotnet: true
        haskell: true
@@ -287,7 +206,7 @@ jobs:
      id: get_version
      run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT

-    - name: Extract metadata for release tags
+    - name: Extract metadata
      id: meta
      uses: docker/metadata-action@v5
      with:
@@ -298,29 +217,7 @@ jobs:
          type=semver,pattern={{major}},value=${{ steps.get_version.outputs.VERSION }}
          type=raw,value=latest

-    # TODO: Re-enable smoke test when disk space issue is resolved
-    # # Step 1: Build for local testing (single platform, no push)
-    # # This creates an identical image to what will be released, just for one platform
-    # - name: Build image for testing
-    #   uses: docker/build-push-action@v6
-    #   with:
-    #     context: .
-    #     file: docker/standalone/Dockerfile
-    #     target: ${{ matrix.target }}
-    #     push: false
-    #     load: true
-    #     tags: ${{ matrix.image_name }}:test
-    #     cache-from: type=gha
-    #     cache-to: type=gha,mode=max
-
-    # # Step 2: Test the image before pushing anything
-    # - name: Smoke test - verify container starts
-    #   env:
-    #     GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-    #   run: ./scripts/docker-smoke-test.sh "${{ matrix.image_name }}:test" "${{ matrix.target }}"
-
-    # Build multi-platform and push to release tags
-    - name: Build and push release images
+    - name: Build and push
      uses: docker/build-push-action@v6
      with:
        context: .
@@ -366,7 +263,7 @@ jobs:

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

@@ -389,12 +286,6 @@ jobs:
        name: typescript-client
        path: ./artifacts/typescript-client

-    - name: Download Control Plane
-      uses: actions/download-artifact@v4
-      with:
-        name: control-plane
-        path: ./artifacts/control-plane
-
    - name: Download Rust CLI (Linux)
      uses: actions/download-artifact@v4
      with:
@@ -427,11 +318,8 @@ jobs:
        cp artifacts/python-packages/hindsight-api/dist/* release-assets/ || true
        cp artifacts/python-packages/hindsight/dist/* release-assets/ || true
        cp artifacts/python-packages/hindsight-integrations/litellm/dist/* release-assets/ || true
-        cp artifacts/python-packages/hindsight-embed/dist/* release-assets/ || true
        # TypeScript client
        cp artifacts/typescript-client/*.tgz release-assets/ || true
-        # Control Plane
-        cp artifacts/control-plane/*.tgz release-assets/ || true
        # Rust CLI binaries
        cp artifacts/rust-cli-linux/hindsight-linux-amd64 release-assets/ || true
        cp artifacts/rust-cli-darwin-amd64/hindsight-darwin-amd64 release-assets/ || true
@@ -20,8 +20,6 @@ jobs:
            path: hindsight-api
          - name: hindsight-client
            path: hindsight-clients/python
-          - name: hindsight-embed
-            path: hindsight-embed

    steps:
    - uses: actions/checkout@v4
@@ -82,58 +80,6 @@ jobs:
    - name: Build TypeScript client
      run: npm run build --workspace=hindsight-clients/typescript

-  build-control-plane:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json
-
-    - name: Install SDK dependencies
-      run: npm ci --workspace=hindsight-clients/typescript
-
-    - name: Build SDK
-      run: npm run build --workspace=hindsight-clients/typescript
-
-    # Install control plane deps and fix hoisted lightningcss binary
-    # lightningcss gets hoisted to root node_modules, so we need to reinstall it there
-    - name: Install Control Plane dependencies
-      run: |
-        npm install --workspace=hindsight-control-plane
-        rm -rf node_modules/lightningcss node_modules/@tailwindcss
-        npm install lightningcss @tailwindcss/postcss @tailwindcss/node
-
-    - name: Build Control Plane
-      run: npm run build --workspace=hindsight-control-plane
-
-    - name: Verify standalone build
-      run: |
-        test -f hindsight-control-plane/standalone/server.js || exit 1
-        test -d hindsight-control-plane/standalone/node_modules || exit 1
-        node hindsight-control-plane/bin/cli.js --help
-
-    - name: Smoke test - verify server starts
-      run: |
-        cd hindsight-control-plane
-        node bin/cli.js --port 9999 &
-        SERVER_PID=$!
-        sleep 5
-        if curl -sf http://localhost:9999 > /dev/null 2>&1; then
-          echo "Server started successfully"
-          kill $SERVER_PID 2>/dev/null || true
-          exit 0
-        else
-          echo "Server failed to respond"
-          kill $SERVER_PID 2>/dev/null || true
-          exit 1
-        fi
-
  build-docs:
    runs-on: ubuntu-latest

@@ -175,90 +121,6 @@ jobs:
      working-directory: hindsight-cli
      run: cargo build --release

-    - name: Upload CLI artifact
-      uses: actions/upload-artifact@v4
-      with:
-        name: hindsight-cli
-        path: hindsight-cli/target/release/hindsight
-        retention-days: 1
-
-  test-rust-cli:
-    runs-on: ubuntu-latest
-    needs: build-rust-cli
-    env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
-      HINDSIGHT_API_URL: http://localhost:8888
-      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Download CLI artifact
-      uses: actions/download-artifact@v4
-      with:
-        name: hindsight-cli
-        path: /tmp/cli
-
-    - name: Make CLI executable
-      run: chmod +x /tmp/cli/hindsight
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-        prune-cache: false
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Build API
-      working-directory: ./hindsight-api
-      run: uv build
-
-    - name: Install API dependencies
-      working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
-
-    - name: Create .env file
-      run: |
-        cat > .env << EOF
-        HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
-        HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
-        EOF
-
-    - name: Start API server
-      run: |
-        ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
-        echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
-          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
-            echo "API server is ready after ${i}s"
-            break
-          fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
-            cat /tmp/api-server.log
-            exit 1
-          fi
-          sleep 1
-        done
-
-    - name: Run CLI smoke test
-      run: |
-        HINDSIGHT_CLI=/tmp/cli/hindsight ./hindsight-cli/smoke-test.sh
-
-    - name: Show API server logs
-      if: always()
-      run: |
-        echo "=== API Server Logs ==="
-        cat /tmp/api-server.log || echo "No API server log found"
-
  lint-helm-chart:
    runs-on: ubuntu-latest

@@ -291,7 +153,7 @@ jobs:
    - name: Free Disk Space
      uses: jlumbroso/free-disk-space@main
      with:
-        tool-cache: true
+        tool-cache: false
        android: true
        dotnet: true
        haskell: true
@@ -309,13 +171,6 @@ jobs:
        file: docker/standalone/Dockerfile
        target: ${{ matrix.target }}
        push: false
-        load: false
-
-    # 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
@@ -325,8 +180,6 @@ jobs:
      GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
      GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-      COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
-      HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -615,97 +468,6 @@ jobs:
        echo "=== API Server Logs ==="
        cat /tmp/api-server.log || echo "No API server log found"

-  test-integration:
-    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_API_URL: http://localhost:8888
-      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      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 API
-      working-directory: ./hindsight-api
-      run: uv build
-
-    - name: Install API dependencies
-      working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
-
-    - name: Install integration test dependencies
-      working-directory: ./hindsight-integration-tests
-      run: uv sync
-
-    - name: Cache HuggingFace models
-      uses: actions/cache@v4
-      with:
-        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
-        restore-keys: |
-          ${{ runner.os }}-huggingface-
-
-    - name: Pre-download models
-      working-directory: ./hindsight-api
-      run: |
-        uv run python -c "
-        from sentence_transformers import SentenceTransformer, CrossEncoder
-        print('Downloading embedding model...')
-        SentenceTransformer('BAAI/bge-small-en-v1.5')
-        print('Downloading cross-encoder model...')
-        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-        print('Models downloaded successfully')
-        "
-
-    - name: Create .env file
-      run: |
-        cat > .env << EOF
-        HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
-        HINDSIGHT_API_LLM_API_KEY=${{ env.HINDSIGHT_API_LLM_API_KEY }}
-        HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
-        EOF
-
-    - name: Start API server
-      run: |
-        ./scripts/dev/start-api.sh > /tmp/api-server.log 2>&1 &
-        echo "Waiting for API server to be ready..."
-        for i in {1..60}; do
-          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
-            echo "API server is ready after ${i}s"
-            break
-          fi
-          if [ $i -eq 60 ]; then
-            echo "API server failed to start after 60s"
-            cat /tmp/api-server.log
-            exit 1
-          fi
-          sleep 1
-        done
-
-    - name: Run integration tests
-      working-directory: ./hindsight-integration-tests
-      run: uv run pytest tests/ -v
-
-    - name: Show API server logs
-      if: always()
-      run: |
-        echo "=== API Server Logs ==="
-        cat /tmp/api-server.log || echo "No API server log found"
-
  test-litellm-integration:
    runs-on: ubuntu-latest

@@ -735,49 +497,8 @@ jobs:
      working-directory: ./hindsight-integrations/litellm
      run: uv run pytest tests -v

-  test-embed:
-    runs-on: ubuntu-latest
-    env:
-      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
-
-    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: Install dependencies
-      working-directory: ./hindsight-embed
-      run: uv sync --index-strategy unsafe-best-match
-
-    - name: Cache HuggingFace models
-      uses: actions/cache@v4
-      with:
-        path: ~/.cache/huggingface
-        key: ${{ runner.os }}-huggingface-embed-${{ hashFiles('hindsight-embed/pyproject.toml') }}
-        restore-keys: |
-          ${{ runner.os }}-huggingface-embed-
-          ${{ runner.os }}-huggingface-
-
-    - name: Run smoke test
-      working-directory: ./hindsight-embed
-      run: ./test.sh
-
  test-doc-examples:
    runs-on: ubuntu-latest
-    needs: build-rust-cli
    env:
      HINDSIGHT_API_LLM_PROVIDER: groq
      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
@@ -789,15 +510,6 @@ jobs:
    steps:
    - uses: actions/checkout@v4

-    - name: Download CLI artifact
-      uses: actions/download-artifact@v4
-      with:
-        name: hindsight-cli
-        path: /usr/local/bin
-
-    - name: Make CLI executable
-      run: chmod +x /usr/local/bin/hindsight
-
    - name: Install uv
      uses: astral-sh/setup-uv@v5
      with:
@@ -871,90 +583,8 @@ jobs:
          node "$f"
        done

-    - name: Configure CLI
-      run: hindsight configure --api-url http://localhost:8888
-
-    - 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()
      run: |
        echo "=== API Server Logs ==="
-        cat /tmp/api-server.log || echo "No API server log found"
-
-  verify-generated-files:
-    runs-on: ubuntu-latest
-    env:
-      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
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        cache: 'npm'
-        cache-dependency-path: package-lock.json
-
-    - name: Install Rust
-      uses: dtolnay/rust-toolchain@stable
-
-    - name: Cache cargo
-      uses: actions/cache@v4
-      with:
-        path: |
-          ~/.cargo/registry
-          ~/.cargo/git
-        key: ${{ runner.os }}-cargo-gen-${{ hashFiles('**/Cargo.lock') }}
-
-    - name: Install Node dependencies
-      run: npm ci
-
-    - name: Install Python dependencies
-      run: |
-        cd hindsight-dev && uv sync --index-strategy unsafe-best-match
-        cd ../hindsight-api && uv sync --index-strategy unsafe-best-match
-        cd ../hindsight-embed && uv sync --index-strategy unsafe-best-match
-
-    - name: Run generate-openapi
-      run: ./scripts/generate-openapi.sh
-
-    - name: Run generate-clients
-      run: ./scripts/generate-clients.sh
-
-    - name: Run lint
-      run: ./scripts/hooks/lint.sh
-
-    - name: Verify no uncommitted changes
-      run: |
-        if [ -n "$(git status --porcelain)" ]; then
-          echo "❌ Error: Generated files are out of sync with committed files."
-          echo ""
-          echo "The following files have changed after running generation scripts:"
-          git status --porcelain
-          echo ""
-          echo "Please run the following commands locally and commit the changes:"
-          echo "  ./scripts/generate-openapi.sh"
-          echo "  ./scripts/generate-clients.sh"
-          echo "  ./scripts/hooks/lint.sh"
-          echo ""
-          git diff --stat
-          exit 1
-        fi
-        echo "✓ All generated files are up to date"
+        cat /tmp/api-server.log || echo "No API server log found"
@@ -5,18 +5,15 @@ build/
 dist/
 wheels/
 *.egg-info
-.mcp.json
-.osgrep
+
 # Virtual environments
 .venv

 # Node
 node_modules/

-# Environment variables and local config
+# Environment variables
 .env
-docker-compose.yml
-docker-compose.override.yml

 # IDE
 .idea/
@@ -42,8 +39,4 @@ hindsight-docs/static/llms-full.txt
 hindsight-dev/benchmarks/locomo/results/
 hindsight-dev/benchmarks/longmemeval/results/
 hindsight-cli/target
-hindsight-clients/rust/target
-.claude
-whats-next.md
-TASK.md
-CHANGELOG.md
+hindsight-clients/rust/target
@@ -14,7 +14,6 @@ This document captures architectural decisions and coding conventions for the Hi
 hindsight/              # Python package for embedded usage
 hindsight-api/          # FastAPI server (core memory engine)
 hindsight-cli/          # Rust CLI client
-hindsight-embed/        # Embedded CLI (no server needed)
 hindsight-control-plane/ # Next.js admin UI
 hindsight-docs/         # Docusaurus documentation site
 hindsight-dev/          # Development tools and benchmarks
@@ -149,5 +148,4 @@ Note: The maintained wrapper `hindsight_client.py` and `README.md` are preserved

 # Branding
 ## Colors
- Primary: gradient from #0074d9 to #009296
-
+- Primary: gradient from #0074d9 to #009296  
@@ -1,186 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
-## Project Overview
-
-Hindsight is an agent memory system that provides long-term memory for AI agents using biomimetic data structures. Memories are organized as:
- **World facts**: General knowledge ("The sky is blue")
- **Experience facts**: Personal experiences ("I visited Paris in 2023")
- **Opinion facts**: Beliefs with confidence scores ("Paris is beautiful" - 0.9 confidence)
- **Observations**: Complex mental models derived from reflection
-
-## Development Commands
-
-### API Server (Python/FastAPI)
-```bash
-# Start API server (loads .env automatically)
-./scripts/dev/start-api.sh
-
-# Run all tests (parallelized with pytest-xdist)
-cd hindsight-api && uv run pytest tests/
-
-# Run specific test file
-cd hindsight-api && uv run pytest tests/test_http_api_integration.py -v
-
-# Run single test function
-cd hindsight-api && uv run pytest tests/test_retain.py::test_retain_simple -v
-
-# Lint and format
-cd hindsight-api && uv run ruff check .
-cd hindsight-api && uv run ruff format .
-
-# Type checking (uses ty - extremely fast type checker from Astral)
-cd hindsight-api && uv run ty check hindsight_api/
-```
-
-### Control Plane (Next.js)
-```bash
-./scripts/dev/start-control-plane.sh
-# Or manually:
-cd hindsight-control-plane && npm run dev
-```
-
-### Documentation Site (Docusaurus)
-```bash
-./scripts/dev/start-docs.sh
-```
-
-### Generating Clients/OpenAPI
-```bash
-# Regenerate OpenAPI spec after API changes (REQUIRED after changing endpoints)
-./scripts/generate-openapi.sh
-
-# Regenerate all client SDKs (Python, TypeScript, Rust)
-./scripts/generate-clients.sh
-```
-
-### Benchmarks
-```bash
-./scripts/benchmarks/run-longmemeval.sh
-./scripts/benchmarks/run-locomo.sh
-./scripts/benchmarks/start-visualizer.sh  # View results at localhost:8001
-```
-
-## Architecture
-
-### Monorepo Structure
- **hindsight-api/**: Core FastAPI server with memory engine (Python, uv)
- **hindsight/**: Embedded Python bundle (hindsight-all package)
- **hindsight-control-plane/**: Admin UI (Next.js, npm)
- **hindsight-cli/**: CLI tool (Rust, cargo, uses progenitor for API client)
- **hindsight-clients/**: Generated SDK clients (Python, TypeScript, Rust)
- **hindsight-docs/**: Docusaurus documentation site
- **hindsight-integrations/**: Framework integrations (LiteLLM, OpenAI)
- **hindsight-dev/**: Development tools and benchmarks
-
-### Core Engine (hindsight-api/hindsight_api/engine/)
- `memory_engine.py`: Main orchestrator (~170KB) for retain/recall/reflect operations
- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, Groq, Ollama, LM Studio
- `embeddings.py`: Embedding generation (local sentence-transformers or TEI)
- `cross_encoder.py`: Reranking (local or TEI)
- `entity_resolver.py`: Entity extraction and normalization
- `query_analyzer.py`: Query intent analysis
-
-**retain/**: Memory ingestion pipeline
- `orchestrator.py`: Coordinates the retain flow
- `fact_extraction.py`: LLM-based fact extraction from content
- `link_utils.py`: Entity link creation and management
-
-**search/**: Multi-strategy retrieval
- `retrieval.py`: Main retrieval orchestrator
- `graph_retrieval.py`: Entity/relationship graph traversal
- `mpfp_retrieval.py`: Multi-Path Fact Propagation retrieval
- `fusion.py`: Reciprocal rank fusion for combining results
- `reranking.py`: Cross-encoder reranking
-
-### API Layer (hindsight-api/hindsight_api/api/)
- `http.py`: FastAPI HTTP routers (~80KB) for all REST endpoints
- `mcp.py`: Model Context Protocol server implementation
-
-Main operations:
- **Retain**: Store memories, extracts facts/entities/relationships
- **Recall**: Retrieve memories via 4 parallel strategies (semantic, BM25, graph, temporal) + reranking
- **Reflect**: Deep analysis forming new opinions/observations (disposition-aware)
-
-### Database
-PostgreSQL with pgvector. Schema managed via Alembic migrations in `hindsight-api/hindsight_api/alembic/`. Migrations run automatically on API startup.
-
-Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
-
-## Key Conventions
-
-### Code Quality
-**Always run the lint script after making Python or TypeScript/Node changes:**
-```bash
-./scripts/hooks/lint.sh
-```
-This runs the same checks as the pre-commit hook (Ruff for Python, ESLint/Prettier for TypeScript).
-
-### Memory Banks
- Each bank is an isolated memory store (like a "brain" for one user/agent)
- Banks have dispositions (skepticism, literalism, empathy traits 1-5) affecting reflect
- Banks can have background context
- Bank isolation is strict - no cross-bank data leakage
-
-### API Design
- All endpoints operate on a single bank per request
- Multi-bank queries are client responsibility to orchestrate
- Disposition traits only affect reflect, not recall
-
-### Python Style
- Python 3.11+, type hints required
- Async throughout (asyncpg, async FastAPI)
- Pydantic models for request/response
- Ruff for linting (line-length 120)
- No Python files at project root - maintain clean directory structure
-
-### TypeScript Style
- Next.js App Router for control plane
- Tailwind CSS with shadcn/ui components
-
-### Adding New API Configuration Flags
-
-When adding a new environment variable configuration:
-
-1. **config.py** (`hindsight-api/hindsight_api/config.py`):
-   - Add `ENV_*` constant for the environment variable name
-   - Add `DEFAULT_*` constant for the default value
-   - Add field to `HindsightConfig` dataclass
-   - Add initialization in `from_env()` method
-
-2. **main.py** (`hindsight-api/hindsight_api/main.py`):
-   - Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")
-
-3. **Use the config** in code:
-   ```python
-   from ...config import get_config
-   config = get_config()
-   value = config.your_new_field
-   ```
-
-4. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
-   - Add to appropriate section table with Variable, Description, Default
-
-## Environment Setup
-
-```bash
-cp .env.example .env
-# Edit .env with LLM API key
-
-# Python deps
-uv sync --directory hindsight-api/
-
-# Node deps (uses npm workspaces)
-npm install
-```
-
-Required env vars:
- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, ollama, lmstudio
- `HINDSIGHT_API_LLM_API_KEY`: Your API key
- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., o3-mini, claude-sonnet-4-20250514)
-
-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)
@@ -51,36 +51,7 @@ cd hindsight-api
 uv run pytest tests/
 ```

-### Code Style
-
-We use [Ruff](https://docs.astral.sh/ruff/) for Python linting and formatting, and ESLint/Prettier for TypeScript.
-
-#### Setting up git hooks (recommended)
-
-Set up git hooks to automatically lint and format code before each commit:
-
-```bash
-./scripts/setup-hooks.sh
-```
-
-This configures git to use the hooks in `.githooks/`, which run all scripts in `scripts/hooks/` on commit. The lint hook runs in parallel:
- **Python**: `ruff check --fix`, `ruff format`, `ty check`
- **TypeScript**: `eslint --fix`, `prettier`
-
-#### Manual linting and formatting
-
-```bash
-# Run all lints (same as pre-commit)
-./scripts/hooks/lint.sh
-
-# Or run individually for Python:
-cd hindsight-api
-uv run ruff check --fix .   # Lint and auto-fix
-uv run ruff format .        # Format code
-uv run ty check hindsight_api  # Type check
-```
-
-#### Style guidelines
+### Code style

 - Use Python type hints
 - Follow existing code patterns
@@ -81,8 +81,6 @@ docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
  ghcr.io/vectorize-io/hindsight:latest
 ```

-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

@@ -2,19 +2,16 @@
 # Supports building API-only, Control Plane-only, or both
 #
 # Build args:
-#   INCLUDE_API=true/false       - Include API (default: true)
-#   INCLUDE_CP=true/false        - Include Control Plane (default: true)
-#   PRELOAD_ML_MODELS=true/false - Pre-download ML models during build (default: true)
+#   INCLUDE_API=true/false  - Include API (default: true)
+#   INCLUDE_CP=true/false   - Include Control Plane (default: true)
 #
 # Examples:
-#   docker build -t hindsight .                                          # Both (standalone)
-#   docker build -t hindsight-api --build-arg INCLUDE_CP=false .         # API only
-#   docker build -t hindsight-cp --build-arg INCLUDE_API=false .         # Control Plane only
-#   docker build -t hindsight --build-arg PRELOAD_ML_MODELS=false .      # Skip ML model preload
+#   docker build -t hindsight .                                    # Both (standalone)
+#   docker build -t hindsight-api --build-arg INCLUDE_CP=false .   # API only
+#   docker build -t hindsight-cp --build-arg INCLUDE_API=false .   # Control Plane only

 ARG INCLUDE_API=true
 ARG INCLUDE_CP=true
-ARG PRELOAD_ML_MODELS=true

 # =============================================================================
 # Stage: API Builder
@@ -75,48 +72,30 @@ FROM node:20-slim AS cp-builder
 ARG INCLUDE_CP
 RUN if [ "$INCLUDE_CP" != "true" ]; then echo "Skipping CP build" && exit 0; fi

-# Create directory structure matching the monorepo layout
-# This is required because build:standalone script expects .next/standalone/memory-poc/hindsight-control-plane
-WORKDIR /app/memory-poc/hindsight-control-plane
+WORKDIR /app
+
+# Copy built SDK
+COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk

 # Install Control Plane dependencies
 # Only copy package.json (not package-lock.json) to ensure npm installs
 # correct platform-specific native bindings for lightningcss/tailwindcss
 COPY hindsight-control-plane/package.json ./
-# Remove the file: dependency on SDK (we'll copy it directly later)
-RUN sed -i '/"@vectorize-io\/hindsight-client":/d' package.json
 RUN npm install

 # Copy Control Plane source (excluding node_modules via .dockerignore)
 COPY hindsight-control-plane/ ./
 # Remove package-lock.json to avoid conflicts with installed native bindings
-# Also remove the file: dependency from package.json (restored by COPY above)
-RUN rm -f package-lock.json && sed -i '/"@vectorize-io\/hindsight-client":/d' package.json
+RUN rm -f package-lock.json

-# 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
+# Link SDK (temporary for build)
+RUN cd /app/sdk && npm link && cd /app && npm link @vectorize-io/hindsight-client

-# 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
+# Build Control Plane
+RUN npm run build

-# Create standalone directory structure manually
-# Note: Must exclude node_modules from find to avoid wrong server.js from next/dist/experimental/testmode/
-# Note: Must explicitly copy .next since glob * doesn't match hidden directories
-RUN STANDALONE_ROOT=$(find .next/standalone -path '*/node_modules' -prune -o -name 'server.js' -print | head -1 | xargs dirname) && \
-    mkdir -p standalone && \
-    cp -r "$STANDALONE_ROOT"/* standalone/ && \
-    cp -r "$STANDALONE_ROOT"/.next standalone/.next && \
-    # Copy node_modules if separate from app dir (monorepo structure)
-    if [ -d ".next/standalone/node_modules" ] && [ "$STANDALONE_ROOT" != ".next/standalone" ]; then \
-      cp -r .next/standalone/node_modules standalone/node_modules; \
-    fi && \
-    cp -r .next/static standalone/.next/static && \
-    mkdir -p standalone/public && \
-    cp -r public/* standalone/public/ 2>/dev/null || true && \
-    # Verify required files exist
-    test -f standalone/server.js || (echo "ERROR: server.js missing!" && exit 1) && \
-    test -f standalone/.next/BUILD_ID || (echo "ERROR: BUILD_ID missing!" && exit 1)
+# Create public directory if it doesn't exist
+RUN mkdir -p public

 # =============================================================================
 # Stage: Final Image - API Only
@@ -125,18 +104,18 @@ FROM python:3.11-slim AS api-only

 WORKDIR /app

-# Note: libicu version varies by Debian version - try common versions in order
+# Install pg0 dependencies
 RUN apt-get update && apt-get install -y \
    curl \
-    procps \
    libxml2 \
    libssl3 \
    libgssapi-krb5-2 \
    libossp-uuid16 \
-    && (apt-get install -y libicu72 2>/dev/null || apt-get install -y libicu74 2>/dev/null || apt-get install -y libicu76 2>/dev/null || true) \
+    && apt-get install -y libicu72 || apt-get install -y libicu74 || apt-get install -y libicu* \
    && rm -rf /var/lib/apt/lists/* \
    && pip install --no-cache-dir uv

+# Create non-root user (PostgreSQL cannot run as root)
 RUN useradd -m -s /bin/bash hindsight

 # Copy API with virtual environment from builder
@@ -146,23 +125,28 @@ COPY --from=api-builder /app/api /app/api
 COPY docker/standalone/start-all.sh /app/start-all.sh
 RUN chmod +x /app/start-all.sh

-RUN chown -R hindsight:hindsight /app
+# Create data directory for pg0 and set ownership
+RUN mkdir -p /app/data && chown -R hindsight:hindsight /app

+# Switch to non-root user
 USER hindsight

+# Set PATH for hindsight user
 ENV PATH="/app/api/.venv/bin:${PATH}"

-# Pre-download ML models to avoid runtime download (conditional)
-ARG PRELOAD_ML_MODELS
-RUN if [ "$PRELOAD_ML_MODELS" = "true" ]; then \
-    /app/api/.venv/bin/python -c "\
+# Pre-cache PostgreSQL binaries by starting/stopping pg0-embedded
+ENV PG0_HOME=/home/hindsight/.pg0-cache
+
+ENV PG0_HOME=/home/hindsight/.pg0
+
+# Pre-download ML models to avoid runtime download
+RUN /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')"; \
-    else echo "Skipping ML model preload"; fi
+print('Models cached successfully')"

 EXPOSE 8888

@@ -187,9 +171,9 @@ COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk

 # Copy Control Plane standalone build
 WORKDIR /app/control-plane
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/standalone ./
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/.next/static ./.next/static
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/public ./public
+COPY --from=cp-builder /app/.next/standalone ./
+COPY --from=cp-builder /app/.next/static ./.next/static
+COPY --from=cp-builder /app/public ./public

 WORKDIR /app

@@ -216,21 +200,20 @@ FROM python:3.11-slim AS standalone

 WORKDIR /app

-# Install Node.js, curl, uv, and system dependencies
-# Note: libicu version varies by Debian version - try common versions in order
+# Install Node.js, curl, uv, and pg0 dependencies
 RUN apt-get update && apt-get install -y \
    curl \
-    procps \
    libxml2 \
    libssl3 \
    libgssapi-krb5-2 \
    libossp-uuid16 \
-    && (apt-get install -y libicu72 2>/dev/null || apt-get install -y libicu74 2>/dev/null || apt-get install -y libicu76 2>/dev/null || true) \
+    && apt-get install -y libicu72 || apt-get install -y libicu74 || apt-get install -y libicu* \
    && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
    && apt-get install -y nodejs \
    && rm -rf /var/lib/apt/lists/* \
    && pip install --no-cache-dir uv

+# Create non-root user (PostgreSQL cannot run as root)
 RUN useradd -m -s /bin/bash hindsight

 # Copy API with virtual environment from builder
@@ -241,9 +224,9 @@ COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk

 # Copy Control Plane standalone build
 WORKDIR /app/control-plane
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/standalone ./
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/.next/static ./.next/static
-COPY --from=cp-builder /app/memory-poc/hindsight-control-plane/public ./public
+COPY --from=cp-builder /app/.next/standalone ./
+COPY --from=cp-builder /app/.next/static ./.next/static
+COPY --from=cp-builder /app/public ./public

 WORKDIR /app

@@ -251,23 +234,35 @@ WORKDIR /app
 COPY docker/standalone/start-all.sh /app/start-all.sh
 RUN chmod +x /app/start-all.sh

-RUN chown -R hindsight:hindsight /app
+# Create data directory for pg0 and set ownership
+RUN mkdir -p /app/data && chown -R hindsight:hindsight /app

+# Switch to non-root user
 USER hindsight

+# Set PATH for hindsight user
 ENV PATH="/app/api/.venv/bin:${PATH}"

-# Pre-download ML models to avoid runtime download (conditional)
-ARG PRELOAD_ML_MODELS
-RUN if [ "$PRELOAD_ML_MODELS" = "true" ]; then \
-    /app/api/.venv/bin/python -c "\
+# Pre-cache PostgreSQL binaries by starting/stopping pg0-embedded
+ENV PG0_HOME=/home/hindsight/.pg0-cache
+RUN /app/api/.venv/bin/python -c "\
+from pg0 import Pg0; \
+print('Pre-caching PostgreSQL binaries...'); \
+pg = Pg0(name='hindsight', port=5555, username='hindsight', password='hindsight', database='hindsight'); \
+pg.start(); \
+pg.stop(); \
+print('PostgreSQL pre-cached to PG0_HOME')" || echo "Pre-download skipped"
+
+ENV PG0_HOME=/home/hindsight/.pg0
+
+# Pre-download ML models to avoid runtime download
+RUN /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')"; \
-    else echo "Skipping ML model preload"; fi
+print('Models cached successfully')"

 EXPOSE 8888 9999

@@ -5,70 +5,16 @@ set -e
 ENABLE_API="${HINDSIGHT_ENABLE_API:-true}"
 ENABLE_CP="${HINDSIGHT_ENABLE_CP:-true}"

-# =============================================================================
-# Dependency waiting (opt-in via HINDSIGHT_WAIT_FOR_DEPS=true)
-#
-# Problem: When running with LM Studio, the LLM may take time to load models.
-# If Hindsight starts before LM Studio is ready, it fails on LLM verification.
-# This wait loop ensures dependencies are ready before starting.
-# =============================================================================
-if [ "${HINDSIGHT_WAIT_FOR_DEPS:-false}" = "true" ]; then
-    LLM_BASE_URL="${HINDSIGHT_API_LLM_BASE_URL:-http://host.docker.internal:1234/v1}"
-    MAX_RETRIES="${HINDSIGHT_RETRY_MAX:-0}"  # 0 = infinite
-    RETRY_INTERVAL="${HINDSIGHT_RETRY_INTERVAL:-10}"
-
-    # Check if external database is configured (skip check for embedded pg0)
-    SKIP_DB_CHECK=false
-    if [ -z "${HINDSIGHT_API_DATABASE_URL}" ]; then
-        SKIP_DB_CHECK=true
-    else
-        DB_CHECK_HOST=$(echo "$HINDSIGHT_API_DATABASE_URL" | sed -E 's|.*@([^:/]+):([0-9]+)/.*|\1 \2|')
+# Copy pre-cached PostgreSQL data if runtime directory is empty (first run with volume)
+if [ "$ENABLE_API" = "true" ]; then
+    PG0_CACHE="/home/hindsight/.pg0-cache"
+    PG0_HOME="/home/hindsight/.pg0"
+    if [ -d "$PG0_CACHE" ] && [ "$(ls -A $PG0_CACHE 2>/dev/null)" ]; then
+        if [ ! "$(ls -A $PG0_HOME 2>/dev/null)" ]; then
+            echo "📦 Copying pre-cached PostgreSQL data..."
+            cp -r "$PG0_CACHE"/* "$PG0_HOME"/ 2>/dev/null || true
+        fi
    fi
-
-    check_db() {
-        if $SKIP_DB_CHECK; then
-            return 0
-        fi
-        if command -v pg_isready &> /dev/null; then
-            pg_isready -h $(echo $DB_CHECK_HOST | cut -d' ' -f1) -p $(echo $DB_CHECK_HOST | cut -d' ' -f2) &>/dev/null
-        else
-            python3 -c "import socket; s=socket.socket(); s.settimeout(5); exit(0 if s.connect_ex(('$(echo $DB_CHECK_HOST | cut -d' ' -f1)', $(echo $DB_CHECK_HOST | cut -d' ' -f2))) == 0 else 1)" 2>/dev/null
-        fi
-    }
-
-    check_llm() {
-        curl -sf "${LLM_BASE_URL}/models" --connect-timeout 5 &>/dev/null
-    }
-
-    echo "⏳ Waiting for dependencies to be ready..."
-    attempt=1
-
-    while true; do
-        db_ok=false
-        llm_ok=false
-
-        if check_db; then
-            db_ok=true
-        fi
-
-        if check_llm; then
-            llm_ok=true
-        fi
-
-        if $db_ok && $llm_ok; then
-            echo "✅ Dependencies ready!"
-            break
-        fi
-
-        if [ "$MAX_RETRIES" -ne 0 ] && [ "$attempt" -ge "$MAX_RETRIES" ]; then
-            echo "❌ Max retries ($MAX_RETRIES) reached. Dependencies not available."
-            exit 1
-        fi
-
-        echo "   Attempt $attempt: DB=$( $db_ok && echo 'ok' || echo 'waiting' ), LLM=$( $llm_ok && echo 'ok' || echo 'waiting' )"
-        sleep "$RETRY_INTERVAL"
-        ((attempt++))
-    done
 fi

 # Track PIDs for wait
@@ -77,8 +23,7 @@ PIDS=()
 # Start API if enabled
 if [ "$ENABLE_API" = "true" ]; then
    cd /app/api
-    # Run API directly - Python's PYTHONUNBUFFERED=1 handles output buffering
-    hindsight-api &
+    hindsight-api 2>&1 | sed -u 's/^/[api] /' &
    API_PID=$!
    PIDS+=($API_PID)

@@ -97,7 +42,7 @@ fi
 if [ "$ENABLE_CP" = "true" ]; then
    echo "🎛️  Starting Control Plane..."
    cd /app/control-plane
-    PORT=9999 node server.js &
+    PORT=9999 node server.js 2>&1 | grep -v -E "^[[:space:]]*(▲|✓|-|$)" | sed -u 's/^/[control-plane] /' &
    CP_PID=$!
    PIDS+=($CP_PID)
 else
@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.2.1
-appVersion: "0.2.1"
+version: 0.1.8
+appVersion: "0.1.8"
 keywords:
  - ai
  - memory
@@ -110,14 +110,3 @@ API URL for control plane
 {{- define "hindsight.apiUrl" -}}
 {{- printf "http://%s-api:%d" (include "hindsight.fullname" .) (.Values.api.service.port | int) }}
 {{- end }}
-
-{{/*
-Get the name of the secret to use
-*/}}
-{{- define "hindsight.secretName" -}}
-{{- if .Values.existingSecret }}
-{{- .Values.existingSecret }}
-{{- else }}
-{{- printf "%s-secret" (include "hindsight.fullname" .) }}
-{{- end }}
-{{- end }}
@@ -15,9 +15,7 @@ spec:
  template:
    metadata:
      annotations:
-        {{- if not .Values.existingSecret }}
        checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
-        {{- end }}
        {{- with .Values.podAnnotations }}
        {{- toYaml . | nindent 8 }}
        {{- end }}
@@ -39,36 +37,27 @@ spec:
        - name: http
          containerPort: {{ .Values.api.service.targetPort }}
          protocol: TCP
-        {{- if .Values.existingSecret }}
-        envFrom:
-        - secretRef:
-            name: {{ .Values.existingSecret }}
-        {{- end }}
        env:
-        {{- /* POSTGRES_PASSWORD must be defined before DATABASE_URL for $(VAR) interpolation */}}
+        - name: HINDSIGHT_API_DATABASE_URL
+          value: {{ include "hindsight.databaseUrl" . | quote }}
        {{- if not .Values.postgresql.enabled }}
        - name: POSTGRES_PASSWORD
          valueFrom:
            secretKeyRef:
-              name: {{ include "hindsight.secretName" . }}
+              name: {{ include "hindsight.fullname" . }}-secret
              key: postgres-password
        {{- end }}
-        - name: HINDSIGHT_API_DATABASE_URL
-          value: {{ include "hindsight.databaseUrl" . | quote }}
        {{- range $key, $value := .Values.api.env }}
        - name: {{ $key }}
          value: {{ $value | quote }}
        {{- end }}
-        {{- /* Only use api.secrets when not using existingSecret (for chart-managed secrets) */}}
-        {{- if not .Values.existingSecret }}
        {{- range $key, $value := .Values.api.secrets }}
        - name: {{ $key }}
          valueFrom:
            secretKeyRef:
-              name: {{ include "hindsight.secretName" $ }}
+              name: {{ include "hindsight.fullname" $ }}-secret
              key: {{ $key }}
        {{- end }}
-        {{- end }}
        livenessProbe:
          {{- toYaml .Values.api.livenessProbe | nindent 10 }}
        readinessProbe:
@@ -15,9 +15,7 @@ spec:
  template:
    metadata:
      annotations:
-        {{- if not .Values.existingSecret }}
        checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
-        {{- end }}
        {{- with .Values.podAnnotations }}
        {{- toYaml . | nindent 8 }}
        {{- end }}
@@ -39,11 +37,6 @@ spec:
        - name: http
          containerPort: {{ .Values.controlPlane.service.targetPort }}
          protocol: TCP
-        {{- if .Values.existingSecret }}
-        envFrom:
-        - secretRef:
-            name: {{ .Values.existingSecret }}
-        {{- end }}
        env:
        - name: HINDSIGHT_CP_DATAPLANE_API_URL
          value: {{ include "hindsight.apiUrl" . | quote }}
@@ -51,16 +44,13 @@ spec:
        - name: {{ $key }}
          value: {{ $value | quote }}
        {{- end }}
-        {{- /* Only use controlPlane.secrets when not using existingSecret (for chart-managed secrets) */}}
-        {{- if not .Values.existingSecret }}
        {{- range $key, $value := .Values.controlPlane.secrets }}
        - name: {{ $key }}
          valueFrom:
            secretKeyRef:
-              name: {{ include "hindsight.secretName" $ }}
+              name: {{ include "hindsight.fullname" $ }}-secret
              key: {{ $key }}
        {{- end }}
-        {{- end }}
        livenessProbe:
          {{- toYaml .Values.controlPlane.livenessProbe | nindent 10 }}
        readinessProbe:
@@ -1,8 +1,7 @@
-{{- if not .Values.existingSecret }}
 apiVersion: v1
 kind: Secret
 metadata:
-  name: {{ include "hindsight.secretName" . }}
+  name: {{ include "hindsight.fullname" . }}-secret
  labels:
    {{- include "hindsight.labels" . | nindent 4 }}
 type: Opaque
@@ -16,4 +15,3 @@ data:
  {{- if and (not .Values.postgresql.enabled) .Values.postgresql.external.password }}
  postgres-password: {{ .Values.postgresql.external.password | b64enc | quote }}
  {{- end }}
-{{- end }}
@@ -3,15 +3,6 @@
 # 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
-# Required keys:
-#   - postgres-password: PostgreSQL password (when postgresql.enabled=false)
-# Optional keys (any key becomes an env var):
-#   - HINDSIGHT_API_LLM_API_KEY: API key for LLM provider
-#   - Any other env vars you want to inject
-# existingSecret: "my-hindsight-secret"
-
 # Global settings
 replicaCount: 1

@@ -80,7 +80,7 @@ Configure via environment variables:
 | Variable | Description | Default |
 |----------|-------------|---------|
 | `HINDSIGHT_API_DATABASE_URL` | PostgreSQL connection string | `pg0` (embedded) |
-| `HINDSIGHT_API_LLM_PROVIDER` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio` | `openai` |
+| `HINDSIGHT_API_LLM_PROVIDER` | `openai`, `groq`, `gemini`, `ollama` | `openai` |
 | `HINDSIGHT_API_LLM_API_KEY` | API key for LLM provider | - |
 | `HINDSIGHT_API_LLM_MODEL` | Model name | `gpt-4o-mini` |
 | `HINDSIGHT_API_HOST` | Server bind address | `0.0.0.0` |
@@ -21,11 +21,9 @@ from .engine.search.trace import (
    WeightComponents,
 )
 from .engine.search.tracer import SearchTracer
-from .models import RequestContext

 __all__ = [
    "MemoryEngine",
-    "RequestContext",
    "HindsightConfig",
    "get_config",
    "SearchTrace",
@@ -1 +0,0 @@
-# Admin CLI for Hindsight
@@ -1,252 +0,0 @@
-"""
-Hindsight Admin CLI - backup and restore operations.
-"""
-
-import asyncio
-import io
-import json
-import logging
-import zipfile
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any
-
-import asyncpg
-import typer
-
-from ..config import HindsightConfig
-from ..pg0 import parse_pg0_url, resolve_database_url
-
-
-def _fq_table(table: str, schema: str) -> str:
-    """Get fully-qualified table name with schema prefix."""
-    return f"{schema}.{table}"
-
-
-# Setup logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(message)s",
-)
-logger = logging.getLogger(__name__)
-
-app = typer.Typer(name="hindsight-admin", help="Hindsight administrative commands")
-
-# Tables to backup/restore in dependency order
-# Import must happen in this order due to foreign key constraints
-BACKUP_TABLES = [
-    "banks",
-    "documents",
-    "entities",
-    "chunks",
-    "memory_units",
-    "unit_entities",
-    "entity_cooccurrences",
-    "memory_links",
-]
-
-MANIFEST_VERSION = "1"
-
-
-async def _backup(database_url: str, output_path: Path, schema: str = "public") -> dict[str, Any]:
-    """Backup all tables to a zip file using binary COPY protocol."""
-    conn = await asyncpg.connect(database_url)
-    try:
-        tables: dict[str, Any] = {}
-        manifest: dict[str, Any] = {
-            "version": MANIFEST_VERSION,
-            "created_at": datetime.now(timezone.utc).isoformat(),
-            "schema": schema,
-            "tables": tables,
-        }
-
-        # Use a transaction with REPEATABLE READ isolation to get a consistent
-        # snapshot across all tables. This prevents race conditions where
-        # entity_cooccurrences could reference entities created after the
-        # entities table was backed up.
-        async with conn.transaction(isolation="repeatable_read"):
-            with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zf:
-                for i, table in enumerate(BACKUP_TABLES, 1):
-                    typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] Backing up {table}...", nl=False)
-
-                    buffer = io.BytesIO()
-
-                    # Use binary COPY for exact type preservation
-                    # asyncpg requires schema_name as separate parameter
-                    await conn.copy_from_table(table, schema_name=schema, output=buffer, format="binary")
-
-                    data = buffer.getvalue()
-                    zf.writestr(f"{table}.bin", data)
-
-                    # Get row count for manifest
-                    qualified_table = _fq_table(table, schema)
-                    row_count = await conn.fetchval(f"SELECT COUNT(*) FROM {qualified_table}")
-                    tables[table] = {
-                        "rows": row_count,
-                        "size_bytes": len(data),
-                    }
-
-                    typer.echo(f" {row_count} rows")
-
-                zf.writestr("manifest.json", json.dumps(manifest, indent=2))
-
-        return manifest
-    finally:
-        await conn.close()
-
-
-async def _restore(database_url: str, input_path: Path, schema: str = "public") -> dict[str, Any]:
-    """Restore all tables from a zip file using binary COPY protocol."""
-    conn = await asyncpg.connect(database_url)
-    try:
-        with zipfile.ZipFile(input_path, "r") as zf:
-            # Read and validate manifest
-            manifest: dict[str, Any] = json.loads(zf.read("manifest.json"))
-            if manifest.get("version") != MANIFEST_VERSION:
-                raise ValueError(f"Unsupported backup version: {manifest.get('version')}")
-
-            # Use a transaction for atomic restore - either all tables are
-            # restored or none are, preventing partial/inconsistent state.
-            async with conn.transaction():
-                typer.echo("  Clearing existing data...")
-                # Truncate tables in reverse order (respects FK constraints)
-                for table in reversed(BACKUP_TABLES):
-                    qualified_table = _fq_table(table, schema)
-                    await conn.execute(f"TRUNCATE TABLE {qualified_table} CASCADE")
-
-                # Restore tables in forward order
-                for i, table in enumerate(BACKUP_TABLES, 1):
-                    filename = f"{table}.bin"
-                    if filename not in zf.namelist():
-                        typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] {table}: skipped (not in backup)")
-                        continue
-
-                    expected_rows = manifest["tables"].get(table, {}).get("rows", "?")
-                    typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] Restoring {table}... {expected_rows} rows")
-
-                    data = zf.read(filename)
-                    buffer = io.BytesIO(data)
-                    # asyncpg requires schema_name as separate parameter
-                    await conn.copy_to_table(table, schema_name=schema, source=buffer, format="binary")
-
-                # Refresh materialized view
-                typer.echo("  Refreshing materialized views...")
-                await conn.execute(f"REFRESH MATERIALIZED VIEW {_fq_table('memory_units_bm25', schema)}")
-
-        return manifest
-    finally:
-        await conn.close()
-
-
-async def _run_backup(db_url: str, output: Path, schema: str = "public") -> dict[str, Any]:
-    """Resolve database URL and run backup."""
-    is_pg0, instance_name, _ = parse_pg0_url(db_url)
-    if is_pg0:
-        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
-    resolved_url = await resolve_database_url(db_url)
-    return await _backup(resolved_url, output, schema)
-
-
-async def _run_restore(db_url: str, input_file: Path, schema: str = "public") -> dict[str, Any]:
-    """Resolve database URL and run restore."""
-    is_pg0, instance_name, _ = parse_pg0_url(db_url)
-    if is_pg0:
-        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
-    resolved_url = await resolve_database_url(db_url)
-    return await _restore(resolved_url, input_file, schema)
-
-
-@app.command()
-def backup(
-    output: Path = typer.Argument(..., help="Output file path (.zip)"),
-    schema: str = typer.Option("public", "--schema", "-s", help="Database schema to backup"),
-):
-    """Backup the Hindsight database to a zip file."""
-    config = HindsightConfig.from_env()
-
-    if not config.database_url:
-        typer.echo("Error: Database URL not configured.", err=True)
-        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
-        raise typer.Exit(1)
-
-    if output.suffix != ".zip":
-        output = output.with_suffix(".zip")
-
-    typer.echo(f"Backing up database (schema: {schema}) to {output}...")
-
-    manifest = asyncio.run(_run_backup(config.database_url, output, schema))
-
-    total_rows = sum(t["rows"] for t in manifest["tables"].values())
-    typer.echo(f"Backed up {total_rows} rows across {len(BACKUP_TABLES)} tables")
-    typer.echo(f"Backup saved to {output}")
-
-
-@app.command()
-def restore(
-    input_file: Path = typer.Argument(..., help="Input backup file (.zip)"),
-    schema: str = typer.Option("public", "--schema", "-s", help="Database schema to restore to"),
-    yes: bool = typer.Option(False, "--yes", "-y", help="Skip confirmation prompt"),
-):
-    """Restore the database from a backup file. WARNING: This deletes all existing data."""
-    config = HindsightConfig.from_env()
-
-    if not config.database_url:
-        typer.echo("Error: Database URL not configured.", err=True)
-        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
-        raise typer.Exit(1)
-
-    if not input_file.exists():
-        typer.echo(f"Error: File not found: {input_file}", err=True)
-        raise typer.Exit(1)
-
-    if not yes:
-        typer.confirm(
-            "This will DELETE all existing data and replace it with the backup. Continue?",
-            abort=True,
-        )
-
-    typer.echo(f"Restoring database (schema: {schema}) from {input_file}...")
-
-    manifest = asyncio.run(_run_restore(config.database_url, input_file, schema))
-
-    total_rows = sum(t["rows"] for t in manifest["tables"].values())
-    typer.echo(f"Restored {total_rows} rows across {len(BACKUP_TABLES)} tables")
-    typer.echo("Restore complete")
-
-
-async def _run_migration(db_url: str, schema: str = "public") -> None:
-    """Resolve database URL and run migrations."""
-    from ..migrations import run_migrations
-
-    is_pg0, instance_name, _ = parse_pg0_url(db_url)
-    if is_pg0:
-        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
-    resolved_url = await resolve_database_url(db_url)
-    run_migrations(resolved_url, schema=schema)
-
-
-@app.command(name="run-db-migration")
-def run_db_migration(
-    schema: str = typer.Option("public", "--schema", "-s", help="Database schema to run migrations on"),
-):
-    """Run database migrations to the latest version."""
-    config = HindsightConfig.from_env()
-
-    if not config.database_url:
-        typer.echo("Error: Database URL not configured.", err=True)
-        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
-        raise typer.Exit(1)
-
-    typer.echo(f"Running database migrations (schema: {schema})...")
-
-    asyncio.run(_run_migration(config.database_url, schema))
-
-    typer.echo("Database migrations completed successfully")
-
-
-def main():
-    app()
-
-
-if __name__ == "__main__":
-    main()
@@ -109,9 +109,6 @@ def run_migrations_online() -> None:

    get_database_url()  # Process and set the database URL in config

-    # Check if we're targeting a specific schema (for multi-tenant isolation)
-    target_schema = config.get_main_option("target_schema")
-
    connectable = engine_from_config(
        config.get_section(config.config_ini_section, {}),
        prefix="sqlalchemy.",
@@ -124,34 +121,14 @@ def run_migrations_online() -> None:
    def set_read_write_mode(dbapi_connection, connection_record):
        cursor = dbapi_connection.cursor()
        cursor.execute("SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE")
-        # If targeting a specific schema, set search_path
-        # Include public in search_path for access to shared extensions (pgvector)
-        if target_schema:
-            cursor.execute(f'CREATE SCHEMA IF NOT EXISTS "{target_schema}"')
-            cursor.execute(f'SET search_path TO "{target_schema}", public')
        cursor.close()

    with connectable.connect() as connection:
        # Also explicitly set read-write mode on this connection
        connection.execute(text("SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE"))
-
-        # If targeting a specific schema, set search_path
-        # Include public in search_path for access to shared extensions (pgvector)
-        if target_schema:
-            connection.execute(text(f'CREATE SCHEMA IF NOT EXISTS "{target_schema}"'))
-            connection.execute(text(f'SET search_path TO "{target_schema}", public'))
-
        connection.commit()  # Commit the SET command

-        # Configure context with version_table_schema if using a specific schema
-        context_opts = {
-            "connection": connection,
-            "target_metadata": target_metadata,
-        }
-        if target_schema:
-            context_opts["version_table_schema"] = target_schema
-
-        context.configure(**context_opts)
+        context.configure(connection=connection, target_metadata=target_metadata)

        with context.begin_transaction():
            context.run_migrations()
@@ -6,7 +6,7 @@ Create Date: 2024-12-04 15:00:00.000000

 """

-from alembic import context, op
+from alembic import op

 # revision identifiers, used by Alembic.
 revision = "d9f6a3b4c5e2"
@@ -15,22 +15,14 @@ branch_labels = None
 depends_on = None


-def _get_schema_prefix() -> str:
-    """Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
 def upgrade():
-    schema = _get_schema_prefix()
-
    # Drop old check constraint FIRST (before updating data)
    op.drop_constraint("memory_units_fact_type_check", "memory_units", type_="check")

    # Update existing 'bank' values to 'experience'
-    op.execute(f"UPDATE {schema}memory_units SET fact_type = 'experience' WHERE fact_type = 'bank'")
+    op.execute("UPDATE memory_units SET fact_type = 'experience' WHERE fact_type = 'bank'")
    # Also update any 'interactions' values (in case of partial migration)
-    op.execute(f"UPDATE {schema}memory_units SET fact_type = 'experience' WHERE fact_type = 'interactions'")
+    op.execute("UPDATE memory_units SET fact_type = 'experience' WHERE fact_type = 'interactions'")

    # Create new check constraint with 'experience' instead of 'bank'
    op.create_check_constraint(
@@ -39,13 +31,11 @@ def upgrade():


 def downgrade():
-    schema = _get_schema_prefix()
-
    # Drop new check constraint FIRST
    op.drop_constraint("memory_units_fact_type_check", "memory_units", type_="check")

    # Update 'experience' back to 'bank'
-    op.execute(f"UPDATE {schema}memory_units SET fact_type = 'bank' WHERE fact_type = 'experience'")
+    op.execute("UPDATE memory_units SET fact_type = 'bank' WHERE fact_type = 'experience'")

    # Recreate old check constraint
    op.create_check_constraint(
@@ -12,7 +12,7 @@ system (skepticism, literalism, empathy with 1-5 integer values).
 from collections.abc import Sequence

 import sqlalchemy as sa
-from alembic import context, op
+from alembic import op

 # revision identifiers, used by Alembic.
 revision: str = "e0a1b2c3d4e5"
@@ -21,36 +21,9 @@ 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 (e.g., 'tenant_x.' or '' for public)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def _get_target_schema() -> str:
-    """Get the target schema name (tenant schema or 'public')."""
-    schema = context.config.get_main_option("target_schema")
-    return schema if schema else "public"
-
-
 def upgrade() -> None:
    """Convert Big Five disposition to 3-trait disposition."""
    conn = op.get_bind()
-    schema = _get_schema_prefix()
-    target_schema = _get_target_schema()
-
-    # Check if disposition column exists (should have been created by previous migration)
-    result = conn.execute(
-        sa.text("""
-        SELECT column_name
-        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
-    """),
-        {"schema": target_schema},
-    )
-    if not result.fetchone():
-        # Column doesn't exist yet (shouldn't happen but be safe)
-        return

    # Update all existing banks to use the new disposition format
    # Convert from old format to new format with reasonable mappings:
@@ -59,18 +32,18 @@ def upgrade() -> None:
    # - empathy: derived from agreeableness + inverse of neuroticism
    # Default all to 3 (neutral) for simplicity
    conn.execute(
-        sa.text(f"""
-        UPDATE {schema}banks
-        SET disposition = '{{"skepticism": 3, "literalism": 3, "empathy": 3}}'::jsonb
+        sa.text("""
+        UPDATE banks
+        SET disposition = '{"skepticism": 3, "literalism": 3, "empathy": 3}'::jsonb
        WHERE disposition IS NOT NULL
    """)
    )

    # Update the default for new banks
    conn.execute(
-        sa.text(f"""
-        ALTER TABLE {schema}banks
-        ALTER COLUMN disposition SET DEFAULT '{{"skepticism": 3, "literalism": 3, "empathy": 3}}'::jsonb
+        sa.text("""
+        ALTER TABLE banks
+        ALTER COLUMN disposition SET DEFAULT '{"skepticism": 3, "literalism": 3, "empathy": 3}'::jsonb
    """)
    )

@@ -78,34 +51,20 @@ def upgrade() -> None:
 def downgrade() -> None:
    """Convert back to Big Five disposition."""
    conn = op.get_bind()
-    schema = _get_schema_prefix()
-    target_schema = _get_target_schema()
-
-    # Check if disposition column exists
-    result = conn.execute(
-        sa.text("""
-        SELECT column_name
-        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
-    """),
-        {"schema": target_schema},
-    )
-    if not result.fetchone():
-        return

    # Revert to Big Five format with default values
    conn.execute(
-        sa.text(f"""
-        UPDATE {schema}banks
-        SET disposition = '{{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}}'::jsonb
+        sa.text("""
+        UPDATE banks
+        SET disposition = '{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}'::jsonb
        WHERE disposition IS NOT NULL
    """)
    )

    # Update the default for new banks
    conn.execute(
-        sa.text(f"""
-        ALTER TABLE {schema}banks
-        ALTER COLUMN disposition SET DEFAULT '{{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}}'::jsonb
+        sa.text("""
+        ALTER TABLE banks
+        ALTER COLUMN disposition SET DEFAULT '{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}'::jsonb
    """)
    )
@@ -9,7 +9,7 @@ Create Date: 2024-12-04
 from collections.abc import Sequence

 import sqlalchemy as sa
-from alembic import context, op
+from alembic import op
 from sqlalchemy.dialects import postgresql

 # revision identifiers, used by Alembic.
@@ -19,25 +19,17 @@ branch_labels: str | Sequence[str] | None = None
 depends_on: str | Sequence[str] | None = None


-def _get_target_schema() -> str:
-    """Get the target schema name (tenant schema or 'public')."""
-    schema = context.config.get_main_option("target_schema")
-    return schema if schema else "public"
-
-
 def upgrade() -> None:
    """Rename personality column to disposition in banks table (if it exists)."""
    conn = op.get_bind()
-    target_schema = _get_target_schema()

    # Check if 'personality' column exists (old database)
    result = conn.execute(
        sa.text("""
        SELECT column_name
        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'personality'
-    """),
-        {"schema": target_schema},
+        WHERE table_name = 'banks' AND column_name = 'personality'
+    """)
    )
    has_personality = result.fetchone() is not None

@@ -46,9 +38,8 @@ def upgrade() -> None:
        sa.text("""
        SELECT column_name
        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
-    """),
-        {"schema": target_schema},
+        WHERE table_name = 'banks' AND column_name = 'disposition'
+    """)
    )
    has_disposition = result.fetchone() is not None

@@ -72,14 +63,12 @@ def upgrade() -> None:
 def downgrade() -> None:
    """Revert disposition column back to personality."""
    conn = op.get_bind()
-    target_schema = _get_target_schema()
    result = conn.execute(
        sa.text("""
        SELECT column_name
        FROM information_schema.columns
-        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
-    """),
-        {"schema": target_schema},
+        WHERE table_name = 'banks' AND column_name = 'disposition'
+    """)
    )
    if result.fetchone():
        op.alter_column("banks", "disposition", new_column_name="personality")
@@ -5,7 +5,6 @@ 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
@@ -46,18 +45,6 @@ def create_app(
        # Both HTTP and MCP
        app = create_app(memory, mcp_api_enabled=True)
    """
-    mcp_app = None
-
-    # Create MCP app first if enabled (we need its lifespan for chaining)
-    if mcp_api_enabled:
-        try:
-            from .mcp import create_mcp_app
-
-            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]")
-            raise

    # Import and create HTTP API if enabled
    if http_api_enabled:
@@ -70,31 +57,20 @@ def create_app(
        app = FastAPI(title="Hindsight API", version="0.0.7")
        logger.info("HTTP REST API disabled")

-    # 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
+    # Mount MCP server if enabled
+    if mcp_api_enabled:
+        try:
+            from .mcp import create_mcp_app

-        # Store the original lifespan
-        original_lifespan = app.router.lifespan_context
-
-        @asynccontextmanager
-        async def chained_lifespan(app_instance: FastAPI):
-            """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
-
-        # Mount the MCP middleware
-        app.mount(mcp_mount_path, mcp_app)
-        logger.info(f"MCP server enabled at {mcp_mount_path}/")
+            # Create MCP app with dynamic bank_id support
+            # Supports: /mcp/{bank_id}/sse (bank-specific SSE endpoint)
+            mcp_app = create_mcp_app(memory=memory)
+            app.mount(mcp_mount_path, mcp_app)
+            logger.info(f"MCP server enabled at {mcp_mount_path}/{{bank_id}}/sse")
+        except ImportError as e:
+            logger.error(f"MCP server requested but dependencies not available: {e}")
+            logger.error("Install with: pip install hindsight-api[mcp]")
+            raise

    return app

@@ -9,7 +9,6 @@ from fastmcp import FastMCP

 from hindsight_api import MemoryEngine
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
-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()
@@ -27,15 +26,12 @@ logging.basicConfig(
 )
 logger = logging.getLogger(__name__)

-# Default bank_id from environment variable
-DEFAULT_BANK_ID = os.environ.get("HINDSIGHT_MCP_BANK_ID", "default")
-
-# Context variable to hold the current bank_id
+# Context variable to hold the current bank_id from the URL path
 _current_bank_id: ContextVar[str | None] = ContextVar("current_bank_id", default=None)


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


@@ -47,18 +43,12 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
        memory: MemoryEngine instance (required)

    Returns:
-        Configured FastMCP server instance with stateless_http enabled
+        Configured FastMCP server instance
    """
-    # Use stateless_http=True for Claude Code compatibility
-    mcp = FastMCP("hindsight-mcp-server", stateless_http=True)
+    mcp = FastMCP("hindsight-mcp-server")

    @mcp.tool()
-    async def retain(
-        content: str,
-        context: str = "general",
-        async_processing: bool = True,
-        bank_id: str | None = None,
-    ) -> str:
+    async def retain(content: str, context: str = "general") -> str:
        """
        Store important information to long-term memory.

@@ -74,34 +64,17 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
        Args:
            content: The fact/memory to store (be specific and include relevant details)
            context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
-            async_processing: If True, queue for background processing and return immediately. If False, wait for completion. Default: True
-            bank_id: Optional bank to store in (defaults to session bank). Use for cross-bank operations.
        """
        try:
-            target_bank = bank_id or get_current_bank_id()
-            if target_bank is None:
-                return "Error: No bank_id configured"
-            contents = [{"content": content, "context": context}]
-            if async_processing:
-                # Queue for background processing and return immediately
-                result = await memory.submit_async_retain(
-                    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:
-                # Wait for completion
-                await memory.retain_batch_async(
-                    bank_id=target_bank,
-                    contents=contents,
-                    request_context=RequestContext(),
-                )
-                return f"Memory stored successfully in bank '{target_bank}'"
+            bank_id = get_current_bank_id()
+            await memory.retain_batch_async(bank_id=bank_id, contents=[{"content": content, "context": context}])
+            return "Memory stored successfully"
        except Exception as e:
            logger.error(f"Error storing memory: {e}", exc_info=True)
            return f"Error: {str(e)}"

    @mcp.tool()
-    async def recall(query: str, max_tokens: int = 4096, bank_id: str | None = None) -> str:
+    async def recall(query: str, max_results: int = 10) -> str:
        """
        Search memories to provide personalized, context-aware responses.

@@ -113,165 +86,43 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:

        Args:
            query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
-            max_tokens: Maximum tokens in the response (default: 4096)
-            bank_id: Optional bank to search in (defaults to session bank). Use for cross-bank operations.
+            max_results: Maximum number of results to return (default: 10)
        """
        try:
-            target_bank = bank_id or get_current_bank_id()
-            if target_bank is None:
-                return "Error: No bank_id configured"
+            bank_id = get_current_bank_id()
            from hindsight_api.engine.memory_engine import Budget

-            recall_result = await memory.recall_async(
-                bank_id=target_bank,
-                query=query,
-                fact_type=list(VALID_RECALL_FACT_TYPES),
-                budget=Budget.HIGH,
-                max_tokens=max_tokens,
-                request_context=RequestContext(),
+            search_result = await memory.recall_async(
+                bank_id=bank_id, query=query, fact_type=list(VALID_RECALL_FACT_TYPES), budget=Budget.LOW
            )

-            # Use model's JSON serialization
-            return recall_result.model_dump_json(indent=2)
+            results = [
+                {
+                    "id": fact.id,
+                    "text": fact.text,
+                    "type": fact.fact_type,
+                    "context": fact.context,
+                    "event_date": fact.event_date,
+                }
+                for fact in search_result.results[:max_results]
+            ]
+
+            return json.dumps({"results": results}, indent=2)
        except Exception as e:
            logger.error(f"Error searching: {e}", exc_info=True)
-            return f'{{"error": "{e}", "results": []}}'
-
-    @mcp.tool()
-    async def reflect(query: str, context: str | None = None, budget: str = "low", bank_id: str | None = None) -> str:
-        """
-        Generate thoughtful analysis by synthesizing stored memories with the bank's personality.
-
-        WHEN TO USE THIS TOOL:
-        Use reflect when you need reasoned analysis, not just fact retrieval. This tool
-        thinks through the question using everything the bank knows and its personality traits.
-
-        EXAMPLES OF GOOD QUERIES:
-        - "What patterns have emerged in how I approach debugging?"
-        - "Based on my past decisions, what architectural style do I prefer?"
-        - "What might be the best approach for this problem given what you know about me?"
-        - "How should I prioritize these tasks based on my goals?"
-
-        HOW IT DIFFERS FROM RECALL:
-        - recall: Returns raw facts matching your search (fast lookup)
-        - reflect: Reasons across memories to form a synthesized answer (deeper analysis)
-
-        Use recall for "what did I say about X?" and reflect for "what should I do about X?"
-
-        Args:
-            query: The question or topic to reflect on
-            context: Optional context about why this reflection is needed
-            budget: Search budget - 'low', 'mid', or 'high' (default: 'low')
-            bank_id: Optional bank to reflect in (defaults to session bank). Use for cross-bank operations.
-        """
-        try:
-            target_bank = bank_id or get_current_bank_id()
-            if target_bank is None:
-                return "Error: No bank_id configured"
-            from hindsight_api.engine.memory_engine import Budget
-
-            # Map string budget to enum
-            budget_map = {"low": Budget.LOW, "mid": Budget.MID, "high": Budget.HIGH}
-            budget_enum = budget_map.get(budget.lower(), Budget.LOW)
-
-            reflect_result = await memory.reflect_async(
-                bank_id=target_bank,
-                query=query,
-                budget=budget_enum,
-                context=context,
-                request_context=RequestContext(),
-            )
-
-            return reflect_result.model_dump_json(indent=2)
-        except Exception as e:
-            logger.error(f"Error reflecting: {e}", exc_info=True)
-            return f'{{"error": "{e}", "text": ""}}'
-
-    @mcp.tool()
-    async def list_banks() -> str:
-        """
-        List all available memory banks.
-
-        Use this tool to discover what memory banks exist in the system.
-        Each bank is an isolated memory store (like a separate "brain").
-
-        Returns:
-            JSON list of banks with their IDs, names, dispositions, and backgrounds.
-        """
-        try:
-            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)
-            return f'{{"error": "{e}", "banks": []}}'
-
-    @mcp.tool()
-    async def create_bank(bank_id: str, name: str | None = None, background: str | None = None) -> str:
-        """
-        Create a new memory bank or get an existing one.
-
-        Memory banks are isolated stores - each one is like a separate "brain" for a user/agent.
-        Banks are auto-created with default settings if they don't exist.
-
-        Args:
-            bank_id: Unique identifier for the bank (e.g., 'user-123', 'agent-alpha')
-            name: Optional human-friendly name for the bank
-            background: Optional background context about the bank's owner/purpose
-        """
-        try:
-            # get_bank_profile auto-creates bank if it doesn't exist
-            profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())
-
-            # Update name/background if provided
-            if name is not None or background is not None:
-                await memory.update_bank(
-                    bank_id,
-                    name=name,
-                    background=background,
-                    request_context=RequestContext(),
-                )
-                # Fetch updated profile
-                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"):
-                profile["disposition"] = profile["disposition"].model_dump()
-            return json.dumps(profile, indent=2)
-        except Exception as e:
-            logger.error(f"Error creating bank: {e}", exc_info=True)
-            return f'{{"error": "{e}"}}'
+            return json.dumps({"error": str(e), "results": []})

    return mcp


 class MCPMiddleware:
-    """ASGI middleware that extracts bank_id from header or path and sets context.
-
-    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)
-
-    For Claude Code, configure with:
-        claude mcp add --transport http hindsight http://localhost:8888/mcp \\
-            --header "X-Bank-Id: my-bank"
-    """
+    """ASGI middleware that extracts bank_id from path and sets context."""

    def __init__(self, app, memory: MemoryEngine):
        self.app = app
        self.memory = memory
        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."""
-        name_lower = name.lower().encode()
-        for header_name, header_value in scope.get("headers", []):
-            if header_name.lower() == name_lower:
-                return header_value.decode()
-        return None
+        self.mcp_app = self.mcp_server.http_app()

    async def __call__(self, scope, receive, send):
        if scope["type"] != "http":
@@ -288,39 +139,32 @@ class MCPMiddleware:
        # 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 = "/"

-        # Try to get bank_id from header first (for Claude Code compatibility)
-        bank_id = self._get_header(scope, "X-Bank-Id")
+        # Extract bank_id from path: /{bank_id}/ or /{bank_id}
+        # http_app expects requests at /
+        if not path.startswith("/") or len(path) <= 1:
+            # No bank_id in path - return error
+            await self._send_error(send, 400, "bank_id required in path: /mcp/{bank_id}/")
+            return

-        # MCP endpoint paths that should not be treated as bank_ids
-        MCP_ENDPOINTS = {"sse", "messages"}
+        # Extract bank_id from first path segment
+        parts = path[1:].split("/", 1)
+        if not parts[0]:
+            await self._send_error(send, 400, "bank_id required in path: /mcp/{bank_id}/")
+            return

-        # If no header, try to extract from path: /{bank_id}/...
-        new_path = path
-        if not bank_id and path.startswith("/") and len(path) > 1:
-            parts = path[1:].split("/", 1)
-            # 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]
-                new_path = "/" + parts[1] if len(parts) > 1 else "/"
-
-        # Fall back to default bank_id
-        if not bank_id:
-            bank_id = DEFAULT_BANK_ID
-            logger.debug(f"Using default bank_id: {bank_id}")
+        bank_id = parts[0]
+        new_path = "/" + parts[1] if len(parts) > 1 else "/"

        # 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
+            # Wrap send to rewrite the SSE endpoint URL to include bank_id
+            # The SSE app sends "event: endpoint\ndata: /messages\n" but we need
+            # the client to POST to /{bank_id}/messages instead
            async def send_wrapper(message):
                if message["type"] == "http.response.body":
                    body = message.get("body", b"")
@@ -356,10 +200,9 @@ def create_mcp_app(memory: MemoryEngine):
    """
    Create an ASGI app that handles MCP requests.

-    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")
+    URL pattern: /mcp/{bank_id}/
+
+    The bank_id is extracted from the URL path and made available to tools.

    Args:
        memory: MemoryEngine instance
@@ -8,11 +8,6 @@ import logging
 import os
 from dataclasses import dataclass

-from dotenv import find_dotenv, load_dotenv
-
-# Load .env file, searching current and parent directories (overrides existing env vars)
-load_dotenv(find_dotenv(usecwd=True), override=True)
-
 logger = logging.getLogger(__name__)

 # Environment variable names
@@ -21,30 +16,10 @@ 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_TIMEOUT = "HINDSIGHT_API_LLM_TIMEOUT"
-ENV_LLM_GROQ_SERVICE_TIER = "HINDSIGHT_API_LLM_GROQ_SERVICE_TIER"
-
-# Per-operation LLM configuration (optional, falls back to global LLM config)
-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_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_EMBEDDINGS_PROVIDER = "HINDSIGHT_API_EMBEDDINGS_PROVIDER"
 ENV_EMBEDDINGS_LOCAL_MODEL = "HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL"
 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_COHERE_API_KEY = "HINDSIGHT_API_COHERE_API_KEY"
-ENV_EMBEDDINGS_COHERE_MODEL = "HINDSIGHT_API_EMBEDDINGS_COHERE_MODEL"
-ENV_RERANKER_COHERE_MODEL = "HINDSIGHT_API_RERANKER_COHERE_MODEL"

 ENV_RERANKER_PROVIDER = "HINDSIGHT_API_RERANKER_PROVIDER"
 ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
@@ -56,41 +31,18 @@ ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
 ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
 ENV_MCP_LOCAL_BANK_ID = "HINDSIGHT_API_MCP_LOCAL_BANK_ID"
-ENV_MCP_INSTRUCTIONS = "HINDSIGHT_API_MCP_INSTRUCTIONS"
-
-# Observation thresholds
-ENV_OBSERVATION_MIN_FACTS = "HINDSIGHT_API_OBSERVATION_MIN_FACTS"
-ENV_OBSERVATION_TOP_ENTITIES = "HINDSIGHT_API_OBSERVATION_TOP_ENTITIES"
-
-# Retain settings
-ENV_RETAIN_MAX_COMPLETION_TOKENS = "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"
-ENV_RETAIN_CHUNK_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_SIZE"
-
-# Optimization flags
-ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
-ENV_LAZY_RERANKER = "HINDSIGHT_API_LAZY_RERANKER"
-
-# Database migrations
-ENV_RUN_MIGRATIONS_ON_STARTUP = "HINDSIGHT_API_RUN_MIGRATIONS_ON_STARTUP"

 # Default values
 DEFAULT_DATABASE_URL = "pg0"
 DEFAULT_LLM_PROVIDER = "openai"
 DEFAULT_LLM_MODEL = "gpt-5-mini"
-DEFAULT_LLM_MAX_CONCURRENT = 32
-DEFAULT_LLM_TIMEOUT = 120.0  # seconds

 DEFAULT_EMBEDDINGS_PROVIDER = "local"
 DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
-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_EMBEDDINGS_COHERE_MODEL = "embed-english-v3.0"
-DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"
-
 DEFAULT_HOST = "0.0.0.0"
 DEFAULT_PORT = 8888
 DEFAULT_LOG_LEVEL = "info"
@@ -98,39 +50,8 @@ DEFAULT_MCP_ENABLED = True
 DEFAULT_GRAPH_RETRIEVER = "bfs"  # Options: "bfs", "mpfp"
 DEFAULT_MCP_LOCAL_BANK_ID = "mcp"

-# Observation thresholds
-DEFAULT_OBSERVATION_MIN_FACTS = 5  # Min facts required to generate entity observations
-DEFAULT_OBSERVATION_TOP_ENTITIES = 5  # Max entities to process per retain batch
-
-# Retain settings
-DEFAULT_RETAIN_MAX_COMPLETION_TOKENS = 64000  # Max tokens for fact extraction LLM call
-DEFAULT_RETAIN_CHUNK_SIZE = 3000  # Max chars per chunk for fact extraction
-
-# Database migrations
-DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
-
-# Default MCP tool descriptions (can be customized via env vars)
-DEFAULT_MCP_RETAIN_DESCRIPTION = """Store important information to long-term memory.
-
-Use this tool PROACTIVELY whenever the user shares:
- Personal facts, preferences, or interests
- Important events or milestones
- User history, experiences, or background
- Decisions, opinions, or stated preferences
- Goals, plans, or future intentions
- Relationships or people mentioned
- Work context, projects, or responsibilities"""
-
-DEFAULT_MCP_RECALL_DESCRIPTION = """Search memories to provide personalized, context-aware responses.
-
-Use this tool PROACTIVELY to:
- Check user's preferences before making suggestions
- Recall user's history to provide continuity
- Remember user's goals and context
- Personalize responses based on past interactions"""
-
-# Default embedding dimension (used by initial migration, adjusted at runtime)
-EMBEDDING_DIMENSION = DEFAULT_EMBEDDING_DIMENSION
+# Required embedding dimension for database schema
+EMBEDDING_DIMENSION = 384


@dataclass
@@ -140,24 +61,11 @@ class HindsightConfig:
    # Database
    database_url: str

-    # LLM (default, used as fallback for per-operation config)
+    # LLM
    llm_provider: str
    llm_api_key: str | None
    llm_model: str
    llm_base_url: str | None
-    llm_max_concurrent: int
-    llm_timeout: float
-
-    # 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
-
-    reflect_llm_provider: str | None
-    reflect_llm_api_key: str | None
-    reflect_llm_model: str | None
-    reflect_llm_base_url: str | None

    # Embeddings
    embeddings_provider: str
@@ -178,21 +86,6 @@ class HindsightConfig:
    # Recall
    graph_retriever: str

-    # Observation thresholds
-    observation_min_facts: int
-    observation_top_entities: int
-
-    # Retain settings
-    retain_max_completion_tokens: int
-    retain_chunk_size: int
-
-    # Optimization flags
-    skip_llm_verification: bool
-    lazy_reranker: bool
-
-    # Database migrations
-    run_migrations_on_startup: bool
-
    @classmethod
    def from_env(cls) -> "HindsightConfig":
        """Create configuration from environment variables."""
@@ -204,17 +97,6 @@ class HindsightConfig:
            llm_api_key=os.getenv(ENV_LLM_API_KEY),
            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_timeout=float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT))),
-            # 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 None,
-            retain_llm_base_url=os.getenv(ENV_RETAIN_LLM_BASE_URL) or 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 None,
-            reflect_llm_base_url=os.getenv(ENV_REFLECT_LLM_BASE_URL) or 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),
@@ -230,21 +112,6 @@ class HindsightConfig:
            mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).lower() == "true",
            # Recall
            graph_retriever=os.getenv(ENV_GRAPH_RETRIEVER, DEFAULT_GRAPH_RETRIEVER),
-            # Optimization flags
-            skip_llm_verification=os.getenv(ENV_SKIP_LLM_VERIFICATION, "false").lower() == "true",
-            lazy_reranker=os.getenv(ENV_LAZY_RERANKER, "false").lower() == "true",
-            # Observation thresholds
-            observation_min_facts=int(os.getenv(ENV_OBSERVATION_MIN_FACTS, str(DEFAULT_OBSERVATION_MIN_FACTS))),
-            observation_top_entities=int(
-                os.getenv(ENV_OBSERVATION_TOP_ENTITIES, str(DEFAULT_OBSERVATION_TOP_ENTITIES))
-            ),
-            # Retain settings
-            retain_max_completion_tokens=int(
-                os.getenv(ENV_RETAIN_MAX_COMPLETION_TOKENS, str(DEFAULT_RETAIN_MAX_COMPLETION_TOKENS))
-            ),
-            retain_chunk_size=int(os.getenv(ENV_RETAIN_CHUNK_SIZE, str(DEFAULT_RETAIN_CHUNK_SIZE))),
-            # Database migrations
-            run_migrations_on_startup=os.getenv(ENV_RUN_MIGRATIONS_ON_STARTUP, "true").lower() == "true",
        )

    def get_llm_base_url(self) -> str:
@@ -257,8 +124,6 @@ class HindsightConfig:
            return "https://api.groq.com/openai/v1"
        elif provider == "ollama":
            return "http://localhost:11434/v1"
-        elif provider == "lmstudio":
-            return "http://localhost:1234/v1"
        else:
            return ""

@@ -277,41 +142,18 @@ class HindsightConfig:
    def configure_logging(self) -> None:
        """Configure Python logging based on the log level."""
        logging.basicConfig(
-            level=self.get_python_log_level(),
-            format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-            force=True,  # Override any existing configuration
+            level=self.get_python_log_level(), format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"
        )

    def log_config(self) -> None:
        """Log the current configuration (without sensitive values)."""
        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
-            retain_model = self.retain_llm_model or self.llm_model
-            logger.info(f"LLM (retain): provider={retain_provider}, model={retain_model}")
-        if self.reflect_llm_provider or self.reflect_llm_model:
-            reflect_provider = self.reflect_llm_provider or self.llm_provider
-            reflect_model = self.reflect_llm_model or self.llm_model
-            logger.info(f"LLM (reflect): provider={reflect_provider}, model={reflect_model}")
        logger.info(f"Embeddings: provider={self.embeddings_provider}")
        logger.info(f"Reranker: provider={self.reranker_provider}")
        logger.info(f"Graph retriever: {self.graph_retriever}")


-# Cached config instance
-_config_cache: HindsightConfig | None = None
-
-
 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()
-    return _config_cache
-
-
-def clear_config_cache() -> None:
-    """Clear the config cache. Useful for testing or reloading config."""
-    global _config_cache
-    _config_cache = None
+    """Get the current configuration from environment variables."""
+    return HindsightConfig.from_env()
@@ -1,204 +0,0 @@
-"""
-Daemon mode support for Hindsight API.
-
-Provides idle timeout and lockfile management for running as a background daemon.
-"""
-
-import asyncio
-import fcntl
-import logging
-import os
-import sys
-import time
-from pathlib import Path
-
-logger = logging.getLogger(__name__)
-
-# Default daemon configuration
-DEFAULT_DAEMON_PORT = 8889
-DEFAULT_IDLE_TIMEOUT = 0  # 0 = no auto-exit (hindsight-embed passes its own timeout)
-LOCKFILE_PATH = Path.home() / ".hindsight" / "daemon.lock"
-DAEMON_LOG_PATH = Path.home() / ".hindsight" / "daemon.log"
-
-
-class IdleTimeoutMiddleware:
-    """ASGI middleware that tracks activity and exits after idle timeout."""
-
-    def __init__(self, app, idle_timeout: int = DEFAULT_IDLE_TIMEOUT):
-        self.app = app
-        self.idle_timeout = idle_timeout
-        self.last_activity = time.time()
-        self._checker_task = None
-
-    async def __call__(self, scope, receive, send):
-        # Update activity timestamp on each request
-        self.last_activity = time.time()
-        await self.app(scope, receive, send)
-
-    def start_idle_checker(self):
-        """Start the background task that checks for idle timeout."""
-        self._checker_task = asyncio.create_task(self._check_idle())
-
-    async def _check_idle(self):
-        """Background task that exits the process after idle timeout."""
-        # If idle_timeout is 0, don't auto-exit
-        if self.idle_timeout <= 0:
-            return
-
-        while True:
-            await asyncio.sleep(30)  # Check every 30 seconds
-            idle_time = time.time() - self.last_activity
-            if idle_time > self.idle_timeout:
-                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)
-                os._exit(0)
-
-
-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():
-    """
-    Fork the current process into a background daemon.
-
-    Uses double-fork technique to properly detach from terminal.
-    """
-    # First fork
-    pid = os.fork()
-    if pid > 0:
-        # Parent exits
-        sys.exit(0)
-
-    # Create new session
-    os.setsid()
-
-    # Second fork to prevent zombie processes
-    pid = os.fork()
-    if pid > 0:
-        sys.exit(0)
-
-    # Redirect standard file descriptors to log file
-    DAEMON_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
-
-    sys.stdout.flush()
-    sys.stderr.flush()
-
-    # Redirect stdin to /dev/null
-    with open("/dev/null", "r") as devnull:
-        os.dup2(devnull.fileno(), sys.stdin.fileno())
-
-    # Redirect stdout/stderr to log file
-    log_fd = open(DAEMON_LOG_PATH, "a")
-    os.dup2(log_fd.fileno(), sys.stdout.fileno())
-    os.dup2(log_fd.fileno(), sys.stderr.fileno())
-
-
-def check_daemon_running(port: int = DEFAULT_DAEMON_PORT) -> bool:
-    """Check if a daemon is running and responsive on the given port."""
-    import socket
-
-    try:
-        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        sock.settimeout(1)
-        result = sock.connect_ex(("127.0.0.1", port))
-        sock.close()
-        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
@@ -11,13 +11,7 @@ from .cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICros
 from .db_utils import acquire_with_retry
 from .embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
 from .llm_wrapper import LLMConfig
-from .memory_engine import (
-    MemoryEngine,
-    UnqualifiedTableError,
-    fq_table,
-    get_current_schema,
-    validate_sql_schema,
-)
+from .memory_engine import MemoryEngine
 from .response_models import MemoryFact, RecallResult, ReflectResult
 from .search.trace import (
    EntryPoint,
@@ -55,9 +49,4 @@ __all__ = [
    "RecallResult",
    "ReflectResult",
    "MemoryFact",
-    # Schema safety utilities
-    "fq_table",
-    "get_current_schema",
-    "validate_sql_schema",
-    "UnqualifiedTableError",
 ]
@@ -13,11 +13,8 @@ from abc import ABC, abstractmethod
 import httpx

 from ..config import (
-    DEFAULT_RERANKER_COHERE_MODEL,
    DEFAULT_RERANKER_LOCAL_MODEL,
    DEFAULT_RERANKER_PROVIDER,
-    ENV_COHERE_API_KEY,
-    ENV_RERANKER_COHERE_MODEL,
    ENV_RERANKER_LOCAL_MODEL,
    ENV_RERANKER_PROVIDER,
    ENV_RERANKER_TEI_URL,
@@ -281,96 +278,6 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
        return all_scores


-class CohereCrossEncoder(CrossEncoderModel):
-    """
-    Cohere cross-encoder implementation using the Cohere Rerank API.
-
-    Supports rerank-english-v3.0 and rerank-multilingual-v3.0 models.
-    """
-
-    def __init__(
-        self,
-        api_key: str,
-        model: str = DEFAULT_RERANKER_COHERE_MODEL,
-        timeout: float = 60.0,
-    ):
-        """
-        Initialize Cohere cross-encoder client.
-
-        Args:
-            api_key: Cohere API key
-            model: Cohere rerank model name (default: rerank-english-v3.0)
-            timeout: Request timeout in seconds (default: 60.0)
-        """
-        self.api_key = api_key
-        self.model = model
-        self.timeout = timeout
-        self._client = None
-
-    @property
-    def provider_name(self) -> str:
-        return "cohere"
-
-    async def initialize(self) -> None:
-        """Initialize the Cohere client."""
-        if self._client is not None:
-            return
-
-        try:
-            import cohere
-        except ImportError:
-            raise ImportError("cohere is required for CohereCrossEncoder. Install it with: pip install cohere")
-
-        logger.info(f"Reranker: initializing Cohere provider with model {self.model}")
-        self._client = cohere.Client(api_key=self.api_key, timeout=self.timeout)
-        logger.info("Reranker: Cohere provider initialized")
-
-    def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
-        """
-        Score query-document pairs using the Cohere Rerank API.
-
-        Args:
-            pairs: List of (query, document) tuples to score
-
-        Returns:
-            List of relevance scores
-        """
-        if self._client is None:
-            raise RuntimeError("Reranker not initialized. Call initialize() first.")
-
-        if not pairs:
-            return []
-
-        # Group pairs by query for efficient batching
-        # Cohere 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]
-
-            response = self._client.rerank(
-                query=query,
-                documents=texts,
-                model=self.model,
-                return_documents=False,
-            )
-
-            # Map scores back to original positions
-            for result in response.results:
-                original_idx = result.index
-                score = result.relevance_score
-                all_scores[indices[original_idx]] = score
-
-        return all_scores
-
-
 def create_cross_encoder_from_env() -> CrossEncoderModel:
    """
    Create a CrossEncoderModel instance based on environment variables.
@@ -391,11 +298,5 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
        model = os.environ.get(ENV_RERANKER_LOCAL_MODEL)
        model_name = model or DEFAULT_RERANKER_LOCAL_MODEL
        return LocalSTCrossEncoder(model_name=model_name)
-    elif provider == "cohere":
-        api_key = os.environ.get(ENV_COHERE_API_KEY)
-        if not api_key:
-            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)
-        return CohereCrossEncoder(api_key=api_key, model=model)
    else:
-        raise ValueError(f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere'")
+        raise ValueError(f"Unknown reranker provider: {provider}. Supported: 'local', 'tei'")
@@ -3,8 +3,8 @@ Embeddings abstraction for the memory system.

 Provides an interface for generating embeddings with different backends.

-The embedding dimension is auto-detected from the model at initialization.
-The database schema is automatically adjusted to match the model's dimension.
+IMPORTANT: All embeddings must produce 384-dimensional vectors to match
+the database schema (pgvector column defined as vector(384)).

 Configuration via environment variables - see hindsight_api.config for all env var names.
 """
@@ -16,18 +16,12 @@ from abc import ABC, abstractmethod
 import httpx

 from ..config import (
-    DEFAULT_EMBEDDINGS_COHERE_MODEL,
    DEFAULT_EMBEDDINGS_LOCAL_MODEL,
-    DEFAULT_EMBEDDINGS_OPENAI_MODEL,
    DEFAULT_EMBEDDINGS_PROVIDER,
-    ENV_COHERE_API_KEY,
-    ENV_EMBEDDINGS_COHERE_MODEL,
+    EMBEDDING_DIMENSION,
    ENV_EMBEDDINGS_LOCAL_MODEL,
-    ENV_EMBEDDINGS_OPENAI_API_KEY,
-    ENV_EMBEDDINGS_OPENAI_MODEL,
    ENV_EMBEDDINGS_PROVIDER,
    ENV_EMBEDDINGS_TEI_URL,
-    ENV_LLM_API_KEY,
 )

 logger = logging.getLogger(__name__)
@@ -37,8 +31,8 @@ class Embeddings(ABC):
    """
    Abstract base class for embedding generation.

-    The embedding dimension is determined by the model and detected at initialization.
-    The database schema is automatically adjusted to match the model's dimension.
+    All implementations MUST generate 384-dimensional embeddings to match
+    the database schema.
    """

    @property
@@ -47,12 +41,6 @@ class Embeddings(ABC):
        """Return a human-readable name for this provider (e.g., 'local', 'tei')."""
        pass

-    @property
-    @abstractmethod
-    def dimension(self) -> int:
-        """Return the embedding dimension produced by this model."""
-        pass
-
    @abstractmethod
    async def initialize(self) -> None:
        """
@@ -66,13 +54,13 @@ class Embeddings(ABC):
    @abstractmethod
    def encode(self, texts: list[str]) -> list[list[float]]:
        """
-        Generate embeddings for a list of texts.
+        Generate 384-dimensional embeddings for a list of texts.

        Args:
            texts: List of text strings to encode

        Returns:
-            List of embedding vectors (each is a list of floats)
+            List of 384-dimensional embedding vectors (each is a list of floats)
        """
        pass

@@ -82,7 +70,9 @@ class LocalSTEmbeddings(Embeddings):
    Local embeddings implementation using SentenceTransformers.

    Call initialize() during startup to load the model and avoid cold starts.
-    The embedding dimension is auto-detected from the model.
+
+    Default model is BAAI/bge-small-en-v1.5 which produces 384-dimensional
+    embeddings matching the database schema.
    """

    def __init__(self, model_name: str | None = None):
@@ -91,22 +81,16 @@ class LocalSTEmbeddings(Embeddings):

        Args:
            model_name: Name of the SentenceTransformer model to use.
+                       Must produce 384-dimensional embeddings.
                       Default: BAAI/bge-small-en-v1.5
        """
        self.model_name = model_name or DEFAULT_EMBEDDINGS_LOCAL_MODEL
        self._model = None
-        self._dimension: int | None = None

    @property
    def provider_name(self) -> str:
        return "local"

-    @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:
        """Load the embedding model."""
        if self._model is not None:
@@ -128,18 +112,26 @@ class LocalSTEmbeddings(Embeddings):
            model_kwargs={"low_cpu_mem_usage": False, "device_map": None},
        )

-        self._dimension = self._model.get_sentence_embedding_dimension()
-        logger.info(f"Embeddings: local provider initialized (dim: {self._dimension})")
+        # Validate dimension matches database schema
+        model_dim = self._model.get_sentence_embedding_dimension()
+        if model_dim != EMBEDDING_DIMENSION:
+            raise ValueError(
+                f"Model {self.model_name} produces {model_dim}-dimensional embeddings, "
+                f"but database schema requires {EMBEDDING_DIMENSION} dimensions. "
+                f"Use a model that produces {EMBEDDING_DIMENSION}-dimensional embeddings."
+            )
+
+        logger.info(f"Embeddings: local provider initialized (dim: {model_dim})")

    def encode(self, texts: list[str]) -> list[list[float]]:
        """
-        Generate embeddings for a list of texts.
+        Generate 384-dimensional embeddings for a list of texts.

        Args:
            texts: List of text strings to encode

        Returns:
-            List of embedding vectors
+            List of 384-dimensional embedding vectors
        """
        if self._model is None:
            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
@@ -154,7 +146,7 @@ class RemoteTEIEmbeddings(Embeddings):
    TEI provides a high-performance inference server for embedding models.
    See: https://github.com/huggingface/text-embeddings-inference

-    The embedding dimension is auto-detected from the server at initialization.
+    The server should be running a model that produces 384-dimensional embeddings.
    """

    def __init__(
@@ -182,18 +174,11 @@ class RemoteTEIEmbeddings(Embeddings):
        self.retry_delay = retry_delay
        self._client: httpx.Client | None = None
        self._model_id: str | None = None
-        self._dimension: int | None = None

    @property
    def provider_name(self) -> str:
        return "tei"

-    @property
-    def dimension(self) -> int:
-        if self._dimension is None:
-            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
-        return self._dimension
-
    def _request_with_retry(self, method: str, url: str, **kwargs) -> httpx.Response:
        """Make an HTTP request with automatic retries on transient errors."""
        import time
@@ -244,24 +229,7 @@ class RemoteTEIEmbeddings(Embeddings):
            response = self._request_with_retry("GET", f"{self.base_url}/info")
            info = response.json()
            self._model_id = info.get("model_id", "unknown")
-
-            # Get dimension from server info or by doing a test embedding
-            if "max_input_length" in info and "model_dtype" in info:
-                # Try to get dimension from info endpoint (some TEI versions expose it)
-                # If not available, do a test embedding
-                pass
-
-            # Do a test embedding to detect dimension
-            test_response = self._request_with_retry(
-                "POST",
-                f"{self.base_url}/embed",
-                json={"inputs": ["test"]},
-            )
-            test_embeddings = test_response.json()
-            if test_embeddings and len(test_embeddings) > 0:
-                self._dimension = len(test_embeddings[0])
-
-            logger.info(f"Embeddings: TEI provider initialized (model: {self._model_id}, dim: {self._dimension})")
+            logger.info(f"Embeddings: TEI provider initialized (model: {self._model_id})")
        except httpx.HTTPError as e:
            raise RuntimeError(f"Failed to connect to TEI server at {self.base_url}: {e}")

@@ -301,234 +269,6 @@ class RemoteTEIEmbeddings(Embeddings):
        return all_embeddings


-class OpenAIEmbeddings(Embeddings):
-    """
-    OpenAI embeddings implementation using the OpenAI API.
-
-    Supports text-embedding-3-small (1536 dims), text-embedding-3-large (3072 dims),
-    and text-embedding-ada-002 (1536 dims, legacy).
-
-    The embedding dimension is auto-detected from the model at initialization.
-    """
-
-    # Known dimensions for OpenAI embedding models
-    MODEL_DIMENSIONS = {
-        "text-embedding-3-small": 1536,
-        "text-embedding-3-large": 3072,
-        "text-embedding-ada-002": 1536,
-    }
-
-    def __init__(
-        self,
-        api_key: str,
-        model: str = DEFAULT_EMBEDDINGS_OPENAI_MODEL,
-        batch_size: int = 100,
-        max_retries: int = 3,
-    ):
-        """
-        Initialize OpenAI embeddings client.
-
-        Args:
-            api_key: OpenAI API key
-            model: OpenAI embedding model name (default: text-embedding-3-small)
-            batch_size: Maximum batch size for embedding requests (default: 100)
-            max_retries: Maximum number of retries for failed requests (default: 3)
-        """
-        self.api_key = api_key
-        self.model = model
-        self.batch_size = batch_size
-        self.max_retries = max_retries
-        self._client = None
-        self._dimension: int | None = None
-
-    @property
-    def provider_name(self) -> str:
-        return "openai"
-
-    @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 OpenAI client and detect dimension."""
-        if self._client is not None:
-            return
-
-        try:
-            from openai import OpenAI
-        except ImportError:
-            raise ImportError("openai is required for OpenAIEmbeddings. Install it with: pip install openai")
-
-        logger.info(f"Embeddings: initializing OpenAI provider with model {self.model}")
-        self._client = OpenAI(api_key=self.api_key, max_retries=self.max_retries)
-
-        # Try to get dimension from known models, otherwise do a test embedding
-        if self.model in self.MODEL_DIMENSIONS:
-            self._dimension = self.MODEL_DIMENSIONS[self.model]
-        else:
-            # Do a test embedding to detect dimension
-            response = self._client.embeddings.create(
-                model=self.model,
-                input=["test"],
-            )
-            if response.data:
-                self._dimension = len(response.data[0].embedding)
-
-        logger.info(f"Embeddings: OpenAI provider initialized (model: {self.model}, dim: {self._dimension})")
-
-    def encode(self, texts: list[str]) -> list[list[float]]:
-        """
-        Generate embeddings using the OpenAI API.
-
-        Args:
-            texts: List of text strings to encode
-
-        Returns:
-            List of embedding vectors
-        """
-        if self._client 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]
-
-            response = self._client.embeddings.create(
-                model=self.model,
-                input=batch,
-            )
-
-            # Sort by index to ensure correct order
-            batch_embeddings = sorted(response.data, key=lambda x: x.index)
-            all_embeddings.extend([e.embedding for e in batch_embeddings])
-
-        return all_embeddings
-
-
-class CohereEmbeddings(Embeddings):
-    """
-    Cohere embeddings implementation using the Cohere API.
-
-    Supports embed-english-v3.0 (1024 dims) and embed-multilingual-v3.0 (1024 dims).
-
-    The embedding dimension is auto-detected from the model at initialization.
-    """
-
-    # Known dimensions for Cohere embedding models
-    MODEL_DIMENSIONS = {
-        "embed-english-v3.0": 1024,
-        "embed-multilingual-v3.0": 1024,
-        "embed-english-light-v3.0": 384,
-        "embed-multilingual-light-v3.0": 384,
-        "embed-english-v2.0": 4096,
-        "embed-multilingual-v2.0": 768,
-    }
-
-    def __init__(
-        self,
-        api_key: str,
-        model: str = DEFAULT_EMBEDDINGS_COHERE_MODEL,
-        batch_size: int = 96,
-        timeout: float = 60.0,
-        input_type: str = "search_document",
-    ):
-        """
-        Initialize Cohere embeddings client.
-
-        Args:
-            api_key: Cohere API key
-            model: Cohere embedding model name (default: embed-english-v3.0)
-            batch_size: Maximum batch size for embedding requests (default: 96, Cohere's limit)
-            timeout: Request timeout in seconds (default: 60.0)
-            input_type: Input type for embeddings (default: search_document).
-                       Options: search_document, search_query, classification, clustering
-        """
-        self.api_key = api_key
-        self.model = model
-        self.batch_size = batch_size
-        self.timeout = timeout
-        self.input_type = input_type
-        self._client = None
-        self._dimension: int | None = None
-
-    @property
-    def provider_name(self) -> str:
-        return "cohere"
-
-    @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 Cohere client and detect dimension."""
-        if self._client is not None:
-            return
-
-        try:
-            import cohere
-        except ImportError:
-            raise ImportError("cohere is required for CohereEmbeddings. Install it with: pip install cohere")
-
-        logger.info(f"Embeddings: initializing Cohere provider with model {self.model}")
-        self._client = cohere.Client(api_key=self.api_key, timeout=self.timeout)
-
-        # Try to get dimension from known models, otherwise do a test embedding
-        if self.model in self.MODEL_DIMENSIONS:
-            self._dimension = self.MODEL_DIMENSIONS[self.model]
-        else:
-            # Do a test embedding to detect dimension
-            response = self._client.embed(
-                texts=["test"],
-                model=self.model,
-                input_type=self.input_type,
-            )
-            if response.embeddings:
-                self._dimension = len(response.embeddings[0])
-
-        logger.info(f"Embeddings: Cohere provider initialized (model: {self.model}, dim: {self._dimension})")
-
-    def encode(self, texts: list[str]) -> list[list[float]]:
-        """
-        Generate embeddings using the Cohere API.
-
-        Args:
-            texts: List of text strings to encode
-
-        Returns:
-            List of embedding vectors
-        """
-        if self._client 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]
-
-            response = self._client.embed(
-                texts=batch,
-                model=self.model,
-                input_type=self.input_type,
-            )
-
-            all_embeddings.extend(response.embeddings)
-
-        return all_embeddings
-
-
 def create_embeddings_from_env() -> Embeddings:
    """
    Create an Embeddings instance based on environment variables.
@@ -549,21 +289,5 @@ def create_embeddings_from_env() -> Embeddings:
        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)
-        if not api_key:
-            raise ValueError(
-                f"{ENV_EMBEDDINGS_OPENAI_API_KEY} or {ENV_LLM_API_KEY} is required "
-                f"when {ENV_EMBEDDINGS_PROVIDER} is 'openai'"
-            )
-        model = os.environ.get(ENV_EMBEDDINGS_OPENAI_MODEL, DEFAULT_EMBEDDINGS_OPENAI_MODEL)
-        return OpenAIEmbeddings(api_key=api_key, model=model)
-    elif provider == "cohere":
-        api_key = os.environ.get(ENV_COHERE_API_KEY)
-        if not api_key:
-            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)
-        return CohereEmbeddings(api_key=api_key, model=model)
    else:
-        raise ValueError(f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei', 'openai', 'cohere'")
+        raise ValueError(f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei'")
@@ -11,7 +11,6 @@ from difflib import SequenceMatcher
 import asyncpg

 from .db_utils import acquire_with_retry
-from .memory_engine import fq_table

 # Load spaCy model (singleton)
 _nlp = None
@@ -69,9 +68,9 @@ class EntityResolver:
    ) -> list[str]:
        # Query ALL candidates for this bank
        all_entities = await conn.fetch(
-            f"""
+            """
            SELECT canonical_name, id, metadata, last_seen, mention_count
-            FROM {fq_table("entities")}
+            FROM entities
            WHERE bank_id = $1
            """,
            bank_id,
@@ -83,11 +82,11 @@ class EntityResolver:
        # Query ALL co-occurrences for this bank's entities in one query
        # This builds a map of entity_id -> set of co-occurring entity names
        all_cooccurrences = await conn.fetch(
-            f"""
+            """
            SELECT ec.entity_id_1, ec.entity_id_2, ec.cooccurrence_count
-            FROM {fq_table("entity_cooccurrences")} ec
-            WHERE ec.entity_id_1 IN (SELECT id FROM {fq_table("entities")} WHERE bank_id = $1)
-               OR ec.entity_id_2 IN (SELECT id FROM {fq_table("entities")} WHERE bank_id = $1)
+            FROM entity_cooccurrences ec
+            WHERE ec.entity_id_1 IN (SELECT id FROM entities WHERE bank_id = $1)
+               OR ec.entity_id_2 IN (SELECT id FROM entities WHERE bank_id = $1)
            """,
            bank_id,
        )
@@ -196,8 +195,8 @@ class EntityResolver:
        # Batch update existing entities
        if entities_to_update:
            await conn.executemany(
-                f"""
-                UPDATE {fq_table("entities")} SET
+                """
+                UPDATE entities SET
                    mention_count = mention_count + 1,
                    last_seen = $2
                WHERE id = $1::uuid
@@ -233,13 +232,13 @@ class EntityResolver:
            # Batch INSERT ... ON CONFLICT with RETURNING
            # This is much faster than individual inserts
            rows = await conn.fetch(
-                f"""
-                INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
+                """
+                INSERT INTO entities (bank_id, canonical_name, first_seen, last_seen, mention_count)
                SELECT $1, name, event_date, event_date, 1
                FROM unnest($2::text[], $3::timestamptz[]) AS t(name, event_date)
                ON CONFLICT (bank_id, LOWER(canonical_name))
                DO UPDATE SET
-                    mention_count = {fq_table("entities")}.mention_count + 1,
+                    mention_count = entities.mention_count + 1,
                    last_seen = EXCLUDED.last_seen
                RETURNING id
                """,
@@ -280,9 +279,9 @@ class EntityResolver:
        async with acquire_with_retry(self.pool) as conn:
            # Find candidate entities with similar name
            candidates = await conn.fetch(
-                f"""
+                """
                SELECT id, canonical_name, metadata, last_seen
-                FROM {fq_table("entities")}
+                FROM entities
                WHERE bank_id = $1
                  AND (
                    canonical_name ILIKE $2
@@ -327,10 +326,10 @@ class EntityResolver:
                # Get entities that co-occurred with this candidate before
                # Use the materialized co-occurrence cache for fast lookup
                co_entity_rows = await conn.fetch(
-                    f"""
+                    """
                    SELECT e.canonical_name, ec.cooccurrence_count
-                    FROM {fq_table("entity_cooccurrences")} ec
-                    JOIN {fq_table("entities")} e ON (
+                    FROM entity_cooccurrences ec
+                    JOIN entities e ON (
                        CASE
                            WHEN ec.entity_id_1 = $1 THEN ec.entity_id_2
                            WHEN ec.entity_id_2 = $1 THEN ec.entity_id_1
@@ -366,8 +365,8 @@ class EntityResolver:
            if best_score > threshold:
                # Update entity
                await conn.execute(
-                    f"""
-                    UPDATE {fq_table("entities")}
+                    """
+                    UPDATE entities
                    SET mention_count = mention_count + 1,
                        last_seen = $1
                    WHERE id = $2
@@ -403,12 +402,12 @@ class EntityResolver:
            Entity ID
        """
        entity_id = await conn.fetchval(
-            f"""
-            INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
+            """
+            INSERT INTO entities (bank_id, canonical_name, first_seen, last_seen, mention_count)
            VALUES ($1, $2, $3, $4, 1)
            ON CONFLICT (bank_id, LOWER(canonical_name))
            DO UPDATE SET
-                mention_count = {fq_table("entities")}.mention_count + 1,
+                mention_count = entities.mention_count + 1,
                last_seen = EXCLUDED.last_seen
            RETURNING id
            """,
@@ -431,8 +430,8 @@ class EntityResolver:
        async with acquire_with_retry(self.pool) as conn:
            # Insert unit-entity link
            await conn.execute(
-                f"""
-                INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
+                """
+                INSERT INTO unit_entities (unit_id, entity_id)
                VALUES ($1, $2)
                ON CONFLICT DO NOTHING
                """,
@@ -442,9 +441,9 @@ class EntityResolver:

            # Update co-occurrence cache: find other entities in this unit
            rows = await conn.fetch(
-                f"""
+                """
                SELECT entity_id
-                FROM {fq_table("unit_entities")}
+                FROM unit_entities
                WHERE unit_id = $1 AND entity_id != $2
                """,
                unit_id,
@@ -473,12 +472,12 @@ class EntityResolver:
            entity_id_1, entity_id_2 = entity_id_2, entity_id_1

        await conn.execute(
-            f"""
-            INSERT INTO {fq_table("entity_cooccurrences")} (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
+            """
+            INSERT INTO entity_cooccurrences (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
            VALUES ($1, $2, 1, NOW())
            ON CONFLICT (entity_id_1, entity_id_2)
            DO UPDATE SET
-                cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
+                cooccurrence_count = entity_cooccurrences.cooccurrence_count + 1,
                last_cooccurred = NOW()
            """,
            entity_id_1,
@@ -507,8 +506,8 @@ class EntityResolver:
    async def _link_units_to_entities_batch_impl(self, conn, unit_entity_pairs: list[tuple[str, str]]):
        # Batch insert all unit-entity links
        await conn.executemany(
-            f"""
-            INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
+            """
+            INSERT INTO unit_entities (unit_id, entity_id)
            VALUES ($1, $2)
            ON CONFLICT DO NOTHING
            """,
@@ -542,12 +541,12 @@ class EntityResolver:
        if cooccurrence_pairs:
            now = datetime.now(UTC)
            await conn.executemany(
-                f"""
-                INSERT INTO {fq_table("entity_cooccurrences")} (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
+                """
+                INSERT INTO entity_cooccurrences (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
                VALUES ($1, $2, $3, $4)
                ON CONFLICT (entity_id_1, entity_id_2)
                DO UPDATE SET
-                    cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
+                    cooccurrence_count = entity_cooccurrences.cooccurrence_count + 1,
                    last_cooccurred = EXCLUDED.last_cooccurred
                """,
                [(e1, e2, 1, now) for e1, e2 in cooccurrence_pairs],
@@ -566,9 +565,9 @@ class EntityResolver:
        """
        async with acquire_with_retry(self.pool) as conn:
            rows = await conn.fetch(
-                f"""
+                """
                SELECT unit_id
-                FROM {fq_table("unit_entities")}
+                FROM unit_entities
                WHERE entity_id = $1
                ORDER BY unit_id
                LIMIT $2
@@ -595,8 +594,8 @@ class EntityResolver:
        """
        async with acquire_with_retry(self.pool) as conn:
            row = await conn.fetchrow(
-                f"""
-                SELECT id FROM {fq_table("entities")}
+                """
+                SELECT id FROM entities
                WHERE bank_id = $1
                  AND canonical_name ILIKE $2
                ORDER BY mention_count DESC
@@ -1,598 +0,0 @@
-"""Abstract interface for MemoryEngine public methods.
-
-This module defines the public API that HTTP endpoints and extensions should use
-to interact with the memory system. All methods require a RequestContext for
-authentication when a TenantExtension is configured.
-"""
-
-from abc import ABC, abstractmethod
-from datetime import datetime
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from hindsight_api.engine.memory_engine import Budget
-    from hindsight_api.engine.response_models import RecallResult, ReflectResult
-    from hindsight_api.models import RequestContext
-
-
-class MemoryEngineInterface(ABC):
-    """
-    Abstract interface for the Memory Engine.
-
-    This defines the public API that should be used by HTTP endpoints and extensions.
-    All methods require a RequestContext for authentication.
-    """
-
-    # =========================================================================
-    # Health & Status
-    # =========================================================================
-
-    @abstractmethod
-    async def health_check(self) -> dict:
-        """
-        Check the health of the memory system.
-
-        Returns:
-            Dict with 'status' key ('healthy' or 'unhealthy') and additional info.
-        """
-        ...
-
-    # =========================================================================
-    # Core Memory Operations
-    # =========================================================================
-
-    @abstractmethod
-    async def retain_batch_async(
-        self,
-        bank_id: str,
-        contents: list[dict[str, Any]],
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Retain a batch of memory items.
-
-        Args:
-            bank_id: The memory bank ID.
-            contents: List of content dicts with 'content', optional 'event_date',
-                     'context', 'metadata', 'document_id'.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with processing results.
-        """
-        ...
-
-    @abstractmethod
-    async def recall_async(
-        self,
-        bank_id: str,
-        query: str,
-        *,
-        budget: "Budget | None" = None,
-        max_tokens: int = 4096,
-        enable_trace: bool = False,
-        fact_type: list[str] | None = None,
-        question_date: datetime | None = None,
-        include_entities: bool = False,
-        max_entity_tokens: int = 500,
-        include_chunks: bool = False,
-        max_chunk_tokens: int = 8192,
-        request_context: "RequestContext",
-    ) -> "RecallResult":
-        """
-        Recall memories relevant to a query.
-
-        Args:
-            bank_id: The memory bank ID.
-            query: The search query.
-            budget: Search budget (LOW, MID, HIGH).
-            max_tokens: Maximum tokens in response.
-            enable_trace: Include trace information.
-            fact_type: Filter by fact types.
-            question_date: Context date for temporal relevance.
-            include_entities: Include entity observations.
-            max_entity_tokens: Max tokens for entity observations.
-            include_chunks: Include raw chunks.
-            max_chunk_tokens: Max tokens for chunks.
-            request_context: Request context for authentication.
-
-        Returns:
-            RecallResult with matching memories.
-        """
-        ...
-
-    @abstractmethod
-    async def reflect_async(
-        self,
-        bank_id: str,
-        query: str,
-        *,
-        budget: "Budget | None" = None,
-        context: str | None = None,
-        max_tokens: int = 4096,
-        response_schema: dict | None = None,
-        request_context: "RequestContext",
-    ) -> "ReflectResult":
-        """
-        Reflect on a query and generate a thoughtful response.
-
-        Args:
-            bank_id: The memory bank ID.
-            query: The question to reflect on.
-            budget: Search budget for retrieving context.
-            context: Additional context for the reflection.
-            max_tokens: Maximum tokens for the response.
-            response_schema: Optional JSON Schema for structured output.
-            request_context: Request context for authentication.
-
-        Returns:
-            ReflectResult with generated response and supporting facts.
-        """
-        ...
-
-    # =========================================================================
-    # Bank Management
-    # =========================================================================
-
-    @abstractmethod
-    async def list_banks(
-        self,
-        *,
-        request_context: "RequestContext",
-    ) -> list[dict[str, Any]]:
-        """
-        List all memory banks.
-
-        Args:
-            request_context: Request context for authentication.
-
-        Returns:
-            List of bank info dicts.
-        """
-        ...
-
-    @abstractmethod
-    async def get_bank_profile(
-        self,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Get bank profile including disposition and background.
-
-        Args:
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Bank profile dict.
-        """
-        ...
-
-    @abstractmethod
-    async def update_bank_disposition(
-        self,
-        bank_id: str,
-        disposition: dict[str, int],
-        *,
-        request_context: "RequestContext",
-    ) -> None:
-        """
-        Update bank disposition traits.
-
-        Args:
-            bank_id: The memory bank ID.
-            disposition: Dict with trait values.
-            request_context: Request context for authentication.
-        """
-        ...
-
-    @abstractmethod
-    async def merge_bank_background(
-        self,
-        bank_id: str,
-        new_info: str,
-        *,
-        update_disposition: bool = True,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Merge new background information into bank profile.
-
-        Args:
-            bank_id: The memory bank ID.
-            new_info: New background information to merge.
-            update_disposition: Whether to infer disposition from background.
-            request_context: Request context for authentication.
-
-        Returns:
-            Updated background info.
-        """
-        ...
-
-    @abstractmethod
-    async def delete_bank(
-        self,
-        bank_id: str,
-        *,
-        fact_type: str | None = None,
-        request_context: "RequestContext",
-    ) -> dict[str, int]:
-        """
-        Delete a bank or its memories.
-
-        Args:
-            bank_id: The memory bank ID.
-            fact_type: If specified, only delete memories of this type.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with deletion counts.
-        """
-        ...
-
-    # =========================================================================
-    # Memory Units
-    # =========================================================================
-
-    @abstractmethod
-    async def list_memory_units(
-        self,
-        bank_id: str,
-        *,
-        fact_type: str | None = None,
-        search_query: str | None = None,
-        limit: int = 100,
-        offset: int = 0,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        List memory units with pagination.
-
-        Args:
-            bank_id: The memory bank ID.
-            fact_type: Filter by fact type.
-            search_query: Full-text search query.
-            limit: Maximum results.
-            offset: Pagination offset.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with 'items', 'total', 'limit', 'offset'.
-        """
-        ...
-
-    @abstractmethod
-    async def delete_memory_unit(
-        self,
-        unit_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Delete a specific memory unit.
-
-        Args:
-            unit_id: The memory unit ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Deletion result.
-        """
-        ...
-
-    @abstractmethod
-    async def get_graph_data(
-        self,
-        bank_id: str,
-        *,
-        fact_type: str | None = None,
-        limit: int = 1000,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Get graph data for visualization.
-
-        Args:
-            bank_id: The memory bank ID.
-            fact_type: Filter by fact type.
-            limit: Maximum number of items to return (default: 1000).
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with nodes, edges, table_rows, total_units, limit.
-        """
-        ...
-
-    # =========================================================================
-    # Documents
-    # =========================================================================
-
-    @abstractmethod
-    async def list_documents(
-        self,
-        bank_id: str,
-        *,
-        search_query: str | None = None,
-        limit: int = 100,
-        offset: int = 0,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        List documents with pagination.
-
-        Args:
-            bank_id: The memory bank ID.
-            search_query: Search query.
-            limit: Maximum results.
-            offset: Pagination offset.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with 'items', 'total', 'limit', 'offset'.
-        """
-        ...
-
-    @abstractmethod
-    async def get_document(
-        self,
-        document_id: str,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any] | None:
-        """
-        Get a specific document.
-
-        Args:
-            document_id: The document ID.
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Document dict or None if not found.
-        """
-        ...
-
-    @abstractmethod
-    async def delete_document(
-        self,
-        document_id: str,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, int]:
-        """
-        Delete a document and its memory units.
-
-        Args:
-            document_id: The document ID.
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with deletion counts.
-        """
-        ...
-
-    @abstractmethod
-    async def get_chunk(
-        self,
-        chunk_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any] | None:
-        """
-        Get a specific chunk.
-
-        Args:
-            chunk_id: The chunk ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Chunk dict or None if not found.
-        """
-        ...
-
-    # =========================================================================
-    # Entities
-    # =========================================================================
-
-    @abstractmethod
-    async def list_entities(
-        self,
-        bank_id: str,
-        *,
-        limit: int = 100,
-        request_context: "RequestContext",
-    ) -> list[dict[str, Any]]:
-        """
-        List entities for a bank.
-
-        Args:
-            bank_id: The memory bank ID.
-            limit: Maximum results.
-            request_context: Request context for authentication.
-
-        Returns:
-            List of entity dicts.
-        """
-        ...
-
-    @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
-    # =========================================================================
-
-    @abstractmethod
-    async def get_bank_stats(
-        self,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Get statistics about memory nodes and links for a bank.
-
-        Args:
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with node_counts, link_counts, link_counts_by_fact_type,
-            link_breakdown, and operations stats.
-        """
-        ...
-
-    @abstractmethod
-    async def get_entity(
-        self,
-        bank_id: str,
-        entity_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any] | None:
-        """
-        Get entity details including metadata and observations.
-
-        Args:
-            bank_id: The memory bank ID.
-            entity_id: The entity ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            Entity dict with id, canonical_name, mention_count, first_seen,
-            last_seen, metadata, and observations. None if not found.
-        """
-        ...
-
-    @abstractmethod
-    async def list_operations(
-        self,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> list[dict[str, Any]]:
-        """
-        List async operations for a bank.
-
-        Args:
-            bank_id: The memory bank ID.
-            request_context: Request context for authentication.
-
-        Returns:
-            List of operation dicts with id, task_type, status, etc.
-        """
-        ...
-
-    @abstractmethod
-    async def cancel_operation(
-        self,
-        bank_id: str,
-        operation_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Cancel a pending async operation.
-
-        Args:
-            bank_id: The memory bank ID.
-            operation_id: The operation ID to cancel.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with success status and message.
-
-        Raises:
-            ValueError: If operation not found.
-        """
-        ...
-
-    @abstractmethod
-    async def update_bank(
-        self,
-        bank_id: str,
-        *,
-        name: str | None = None,
-        background: str | None = None,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Update bank name and/or background.
-
-        Args:
-            bank_id: The memory bank ID.
-            name: New bank name (optional).
-            background: New background text (optional, replaces existing).
-            request_context: Request context for authentication.
-
-        Returns:
-            Updated bank profile dict.
-        """
-        ...
-
-    @abstractmethod
-    async def submit_async_retain(
-        self,
-        bank_id: str,
-        contents: list[dict[str, Any]],
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Submit a batch retain operation to run asynchronously.
-
-        Args:
-            bank_id: The memory bank ID.
-            contents: List of content dicts to retain.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with operation_id and items_count.
-        """
-        ...
@@ -3,29 +3,16 @@ LLM wrapper for unified configuration across providers.
 """

 import asyncio
-import json
 import logging
 import os
-import re
 import time
 from typing import Any

-import httpx
 from google import genai
 from google.genai import errors as genai_errors
 from google.genai import types as genai_types
 from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinishReasonError

-from ..config import (
-    DEFAULT_LLM_MAX_CONCURRENT,
-    DEFAULT_LLM_TIMEOUT,
-    ENV_LLM_GROQ_SERVICE_TIER,
-    ENV_LLM_MAX_CONCURRENT,
-    ENV_LLM_TIMEOUT,
-)
-from ..metrics import get_metrics_collector
-from .response_models import TokenUsage
-
 # Seed applied to every Groq request for deterministic behavior.
 DEFAULT_LLM_SEED = 4242

@@ -35,9 +22,7 @@ logger = logging.getLogger(__name__)
 logging.getLogger("httpx").setLevel(logging.WARNING)

 # Global semaphore to limit concurrent LLM requests across all instances
-# Set HINDSIGHT_API_LLM_MAX_CONCURRENT=1 for local LLMs (LM Studio, Ollama)
-_llm_max_concurrent = int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_CONCURRENT)))
-_global_llm_semaphore = asyncio.Semaphore(_llm_max_concurrent)
+_global_llm_semaphore = asyncio.Semaphore(32)


 class OutputTooLongError(Exception):
@@ -66,87 +51,53 @@ class LLMProvider:
        base_url: str,
        model: str,
        reasoning_effort: str = "low",
-        groq_service_tier: str | None = None,
    ):
        """
        Initialize LLM provider.

        Args:
-            provider: Provider name ("openai", "groq", "ollama", "gemini", "anthropic", "lmstudio").
+            provider: Provider name ("openai", "groq", "ollama", "gemini").
            api_key: API key.
            base_url: Base URL for the API.
            model: Model name.
            reasoning_effort: Reasoning effort level for supported providers.
-            groq_service_tier: Groq service tier ("on_demand", "flex", "auto"). Default: None (uses Groq's default).
        """
        self.provider = provider.lower()
        self.api_key = api_key
        self.base_url = base_url
        self.model = model
        self.reasoning_effort = reasoning_effort
-        # Default to 'auto' for best performance, users can override to 'on_demand' for free tier
-        self.groq_service_tier = groq_service_tier or os.getenv(ENV_LLM_GROQ_SERVICE_TIER, "auto")

        # Validate provider
-        valid_providers = ["openai", "groq", "ollama", "gemini", "anthropic", "lmstudio", "mock"]
+        valid_providers = ["openai", "groq", "ollama", "gemini"]
        if self.provider not in valid_providers:
            raise ValueError(f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}")

-        # Mock provider tracking (for testing)
-        self._mock_calls: list[dict] = []
-        self._mock_response: Any = None
-
        # 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"

-        # Validate API key (not needed for ollama, lmstudio, or mock)
-        if self.provider not in ("ollama", "lmstudio", "mock") and not self.api_key:
+        # Validate API key (not needed for ollama)
+        if self.provider != "ollama" and not self.api_key:
            raise ValueError(f"API key not found for {self.provider}")

-        # Get timeout config (set HINDSIGHT_API_LLM_TIMEOUT for local LLMs that need longer timeouts)
-        self.timeout = float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))
-
        # Create client based on provider
-        self._client = None
-        self._gemini_client = None
-        self._anthropic_client = None
-
-        if self.provider == "mock":
-            # Mock provider - no client needed
-            pass
-        elif self.provider == "gemini":
+        if self.provider == "gemini":
            self._gemini_client = genai.Client(api_key=self.api_key)
-        elif self.provider == "anthropic":
-            from anthropic import AsyncAnthropic
-
-            # Only pass base_url if it's set (Anthropic uses default URL otherwise)
-            anthropic_kwargs = {"api_key": self.api_key}
-            if self.base_url:
-                anthropic_kwargs["base_url"] = self.base_url
-            if self.timeout:
-                anthropic_kwargs["timeout"] = self.timeout
-            self._anthropic_client = AsyncAnthropic(**anthropic_kwargs)
-        elif self.provider in ("ollama", "lmstudio"):
-            # Use dummy key if not provided for local
-            api_key = self.api_key or "local"
-            client_kwargs = {"api_key": api_key, "base_url": self.base_url, "max_retries": 0}
-            if self.timeout:
-                client_kwargs["timeout"] = self.timeout
-            self._client = AsyncOpenAI(**client_kwargs)
+            self._client = None
+        elif self.provider == "ollama":
+            self._client = AsyncOpenAI(api_key="ollama", base_url=self.base_url, max_retries=0)
+            self._gemini_client = None
        else:
            # Only pass base_url if it's set (OpenAI uses default URL otherwise)
            client_kwargs = {"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)
+            self._gemini_client = None

    async def verify_connection(self) -> None:
        """
@@ -161,7 +112,7 @@ class LLMProvider:
            )
            await self.call(
                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=100,
+                max_completion_tokens=10,
                max_retries=2,
                initial_backoff=0.5,
                max_backoff=2.0,
@@ -182,8 +133,6 @@ class LLMProvider:
        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.
@@ -198,12 +147,9 @@ class LLMProvider:
            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). Guarantees all required fields.
-            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 from the LLM call.
+            Parsed response if response_format is provided, otherwise text content.

        Raises:
            OutputTooLongError: If output exceeds token limits.
@@ -211,56 +157,12 @@ class LLMProvider:
        """
        async with _global_llm_semaphore:
            start_time = time.time()
-
-            # Handle Mock provider (for testing)
-            if self.provider == "mock":
-                return await self._call_mock(
-                    messages,
-                    response_format,
-                    scope,
-                    return_usage,
-                )
+            import json

            # Handle Gemini provider separately
            if self.provider == "gemini":
                return await self._call_gemini(
-                    messages,
-                    response_format,
-                    max_retries,
-                    initial_backoff,
-                    max_backoff,
-                    skip_validation,
-                    start_time,
-                    return_usage,
-                )
-
-            # Handle Anthropic provider separately
-            if self.provider == "anthropic":
-                return await self._call_anthropic(
-                    messages,
-                    response_format,
-                    max_completion_tokens,
-                    max_retries,
-                    initial_backoff,
-                    max_backoff,
-                    skip_validation,
-                    start_time,
-                    return_usage,
-                )
-
-            # 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,
-                    response_format,
-                    max_completion_tokens,
-                    temperature,
-                    max_retries,
-                    initial_backoff,
-                    max_backoff,
-                    skip_validation,
-                    start_time,
-                    return_usage,
+                    messages, response_format, max_retries, initial_backoff, max_backoff, skip_validation, start_time
                )

            call_params = {
@@ -270,7 +172,7 @@ class LLMProvider:

            # Check if model supports reasoning parameter (o1, o3, gpt-5 families)
            model_lower = self.model.lower()
-            is_reasoning_model = any(x in model_lower for x in ["gpt-5", "o1", "o3", "deepseek"])
+            is_reasoning_model = any(x in model_lower for x in ["gpt-5", "o1", "o3"])

            # For GPT-4 and GPT-4.1 models, cap max_completion_tokens to 32000
            # For GPT-4o models, cap to 16384
@@ -292,114 +194,41 @@ class LLMProvider:
                call_params["temperature"] = temperature

            # Set reasoning_effort for reasoning models (OpenAI gpt-5, o1, o3)
-            if is_reasoning_model:
+            if is_reasoning_model and self.provider == "openai":
                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 (requires paid plan for flex/auto)
-                if self.groq_service_tier:
-                    extra_body["service_tier"] = self.groq_service_tier
-                # Add reasoning parameters for reasoning models
+                extra_body = {"service_tier": "auto"}
+                # Only add reasoning parameters for reasoning models
                if is_reasoning_model:
+                    extra_body["reasoning_effort"] = self.reasoning_effort
                    extra_body["include_reasoning"] = False
-                if extra_body:
-                    call_params["extra_body"] = extra_body
+                call_params["extra_body"] = extra_body

            last_exception = None

            for attempt in range(max_retries + 1):
                try:
                    if response_format is not None:
-                        schema = None
+                        # Add schema to system message for JSON mode
                        if 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 strict_schema and schema is not None:
-                            # Use OpenAI's strict JSON schema enforcement
-                            # This guarantees all required fields are returned
-                            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":
+                                call_params["messages"][0]["content"] += schema_msg
+                            elif call_params["messages"]:
+                                call_params["messages"][0]["content"] = (
+                                    schema_msg + "\n\n" + call_params["messages"][0]["content"]
+                                )

-                                if call_params["messages"] and call_params["messages"][0].get("role") == "system":
-                                    call_params["messages"][0]["content"] += schema_msg
-                                elif call_params["messages"]:
-                                    call_params["messages"][0]["content"] = (
-                                        schema_msg + "\n\n" + call_params["messages"][0]["content"]
-                                    )
-                            if self.provider not in ("lmstudio", "ollama"):
-                                # LM Studio and Ollama don't support json_object response format reliably
-                                # We rely on the schema in the system message instead
-                                call_params["response_format"] = {"type": "json_object"}
-
-                        logger.debug(f"Sending request to {self.provider}/{self.model} (timeout={self.timeout})")
+                        call_params["response_format"] = {"type": "json_object"}
                        response = await self._client.chat.completions.create(**call_params)
-                        logger.debug(f"Received response from {self.provider}/{self.model}")

                        content = response.choices[0].message.content
-
-                        # Strip reasoning model thinking tags
-                        # Supports: <think>, <thinking>, <reasoning>, |startthink|/|endthink|
-                        # for reasoning models that embed thinking in their output (e.g., Qwen3, DeepSeek)
-                        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 (first 500 and last 200 chars)
-                                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 - LLM may return valid JSON on next attempt
-                                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
+                        json_data = json.loads(content)

                        if skip_validation:
                            result = json_data
@@ -409,45 +238,21 @@ class LLMProvider:
                        response = await self._client.chat.completions.create(**call_params)
                        result = response.choices[0].message.content

-                    # Record token usage metrics
+                    # Log slow calls
                    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,
-                    )
-
-                    # Log slow calls
-                    if duration > 10.0 and usage:
-                        ratio = max(1, output_tokens) / max(1, input_tokens)
+                    if duration > 10.0:
+                        ratio = max(1, usage.completion_tokens) / usage.prompt_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: 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}"
+                            f"input_tokens={usage.prompt_tokens}, output_tokens={usage.completion_tokens}, "
+                            f"total_tokens={usage.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:
@@ -496,317 +301,6 @@ class LLMProvider:
                raise last_exception
            raise RuntimeError("LLM call failed after all retries with no exception captured")

-    async def _call_anthropic(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None,
-        max_completion_tokens: int | None,
-        max_retries: int,
-        initial_backoff: float,
-        max_backoff: float,
-        skip_validation: bool,
-        start_time: float,
-        return_usage: bool = False,
-    ) -> Any:
-        """Handle Anthropic-specific API calls."""
-        from anthropic import APIConnectionError, APIStatusError, RateLimitError
-
-        # 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 = {
-            "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
-
-        last_exception = None
-
-        for attempt in range(max_retries + 1):
-            try:
-                response = await self._anthropic_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="memory",
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
-                # Log slow calls
-                if duration > 10.0:
-                    logger.info(
-                        f"slow llm call: scope=memory, 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_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,
-        start_time: float,
-        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.
-        """
-        # 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 = {
-            "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
-        # Benchmarking shows num_ctx=16384 + num_batch=512 is optimal
-        options = {
-            "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
-                    # Ollama returns prompt_eval_count (input) and eval_count (output)
-                    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="memory",
-                        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 _call_gemini(
        self,
        messages: list[dict[str, str]],
@@ -816,9 +310,10 @@ class LLMProvider:
        max_backoff: float,
        skip_validation: bool,
        start_time: float,
-        return_usage: bool = False,
    ) -> Any:
        """Handle Gemini-specific API calls."""
+        import json
+
        # Convert OpenAI-style messages to Gemini format
        system_instruction = None
        gemini_contents = []
@@ -893,42 +388,16 @@ class LLMProvider:
                else:
                    result = content

-                # Record metrics and log slow calls
-                duration = time.time() - start_time
-                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 LLM metrics
-                metrics = get_metrics_collector()
-                metrics.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope="memory",
-                    duration=duration,
-                    input_tokens=input_tokens,
-                    output_tokens=output_tokens,
-                    success=True,
-                )
-
                # Log slow calls
-                if duration > 10.0 and input_tokens > 0:
+                duration = time.time() - start_time
+                if duration > 10.0 and hasattr(response, "usage_metadata") and response.usage_metadata:
+                    usage = response.usage_metadata
                    logger.info(
-                        f"slow llm call: scope=memory, model={self.provider}/{self.model}, "
-                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
+                        f"slow llm call: model={self.provider}/{self.model}, "
+                        f"input_tokens={usage.prompt_token_count}, output_tokens={usage.candidates_token_count}, "
                        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:
@@ -970,68 +439,11 @@ class LLMProvider:
            raise last_exception
        raise RuntimeError("Gemini call failed after all retries")

-    async def _call_mock(
-        self,
-        messages: list[dict[str, str]],
-        response_format: Any | None,
-        scope: str,
-        return_usage: bool,
-    ) -> Any:
-        """
-        Handle mock provider calls for testing.
-
-        Records the call and returns a configurable mock response.
-        """
-        # 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}")
-
-        # 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
-
-    def set_mock_response(self, response: Any) -> None:
-        """Set the response to return from mock calls."""
-        self._mock_response = response
-
-    def get_mock_calls(self) -> list[dict]:
-        """Get the list of recorded mock calls."""
-        return self._mock_calls
-
-    def clear_mock_calls(self) -> None:
-        """Clear the recorded mock calls."""
-        self._mock_calls = []
-
    @classmethod
    def for_memory(cls) -> "LLMProvider":
        """Create provider for memory operations from environment variables."""
        provider = os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq")
        api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY")
-        if not api_key:
-            raise ValueError("HINDSIGHT_API_LLM_API_KEY environment variable is required")
        base_url = os.getenv("HINDSIGHT_API_LLM_BASE_URL", "")
        model = os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b")

@@ -1042,10 +454,6 @@ class LLMProvider:
        """Create provider for answer generation. Falls back to memory config if not set."""
        provider = os.getenv("HINDSIGHT_API_ANSWER_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
        api_key = os.getenv("HINDSIGHT_API_ANSWER_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY"))
-        if not api_key:
-            raise ValueError(
-                "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_ANSWER_LLM_API_KEY environment variable is required"
-            )
        base_url = os.getenv("HINDSIGHT_API_ANSWER_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
        model = os.getenv("HINDSIGHT_API_ANSWER_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"))

@@ -1056,10 +464,6 @@ class LLMProvider:
        """Create provider for judge/evaluator operations. Falls back to memory config if not set."""
        provider = os.getenv("HINDSIGHT_API_JUDGE_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
        api_key = os.getenv("HINDSIGHT_API_JUDGE_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY"))
-        if not api_key:
-            raise ValueError(
-                "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_JUDGE_LLM_API_KEY environment variable is required"
-            )
        base_url = os.getenv("HINDSIGHT_API_JUDGE_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
        model = os.getenv("HINDSIGHT_API_JUDGE_LLM_MODEL", os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"))

@@ -14,37 +14,6 @@ from pydantic import BaseModel, ConfigDict, Field
 VALID_RECALL_FACT_TYPES = frozenset(["world", "experience", "opinion"])


-class TokenUsage(BaseModel):
-    """
-    Token usage metrics for LLM calls.
-
-    Tracks input/output tokens for a single request to enable
-    per-request cost tracking and monitoring.
-    """
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "input_tokens": 1500,
-                "output_tokens": 500,
-                "total_tokens": 2000,
-            }
-        }
-    )
-
-    input_tokens: int = Field(default=0, description="Number of input/prompt tokens consumed")
-    output_tokens: int = Field(default=0, description="Number of output/completion tokens generated")
-    total_tokens: int = Field(default=0, description="Total tokens (input + output)")
-
-    def __add__(self, other: "TokenUsage") -> "TokenUsage":
-        """Allow aggregating token usage from multiple calls."""
-        return TokenUsage(
-            input_tokens=self.input_tokens + other.input_tokens,
-            output_tokens=self.output_tokens + other.output_tokens,
-            total_tokens=self.total_tokens + other.total_tokens,
-        )
-
-
 class DispositionTraits(BaseModel):
    """
    Disposition traits for a memory bank.
@@ -154,8 +123,7 @@ class ReflectResult(BaseModel):
    Result from a reflect operation.

    Contains the formulated answer, the facts it was based on (organized by type),
-    any new opinions that were formed during the reflection process, and optionally
-    structured output if a response schema was provided.
+    and any new opinions that were formed during the reflection process.
    """

    model_config = ConfigDict(
@@ -177,8 +145,6 @@ class ReflectResult(BaseModel):
                    "opinion": [],
                },
                "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},
            }
        }
    )
@@ -188,14 +154,6 @@ class ReflectResult(BaseModel):
        description="Facts used to formulate the answer, organized by type (world, experience, opinion)"
    )
    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.",
-    )
-    usage: TokenUsage | None = Field(
-        default=None,
-        description="Token usage metrics for the LLM calls made during this reflect operation.",
-    )


 class Opinion(BaseModel):
@@ -10,7 +10,6 @@ from typing import TypedDict
 from pydantic import BaseModel, Field

 from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
 from ..response_models import DispositionTraits

 logger = logging.getLogger(__name__)
@@ -52,9 +51,9 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
    async with acquire_with_retry(pool) as conn:
        # Try to get existing bank
        row = await conn.fetchrow(
-            f"""
+            """
            SELECT name, disposition, background
-            FROM {fq_table("banks")} WHERE bank_id = $1
+            FROM banks WHERE bank_id = $1
            """,
            bank_id,
        )
@@ -71,8 +70,8 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:

        # Bank doesn't exist, create with defaults
        await conn.execute(
-            f"""
-            INSERT INTO {fq_table("banks")} (bank_id, name, disposition, background)
+            """
+            INSERT INTO banks (bank_id, name, disposition, background)
            VALUES ($1, $2, $3::jsonb, $4)
            ON CONFLICT (bank_id) DO NOTHING
            """,
@@ -99,8 +98,8 @@ async def update_bank_disposition(pool, bank_id: str, disposition: dict[str, int

    async with acquire_with_retry(pool) as conn:
        await conn.execute(
-            f"""
-            UPDATE {fq_table("banks")}
+            """
+            UPDATE banks
            SET disposition = $2::jsonb,
                updated_at = NOW()
            WHERE bank_id = $1
@@ -141,8 +140,8 @@ async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, u
        if inferred_disposition:
            # Update both background and disposition
            await conn.execute(
-                f"""
-                UPDATE {fq_table("banks")}
+                """
+                UPDATE banks
                SET background = $2,
                    disposition = $3::jsonb,
                    updated_at = NOW()
@@ -155,8 +154,8 @@ async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, u
        else:
            # Update only background
            await conn.execute(
-                f"""
-                UPDATE {fq_table("banks")}
+                """
+                UPDATE banks
                SET background = $2,
                    updated_at = NOW()
                WHERE bank_id = $1
@@ -362,9 +361,9 @@ async def list_banks(pool) -> list:
    """
    async with acquire_with_retry(pool) as conn:
        rows = await conn.fetch(
-            f"""
+            """
            SELECT bank_id, name, disposition, background, created_at, updated_at
-            FROM {fq_table("banks")}
+            FROM banks
            ORDER BY updated_at DESC
            """
        )
@@ -6,7 +6,6 @@ Handles storage of document chunks in the database.

 import logging

-from ..memory_engine import fq_table
 from .types import ChunkMetadata

 logger = logging.getLogger(__name__)
@@ -43,8 +42,8 @@ async def store_chunks_batch(conn, bank_id: str, document_id: str, chunks: list[

    # Batch insert all chunks
    await conn.execute(
-        f"""
-        INSERT INTO {fq_table("chunks")} (chunk_id, document_id, bank_id, chunk_text, chunk_index)
+        """
+        INSERT INTO chunks (chunk_id, document_id, bank_id, chunk_text, chunk_index)
        SELECT * FROM unnest($1::text[], $2::text[], $3::text[], $4::text[], $5::integer[])
        """,
        chunk_ids,
@@ -13,23 +13,16 @@ logger = logging.getLogger(__name__)


 async def process_entities_batch(
-    entity_resolver,
-    conn,
-    bank_id: str,
-    unit_ids: list[str],
-    facts: list[ProcessedFact],
-    log_buffer: list[str] = None,
-    user_entities_per_content: dict[int, list[dict]] = None,
+    entity_resolver, conn, bank_id: str, unit_ids: list[str], facts: list[ProcessedFact], log_buffer: list[str] = None
 ) -> list[EntityLink]:
    """
    Process entities for all facts and create entity links.

    This function:
    1. Extracts entity mentions from fact texts
-    2. Merges user-provided entities with LLM-extracted entities
-    3. Resolves entity names to canonical entities
-    4. Creates entity records in the database
-    5. Returns entity links ready for insertion
+    2. Resolves entity names to canonical entities
+    3. Creates entity records in the database
+    4. Returns entity links ready for insertion

    Args:
        entity_resolver: EntityResolver instance for entity resolution
@@ -38,7 +31,6 @@ async def process_entities_batch(
        unit_ids: List of unit IDs (same length as facts)
        facts: List of ProcessedFact objects
        log_buffer: Optional buffer for detailed logging
-        user_entities_per_content: Dict mapping content_index to list of user-provided entities

    Returns:
        List of EntityLink objects for batch insertion
@@ -49,35 +41,14 @@ async def process_entities_batch(
    if len(unit_ids) != len(facts):
        raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and facts ({len(facts)})")

-    user_entities_per_content = user_entities_per_content or {}
-
    # Extract data for link_utils function
    fact_texts = [fact.fact_text for fact in facts]
    # Use occurred_start if available, otherwise use mentioned_at for entity timestamps
    fact_dates = [fact.occurred_start if fact.occurred_start is not None else fact.mentioned_at for fact in facts]
-
-    # Convert EntityRef objects to dict format and merge with user-provided entities
-    entities_per_fact = []
-    for fact in facts:
-        # Start with LLM-extracted entities
-        llm_entities = [{"text": entity.name, "type": "CONCEPT"} for entity in (fact.entities or [])]
-
-        # Get user entities for this content (use content_index from fact)
-        user_entities = user_entities_per_content.get(fact.content_index, [])
-
-        # Merge with case-insensitive deduplication
-        seen_texts = {e["text"].lower() for e in llm_entities}
-        for user_entity in user_entities:
-            if user_entity["text"].lower() not in seen_texts:
-                llm_entities.append(
-                    {
-                        "text": user_entity["text"],
-                        "type": user_entity.get("type", "CONCEPT"),
-                    }
-                )
-                seen_texts.add(user_entity["text"].lower())
-
-        entities_per_fact.append(llm_entities)
+    # Convert EntityRef objects to dict format expected by link_utils
+    entities_per_fact = [
+        [{"text": entity.name, "type": "CONCEPT"} for entity in (fact.entities or [])] for fact in facts
+    ]

    # Use existing link_utils function for entity processing
    entity_links = await link_utils.extract_entities_batch_optimized(
@@ -14,47 +14,7 @@ from typing import Literal

 from pydantic import BaseModel, ConfigDict, Field, field_validator

-from ...config import get_config
 from ..llm_wrapper import LLMConfig, OutputTooLongError
-from ..response_models import TokenUsage
-
-
-def _infer_temporal_date(fact_text: str, event_date: datetime) -> str | None:
-    """
-    Infer a temporal date from fact text when LLM didn't provide occurred_start.
-
-    This is a fallback for when the LLM fails to extract temporal information
-    from relative time expressions like "last night", "yesterday", etc.
-    """
-    import re
-
-    fact_lower = fact_text.lower()
-
-    # Map relative time expressions to day offsets
-    temporal_patterns = {
-        r"\blast night\b": -1,
-        r"\byesterday\b": -1,
-        r"\btoday\b": 0,
-        r"\bthis morning\b": 0,
-        r"\bthis afternoon\b": 0,
-        r"\bthis evening\b": 0,
-        r"\btonigh?t\b": 0,
-        r"\btomorrow\b": 1,
-        r"\blast week\b": -7,
-        r"\bthis week\b": 0,
-        r"\bnext week\b": 7,
-        r"\blast month\b": -30,
-        r"\bthis month\b": 0,
-        r"\bnext month\b": 30,
-    }
-
-    for pattern, offset_days in temporal_patterns.items():
-        if re.search(pattern, fact_lower):
-            target_date = event_date + timedelta(days=offset_days)
-            return target_date.replace(hour=0, minute=0, second=0, microsecond=0).isoformat()
-
-    # If no relative time expression found, return None
-    return None


 def _sanitize_text(text: str) -> str:
@@ -111,44 +71,22 @@ class Fact(BaseModel):


 class CausalRelation(BaseModel):
-    """Causal relationship from this fact to a previous fact (stored format)."""
+    """Causal relationship between facts."""

-    target_fact_index: int = Field(description="Index of the related fact in the facts array (0-based).")
-    relation_type: Literal["caused_by", "enabled_by", "prevented_by"] = Field(
-        description="How this fact relates to the target: "
-        "'caused_by' = this fact was caused by the target, "
-        "'enabled_by' = this fact was enabled by the target, "
-        "'prevented_by' = this fact was prevented by the target"
+    target_fact_index: int = Field(
+        description="Index of the related fact in the facts array (0-based). "
+        "This creates a directed causal link to another fact in the extraction."
    )
-    strength: float = Field(
-        description="Strength of relationship (0.0 to 1.0)",
-        ge=0.0,
-        le=1.0,
-        default=1.0,
-    )
-
-
-class FactCausalRelation(BaseModel):
-    """
-    Causal relationship from this fact to a PREVIOUS fact (embedded in each fact).
-
-    Uses index-based references but ONLY allows referencing facts that appear
-    BEFORE this fact in the list. This prevents hallucination of invalid indices.
-    """
-
-    target_index: int = Field(
-        description="Index of the PREVIOUS fact this relates to (0-based). "
-        "MUST be less than this fact's position in the list. "
-        "Example: if this is fact #5, target_index can only be 0, 1, 2, 3, or 4."
-    )
-    relation_type: Literal["caused_by", "enabled_by", "prevented_by"] = Field(
-        description="How this fact relates to the target fact: "
+    relation_type: Literal["causes", "caused_by", "enables", "prevents"] = Field(
+        description="Type of causal relationship: "
+        "'causes' = this fact directly causes the target fact, "
        "'caused_by' = this fact was caused by the target fact, "
-        "'enabled_by' = this fact was enabled by the target fact, "
-        "'prevented_by' = this fact was blocked/prevented by the target fact"
+        "'enables' = this fact enables/allows the target fact, "
+        "'prevents' = this fact prevents/blocks the target fact"
    )
    strength: float = Field(
-        description="Strength of relationship (0.0 to 1.0). 1.0 = strong, 0.5 = moderate",
+        description="Strength of causal relationship (0.0 to 1.0). "
+        "1.0 = direct/strong causation, 0.5 = moderate, 0.3 = weak/indirect",
        ge=0.0,
        le=1.0,
        default=1.0,
@@ -238,12 +176,8 @@ class ExtractedFact(BaseModel):
        default=None,
        description="Named entities, objects, AND abstract concepts from the fact. Include: people names, organizations, places, significant objects (e.g., 'coffee maker', 'car'), AND abstract concepts/themes (e.g., 'friendship', 'career growth', 'loss', 'celebration'). Extract anything that could help link related facts together.",
    )
-
-    # Causal relations to PREVIOUS facts only (prevents hallucination of invalid indices)
-    causal_relations: list[FactCausalRelation] | None = Field(
-        default=None,
-        description="Causal links to PREVIOUS facts only. target_index MUST be less than this fact's position. "
-        "Example: fact #3 can only reference facts 0, 1, or 2. Max 2 relations per fact.",
+    causal_relations: list[CausalRelation] | None = Field(
+        default=None, description="Causal links to other facts. Can be null."
    )

    @field_validator("entities", mode="before")
@@ -254,6 +188,14 @@ class ExtractedFact(BaseModel):
            return []
        return v

+    @field_validator("causal_relations", mode="before")
+    @classmethod
+    def ensure_causal_relations_list(cls, v):
+        """Ensure causal_relations is always a list (convert None to empty list)."""
+        if v is None:
+            return []
+        return v
+
    def build_fact_text(self) -> str:
        """Combine all dimensions into a single comprehensive fact string."""
        parts = [self.what]
@@ -273,7 +215,7 @@ class ExtractedFact(BaseModel):


 class FactExtractionResponse(BaseModel):
-    """Response containing all extracted facts (causal relations are embedded in each fact)."""
+    """Response containing all extracted facts."""

    facts: list[ExtractedFact] = Field(description="List of extracted factual statements")

@@ -376,7 +318,7 @@ async def _extract_facts_from_chunk(
    llm_config: "LLMConfig",
    agent_name: str = None,
    extract_opinions: bool = False,
-) -> tuple[list[dict[str, str]], TokenUsage]:
+) -> list[dict[str, str]]:
    """
    Extract facts from a single chunk (internal helper for parallel processing).

@@ -397,9 +339,6 @@ async def _extract_facts_from_chunk(

    prompt = f"""Extract facts from text into structured format with FOUR required dimensions - BE EXTREMELY DETAILED.

-LANGUAGE REQUIREMENT: Detect the language of the input text. All extracted facts, entity names, descriptions,
-and other output MUST be in the SAME language as the input. Do not translate to English if the input is in another language.
-
 {fact_types_instruction}


@@ -595,52 +534,7 @@ WHAT TO EXTRACT vs SKIP
 ══════════════════════════════════════════════════════════════════════════

 ✅ EXTRACT: User preferences (ALWAYS as separate facts!), feelings, plans, events, relationships, achievements
-❌ SKIP: Greetings, filler ("thanks", "cool"), purely structural statements
-
-══════════════════════════════════════════════════════════════════════════
-CAUSAL RELATIONSHIPS (EMBEDDED IN EACH FACT - REFERENCE PREVIOUS FACTS ONLY)
-══════════════════════════════════════════════════════════════════════════
-
-Each fact can have a `causal_relations` array that links to PREVIOUS facts only.
-⚠️ CRITICAL: target_index MUST be less than this fact's position in the list!
-
-If you're writing fact #5, you can only reference facts 0, 1, 2, 3, or 4.
-This ensures all references are valid.
-
-Relationship types (all describe how THIS fact relates to the target):
- "caused_by": This fact was caused by the target fact
- "enabled_by": This fact was enabled/allowed by the target fact
- "prevented_by": This fact was blocked/prevented by the target fact
-
-Max 2 causal relations per fact. Only add if there's a clear causal link.
-
-Example (Event Date: March 15, 2024):
-Input: "I lost my job in January. Because of that, I couldn't pay rent. So I had to move to a cheaper apartment."
-
-Output facts:
-```json
-{{
-  "facts": [
-    {{
-      "what": "User lost their job in January due to company layoffs",
-      ...other fields...
-      "causal_relations": null  // First fact - nothing to reference
-    }},
-    {{
-      "what": "User couldn't pay rent because of job loss",
-      ...other fields...
-      "causal_relations": [{{"target_index": 0, "relation_type": "caused_by", "strength": 1.0}}]
-    }},
-    {{
-      "what": "User moved to a cheaper apartment",
-      ...other fields...
-      "causal_relations": [{{"target_index": 1, "relation_type": "caused_by", "strength": 0.9}}]
-    }}
-  ]
-}}
-```
-
-This creates: Job loss (0) ← Can't pay rent (1) ← Moved apartment (2)"""
+❌ SKIP: Greetings, filler ("thanks", "cool"), purely structural statements"""

    import logging

@@ -651,7 +545,6 @@ This creates: Job loss (0) ← Can't pay rent (1) ← Moved apartment (2)"""
    # Retry logic for JSON validation errors
    max_retries = 2
    last_error = None
-    config = get_config()

    # Sanitize input text to prevent Unicode encoding errors (e.g., unpaired surrogates)
    sanitized_chunk = _sanitize_text(chunk)
@@ -670,19 +563,16 @@ Context: {sanitized_context}
 Text:
 {sanitized_chunk}"""

-    usage = TokenUsage()  # Track cumulative usage across retries
    for attempt in range(max_retries):
        try:
-            extraction_response_json, call_usage = await llm_config.call(
+            extraction_response_json = await llm_config.call(
                messages=[{"role": "system", "content": prompt}, {"role": "user", "content": user_message}],
                response_format=FactExtractionResponse,
                scope="memory_extract_facts",
                temperature=0.1,
-                max_completion_tokens=config.retain_max_completion_tokens,
+                max_completion_tokens=65000,
                skip_validation=True,  # Get raw JSON, we'll validate leniently
-                return_usage=True,
            )
-            usage = usage + call_usage  # Aggregate usage across retries

            # Lenient parsing of facts from raw JSON
            chunk_facts = []
@@ -700,10 +590,9 @@ Text:
                        f"LLM returned non-dict JSON after {max_retries} attempts: {type(extraction_response_json).__name__}. "
                        f"Raw: {str(extraction_response_json)[:500]}"
                    )
-                    return [], usage
+                    return []

            raw_facts = extraction_response_json.get("facts", [])
-
            if not raw_facts:
                logger.debug(
                    f"LLM response missing 'facts' field or returned empty list. "
@@ -787,18 +676,13 @@ Text:
                if fact_kind == "event":
                    occurred_start = get_value("occurred_start")
                    occurred_end = get_value("occurred_end")
-
-                    # If LLM didn't set temporal fields, try to extract them from the fact text
-                    if not occurred_start:
-                        fact_data["occurred_start"] = _infer_temporal_date(combined_text, event_date)
-                    else:
+                    if occurred_start:
                        fact_data["occurred_start"] = occurred_start
-
-                    # For point events: if occurred_end not set, default to occurred_start
-                    if occurred_end:
-                        fact_data["occurred_end"] = occurred_end
-                    elif fact_data.get("occurred_start"):
-                        fact_data["occurred_end"] = fact_data["occurred_start"]
+                        # For point events: if occurred_end not set, default to occurred_start
+                        if occurred_end:
+                            fact_data["occurred_end"] = occurred_end
+                        else:
+                            fact_data["occurred_end"] = occurred_start

                # Add entities if present (validate as Entity objects)
                # LLM sometimes returns strings instead of {"text": "..."} format
@@ -818,41 +702,19 @@ Text:
                    if validated_entities:
                        fact_data["entities"] = validated_entities

-                # Add per-fact causal relations (new schema: target_index must be < current fact index)
-                validated_relations = []
-                causal_relations_raw = get_value("causal_relations")
-                if causal_relations_raw:
-                    for rel in causal_relations_raw:
-                        if not isinstance(rel, dict):
-                            continue
-                        # New schema uses target_index
-                        target_idx = rel.get("target_index")
-                        relation_type = rel.get("relation_type")
-                        strength = rel.get("strength", 1.0)
-
-                        if target_idx is None or relation_type is None:
-                            continue
-
-                        # Validate: target_index must be < current fact index
-                        if target_idx < 0 or target_idx >= i:
-                            logger.debug(
-                                f"Invalid target_index {target_idx} for fact {i} (must be 0 to {i - 1}). Skipping."
-                            )
-                            continue
-
-                        try:
-                            validated_relations.append(
-                                CausalRelation(
-                                    target_fact_index=target_idx,
-                                    relation_type=relation_type,
-                                    strength=strength,
-                                )
-                            )
-                        except Exception as e:
-                            logger.debug(f"Invalid causal relation {rel}: {e}")
-
-                if validated_relations:
-                    fact_data["causal_relations"] = validated_relations
+                # Add causal relations if present (validate as CausalRelation objects)
+                # Filter out invalid relations (missing required fields)
+                causal_relations = get_value("causal_relations")
+                if causal_relations:
+                    validated_relations = []
+                    for rel in causal_relations:
+                        if isinstance(rel, dict) and "target_fact_index" in rel and "relation_type" in rel:
+                            try:
+                                validated_relations.append(CausalRelation.model_validate(rel))
+                            except Exception as e:
+                                logger.warning(f"Invalid causal relation {rel}: {e}")
+                    if validated_relations:
+                        fact_data["causal_relations"] = validated_relations

                # Always set mentioned_at to the event_date (when the conversation/document occurred)
                fact_data["mentioned_at"] = event_date.isoformat()
@@ -873,7 +735,7 @@ Text:
                )
                continue

-            return chunk_facts, usage
+            return chunk_facts

        except BadRequestError as e:
            last_error = e
@@ -900,7 +762,7 @@ async def _extract_facts_with_auto_split(
    llm_config: LLMConfig,
    agent_name: str = None,
    extract_opinions: bool = False,
-) -> tuple[list[dict[str, str]], TokenUsage]:
+) -> list[dict[str, str]]:
    """
    Extract facts from a chunk with automatic splitting if output exceeds token limits.

@@ -918,7 +780,7 @@ async def _extract_facts_with_auto_split(
        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)
+        List of fact dictionaries extracted from the chunk (possibly from sub-chunks)
    """
    import logging

@@ -997,14 +859,12 @@ async def _extract_facts_with_auto_split(

        # Combine results from both halves
        all_facts = []
-        total_usage = TokenUsage()
-        for sub_facts, sub_usage in sub_results:
-            all_facts.extend(sub_facts)
-            total_usage = total_usage + sub_usage
+        for sub_result in sub_results:
+            all_facts.extend(sub_result)

        logger.info(f"Successfully extracted {len(all_facts)} facts from split chunk {chunk_index + 1}")

-        return all_facts, total_usage
+        return all_facts


 async def extract_facts_from_text(
@@ -1014,7 +874,7 @@ async def extract_facts_from_text(
    agent_name: str,
    context: str = "",
    extract_opinions: bool = False,
-) -> tuple[list[Fact], list[tuple[str, int]], TokenUsage]:
+) -> tuple[list[Fact], list[tuple[str, int]]]:
    """
    Extract semantic facts from conversational or narrative text using LLM.

@@ -1033,13 +893,11 @@ async def extract_facts_from_text(
        extract_opinions: If True, extract ONLY opinions. If False, extract world and bank facts (no opinions)

    Returns:
-        Tuple of (facts, chunks, usage) where:
+        Tuple of (facts, chunks) where:
        - facts: List of Fact model instances
        - 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)
+    chunks = chunk_text(text, max_chars=3000)
    tasks = [
        _extract_facts_with_auto_split(
            chunk=chunk,
@@ -1056,12 +914,10 @@ async def extract_facts_from_text(
    chunk_results = await asyncio.gather(*tasks)
    all_facts = []
    chunk_metadata = []  # [(chunk_text, fact_count), ...]
-    total_usage = TokenUsage()
-    for chunk, (chunk_facts, chunk_usage) in zip(chunks, chunk_results):
+    for chunk, chunk_facts in zip(chunks, chunk_results):
        all_facts.extend(chunk_facts)
        chunk_metadata.append((chunk, len(chunk_facts)))
-        total_usage = total_usage + chunk_usage
-    return all_facts, chunk_metadata, total_usage
+    return all_facts, chunk_metadata


 # ============================================================================
@@ -1082,7 +938,7 @@ SECONDS_PER_FACT = 10

 async def extract_facts_from_contents(
    contents: list[RetainContent], llm_config, agent_name: str, extract_opinions: bool = False
-) -> tuple[list[ExtractedFactType], list[ChunkMetadata], TokenUsage]:
+) -> tuple[list[ExtractedFactType], list[ChunkMetadata]]:
    """
    Extract facts from multiple content items in parallel.

@@ -1099,10 +955,10 @@ async def extract_facts_from_contents(
        extract_opinions: If True, extract only opinions; otherwise world/bank facts

    Returns:
-        Tuple of (extracted_facts, chunks_metadata, usage)
+        Tuple of (extracted_facts, chunks_metadata)
    """
    if not contents:
-        return [], [], TokenUsage()
+        return [], []

    # Step 1: Create parallel fact extraction tasks
    fact_extraction_tasks = []
@@ -1125,15 +981,11 @@ async def extract_facts_from_contents(
    # Step 3: Flatten and convert to typed objects
    extracted_facts: list[ExtractedFactType] = []
    chunks_metadata: list[ChunkMetadata] = []
-    total_usage = TokenUsage()

    global_chunk_idx = 0
    global_fact_idx = 0

-    for content_index, (content, (facts_from_llm, chunks_from_llm, content_usage)) in enumerate(
-        zip(contents, all_fact_results)
-    ):
-        total_usage = total_usage + content_usage
+    for content_index, (content, (facts_from_llm, chunks_from_llm)) in enumerate(zip(contents, all_fact_results)):
        chunk_start_idx = global_chunk_idx

        # Convert chunk tuples to ChunkMetadata objects
@@ -1187,7 +1039,7 @@ async def extract_facts_from_contents(
    # Step 4: Add time offsets to preserve ordering within each content
    _add_temporal_offsets(extracted_facts, contents)

-    return extracted_facts, chunks_metadata, total_usage
+    return extracted_facts, chunks_metadata


 def _parse_datetime(date_str: str):
@@ -7,7 +7,6 @@ Handles insertion of facts into the database.
 import json
 import logging

-from ..memory_engine import fq_table
 from .types import ProcessedFact

 logger = logging.getLogger(__name__)
@@ -68,8 +67,8 @@ async def insert_facts_batch(

    # Batch insert all facts
    results = await conn.fetch(
-        f"""
-        INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+        """
+        INSERT INTO memory_units (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
                                 context, fact_type, confidence_score, access_count, metadata, chunk_id, document_id)
        SELECT $1, * FROM unnest(
            $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
@@ -108,8 +107,8 @@ async def ensure_bank_exists(conn, bank_id: str) -> None:
        bank_id: Bank identifier
    """
    await conn.execute(
-        f"""
-        INSERT INTO {fq_table("banks")} (bank_id, disposition, background)
+        """
+        INSERT INTO banks (bank_id, disposition, background)
        VALUES ($1, $2::jsonb, $3)
        ON CONFLICT (bank_id) DO UPDATE
        SET updated_at = NOW()
@@ -142,14 +141,12 @@ async def handle_document_tracking(
    # Always delete old document first if it exists (cascades to units and links)
    # Only delete on the first batch to avoid deleting data we just inserted
    if is_first_batch:
-        await conn.fetchval(
-            f"DELETE FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2 RETURNING id", document_id, bank_id
-        )
+        await conn.fetchval("DELETE FROM documents WHERE id = $1 AND bank_id = $2 RETURNING id", document_id, bank_id)

    # Insert document (or update if exists from concurrent operations)
    await conn.execute(
-        f"""
-        INSERT INTO {fq_table("documents")} (id, bank_id, original_text, content_hash, metadata, retain_params)
+        """
+        INSERT INTO documents (id, bank_id, original_text, content_hash, metadata, retain_params)
        VALUES ($1, $2, $3, $4, $5, $6)
        ON CONFLICT (id, bank_id) DO UPDATE
        SET original_text = EXCLUDED.original_text,
@@ -7,7 +7,6 @@ import time
 from datetime import UTC, datetime, timedelta
 from uuid import UUID

-from ..memory_engine import fq_table
 from .types import EntityLink

 logger = logging.getLogger(__name__)
@@ -291,9 +290,9 @@ async def extract_entities_batch_optimized(

            entity_id_list = [uuid.UUID(eid) if isinstance(eid, str) else eid for eid in all_entity_ids]
            rows = await conn.fetch(
-                f"""
+                """
                SELECT entity_id, unit_id
-                FROM {fq_table("unit_entities")}
+                FROM unit_entities
                WHERE entity_id = ANY($1::uuid[])
                """,
                entity_id_list,
@@ -414,9 +413,9 @@ async def create_temporal_links_batch_per_fact(
        # Get the event_date for each new unit
        fetch_dates_start = time_mod.time()
        rows = await conn.fetch(
-            f"""
+            """
            SELECT id, event_date
-            FROM {fq_table("memory_units")}
+            FROM memory_units
            WHERE id::text = ANY($1)
            """,
            unit_ids,
@@ -433,9 +432,9 @@ async def create_temporal_links_batch_per_fact(

        fetch_neighbors_start = time_mod.time()
        all_candidates = await conn.fetch(
-            f"""
+            """
            SELECT id, event_date
-            FROM {fq_table("memory_units")}
+            FROM memory_units
            WHERE bank_id = $1
              AND event_date BETWEEN $2 AND $3
              AND id::text != ALL($4)
@@ -480,8 +479,8 @@ async def create_temporal_links_batch_per_fact(
        if links:
            insert_start = time_mod.time()
            await conn.executemany(
-                f"""
-                INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                """
+                INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
                VALUES ($1, $2, $3, $4, $5)
                ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
                """,
@@ -536,9 +535,9 @@ async def create_semantic_links_batch(
        # Fetch ALL existing units with embeddings in ONE query
        fetch_start = time_mod.time()
        all_existing = await conn.fetch(
-            f"""
+            """
            SELECT id, embedding
-            FROM {fq_table("memory_units")}
+            FROM memory_units
            WHERE bank_id = $1
              AND embedding IS NOT NULL
              AND id::text != ALL($2)
@@ -645,8 +644,8 @@ async def create_semantic_links_batch(
        if all_links:
            insert_start = time_mod.time()
            await conn.executemany(
-                f"""
-                INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                """
+                INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
                VALUES ($1, $2, $3, $4, $5)
                ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
                """,
@@ -722,8 +721,8 @@ async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: i

    # Insert from temp table with ON CONFLICT (single query for all rows)
    insert_start = time_mod.time()
-    await conn.execute(f"""
-        INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
+    await conn.execute("""
+        INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
        SELECT from_unit_id, to_unit_id, link_type, weight, entity_id
        FROM _temp_entity_links
        ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
@@ -809,8 +808,8 @@ async def create_causal_links_batch(
            insert_start = time_mod.time()
            try:
                await conn.executemany(
-                    f"""
-                    INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                    """
+                    INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
                    VALUES ($1, $2, $3, $4, $5)
                    ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
                    """,
@@ -9,8 +9,6 @@ import time
 import uuid
 from datetime import UTC, datetime

-from ...config import get_config
-from ..memory_engine import fq_table
 from ..search import observation_utils
 from . import embedding_utils
 from .types import EntityLink
@@ -50,9 +48,8 @@ async def regenerate_observations_batch(
        entity_links: Entity links from this batch
        log_buffer: Optional log buffer for timing
    """
-    config = get_config()
-    TOP_N_ENTITIES = config.observation_top_entities
-    MIN_FACTS_THRESHOLD = config.observation_min_facts
+    TOP_N_ENTITIES = 5
+    MIN_FACTS_THRESHOLD = 5

    if not entity_links:
        return
@@ -78,8 +75,8 @@ async def regenerate_observations_batch(

    # Batch query for entity names
    entity_rows = await conn.fetch(
-        f"""
-        SELECT id, canonical_name FROM {fq_table("entities")}
+        """
+        SELECT id, canonical_name FROM entities
        WHERE id = ANY($1) AND bank_id = $2
        """,
        entity_uuids,
@@ -89,10 +86,10 @@ async def regenerate_observations_batch(

    # Batch query for fact counts
    fact_counts = await conn.fetch(
-        f"""
+        """
        SELECT ue.entity_id, COUNT(*) as cnt
-        FROM {fq_table("unit_entities")} ue
-        JOIN {fq_table("memory_units")} mu ON ue.unit_id = mu.id
+        FROM unit_entities ue
+        JOIN memory_units mu ON ue.unit_id = mu.id
        WHERE ue.entity_id = ANY($1) AND mu.bank_id = $2
        GROUP BY ue.entity_id
        """,
@@ -157,10 +154,10 @@ async def _regenerate_entity_observations(

    # Get all facts mentioning this entity (exclude observations themselves)
    rows = await conn.fetch(
-        f"""
+        """
        SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.fact_type
-        FROM {fq_table("memory_units")} mu
-        JOIN {fq_table("unit_entities")} ue ON mu.id = ue.unit_id
+        FROM memory_units mu
+        JOIN unit_entities ue ON mu.id = ue.unit_id
        WHERE mu.bank_id = $1
          AND ue.entity_id = $2
          AND mu.fact_type IN ('world', 'experience')
@@ -196,12 +193,12 @@ async def _regenerate_entity_observations(

    # Delete old observations for this entity
    await conn.execute(
-        f"""
-        DELETE FROM {fq_table("memory_units")}
+        """
+        DELETE FROM memory_units
        WHERE id IN (
            SELECT mu.id
-            FROM {fq_table("memory_units")} mu
-            JOIN {fq_table("unit_entities")} ue ON mu.id = ue.unit_id
+            FROM memory_units mu
+            JOIN unit_entities ue ON mu.id = ue.unit_id
            WHERE mu.bank_id = $1
              AND mu.fact_type = 'observation'
              AND ue.entity_id = $2
@@ -220,8 +217,8 @@ async def _regenerate_entity_observations(

    for obs_text, embedding in zip(observations, embeddings):
        result = await conn.fetchrow(
-            f"""
-            INSERT INTO {fq_table("memory_units")} (
+            """
+            INSERT INTO memory_units (
                bank_id, text, embedding, context, event_date,
                occurred_start, occurred_end, mentioned_at,
                fact_type, access_count
@@ -243,8 +240,8 @@ async def _regenerate_entity_observations(

        # Link observation to entity
        await conn.execute(
-            f"""
-            INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
+            """
+            INSERT INTO unit_entities (unit_id, entity_id)
            VALUES ($1, $2)
            """,
            uuid.UUID(obs_id),
@@ -8,6 +8,7 @@ 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
@@ -18,7 +19,6 @@ def utcnow():
    return datetime.now(UTC)


-from ..response_models import TokenUsage
 from . import (
    chunk_storage,
    deduplication,
@@ -29,7 +29,7 @@ from . import (
    link_creation,
    observation_regeneration,
 )
-from .types import ExtractedFact, ProcessedFact, RetainContent, RetainContentDict
+from .types import ExtractedFact, ProcessedFact, RetainContent

 logger = logging.getLogger(__name__)

@@ -43,12 +43,12 @@ async def retain_batch(
    format_date_fn,
    duplicate_checker_fn,
    bank_id: str,
-    contents_dicts: list[RetainContentDict],
+    contents_dicts: list[dict[str, Any]],
    document_id: str | None = None,
    is_first_batch: bool = True,
    fact_type_override: str | None = None,
    confidence_score: float | None = None,
-) -> tuple[list[list[str]], TokenUsage]:
+) -> list[list[str]]:
    """
    Process a batch of content through the retain pipeline.

@@ -68,7 +68,7 @@ async def retain_batch(
        confidence_score: Confidence score for opinions

    Returns:
-        Tuple of (unit ID lists, token usage for fact extraction)
+        List of unit ID lists (one list per content item)
    """
    start_time = time.time()
    total_chars = sum(len(item.get("content", "")) for item in contents_dicts)
@@ -92,7 +92,6 @@ async def retain_batch(
            context=item.get("context", ""),
            event_date=item.get("event_date") or utcnow(),
            metadata=item.get("metadata", {}),
-            entities=item.get("entities", []),
        )
        contents.append(content)

@@ -100,7 +99,7 @@ async def retain_batch(
    step_start = time.time()
    extract_opinions = fact_type_override == "opinion"

-    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(
+    extracted_facts, chunks = await fact_extraction.extract_facts_from_contents(
        contents, llm_config, agent_name, extract_opinions
    )
    log_buffer.append(
@@ -108,64 +107,7 @@ async def retain_batch(
    )

    if not extracted_facts:
-        # Still need to create document if document_id was provided
-        async with acquire_with_retry(pool) as conn:
-            async with conn.transaction():
-                await fact_storage.ensure_bank_exists(conn, bank_id)
-
-                # Handle document tracking even with no facts
-                if document_id:
-                    combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
-                    retain_params = {}
-                    if contents_dicts:
-                        first_item = contents_dicts[0]
-                        if first_item.get("context"):
-                            retain_params["context"] = first_item["context"]
-                        if first_item.get("event_date"):
-                            retain_params["event_date"] = (
-                                first_item["event_date"].isoformat()
-                                if hasattr(first_item["event_date"], "isoformat")
-                                else str(first_item["event_date"])
-                            )
-                        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
-                    )
-                else:
-                    # Check for per-item document_ids
-                    from collections import defaultdict
-
-                    contents_by_doc = defaultdict(list)
-                    for idx, content_dict in enumerate(contents_dicts):
-                        doc_id = content_dict.get("document_id")
-                        if doc_id:
-                            contents_by_doc[doc_id].append((idx, content_dict))
-
-                    for doc_id, doc_contents in contents_by_doc.items():
-                        combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
-                        retain_params = {}
-                        if doc_contents:
-                            first_item = doc_contents[0][1]
-                            if first_item.get("context"):
-                                retain_params["context"] = first_item["context"]
-                            if first_item.get("event_date"):
-                                retain_params["event_date"] = (
-                                    first_item["event_date"].isoformat()
-                                    if hasattr(first_item["event_date"], "isoformat")
-                                    else str(first_item["event_date"])
-                                )
-                            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
-                        )
-
-        total_time = time.time() - start_time
-        logger.info(
-            f"RETAIN_BATCH COMPLETE: 0 facts extracted from {len(contents)} contents in {total_time:.3f}s (document tracked, no facts)"
-        )
-        return [[] for _ in contents], usage
+        return [[] for _ in contents]

    # Apply fact_type_override if provided
    if fact_type_override:
@@ -345,7 +287,7 @@ async def retain_batch(
            non_duplicate_facts = deduplication.filter_duplicates(processed_facts, is_duplicate_flags)

            if not non_duplicate_facts:
-                return [[] for _ in contents], usage
+                return [[] for _ in contents]

            # Insert facts (document_id is now stored per-fact)
            step_start = time.time()
@@ -354,18 +296,8 @@ async def retain_batch(

            # Process entities
            step_start = time.time()
-            # Build map of content_index -> user entities for merging
-            user_entities_per_content = {
-                idx: content.entities for idx, content in enumerate(contents) if content.entities
-            }
            entity_links = await entity_processing.process_entities_batch(
-                entity_resolver,
-                conn,
-                bank_id,
-                unit_ids,
-                non_duplicate_facts,
-                log_buffer,
-                user_entities_per_content=user_entities_per_content,
+                entity_resolver, conn, bank_id, unit_ids, non_duplicate_facts, log_buffer
            )
            log_buffer.append(f"[6] Process entities: {len(entity_links)} links in {time.time() - step_start:.3f}s")

@@ -416,7 +348,7 @@ async def retain_batch(

        logger.info("\n" + "\n".join(log_buffer) + "\n")

-        return result_unit_ids, usage
+        return result_unit_ids


 def _map_results_to_contents(
@@ -7,35 +7,9 @@ from content input to fact storage.

 from dataclasses import dataclass, field
 from datetime import UTC, datetime
-from typing import TypedDict
 from uuid import UUID


-class RetainContentDict(TypedDict, total=False):
-    """Type definition for content items in retain_batch_async.
-
-    Fields:
-        content: Text content to store (required)
-        context: Context about the content (optional)
-        event_date: When the content occurred (optional, defaults to now)
-        metadata: Custom key-value metadata (optional)
-        document_id: Document ID for this content item (optional)
-        entities: User-provided entities to merge with extracted entities (optional)
-    """
-
-    content: str  # Required
-    context: str
-    event_date: datetime
-    metadata: dict[str, str]
-    document_id: str
-    entities: list[dict[str, str]]  # [{"text": "...", "type": "..."}]
-
-
-def _now_utc() -> datetime:
-    """Factory function for default event_date."""
-    return datetime.now(UTC)
-
-
@dataclass
 class RetainContent:
    """
@@ -46,9 +20,15 @@ class RetainContent:

    content: str
    context: str = ""
-    event_date: datetime = field(default_factory=_now_utc)
+    event_date: datetime | None = None
    metadata: dict[str, str] = field(default_factory=dict)
-    entities: list[dict[str, str]] = field(default_factory=list)  # User-provided entities
+
+    def __post_init__(self):
+        """Ensure event_date is set."""
+        if self.event_date is None:
+            from datetime import datetime
+
+            self.event_date = datetime.now(UTC)


@dataclass
@@ -155,9 +135,6 @@ class ProcessedFact:
    # DB fields (set after insertion)
    unit_id: UUID | None = None

-    # Track which content this fact came from (for user entity merging)
-    content_index: int = 0
-
    @property
    def is_duplicate(self) -> bool:
        """Check if this fact was marked as a duplicate."""
@@ -200,7 +177,6 @@ class ProcessedFact:
            entities=entities,
            causal_relations=extracted_fact.causal_relations,
            chunk_id=chunk_id,
-            content_index=extracted_fact.content_index,
        )


@@ -10,7 +10,6 @@ import logging
 from abc import ABC, abstractmethod

 from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
 from .types import RetrievalResult

 logger = logging.getLogger(__name__)
@@ -140,11 +139,11 @@ class BFSGraphRetriever(GraphRetriever):

        # Step 1: Find entry points
        entry_points = await conn.fetch(
-            f"""
+            """
            SELECT id, text, context, event_date, occurred_start, occurred_end,
                   mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
                   1 - (embedding <=> $1::vector) AS similarity
-            FROM {fq_table("memory_units")}
+            FROM memory_units
            WHERE bank_id = $2
              AND embedding IS NOT NULL
              AND fact_type = $3
@@ -189,13 +188,13 @@ class BFSGraphRetriever(GraphRetriever):
            if batch_nodes and budget_remaining > 0:
                max_neighbors = len(batch_nodes) * 20
                neighbors = await conn.fetch(
-                    f"""
+                    """
                    SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end,
                           mu.mentioned_at, mu.access_count, mu.embedding, mu.fact_type,
                           mu.document_id, mu.chunk_id,
                           ml.weight, ml.link_type, ml.from_unit_id
-                    FROM {fq_table("memory_links")} ml
-                    JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
+                    FROM memory_links ml
+                    JOIN memory_units mu ON ml.to_unit_id = mu.id
                    WHERE ml.from_unit_id = ANY($1::uuid[])
                      AND ml.weight >= $2
                      AND mu.fact_type = $3
@@ -20,7 +20,6 @@ from collections import defaultdict
 from dataclasses import dataclass, field

 from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
 from .graph_retrieval import GraphRetriever
 from .types import RetrievalResult

@@ -218,10 +217,10 @@ async def load_typed_adjacency(pool, bank_id: str) -> TypedAdjacency:
    """
    async with acquire_with_retry(pool) as conn:
        rows = await conn.fetch(
-            f"""
+            """
            SELECT ml.from_unit_id, ml.to_unit_id, ml.link_type, ml.weight
-            FROM {fq_table("memory_links")} ml
-            JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
+            FROM memory_links ml
+            JOIN memory_units mu ON ml.from_unit_id = mu.id
            WHERE mu.bank_id = $1
              AND ml.weight >= 0.1
            ORDER BY ml.from_unit_id, ml.weight DESC
@@ -253,10 +252,10 @@ async def fetch_memory_units_by_ids(

    async with acquire_with_retry(pool) as conn:
        rows = await conn.fetch(
-            f"""
+            """
            SELECT id, text, context, event_date, occurred_start, occurred_end,
                   mentioned_at, access_count, embedding, fact_type, document_id, chunk_id
-            FROM {fq_table("memory_units")}
+            FROM memory_units
            WHERE id = ANY($1::uuid[])
              AND fact_type = $2
            """,
@@ -419,9 +418,9 @@ class MPFPGraphRetriever(GraphRetriever):
        """Fallback: find semantic seeds via embedding search."""
        async with acquire_with_retry(pool) as conn:
            rows = await conn.fetch(
-                f"""
+                """
                SELECT id, 1 - (embedding <=> $1::vector) AS similarity
-                FROM {fq_table("memory_units")}
+                FROM memory_units
                WHERE bank_id = $2
                  AND embedding IS NOT NULL
                  AND fact_type = $3
@@ -26,23 +26,6 @@ class CrossEncoderReranker:

            cross_encoder = create_cross_encoder_from_env()
        self.cross_encoder = cross_encoder
-        self._initialized = False
-
-    async def ensure_initialized(self):
-        """Ensure the cross-encoder model is initialized (for lazy initialization)."""
-        if self._initialized:
-            return
-
-        import asyncio
-
-        cross_encoder = self.cross_encoder
-        # For local providers, run in thread pool to avoid blocking event loop
-        if cross_encoder.provider_name == "local":
-            loop = asyncio.get_event_loop()
-            await loop.run_in_executor(None, lambda: asyncio.run(cross_encoder.initialize()))
-        else:
-            await cross_encoder.initialize()
-        self._initialized = True

    def rerank(self, query: str, candidates: list[MergedCandidate]) -> list[ScoredResult]:
        """
@@ -16,7 +16,6 @@ from typing import Optional

 from ...config import get_config
 from ..db_utils import acquire_with_retry
-from ..memory_engine import fq_table
 from .graph_retrieval import BFSGraphRetriever, GraphRetriever
 from .mpfp_retrieval import MPFPGraphRetriever
 from .types import RetrievalResult
@@ -81,10 +80,10 @@ async def retrieve_semantic(
        List of RetrievalResult objects
    """
    results = await conn.fetch(
-        f"""
+        """
        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
+        FROM memory_units
        WHERE bank_id = $2
          AND embedding IS NOT NULL
          AND fact_type = $3
@@ -132,10 +131,10 @@ async def retrieve_bm25(conn, query_text: str, bank_id: str, fact_type: str, lim
    query_tsquery = " | ".join(tokens)

    results = await conn.fetch(
-        f"""
+        """
        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
               ts_rank_cd(search_vector, to_tsquery('english', $1)) AS bm25_score
-        FROM {fq_table("memory_units")}
+        FROM memory_units
        WHERE bank_id = $2
          AND fact_type = $3
          AND search_vector @@ to_tsquery('english', $1)
@@ -189,10 +188,10 @@ async def retrieve_temporal(
        end_date = end_date.replace(tzinfo=UTC)

    entry_points = await conn.fetch(
-        f"""
+        """
        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
+        FROM memory_units
        WHERE bank_id = $2
          AND fact_type = $3
          AND embedding IS NOT NULL
@@ -273,12 +272,12 @@ async def retrieve_temporal(
        # Get neighbors via temporal and causal links
        if budget_remaining > 0:
            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.access_count, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id,
                       ml.weight, ml.link_type,
                       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
+                FROM memory_links ml
+                JOIN memory_units mu ON ml.to_unit_id = mu.id
                WHERE ml.from_unit_id = $2
                  AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
                  AND ml.weight >= 0.1
@@ -547,11 +546,11 @@ async def _get_temporal_entry_points(
        end_date = end_date.replace(tzinfo=UTC)

    rows = await conn.fetch(
-        f"""
+        """
        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at,
               access_count, embedding, fact_type, document_id, chunk_id,
               1 - (embedding <=> $1::vector) AS similarity
-        FROM {fq_table("memory_units")}
+        FROM memory_units
        WHERE bank_id = $2
          AND fact_type = $3
          AND embedding IS NOT NULL
@@ -101,7 +101,7 @@ def build_think_prompt(
    name: str,
    disposition: DispositionTraits,
    background: str,
-    context: str | None = None,
+    context: str = None,
 ) -> str:
    """Build the think prompt for the LLM."""
    disposition_desc = build_disposition_description(disposition)
@@ -172,7 +172,7 @@ 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. 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."


 async def extract_opinions_from_text(llm_config, text: str, query: str) -> list[Opinion]:
@@ -115,7 +115,7 @@ class SearchTracer:
        node_id: str,
        text: str,
        context: str,
-        event_date: datetime | None,
+        event_date: datetime,
        access_count: int,
        is_entry_point: bool,
        parent_node_id: str | None,
@@ -89,38 +89,6 @@ class TaskBackend(ABC):
            traceback.print_exc()


-class SyncTaskBackend(TaskBackend):
-    """
-    Synchronous task backend that executes tasks immediately.
-
-    This is useful for embedded/CLI usage where we don't want background
-    workers that prevent clean exit. Tasks are executed inline rather than
-    being queued.
-    """
-
-    async def initialize(self):
-        """No-op for sync backend."""
-        self._initialized = True
-        logger.debug("SyncTaskBackend initialized")
-
-    async def submit_task(self, task_dict: dict[str, Any]):
-        """
-        Execute the task immediately (synchronously).
-
-        Args:
-            task_dict: Task dictionary to execute
-        """
-        if not self._initialized:
-            await self.initialize()
-
-        await self._execute_task(task_dict)
-
-    async def shutdown(self):
-        """No-op for sync backend."""
-        self._initialized = False
-        logger.debug("SyncTaskBackend shutdown")
-
-
 class AsyncIOQueueBackend(TaskBackend):
    """
    Task backend implementation using asyncio queues.
@@ -49,7 +49,7 @@ async def extract_facts(
    if not text or not text.strip():
        return [], []

-    facts, chunks, _ = await extract_facts_from_text(
+    facts, chunks = await extract_facts_from_text(
        text,
        event_date,
        context=context,
@@ -1,66 +0,0 @@
-"""
-Hindsight Extensions System.
-
-Extensions allow customizing and extending Hindsight behavior without modifying core code.
-Extensions are loaded via environment variables pointing to implementation classes.
-
-Example:
-    HINDSIGHT_API_OPERATION_VALIDATOR_EXTENSION=mypackage.validators:MyValidator
-    HINDSIGHT_API_OPERATION_VALIDATOR_MAX_RETRIES=3
-
-    HINDSIGHT_API_HTTP_EXTENSION=mypackage.http:MyHttpExtension
-    HINDSIGHT_API_HTTP_SOME_CONFIG=value
-
-Extensions receive an ExtensionContext that provides a controlled API for interacting
-with the system (e.g., running migrations for tenant schemas).
-"""
-
-from hindsight_api.extensions.base import Extension
-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.operation_validator import (
-    OperationValidationError,
-    OperationValidatorExtension,
-    RecallContext,
-    RecallResult,
-    ReflectContext,
-    ReflectResultContext,
-    RetainContext,
-    RetainResult,
-    ValidationResult,
-)
-from hindsight_api.extensions.tenant import (
-    AuthenticationError,
-    TenantContext,
-    TenantExtension,
-)
-from hindsight_api.models import RequestContext
-
-__all__ = [
-    # Base
-    "Extension",
-    "load_extension",
-    # Context
-    "ExtensionContext",
-    "DefaultExtensionContext",
-    # HTTP Extension
-    "HttpExtension",
-    # Operation Validator
-    "OperationValidationError",
-    "OperationValidatorExtension",
-    "RecallContext",
-    "RecallResult",
-    "ReflectContext",
-    "ReflectResultContext",
-    "RetainContext",
-    "RetainResult",
-    "ValidationResult",
-    # Tenant/Auth
-    "ApiKeyTenantExtension",
-    "AuthenticationError",
-    "RequestContext",
-    "TenantContext",
-    "TenantExtension",
-]
@@ -1,81 +0,0 @@
-"""Base Extension class for all Hindsight extensions."""
-
-from abc import ABC
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from hindsight_api.extensions.context import ExtensionContext
-
-
-class Extension(ABC):
-    """
-    Base class for all Hindsight extensions.
-
-    Extensions are loaded via environment variables and receive configuration
-    from prefixed environment variables.
-
-    Example:
-        HINDSIGHT_API_MY_EXTENSION=mypackage.ext:MyExtension
-        HINDSIGHT_API_MY_SOME_CONFIG=value
-
-        The extension receives: {"some_config": "value"}
-
-    Extensions also receive an ExtensionContext that provides a controlled API
-    for interacting with the system (e.g., running migrations for tenant schemas).
-    """
-
-    def __init__(self, config: dict[str, str]):
-        """
-        Initialize the extension with configuration.
-
-        Args:
-            config: Dictionary of configuration values from environment variables.
-                    Keys are lowercased with the prefix stripped.
-        """
-        self.config = config
-        self._context: "ExtensionContext | None" = None
-
-    def set_context(self, context: "ExtensionContext") -> None:
-        """
-        Set the extension context.
-
-        Called by the extension loader after instantiation.
-        Extensions should not call this directly.
-
-        Args:
-            context: The ExtensionContext providing system APIs.
-        """
-        self._context = context
-
-    @property
-    def context(self) -> "ExtensionContext":
-        """
-        Get the extension context.
-
-        Returns:
-            The ExtensionContext providing system APIs.
-
-        Raises:
-            RuntimeError: If context has not been set yet.
-        """
-        if self._context is None:
-            raise RuntimeError(
-                "Extension context not set. Context is available after the extension is loaded by the system."
-            )
-        return self._context
-
-    async def on_startup(self) -> None:
-        """
-        Called when the application starts.
-
-        Override to perform initialization tasks like connecting to external services.
-        """
-        pass
-
-    async def on_shutdown(self) -> None:
-        """
-        Called when the application shuts down.
-
-        Override to perform cleanup tasks like closing connections.
-        """
-        pass
@@ -1,18 +0,0 @@
-"""
-Built-in extension implementations.
-
-These are ready-to-use implementations of the extension interfaces.
-They can be used directly or serve as examples for custom implementations.
-
-Available built-in extensions:
-    - ApiKeyTenantExtension: Simple API key validation with public schema
-
-Example usage:
-    HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
-"""
-
-from hindsight_api.extensions.builtin.tenant import ApiKeyTenantExtension
-
-__all__ = [
-    "ApiKeyTenantExtension",
-]
@@ -1,33 +0,0 @@
-"""Built-in tenant extension implementations."""
-
-from hindsight_api.extensions.tenant import AuthenticationError, TenantContext, TenantExtension
-from hindsight_api.models import RequestContext
-
-
-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 '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
-
-    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.
-    """
-
-    def __init__(self, config: dict[str, str]):
-        super().__init__(config)
-        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")
-
-    async def authenticate(self, context: RequestContext) -> TenantContext:
-        """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="public")
@@ -1,117 +0,0 @@
-"""Extension context providing a controlled API for extensions to interact with the system."""
-
-from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from hindsight_api.engine.interface import MemoryEngineInterface
-
-
-class ExtensionContext(ABC):
-    """
-    Abstract context providing a controlled API for extensions.
-
-    Extensions receive this context instead of direct access to internal
-    components like MemoryEngine or database connections. This provides:
-    - A stable API that won't break when internals change
-    - Security by limiting what extensions can access
-    - Clear documentation of what extensions can do
-
-    Built-in implementation:
-        hindsight_api.extensions.builtin.context.DefaultExtensionContext
-
-    Example usage in an extension:
-        class MyTenantExtension(TenantExtension):
-            async def on_startup(self) -> None:
-                # Run migrations for a new tenant schema
-                await self.context.run_migration("tenant_acme")
-
-        class MyHttpExtension(HttpExtension):
-            def get_router(self, memory):
-                # Use memory engine for custom endpoints
-                engine = self.context.get_memory_engine()
-                ...
-    """
-
-    @abstractmethod
-    async def run_migration(self, schema: str) -> None:
-        """
-        Run database migrations for a specific schema.
-
-        This creates the schema if it doesn't exist and runs all pending
-        migrations. Uses advisory locks to coordinate between distributed workers.
-
-        Args:
-            schema: PostgreSQL schema name (e.g., "tenant_acme").
-                    The schema will be created if it doesn't exist.
-
-        Raises:
-            RuntimeError: If migrations fail to complete.
-
-        Example:
-            # Provision a new tenant schema
-            await context.run_migration("tenant_acme")
-        """
-        ...
-
-    @abstractmethod
-    def get_memory_engine(self) -> "MemoryEngineInterface":
-        """
-        Get the memory engine interface.
-
-        Returns the MemoryEngineInterface for performing memory operations
-        like retain, recall, reflect, and entity/document management.
-
-        Returns:
-            MemoryEngineInterface instance.
-
-        Example:
-            engine = context.get_memory_engine()
-            result = await engine.recall_async(bank_id, query)
-        """
-        ...
-
-
-class DefaultExtensionContext(ExtensionContext):
-    """
-    Default implementation of ExtensionContext.
-
-    Uses the system's database URL and migration infrastructure.
-    """
-
-    def __init__(
-        self,
-        database_url: str,
-        memory_engine: "MemoryEngineInterface | None" = None,
-    ):
-        """
-        Initialize the context.
-
-        Args:
-            database_url: SQLAlchemy database URL for migrations.
-            memory_engine: Optional MemoryEngine instance for memory operations.
-        """
-        self._database_url = database_url
-        self._memory_engine = memory_engine
-
-    async def run_migration(self, schema: str) -> None:
-        """Run migrations for a specific schema."""
-        from hindsight_api.migrations import run_migrations
-
-        # Prefer getting URL from memory engine (handles pg0 case where URL is set after init)
-        db_url = self._database_url
-        if self._memory_engine is not None:
-            engine_url = getattr(self._memory_engine, "db_url", None)
-            if engine_url:
-                db_url = engine_url
-
-        run_migrations(db_url, schema=schema)
-
-    def get_memory_engine(self) -> "MemoryEngineInterface":
-        """Get the memory engine interface."""
-        if self._memory_engine is None:
-            raise RuntimeError(
-                "Memory engine not configured in ExtensionContext. "
-                "Ensure the context was created with a memory_engine parameter."
-            )
-        return self._memory_engine
@@ -1,89 +0,0 @@
-"""
-HTTP Extension for adding custom endpoints to the Hindsight API.
-
-This extension allows adding custom HTTP endpoints under the /ext/ path prefix.
-The extension provides a FastAPI router that is mounted on the main application.
-"""
-
-from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING
-
-from fastapi import APIRouter
-
-from hindsight_api.extensions.base import Extension
-
-if TYPE_CHECKING:
-    from hindsight_api import MemoryEngine
-
-
-class HttpExtension(Extension, ABC):
-    """
-    Base class for HTTP extensions that add custom API endpoints.
-
-    HTTP extensions provide a FastAPI router that gets mounted under /ext/.
-    The extension has full control over the routes, request/response models, and handlers.
-
-    Example:
-        ```python
-        from fastapi import APIRouter
-        from hindsight_api.extensions import HttpExtension
-
-        class MyHttpExtension(HttpExtension):
-            def get_router(self, memory: MemoryEngine) -> APIRouter:
-                router = APIRouter()
-
-                @router.get("/hello")
-                async def hello():
-                    return {"message": "Hello from extension!"}
-
-                @router.post("/custom/{bank_id}/action")
-                async def custom_action(bank_id: str):
-                    # Access memory engine for database operations
-                    pool = await memory._get_pool()
-                    # ... custom logic
-                    return {"status": "ok"}
-
-                return router
-        ```
-
-    The routes will be available at:
-        - GET /ext/hello
-        - POST /ext/custom/{bank_id}/action
-
-    Configuration via environment variables:
-        HINDSIGHT_API_HTTP_EXTENSION=mypackage.ext:MyHttpExtension
-        HINDSIGHT_API_HTTP_SOME_CONFIG=value
-
-    The extension receives config: {"some_config": "value"}
-    """
-
-    @abstractmethod
-    def get_router(self, memory: "MemoryEngine") -> APIRouter:
-        """
-        Return a FastAPI router with custom endpoints.
-
-        The router will be mounted at /ext/ on the main application.
-        All routes defined in the router will be prefixed with /ext/.
-
-        Args:
-            memory: The MemoryEngine instance for database access and core operations.
-                   Use this to access the connection pool, run queries, or call
-                   memory operations like retain, recall, etc.
-
-        Returns:
-            A FastAPI APIRouter with the custom endpoints defined.
-
-        Example:
-            ```python
-            def get_router(self, memory: MemoryEngine) -> APIRouter:
-                router = APIRouter(tags=["My Extension"])
-
-                @router.get("/status")
-                async def status():
-                    health = await memory.health_check()
-                    return {"extension": "healthy", "memory": health}
-
-                return router
-            ```
-        """
-        pass
@@ -1,125 +0,0 @@
-"""Extension loader utilities."""
-
-import importlib
-import logging
-import os
-from typing import TYPE_CHECKING, TypeVar
-
-from hindsight_api.extensions.base import Extension
-
-if TYPE_CHECKING:
-    from hindsight_api.extensions.context import ExtensionContext
-
-logger = logging.getLogger(__name__)
-
-T = TypeVar("T", bound=Extension)
-
-
-class ExtensionLoadError(Exception):
-    """Raised when an extension fails to load."""
-
-    pass
-
-
-def load_extension(
-    prefix: str,
-    base_class: type[T],
-    env_prefix: str = "HINDSIGHT_API",
-    context: "ExtensionContext | None" = None,
-) -> T | None:
-    """
-    Load an extension from environment variable configuration.
-
-    The extension class is specified via {env_prefix}_{prefix}_EXTENSION environment
-    variable in the format "module.path:ClassName".
-
-    Configuration for the extension is collected from all environment variables
-    matching {env_prefix}_{prefix}_* (excluding the EXTENSION variable itself).
-
-    Args:
-        prefix: The extension prefix (e.g., "OPERATION_VALIDATOR").
-        base_class: The base class that the extension must inherit from.
-        env_prefix: The environment variable prefix (default: "HINDSIGHT_API").
-        context: Optional ExtensionContext to provide system APIs to the extension.
-
-    Returns:
-        An instance of the extension, or None if not configured.
-
-    Raises:
-        ExtensionLoadError: If the extension fails to load or validate.
-
-    Example:
-        HINDSIGHT_API_OPERATION_VALIDATOR_EXTENSION=mypackage.validators:MyValidator
-        HINDSIGHT_API_OPERATION_VALIDATOR_MAX_REQUESTS=100
-
-        ext = load_extension("OPERATION_VALIDATOR", OperationValidatorExtension)
-        # ext.config == {"max_requests": "100"}
-    """
-    env_var = f"{env_prefix}_{prefix}_EXTENSION"
-    ext_path = os.getenv(env_var)
-
-    if not ext_path:
-        logger.debug(f"No extension configured for {env_var}")
-        return None
-
-    logger.info(f"Loading extension from {env_var}={ext_path}")
-
-    # Parse "module.path:ClassName"
-    if ":" not in ext_path:
-        raise ExtensionLoadError(f"Invalid extension path '{ext_path}'. Expected format: 'module.path:ClassName'")
-
-    module_path, class_name = ext_path.rsplit(":", 1)
-
-    # Import the module
-    try:
-        module = importlib.import_module(module_path)
-    except ImportError as e:
-        raise ExtensionLoadError(f"Failed to import extension module '{module_path}': {e}") from e
-
-    # Get the class
-    try:
-        ext_class = getattr(module, class_name)
-    except AttributeError as e:
-        raise ExtensionLoadError(f"Extension class '{class_name}' not found in module '{module_path}'") from e
-
-    # Validate inheritance
-    if not isinstance(ext_class, type) or not issubclass(ext_class, base_class):
-        raise ExtensionLoadError(f"Extension class '{ext_class.__name__}' must inherit from '{base_class.__name__}'")
-
-    # Collect configuration from environment variables
-    config = _collect_config(env_prefix, prefix)
-
-    logger.info(f"Loaded extension {ext_class.__name__} with config keys: {list(config.keys())}")
-
-    # Instantiate the extension
-    try:
-        extension = ext_class(config)
-    except Exception as e:
-        raise ExtensionLoadError(f"Failed to instantiate extension '{ext_class.__name__}': {e}") from e
-
-    # Set the context if provided
-    if context is not None:
-        extension.set_context(context)
-        logger.debug(f"Set context on extension {ext_class.__name__}")
-
-    return extension
-
-
-def _collect_config(env_prefix: str, prefix: str) -> dict[str, str]:
-    """
-    Collect configuration from environment variables.
-
-    Collects all variables matching {env_prefix}_{prefix}_* except for
-    {env_prefix}_{prefix}_EXTENSION, strips the prefix, and lowercases keys.
-    """
-    config = {}
-    full_prefix = f"{env_prefix}_{prefix}_"
-    extension_var = f"{full_prefix}EXTENSION"
-
-    for key, value in os.environ.items():
-        if key.startswith(full_prefix) and key != extension_var:
-            # Strip prefix and lowercase the key
-            config_key = key[len(full_prefix) :].lower()
-            config[config_key] = value
-
-    return config
@@ -1,327 +0,0 @@
-"""Operation Validator Extension for validating retain/recall/reflect operations."""
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import TYPE_CHECKING, Any
-
-from hindsight_api.extensions.base import Extension
-
-if TYPE_CHECKING:
-    from hindsight_api.engine.memory_engine import Budget
-    from hindsight_api.engine.response_models import RecallResult as RecallResultModel
-    from hindsight_api.engine.response_models import ReflectResult
-    from hindsight_api.models import RequestContext
-
-
-class OperationValidationError(Exception):
-    """Raised when an operation fails validation."""
-
-    def __init__(self, reason: str, status_code: int = 403):
-        self.reason = reason
-        self.status_code = status_code
-        super().__init__(f"Operation validation failed: {reason}")
-
-
-@dataclass
-class ValidationResult:
-    """Result of an operation validation."""
-
-    allowed: bool
-    reason: str | None = None
-    status_code: int = 403  # Default to Forbidden
-
-    @classmethod
-    def accept(cls) -> "ValidationResult":
-        """Create an accepted validation result."""
-        return cls(allowed=True)
-
-    @classmethod
-    def reject(cls, reason: str, status_code: int = 403) -> "ValidationResult":
-        """Create a rejected validation result with a reason and HTTP status code."""
-        return cls(allowed=False, reason=reason, status_code=status_code)
-
-
-# =============================================================================
-# Pre-operation Contexts (all user-provided parameters)
-# =============================================================================
-
-
-@dataclass
-class RetainContext:
-    """Context for a retain operation validation (pre-operation).
-
-    Contains ALL user-provided parameters for the retain operation.
-    """
-
-    bank_id: str
-    contents: list[dict]  # List of {content, context, event_date, document_id}
-    request_context: "RequestContext"
-    document_id: str | None = None
-    fact_type_override: str | None = None
-    confidence_score: float | None = None
-
-
-@dataclass
-class RecallContext:
-    """Context for a recall operation validation (pre-operation).
-
-    Contains ALL user-provided parameters for the recall operation.
-    """
-
-    bank_id: str
-    query: str
-    request_context: "RequestContext"
-    budget: "Budget | None" = None
-    max_tokens: int = 4096
-    enable_trace: bool = False
-    fact_types: list[str] = field(default_factory=list)
-    question_date: datetime | None = None
-    include_entities: bool = False
-    max_entity_tokens: int = 500
-    include_chunks: bool = False
-    max_chunk_tokens: int = 8192
-
-
-@dataclass
-class ReflectContext:
-    """Context for a reflect operation validation (pre-operation).
-
-    Contains ALL user-provided parameters for the reflect operation.
-    """
-
-    bank_id: str
-    query: str
-    request_context: "RequestContext"
-    budget: "Budget | None" = None
-    context: str | None = None
-
-
-# =============================================================================
-# Post-operation Contexts (includes results)
-# =============================================================================
-
-
-@dataclass
-class RetainResult:
-    """Result context for post-retain hook.
-
-    Contains the operation parameters and the result.
-    """
-
-    bank_id: str
-    contents: list[dict]
-    request_context: "RequestContext"
-    document_id: str | None
-    fact_type_override: str | None
-    confidence_score: float | None
-    # Result
-    unit_ids: list[list[str]]  # List of unit IDs per content item
-    success: bool = True
-    error: str | None = None
-
-
-@dataclass
-class RecallResult:
-    """Result context for post-recall hook.
-
-    Contains the operation parameters and the result.
-    """
-
-    bank_id: str
-    query: str
-    request_context: "RequestContext"
-    budget: "Budget | None"
-    max_tokens: int
-    enable_trace: bool
-    fact_types: list[str]
-    question_date: datetime | None
-    include_entities: bool
-    max_entity_tokens: int
-    include_chunks: bool
-    max_chunk_tokens: int
-    # Result
-    result: "RecallResultModel | None" = None
-    success: bool = True
-    error: str | None = None
-
-
-@dataclass
-class ReflectResultContext:
-    """Result context for post-reflect hook.
-
-    Contains the operation parameters and the result.
-    """
-
-    bank_id: str
-    query: str
-    request_context: "RequestContext"
-    budget: "Budget | None"
-    context: str | None
-    # Result
-    result: "ReflectResult | None" = None
-    success: bool = True
-    error: str | None = None
-
-
-class OperationValidatorExtension(Extension, ABC):
-    """
-    Validates and hooks into retain/recall/reflect operations.
-
-    This extension allows implementing custom logic such as:
-    - Rate limiting (pre-operation)
-    - Quota enforcement (pre-operation)
-    - Permission checks (pre-operation)
-    - Content filtering (pre-operation)
-    - Usage tracking (post-operation)
-    - Audit logging (post-operation)
-    - Metrics collection (post-operation)
-
-    Enable via environment variable:
-        HINDSIGHT_API_OPERATION_VALIDATOR_EXTENSION=mypackage.validators:MyValidator
-
-    Configuration is passed from prefixed environment variables:
-        HINDSIGHT_API_OPERATION_VALIDATOR_MAX_REQUESTS=100
-        -> config = {"max_requests": "100"}
-
-    Hook execution order:
-        1. validate_retain/validate_recall/validate_reflect (pre-operation)
-        2. [operation executes]
-        3. on_retain_complete/on_recall_complete/on_reflect_complete (post-operation)
-    """
-
-    # =========================================================================
-    # Pre-operation validation hooks (abstract - must be implemented)
-    # =========================================================================
-
-    @abstractmethod
-    async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
-        """
-        Validate a retain operation before execution.
-
-        Called before the retain operation is processed. Return ValidationResult.reject()
-        to prevent the operation from executing.
-
-        Args:
-            ctx: Context containing all user-provided parameters:
-                - bank_id: Bank identifier
-                - contents: List of content dicts
-                - request_context: Request context with auth info
-                - document_id: Optional document ID
-                - fact_type_override: Optional fact type override
-                - confidence_score: Optional confidence score
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        ...
-
-    @abstractmethod
-    async def validate_recall(self, ctx: RecallContext) -> ValidationResult:
-        """
-        Validate a recall operation before execution.
-
-        Called before the recall operation is processed. Return ValidationResult.reject()
-        to prevent the operation from executing.
-
-        Args:
-            ctx: Context containing all user-provided parameters:
-                - bank_id: Bank identifier
-                - query: Search query
-                - request_context: Request context with auth info
-                - budget: Budget level
-                - max_tokens: Maximum tokens to return
-                - enable_trace: Whether to include trace info
-                - fact_types: List of fact types to search
-                - question_date: Optional date context for query
-                - include_entities: Whether to include entity data
-                - max_entity_tokens: Max tokens for entities
-                - include_chunks: Whether to include chunks
-                - max_chunk_tokens: Max tokens for chunks
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        ...
-
-    @abstractmethod
-    async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
-        """
-        Validate a reflect operation before execution.
-
-        Called before the reflect operation is processed. Return ValidationResult.reject()
-        to prevent the operation from executing.
-
-        Args:
-            ctx: Context containing all user-provided parameters:
-                - bank_id: Bank identifier
-                - query: Question to answer
-                - request_context: Request context with auth info
-                - budget: Budget level
-                - context: Optional additional context
-
-        Returns:
-            ValidationResult indicating whether the operation is allowed.
-        """
-        ...
-
-    # =========================================================================
-    # Post-operation hooks (optional - override to implement)
-    # =========================================================================
-
-    async def on_retain_complete(self, result: RetainResult) -> None:
-        """
-        Called after a retain operation completes (success or failure).
-
-        Override this method to implement post-operation logic such as:
-        - Usage tracking
-        - Audit logging
-        - Metrics collection
-        - Notifications
-
-        Args:
-            result: Result context containing:
-                - All original operation parameters
-                - unit_ids: List of created unit IDs (if success)
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
-
-    async def on_recall_complete(self, result: RecallResult) -> None:
-        """
-        Called after a recall operation completes (success or failure).
-
-        Override this method to implement post-operation logic such as:
-        - Usage tracking
-        - Audit logging
-        - Metrics collection
-        - Query analytics
-
-        Args:
-            result: Result context containing:
-                - All original operation parameters
-                - result: RecallResultModel (if success)
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
-
-    async def on_reflect_complete(self, result: ReflectResultContext) -> None:
-        """
-        Called after a reflect operation completes (success or failure).
-
-        Override this method to implement post-operation logic such as:
-        - Usage tracking
-        - Audit logging
-        - Metrics collection
-        - Response analytics
-
-        Args:
-            result: Result context containing:
-                - All original operation parameters
-                - result: ReflectResult (if success)
-                - success: Whether the operation succeeded
-                - error: Error message (if failed)
-        """
-        pass
@@ -1,63 +0,0 @@
-"""Tenant Extension for multi-tenancy and API key authentication."""
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-
-from hindsight_api.extensions.base import Extension
-from hindsight_api.models import RequestContext
-
-
-class AuthenticationError(Exception):
-    """Raised when authentication fails."""
-
-    def __init__(self, reason: str):
-        self.reason = reason
-        super().__init__(f"Authentication failed: {reason}")
-
-
-@dataclass
-class TenantContext:
-    """
-    Tenant context returned by authentication.
-
-    Contains the PostgreSQL schema name for tenant isolation.
-    All database queries will use fully-qualified table names
-    with this schema (e.g., schema_name.memory_units).
-    """
-
-    schema_name: str
-
-
-class TenantExtension(Extension, ABC):
-    """
-    Extension for multi-tenancy and API key authentication.
-
-    This extension validates incoming requests and returns the tenant context
-    including the PostgreSQL schema to use for database operations.
-
-    Built-in implementation:
-        hindsight_api.extensions.builtin.tenant.ApiKeyTenantExtension
-
-    Enable via environment variable:
-        HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
-        HINDSIGHT_API_TENANT_API_KEY=your-secret-key
-
-    The returned schema_name is used for fully-qualified table names in queries,
-    enabling tenant isolation at the database level.
-    """
-
-    @abstractmethod
-    async def authenticate(self, context: RequestContext) -> TenantContext:
-        """
-        Authenticate the action context and return tenant context.
-
-        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.
-        """
-        ...
@@ -4,9 +4,6 @@ Command-line interface for Hindsight API.
 Run the server with:
    hindsight-api

-Run as background daemon:
-    hindsight-api --daemon
-
 Stop with Ctrl+C.
 """

@@ -24,14 +21,9 @@ from . import MemoryEngine
 from .api import create_app
 from .banner import print_banner
 from .config import HindsightConfig, get_config
-from .daemon import (
-    DEFAULT_DAEMON_PORT,
-    DEFAULT_IDLE_TIMEOUT,
-    DaemonLock,
-    IdleTimeoutMiddleware,
-    daemonize,
-)
-from .extensions import DefaultExtensionContext, OperationValidatorExtension, TenantExtension, load_extension
+
+print()
+print_banner()

 # Filter deprecation warnings from third-party libraries
 warnings.filterwarnings("ignore", message="websockets.legacy is deprecated")
@@ -114,52 +106,8 @@ def main():
    parser.add_argument("--ssl-keyfile", default=None, help="SSL key file")
    parser.add_argument("--ssl-certfile", default=None, help="SSL certificate file")

-    # Daemon mode options
-    parser.add_argument(
-        "--daemon",
-        action="store_true",
-        help=f"Run as background daemon (uses port {DEFAULT_DAEMON_PORT}, auto-exits after idle)",
-    )
-    parser.add_argument(
-        "--idle-timeout",
-        type=int,
-        default=DEFAULT_IDLE_TIMEOUT,
-        help=f"Idle timeout in seconds before auto-exit in daemon mode (default: {DEFAULT_IDLE_TIMEOUT})",
-    )
-
    args = parser.parse_args()

-    # Daemon mode handling
-    if args.daemon:
-        # 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
-        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()
-        print_banner()
-
    # Configure Python logging based on log level
    # Update config with CLI override if provided
    if args.log_level != config.log_level:
@@ -169,16 +117,6 @@ def main():
            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_timeout=config.llm_timeout,
-            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,
-            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,
            embeddings_provider=config.embeddings_provider,
            embeddings_local_model=config.embeddings_local_model,
            embeddings_tei_url=config.embeddings_tei_url,
@@ -189,53 +127,16 @@ def main():
            port=args.port,
            log_level=args.log_level,
            mcp_enabled=config.mcp_enabled,
-            graph_retriever=config.graph_retriever,
-            observation_min_facts=config.observation_min_facts,
-            observation_top_entities=config.observation_top_entities,
-            retain_max_completion_tokens=config.retain_max_completion_tokens,
-            retain_chunk_size=config.retain_chunk_size,
-            skip_llm_verification=config.skip_llm_verification,
-            lazy_reranker=config.lazy_reranker,
-            run_migrations_on_startup=config.run_migrations_on_startup,
        )
    config.configure_logging()
-    if not args.daemon:
-        config.log_config()

    # Register cleanup handlers
    atexit.register(_cleanup)
    signal.signal(signal.SIGINT, _signal_handler)
    signal.signal(signal.SIGTERM, _signal_handler)

-    # Load operation validator extension if configured
-    operation_validator = load_extension("OPERATION_VALIDATOR", OperationValidatorExtension)
-    if operation_validator:
-        import logging
-
-        logging.info(f"Loaded operation validator: {operation_validator.__class__.__name__}")
-
-    # Load tenant extension if configured
-    tenant_extension = load_extension("TENANT", TenantExtension)
-    if tenant_extension:
-        import logging
-
-        logging.info(f"Loaded tenant extension: {tenant_extension.__class__.__name__}")
-
    # Create MemoryEngine (reads configuration from environment)
-    _memory = MemoryEngine(
-        operation_validator=operation_validator,
-        tenant_extension=tenant_extension,
-        run_migrations=config.run_migrations_on_startup,
-    )
-
-    # Set extension context on tenant extension (needed for schema provisioning)
-    if tenant_extension:
-        extension_context = DefaultExtensionContext(
-            database_url=config.database_url,
-            memory_engine=_memory,
-        )
-        tenant_extension.set_context(extension_context)
-        logging.info("Extension context set on tenant extension")
+    _memory = MemoryEngine()

    # Create FastAPI app
    app = create_app(
@@ -246,12 +147,6 @@ def main():
        initialize_memory=True,
    )

-    # Wrap with idle timeout middleware in daemon mode
-    idle_middleware = None
-    if args.daemon:
-        idle_middleware = IdleTimeoutMiddleware(app, idle_timeout=args.idle_timeout)
-        app = idle_middleware
-
    # Prepare uvicorn config
    uvicorn_config = {
        "app": app,
@@ -275,40 +170,20 @@ def main():
    if args.ssl_certfile:
        uvicorn_config["ssl_certfile"] = args.ssl_certfile

-    # Print startup info (not in daemon mode)
-    if not args.daemon:
-        from .banner import print_startup_info
+    from .banner import print_startup_info

-        print_startup_info(
-            host=args.host,
-            port=args.port,
-            database_url=config.database_url,
-            llm_provider=config.llm_provider,
-            llm_model=config.llm_model,
-            embeddings_provider=config.embeddings_provider,
-            reranker_provider=config.reranker_provider,
-            mcp_enabled=config.mcp_enabled,
-        )
+    print_startup_info(
+        host=args.host,
+        port=args.port,
+        database_url=config.database_url,
+        llm_provider=config.llm_provider,
+        llm_model=config.llm_model,
+        embeddings_provider=config.embeddings_provider,
+        reranker_provider=config.reranker_provider,
+        mcp_enabled=config.mcp_enabled,
+    )

-    # 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 threading
-
-        def run_idle_checker():
-            import time
-
-            time.sleep(2)  # Wait for uvicorn to start
-            try:
-                loop = asyncio.new_event_loop()
-                asyncio.set_event_loop(loop)
-                loop.run_until_complete(idle_middleware._check_idle())
-            except Exception:
-                pass
-
-        threading.Thread(target=run_idle_checker, daemon=True).start()
-
-    uvicorn.run(**uvicorn_config)  # type: ignore[invalid-argument-type] - dict kwargs
+    uvicorn.run(**uvicorn_config)


 if __name__ == "__main__":
@@ -28,15 +28,7 @@ Environment variables:
    HINDSIGHT_API_LLM_PROVIDER: Optional. LLM provider (default: "openai").
    HINDSIGHT_API_LLM_MODEL: Optional. LLM model (default: "gpt-4o-mini").
    HINDSIGHT_API_MCP_LOCAL_BANK_ID: Optional. Memory bank ID (default: "mcp").
-    HINDSIGHT_API_LOG_LEVEL: Optional. Log level (default: "warning").
-    HINDSIGHT_API_MCP_INSTRUCTIONS: Optional. Additional instructions appended to both retain and recall tools.
-
-Example custom instructions (these are ADDED to the default behavior):
-    To also store assistant actions:
-        HINDSIGHT_API_MCP_INSTRUCTIONS="Also store every action you take, including tool calls, code written, and decisions made."
-
-    To also store conversation summaries:
-        HINDSIGHT_API_MCP_INSTRUCTIONS="Also store summaries of important conversations and their outcomes."
+    HINDSIGHT_API_LOG_LEVEL: Optional. Log level (default: "info").
 """

 import logging
@@ -44,19 +36,14 @@ import os
 import sys

 from mcp.server.fastmcp import FastMCP
-from mcp.types import Icon

 from hindsight_api.config import (
    DEFAULT_MCP_LOCAL_BANK_ID,
-    DEFAULT_MCP_RECALL_DESCRIPTION,
-    DEFAULT_MCP_RETAIN_DESCRIPTION,
-    ENV_MCP_INSTRUCTIONS,
    ENV_MCP_LOCAL_BANK_ID,
 )

-# Configure logging - default to warning to avoid polluting stderr during MCP init
-# MCP clients interpret stderr output as errors, so we suppress INFO logs by default
-_log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "warning").lower()
+# Configure logging - default to info
+_log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
 _log_level_map = {
    "critical": logging.CRITICAL,
    "error": logging.ERROR,
@@ -87,27 +74,27 @@ def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
    from hindsight_api import MemoryEngine
    from hindsight_api.engine.memory_engine import Budget
    from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
-    from hindsight_api.models import RequestContext

    # Create memory engine with pg0 embedded database if not provided
    if memory is None:
        memory = MemoryEngine(db_url="pg0://hindsight-mcp")

-    # Get custom instructions from environment variable (appended to both tools)
-    extra_instructions = os.environ.get(ENV_MCP_INSTRUCTIONS, "")
-
-    retain_description = DEFAULT_MCP_RETAIN_DESCRIPTION
-    recall_description = DEFAULT_MCP_RECALL_DESCRIPTION
-
-    if extra_instructions:
-        retain_description = f"{DEFAULT_MCP_RETAIN_DESCRIPTION}\n\nAdditional instructions: {extra_instructions}"
-        recall_description = f"{DEFAULT_MCP_RECALL_DESCRIPTION}\n\nAdditional instructions: {extra_instructions}"
-
    mcp = FastMCP("hindsight")

-    @mcp.tool(description=retain_description)
+    @mcp.tool()
    async def retain(content: str, context: str = "general") -> dict:
        """
+        Store important information to long-term memory.
+
+        Use this tool PROACTIVELY whenever the user shares:
+        - Personal facts, preferences, or interests
+        - Important events or milestones
+        - User history, experiences, or background
+        - Decisions, opinions, or stated preferences
+        - Goals, plans, or future intentions
+        - Relationships or people mentioned
+        - Work context, projects, or responsibilities
+
        Args:
            content: The fact/memory to store (be specific and include relevant details)
            context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
@@ -116,11 +103,7 @@ def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:

        async def _retain():
            try:
-                await memory.retain_batch_async(
-                    bank_id=bank_id,
-                    contents=[{"content": content, "context": context}],
-                    request_context=RequestContext(),
-                )
+                await memory.retain_batch_async(bank_id=bank_id, contents=[{"content": content, "context": context}])
            except Exception as e:
                logger.error(f"Error storing memory: {e}", exc_info=True)

@@ -128,9 +111,17 @@ def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
        asyncio.create_task(_retain())
        return {"status": "accepted", "message": "Memory storage initiated"}

-    @mcp.tool(description=recall_description)
+    @mcp.tool()
    async def recall(query: str, max_tokens: int = 4096, budget: str = "low") -> dict:
        """
+        Search memories to provide personalized, context-aware responses.
+
+        Use this tool PROACTIVELY to:
+        - Check user's preferences before making suggestions
+        - Recall user's history to provide continuity
+        - Remember user's goals and context
+        - Personalize responses based on past interactions
+
        Args:
            query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
            max_tokens: Maximum tokens to return in results (default: 4096)
@@ -147,7 +138,6 @@ def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
                fact_type=list(VALID_RECALL_FACT_TYPES),
                budget=budget_enum,
                max_tokens=max_tokens,
-                request_context=RequestContext(),
            )

            return search_result.model_dump()
@@ -163,9 +153,10 @@ async def _initialize_and_run(bank_id: str):
    from hindsight_api import MemoryEngine

    # Create and initialize memory engine with pg0 embedded database
-    # Note: We avoid printing to stderr during init as MCP clients show it as "errors"
+    print("Initializing memory engine...", file=sys.stderr)
    memory = MemoryEngine(db_url="pg0://hindsight-mcp")
    await memory.initialize()
+    print("Memory engine initialized.", file=sys.stderr)

    # Create and run the server
    mcp = create_local_mcp_server(bank_id, memory=memory)
@@ -188,8 +179,8 @@ def main():
    # Get bank ID from environment, default to "mcp"
    bank_id = os.environ.get(ENV_MCP_LOCAL_BANK_ID, DEFAULT_MCP_LOCAL_BANK_ID)

-    # Note: We don't print to stderr as MCP clients display it as "error output"
-    # Use HINDSIGHT_API_LOG_LEVEL=debug for verbose startup logging
+    # Print startup message to stderr (stdout is reserved for MCP protocol)
+    print(f"Hindsight MCP server starting (bank_id={bank_id})...", file=sys.stderr)

    # Run the async initialization and server
    asyncio.run(_initialize_and_run(bank_id))
@@ -5,7 +5,6 @@ This module provides metrics for:
 - Operation latency (retain, recall, reflect) with percentiles
 - Token usage (input/output) per operation
 - Per-bank granularity via labels
- LLM call latency and token usage with scope dimension
 """

 import logging
@@ -15,54 +14,8 @@ from contextlib import contextmanager
 from opentelemetry import metrics
 from opentelemetry.exporter.prometheus import PrometheusMetricReader
 from opentelemetry.sdk.metrics import MeterProvider
-from opentelemetry.sdk.metrics.view import ExplicitBucketHistogramAggregation, View
 from opentelemetry.sdk.resources import Resource

-# Custom bucket boundaries for operation duration (in seconds)
-# Fine granularity in 0-30s range where most operations complete
-DURATION_BUCKETS = (0.1, 0.25, 0.5, 0.75, 1.0, 2.0, 3.0, 5.0, 7.5, 10.0, 15.0, 20.0, 30.0, 60.0, 120.0)
-
-# LLM duration buckets (finer granularity for faster LLM calls)
-LLM_DURATION_BUCKETS = (0.1, 0.25, 0.5, 1.0, 2.0, 3.0, 5.0, 10.0, 15.0, 30.0, 60.0, 120.0)
-
-
-def get_token_bucket(token_count: int) -> str:
-    """
-    Convert a token count to a bucket label for use as a dimension.
-
-    This allows analyzing token usage patterns without high-cardinality issues.
-
-    Buckets:
-    - "0-100": Very small requests/responses
-    - "100-500": Small requests/responses
-    - "500-1k": Medium requests/responses
-    - "1k-5k": Large requests/responses
-    - "5k-10k": Very large requests/responses
-    - "10k-50k": Huge requests/responses
-    - "50k+": Extremely large requests/responses
-
-    Args:
-        token_count: Number of tokens
-
-    Returns:
-        Bucket label string
-    """
-    if token_count < 100:
-        return "0-100"
-    elif token_count < 500:
-        return "100-500"
-    elif token_count < 1000:
-        return "500-1k"
-    elif token_count < 5000:
-        return "1k-5k"
-    elif token_count < 10000:
-        return "5k-10k"
-    elif token_count < 50000:
-        return "10k-50k"
-    else:
-        return "50k+"
-
-
 logger = logging.getLogger(__name__)

 # Global meter instance
@@ -95,22 +48,8 @@ def initialize_metrics(service_name: str = "hindsight-api", service_version: str
    # Create Prometheus metric reader
    prometheus_reader = PrometheusMetricReader()

-    # Create view with custom bucket boundaries for duration histogram
-    duration_view = View(
-        instrument_name="hindsight.operation.duration",
-        aggregation=ExplicitBucketHistogramAggregation(boundaries=DURATION_BUCKETS),
-    )
-
-    # Create view with custom bucket boundaries for LLM duration histogram
-    llm_duration_view = View(
-        instrument_name="hindsight.llm.duration",
-        aggregation=ExplicitBucketHistogramAggregation(boundaries=LLM_DURATION_BUCKETS),
-    )
-
-    # Create meter provider with Prometheus exporter and custom views
-    provider = MeterProvider(
-        resource=resource, metric_readers=[prometheus_reader], views=[duration_view, llm_duration_view]
-    )
+    # Create meter provider with Prometheus exporter
+    provider = MeterProvider(resource=resource, metric_readers=[prometheus_reader])

    # Set the global meter provider
    metrics.set_meter_provider(provider)
@@ -132,39 +71,20 @@ class MetricsCollectorBase:
    """Base class for metrics collectors."""

    @contextmanager
-    def record_operation(
-        self,
-        operation: str,
-        bank_id: str,
-        source: str = "api",
-        budget: str | None = None,
-        max_tokens: int | None = None,
-    ):
+    def record_operation(self, operation: str, bank_id: str, budget: str | None = None, max_tokens: int | None = None):
        """Context manager to record operation duration and status."""
        raise NotImplementedError

-    def record_llm_call(
+    def record_tokens(
        self,
-        provider: str,
-        model: str,
-        scope: str,
-        duration: float,
+        operation: str,
+        bank_id: str,
        input_tokens: int = 0,
        output_tokens: int = 0,
-        success: bool = True,
+        budget: str | None = None,
+        max_tokens: int | None = None,
    ):
-        """
-        Record metrics for an LLM call.
-
-        Args:
-            provider: LLM provider name (openai, anthropic, gemini, groq, ollama, lmstudio)
-            model: Model name
-            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
-            success: Whether the call was successful
-        """
+        """Record token usage for an operation."""
        raise NotImplementedError


@@ -172,28 +92,20 @@ class NoOpMetricsCollector(MetricsCollectorBase):
    """No-op metrics collector that does nothing. Used when metrics are disabled."""

    @contextmanager
-    def record_operation(
-        self,
-        operation: str,
-        bank_id: str,
-        source: str = "api",
-        budget: str | None = None,
-        max_tokens: int | None = None,
-    ):
+    def record_operation(self, operation: str, bank_id: str, budget: str | None = None, max_tokens: int | None = None):
        """No-op context manager."""
        yield

-    def record_llm_call(
+    def record_tokens(
        self,
-        provider: str,
-        model: str,
-        scope: str,
-        duration: float,
+        operation: str,
+        bank_id: str,
        input_tokens: int = 0,
        output_tokens: int = 0,
-        success: bool = True,
+        budget: str | None = None,
+        max_tokens: int | None = None,
    ):
-        """No-op LLM call recording."""
+        """No-op token recording."""
        pass


@@ -213,52 +125,33 @@ class MetricsCollector(MetricsCollectorBase):
            name="hindsight.operation.duration", description="Duration of Hindsight operations in seconds", unit="s"
        )

+        # Token usage counters
+        self.tokens_input = self.meter.create_counter(
+            name="hindsight.tokens.input", description="Number of input tokens consumed", unit="tokens"
+        )
+
+        self.tokens_output = self.meter.create_counter(
+            name="hindsight.tokens.output", description="Number of output tokens generated", unit="tokens"
+        )
+
        # Operation counter (success/failure)
        self.operation_total = self.meter.create_counter(
            name="hindsight.operation.total", description="Total number of operations executed", unit="operations"
        )

-        # LLM call latency histogram (in seconds)
-        # Records duration of LLM API calls with provider, model, and scope dimensions
-        self.llm_duration = self.meter.create_histogram(
-            name="hindsight.llm.duration", description="Duration of LLM API calls in seconds", unit="s"
-        )
-
-        # LLM token usage counters with bucket labels
-        self.llm_tokens_input = self.meter.create_counter(
-            name="hindsight.llm.tokens.input", description="Number of input tokens for LLM calls", unit="tokens"
-        )
-
-        self.llm_tokens_output = self.meter.create_counter(
-            name="hindsight.llm.tokens.output", description="Number of output tokens from LLM calls", unit="tokens"
-        )
-
-        # LLM call counter (success/failure)
-        self.llm_calls_total = self.meter.create_counter(
-            name="hindsight.llm.calls.total", description="Total number of LLM API calls", unit="calls"
-        )
-
    @contextmanager
-    def record_operation(
-        self,
-        operation: str,
-        bank_id: str,
-        source: str = "api",
-        budget: str | None = None,
-        max_tokens: int | None = None,
-    ):
+    def record_operation(self, operation: str, bank_id: str, budget: str | None = None, max_tokens: int | None = None):
        """
        Context manager to record operation duration and status.

        Usage:
-            with metrics.record_operation("recall", bank_id="user123", source="api", budget="mid", max_tokens=4096):
+            with metrics.record_operation("recall", bank_id="user123", budget="mid", max_tokens=4096):
                # ... perform operation
                pass

        Args:
-            operation: Operation name (retain, recall, reflect, entity_observation)
+            operation: Operation name (retain, recall, reflect)
            bank_id: Memory bank ID
-            source: Source of the operation (api, reflect, internal)
            budget: Optional budget level (low, mid, high)
            max_tokens: Optional max tokens for the operation
        """
@@ -266,7 +159,6 @@ class MetricsCollector(MetricsCollectorBase):
        attributes = {
            "operation": operation,
            "bank_id": bank_id,
-            "source": source,
        }
        if budget:
            attributes["budget"] = budget
@@ -289,56 +181,40 @@ class MetricsCollector(MetricsCollectorBase):
            # Record operation count
            self.operation_total.add(1, attributes)

-    def record_llm_call(
+    def record_tokens(
        self,
-        provider: str,
-        model: str,
-        scope: str,
-        duration: float,
+        operation: str,
+        bank_id: str,
        input_tokens: int = 0,
        output_tokens: int = 0,
-        success: bool = True,
+        budget: str | None = None,
+        max_tokens: int | None = None,
    ):
        """
-        Record metrics for an LLM call.
+        Record token usage for an operation.

        Args:
-            provider: LLM provider name (openai, anthropic, gemini, groq, ollama, lmstudio)
-            model: Model name
-            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
-            success: Whether the call was successful
+            operation: Operation name (retain, recall, reflect)
+            bank_id: Memory bank ID
+            input_tokens: Number of input tokens
+            output_tokens: Number of output tokens
+            budget: Optional budget level
+            max_tokens: Optional max tokens for the operation
        """
-        # Base attributes for all metrics
-        base_attributes = {
-            "provider": provider,
-            "model": model,
-            "scope": scope,
-            "success": str(success).lower(),
+        attributes = {
+            "operation": operation,
+            "bank_id": bank_id,
        }
+        if budget:
+            attributes["budget"] = budget
+        if max_tokens:
+            attributes["max_tokens"] = str(max_tokens)

-        # Record duration
-        self.llm_duration.record(duration, base_attributes)
-
-        # Record call count
-        self.llm_calls_total.add(1, base_attributes)
-
-        # Record tokens with bucket labels for cardinality control
        if input_tokens > 0:
-            input_attributes = {
-                **base_attributes,
-                "token_bucket": get_token_bucket(input_tokens),
-            }
-            self.llm_tokens_input.add(input_tokens, input_attributes)
+            self.tokens_input.add(input_tokens, attributes)

        if output_tokens > 0:
-            output_attributes = {
-                **base_attributes,
-                "token_bucket": get_token_bucket(output_tokens),
-            }
-            self.llm_tokens_output.add(output_tokens, output_attributes)
+            self.tokens_output.add(output_tokens, attributes)


 # Global metrics collector instance (defaults to no-op)
@@ -6,16 +6,12 @@ on application startup. It is designed to be safe for concurrent
 execution using PostgreSQL advisory locks to coordinate between
 distributed workers.

-Supports multi-tenant schema isolation: migrations can target a specific
-PostgreSQL schema, allowing each tenant to have isolated tables.
-
 Important: All migrations must be backward-compatible to allow
 safe rolling deployments.

 No alembic.ini required - all configuration is done programmatically.
 """

-import hashlib
 import logging
 import os
 from pathlib import Path
@@ -30,29 +26,11 @@ logger = logging.getLogger(__name__)
 MIGRATION_LOCK_ID = 123456789


-def _get_schema_lock_id(schema: str) -> int:
-    """
-    Generate a unique advisory lock ID for a schema.
-
-    Uses hash of schema name to create a deterministic lock ID.
-    """
-    # Use hash to create a unique lock ID per schema
-    # Keep within PostgreSQL's bigint range
-    hash_bytes = hashlib.sha256(schema.encode()).digest()[:8]
-    return int.from_bytes(hash_bytes, byteorder="big") % (2**31)
-
-
-def _run_migrations_internal(database_url: str, script_location: str, schema: str | None = None) -> None:
+def _run_migrations_internal(database_url: str, script_location: str) -> None:
    """
    Internal function to run migrations without locking.
-
-    Args:
-        database_url: SQLAlchemy database URL
-        script_location: Path to alembic scripts
-        schema: Target schema (None for default/public)
    """
-    schema_name = schema or "public"
-    logger.info(f"Running database migrations to head for schema '{schema_name}'...")
+    logger.info("Running database migrations to head...")
    logger.info(f"Database URL: {database_url}")
    logger.info(f"Script location: {script_location}")

@@ -72,22 +50,13 @@ def _run_migrations_internal(database_url: str, script_location: str, schema: st
    # Set path_separator to avoid deprecation warning
    alembic_cfg.set_main_option("path_separator", "os")

-    # If targeting a specific schema, pass it to env.py via config
-    # env.py will handle setting search_path and version_table_schema
-    if schema:
-        alembic_cfg.set_main_option("target_schema", schema)
-
-    # Run migrations
+    # Run migrations to head (latest version)
    command.upgrade(alembic_cfg, "head")

-    logger.info(f"Database migrations completed successfully for schema '{schema_name}'")
+    logger.info("Database migrations completed successfully")


-def run_migrations(
-    database_url: str,
-    script_location: str | None = None,
-    schema: str | None = None,
-) -> None:
+def run_migrations(database_url: str, script_location: str | None = None) -> None:
    """
    Run database migrations to the latest version using programmatic Alembic configuration.

@@ -96,28 +65,19 @@ def run_migrations(
    - Other workers wait for the lock, then verify migrations are complete
    - If schema is already up-to-date, this is a fast no-op

-    Supports multi-tenant schema isolation: when a schema is specified, migrations
-    run in that schema instead of public. This allows tenant extensions to provision
-    new tenant schemas with their own isolated tables.
-
    Args:
        database_url: SQLAlchemy database URL (e.g., "postgresql://user:pass@host/db")
        script_location: Path to alembic migrations directory (e.g., "/path/to/alembic").
                        If None, defaults to hindsight-api/alembic directory.
-        schema: Target PostgreSQL schema name. If None, uses default (public).
-                When specified, creates the schema if needed and runs migrations there.

    Raises:
        RuntimeError: If migrations fail to complete
        FileNotFoundError: If script_location doesn't exist

    Example:
-        # Using default location and public schema
+        # Using default location (hindsight_api package)
        run_migrations("postgresql://user:pass@host/db")

-        # Run migrations for a specific tenant schema
-        run_migrations("postgresql://user:pass@host/db", schema="tenant_acme")
-
        # Using custom location (when importing from another project)
        run_migrations(
            "postgresql://user:pass@host/db",
@@ -139,25 +99,21 @@ def run_migrations(
                f"Alembic script location not found at {script_location}. Database migrations cannot be run."
            )

-        # Use schema-specific lock ID for multi-tenant isolation
-        lock_id = _get_schema_lock_id(schema) if schema else MIGRATION_LOCK_ID
-        schema_name = schema or "public"
-
        # Use PostgreSQL advisory lock to coordinate between distributed workers
        engine = create_engine(database_url)
        with engine.connect() as conn:
            # pg_advisory_lock blocks until the lock is acquired
            # The lock is automatically released when the connection closes
-            logger.debug(f"Acquiring migration advisory lock for schema '{schema_name}' (id={lock_id})...")
-            conn.execute(text(f"SELECT pg_advisory_lock({lock_id})"))
+            logger.debug(f"Acquiring migration advisory lock (id={MIGRATION_LOCK_ID})...")
+            conn.execute(text(f"SELECT pg_advisory_lock({MIGRATION_LOCK_ID})"))
            logger.debug("Migration advisory lock acquired")

            try:
                # Run migrations while holding the lock
-                _run_migrations_internal(database_url, script_location, schema=schema)
+                _run_migrations_internal(database_url, script_location)
            finally:
                # Explicitly release the lock (also released on connection close)
-                conn.execute(text(f"SELECT pg_advisory_unlock({lock_id})"))
+                conn.execute(text(f"SELECT pg_advisory_unlock({MIGRATION_LOCK_ID})"))
                logger.debug("Migration advisory lock released")

    except FileNotFoundError:
@@ -229,131 +185,3 @@ def check_migration_status(
    except Exception as e:
        logger.warning(f"Unable to check migration status: {e}")
        return None, None
-
-
-def ensure_embedding_dimension(
-    database_url: str,
-    required_dimension: int,
-    schema: str | None = None,
-) -> None:
-    """
-    Ensure the embedding column dimension matches the model's dimension.
-
-    This function checks the current vector column dimension in the database
-    and adjusts it if necessary:
-    - If dimensions match: no action needed
-    - If dimensions differ and table is empty: ALTER COLUMN to new dimension
-    - If dimensions differ and table has data: raise error with migration guidance
-
-    Args:
-        database_url: SQLAlchemy database URL
-        required_dimension: The embedding dimension required by the model
-        schema: Target PostgreSQL schema name (None for public)
-
-    Raises:
-        RuntimeError: If dimension mismatch with existing data
-    """
-    schema_name = schema or "public"
-
-    engine = create_engine(database_url)
-    with engine.connect() as conn:
-        # Check if memory_units table exists
-        table_exists = conn.execute(
-            text("""
-                SELECT EXISTS (
-                    SELECT 1 FROM information_schema.tables
-                    WHERE table_schema = :schema AND table_name = 'memory_units'
-                )
-            """),
-            {"schema": schema_name},
-        ).scalar()
-
-        if not table_exists:
-            logger.debug(f"memory_units table does not exist in schema '{schema_name}', skipping dimension check")
-            return
-
-        # Get current column dimension from pg_attribute
-        # pgvector stores dimension in atttypmod
-        current_dim = conn.execute(
-            text("""
-                SELECT atttypmod
-                FROM pg_attribute a
-                JOIN pg_class c ON a.attrelid = c.oid
-                JOIN pg_namespace n ON c.relnamespace = n.oid
-                WHERE n.nspname = :schema
-                  AND c.relname = 'memory_units'
-                  AND a.attname = 'embedding'
-            """),
-            {"schema": schema_name},
-        ).scalar()
-
-        if current_dim is None:
-            logger.warning("Could not determine current embedding dimension, skipping check")
-            return
-
-        # pgvector stores dimension directly in atttypmod (no offset like other types)
-        current_dimension = current_dim
-
-        if current_dimension == required_dimension:
-            logger.debug(f"Embedding dimension OK: {current_dimension}")
-            return
-
-        logger.info(
-            f"Embedding dimension mismatch: database has {current_dimension}, model requires {required_dimension}"
-        )
-
-        # Check if table has data
-        row_count = conn.execute(
-            text(f"SELECT COUNT(*) FROM {schema_name}.memory_units WHERE embedding IS NOT NULL")
-        ).scalar()
-
-        if row_count > 0:
-            raise RuntimeError(
-                f"Cannot change embedding dimension from {current_dimension} to {required_dimension}: "
-                f"memory_units table contains {row_count} rows with embeddings. "
-                f"To change dimensions, you must either:\n"
-                f"  1. Re-embed all data: DELETE FROM {schema_name}.memory_units; then restart\n"
-                f"  2. Use a model with {current_dimension}-dimensional embeddings"
-            )
-
-        # Table is empty, safe to alter column
-        logger.info(f"Altering embedding column dimension from {current_dimension} to {required_dimension}")
-
-        # 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 $$
-                DECLARE idx_name TEXT;
-                BEGIN
-                    FOR idx_name IN
-                        SELECT indexname FROM pg_indexes
-                        WHERE schemaname = '{schema_name}'
-                          AND tablename = 'memory_units'
-                          AND indexdef LIKE '%hnsw%'
-                          AND indexdef LIKE '%embedding%'
-                    LOOP
-                        EXECUTE 'DROP INDEX IF EXISTS {schema_name}.' || idx_name;
-                    END LOOP;
-                END $$;
-            """)
-        )
-
-        # Alter the column type
-        conn.execute(
-            text(f"ALTER TABLE {schema_name}.memory_units ALTER COLUMN embedding TYPE vector({required_dimension})")
-        )
-        conn.commit()
-
-        # 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}")
@@ -2,27 +2,9 @@
 SQLAlchemy models for the memory system.
 """

-from dataclasses import dataclass
 from datetime import datetime
 from uuid import UUID as PyUUID

-
-@dataclass
-class RequestContext:
-    """
-    Context for request authentication and authorization.
-
-    This dataclass carries authentication data from HTTP requests to the
-    memory engine operations. It can be extended to include additional
-    context like headers, tokens, user info, etc.
-    """
-
-    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 (not user-visible)
-
-
 from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
    CheckConstraint,
@@ -41,8 +23,6 @@ from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP, UUID
 from sqlalchemy.ext.asyncio import AsyncAttrs
 from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship

-from .config import EMBEDDING_DIMENSION
-

 class Base(AsyncAttrs, DeclarativeBase):
    """Base class for all models."""
@@ -83,7 +63,7 @@ class MemoryUnit(Base):
    bank_id: Mapped[str] = mapped_column(Text, nullable=False)
    document_id: Mapped[str | None] = mapped_column(Text)
    text: Mapped[str] = mapped_column(Text, nullable=False)
-    embedding = mapped_column(Vector(EMBEDDING_DIMENSION))  # pgvector type
+    embedding = mapped_column(Vector(384))  # pgvector type
    context: Mapped[str | None] = mapped_column(Text)
    event_date: Mapped[datetime] = mapped_column(
        TIMESTAMP(timezone=True), nullable=False
@@ -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)  # type: ignore[invalid-argument-type] - dict kwargs
+            self._pg0 = Pg0(**kwargs)
        return self._pg0

    async def start(self, max_retries: int = 5, retry_delay: float = 4.0) -> str:
@@ -132,56 +132,3 @@ async def stop_embedded_postgres() -> None:
    global _default_instance
    if _default_instance:
        await _default_instance.stop()
-
-
-def parse_pg0_url(db_url: str) -> tuple[bool, str | None, int | None]:
-    """
-    Parse a database URL and check if it's a pg0:// embedded database URL.
-
-    Supports:
-    - "pg0" -> default instance "hindsight"
-    - "pg0://instance-name" -> named instance
-    - "pg0://instance-name:port" -> named instance with explicit port
-    - Any other URL (e.g., postgresql://) -> not a pg0 URL
-
-    Args:
-        db_url: The database URL to parse
-
-    Returns:
-        Tuple of (is_pg0, instance_name, port)
-        - is_pg0: True if this is a pg0 URL
-        - instance_name: The instance name (or None if not pg0)
-        - port: The explicit port (or None for auto-assign)
-    """
-    if db_url == "pg0":
-        return True, "hindsight", None
-
-    if db_url.startswith("pg0://"):
-        url_part = db_url[6:]  # Remove "pg0://"
-        if ":" in url_part:
-            instance_name, port_str = url_part.rsplit(":", 1)
-            return True, instance_name or "hindsight", int(port_str)
-        else:
-            return True, url_part or "hindsight", None
-
-    return False, None, None
-
-
-async def resolve_database_url(db_url: str) -> str:
-    """
-    Resolve a database URL, handling pg0:// embedded database URLs.
-
-    If the URL is a pg0:// URL, starts the embedded PostgreSQL and returns
-    the actual postgresql:// connection URL. Otherwise, returns the URL unchanged.
-
-    Args:
-        db_url: Database URL (pg0://, pg0, or postgresql://)
-
-    Returns:
-        The resolved postgresql:// connection URL
-    """
-    is_pg0, instance_name, port = parse_pg0_url(db_url)
-    if is_pg0:
-        pg0 = EmbeddedPostgres(name=instance_name, port=port)
-        return await pg0.ensure_running()
-    return db_url
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "hindsight-api"
-version = "0.2.1"
+version = "0.1.8"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -37,9 +37,6 @@ dependencies = [
    "opentelemetry-exporter-prometheus>=0.41b0",
    "dateparser>=1.2.2",
    "google-genai>=1.0.0",
-    "anthropic>=0.40.0",
-    "typer>=0.9.0",
-    "cohere>=5.0.0",
 ]

 [project.optional-dependencies]
@@ -54,7 +51,6 @@ test = [
 [project.scripts]
 hindsight-api = "hindsight_api.main:main"
 hindsight-local-mcp = "hindsight_api.mcp_local:main"
-hindsight-admin = "hindsight_api.admin.cli:main"

 [tool.hatch.build.targets.wheel]
 packages = ["hindsight_api"]
@@ -78,7 +74,7 @@ log_cli = true
 log_cli_level = "INFO"
 log_cli_format = "%(asctime)s - %(levelname)s - %(name)s - %(message)s"
 log_cli_date_format = "%Y-%m-%d %H:%M:%S"
-addopts = "--timeout 120 -n 8 --dist loadgroup --durations=10 -v"
+addopts = "--timeout 120 -n 8 --durations=10 -v"
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "function"
 log_auto_indent = true
@@ -96,7 +92,6 @@ dev = [
    "python-dotenv>=1.2.1",
    "filelock>=3.0.0",
    "ruff>=0.8.0",
-    "ty>=0.0.1",
 ]

 [tool.ruff]
@@ -126,28 +121,3 @@ ignore = [
 [tool.ruff.format]
 quote-style = "double"
 indent-style = "space"
-
-[tool.ty]
-# Type checking configuration
-# ty is an extremely fast Python type checker from Astral (same team as ruff/uv)
-
-[tool.ty.environment]
-python-version = "3.11"
-
-[tool.ty.src]
-exclude = [
-    "tests/",
-    "hindsight_api/alembic/",
-]
-
-[tool.ty.rules]
-# Disable noisy rules while keeping important ones
-invalid-argument-type = "ignore"    # False positives with **kwargs patterns
-invalid-return-type = "ignore"      # Often intentional in async code
-invalid-parameter-default = "ignore" # Optional params with None default
-possibly-missing-attribute = "ignore" # Common with Optional types
-invalid-raise = "ignore"            # False positives with exception tracking
-call-non-callable = "ignore"        # False positives with Optional types
-invalid-key = "ignore"              # Pydantic ConfigDict not understood
-invalid-method-override = "ignore"  # Intentional signature differences
-unresolved-reference = "ignore"     # Forward references not always resolved
@@ -8,7 +8,7 @@ import os
 import filelock
 from pathlib import Path
 from dotenv import load_dotenv
-from hindsight_api import MemoryEngine, LLMConfig, LocalSTEmbeddings, RequestContext
+from hindsight_api import MemoryEngine, LLMConfig, LocalSTEmbeddings

 from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder
 from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
@@ -99,12 +99,6 @@ def pg0_db_url(db_url, tmp_path_factory, worker_id):
    return url


-@pytest.fixture(scope="function")
-def request_context():
-    """Provide a default RequestContext for tests."""
-    return RequestContext()
-
-
@pytest.fixture(scope="session")
 def llm_config():
    """
@@ -1,292 +0,0 @@
-"""
-Tests for admin backup and restore functionality.
-
-These tests use an isolated schema to avoid interfering with other tests.
-The backup/restore operations truncate tables, which would cause deadlocks
-and race conditions if run against the shared public schema.
-"""
-
-import tempfile
-import uuid
-import zipfile
-from pathlib import Path
-
-import asyncpg
-import pytest
-import pytest_asyncio
-
-from hindsight_api.admin.cli import _backup, _restore, BACKUP_TABLES
-from hindsight_api.migrations import run_migrations
-
-
-# Run these tests sequentially since they do full DB backup/restore
-pytestmark = pytest.mark.xdist_group(name="backup_restore")
-
-
-@pytest_asyncio.fixture(scope="function")
-async def backup_test_schema(pg0_db_url, embeddings):
-    """Create an isolated schema for backup/restore tests.
-
-    Uses a unique schema name per test invocation to avoid conflicts with
-    parallel test runs or leftover state from interrupted runs.
-
-    Returns a tuple of (db_url, schema_name, fq_helper, embeddings).
-    """
-    # Initialize embeddings if not already done
-    await embeddings.initialize()
-
-    # Use unique schema name to avoid conflicts
-    schema_name = f"backup_test_{uuid.uuid4().hex[:8]}"
-
-    def _fq(table: str) -> str:
-        """Get fully-qualified table name in test schema."""
-        return f"{schema_name}.{table}"
-
-    conn = await asyncpg.connect(pg0_db_url)
-    try:
-        await conn.execute(f"CREATE SCHEMA {schema_name}")
-    finally:
-        await conn.close()
-
-    # Run migrations on the isolated schema
-    run_migrations(pg0_db_url, schema=schema_name)
-
-    yield pg0_db_url, schema_name, _fq, embeddings
-
-    # Cleanup after test
-    conn = await asyncpg.connect(pg0_db_url)
-    try:
-        await conn.execute(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE")
-    finally:
-        await conn.close()
-
-
-@pytest.mark.asyncio
-async def test_backup_restore_roundtrip(backup_test_schema):
-    """Test that backup and restore preserves all data correctly."""
-    db_url, schema_name, _fq, embeddings = backup_test_schema
-    bank_id = f"test-backup-{uuid.uuid4().hex[:8]}"
-    conn = await asyncpg.connect(db_url)
-
-    try:
-        # Create a bank
-        await conn.execute(
-            f"INSERT INTO {_fq('banks')} (bank_id) VALUES ($1) ON CONFLICT DO NOTHING",
-            bank_id,
-        )
-
-        # Create some test memory units with embeddings
-        # Convert embedding list to pgvector format string
-        embedding_list = embeddings.encode(["Test content about Alice"])[0]
-        embedding_str = "[" + ",".join(str(x) for x in embedding_list) + "]"
-        for text in [
-            "Alice is a software engineer who loves Python.",
-            "Bob works with Alice on the backend team.",
-            "The team uses PostgreSQL for their database.",
-        ]:
-            await conn.execute(
-                f"""INSERT INTO {_fq('memory_units')}
-                    (bank_id, text, fact_type, embedding, event_date)
-                    VALUES ($1, $2, 'world', $3::vector, NOW())""",
-                bank_id,
-                text,
-                embedding_str,
-            )
-
-        # Get counts before backup
-        counts_before = {}
-        for table in BACKUP_TABLES:
-            counts_before[table] = await conn.fetchval(f"SELECT COUNT(*) FROM {_fq(table)}")
-
-        # Verify we have data
-        assert counts_before["banks"] > 0
-        assert counts_before["memory_units"] > 0
-
-    finally:
-        await conn.close()
-
-    # Backup to a temp file
-    with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as f:
-        backup_path = Path(f.name)
-
-    try:
-        manifest = await _backup(db_url, backup_path, schema=schema_name)
-
-        # Verify backup file exists and is valid
-        assert backup_path.exists()
-        assert backup_path.stat().st_size > 0
-
-        # Verify manifest
-        assert manifest["version"] == "1"
-        assert "created_at" in manifest
-        for table in BACKUP_TABLES:
-            assert table in manifest["tables"]
-            assert manifest["tables"][table]["rows"] == counts_before[table]
-
-        # Verify zip contents
-        with zipfile.ZipFile(backup_path, "r") as zf:
-            assert "manifest.json" in zf.namelist()
-            for table in BACKUP_TABLES:
-                assert f"{table}.bin" in zf.namelist()
-
-        # Clear all data
-        conn = await asyncpg.connect(db_url)
-        try:
-            for table in reversed(BACKUP_TABLES):
-                await conn.execute(f"TRUNCATE TABLE {_fq(table)} CASCADE")
-
-            # Verify data is gone
-            for table in BACKUP_TABLES:
-                count = await conn.fetchval(f"SELECT COUNT(*) FROM {_fq(table)}")
-                assert count == 0, f"Table {table} should be empty after truncate"
-        finally:
-            await conn.close()
-
-        # Restore from backup
-        await _restore(db_url, backup_path, schema=schema_name)
-
-        # Verify counts match original
-        conn = await asyncpg.connect(db_url)
-        try:
-            for table in BACKUP_TABLES:
-                count = await conn.fetchval(f"SELECT COUNT(*) FROM {_fq(table)}")
-                assert count == counts_before[table], f"Table {table} count mismatch after restore"
-
-            # Verify data content is preserved
-            texts = await conn.fetch(
-                f"SELECT text FROM {_fq('memory_units')} WHERE bank_id = $1",
-                bank_id,
-            )
-            text_content = " ".join(r["text"] for r in texts)
-            assert "Alice" in text_content or "software" in text_content
-        finally:
-            await conn.close()
-
-    finally:
-        # Cleanup
-        if backup_path.exists():
-            backup_path.unlink()
-
-
-@pytest.mark.asyncio
-async def test_backup_restore_preserves_all_column_types(backup_test_schema):
-    """Test that all column types are preserved: vectors, UUIDs, timestamps, JSONB."""
-    db_url, schema_name, _fq, embeddings = backup_test_schema
-    bank_id = f"test-types-{uuid.uuid4().hex[:8]}"
-    conn = await asyncpg.connect(db_url)
-
-    try:
-        # Create a bank
-        await conn.execute(
-            f"INSERT INTO {_fq('banks')} (bank_id) VALUES ($1) ON CONFLICT DO NOTHING",
-            bank_id,
-        )
-
-        # Create a memory unit with all column types
-        # Convert embedding list to pgvector format string
-        embedding_list = embeddings.encode(["John Smith engineer"])[0]
-        embedding_str = "[" + ",".join(str(x) for x in embedding_list) + "]"
-        await conn.execute(
-            f"""INSERT INTO {_fq('memory_units')}
-                (bank_id, text, fact_type, embedding, event_date, metadata)
-                VALUES ($1, $2, 'world', $3::vector, NOW(), $4)""",
-            bank_id,
-            "John Smith is a senior engineer at Acme Corp since 2020.",
-            embedding_str,
-            '{"key": "value"}',
-        )
-
-        # Create an entity
-        await conn.execute(
-            f"""INSERT INTO {_fq('entities')}
-                (bank_id, canonical_name, metadata)
-                VALUES ($1, $2, $3)""",
-            bank_id,
-            "John Smith",
-            '{"role": "engineer"}',
-        )
-
-        # Get original data
-        original_unit = await conn.fetchrow(
-            f"""SELECT id, embedding, event_date, created_at, metadata, text
-               FROM {_fq('memory_units')} WHERE bank_id = $1 LIMIT 1""",
-            bank_id,
-        )
-        original_entity = await conn.fetchrow(
-            f"""SELECT id, first_seen, last_seen, metadata, canonical_name
-               FROM {_fq('entities')} WHERE bank_id = $1 LIMIT 1""",
-            bank_id,
-        )
-        original_bank = await conn.fetchrow(
-            f"SELECT bank_id, created_at, updated_at FROM {_fq('banks')} WHERE bank_id = $1",
-            bank_id,
-        )
-    finally:
-        await conn.close()
-
-    assert original_unit is not None, "Should have created memory units"
-    assert original_unit["embedding"] is not None, "Should have embedding"
-    assert original_unit["id"] is not None, "Should have UUID"
-    assert original_entity is not None, "Should have created entities"
-
-    with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as f:
-        backup_path = Path(f.name)
-
-    try:
-        await _backup(db_url, backup_path, schema=schema_name)
-
-        # Clear all data
-        conn = await asyncpg.connect(db_url)
-        try:
-            for table in reversed(BACKUP_TABLES):
-                await conn.execute(f"TRUNCATE TABLE {_fq(table)} CASCADE")
-        finally:
-            await conn.close()
-
-        await _restore(db_url, backup_path, schema=schema_name)
-
-        # Verify all column types are preserved exactly
-        conn = await asyncpg.connect(db_url)
-        try:
-            restored_unit = await conn.fetchrow(
-                f"""SELECT id, embedding, event_date, created_at, metadata, text
-                   FROM {_fq('memory_units')} WHERE bank_id = $1 LIMIT 1""",
-                bank_id,
-            )
-            restored_entity = await conn.fetchrow(
-                f"""SELECT id, first_seen, last_seen, metadata, canonical_name
-                   FROM {_fq('entities')} WHERE bank_id = $1 LIMIT 1""",
-                bank_id,
-            )
-            restored_bank = await conn.fetchrow(
-                f"SELECT bank_id, created_at, updated_at FROM {_fq('banks')} WHERE bank_id = $1",
-                bank_id,
-            )
-        finally:
-            await conn.close()
-
-        # Verify memory_units
-        assert restored_unit is not None, "Should have restored memory unit"
-        assert restored_unit["id"] == original_unit["id"], "UUID should match exactly"
-        assert restored_unit["text"] == original_unit["text"], "Text should match"
-        assert list(restored_unit["embedding"]) == list(original_unit["embedding"]), "Vector embedding should match exactly"
-        assert restored_unit["event_date"] == original_unit["event_date"], "Timestamp should match exactly"
-        assert restored_unit["created_at"] == original_unit["created_at"], "Created timestamp should match"
-        assert restored_unit["metadata"] == original_unit["metadata"], "JSONB metadata should match"
-
-        # Verify entities
-        assert restored_entity is not None, "Should have restored entity"
-        assert restored_entity["id"] == original_entity["id"], "Entity UUID should match"
-        assert restored_entity["canonical_name"] == original_entity["canonical_name"], "Entity name should match"
-        assert restored_entity["first_seen"] == original_entity["first_seen"], "Entity first_seen should match"
-        assert restored_entity["last_seen"] == original_entity["last_seen"], "Entity last_seen should match"
-        assert restored_entity["metadata"] == original_entity["metadata"], "Entity metadata should match"
-
-        # Verify banks
-        assert restored_bank is not None, "Should have restored bank"
-        assert restored_bank["bank_id"] == original_bank["bank_id"], "Bank ID should match"
-        assert restored_bank["created_at"] == original_bank["created_at"], "Bank created_at should match"
-
-    finally:
-        if backup_path.exists():
-            backup_path.unlink()
@@ -3,7 +3,7 @@ Tests for agent management API (profile, disposition, background).
 """
 import pytest
 import uuid
-from hindsight_api import MemoryEngine, RequestContext
+from hindsight_api import MemoryEngine
 from hindsight_api.api import CreateBankRequest, DispositionTraits
 from hindsight_api.engine.memory_engine import Budget

@@ -17,11 +17,11 @@ class TestAgentProfile:
    """Tests for agent profile management."""

    @pytest.mark.asyncio
-    async def test_get_agent_profile_creates_default(self, memory: MemoryEngine, request_context):
+    async def test_get_agent_profile_creates_default(self, memory: MemoryEngine):
        """Test that getting a profile for a new agent creates default disposition."""
        bank_id = unique_agent_id("test_profile_default")

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)

        assert profile is not None
        assert "disposition" in profile
@@ -35,11 +35,11 @@ class TestAgentProfile:
        assert profile["background"] == ""

    @pytest.mark.asyncio
-    async def test_update_agent_disposition(self, memory: MemoryEngine, request_context):
+    async def test_update_agent_disposition(self, memory: MemoryEngine):
        """Test updating agent disposition traits."""
        bank_id = unique_agent_id("test_profile_update")

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)
        assert profile["disposition"].skepticism == 3

        new_disposition = {
@@ -47,26 +47,26 @@ class TestAgentProfile:
            "literalism": 4,
            "empathy": 2,
        }
-        await memory.update_bank_disposition(bank_id, new_disposition, request_context=request_context)
+        await memory.update_bank_disposition(bank_id, new_disposition)

-        updated_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        updated_profile = await memory.get_bank_profile(bank_id)
        disposition = updated_profile["disposition"]
        assert disposition.skepticism == new_disposition["skepticism"]
        assert disposition.literalism == new_disposition["literalism"]
        assert disposition.empathy == new_disposition["empathy"]

    @pytest.mark.asyncio
-    async def test_list_agents(self, memory: MemoryEngine, request_context):
+    async def test_list_agents(self, memory: MemoryEngine):
        """Test listing all agents."""
        agent_id_1 = unique_agent_id("test_list")
        agent_id_2 = unique_agent_id("test_list")
        agent_id_3 = unique_agent_id("test_list")

-        await memory.get_bank_profile(agent_id_1, request_context=request_context)
-        await memory.get_bank_profile(agent_id_2, request_context=request_context)
-        await memory.get_bank_profile(agent_id_3, request_context=request_context)
+        await memory.get_bank_profile(agent_id_1)
+        await memory.get_bank_profile(agent_id_2)
+        await memory.get_bank_profile(agent_id_3)

-        agents = await memory.list_banks(request_context=request_context)
+        agents = await memory.list_banks()

        agent_ids = [a["bank_id"] for a in agents]
        assert agent_id_1 in agent_ids
@@ -85,50 +85,46 @@ class TestAgentBackground:
    """Tests for agent background management."""

    @pytest.mark.asyncio
-    async def test_merge_agent_background(self, memory: MemoryEngine, request_context):
+    async def test_merge_agent_background(self, memory: MemoryEngine):
        """Test merging agent background information."""
        bank_id = unique_agent_id("test_profile_merge")

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)
        assert profile["background"] == ""

        result1 = await memory.merge_bank_background(
            bank_id,
            "I was born in Texas",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )
        assert "Texas" in result1["background"]

        result2 = await memory.merge_bank_background(
            bank_id,
            "I have 10 years of startup experience",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )
        assert "Texas" in result2["background"] or "startup" in result2["background"]

-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        final_profile = await memory.get_bank_profile(bank_id)
        assert final_profile["background"] != ""

    @pytest.mark.asyncio
-    async def test_merge_background_handles_conflicts(self, memory: MemoryEngine, request_context):
+    async def test_merge_background_handles_conflicts(self, memory: MemoryEngine):
        """Test that merging background handles conflicts (new overwrites old)."""
        bank_id = unique_agent_id("test_profile_conflict")

        result1 = await memory.merge_bank_background(
            bank_id,
            "I was born in Colorado",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )
        assert "Colorado" in result1["background"]

        result2 = await memory.merge_bank_background(
            bank_id,
            "You were born in Texas",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )
        assert "Texas" in result2["background"]

@@ -137,7 +133,7 @@ class TestAgentEndpoint:
    """Tests for agent PUT endpoint logic."""

    @pytest.mark.asyncio
-    async def test_put_agent_create(self, memory: MemoryEngine, request_context):
+    async def test_put_agent_create(self, memory: MemoryEngine):
        """Test creating an agent via PUT endpoint."""
        bank_id = unique_agent_id("test_put_create")

@@ -150,13 +146,12 @@ class TestAgentEndpoint:
            background="I am a creative software engineer"
        )

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)

        if request.disposition is not None:
            await memory.update_bank_disposition(
                bank_id,
-                request.disposition.model_dump(),
-                request_context=request_context,
+                request.disposition.model_dump()
            )

        if request.background is not None:
@@ -173,14 +168,14 @@ class TestAgentEndpoint:
                    request.background
                )

-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        final_profile = await memory.get_bank_profile(bank_id)

        assert final_profile["disposition"].skepticism == 4
        assert final_profile["disposition"].literalism == 5
        assert final_profile["background"] == "I am a creative software engineer"

    @pytest.mark.asyncio
-    async def test_put_agent_partial_update(self, memory: MemoryEngine, request_context):
+    async def test_put_agent_partial_update(self, memory: MemoryEngine):
        """Test updating only background."""
        bank_id = unique_agent_id("test_put_partial")

@@ -188,7 +183,7 @@ class TestAgentEndpoint:
            background="I am a data scientist"
        )

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)

        if request.background is not None:
            pool = await memory._get_pool()
@@ -204,7 +199,7 @@ class TestAgentEndpoint:
                    request.background
                )

-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        final_profile = await memory.get_bank_profile(bank_id)

        assert final_profile["disposition"].skepticism == 3  # Default
        assert final_profile["background"] == "I am a data scientist"
@@ -214,7 +209,7 @@ class TestAgentDispositionIntegration:
    """Tests for disposition integration with other features."""

    @pytest.mark.asyncio
-    async def test_think_uses_disposition(self, memory: MemoryEngine, request_context):
+    async def test_think_uses_disposition(self, memory: MemoryEngine):
        """Test that THINK operation uses agent disposition."""
        bank_id = unique_agent_id("test_think")

@@ -223,13 +218,12 @@ class TestAgentDispositionIntegration:
            "literalism": 4,  # High literalism
            "empathy": 2,     # Low empathy
        }
-        await memory.update_bank_disposition(bank_id, disposition, request_context=request_context)
+        await memory.update_bank_disposition(bank_id, disposition)

        await memory.merge_bank_background(
            bank_id,
            "I am a creative artist who values innovation over tradition",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )

        await memory.retain_batch_async(
@@ -238,14 +232,13 @@ class TestAgentDispositionIntegration:
                {"content": "Traditional painting techniques have been used for centuries"},
                {"content": "Modern digital art is changing the art world"}
            ],
-            request_context=request_context,
+            document_id="art_facts"
        )

        result = await memory.reflect_async(
            bank_id=bank_id,
            query="What do you think about traditional vs modern art?",
-            budget=Budget.LOW,
-            request_context=request_context,
+            budget=Budget.LOW
        )

        assert result.text is not None
@@ -6,7 +6,7 @@ import os


@pytest.mark.asyncio
-async def test_large_batch_auto_chunks(memory, request_context):
+async def test_large_batch_auto_chunks(memory):
    bank_id = "test_chunking_agent"
    # Create a large batch that should trigger chunking
    # Each item is ~2000 chars, so 30 items = 60k chars (exceeds 50k threshold)
@@ -24,8 +24,7 @@ async def test_large_batch_auto_chunks(memory, request_context):
    # Ingest the large batch (should auto-chunk)
    result = await memory.retain_batch_async(
        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
+        contents=contents
    )

    # Verify we got results back
@@ -34,7 +33,7 @@ async def test_large_batch_auto_chunks(memory, request_context):


@pytest.mark.asyncio
-async def test_small_batch_no_chunking(memory, request_context):
+async def test_small_batch_no_chunking(memory):
    bank_id = "test_no_chunking_agent"

    # Create a small batch that should NOT trigger chunking
@@ -51,8 +50,7 @@ async def test_small_batch_no_chunking(memory, request_context):
    # Ingest the small batch (should NOT auto-chunk)
    result = await memory.retain_batch_async(
        bank_id=bank_id,
-        contents=contents,
-        request_context=request_context,
+        contents=contents
    )

    # Verify we got results back
@@ -1,223 +0,0 @@
-"""
-Test suite for causal relations extraction and validation.
-
-Tests that:
-1. Causal relations only reference previous facts (target_index < current fact index)
-2. Invalid causal relation indices are rejected
-3. The new per-fact causal relations schema works correctly
-"""
-
-from datetime import datetime
-
-import pytest
-
-from hindsight_api import LLMConfig
-from hindsight_api.engine.retain.fact_extraction import extract_facts_from_text
-
-
-class TestCausalRelationsValidation:
-    """Tests for causal relations index validation."""
-
-    @pytest.mark.asyncio
-    async def test_causal_relations_only_reference_previous_facts(self):
-        """
-        Test that causal relations can only reference facts that appear before them.
-
-        This test verifies the new schema that prevents hallucination of invalid
-        fact indices by constraining target_index to be less than the current fact's index.
-        """
-        # Text with clear causal chain
-        text = """
-        I lost my job in January due to company layoffs.
-        Because I lost my job, I couldn't pay my rent.
-        Since I couldn't afford rent, I had to move to a cheaper apartment.
-        After moving, I started looking for a new job.
-        """
-
-        context = "Personal life update"
-        llm_config = LLMConfig.for_memory()
-        event_date = datetime(2024, 3, 15)
-
-        facts, _, usage = await extract_facts_from_text(
-            text=text,
-            event_date=event_date,
-            context=context,
-            llm_config=llm_config,
-            agent_name="TestUser",
-        )
-
-        assert len(facts) > 0, "Should extract at least one fact"
-
-        # Verify all causal relations reference valid previous facts
-        for i, fact in enumerate(facts):
-            if fact.causal_relations:
-                for rel in fact.causal_relations:
-                    assert rel.target_fact_index < i, (
-                        f"Fact {i} has causal relation to fact {rel.target_fact_index}, "
-                        f"but target_index must be < current index ({i})"
-                    )
-                    assert rel.target_fact_index >= 0, (
-                        f"Fact {i} has negative causal relation index: {rel.target_fact_index}"
-                    )
-                    assert rel.relation_type in ["caused_by", "enabled_by", "prevented_by"], (
-                        f"Invalid relation_type: {rel.relation_type}"
-                    )
-
-    @pytest.mark.asyncio
-    async def test_first_fact_has_no_causal_relations(self):
-        """
-        Test that the first fact (index 0) cannot have causal relations.
-
-        Since causal relations can only reference previous facts,
-        and there are no facts before index 0, the first fact should
-        have no causal relations.
-        """
-        text = """
-        The user started a new machine learning project.
-        The project requires learning TensorFlow.
-        Learning TensorFlow is challenging but rewarding.
-        """
-
-        context = "Project update"
-        llm_config = LLMConfig.for_memory()
-        event_date = datetime(2024, 6, 1)
-
-        facts, _, _ = await extract_facts_from_text(
-            text=text,
-            event_date=event_date,
-            context=context,
-            llm_config=llm_config,
-            agent_name="TestUser",
-        )
-
-        assert len(facts) > 0, "Should extract at least one fact"
-
-        # First fact should have no causal relations (nothing to reference)
-        if facts[0].causal_relations:
-            # If there are causal relations on the first fact, they should be empty
-            # or the validation should have filtered them out
-            for rel in facts[0].causal_relations:
-                # This should never happen due to validation
-                assert False, (
-                    f"First fact should not have causal relations, "
-                    f"but found: target_index={rel.target_fact_index}"
-                )
-
-    @pytest.mark.asyncio
-    async def test_causal_chain_extraction(self):
-        """
-        Test that a clear causal chain is extracted with valid relations.
-        """
-        text = """
-        Emily got promoted to senior engineer last month.
-        Because of her promotion, she received a significant salary increase.
-        With the extra money, she decided to buy a new car.
-        """
-
-        context = "Personal achievement story"
-        llm_config = LLMConfig.for_memory()
-        event_date = datetime(2024, 7, 15)
-
-        facts, _, _ = await extract_facts_from_text(
-            text=text,
-            event_date=event_date,
-            context=context,
-            llm_config=llm_config,
-            agent_name="TestUser",
-        )
-
-        assert len(facts) > 0, "Should extract facts about the causal chain"
-
-        # Collect all causal relations
-        all_relations = []
-        for i, fact in enumerate(facts):
-            if fact.causal_relations:
-                for rel in fact.causal_relations:
-                    all_relations.append({
-                        "from_fact": i,
-                        "to_fact": rel.target_fact_index,
-                        "type": rel.relation_type,
-                    })
-
-        # If causal relations were extracted, verify they form a valid chain
-        if all_relations:
-            for rel in all_relations:
-                assert rel["to_fact"] < rel["from_fact"], (
-                    f"Causal relation from fact {rel['from_fact']} to fact {rel['to_fact']} "
-                    f"is invalid (target must be < source)"
-                )
-
-    @pytest.mark.asyncio
-    async def test_token_efficiency_with_causal_relations(self):
-        """
-        Test that causal relations don't cause excessive output tokens.
-
-        This test verifies that the new schema (per-fact causal relations
-        with index constraints) doesn't waste tokens on invalid relations.
-        """
-        text = """
-        The company announced budget cuts in Q1.
-        Due to the budget cuts, the marketing team was reduced.
-        The reduced team meant fewer campaigns could be run.
-        With fewer campaigns, lead generation dropped.
-        Lower leads resulted in decreased sales.
-        """
-
-        context = "Business impact analysis"
-        llm_config = LLMConfig.for_memory()
-        event_date = datetime(2024, 4, 1)
-
-        facts, _, usage = await extract_facts_from_text(
-            text=text,
-            event_date=event_date,
-            context=context,
-            llm_config=llm_config,
-            agent_name="TestUser",
-        )
-
-        assert len(facts) > 0, "Should extract facts"
-
-        # Calculate output/input ratio
-        if usage.input_tokens > 0:
-            ratio = usage.output_tokens / usage.input_tokens
-            # The ratio should be reasonable (< 5x) with the new schema
-            # Previously it could be 7-10x due to hallucinated indices
-            assert ratio < 6, (
-                f"Output/input token ratio {ratio:.2f}x is too high. "
-                f"Input: {usage.input_tokens}, Output: {usage.output_tokens}"
-            )
-
-    @pytest.mark.asyncio
-    async def test_relation_types_are_backward_looking(self):
-        """
-        Test that all relation types describe how the current fact
-        relates to a previous fact (caused_by, enabled_by, prevented_by).
-        """
-        text = """
-        Alice learned Python programming.
-        Because she knew Python, she got a job as a data scientist.
-        Her data science skills enabled her to lead the analytics team.
-        """
-
-        context = "Career progression"
-        llm_config = LLMConfig.for_memory()
-        event_date = datetime(2024, 5, 1)
-
-        facts, _, _ = await extract_facts_from_text(
-            text=text,
-            event_date=event_date,
-            context=context,
-            llm_config=llm_config,
-            agent_name="TestUser",
-        )
-
-        # Verify relation types are all backward-looking
-        valid_types = {"caused_by", "enabled_by", "prevented_by"}
-
-        for i, fact in enumerate(facts):
-            if fact.causal_relations:
-                for rel in fact.causal_relations:
-                    assert rel.relation_type in valid_types, (
-                        f"Invalid relation_type '{rel.relation_type}'. "
-                        f"Must be one of: {valid_types}"
-                    )
@@ -1,202 +0,0 @@
-"""
-Test suite for causal relationship extraction.
-
-Tests that the fact extraction system correctly identifies and validates
-causal relationships between facts, with valid indices.
-"""
-
-from datetime import datetime
-
-import pytest
-
-from hindsight_api import LLMConfig
-from hindsight_api.engine.retain.fact_extraction import extract_facts_from_text
-
-
-class TestCausalRelationships:
-    """Tests for causal relationship extraction and validation."""
-
-    @pytest.mark.asyncio
-    async def test_causal_chain_extraction(self):
-        """
-        Test that a clear causal chain is extracted with valid relationships.
-
-        Story: Lost job -> couldn't pay rent -> had to move -> found new apartment
-
-        This is a 4-fact causal chain where each fact causes the next.
-        The extracted causal relations should have valid indices (0-3).
-        """
-        text = """
-I lost my job at the tech company in January because of layoffs.
-Because I lost my job, I couldn't pay my rent anymore.
-Since I couldn't afford rent, I had to move out of my apartment.
-After searching for weeks, I finally found a cheaper apartment in Brooklyn.
-"""
-
-        context = "Personal story about housing change"
-        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"
-        )
-
-        assert len(facts) >= 3, f"Should extract at least 3 facts from the causal chain. Got {len(facts)}"
-
-        # Collect all causal relations from all facts
-        all_causal_relations = []
-        for i, fact in enumerate(facts):
-            if fact.causal_relations:
-                for rel in fact.causal_relations:
-                    all_causal_relations.append(
-                        {
-                            "from_fact_index": i,
-                            "to_fact_index": rel.target_fact_index,
-                            "relation_type": rel.relation_type,
-                            "strength": rel.strength,
-                            "from_fact_text": fact.fact[:50],
-                        }
-                    )
-
-        # Verify that ALL causal relation indices are valid
-        # New constraint: target_index must be < from_fact_index (can only reference PREVIOUS facts)
-        num_facts = len(facts)
-        invalid_relations = []
-        for rel in all_causal_relations:
-            # Must be non-negative and less than the current fact's index
-            if rel["to_fact_index"] < 0 or rel["to_fact_index"] >= rel["from_fact_index"]:
-                invalid_relations.append(rel)
-
-        assert len(invalid_relations) == 0, (
-            f"Found {len(invalid_relations)} causal relations with invalid indices! "
-            f"Each target_fact_index must be < from_fact_index (can only reference previous facts). "
-            f"Invalid relations: {invalid_relations}"
-        )
-
-        # Should have at least some causal relations extracted
-        assert len(all_causal_relations) >= 2, (
-            f"Should extract at least 2 causal relationships from this clear chain. "
-            f"Got {len(all_causal_relations)}: {all_causal_relations}"
-        )
-
-        # Verify relation types are valid (passive only - facts reference PREVIOUS facts)
-        valid_types = {"caused_by", "enabled_by", "prevented_by"}
-        for rel in all_causal_relations:
-            assert rel["relation_type"] in valid_types, (
-                f"Invalid relation_type '{rel['relation_type']}'. Must be one of {valid_types}"
-            )
-
-    @pytest.mark.asyncio
-    async def test_complex_causal_web(self):
-        """
-        Test a more complex scenario with multiple interconnected causes.
-
-        This tests the LLM's ability to identify multiple causal links and
-        ensure all referenced indices exist.
-        """
-        text = """
-The heavy rain caused flooding in the basement.
-The flooding damaged the electrical system.
-Because of the electrical damage, we had to call an electrician.
-The electrician found that the wiring was old and needed replacement.
-We decided to renovate the entire basement while fixing the wiring.
-The renovation took three months and cost $15,000.
-"""
-
-        context = "Home repair story"
-        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"
-        )
-
-        assert len(facts) >= 4, f"Should extract at least 4 facts. Got {len(facts)}"
-
-        # Validate all causal relation indices (must reference PREVIOUS facts only)
-        for i, fact in enumerate(facts):
-            if fact.causal_relations:
-                for rel in fact.causal_relations:
-                    assert 0 <= rel.target_fact_index < i, (
-                        f"Fact {i} has causal relation to invalid index {rel.target_fact_index}. "
-                        f"Must reference previous facts only (valid range: 0 to {i - 1}). "
-                        f"Fact text: {fact.fact[:80]}..."
-                    )
-
-    @pytest.mark.asyncio
-    async def test_no_self_referencing_causal_relations(self):
-        """
-        Test that facts don't have causal relations pointing to themselves.
-        """
-        text = """
-I started learning Python because I wanted to automate my work tasks.
-Learning Python led me to discover machine learning.
-Machine learning fascinated me so much that I changed my career to data science.
-"""
-
-        context = "Career change story"
-        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"
-        )
-
-        # Check no fact references itself
-        for i, fact in enumerate(facts):
-            if fact.causal_relations:
-                for rel in fact.causal_relations:
-                    assert rel.target_fact_index != i, (
-                        f"Fact {i} has a self-referencing causal relation! Fact text: {fact.fact}"
-                    )
-
-    @pytest.mark.asyncio
-    async def test_bidirectional_causal_relationships(self):
-        """
-        Test that bidirectional causal relationships (causes and caused_by)
-        are handled correctly.
-        """
-        text = """
-My promotion at work caused me to move to New York.
-Moving to New York was caused by my promotion at work.
-The new role enabled me to lead a team of engineers.
-"""
-
-        context = "Work promotion story"
-        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"
-        )
-
-        # Validate all indices (must reference PREVIOUS facts only)
-        for i, fact in enumerate(facts):
-            if fact.causal_relations:
-                for rel in fact.causal_relations:
-                    assert 0 <= rel.target_fact_index < i, (
-                        f"Invalid target_fact_index {rel.target_fact_index} in fact {i}. "
-                        f"Must reference previous facts only (valid range: 0 to {i - 1})"
-                    )
-
-    @pytest.mark.asyncio
-    async def test_causal_relation_strength_values(self):
-        """
-        Test that causal relation strength values are within valid range [0.0, 1.0].
-        """
-        text = """
-The stock market crash directly caused the company to lay off employees.
-The layoffs indirectly led to reduced consumer spending in the area.
-Reduced spending somewhat affected local businesses.
-"""
-
-        context = "Economic impact story"
-        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"
-        )
-
-        for i, fact in enumerate(facts):
-            if fact.causal_relations:
-                for rel in fact.causal_relations:
-                    assert 0.0 <= rel.strength <= 1.0, (
-                        f"Causal relation strength {rel.strength} is outside valid range [0.0, 1.0]. "
-                        f"Fact {i}: {fact.fact[:50]}..."
-                    )
@@ -10,7 +10,6 @@ import pytest
 from datetime import datetime, timezone
 from hindsight_api.engine.search.types import RetrievalResult, MergedCandidate, ScoredResult
 from hindsight_api.engine.memory_engine import Budget
-from hindsight_api import RequestContext


 class TestRRFNormalization:
@@ -126,7 +125,7 @@ class TestCombinedScoringFormula:


@pytest.mark.asyncio
-async def test_trace_has_normalized_rrf(memory, request_context):
+async def test_trace_has_normalized_rrf(memory):
    """Integration test: verify trace contains normalized RRF values, not raw."""
    bank_id = f"test_scoring_{datetime.now(timezone.utc).timestamp()}"

@@ -136,25 +135,21 @@ async def test_trace_has_normalized_rrf(memory, request_context):
            bank_id=bank_id,
            content="Python is a programming language created by Guido van Rossum",
            context="tech facts",
-            request_context=request_context,
        )
        await memory.retain_async(
            bank_id=bank_id,
            content="JavaScript was created by Brendan Eich at Netscape",
            context="tech facts",
-            request_context=request_context,
        )
        await memory.retain_async(
            bank_id=bank_id,
            content="The Eiffel Tower is located in Paris, France",
            context="geography facts",
-            request_context=request_context,
        )
        await memory.retain_async(
            bank_id=bank_id,
            content="Mount Everest is the tallest mountain on Earth",
            context="geography facts",
-            request_context=request_context,
        )

        # Search with tracing
@@ -165,7 +160,6 @@ async def test_trace_has_normalized_rrf(memory, request_context):
            budget=Budget.LOW,
            max_tokens=1024,
            enable_trace=True,
-            request_context=request_context,
        )

        assert result.trace is not None, "Trace should be present"
@@ -216,11 +210,11 @@ async def test_trace_has_normalized_rrf(memory, request_context):
            print(f"  - First result score components: {sc}")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_rrf_normalized_not_raw_in_trace(memory, request_context):
+async def test_rrf_normalized_not_raw_in_trace(memory):
    """Verify that raw RRF scores (0.04-0.06 range) don't appear as normalized values."""
    bank_id = f"test_rrf_raw_{datetime.now(timezone.utc).timestamp()}"

@@ -231,7 +225,6 @@ async def test_rrf_normalized_not_raw_in_trace(memory, request_context):
                bank_id=bank_id,
                content=f"Test fact number {i} about various topics",
                context="test context",
-                request_context=request_context,
            )

        result = await memory.recall_async(
@@ -241,7 +234,6 @@ async def test_rrf_normalized_not_raw_in_trace(memory, request_context):
            budget=Budget.LOW,
            max_tokens=512,
            enable_trace=True,
-            request_context=request_context,
        )

        trace = result.trace
@@ -276,11 +268,11 @@ async def test_rrf_normalized_not_raw_in_trace(memory, request_context):
        print("\n✓ RRF raw vs normalized test passed!")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_combined_score_matches_components(memory, request_context):
+async def test_combined_score_matches_components(memory):
    """Verify the final score actually equals the weighted sum of components."""
    bank_id = f"test_combined_{datetime.now(timezone.utc).timestamp()}"

@@ -289,13 +281,11 @@ async def test_combined_score_matches_components(memory, request_context):
            bank_id=bank_id,
            content="The quick brown fox jumps over the lazy dog",
            context="test",
-            request_context=request_context,
        )
        await memory.retain_async(
            bank_id=bank_id,
            content="A quick test of the emergency broadcast system",
            context="test",
-            request_context=request_context,
        )

        result = await memory.recall_async(
@@ -305,7 +295,6 @@ async def test_combined_score_matches_components(memory, request_context):
            budget=Budget.LOW,
            max_tokens=512,
            enable_trace=True,
-            request_context=request_context,
        )

        trace = result.trace
@@ -331,4 +320,4 @@ async def test_combined_score_matches_components(memory, request_context):
        print("\n✓ Combined score verification test passed!")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)
@@ -1,602 +0,0 @@
-"""
-Tests for custom embedding dimensions and automatic dimension detection.
-
-Uses isolated PostgreSQL schemas to avoid affecting other tests.
-Includes tests for:
- Automatic embedding dimension detection and database schema adjustment
- OpenAI embeddings provider with 1536 dimensions
-"""
-
-import asyncio
-import os
-import pytest
-from datetime import datetime
-from sqlalchemy import create_engine, text
-
-from hindsight_api import MemoryEngine, RequestContext
-from hindsight_api.engine.embeddings import LocalSTEmbeddings, OpenAIEmbeddings, CohereEmbeddings
-from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder, CohereCrossEncoder
-from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
-from hindsight_api.extensions import TenantExtension, TenantContext
-from hindsight_api.migrations import run_migrations, ensure_embedding_dimension
-
-
-# =============================================================================
-# Shared Utilities
-# =============================================================================
-
-
-class SchemaTenantExtension(TenantExtension):
-    """Tenant extension that routes all requests to a specific schema (for testing)."""
-
-    def __init__(self, schema_name: str):
-        self.schema_name = schema_name
-
-    async def authenticate(self, request_context: RequestContext) -> TenantContext:
-        return TenantContext(schema_name=self.schema_name)
-
-
-def get_test_schema(prefix: str, worker_id: str) -> str:
-    """Get unique schema name per xdist worker."""
-    if worker_id == "master" or not worker_id:
-        return prefix
-    return f"{prefix}_{worker_id}"
-
-
-def create_isolated_schema(db_url: str, schema_name: str, dimension: int | None = None):
-    """Create an isolated schema with migrations and optional dimension adjustment."""
-    engine = create_engine(db_url)
-
-    # Create schema (drop first if exists from previous failed run)
-    with engine.connect() as conn:
-        conn.execute(text(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE"))
-        conn.execute(text(f"CREATE SCHEMA {schema_name}"))
-        conn.commit()
-
-    # Run migrations in the isolated schema
-    run_migrations(db_url, schema=schema_name)
-
-    # Adjust embedding dimension if specified
-    if dimension is not None:
-        ensure_embedding_dimension(db_url, dimension, schema=schema_name)
-
-
-def drop_schema(db_url: str, schema_name: str):
-    """Drop an isolated schema."""
-    engine = create_engine(db_url)
-    with engine.connect() as conn:
-        conn.execute(text(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE"))
-        conn.commit()
-
-
-def get_column_dimension(db_url: str, schema: str = "public") -> int | None:
-    """Get the current embedding column dimension from the database."""
-    engine = create_engine(db_url)
-    with engine.connect() as conn:
-        result = conn.execute(
-            text("""
-                SELECT atttypmod
-                FROM pg_attribute a
-                JOIN pg_class c ON a.attrelid = c.oid
-                JOIN pg_namespace n ON c.relnamespace = n.oid
-                WHERE n.nspname = :schema
-                  AND c.relname = 'memory_units'
-                  AND a.attname = 'embedding'
-            """),
-            {"schema": schema},
-        ).scalar()
-        return result
-
-
-def get_row_count(db_url: str, schema: str = "public") -> int:
-    """Get the number of rows with embeddings in memory_units."""
-    engine = create_engine(db_url)
-    with engine.connect() as conn:
-        return conn.execute(
-            text(f"SELECT COUNT(*) FROM {schema}.memory_units WHERE embedding IS NOT NULL")
-        ).scalar()
-
-
-def insert_test_embedding(db_url: str, schema: str, dimension: int):
-    """Insert a test row with a dummy embedding."""
-    engine = create_engine(db_url)
-    embedding = [0.1] * dimension
-    embedding_str = "[" + ",".join(str(x) for x in embedding) + "]"
-
-    with engine.connect() as conn:
-        conn.execute(
-            text(f"""
-                INSERT INTO {schema}.memory_units (bank_id, text, embedding, event_date, fact_type)
-                VALUES ('test-bank', 'test text', '{embedding_str}'::vector, NOW(), 'world')
-            """)
-        )
-        conn.commit()
-
-
-def clear_embeddings(db_url: str, schema: str):
-    """Clear all rows from memory_units."""
-    engine = create_engine(db_url)
-    with engine.connect() as conn:
-        conn.execute(text(f"DELETE FROM {schema}.memory_units"))
-        conn.commit()
-
-
-# =============================================================================
-# Embedding Dimension Tests (Local Embeddings)
-# =============================================================================
-
-
-@pytest.fixture(scope="class")
-def dimension_test_schema(pg0_db_url, worker_id):
-    """Create an isolated schema for dimension tests."""
-    schema_name = get_test_schema("test_embed_dim", worker_id)
-    create_isolated_schema(pg0_db_url, schema_name)
-    yield pg0_db_url, schema_name
-    drop_schema(pg0_db_url, schema_name)
-
-
-class TestEmbeddingDimension:
-    """Tests for embedding dimension detection and adjustment."""
-
-    def test_dimension_matches_no_change(self, dimension_test_schema):
-        """When dimension matches, no changes should be made."""
-        db_url, schema = dimension_test_schema
-
-        # Get initial dimension (should be 384 from migration)
-        initial_dim = get_column_dimension(db_url, schema)
-        assert initial_dim == 384, f"Expected 384, got {initial_dim}"
-
-        # Call ensure_embedding_dimension with matching dimension
-        ensure_embedding_dimension(db_url, 384, schema=schema)
-
-        # Dimension should still be 384
-        assert get_column_dimension(db_url, schema) == 384
-
-    def test_dimension_change_empty_table(self, dimension_test_schema):
-        """When table is empty, dimension can be changed."""
-        db_url, schema = dimension_test_schema
-
-        # Ensure table is empty
-        clear_embeddings(db_url, schema)
-        assert get_row_count(db_url, schema) == 0
-
-        # Change dimension to 768
-        ensure_embedding_dimension(db_url, 768, schema=schema)
-
-        # Verify dimension changed
-        new_dim = get_column_dimension(db_url, schema)
-        assert new_dim == 768, f"Expected 768, got {new_dim}"
-
-        # Change back to 384 for other tests
-        ensure_embedding_dimension(db_url, 384, schema=schema)
-        assert get_column_dimension(db_url, schema) == 384
-
-    def test_dimension_change_blocked_with_data(self, dimension_test_schema):
-        """When table has data, dimension change should be blocked."""
-        db_url, schema = dimension_test_schema
-
-        # Ensure table is empty first
-        clear_embeddings(db_url, schema)
-
-        # Insert a test row with 384-dim embedding
-        insert_test_embedding(db_url, schema, 384)
-        assert get_row_count(db_url, schema) == 1
-
-        # Try to change dimension - should raise error
-        with pytest.raises(RuntimeError) as exc_info:
-            ensure_embedding_dimension(db_url, 768, schema=schema)
-
-        assert "Cannot change embedding dimension" in str(exc_info.value)
-        assert "1 rows with embeddings" in str(exc_info.value)
-
-        # Dimension should be unchanged
-        assert get_column_dimension(db_url, schema) == 384
-
-        # Cleanup
-        clear_embeddings(db_url, schema)
-
-    def test_local_embeddings_dimension_detection(self, embeddings):
-        """Test that LocalSTEmbeddings correctly detects dimension."""
-        # Initialize embeddings if not already done
-        loop = asyncio.new_event_loop()
-        try:
-            loop.run_until_complete(embeddings.initialize())
-        finally:
-            loop.close()
-
-        # bge-small-en-v1.5 produces 384-dim embeddings
-        assert embeddings.dimension == 384
-
-        # Verify by generating an actual embedding
-        result = embeddings.encode(["test"])
-        assert len(result) == 1
-        assert len(result[0]) == 384
-
-
-# =============================================================================
-# OpenAI Embeddings Tests
-# =============================================================================
-
-
-def has_openai_api_key() -> bool:
-    """Check if OpenAI API key is available."""
-    return bool(os.environ.get("HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY"))
-
-
-def get_openai_api_key() -> str:
-    """Get OpenAI API key from environment."""
-    return os.environ.get("HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY", "")
-
-
-@pytest.fixture(scope="module")
-def openai_embeddings():
-    """Create OpenAI embeddings instance."""
-    if not has_openai_api_key():
-        pytest.skip("OpenAI API key not available (set HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY)")
-
-    embeddings = OpenAIEmbeddings(
-        api_key=get_openai_api_key(),
-        model="text-embedding-3-small",
-    )
-    loop = asyncio.new_event_loop()
-    try:
-        loop.run_until_complete(embeddings.initialize())
-    finally:
-        loop.close()
-    return embeddings
-
-
-@pytest.fixture(scope="module")
-def openai_test_schema(pg0_db_url, worker_id, openai_embeddings):
-    """Create an isolated schema for OpenAI embedding tests."""
-    schema_name = get_test_schema("test_openai_embed", worker_id)
-    create_isolated_schema(pg0_db_url, schema_name, dimension=openai_embeddings.dimension)
-    yield pg0_db_url, schema_name
-    drop_schema(pg0_db_url, schema_name)
-
-
-@pytest.fixture
-def cross_encoder():
-    """Provide a cross encoder for tests."""
-    return LocalSTCrossEncoder()
-
-
-@pytest.fixture
-def query_analyzer():
-    """Provide a query analyzer for tests."""
-    return DateparserQueryAnalyzer()
-
-
-@pytest.fixture
-def test_bank_id():
-    """Provide a unique bank ID for this test run."""
-    return f"openai_test_{datetime.now().timestamp()}"
-
-
-@pytest.fixture
-def request_context():
-    """Provide a default RequestContext for tests."""
-    return RequestContext()
-
-
-class TestOpenAIEmbeddings:
-    """Tests for OpenAI embeddings provider."""
-
-    def test_openai_embeddings_initialization(self, openai_embeddings):
-        """Test that OpenAI embeddings initializes correctly."""
-        assert openai_embeddings.dimension == 1536
-        assert openai_embeddings.provider_name == "openai"
-
-    def test_openai_embeddings_encode(self, openai_embeddings):
-        """Test that OpenAI embeddings can encode text."""
-        texts = ["Hello, world!", "This is a test."]
-        embeddings = openai_embeddings.encode(texts)
-
-        assert len(embeddings) == 2
-        assert len(embeddings[0]) == 1536
-        assert len(embeddings[1]) == 1536
-        assert all(isinstance(x, float) for x in embeddings[0])
-
-    @pytest.mark.asyncio
-    async def test_openai_embeddings_retain_recall(
-        self,
-        openai_test_schema,
-        openai_embeddings,
-        cross_encoder,
-        query_analyzer,
-        test_bank_id,
-        request_context,
-    ):
-        """Test retain and recall operations with OpenAI embeddings."""
-        db_url, schema_name = openai_test_schema
-
-        memory = MemoryEngine(
-            db_url=db_url,
-            memory_llm_provider=os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"),
-            memory_llm_api_key=os.getenv("HINDSIGHT_API_LLM_API_KEY"),
-            memory_llm_model=os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"),
-            memory_llm_base_url=os.getenv("HINDSIGHT_API_LLM_BASE_URL") or None,
-            embeddings=openai_embeddings,
-            cross_encoder=cross_encoder,
-            query_analyzer=query_analyzer,
-            pool_min_size=1,
-            pool_max_size=3,
-            run_migrations=False,
-            tenant_extension=SchemaTenantExtension(schema_name),
-        )
-
-        try:
-            await memory.initialize()
-
-            # Store some memories
-            await memory.retain_async(
-                bank_id=test_bank_id,
-                content="Alice works as a software engineer at Google.",
-                context="career discussion",
-                request_context=request_context,
-            )
-
-            await memory.retain_async(
-                bank_id=test_bank_id,
-                content="Bob is a data scientist specializing in machine learning.",
-                context="team introductions",
-                request_context=request_context,
-            )
-
-            # Recall memories
-            result = await memory.recall_async(
-                bank_id=test_bank_id,
-                query="Who works in technology?",
-                request_context=request_context,
-            )
-
-            assert result is not None
-            assert len(result.results) > 0
-
-            memory_texts = [m.text for m in result.results]
-            assert any(
-                "Alice" in text or "Bob" in text or "software" in text or "data scientist" in text
-                for text in memory_texts
-            ), f"Expected to find relevant memories, got: {memory_texts}"
-
-        finally:
-            try:
-                if memory._pool and not memory._pool._closing:
-                    await memory.close()
-            except Exception:
-                pass
-
-    @pytest.mark.asyncio
-    async def test_openai_embeddings_batch_retain(
-        self,
-        openai_test_schema,
-        openai_embeddings,
-        cross_encoder,
-        query_analyzer,
-        test_bank_id,
-        request_context,
-    ):
-        """Test batch retain with OpenAI embeddings."""
-        db_url, schema_name = openai_test_schema
-
-        memory = MemoryEngine(
-            db_url=db_url,
-            memory_llm_provider=os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"),
-            memory_llm_api_key=os.getenv("HINDSIGHT_API_LLM_API_KEY"),
-            memory_llm_model=os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"),
-            memory_llm_base_url=os.getenv("HINDSIGHT_API_LLM_BASE_URL") or None,
-            embeddings=openai_embeddings,
-            cross_encoder=cross_encoder,
-            query_analyzer=query_analyzer,
-            pool_min_size=1,
-            pool_max_size=3,
-            run_migrations=False,
-            tenant_extension=SchemaTenantExtension(schema_name),
-        )
-
-        try:
-            await memory.initialize()
-
-            contents = [
-                {"content": "Python is my favorite programming language.", "context": "preferences"},
-                {"content": "I prefer dark mode for all my applications.", "context": "preferences"},
-                {"content": "Coffee is essential for morning productivity.", "context": "habits"},
-            ]
-
-            result = await memory.retain_batch_async(
-                bank_id=test_bank_id,
-                contents=contents,
-                request_context=request_context,
-            )
-
-            assert len(result) == 3
-
-            recall_result = await memory.recall_async(
-                bank_id=test_bank_id,
-                query="What are my preferences?",
-                request_context=request_context,
-            )
-
-            assert recall_result is not None
-            assert len(recall_result.results) > 0
-
-        finally:
-            try:
-                if memory._pool and not memory._pool._closing:
-                    await memory.close()
-            except Exception:
-                pass
-
-
-# =============================================================================
-# Cohere Embeddings Tests
-# =============================================================================
-
-
-def has_cohere_api_key() -> bool:
-    """Check if Cohere API key is available."""
-    return bool(os.environ.get("COHERE_API_KEY"))
-
-
-def get_cohere_api_key() -> str:
-    """Get Cohere API key from environment."""
-    return os.environ.get("COHERE_API_KEY", "")
-
-
-@pytest.fixture(scope="module")
-def cohere_embeddings():
-    """Create Cohere embeddings instance."""
-    if not has_cohere_api_key():
-        pytest.skip("Cohere API key not available (set COHERE_API_KEY)")
-
-    embeddings = CohereEmbeddings(
-        api_key=get_cohere_api_key(),
-        model="embed-english-v3.0",
-    )
-    loop = asyncio.new_event_loop()
-    try:
-        loop.run_until_complete(embeddings.initialize())
-    finally:
-        loop.close()
-    return embeddings
-
-
-@pytest.fixture(scope="module")
-def cohere_cross_encoder():
-    """Create Cohere cross-encoder instance."""
-    if not has_cohere_api_key():
-        pytest.skip("Cohere API key not available (set COHERE_API_KEY)")
-
-    cross_encoder = CohereCrossEncoder(
-        api_key=get_cohere_api_key(),
-        model="rerank-english-v3.0",
-    )
-    loop = asyncio.new_event_loop()
-    try:
-        loop.run_until_complete(cross_encoder.initialize())
-    finally:
-        loop.close()
-    return cross_encoder
-
-
-@pytest.fixture(scope="module")
-def cohere_test_schema(pg0_db_url, worker_id, cohere_embeddings):
-    """Create an isolated schema for Cohere embedding tests."""
-    schema_name = get_test_schema("test_cohere_embed", worker_id)
-    create_isolated_schema(pg0_db_url, schema_name, dimension=cohere_embeddings.dimension)
-    yield pg0_db_url, schema_name
-    drop_schema(pg0_db_url, schema_name)
-
-
-class TestCohereEmbeddings:
-    """Tests for Cohere embeddings provider."""
-
-    def test_cohere_embeddings_initialization(self, cohere_embeddings):
-        """Test that Cohere embeddings initializes correctly."""
-        assert cohere_embeddings.dimension == 1024
-        assert cohere_embeddings.provider_name == "cohere"
-
-    def test_cohere_embeddings_encode(self, cohere_embeddings):
-        """Test that Cohere embeddings can encode text."""
-        texts = ["Hello, world!", "This is a test."]
-        embeddings = cohere_embeddings.encode(texts)
-
-        assert len(embeddings) == 2
-        assert len(embeddings[0]) == 1024
-        assert len(embeddings[1]) == 1024
-        assert all(isinstance(x, float) for x in embeddings[0])
-
-
-class TestCohereCrossEncoder:
-    """Tests for Cohere cross-encoder/reranker."""
-
-    def test_cohere_cross_encoder_initialization(self, cohere_cross_encoder):
-        """Test that Cohere cross-encoder initializes correctly."""
-        assert cohere_cross_encoder.provider_name == "cohere"
-
-    def test_cohere_cross_encoder_predict(self, cohere_cross_encoder):
-        """Test that Cohere cross-encoder can score pairs."""
-        pairs = [
-            ("What is the capital of France?", "Paris is the capital of France."),
-            ("What is the capital of France?", "The Eiffel Tower is in Paris."),
-            ("What is the capital of France?", "Python is a programming language."),
-        ]
-        scores = cohere_cross_encoder.predict(pairs)
-
-        assert len(scores) == 3
-        assert all(isinstance(s, float) for s in scores)
-        # The first result should be most relevant
-        assert scores[0] > scores[2], "Direct answer should score higher than unrelated text"
-
-
-class TestCohereIntegration:
-    """Integration tests for Cohere embeddings with memory engine."""
-
-    @pytest.mark.asyncio
-    async def test_cohere_embeddings_retain_recall(
-        self,
-        cohere_test_schema,
-        cohere_embeddings,
-        cohere_cross_encoder,
-        query_analyzer,
-        request_context,
-    ):
-        """Test retain and recall operations with Cohere embeddings."""
-        db_url, schema_name = cohere_test_schema
-        test_bank_id = f"cohere_test_{datetime.now().timestamp()}"
-
-        memory = MemoryEngine(
-            db_url=db_url,
-            memory_llm_provider=os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"),
-            memory_llm_api_key=os.getenv("HINDSIGHT_API_LLM_API_KEY"),
-            memory_llm_model=os.getenv("HINDSIGHT_API_LLM_MODEL", "openai/gpt-oss-120b"),
-            memory_llm_base_url=os.getenv("HINDSIGHT_API_LLM_BASE_URL") or None,
-            embeddings=cohere_embeddings,
-            cross_encoder=cohere_cross_encoder,
-            query_analyzer=query_analyzer,
-            pool_min_size=1,
-            pool_max_size=3,
-            run_migrations=False,
-            tenant_extension=SchemaTenantExtension(schema_name),
-        )
-
-        try:
-            await memory.initialize()
-
-            # Store some memories
-            await memory.retain_async(
-                bank_id=test_bank_id,
-                content="Alice works as a software engineer at Google.",
-                context="career discussion",
-                request_context=request_context,
-            )
-
-            await memory.retain_async(
-                bank_id=test_bank_id,
-                content="Bob is a data scientist specializing in machine learning.",
-                context="team introductions",
-                request_context=request_context,
-            )
-
-            # Recall memories
-            result = await memory.recall_async(
-                bank_id=test_bank_id,
-                query="Who works in technology?",
-                request_context=request_context,
-            )
-
-            assert result is not None
-            assert len(result.results) > 0
-
-            memory_texts = [m.text for m in result.results]
-            assert any(
-                "Alice" in text or "Bob" in text or "software" in text or "data scientist" in text
-                for text in memory_texts
-            ), f"Expected to find relevant memories, got: {memory_texts}"
-
-        finally:
-            try:
-                if memory._pool and not memory._pool._closing:
-                    await memory.close()
-            except Exception:
-                pass
@@ -4,11 +4,10 @@ Tests for document tracking and upsert functionality.
 import logging
 import pytest
 from datetime import datetime, timezone
-from hindsight_api import RequestContext


@pytest.mark.asyncio
-async def test_document_creation_and_retrieval(memory, request_context):
+async def test_document_creation_and_retrieval(memory):
    """Test that documents are created and can be retrieved."""
    bank_id = f"test_doc_{datetime.now(timezone.utc).timestamp()}"

@@ -20,12 +19,11 @@ async def test_document_creation_and_retrieval(memory, request_context):
            bank_id=bank_id,
            content="Alice works at Google. Bob works at Microsoft.",
            context="Team meeting",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        # Retrieve document
-        doc = await memory.get_document(document_id, bank_id, request_context=request_context)
+        doc = await memory.get_document(document_id, bank_id)

        assert doc is not None
        assert doc["id"] == document_id
@@ -34,11 +32,11 @@ async def test_document_creation_and_retrieval(memory, request_context):
        assert doc["memory_unit_count"] > 0

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_document_upsert(memory, request_context):
+async def test_document_upsert(memory):
    """Test that providing the same document_id automatically upserts (deletes old units and creates new ones)."""
    bank_id = f"test_upsert_{datetime.now(timezone.utc).timestamp()}"

@@ -50,12 +48,11 @@ async def test_document_upsert(memory, request_context):
            bank_id=bank_id,
            content="Alice works at Google.",
            context="Initial",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        # Get document stats
-        doc_v1 = await memory.get_document(document_id, bank_id, request_context=request_context)
+        doc_v1 = await memory.get_document(document_id, bank_id)
        count_v1 = doc_v1["memory_unit_count"]

        # Update with different content (automatic upsert when same document_id is provided)
@@ -63,12 +60,11 @@ async def test_document_upsert(memory, request_context):
            bank_id=bank_id,
            content="Alice works at Microsoft. Bob works at Apple.",
            context="Updated",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        # Get updated document stats
-        doc_v2 = await memory.get_document(document_id, bank_id, request_context=request_context)
+        doc_v2 = await memory.get_document(document_id, bank_id)
        count_v2 = doc_v2["memory_unit_count"]

        # Verify old units were replaced
@@ -79,11 +75,11 @@ async def test_document_upsert(memory, request_context):
        assert set(units_v1).isdisjoint(set(units_v2))

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_document_deletion(memory, request_context):
+async def test_document_deletion(memory):
    """Test that deleting a document cascades to memory units."""
    bank_id = f"test_delete_{datetime.now(timezone.utc).timestamp()}"

@@ -95,30 +91,29 @@ async def test_document_deletion(memory, request_context):
            bank_id=bank_id,
            content="Alice works at Google.",
            context="Test",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        # Verify it exists
-        doc = await memory.get_document(document_id, bank_id, request_context=request_context)
+        doc = await memory.get_document(document_id, bank_id)
        assert doc is not None
        assert doc["memory_unit_count"] > 0

        # Delete document
-        result = await memory.delete_document(document_id, bank_id, request_context=request_context)
+        result = await memory.delete_document(document_id, bank_id)
        assert result["document_deleted"] == 1
        assert result["memory_units_deleted"] > 0

        # Verify it's gone
-        doc_after = await memory.get_document(document_id, bank_id, request_context=request_context)
+        doc_after = await memory.get_document(document_id, bank_id)
        assert doc_after is None

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_memory_without_document(memory, request_context):
+async def test_memory_without_document(memory):
    """Test that memories can still be created without document tracking."""
    bank_id = f"test_no_doc_{datetime.now(timezone.utc).timestamp()}"

@@ -127,11 +122,10 @@ async def test_memory_without_document(memory, request_context):
        units = await memory.retain_async(
            bank_id=bank_id,
            content="Alice works at Google.",
-            context="Test",
-            request_context=request_context,
+            context="Test"
        )

        assert len(units) > 0

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)
@@ -1,796 +0,0 @@
-"""Tests for the Hindsight extensions system."""
-
-from collections import defaultdict
-
-import pytest
-from fastapi import APIRouter
-from fastapi.testclient import TestClient
-
-from hindsight_api.extensions import (
-    ApiKeyTenantExtension,
-    AuthenticationError,
-    Extension,
-    HttpExtension,
-    OperationValidationError,
-    OperationValidatorExtension,
-    RecallContext,
-    RecallResult,
-    ReflectContext,
-    ReflectResultContext,
-    RequestContext,
-    RetainContext,
-    RetainResult,
-    TenantContext,
-    TenantExtension,
-    ValidationResult,
-    load_extension,
-)
-
-
-class TestExtensionLoader:
-    """Tests for extension loading and lifecycle."""
-
-    def test_load_extension_with_config(self, monkeypatch):
-        """Extension receives config from prefixed env vars and supports lifecycle."""
-        monkeypatch.setenv(
-            "HINDSIGHT_API_TEST_EXTENSION",
-            "tests.test_extensions:LifecycleTestExtension",
-        )
-        monkeypatch.setenv("HINDSIGHT_API_TEST_API_URL", "https://example.com")
-        monkeypatch.setenv("HINDSIGHT_API_TEST_MAX_RETRIES", "5")
-
-        ext = load_extension("TEST", Extension)
-
-        assert ext is not None
-        assert ext.config["api_url"] == "https://example.com"
-        assert ext.config["max_retries"] == "5"
-
-    @pytest.mark.asyncio
-    async def test_extension_lifecycle(self, monkeypatch):
-        """Extension on_startup and on_shutdown are called."""
-        monkeypatch.setenv(
-            "HINDSIGHT_API_TEST_EXTENSION",
-            "tests.test_extensions:LifecycleTestExtension",
-        )
-
-        ext = load_extension("TEST", Extension)
-
-        assert not ext.started
-        assert not ext.stopped
-
-        await ext.on_startup()
-        assert ext.started
-
-        await ext.on_shutdown()
-        assert ext.stopped
-
-
-class LifecycleTestExtension(Extension):
-    """Test extension for config and lifecycle tests."""
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.started = False
-        self.stopped = False
-
-    async def on_startup(self):
-        self.started = True
-
-    async def on_shutdown(self):
-        self.stopped = True
-
-
-class RateLimitingValidator(OperationValidatorExtension):
-    """
-    Mock validator that blocks after N attempts per bank_id.
-
-    Used for testing the extension integration with MemoryEngine.
-    """
-
-    def __init__(self, config: dict):
-        super().__init__(config)
-        self.max_attempts = int(config.get("max_attempts", "2"))
-        self.retain_counts: dict[str, int] = defaultdict(int)
-        self.recall_counts: dict[str, int] = defaultdict(int)
-        self.reflect_counts: dict[str, int] = defaultdict(int)
-
-    async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
-        self.retain_counts[ctx.bank_id] += 1
-        if self.retain_counts[ctx.bank_id] > self.max_attempts:
-            return ValidationResult.reject(
-                f"Retain limit exceeded for bank {ctx.bank_id}"
-            )
-        return ValidationResult.accept()
-
-    async def validate_recall(self, ctx: RecallContext) -> ValidationResult:
-        self.recall_counts[ctx.bank_id] += 1
-        if self.recall_counts[ctx.bank_id] > self.max_attempts:
-            return ValidationResult.reject(
-                f"Recall limit exceeded for bank {ctx.bank_id}"
-            )
-        return ValidationResult.accept()
-
-    async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
-        self.reflect_counts[ctx.bank_id] += 1
-        if self.reflect_counts[ctx.bank_id] > self.max_attempts:
-            return ValidationResult.reject(
-                f"Reflect limit exceeded for bank {ctx.bank_id}"
-            )
-        return ValidationResult.accept()
-
-
-class TrackingValidator(OperationValidatorExtension):
-    """
-    Mock validator that tracks all pre and post hook calls with full parameters.
-
-    Used for testing that hooks receive all user-provided parameters.
-    """
-
-    def __init__(self, config: dict):
-        super().__init__(config)
-        # Pre-hook tracking
-        self.pre_retain_calls: list[RetainContext] = []
-        self.pre_recall_calls: list[RecallContext] = []
-        self.pre_reflect_calls: list[ReflectContext] = []
-        # Post-hook tracking
-        self.post_retain_calls: list[RetainResult] = []
-        self.post_recall_calls: list[RecallResult] = []
-        self.post_reflect_calls: list[ReflectResultContext] = []
-
-    async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
-        self.pre_retain_calls.append(ctx)
-        return ValidationResult.accept()
-
-    async def validate_recall(self, ctx: RecallContext) -> ValidationResult:
-        self.pre_recall_calls.append(ctx)
-        return ValidationResult.accept()
-
-    async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
-        self.pre_reflect_calls.append(ctx)
-        return ValidationResult.accept()
-
-    async def on_retain_complete(self, result: RetainResult) -> None:
-        self.post_retain_calls.append(result)
-
-    async def on_recall_complete(self, result: RecallResult) -> None:
-        self.post_recall_calls.append(result)
-
-    async def on_reflect_complete(self, result: ReflectResultContext) -> None:
-        self.post_reflect_calls.append(result)
-
-
-class TestMemoryEngineValidation:
-    """Tests for validation integration with MemoryEngine.
-
-    The OperationValidatorExtension is integrated at the MemoryEngine level,
-    so all interfaces (HTTP API, MCP, SDK) get the same validation behavior.
-
-    For retain, the batch is validated as a whole (all or nothing) using
-    retain_batch_async which is the public method used by the HTTP API.
-    """
-
-    @pytest.mark.asyncio
-    async def test_retain_batch_validation(self, memory_with_validator):
-        """Retain batch is validated as a whole - accepts or rejects entire batch."""
-        memory = memory_with_validator
-        bank_id = "test-retain-batch"
-        ctx = RequestContext()
-
-        # First batch should succeed
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[
-                {"content": "First item"},
-                {"content": "Second item"},
-            ],
-            request_context=ctx,
-        )
-
-        # Second batch should succeed (2nd attempt)
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[{"content": "Third item"}],
-            request_context=ctx,
-        )
-
-        # Third batch should be blocked entirely (exceeds limit)
-        with pytest.raises(OperationValidationError) as exc_info:
-            await memory.retain_batch_async(
-                bank_id=bank_id,
-                contents=[
-                    {"content": "Should not be stored"},
-                    {"content": "Neither should this"},
-                ],
-                request_context=ctx,
-            )
-
-        assert "limit exceeded" in str(exc_info.value).lower()
-
-    @pytest.mark.asyncio
-    async def test_recall_validation(self, memory_with_validator):
-        """Recall is validated before execution."""
-        memory = memory_with_validator
-        bank_id = "test-recall-validation"
-        ctx = RequestContext()
-
-        # First recall should pass validation
-        await memory.recall_async(bank_id, "test query", fact_type=["world"], request_context=ctx)
-
-        # Second recall should pass validation
-        await memory.recall_async(bank_id, "another query", fact_type=["world"], request_context=ctx)
-
-        # Third recall should be blocked by validator
-        with pytest.raises(OperationValidationError) as exc_info:
-            await memory.recall_async(bank_id, "blocked query", fact_type=["world"], request_context=ctx)
-
-        assert "limit exceeded" in str(exc_info.value).lower()
-
-    @pytest.mark.asyncio
-    async def test_reflect_validation(self, memory_with_validator):
-        """Reflect is validated before execution."""
-        memory = memory_with_validator
-        bank_id = "test-reflect-validation"
-        ctx = RequestContext()
-
-        # First reflect should pass validation (may fail internally but validation passes)
-        try:
-            await memory.reflect_async(bank_id, "test question", request_context=ctx)
-        except OperationValidationError:
-            raise  # Re-raise validation errors
-        except Exception:
-            pass  # Other errors are fine (e.g., no data)
-
-        # Second reflect should pass validation
-        try:
-            await memory.reflect_async(bank_id, "another question", request_context=ctx)
-        except OperationValidationError:
-            raise
-        except Exception:
-            pass
-
-        # Third reflect should be blocked by validator
-        with pytest.raises(OperationValidationError) as exc_info:
-            await memory.reflect_async(bank_id, "blocked question", request_context=ctx)
-
-        assert "limit exceeded" in str(exc_info.value).lower()
-
-
-@pytest.fixture
-def memory_with_validator(memory):
-    """Memory engine with a rate-limiting validator (max 2 attempts per bank)."""
-    validator = RateLimitingValidator({"max_attempts": "2"})
-    memory._operation_validator = validator
-    return memory
-
-
-@pytest.fixture
-def memory_with_tracking_validator(memory):
-    """Memory engine with a tracking validator that records all hook calls."""
-    validator = TrackingValidator({})
-    memory._operation_validator = validator
-    return memory, validator
-
-
-class TestOperationHooksParameters:
-    """Tests for pre and post operation hooks receiving all user-provided parameters."""
-
-    @pytest.mark.asyncio
-    async def test_retain_pre_hook_receives_all_parameters(self, memory_with_tracking_validator):
-        """Pre-retain hook receives all user-provided parameters."""
-        memory, validator = memory_with_tracking_validator
-        bank_id = "test-retain-params"
-        ctx = RequestContext(api_key="test-key")
-        contents = [{"content": "Test content", "context": "test context"}]
-        document_id = "doc-123"
-
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=contents,
-            document_id=document_id,
-            fact_type_override="world",
-            confidence_score=0.9,
-            request_context=ctx,
-        )
-
-        assert len(validator.pre_retain_calls) == 1
-        pre_ctx = validator.pre_retain_calls[0]
-
-        # Verify all parameters are present
-        assert pre_ctx.bank_id == bank_id
-        # Note: contents is copied before document_id is applied to individual items
-        assert len(pre_ctx.contents) == len(contents)
-        assert pre_ctx.contents[0]["content"] == contents[0]["content"]
-        assert pre_ctx.document_id == document_id
-        assert pre_ctx.fact_type_override == "world"
-        assert pre_ctx.confidence_score == 0.9
-        assert pre_ctx.request_context == ctx
-
-    @pytest.mark.asyncio
-    async def test_retain_post_hook_receives_all_parameters_and_result(self, memory_with_tracking_validator):
-        """Post-retain hook receives all parameters plus the result."""
-        memory, validator = memory_with_tracking_validator
-        bank_id = "test-retain-post"
-        ctx = RequestContext(api_key="test-key")
-        contents = [{"content": "Test content for post hook"}]
-        document_id = "doc-456"
-
-        result = await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=contents,
-            document_id=document_id,
-            fact_type_override="experience",
-            confidence_score=0.8,
-            request_context=ctx,
-        )
-
-        assert len(validator.post_retain_calls) == 1
-        post_result = validator.post_retain_calls[0]
-
-        # Verify all parameters are present
-        assert post_result.bank_id == bank_id
-        assert post_result.document_id == document_id
-        assert post_result.fact_type_override == "experience"
-        assert post_result.confidence_score == 0.8
-        assert post_result.request_context == ctx
-
-        # Verify result data
-        assert post_result.success is True
-        assert post_result.error is None
-        assert post_result.unit_ids == result  # Should match the return value
-
-    @pytest.mark.asyncio
-    async def test_recall_pre_hook_receives_all_parameters(self, memory_with_tracking_validator):
-        """Pre-recall hook receives all user-provided parameters."""
-        from datetime import datetime, timezone
-        from hindsight_api.engine.memory_engine import Budget
-
-        memory, validator = memory_with_tracking_validator
-        bank_id = "test-recall-params"
-        ctx = RequestContext(api_key="test-key")
-        query = "test query"
-        question_date = datetime(2024, 1, 15, tzinfo=timezone.utc)
-
-        await memory.recall_async(
-            bank_id=bank_id,
-            query=query,
-            budget=Budget.HIGH,
-            max_tokens=2048,
-            enable_trace=True,
-            fact_type=["world", "experience"],
-            question_date=question_date,
-            include_entities=True,
-            max_entity_tokens=300,
-            include_chunks=True,
-            max_chunk_tokens=4096,
-            request_context=ctx,
-        )
-
-        assert len(validator.pre_recall_calls) == 1
-        pre_ctx = validator.pre_recall_calls[0]
-
-        # Verify all parameters are present
-        assert pre_ctx.bank_id == bank_id
-        assert pre_ctx.query == query
-        assert pre_ctx.budget == Budget.HIGH
-        assert pre_ctx.max_tokens == 2048
-        assert pre_ctx.enable_trace is True
-        assert pre_ctx.fact_types == ["world", "experience"]
-        assert pre_ctx.question_date == question_date
-        assert pre_ctx.include_entities is True
-        assert pre_ctx.max_entity_tokens == 300
-        assert pre_ctx.include_chunks is True
-        assert pre_ctx.max_chunk_tokens == 4096
-        assert pre_ctx.request_context == ctx
-
-    @pytest.mark.asyncio
-    async def test_recall_post_hook_receives_all_parameters_and_result(self, memory_with_tracking_validator):
-        """Post-recall hook receives all parameters plus the result."""
-        from hindsight_api.engine.memory_engine import Budget
-
-        memory, validator = memory_with_tracking_validator
-        bank_id = "test-recall-post"
-        ctx = RequestContext(api_key="test-key")
-
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="test query for post",
-            budget=Budget.LOW,
-            max_tokens=1024,
-            fact_type=["world"],
-            request_context=ctx,
-        )
-
-        assert len(validator.post_recall_calls) == 1
-        post_result = validator.post_recall_calls[0]
-
-        # Verify all parameters are present
-        assert post_result.bank_id == bank_id
-        assert post_result.query == "test query for post"
-        assert post_result.budget == Budget.LOW
-        assert post_result.max_tokens == 1024
-        assert post_result.fact_types == ["world"]
-        assert post_result.request_context == ctx
-
-        # Verify result data
-        assert post_result.success is True
-        assert post_result.error is None
-        assert post_result.result == result  # Should match the return value
-
-    @pytest.mark.asyncio
-    async def test_reflect_pre_hook_receives_all_parameters(self, memory_with_tracking_validator):
-        """Pre-reflect hook receives all user-provided parameters."""
-        from hindsight_api.engine.memory_engine import Budget
-
-        memory, validator = memory_with_tracking_validator
-        bank_id = "test-reflect-params"
-        ctx = RequestContext(api_key="test-key")
-
-        try:
-            await memory.reflect_async(
-                bank_id=bank_id,
-                query="test question",
-                budget=Budget.MID,
-                context="additional context",
-                request_context=ctx,
-            )
-        except Exception:
-            pass  # May fail if no data, but pre-hook should still be called
-
-        assert len(validator.pre_reflect_calls) == 1
-        pre_ctx = validator.pre_reflect_calls[0]
-
-        # Verify all parameters are present
-        assert pre_ctx.bank_id == bank_id
-        assert pre_ctx.query == "test question"
-        assert pre_ctx.budget == Budget.MID
-        assert pre_ctx.context == "additional context"
-        assert pre_ctx.request_context == ctx
-
-    @pytest.mark.asyncio
-    async def test_reflect_post_hook_receives_all_parameters_and_result(self, memory_with_tracking_validator):
-        """Post-reflect hook receives all parameters plus the result on success."""
-        from hindsight_api.engine.memory_engine import Budget
-
-        memory, validator = memory_with_tracking_validator
-        bank_id = "test-reflect-post"
-        ctx = RequestContext(api_key="test-key")
-
-        # Store some content first so reflect has something to work with
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[{"content": "Alice is a software engineer at Google."}],
-            request_context=ctx,
-        )
-
-        result = await memory.reflect_async(
-            bank_id=bank_id,
-            query="What does Alice do?",
-            budget=Budget.LOW,
-            context="work context",
-            request_context=ctx,
-        )
-
-        assert len(validator.post_reflect_calls) == 1
-        post_result = validator.post_reflect_calls[0]
-
-        # Verify all parameters are present
-        assert post_result.bank_id == bank_id
-        assert post_result.query == "What does Alice do?"
-        assert post_result.budget == Budget.LOW
-        assert post_result.context == "work context"
-        assert post_result.request_context == ctx
-
-        # Verify result data
-        assert post_result.success is True
-        assert post_result.error is None
-        assert post_result.result == result  # Should match the return value
-        assert post_result.result.text is not None
-
-    @pytest.mark.asyncio
-    async def test_post_hooks_called_in_order_after_pre_hooks(self, memory_with_tracking_validator):
-        """Post hooks are called after pre hooks and after operation completes."""
-        memory, validator = memory_with_tracking_validator
-        bank_id = "test-hook-order"
-        ctx = RequestContext()
-
-        # Retain operation
-        await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[{"content": "Test content"}],
-            request_context=ctx,
-        )
-
-        # Pre-hook should be called before post-hook
-        assert len(validator.pre_retain_calls) == 1
-        assert len(validator.post_retain_calls) == 1
-
-        # Recall operation
-        await memory.recall_async(
-            bank_id=bank_id,
-            query="test",
-            fact_type=["world"],
-            request_context=ctx,
-        )
-
-        assert len(validator.pre_recall_calls) == 1
-        assert len(validator.post_recall_calls) == 1
-
-
-class TestTenantExtension:
-    """Tests for TenantExtension and ApiKeyTenantExtension."""
-
-    @pytest.mark.asyncio
-    async def test_api_key_tenant_extension_valid_key(self):
-        """ApiKeyTenantExtension accepts valid API key."""
-        ext = ApiKeyTenantExtension({"api_key": "secret-key-123"})
-
-        result = await ext.authenticate(RequestContext(api_key="secret-key-123"))
-
-        assert result.schema_name == "public"
-
-    @pytest.mark.asyncio
-    async def test_api_key_tenant_extension_invalid_key(self):
-        """ApiKeyTenantExtension rejects invalid API key."""
-        ext = ApiKeyTenantExtension({"api_key": "secret-key-123"})
-
-        with pytest.raises(AuthenticationError) as exc_info:
-            await ext.authenticate(RequestContext(api_key="wrong-key"))
-
-        assert "Invalid API key" in str(exc_info.value)
-
-    @pytest.mark.asyncio
-    async def test_api_key_tenant_extension_missing_key(self):
-        """ApiKeyTenantExtension rejects missing API key."""
-        ext = ApiKeyTenantExtension({"api_key": "secret-key-123"})
-
-        with pytest.raises(AuthenticationError):
-            await ext.authenticate(RequestContext(api_key=None))
-
-    def test_api_key_tenant_extension_requires_config(self):
-        """ApiKeyTenantExtension requires api_key in config."""
-        with pytest.raises(ValueError) as exc_info:
-            ApiKeyTenantExtension({})
-
-        assert "HINDSIGHT_API_TENANT_API_KEY is required" in str(exc_info.value)
-
-
-class TestMemoryEngineTenantAuth:
-    """Tests for tenant authentication in MemoryEngine."""
-
-    @pytest.mark.asyncio
-    async def test_retain_requires_tenant_request_when_extension_configured(
-        self, memory_with_tenant
-    ):
-        """Retain fails without RequestContext when tenant extension is configured."""
-        memory = memory_with_tenant
-
-        with pytest.raises(AuthenticationError) as exc_info:
-            await memory.retain_batch_async(
-                bank_id="test-bank",
-                contents=[{"content": "test"}],
-                request_context=None,  # Missing!
-            )
-
-        assert "RequestContext is required" in str(exc_info.value)
-
-    @pytest.mark.asyncio
-    async def test_retain_succeeds_with_valid_tenant_request(self, memory_with_tenant):
-        """Retain succeeds with valid RequestContext."""
-        memory = memory_with_tenant
-
-        # Should not raise
-        await memory.retain_batch_async(
-            bank_id="test-bank-tenant",
-            contents=[{"content": "test content"}],
-            request_context=RequestContext(api_key="test-api-key"),
-        )
-
-    @pytest.mark.asyncio
-    async def test_retain_fails_with_invalid_api_key(self, memory_with_tenant):
-        """Retain fails with invalid API key."""
-        memory = memory_with_tenant
-
-        with pytest.raises(AuthenticationError) as exc_info:
-            await memory.retain_batch_async(
-                bank_id="test-bank",
-                contents=[{"content": "test"}],
-                request_context=RequestContext(api_key="wrong-key"),
-            )
-
-        assert "Invalid API key" in str(exc_info.value)
-
-    @pytest.mark.asyncio
-    async def test_recall_requires_tenant_request_when_extension_configured(
-        self, memory_with_tenant
-    ):
-        """Recall fails without RequestContext when tenant extension is configured."""
-        memory = memory_with_tenant
-
-        with pytest.raises(AuthenticationError):
-            await memory.recall_async(
-                bank_id="test-bank",
-                query="test query",
-                fact_type=["world"],
-                request_context=None,
-            )
-
-    @pytest.mark.asyncio
-    async def test_no_tenant_request_needed_without_extension(self, memory):
-        """Operations work with empty RequestContext when no tenant extension configured."""
-        # Should not raise - no tenant extension configured, just pass empty RequestContext
-        await memory.retain_batch_async(
-            bank_id="test-bank-no-tenant",
-            contents=[{"content": "test content"}],
-            request_context=RequestContext(),
-        )
-
-
-@pytest.fixture
-def memory_with_tenant(memory):
-    """Memory engine with a tenant extension (API key auth)."""
-    tenant_ext = ApiKeyTenantExtension({"api_key": "test-api-key"})
-    memory._tenant_extension = tenant_ext
-    return memory
-
-
-class SampleHttpExtension(HttpExtension):
-    """Sample HTTP extension for testing that provides custom endpoints."""
-
-    def __init__(self, config: dict):
-        super().__init__(config)
-        self.started = False
-        self.stopped = False
-        self.request_count = 0
-
-    async def on_startup(self):
-        self.started = True
-
-    async def on_shutdown(self):
-        self.stopped = True
-
-    def get_router(self, memory) -> APIRouter:
-        router = APIRouter()
-
-        @router.get("/hello")
-        async def hello():
-            self.request_count += 1
-            return {"message": "Hello from extension!"}
-
-        @router.get("/config")
-        async def get_config():
-            return {"config": self.config}
-
-        @router.get("/health-check")
-        async def extension_health():
-            health = await memory.health_check()
-            return {"extension": "healthy", "memory": health}
-
-        @router.post("/echo")
-        async def echo(data: dict):
-            return {"echoed": data}
-
-        return router
-
-
-class TestHttpExtensionIntegration:
-    """Tests for HTTP extension integration."""
-
-    def test_load_http_extension(self, monkeypatch):
-        """HttpExtension can be loaded from environment variable."""
-        monkeypatch.setenv(
-            "HINDSIGHT_API_HTTP_EXTENSION",
-            "tests.test_extensions:SampleHttpExtension",
-        )
-        monkeypatch.setenv("HINDSIGHT_API_HTTP_CUSTOM_PARAM", "custom_value")
-
-        ext = load_extension("HTTP", HttpExtension)
-
-        assert ext is not None
-        assert isinstance(ext, SampleHttpExtension)
-        assert ext.config["custom_param"] == "custom_value"
-
-    def test_http_extension_router_mounted_at_ext(self, memory):
-        """HTTP extension router is mounted at /ext/."""
-        from hindsight_api.api.http import create_app
-
-        ext = SampleHttpExtension({"test_key": "test_value"})
-        app = create_app(memory, initialize_memory=False, http_extension=ext)
-
-        client = TestClient(app)
-
-        # Extension endpoint should be accessible at /ext/
-        response = client.get("/ext/hello")
-        assert response.status_code == 200
-        assert response.json() == {"message": "Hello from extension!"}
-
-        # Should track request count
-        assert ext.request_count == 1
-
-        # Old path should NOT work
-        response = client.get("/extension/hello")
-        assert response.status_code == 404
-
-    def test_http_extension_config_endpoint(self, memory):
-        """Extension can expose its config via custom endpoint."""
-        from hindsight_api.api.http import create_app
-
-        ext = SampleHttpExtension({"api_key": "secret", "limit": "100"})
-        app = create_app(memory, initialize_memory=False, http_extension=ext)
-
-        client = TestClient(app)
-
-        response = client.get("/ext/config")
-        assert response.status_code == 200
-        assert response.json()["config"]["api_key"] == "secret"
-        assert response.json()["config"]["limit"] == "100"
-
-    def test_http_extension_can_access_memory(self, memory):
-        """Extension endpoints can access memory engine."""
-        from hindsight_api.api.http import create_app
-
-        ext = SampleHttpExtension({})
-        app = create_app(memory, initialize_memory=False, http_extension=ext)
-
-        client = TestClient(app)
-
-        response = client.get("/ext/health-check")
-        assert response.status_code == 200
-        data = response.json()
-        assert data["extension"] == "healthy"
-        assert "memory" in data
-
-    def test_http_extension_post_endpoint(self, memory):
-        """Extension can handle POST requests with JSON body."""
-        from hindsight_api.api.http import create_app
-
-        ext = SampleHttpExtension({})
-        app = create_app(memory, initialize_memory=False, http_extension=ext)
-
-        client = TestClient(app)
-
-        response = client.post("/ext/echo", json={"key": "value", "number": 42})
-        assert response.status_code == 200
-        assert response.json() == {"echoed": {"key": "value", "number": 42}}
-
-    def test_http_extension_not_mounted_when_none(self, memory):
-        """No extension routes when http_extension is None."""
-        from hindsight_api.api.http import create_app
-
-        app = create_app(memory, initialize_memory=False, http_extension=None)
-
-        client = TestClient(app)
-
-        # Extension endpoint should not exist
-        response = client.get("/ext/hello")
-        assert response.status_code == 404
-
-    @pytest.mark.asyncio
-    async def test_http_extension_lifecycle(self):
-        """HTTP extension on_startup and on_shutdown are called."""
-        ext = SampleHttpExtension({})
-
-        assert not ext.started
-        assert not ext.stopped
-
-        await ext.on_startup()
-        assert ext.started
-
-        await ext.on_shutdown()
-        assert ext.stopped
-
-    def test_core_routes_still_work_with_extension(self, memory):
-        """Core API routes still work when extension is mounted."""
-        from hindsight_api.api.http import create_app
-
-        ext = SampleHttpExtension({})
-        app = create_app(memory, initialize_memory=False, http_extension=ext)
-
-        client = TestClient(app)
-
-        # Health endpoint should work
-        response = client.get("/health")
-        assert response.status_code in (200, 503)  # May be unhealthy if DB not connected
-
-        # Banks list endpoint should work
-        response = client.get("/v1/default/banks")
-        assert response.status_code in (200, 500)  # May fail if DB not ready
@@ -43,7 +43,7 @@ Marcus felt anxious about the upcoming interview.
        context = "Personal journal entry"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -75,7 +75,7 @@ The music was so loud I could barely hear myself think.
        context = "Personal experience"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -108,7 +108,7 @@ Maybe we should reconsider the timeline.
        context = "Team discussion"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -141,7 +141,7 @@ I'm unable to attend the conference due to scheduling conflicts.
        context = "Personal profile discussion"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -173,7 +173,7 @@ Unlike last year, we're ahead of schedule.
        context = "Project review"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -206,7 +206,7 @@ She's enthusiastic about the opportunity.
        context = "Team meeting"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -239,7 +239,7 @@ I'm planning to switch careers because I'm not fulfilled in my current role.
        context = "Personal goals discussion"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -276,7 +276,7 @@ Family is the most important thing to her.
        context = "Personal values discussion"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -310,7 +310,7 @@ I prefer presenting in person rather than virtually because I can read the room

        event_date = datetime(2024, 11, 13)

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=event_date,
            context=context,
@@ -366,7 +366,7 @@ I'm planning to visit Tokyo next month.

        event_date = datetime(2024, 11, 13)

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=event_date,
            context=context,
@@ -402,8 +402,6 @@ I'm planning to visit Tokyo next month.

        Ideally: If conversation is on August 14, 2023 and text says "last night",
        the date field should be August 13. We accept 13 or 14 as LLM may vary.
-
-        Retries up to 3 times to account for LLM inconsistencies.
        """
        text = """
 Melanie: Hey Caroline! Last night was amazing! We celebrated my daughter's birthday
@@ -412,69 +410,41 @@ with a concert surrounded by music, joy and the warm summer breeze.

        context = "Conversation between Melanie and Caroline"
        llm_config = LLMConfig.for_memory()
+
        event_date = datetime(2023, 8, 14, 14, 24)

-        last_error = None
-        max_retries = 3
+        facts, _ = await extract_facts_from_text(
+            text=text,
+            event_date=event_date,
+            context=context,
+            llm_config=llm_config,
+            agent_name="Melanie"
+        )

-        for attempt in range(max_retries):
-            try:
-                facts, _, _ = await extract_facts_from_text(
-                    text=text,
-                    event_date=event_date,
-                    context=context,
-                    llm_config=llm_config,
-                    agent_name="Melanie"
-                )
+        assert len(facts) > 0, "Should extract at least one fact"

-                assert len(facts) > 0, "Should extract at least one fact"
+        birthday_fact = None
+        for fact in facts:
+            if "birthday" in fact.fact.lower() or "concert" in fact.fact.lower():
+                birthday_fact = fact
+                break

-                birthday_fact = None
-                for fact in facts:
-                    if "birthday" in fact.fact.lower() or "concert" in fact.fact.lower():
-                        birthday_fact = fact
-                        break
+        assert birthday_fact is not None, "Should extract fact about birthday celebration"

-                assert birthday_fact is not None, "Should extract fact about birthday celebration"
+        fact_date_str = birthday_fact.occurred_start
+        assert fact_date_str is not None, "occurred_start should not be None for temporal events"

-                fact_date_str = birthday_fact.occurred_start
-                assert fact_date_str is not None, "occurred_start should not be None for temporal events"
+        if 'T' in fact_date_str:
+            fact_date = datetime.fromisoformat(fact_date_str.replace('Z', '+00:00'))
+        else:
+            fact_date = datetime.fromisoformat(fact_date_str)

-                if 'T' in fact_date_str:
-                    fact_date = datetime.fromisoformat(fact_date_str.replace('Z', '+00:00'))
-                else:
-                    fact_date = datetime.fromisoformat(fact_date_str)
-
-                assert fact_date.year == 2023, "Year should be 2023"
-                assert fact_date.month == 8, "Month should be August"
-                # Accept day 13 (ideal: last night) or 14 (conversation date) as valid
-                assert fact_date.day in (13, 14), (
-                    f"Day should be 13 or 14 (around Aug 14 event), but got {fact_date.day}."
-                )
-
-                # If we reach here, test passed
-                return
-
-            except AssertionError as e:
-                last_error = e
-                if attempt < max_retries - 1:
-                    print(f"Test attempt {attempt + 1} failed: {e}. Retrying...")
-                    continue
-                else:
-                    # Last attempt failed, re-raise the error
-                    raise e
-            except Exception as e:
-                last_error = e
-                if attempt < max_retries - 1:
-                    print(f"Test attempt {attempt + 1} failed with exception: {e}. Retrying...")
-                    continue
-                else:
-                    # Last attempt failed, re-raise the error
-                    raise e
-
-        # Should not reach here, but just in case
-        if last_error:
-            raise last_error
+        assert fact_date.year == 2023, "Year should be 2023"
+        assert fact_date.month == 8, "Month should be August"
+        # Accept day 13 (ideal: last night) or 14 (conversation date) as valid
+        assert fact_date.day in (13, 14), (
+            f"Day should be 13 or 14 (around Aug 14 event), but got {fact_date.day}."
+        )

    @pytest.mark.asyncio
    async def test_date_field_calculation_yesterday(self):
@@ -488,7 +458,7 @@ Yesterday I went for a morning jog for the first time in a nearby park.

        event_date = datetime(2024, 11, 13)

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=event_date,
            context=context,
@@ -537,7 +507,7 @@ Yesterday I went for a morning jog for the first time in a nearby park.
        This morning I had coffee with Alice.
        """

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=reference_date,
            llm_config=llm_config,
@@ -567,7 +537,7 @@ Yesterday I went for a morning jog for the first time in a nearby park.

        text = "Alice works at Google. She loves Python programming."

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=reference_date,
            llm_config=llm_config,
@@ -594,7 +564,7 @@ Yesterday I went for a morning jog for the first time in a nearby park.
        Bob will start his vacation on April 1st.
        """

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=reference_date,
            llm_config=llm_config,
@@ -645,7 +615,7 @@ great time! Every time I see it, I can't help but smile.

        event_date = datetime(2023, 2, 23)

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=event_date,
            context=context,
@@ -695,7 +665,7 @@ I've learned so much from it.
        context = "Personal update"
        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            context=context,
@@ -758,7 +728,7 @@ Jamie: Congratulations! I'd love to read it.

        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=transcript,
            event_date=datetime(2024, 11, 13),
            llm_config=llm_config,
@@ -803,7 +773,7 @@ We presented our findings to the team yesterday.

        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=text,
            event_date=datetime(2024, 11, 13),
            llm_config=llm_config,
@@ -838,7 +808,7 @@ Jamie: [teasing] We'll see who's right, my Niners pick is solid.

        llm_config = LLMConfig.for_memory()

-        facts, _, _ = await extract_facts_from_text(
+        facts, _ = await extract_facts_from_text(
            text=transcript,
            event_date=datetime(2024, 11, 14),
            context=context,
@@ -872,8 +842,6 @@ Jamie: [teasing] We'll see who's right, my Niners pick is solid.

        This addresses the issue where podcast outros like "that's all for today,
        don't forget to subscribe" were being extracted as facts.
-
-        Note: LLM fact extraction is non-deterministic, so we retry up to 3 times.
        """

        transcript = """
@@ -899,41 +867,26 @@ so the algorithm learns to box out. See you next week!

        llm_config = LLMConfig.for_memory()

-        max_retries = 3
-        last_error = None
+        facts, _ = await extract_facts_from_text(
+            text=transcript,
+            event_date=datetime(2024, 11, 13),
+            llm_config=llm_config,
+            agent_name="Marcus",
+            context=context
+        )

-        for attempt in range(max_retries):
-            try:
-                facts, _, _ = await extract_facts_from_text(
-                    text=transcript,
-                    event_date=datetime(2024, 11, 13),
-                    llm_config=llm_config,
-                    agent_name="Marcus",
-                    context=context
-                )
+        assert len(facts) > 0, "Should extract at least one fact"

-                assert len(facts) > 0, "Should extract at least one fact"
+        # The main goal is to extract substantive content about AI research
+        # Meta-commentary filtering is ideal but not strictly required
+        all_facts_text = " ".join([f.fact.lower() for f in facts])

-                # The main goal is to extract substantive content about AI research
-                # Meta-commentary filtering is ideal but not strictly required
-                all_facts_text = " ".join([f.fact.lower() for f in facts])
-
-                # Should extract the actual AI research content
-                has_substantive_content = any(term in all_facts_text for term in [
-                    "interpretability", "ai", "safety", "research", "models", "decisions"
-                ])
-                assert has_substantive_content, \
-                    f"Should extract substantive AI research content. Facts: {[f.fact for f in facts]}"
-
-                return  # Test passed
-
-            except AssertionError as e:
-                last_error = e
-                if attempt < max_retries - 1:
-                    print(f"Test attempt {attempt + 1} failed: {e}. Retrying...")
-                    continue
-                else:
-                    raise e
+        # Should extract the actual AI research content
+        has_substantive_content = any(term in all_facts_text for term in [
+            "interpretability", "ai", "safety", "research", "models", "decisions"
+        ])
+        assert has_substantive_content, \
+            f"Should extract substantive AI research content. Facts: {[f.fact for f in facts]}"


 # =============================================================================
@@ -944,7 +897,7 @@ class TestDispositionInference:
    """Tests for LLM-based disposition trait inference from background."""

    @pytest.mark.asyncio
-    async def test_background_merge_with_disposition_inference(self, memory, request_context):
+    async def test_background_merge_with_disposition_inference(self, memory):
        """Test that background merge infers disposition traits by default."""
        import uuid
        bank_id = f"test_infer_{uuid.uuid4().hex[:8]}"
@@ -952,8 +905,7 @@ class TestDispositionInference:
        result = await memory.merge_bank_background(
            bank_id,
            "I am a creative software engineer who loves innovation and trying new technologies",
-            update_disposition=True,
-            request_context=request_context,
+            update_disposition=True
        )

        assert "background" in result
@@ -971,31 +923,30 @@ class TestDispositionInference:
            assert 1 <= disposition[trait] <= 5

    @pytest.mark.asyncio
-    async def test_background_merge_without_disposition_inference(self, memory, request_context):
+    async def test_background_merge_without_disposition_inference(self, memory):
        """Test that background merge skips disposition inference when disabled."""
        import uuid
        bank_id = f"test_no_infer_{uuid.uuid4().hex[:8]}"

-        initial_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        initial_profile = await memory.get_bank_profile(bank_id)
        initial_disposition = initial_profile["disposition"]

        result = await memory.merge_bank_background(
            bank_id,
            "I am a data scientist",
-            update_disposition=False,
-            request_context=request_context,
+            update_disposition=False
        )

        assert "background" in result
        assert "disposition" not in result

-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        final_profile = await memory.get_bank_profile(bank_id)
        final_disposition = final_profile["disposition"]

        assert initial_disposition == final_disposition

    @pytest.mark.asyncio
-    async def test_disposition_inference_for_lawyer(self, memory, request_context):
+    async def test_disposition_inference_for_lawyer(self, memory):
        """Test disposition inference for lawyer profile (high skepticism, high literalism)."""
        import uuid
        bank_id = f"test_lawyer_{uuid.uuid4().hex[:8]}"
@@ -1003,8 +954,7 @@ class TestDispositionInference:
        result = await memory.merge_bank_background(
            bank_id,
            "I am a lawyer who focuses on contract details and never takes claims at face value",
-            update_disposition=True,
-            request_context=request_context,
+            update_disposition=True
        )

        disposition = result["disposition"]
@@ -1014,7 +964,7 @@ class TestDispositionInference:
        assert disposition["literalism"] >= 3

    @pytest.mark.asyncio
-    async def test_disposition_inference_for_therapist(self, memory, request_context):
+    async def test_disposition_inference_for_therapist(self, memory):
        """Test disposition inference for therapist profile (high empathy)."""
        import uuid
        bank_id = f"test_therapist_{uuid.uuid4().hex[:8]}"
@@ -1022,8 +972,7 @@ class TestDispositionInference:
        result = await memory.merge_bank_background(
            bank_id,
            "I am a therapist who deeply understands and connects with people's emotional struggles",
-            update_disposition=True,
-            request_context=request_context,
+            update_disposition=True
        )

        disposition = result["disposition"]
@@ -1032,7 +981,7 @@ class TestDispositionInference:
        assert disposition["empathy"] >= 3

    @pytest.mark.asyncio
-    async def test_disposition_updates_in_database(self, memory, request_context):
+    async def test_disposition_updates_in_database(self, memory):
        """Test that inferred disposition is actually stored in database."""
        import uuid
        bank_id = f"test_db_update_{uuid.uuid4().hex[:8]}"
@@ -1040,13 +989,12 @@ class TestDispositionInference:
        result = await memory.merge_bank_background(
            bank_id,
            "I am an innovative designer",
-            update_disposition=True,
-            request_context=request_context,
+            update_disposition=True
        )

        inferred_disposition = result["disposition"]

-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
+        profile = await memory.get_bank_profile(bank_id)
        db_disposition = profile["disposition"]

        # Compare values (db_disposition is a Pydantic model)
@@ -1055,7 +1003,7 @@ class TestDispositionInference:
        assert db_disposition.empathy == inferred_disposition["empathy"]

    @pytest.mark.asyncio
-    async def test_multiple_background_merges_update_disposition(self, memory, request_context):
+    async def test_multiple_background_merges_update_disposition(self, memory):
        """Test that each background merge can update disposition."""
        import uuid
        bank_id = f"test_multi_merge_{uuid.uuid4().hex[:8]}"
@@ -1063,16 +1011,14 @@ class TestDispositionInference:
        result1 = await memory.merge_bank_background(
            bank_id,
            "I am a software engineer",
-            update_disposition=True,
-            request_context=request_context,
+            update_disposition=True
        )
        disposition1 = result1["disposition"]

        result2 = await memory.merge_bank_background(
            bank_id,
            "I love creative problem solving and innovation",
-            update_disposition=True,
-            request_context=request_context,
+            update_disposition=True
        )
        disposition2 = result2["disposition"]

@@ -1080,7 +1026,7 @@ class TestDispositionInference:
        assert "creative" in result2["background"].lower() or "innovation" in result2["background"].lower()

    @pytest.mark.asyncio
-    async def test_background_merge_conflict_resolution_with_disposition(self, memory, request_context):
+    async def test_background_merge_conflict_resolution_with_disposition(self, memory):
        """Test that conflicts are resolved and disposition reflects final background."""
        import uuid
        bank_id = f"test_conflict_{uuid.uuid4().hex[:8]}"
@@ -1088,15 +1034,13 @@ class TestDispositionInference:
        await memory.merge_bank_background(
            bank_id,
            "I was born in Colorado and prefer stability",
-            update_disposition=True,
-            request_context=request_context,
+            update_disposition=True
        )

        result = await memory.merge_bank_background(
            bank_id,
            "You were born in Texas and are very skeptical of people",
-            update_disposition=True,
-            request_context=request_context,
+            update_disposition=True
        )

        background = result["background"]
@@ -7,24 +7,24 @@ distinguish between things said earlier vs later.
 """
 import pytest
 from datetime import datetime, timezone
-from hindsight_api import MemoryEngine, RequestContext
+from hindsight_api import MemoryEngine
 from hindsight_api.engine.memory_engine import Budget
 import os


@pytest.mark.asyncio
-async def test_fact_ordering_within_conversation(memory, request_context):
+async def test_fact_ordering_within_conversation(memory):
    bank_id = "test_ordering_agent"

    # Get/create agent (auto-creates with defaults)
-    await memory.get_bank_profile(bank_id, request_context=request_context)
+    await memory.get_bank_profile(bank_id)

    # Update disposition to match Marcus
    await memory.update_bank_disposition(bank_id, {
        "skepticism": 3,
        "literalism": 3,
        "empathy": 3
-    }, request_context=request_context)
+    })

    # A conversation where Marcus changes his position
    conversation = """
@@ -43,8 +43,7 @@ Marcus: Yeah, I realized I was being too optimistic about their defense.
        content=conversation,
        context="podcast discussion about NFL game",
        event_date=base_event_date,
-        document_id="test_conv_1",
-        request_context=request_context,
+        document_id="test_conv_1"
    )

    # Search for all facts about Marcus's predictions
@@ -53,8 +52,7 @@ Marcus: Yeah, I realized I was being too optimistic about their defense.
        query="Marcus prediction Rams",
        fact_type=['opinion', 'experience', 'world'],
        budget=Budget.LOW,
-        max_tokens=8192,
-        request_context=request_context,
+        max_tokens=8192
    )

    print(f"\n=== Retrieved {len(results.results)} facts ===")
@@ -115,17 +113,17 @@ Marcus: Yeah, I realized I was being too optimistic about their defense.
        print(f"\n✅ Temporal ordering preserved: First prediction came before changed prediction")

    # Cleanup
-    await memory.delete_bank(bank_id, request_context=request_context)
+    await memory.delete_bank(bank_id)

    print(f"\n✅ Test passed: Fact ordering within conversation is preserved")


@pytest.mark.asyncio
-async def test_multiple_documents_ordering(memory, request_context):
+async def test_multiple_documents_ordering(memory):

    bank_id = "test_multi_doc_agent"

-    await memory.get_bank_profile(bank_id, request_context=request_context)  # Auto-creates with defaults
+    await memory.get_bank_profile(bank_id)  # Auto-creates with defaults

    # Two separate conversations with same base time
    base_time = datetime(2024, 11, 14, 10, 0, 0, tzinfo=timezone.utc)
@@ -148,8 +146,7 @@ Alice: I reconsidered the team's experience level.
        contents=[
            {"content": conv1, "context": "project discussion 1", "event_date": base_time},
            {"content": conv2, "context": "project discussion 2", "event_date": base_time}
-        ],
-        request_context=request_context,
+        ]
    )

    # Search for Alice's preferences
@@ -158,8 +155,7 @@ Alice: I reconsidered the team's experience level.
        query="Alice preference React Vue",
        fact_type=['opinion', 'experience'],
        budget=Budget.LOW,
-        max_tokens=8192,
-        request_context=request_context,
+        max_tokens=8192
    )

    print(f"\n=== Retrieved {len(results.results)} agent facts ===")
@@ -179,6 +175,6 @@ Alice: I reconsidered the team's experience level.
        print(f"\n✅ Facts from {len(agent_facts)} statements have {len(unique_timestamps)} unique timestamps")

    # Cleanup
-    await memory.delete_bank(bank_id, request_context=request_context)
+    await memory.delete_bank(bank_id)

    print(f"\n✅ Test passed: Multiple documents maintain separate ordering")
@@ -428,66 +428,6 @@ async def test_document_deletion(api_client):
    assert response.status_code == 404


-@pytest.mark.asyncio
-async def test_document_deletion_with_slashes_in_id(api_client):
-    """
-    Test document deletion when document_id contains forward slashes.
-
-    Regression test for https://github.com/vectorize-io/hindsight/issues/92
-
-    Document IDs with slashes (e.g., "folder/file.md") should work correctly
-    for all operations including creation, listing, retrieval, and deletion.
-    """
-    import urllib.parse
-
-    test_bank_id = f"doc_slash_test_{datetime.now().timestamp()}"
-    document_id_with_slash = "reports/quarterly/q1-2024.md"
-
-    try:
-        # 1. Create a document with slashes in its ID
-        response = await api_client.post(
-            f"/v1/default/banks/{test_bank_id}/memories",
-            json={
-                "items": [
-                    {
-                        "content": "The Q1 2024 report shows significant growth in user engagement.",
-                        "context": "quarterly report",
-                        "document_id": document_id_with_slash
-                    }
-                ]
-            }
-        )
-        assert response.status_code == 200, f"Failed to create document: {response.text}"
-
-        # 2. Verify document exists via list endpoint
-        response = await api_client.get(f"/v1/default/banks/{test_bank_id}/documents")
-        assert response.status_code == 200
-        documents = response.json()
-        doc_ids = [doc["id"] for doc in documents["items"]]
-        assert document_id_with_slash in doc_ids, f"Document should be in list: {doc_ids}"
-
-        # 3. Delete the document (slashes in document_id should work with :path converter)
-        encoded_doc_id = urllib.parse.quote(document_id_with_slash, safe="")
-        response = await api_client.delete(
-            f"/v1/default/banks/{test_bank_id}/documents/{encoded_doc_id}"
-        )
-        assert response.status_code == 200, (
-            f"Failed to delete document with slashes in ID. "
-            f"Status: {response.status_code}, Response: {response.text}"
-        )
-
-        # Verify document is deleted
-        response = await api_client.get(f"/v1/default/banks/{test_bank_id}/documents")
-        assert response.status_code == 200
-        documents = response.json()
-        doc_ids = [doc["id"] for doc in documents["items"]]
-        assert document_id_with_slash not in doc_ids, "Document should be deleted"
-
-    finally:
-        # Cleanup - delete the bank
-        await api_client.delete(f"/v1/default/banks/{test_bank_id}")
-
-
@pytest.mark.asyncio
 async def test_async_retain(api_client):
    """Test asynchronous retain functionality.
@@ -668,298 +608,3 @@ async def test_async_retain_parallel(api_client):
        assert response.status_code == 200
        results = response.json()["results"]
        assert len(results) > 0, f"Should find memories for document {i}"
-
-
-@pytest.mark.asyncio
-async def test_reflect_structured_output(api_client):
-    """Test reflect endpoint with structured output via response_schema.
-
-    When response_schema is provided, the reflect endpoint should return
-    both the natural language text response and a structured_output field
-    containing the response parsed according to the provided JSON schema.
-    """
-    test_bank_id = f"reflect_structured_test_{datetime.now().timestamp()}"
-
-    # Store some memories to reflect on
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "Alice is a senior machine learning engineer with 8 years of experience.",
-                    "context": "team member info"
-                },
-                {
-                    "content": "Bob is a junior data scientist who joined last month.",
-                    "context": "team member info"
-                },
-                {
-                    "content": "The team uses Python and TensorFlow for most projects.",
-                    "context": "tech stack"
-                }
-            ]
-        }
-    )
-    assert response.status_code == 200
-
-    # Define a JSON schema for structured output
-    response_schema = {
-        "type": "object",
-        "properties": {
-            "team_members": {
-                "type": "array",
-                "items": {
-                    "type": "object",
-                    "properties": {
-                        "name": {"type": "string"},
-                        "role": {"type": "string"},
-                        "experience_level": {"type": "string"}
-                    }
-                }
-            },
-            "technologies": {
-                "type": "array",
-                "items": {"type": "string"}
-            },
-            "summary": {"type": "string"}
-        },
-        "required": ["team_members", "summary"]
-    }
-
-    # Call reflect with response_schema
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/reflect",
-        json={
-            "query": "Give me an overview of the team and their tech stack",
-            "response_schema": response_schema
-        }
-    )
-    assert response.status_code == 200
-    result = response.json()
-
-    # Verify text field exists (empty when using structured output)
-    assert "text" in result
-    assert result["text"] == ""
-
-    # Verify structured output exists and has expected structure
-    assert "structured_output" in result
-    assert result["structured_output"] is not None
-
-    structured = result["structured_output"]
-    assert "team_members" in structured
-    assert "summary" in structured
-    assert isinstance(structured["team_members"], list)
-    assert isinstance(structured["summary"], str)
-
-    # Verify team members have the expected fields
-    if len(structured["team_members"]) > 0:
-        member = structured["team_members"][0]
-        assert "name" in member or "role" in member  # At least some fields should be present
-
-
-@pytest.mark.asyncio
-async def test_reflect_without_structured_output(api_client):
-    """Test that reflect works normally without response_schema.
-
-    When response_schema is not provided, the structured_output field
-    should be null/None in the response.
-    """
-    test_bank_id = f"reflect_no_structured_test_{datetime.now().timestamp()}"
-
-    # Store a memory
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "The project deadline is next Friday.",
-                    "context": "project timeline"
-                }
-            ]
-        }
-    )
-    assert response.status_code == 200
-
-    # Call reflect without response_schema
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/reflect",
-        json={
-            "query": "When is the project deadline?"
-        }
-    )
-    assert response.status_code == 200
-    result = response.json()
-
-    # Verify response has text but structured_output is null
-    assert "text" in result
-    assert len(result["text"]) > 0
-    assert result.get("structured_output") is None
-
-
-@pytest.mark.asyncio
-async def test_reflect_with_max_tokens(api_client):
-    """Test reflect endpoint with custom max_tokens parameter.
-
-    The max_tokens parameter controls the maximum tokens for the LLM response.
-    """
-    test_bank_id = f"reflect_max_tokens_test_{datetime.now().timestamp()}"
-
-    # Store a memory
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "Python is a popular programming language for data science and machine learning.",
-                    "context": "tech"
-                }
-            ]
-        }
-    )
-    assert response.status_code == 200
-
-    # Call reflect with custom max_tokens
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/reflect",
-        json={
-            "query": "What is Python used for?",
-            "max_tokens": 500
-        }
-    )
-    assert response.status_code == 200
-    result = response.json()
-
-    # Verify response has text
-    assert "text" in result
-    assert len(result["text"]) > 0
-
-
-@pytest.mark.asyncio
-async def test_reflect_returns_token_usage(api_client):
-    """Test that reflect endpoint returns token usage metrics.
-
-    The usage field should contain input_tokens, output_tokens, and total_tokens
-    from the LLM call made during reflection.
-    """
-    test_bank_id = f"reflect_usage_test_{datetime.now().timestamp()}"
-
-    # Store a memory to reflect on
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "The capital of France is Paris.",
-                    "context": "geography"
-                }
-            ]
-        }
-    )
-    assert response.status_code == 200
-
-    # Call reflect
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/reflect",
-        json={
-            "query": "What is the capital of France?"
-        }
-    )
-    assert response.status_code == 200
-    result = response.json()
-
-    # Verify response has text
-    assert "text" in result
-    assert len(result["text"]) > 0
-
-    # Verify usage field exists and has expected structure
-    assert "usage" in result, "Response should include 'usage' field"
-    usage = result["usage"]
-    assert usage is not None, "Usage should not be None for reflect"
-    assert "input_tokens" in usage, "Usage should have 'input_tokens'"
-    assert "output_tokens" in usage, "Usage should have 'output_tokens'"
-    assert "total_tokens" in usage, "Usage should have 'total_tokens'"
-
-    # Verify token counts are valid
-    assert usage["input_tokens"] > 0, f"Expected input_tokens > 0, got {usage['input_tokens']}"
-    assert usage["output_tokens"] >= 0, f"Expected output_tokens >= 0, got {usage['output_tokens']}"
-    assert usage["total_tokens"] == usage["input_tokens"] + usage["output_tokens"]
-
-    print(f"Reflect token usage: input={usage['input_tokens']}, output={usage['output_tokens']}, total={usage['total_tokens']}")
-
-
-@pytest.mark.asyncio
-async def test_retain_returns_token_usage(api_client):
-    """Test that retain endpoint returns token usage metrics for synchronous operations.
-
-    The usage field should contain input_tokens, output_tokens, and total_tokens
-    from the LLM calls made during fact extraction.
-    """
-    test_bank_id = f"retain_usage_test_{datetime.now().timestamp()}"
-
-    # Store memory synchronously (async=false is default)
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "Alice is a software engineer at TechCorp. She specializes in machine learning.",
-                    "context": "team introduction"
-                }
-            ]
-        }
-    )
-    assert response.status_code == 200
-    result = response.json()
-
-    # Verify basic response
-    assert result["success"] is True
-    assert result["items_count"] == 1
-    assert result["async"] is False
-
-    # Verify usage field exists and has expected structure
-    assert "usage" in result, "Response should include 'usage' field"
-    usage = result["usage"]
-    assert usage is not None, "Usage should not be None for synchronous retain"
-    assert "input_tokens" in usage, "Usage should have 'input_tokens'"
-    assert "output_tokens" in usage, "Usage should have 'output_tokens'"
-    assert "total_tokens" in usage, "Usage should have 'total_tokens'"
-
-    # Verify token counts are valid
-    assert usage["input_tokens"] > 0, f"Expected input_tokens > 0, got {usage['input_tokens']}"
-    assert usage["output_tokens"] >= 0, f"Expected output_tokens >= 0, got {usage['output_tokens']}"
-    assert usage["total_tokens"] == usage["input_tokens"] + usage["output_tokens"]
-
-    print(f"Retain token usage: input={usage['input_tokens']}, output={usage['output_tokens']}, total={usage['total_tokens']}")
-
-
-@pytest.mark.asyncio
-async def test_retain_async_no_usage(api_client):
-    """Test that async retain does not return usage (as it's processed in background).
-
-    When async=true, the usage field should be None since the actual
-    fact extraction happens asynchronously.
-    """
-    test_bank_id = f"retain_async_no_usage_test_{datetime.now().timestamp()}"
-
-    # Store memory asynchronously
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "async": True,
-            "items": [
-                {
-                    "content": "Bob is a data scientist.",
-                    "context": "team introduction"
-                }
-            ]
-        }
-    )
-    assert response.status_code == 200
-    result = response.json()
-
-    # Verify async response
-    assert result["success"] is True
-    assert result["async"] is True
-
-    # Usage should be None for async operations
-    assert result.get("usage") is None, "Async retain should not include usage"
@@ -26,9 +26,6 @@ MODEL_MATRIX = [
    ("gemini", "gemini-2.5-flash"),
    ("gemini", "gemini-2.5-flash-lite"),
    ("gemini", "gemini-3-pro-preview"),
-    # Ollama models (local)
-    ("ollama", "gemma3:12b"),
-    ("ollama", "gemma3:1b"),
 ]


@@ -51,18 +48,12 @@ async def test_llm_provider_memory_operations(provider: str, model: str):
    All models must pass this test.
    """
    api_key = get_api_key_for_provider(provider)
-
-    # Skip Ollama tests in CI (no models available)
-    if provider == "ollama" and os.getenv("CI"):
-        pytest.skip(f"Skipping {provider}/{model}: Ollama not available in CI")
-
-    # Other providers need an API key
-    if provider != "ollama" and not api_key:
+    if not api_key:
        pytest.skip(f"Skipping {provider}/{model}: no API key available")

    llm = LLMProvider(
        provider=provider,
-        api_key=api_key or "",
+        api_key=api_key,
        base_url="",
        model=model,
    )
@@ -1,245 +0,0 @@
-"""
-Test that LLM calls record token metrics via the metrics collector.
-"""
-import os
-from unittest.mock import MagicMock, patch
-import pytest
-from hindsight_api.engine.llm_wrapper import LLMProvider
-from hindsight_api.metrics import (
-    MetricsCollector,
-    NoOpMetricsCollector,
-    get_metrics_collector,
-)
-
-
-def get_groq_api_key() -> str | None:
-    """Get Groq API key from environment."""
-    return os.getenv("GROQ_API_KEY")
-
-
-@pytest.mark.asyncio
-async def test_llm_metrics_recorded_for_groq():
-    """
-    Test that LLM metrics are recorded when making LLM calls via Groq.
-    Uses openai/gpt-oss-20b as recommended by Hindsight.
-    """
-    api_key = get_groq_api_key()
-    if not api_key:
-        pytest.skip("Skipping: GROQ_API_KEY not set")
-
-    # Create a mock metrics collector to track record_llm_call calls
-    mock_collector = MagicMock(spec=MetricsCollector)
-
-    with patch("hindsight_api.engine.llm_wrapper.get_metrics_collector", return_value=mock_collector):
-        llm = LLMProvider(
-            provider="groq",
-            api_key=api_key,
-            base_url="",
-            model="openai/gpt-oss-20b",
-        )
-
-        # Make an LLM call with clear instruction
-        response = await llm.call(
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant. Always respond."},
-                {"role": "user", "content": "What is 2+2? Reply with just the number."}
-            ],
-            max_completion_tokens=50,
-            scope="test_metrics",
-        )
-
-        # Verify record_llm_call was called - this is the main test
-        assert mock_collector.record_llm_call.called, "record_llm_call should have been called"
-
-        # Get the call arguments
-        call_kwargs = mock_collector.record_llm_call.call_args.kwargs
-
-        # Verify the call had correct structure
-        assert call_kwargs["provider"] == "groq", f"Expected provider='groq', got {call_kwargs}"
-        assert call_kwargs["model"] == "openai/gpt-oss-20b", f"Expected model='openai/gpt-oss-20b', got {call_kwargs}"
-        assert call_kwargs["scope"] == "test_metrics", f"Expected scope='test_metrics', got {call_kwargs}"
-        assert call_kwargs["duration"] > 0, f"Expected duration > 0, got {call_kwargs['duration']}"
-        assert call_kwargs["input_tokens"] > 0, f"Expected input_tokens > 0, got {call_kwargs['input_tokens']}"
-        assert call_kwargs["output_tokens"] >= 0, f"Expected output_tokens >= 0, got {call_kwargs['output_tokens']}"
-        assert call_kwargs["success"] is True, f"Expected success=True, got {call_kwargs['success']}"
-
-        print(f"\nLLM metrics recorded:")
-        print(f"  provider: {call_kwargs['provider']}")
-        print(f"  model: {call_kwargs['model']}")
-        print(f"  scope: {call_kwargs['scope']}")
-        print(f"  duration: {call_kwargs['duration']:.3f}s")
-        print(f"  input_tokens: {call_kwargs['input_tokens']}")
-        print(f"  output_tokens: {call_kwargs['output_tokens']}")
-        print(f"  response: {response}")
-
-
-@pytest.mark.asyncio
-async def test_llm_metrics_recorded_for_structured_output():
-    """
-    Test that LLM metrics are recorded for structured output (JSON) calls.
-    """
-    api_key = get_groq_api_key()
-    if not api_key:
-        pytest.skip("Skipping: GROQ_API_KEY not set")
-
-    from pydantic import BaseModel
-
-    class SimpleResponse(BaseModel):
-        greeting: str
-        language: str
-
-    mock_collector = MagicMock(spec=MetricsCollector)
-
-    with patch("hindsight_api.engine.llm_wrapper.get_metrics_collector", return_value=mock_collector):
-        llm = LLMProvider(
-            provider="groq",
-            api_key=api_key,
-            base_url="",
-            model="openai/gpt-oss-20b",
-        )
-
-        # Make a structured output call
-        response = await llm.call(
-            messages=[{"role": "user", "content": "Say hello in French. Return greeting and language."}],
-            response_format=SimpleResponse,
-            max_completion_tokens=100,
-            scope="structured_output_test",
-        )
-
-        # Verify structured response
-        assert isinstance(response, SimpleResponse)
-        assert response.greeting is not None
-        assert response.language is not None
-
-        # Verify record_llm_call was called
-        assert mock_collector.record_llm_call.called, "record_llm_call should have been called"
-
-        call_kwargs = mock_collector.record_llm_call.call_args.kwargs
-        assert call_kwargs["input_tokens"] > 0
-        assert call_kwargs["output_tokens"] > 0
-
-        print(f"\nStructured output LLM metrics:")
-        print(f"  greeting: {response.greeting}")
-        print(f"  language: {response.language}")
-        print(f"  input_tokens: {call_kwargs['input_tokens']}")
-        print(f"  output_tokens: {call_kwargs['output_tokens']}")
-
-
-@pytest.mark.asyncio
-async def test_noop_collector_when_metrics_disabled():
-    """
-    Test that NoOpMetricsCollector is returned when metrics are not initialized.
-    This verifies the fallback behavior doesn't break LLM calls.
-    """
-    api_key = get_groq_api_key()
-    if not api_key:
-        pytest.skip("Skipping: GROQ_API_KEY not set")
-
-    # Without initializing metrics, get_metrics_collector returns NoOpMetricsCollector
-    collector = get_metrics_collector()
-    assert isinstance(collector, NoOpMetricsCollector), "Should return NoOpMetricsCollector when not initialized"
-
-    # Make an LLM call - should work fine with NoOp collector
-    llm = LLMProvider(
-        provider="groq",
-        api_key=api_key,
-        base_url="",
-        model="openai/gpt-oss-20b",
-    )
-
-    response = await llm.call(
-        messages=[{"role": "user", "content": "Say 'test' in one word."}],
-        max_completion_tokens=50,
-    )
-
-    assert response is not None
-    print(f"\nLLM call succeeded with NoOpMetricsCollector: {response}")
-
-
-@pytest.mark.asyncio
-async def test_return_usage_returns_tuple():
-    """
-    Test that return_usage=True returns (result, TokenUsage) tuple.
-    """
-    from hindsight_api.engine.response_models import TokenUsage
-
-    api_key = get_groq_api_key()
-    if not api_key:
-        pytest.skip("Skipping: GROQ_API_KEY not set")
-
-    llm = LLMProvider(
-        provider="groq",
-        api_key=api_key,
-        base_url="",
-        model="openai/gpt-oss-20b",
-    )
-
-    # Call with return_usage=True
-    result, usage = await llm.call(
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": "What is 2+2? Reply with just the number."}
-        ],
-        max_completion_tokens=50,
-        return_usage=True,
-    )
-
-    # Verify result is the response text
-    assert result is not None
-    assert isinstance(result, str)
-
-    # Verify usage is TokenUsage model with valid counts
-    assert isinstance(usage, TokenUsage)
-    assert usage.input_tokens > 0, f"Expected input_tokens > 0, got {usage.input_tokens}"
-    assert usage.output_tokens >= 0, f"Expected output_tokens >= 0, got {usage.output_tokens}"
-    assert usage.total_tokens == usage.input_tokens + usage.output_tokens
-
-    print(f"\nreturn_usage=True test:")
-    print(f"  result: {result}")
-    print(f"  usage: {usage}")
-
-
-@pytest.mark.asyncio
-async def test_return_usage_with_structured_output():
-    """
-    Test that return_usage=True works with structured output (JSON).
-    """
-    from pydantic import BaseModel
-    from hindsight_api.engine.response_models import TokenUsage
-
-    api_key = get_groq_api_key()
-    if not api_key:
-        pytest.skip("Skipping: GROQ_API_KEY not set")
-
-    class MathAnswer(BaseModel):
-        answer: int
-        explanation: str
-
-    llm = LLMProvider(
-        provider="groq",
-        api_key=api_key,
-        base_url="",
-        model="openai/gpt-oss-20b",
-    )
-
-    # Call with return_usage=True and structured output
-    result, usage = await llm.call(
-        messages=[{"role": "user", "content": "What is 5+3? Return the answer and a brief explanation."}],
-        response_format=MathAnswer,
-        max_completion_tokens=100,
-        return_usage=True,
-    )
-
-    # Verify result is the parsed response
-    assert isinstance(result, MathAnswer)
-    assert result.answer == 8
-    assert result.explanation is not None
-
-    # Verify usage is TokenUsage model
-    assert isinstance(usage, TokenUsage)
-    assert usage.input_tokens > 0
-    assert usage.output_tokens > 0
-
-    print(f"\nStructured output with return_usage=True:")
-    print(f"  result: {result}")
-    print(f"  usage: {usage}")
@@ -0,0 +1,177 @@
+"""
+Integration test for the MCP (Model Context Protocol) server.
+
+Tests MCP endpoints by starting a FastAPI server with MCP enabled and using the MCP client.
+
+Note: MCP server is integrated with the web server. These tests require HINDSIGHT_API_MCP_ENABLED=true.
+"""
+import asyncio
+import pytest
+import pytest_asyncio
+import httpx
+from mcp import ClientSession
+from mcp.client.sse import sse_client
+from hindsight_api.api import create_app
+
+
+@pytest_asyncio.fixture
+async def mcp_server(memory):
+    """Start the FastAPI app with MCP enabled and return the SSE URL."""
+    # Memory is already initialized by the conftest fixture (with migrations)
+    app = create_app(
+        memory,
+        initialize_memory=False,
+        mcp_api_enabled=True
+    )
+
+    # Use httpx to create a test server
+    transport = httpx.ASGITransport(app=app)
+    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
+        # The MCP SSE endpoint is at /mcp/sse
+        # We need to yield the base URL for sse_client to connect
+        # However, sse_client expects a real URL, not a test client
+        # So we'll start a real server on a random port
+        pass
+
+    # For now, skip these tests as they require a real server
+    # The sse_client doesn't work with ASGI test transport
+    pytest.skip("MCP tests require a real running server. Run: HINDSIGHT_API_MCP_ENABLED=true uvicorn hindsight_api.api:app")
+
+
+@pytest.mark.asyncio
+async def test_mcp_server_tools_via_sse(mcp_server):
+    """Test MCP server tools via SSE transport using proper MCP client."""
+    sse_url = mcp_server
+
+    async with sse_client(sse_url) as (read, write):
+        async with ClientSession(read, write) as session:
+            await session.initialize()
+
+            # Test 1: List tools
+            tools_list = await session.list_tools()
+            print(f"Tools: {tools_list}")
+            tool_names = [t.name for t in tools_list.tools]
+            assert "hindsight_search" in tool_names
+            assert "hindsight_put" in tool_names
+
+            # Test 2: Call hindsight_put
+            put_result = await session.call_tool(
+                "hindsight_put",
+                arguments={
+                    "content": "User loves Python programming",
+                    "context": "programming_preferences",
+                    "explanation": "Storing user's programming language preference"
+                }
+            )
+            print(f"Put result: {put_result}")
+            assert put_result is not None
+
+            # Wait a bit for indexing
+            await asyncio.sleep(1)
+
+            # Test 3: Call hindsight_search
+            search_result = await session.call_tool(
+                "hindsight_search",
+                arguments={
+                    "query": "What programming languages does the user like?",
+                    "max_tokens": 4096,
+                    "explanation": "Searching for programming preferences"
+                }
+            )
+            print(f"Search result: {search_result}")
+            assert search_result is not None
+
+
+@pytest.mark.asyncio
+async def test_multiple_concurrent_requests(mcp_server):
+    """Test multiple concurrent requests from a single session."""
+    sse_url = mcp_server
+
+    async with sse_client(sse_url) as (read, write):
+        async with ClientSession(read, write) as session:
+            await session.initialize()
+
+            # Fire off 10 concurrent search requests from same session
+            async def make_search(idx):
+                try:
+                    result = await session.call_tool(
+                        "hindsight_search",
+                        arguments={
+                            "query": f"test query {idx}",
+                            "explanation": f"Concurrent test {idx}"
+                        }
+                    )
+                    return idx, "success", result
+                except Exception as e:
+                    return idx, "error", str(e)
+
+            tasks = [make_search(i) for i in range(10)]
+            results = await asyncio.gather(*tasks, return_exceptions=True)
+
+            # Check results
+            successes = 0
+            failures = 0
+
+            for result in results:
+                if isinstance(result, Exception):
+                    print(f"Request failed with exception: {result}")
+                    failures += 1
+                else:
+                    idx, status, data = result
+                    if status == "success":
+                        successes += 1
+                    else:
+                        print(f"Request {idx} failed: {data}")
+                        failures += 1
+
+            print(f"Successes: {successes}, Failures: {failures}")
+
+            # We expect all requests to succeed
+            assert successes >= 8, f"Too many failures: {failures}/10"
+
+
+@pytest.mark.asyncio
+async def test_race_condition_with_rapid_requests(mcp_server):
+    """Test rapid-fire requests with multiple sessions to trigger race condition."""
+    sse_url = mcp_server
+
+    async def rapid_session_search(idx):
+        """Create a new session and immediately make a request."""
+        try:
+            async with sse_client(sse_url) as (read, write):
+                async with ClientSession(read, write) as session:
+                    await session.initialize()
+
+                    # Make request immediately after initialization
+                    result = await session.call_tool(
+                        "hindsight_search",
+                        arguments={
+                            "query": f"rapid query {idx}",
+                            "max_tokens": 2048
+                        }
+                    )
+                    return idx, "success", result
+        except Exception as e:
+            return idx, "error", str(e)
+
+    # Fire 20 requests with minimal delay, each with its own session
+    tasks = [rapid_session_search(i) for i in range(20)]
+    results = await asyncio.gather(*tasks)
+
+    # Analyze results
+    errors = []
+    for idx, status, data in results:
+        if status == "error":
+            errors.append((idx, data))
+
+    if errors:
+        print(f"Found {len(errors)} errors:")
+        for idx, error_msg in errors:
+            print(f"  Request {idx}: {error_msg}")
+
+    # Most requests should succeed
+    assert len(errors) < 5, f"Too many errors: {len(errors)}/20"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])
@@ -9,7 +9,6 @@ def mock_memory():
    """Create a mock MemoryEngine."""
    memory = MagicMock()
    memory.retain_batch_async = AsyncMock()
-    memory.submit_async_retain = AsyncMock(return_value={"operation_id": "test-op-123"})
    memory.recall_async = AsyncMock(return_value=MagicMock(results=[]))
    return memory

@@ -45,11 +44,11 @@ async def test_mcp_tools_use_context_bank_id(mock_memory):
    assert "retain" in tools
    assert "recall" in tools

-    # Test retain with bank_id from context (use async_processing=False for synchronous test)
+    # Test retain with bank_id from context
    token = _current_bank_id.set("context-bank-id")
    try:
        retain_tool = tools["retain"]
-        result = await retain_tool.fn(content="test content", context="test_context", async_processing=False)
+        result = await retain_tool.fn(content="test content", context="test_context")
        assert "successfully" in result.lower()

        # Verify the memory was called with the context bank_id
@@ -1,391 +0,0 @@
-"""Tests for metrics instrumentation."""
-import pytest
-from unittest.mock import MagicMock, patch
-
-from hindsight_api.metrics import (
-    MetricsCollector,
-    MetricsCollectorBase,
-    NoOpMetricsCollector,
-    get_metrics_collector,
-    get_token_bucket,
-    create_metrics_collector,
-    initialize_metrics,
-)
-
-
-class TestNoOpMetricsCollector:
-    """Tests for the no-op metrics collector."""
-
-    def test_record_operation_is_noop(self):
-        """Test that record_operation does nothing."""
-        collector = NoOpMetricsCollector()
-
-        # Should not raise any exception
-        with collector.record_operation("recall", bank_id="test_bank", source="api"):
-            pass
-
-    def test_nested_contexts_work(self):
-        """Test that nested context managers work correctly."""
-        collector = NoOpMetricsCollector()
-
-        # Nested contexts should work without issues
-        with collector.record_operation("reflect", bank_id="test_bank", source="api"):
-            with collector.record_operation("recall", bank_id="test_bank", source="reflect"):
-                pass
-
-    def test_exception_propagates(self):
-        """Test that exceptions inside context are propagated."""
-        collector = NoOpMetricsCollector()
-
-        with pytest.raises(ValueError, match="test error"):
-            with collector.record_operation("recall", bank_id="test_bank"):
-                raise ValueError("test error")
-
-    def test_record_llm_call_is_noop(self):
-        """Test that record_llm_call does nothing."""
-        collector = NoOpMetricsCollector()
-
-        # Should not raise any exception
-        collector.record_llm_call(
-            provider="openai",
-            model="gpt-4",
-            scope="memory",
-            duration=1.5,
-            input_tokens=100,
-            output_tokens=50,
-            success=True,
-        )
-
-
-class TestMetricsCollector:
-    """Tests for the real metrics collector."""
-
-    @pytest.fixture
-    def mock_meter(self):
-        """Create a mock meter for testing."""
-        meter = MagicMock()
-        # Create separate mocks for each histogram (operation_duration, llm_duration)
-        histogram_mocks = [MagicMock(), MagicMock()]
-        meter.create_histogram.side_effect = histogram_mocks
-        # Create separate mocks for each counter
-        # (operation_total, llm_tokens_input, llm_tokens_output, llm_calls_total)
-        counter_mocks = [MagicMock() for _ in range(4)]
-        meter.create_counter.side_effect = counter_mocks
-        return meter
-
-    @pytest.fixture
-    def collector(self, mock_meter):
-        """Create a MetricsCollector with a mock meter."""
-        with patch("hindsight_api.metrics.get_meter", return_value=mock_meter):
-            return MetricsCollector()
-
-    def test_record_operation_records_duration(self, collector):
-        """Test that record_operation records duration."""
-        with collector.record_operation("recall", bank_id="test_bank", source="api"):
-            pass
-
-        # Histogram should have been called
-        collector.operation_duration.record.assert_called_once()
-        call_args = collector.operation_duration.record.call_args
-
-        # First arg is duration (should be > 0)
-        duration = call_args[0][0]
-        assert duration >= 0
-
-        # Second arg is attributes dict
-        attributes = call_args[0][1]
-        assert attributes["operation"] == "recall"
-        assert attributes["bank_id"] == "test_bank"
-        assert attributes["source"] == "api"
-        assert attributes["success"] == "true"
-
-    def test_record_operation_records_failure_on_exception(self, collector):
-        """Test that record_operation records failure when exception occurs."""
-        with pytest.raises(RuntimeError):
-            with collector.record_operation("retain", bank_id="test_bank", source="api"):
-                raise RuntimeError("Test error")
-
-        # Should have recorded with success=false
-        call_args = collector.operation_duration.record.call_args
-        attributes = call_args[0][1]
-        assert attributes["success"] == "false"
-
-    def test_record_operation_with_budget(self, collector):
-        """Test that budget is included in attributes when provided."""
-        with collector.record_operation("recall", bank_id="test_bank", source="api", budget="mid"):
-            pass
-
-        call_args = collector.operation_duration.record.call_args
-        attributes = call_args[0][1]
-        assert attributes["budget"] == "mid"
-
-    def test_record_operation_with_max_tokens(self, collector):
-        """Test that max_tokens is included in attributes when provided."""
-        with collector.record_operation("recall", bank_id="test_bank", source="api", max_tokens=4096):
-            pass
-
-        call_args = collector.operation_duration.record.call_args
-        attributes = call_args[0][1]
-        assert attributes["max_tokens"] == "4096"
-
-    def test_record_operation_source_values(self, collector):
-        """Test different source values: api, reflect, internal."""
-        sources = ["api", "reflect", "internal"]
-
-        for source in sources:
-            collector.operation_duration.record.reset_mock()
-
-            with collector.record_operation("recall", bank_id="test_bank", source=source):
-                pass
-
-            call_args = collector.operation_duration.record.call_args
-            attributes = call_args[0][1]
-            assert attributes["source"] == source
-
-    def test_nested_contexts_track_separately(self, collector):
-        """Test that nested operations are tracked separately with different sources."""
-        # Simulate reflect (api) calling recall (reflect)
-        with collector.record_operation("reflect", bank_id="test_bank", source="api"):
-            with collector.record_operation("recall", bank_id="test_bank", source="reflect"):
-                pass
-
-        # Should have 2 calls to record
-        assert collector.operation_duration.record.call_count == 2
-        assert collector.operation_total.add.call_count == 2
-
-        # Check the calls
-        calls = collector.operation_duration.record.call_args_list
-
-        # First call should be recall (inner context exits first)
-        recall_attrs = calls[0][0][1]
-        assert recall_attrs["operation"] == "recall"
-        assert recall_attrs["source"] == "reflect"
-
-        # Second call should be reflect (outer context exits last)
-        reflect_attrs = calls[1][0][1]
-        assert reflect_attrs["operation"] == "reflect"
-        assert reflect_attrs["source"] == "api"
-
-
-class TestGetMetricsCollector:
-    """Tests for the get_metrics_collector function."""
-
-    def test_returns_noop_by_default(self):
-        """Test that get_metrics_collector returns NoOpMetricsCollector by default."""
-        # Reset global state
-        import hindsight_api.metrics as metrics_module
-        original_collector = metrics_module._metrics_collector
-
-        try:
-            metrics_module._metrics_collector = NoOpMetricsCollector()
-            collector = get_metrics_collector()
-            assert isinstance(collector, NoOpMetricsCollector)
-        finally:
-            metrics_module._metrics_collector = original_collector
-
-
-class TestMetricsCollectorBase:
-    """Tests for the MetricsCollectorBase abstract class."""
-
-    def test_is_abstract(self):
-        """Test that MetricsCollectorBase methods are abstract."""
-        # Create a class that inherits but doesn't implement
-        class IncompleteCollector(MetricsCollectorBase):
-            pass
-
-        collector = IncompleteCollector()
-
-        # Abstract methods should raise NotImplementedError
-        with pytest.raises(NotImplementedError):
-            with collector.record_operation("test", "test"):
-                pass
-
-        with pytest.raises(NotImplementedError):
-            collector.record_llm_call("test", "test", "test", 1.0)
-
-
-class TestGetTokenBucket:
-    """Tests for the get_token_bucket function."""
-
-    def test_bucket_0_100(self):
-        """Test tokens < 100 return '0-100' bucket."""
-        assert get_token_bucket(0) == "0-100"
-        assert get_token_bucket(50) == "0-100"
-        assert get_token_bucket(99) == "0-100"
-
-    def test_bucket_100_500(self):
-        """Test tokens 100-499 return '100-500' bucket."""
-        assert get_token_bucket(100) == "100-500"
-        assert get_token_bucket(250) == "100-500"
-        assert get_token_bucket(499) == "100-500"
-
-    def test_bucket_500_1k(self):
-        """Test tokens 500-999 return '500-1k' bucket."""
-        assert get_token_bucket(500) == "500-1k"
-        assert get_token_bucket(750) == "500-1k"
-        assert get_token_bucket(999) == "500-1k"
-
-    def test_bucket_1k_5k(self):
-        """Test tokens 1000-4999 return '1k-5k' bucket."""
-        assert get_token_bucket(1000) == "1k-5k"
-        assert get_token_bucket(2500) == "1k-5k"
-        assert get_token_bucket(4999) == "1k-5k"
-
-    def test_bucket_5k_10k(self):
-        """Test tokens 5000-9999 return '5k-10k' bucket."""
-        assert get_token_bucket(5000) == "5k-10k"
-        assert get_token_bucket(7500) == "5k-10k"
-        assert get_token_bucket(9999) == "5k-10k"
-
-    def test_bucket_10k_50k(self):
-        """Test tokens 10000-49999 return '10k-50k' bucket."""
-        assert get_token_bucket(10000) == "10k-50k"
-        assert get_token_bucket(25000) == "10k-50k"
-        assert get_token_bucket(49999) == "10k-50k"
-
-    def test_bucket_50k_plus(self):
-        """Test tokens >= 50000 return '50k+' bucket."""
-        assert get_token_bucket(50000) == "50k+"
-        assert get_token_bucket(100000) == "50k+"
-        assert get_token_bucket(1000000) == "50k+"
-
-
-class TestLLMMetrics:
-    """Tests for LLM-specific metrics recording."""
-
-    @pytest.fixture
-    def mock_meter(self):
-        """Create a mock meter for testing."""
-        meter = MagicMock()
-        # Create separate mocks for each histogram (operation_duration, llm_duration)
-        histogram_mocks = [MagicMock(), MagicMock()]
-        meter.create_histogram.side_effect = histogram_mocks
-        # Create separate mocks for each counter
-        # (operation_total, llm_tokens_input, llm_tokens_output, llm_calls_total)
-        counter_mocks = [MagicMock() for _ in range(4)]
-        meter.create_counter.side_effect = counter_mocks
-        return meter
-
-    @pytest.fixture
-    def collector(self, mock_meter):
-        """Create a MetricsCollector with a mock meter."""
-        with patch("hindsight_api.metrics.get_meter", return_value=mock_meter):
-            return MetricsCollector()
-
-    def test_record_llm_call_records_duration(self, collector):
-        """Test that record_llm_call records duration."""
-        collector.record_llm_call(
-            provider="openai",
-            model="gpt-4",
-            scope="memory",
-            duration=1.5,
-            input_tokens=100,
-            output_tokens=50,
-            success=True,
-        )
-
-        # LLM duration histogram should be called
-        collector.llm_duration.record.assert_called_once()
-        call_args = collector.llm_duration.record.call_args
-
-        # First arg is duration
-        assert call_args[0][0] == 1.5
-
-        # Second arg is attributes dict
-        attributes = call_args[0][1]
-        assert attributes["provider"] == "openai"
-        assert attributes["model"] == "gpt-4"
-        assert attributes["scope"] == "memory"
-        assert attributes["success"] == "true"
-
-    def test_record_llm_call_records_failure(self, collector):
-        """Test that record_llm_call records failure status."""
-        collector.record_llm_call(
-            provider="anthropic",
-            model="claude-3",
-            scope="reflect",
-            duration=0.5,
-            success=False,
-        )
-
-        # Check success is false
-        call_args = collector.llm_duration.record.call_args
-        attributes = call_args[0][1]
-        assert attributes["success"] == "false"
-
-    def test_record_llm_call_records_tokens_with_buckets(self, collector):
-        """Test that record_llm_call records tokens with bucket labels."""
-        collector.record_llm_call(
-            provider="openai",
-            model="gpt-4",
-            scope="memory",
-            duration=1.0,
-            input_tokens=2500,  # Should be "1k-5k" bucket
-            output_tokens=150,  # Should be "100-500" bucket
-            success=True,
-        )
-
-        # Input tokens should be recorded with bucket
-        collector.llm_tokens_input.add.assert_called_once()
-        input_call = collector.llm_tokens_input.add.call_args
-        assert input_call[0][0] == 2500
-        assert input_call[0][1]["token_bucket"] == "1k-5k"
-
-        # Output tokens should be recorded with bucket
-        collector.llm_tokens_output.add.assert_called_once()
-        output_call = collector.llm_tokens_output.add.call_args
-        assert output_call[0][0] == 150
-        assert output_call[0][1]["token_bucket"] == "100-500"
-
-    def test_record_llm_call_skips_zero_tokens(self, collector):
-        """Test that zero token values don't record."""
-        collector.record_llm_call(
-            provider="openai",
-            model="gpt-4",
-            scope="memory",
-            duration=1.0,
-            input_tokens=0,
-            output_tokens=0,
-            success=True,
-        )
-
-        # Token counters should not be called
-        collector.llm_tokens_input.add.assert_not_called()
-        collector.llm_tokens_output.add.assert_not_called()
-
-    def test_record_llm_call_increments_call_counter(self, collector):
-        """Test that record_llm_call increments the call counter."""
-        collector.record_llm_call(
-            provider="gemini",
-            model="gemini-pro",
-            scope="entity_observation",
-            duration=2.0,
-            success=True,
-        )
-
-        # Call counter should be incremented
-        collector.llm_calls_total.add.assert_called_once()
-        call_args = collector.llm_calls_total.add.call_args
-        assert call_args[0][0] == 1
-        assert call_args[0][1]["provider"] == "gemini"
-        assert call_args[0][1]["model"] == "gemini-pro"
-        assert call_args[0][1]["scope"] == "entity_observation"
-
-    def test_record_llm_call_different_scopes(self, collector):
-        """Test recording LLM calls with different scopes."""
-        scopes = ["memory", "reflect", "entity_observation", "answer"]
-
-        for scope in scopes:
-            collector.llm_duration.record.reset_mock()
-
-            collector.record_llm_call(
-                provider="openai",
-                model="gpt-4",
-                scope=scope,
-                duration=1.0,
-                success=True,
-            )
-
-            call_args = collector.llm_duration.record.call_args
-            attributes = call_args[0][1]
-            assert attributes["scope"] == scope
@@ -1,336 +0,0 @@
-"""
-Test multilingual support for retain and reflect operations.
-
-Tests that the system correctly handles non-English input and produces
-output in the same language as the input.
-"""
-
-import pytest
-import logging
-from datetime import datetime, timezone
-from hindsight_api.engine.memory_engine import Budget
-from hindsight_api import RequestContext
-
-logger = logging.getLogger(__name__)
-
-
-@pytest.mark.asyncio
-async def test_retain_chinese_content(memory, request_context):
-    """
-    Test that retain correctly extracts facts from Chinese content
-    and keeps the output in Chinese.
-
-    This test verifies:
-    1. Facts are extracted from Chinese text
-    2. The extracted facts contain Chinese characters
-    3. Entity names are preserved in Chinese
-    """
-    bank_id = f"test_chinese_retain_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Chinese content about a person and their activities
-        chinese_content = """
-        张伟是一位资深软件工程师，在腾讯工作了五年。他专门研究分布式系统，
-        并领导了公司微服务架构的开发。他以编写干净、文档完善的代码而闻名。
-
-        李明上个月加入团队担任初级开发人员。他正在学习React和Node.js。
-        李明很有热情，在代码审查中提出很好的问题。他最近完成了他的第一个功能，
-        这是一个用户认证流程。
-
-        团队使用Kubernetes进行容器编排，并部署到阿里云。他们遵循敏捷方法论，
-        采用两周冲刺周期。合并前必须进行代码审查。
-        """
-
-        # Retain the Chinese content
-        unit_ids = await memory.retain_async(
-            bank_id=bank_id,
-            content=chinese_content,
-            context="团队概述",  # Chinese context
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        logger.info(f"Retained {len(unit_ids)} facts from Chinese content")
-        assert len(unit_ids) > 0, "Should have extracted and stored facts from Chinese content"
-
-        # Recall the facts with a Chinese query
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="告诉我关于张伟的信息",  # "Tell me about Zhang Wei"
-            budget=Budget.MID,
-            max_tokens=1000,
-            fact_type=["world"],
-            request_context=request_context,
-        )
-
-        logger.info(f"Recalled {len(result.results)} facts")
-        assert len(result.results) > 0, "Should recall facts about Zhang Wei"
-
-        # Verify that the facts contain Chinese characters
-        # At least one fact should mention 张伟 (Zhang Wei) or related Chinese content
-        chinese_facts_found = 0
-        for fact in result.results:
-            logger.info(f"Fact: {fact.text[:100]}...")
-            # Check for common Chinese characters or the name
-            if any(
-                char in fact.text
-                for char in ["张", "伟", "腾讯", "软件", "工程师", "分布式", "系统", "代码"]
-            ):
-                chinese_facts_found += 1
-
-        logger.info(f"Found {chinese_facts_found} facts with Chinese content")
-        assert chinese_facts_found > 0, (
-            f"Expected facts to contain Chinese characters, but none found. "
-            f"Facts: {[f.text for f in result.results]}"
-        )
-
-        logger.info("Chinese retain test passed - facts preserved in Chinese")
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_reflect_chinese_content(memory, request_context):
-    """
-    Test that reflect correctly generates responses in Chinese
-    when given Chinese facts and a Chinese query.
-
-    This test verifies:
-    1. Reflection produces a response in Chinese
-    2. The response references the Chinese facts
-    3. Opinions are formed and expressed in Chinese
-
-    Note: LLM responses are non-deterministic, so we retry up to 3 times
-    to account for occasional hallucinations of different names.
-    """
-    bank_id = f"test_chinese_reflect_{datetime.now(timezone.utc).timestamp()}"
-    max_retries = 3
-
-    try:
-        # Store some Chinese facts to give context for opinion formation
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="张伟是一位优秀的软件工程师，完成了五个重大项目。他总是按时交付，代码整洁有良好的文档。",
-            context="绩效评估",  # "Performance review"
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="李明最近加入团队。他错过了第一个截止日期，代码有很多bug。",
-            context="绩效评估",
-            event_date=datetime(2024, 2, 1, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        last_error = None
-        for attempt in range(max_retries):
-            try:
-                # Reflect with a Chinese query
-                query = "谁是更可靠的工程师？"  # "Who is a more reliable engineer?"
-                result = await memory.reflect_async(
-                    bank_id=bank_id,
-                    query=query,
-                    budget=Budget.LOW,
-                    request_context=request_context,
-                )
-
-                logger.info(f"Reflection answer (attempt {attempt + 1}): {result.text}")
-
-                # Verify we got an answer
-                assert result.text, "Reflection should return an answer"
-
-                # Check that the response contains Chinese characters
-                # The response should be in Chinese, not English
-                chinese_chars_found = sum(1 for char in result.text if "\u4e00" <= char <= "\u9fff")
-                total_chars = len(result.text.replace(" ", "").replace("\n", ""))
-
-                logger.info(f"Chinese characters: {chinese_chars_found}, Total characters: {total_chars}")
-
-                # At least 30% of characters should be Chinese (allowing for numbers, punctuation)
-                chinese_ratio = chinese_chars_found / max(total_chars, 1)
-                assert chinese_ratio > 0.3, (
-                    f"Expected response to be in Chinese (>30% Chinese characters), "
-                    f"but only {chinese_ratio:.1%} are Chinese. Response: {result.text}"
-                )
-
-                # Check that Chinese names are mentioned
-                # The LLM should use names from the based_on facts, not hallucinate different names
-                # Extract Chinese names from the based_on world facts
-                expected_names = set()
-                for fact in result.based_on.get("world", []):
-                    # Extract Chinese entity names from the fact
-                    for entity in (fact.entities or []):
-                        # Check if entity contains Chinese characters
-                        if any("\u4e00" <= char <= "\u9fff" for char in entity):
-                            expected_names.add(entity)
-
-                # Also check for the specific names we stored
-                expected_names.update(["张伟", "李明"])
-
-                # At least one expected name should appear in the response
-                found_name = any(name in result.text for name in expected_names)
-                assert found_name, (
-                    f"Expected response to mention one of the Chinese names: {expected_names}. Response: {result.text}"
-                )
-
-                logger.info("Chinese reflect test passed - response generated in Chinese")
-                return  # Test passed, exit
-
-            except AssertionError as e:
-                last_error = e
-                if attempt < max_retries - 1:
-                    logger.warning(f"Attempt {attempt + 1} failed: {e}. Retrying...")
-                    continue
-                else:
-                    raise e
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_retain_japanese_content(memory, request_context):
-    """
-    Test that retain correctly handles Japanese content.
-
-    This test verifies multilingual support extends beyond Chinese
-    to other non-Latin languages.
-
-    Note: LLM fact extraction is non-deterministic and may sometimes translate
-    content to English despite instructions. We retry up to 3 times.
-    """
-    max_retries = 3
-    last_error = None
-
-    for attempt in range(max_retries):
-        # Use unique bank_id per attempt to avoid stale data
-        bank_id = f"test_japanese_retain_{datetime.now(timezone.utc).timestamp()}_{attempt}"
-
-        try:
-            # Japanese content about a developer
-            japanese_content = """
-            田中さんはソフトウェアエンジニアで、東京のスタートアップで働いています。
-            彼女はPythonとTypeScriptが得意で、毎日コードレビューをしています。
-            先週、新しいAPIを完成させました。
-            """
-
-            unit_ids = await memory.retain_async(
-                bank_id=bank_id,
-                content=japanese_content,
-                context="チームプロフィール",  # "Team profile"
-                event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-                request_context=request_context,
-            )
-
-            logger.info(f"Retained {len(unit_ids)} facts from Japanese content (attempt {attempt + 1})")
-            assert len(unit_ids) > 0, "Should have extracted facts from Japanese content"
-
-            # Recall with Japanese query
-            result = await memory.recall_async(
-                bank_id=bank_id,
-                query="田中さんについて教えてください",  # "Tell me about Tanaka-san"
-                budget=Budget.MID,
-                max_tokens=1000,
-                fact_type=["world"],
-                request_context=request_context,
-            )
-
-            assert len(result.results) > 0, "Should recall facts about Tanaka"
-
-            # Check for Japanese content in facts
-            japanese_facts_found = 0
-            for fact in result.results:
-                logger.info(f"Fact: {fact.text[:100]}...")
-                # Check for Japanese characters (hiragana, katakana, or kanji)
-                if any(
-                    ("\u3040" <= char <= "\u309f")  # Hiragana
-                    or ("\u30a0" <= char <= "\u30ff")  # Katakana
-                    or ("\u4e00" <= char <= "\u9fff")  # Kanji
-                    for char in fact.text
-                ):
-                    japanese_facts_found += 1
-
-            assert japanese_facts_found > 0, (
-                f"Expected facts to contain Japanese characters. "
-                f"Facts: {[f.text for f in result.results]}"
-            )
-
-            logger.info("Japanese retain test passed - facts preserved in Japanese")
-            return  # Test passed
-
-        except AssertionError as e:
-            last_error = e
-            if attempt < max_retries - 1:
-                logger.warning(f"Attempt {attempt + 1} failed: {e}. Retrying...")
-            else:
-                raise e
-        finally:
-            # Cleanup the bank
-            try:
-                await memory.delete_bank(bank_id, request_context=request_context)
-            except Exception:
-                pass
-
-
-@pytest.mark.asyncio
-async def test_mixed_language_entities(memory, request_context):
-    """
-    Test that entity extraction works correctly with mixed language content.
-
-    Some entities (like company names) might be in English while the
-    description is in Chinese.
-    """
-    bank_id = f"test_mixed_lang_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Mixed language content - Chinese with English company names
-        mixed_content = """
-        王芳在Google北京办公室工作，她是一名高级产品经理。
-        之前她在Microsoft和Amazon工作过。
-        她负责管理YouTube在中国市场的推广策略。
-        """
-
-        unit_ids = await memory.retain_async(
-            bank_id=bank_id,
-            content=mixed_content,
-            context="员工资料",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        assert len(unit_ids) > 0, "Should extract facts from mixed language content"
-
-        # Recall and check entities
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="王芳在哪里工作？",  # "Where does Wang Fang work?"
-            budget=Budget.MID,
-            max_tokens=1000,
-            fact_type=["world"],
-            include_entities=True,
-            request_context=request_context,
-        )
-
-        assert len(result.results) > 0, "Should recall facts about Wang Fang"
-
-        # Check that both Chinese and English entities are preserved
-        all_text = " ".join(f.text for f in result.results)
-        logger.info(f"Combined facts: {all_text}")
-
-        # Should contain Chinese name and/or English company names
-        has_chinese_name = "王芳" in all_text
-        has_english_company = any(
-            company in all_text for company in ["Google", "Microsoft", "Amazon", "YouTube"]
-        )
-
-        assert has_chinese_name or has_english_company, (
-            f"Expected mixed language entities. Facts: {all_text}"
-        )
-
-        logger.info("Mixed language entity test passed")
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
@@ -3,12 +3,11 @@ Test observation generation and entity state functionality.
 """
 import pytest
 from hindsight_api.engine.memory_engine import Budget
-from hindsight_api import RequestContext
 from datetime import datetime, timezone


@pytest.mark.asyncio
-async def test_observation_generation_on_put(memory, request_context):
+async def test_observation_generation_on_put(memory):
    """
    Test that observations are generated SYNCHRONOUSLY when new facts are added.

@@ -37,8 +36,7 @@ async def test_observation_generation_on_put(memory, request_context):
                bank_id=bank_id,
                content=content,
                context="work info",
-                event_date=datetime(2024, 1, 15 + i, tzinfo=timezone.utc),
-                request_context=request_context,
+                event_date=datetime(2024, 1, 15 + i, tzinfo=timezone.utc)
            )

        # Observations are generated SYNCHRONOUSLY during retain,
@@ -77,7 +75,7 @@ async def test_observation_generation_on_put(memory, request_context):
        print(f"Entity: {entity_name} (id: {entity_id})")

        # Get observations for the entity - should be available immediately
-        observations = await memory.get_entity_observations(bank_id, entity_id, limit=10, request_context=request_context)
+        observations = await memory.get_entity_observations(bank_id, entity_id, limit=10)

        print(f"\n=== Observations for {entity_name} ===")
        print(f"Total observations: {len(observations)}")
@@ -104,7 +102,7 @@ async def test_observation_generation_on_put(memory, request_context):


@pytest.mark.asyncio
-async def test_regenerate_entity_observations(memory, request_context):
+async def test_regenerate_entity_observations(memory):
    """
    Test explicit regeneration of observations for an entity.
    """
@@ -116,8 +114,7 @@ async def test_regenerate_entity_observations(memory, request_context):
            bank_id=bank_id,
            content="Sarah is a product manager who loves user research and data analysis.",
            context="work info",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc)
        )

        await memory.wait_for_background_tasks()
@@ -143,15 +140,14 @@ async def test_regenerate_entity_observations(memory, request_context):
            created_ids = await memory.regenerate_entity_observations(
                bank_id=bank_id,
                entity_id=entity_id,
-                entity_name=entity_name,
-                request_context=request_context,
+                entity_name=entity_name
            )

            print(f"\n=== Regenerated Observations ===")
            print(f"Created {len(created_ids)} observations for {entity_name}")

            # Get the observations
-            observations = await memory.get_entity_observations(bank_id, entity_id, limit=10, request_context=request_context)
+            observations = await memory.get_entity_observations(bank_id, entity_id, limit=10)
            for obs in observations:
                print(f"  - {obs.text}")

@@ -174,108 +170,7 @@ async def test_regenerate_entity_observations(memory, request_context):


@pytest.mark.asyncio
-async def test_manual_regenerate_with_few_facts(memory, request_context):
-    """
-    Test that manual regeneration works even with fewer than 5 facts.
-
-    This is important because:
-    - Automatic generation during retain requires MIN_FACTS_THRESHOLD (5)
-    - But manual regeneration via API should work with any number of facts
-    - The UI triggers manual regeneration, so it should work regardless of fact count
-    """
-    bank_id = f"test_manual_regen_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Store only 2 facts - below the automatic threshold
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice works at Google as a senior software engineer.",
-            context="work info",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice loves hiking and outdoor photography.",
-            context="hobbies",
-            event_date=datetime(2024, 1, 16, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        # Find the Alice entity
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            entity_row = await conn.fetchrow(
-                """
-                SELECT id, canonical_name
-                FROM entities
-                WHERE bank_id = $1 AND LOWER(canonical_name) LIKE '%alice%'
-                LIMIT 1
-                """,
-                bank_id
-            )
-
-        assert entity_row is not None, "Alice entity should have been extracted"
-
-        entity_id = str(entity_row['id'])
-        entity_name = entity_row['canonical_name']
-
-        # Check fact count - should be < 5
-        async with pool.acquire() as conn:
-            fact_count = await conn.fetchval(
-                "SELECT COUNT(*) FROM unit_entities WHERE entity_id = $1",
-                entity_row['id']
-            )
-
-        print(f"\n=== Manual Regeneration Test ===")
-        print(f"Entity: {entity_name} (id: {entity_id})")
-        print(f"Linked facts: {fact_count}")
-
-        # Verify we're testing with fewer than the automatic threshold
-        assert fact_count < 5, f"Test requires < 5 facts, but entity has {fact_count}"
-
-        # Before regeneration - should have no observations (auto threshold not met)
-        obs_before = await memory.get_entity_observations(bank_id, entity_id, limit=10, request_context=request_context)
-        print(f"Observations before manual regenerate: {len(obs_before)}")
-
-        # Manually regenerate observations - this should work regardless of fact count
-        created_ids = await memory.regenerate_entity_observations(
-            bank_id=bank_id,
-            entity_id=entity_id,
-            entity_name=entity_name,
-            request_context=request_context,
-        )
-
-        print(f"Observations created by manual regenerate: {len(created_ids)}")
-
-        # Get observations after regeneration
-        observations = await memory.get_entity_observations(bank_id, entity_id, limit=10, request_context=request_context)
-        print(f"Observations after manual regenerate: {len(observations)}")
-        for obs in observations:
-            print(f"  - {obs.text}")
-
-        # Manual regeneration should create observations even with < 5 facts
-        assert len(observations) > 0, \
-            f"Manual regeneration should create observations even with only {fact_count} facts. " \
-            f"The LLM should synthesize at least 1 observation from the available facts."
-
-        # Verify observations contain relevant content
-        obs_texts = " ".join([o.text.lower() for o in observations])
-        assert any(keyword in obs_texts for keyword in ["google", "engineer", "hiking", "photography", "alice"]), \
-            "Observations should contain relevant information about Alice"
-
-        print(f"✓ Manual regeneration works with {fact_count} facts (below automatic threshold of 5)")
-
-    finally:
-        # Cleanup
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            await conn.execute("DELETE FROM memory_units WHERE bank_id = $1", bank_id)
-            await conn.execute("DELETE FROM entities WHERE bank_id = $1", bank_id)
-
-
-@pytest.mark.asyncio
-async def test_search_with_include_entities(memory, request_context):
+async def test_search_with_include_entities(memory):
    """
    Test that search with include_entities=True returns entity observations.

@@ -301,8 +196,7 @@ async def test_search_with_include_entities(memory, request_context):
                bank_id=bank_id,
                content=content,
                context="work info",
-                event_date=datetime(2024, 1, 15 + i, tzinfo=timezone.utc),
-                request_context=request_context,
+                event_date=datetime(2024, 1, 15 + i, tzinfo=timezone.utc)
            )

        # Observations are generated synchronously during retain, no need to wait
@@ -315,8 +209,7 @@ async def test_search_with_include_entities(memory, request_context):
            budget=Budget.LOW,
            max_tokens=2000,
            include_entities=True,
-            max_entity_tokens=500,
-            request_context=request_context,
+            max_entity_tokens=500
        )

        print(f"\n=== Search Results ===")
@@ -370,7 +263,7 @@ async def test_search_with_include_entities(memory, request_context):


@pytest.mark.asyncio
-async def test_get_entity_state(memory, request_context):
+async def test_get_entity_state(memory):
    """
    Test getting the full state of an entity.
    """
@@ -382,8 +275,7 @@ async def test_get_entity_state(memory, request_context):
            bank_id=bank_id,
            content="Bob is a frontend developer who specializes in React and TypeScript.",
            context="work info",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc)
        )

        await memory.wait_for_background_tasks()
@@ -410,8 +302,7 @@ async def test_get_entity_state(memory, request_context):
                bank_id=bank_id,
                entity_id=entity_id,
                entity_name=entity_name,
-                limit=10,
-                request_context=request_context,
+                limit=10
            )

            print(f"\n=== Entity State for {entity_name} ===")
@@ -433,7 +324,7 @@ async def test_get_entity_state(memory, request_context):


@pytest.mark.asyncio
-async def test_observation_fact_type_in_database(memory, request_context):
+async def test_observation_fact_type_in_database(memory):
    """
    Test that observations are stored with correct fact_type in database.
    """
@@ -445,8 +336,7 @@ async def test_observation_fact_type_in_database(memory, request_context):
            bank_id=bank_id,
            content="Charlie is a DevOps engineer who manages the Kubernetes infrastructure.",
            context="work info",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc)
        )

        await memory.wait_for_background_tasks()
@@ -484,7 +374,7 @@ async def test_observation_fact_type_in_database(memory, request_context):


@pytest.mark.asyncio
-async def test_user_entity_prioritized_for_observations(memory, request_context):
+async def test_user_entity_prioritized_for_observations(memory):
    """
    Test that the 'user' entity gets observations even when many other entities exist.

@@ -520,8 +410,7 @@ async def test_user_entity_prioritized_for_observations(memory, request_context)
                bank_id=bank_id,
                content=content,
                context="personal info",
-                event_date=datetime(2024, 1, 15 + i, tzinfo=timezone.utc),
-                request_context=request_context,
+                event_date=datetime(2024, 1, 15 + i, tzinfo=timezone.utc)
            )

        # Observations are generated synchronously during retain
@@ -577,7 +466,7 @@ async def test_user_entity_prioritized_for_observations(memory, request_context)
            f"User entity should have at least 5 facts, but has {user_fact_count}"

        # Get observations for user entity
-        observations = await memory.get_entity_observations(bank_id, user_entity_id, limit=10, request_context=request_context)
+        observations = await memory.get_entity_observations(bank_id, user_entity_id, limit=10)

        print(f"\n=== User Entity Observations ===")
        print(f"Total observations: {len(observations)}")
@@ -1,277 +0,0 @@
-"""
-Tests for per-operation LLM configuration.
-
-Verifies that retain and reflect operations use their respective LLM configs.
-"""
-
-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_set = {
-        "HINDSIGHT_API_SKIP_LLM_VERIFICATION": "true",
-        "HINDSIGHT_API_LAZY_RERANKER": "true",
-        "HINDSIGHT_API_LLM_PROVIDER": "mock",
-        "HINDSIGHT_API_LLM_MODEL": "default-model",
-        "HINDSIGHT_API_RETAIN_LLM_PROVIDER": "mock",
-        "HINDSIGHT_API_RETAIN_LLM_MODEL": "retain-model",
-        "HINDSIGHT_API_REFLECT_LLM_PROVIDER": "mock",
-        "HINDSIGHT_API_REFLECT_LLM_MODEL": "reflect-model",
-    }
-
-    # Save original values
-    original_values = {}
-    for key in env_vars_to_set:
-        original_values[key] = os.environ.get(key)
-
-    # Set test values
-    for key, value in env_vars_to_set.items():
-        os.environ[key] = value
-
-    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()
-
-
-class TestPerOperationLLMConfig:
-    """Test that per-operation LLM configs are correctly applied."""
-
-    def test_config_loads_per_operation_settings(self):
-        """Test that config correctly loads per-operation LLM settings."""
-        from hindsight_api.config import get_config
-
-        config = get_config()
-
-        # Default config
-        assert config.llm_provider == "mock"
-        assert config.llm_model == "default-model"
-
-        # Retain config
-        assert config.retain_llm_provider == "mock"
-        assert config.retain_llm_model == "retain-model"
-
-        # Reflect config
-        assert config.reflect_llm_provider == "mock"
-        assert config.reflect_llm_model == "reflect-model"
-
-    def test_memory_engine_creates_separate_llm_configs(self):
-        """Test that MemoryEngine creates separate LLM configs for each operation."""
-        from hindsight_api import MemoryEngine
-
-        engine = MemoryEngine(
-            skip_llm_verification=True,
-            lazy_reranker=True,
-        )
-
-        # Verify default config
-        assert engine._llm_config.provider == "mock"
-        assert engine._llm_config.model == "default-model"
-
-        # Verify retain config
-        assert engine._retain_llm_config.provider == "mock"
-        assert engine._retain_llm_config.model == "retain-model"
-
-        # Verify reflect config
-        assert engine._reflect_llm_config.provider == "mock"
-        assert engine._reflect_llm_config.model == "reflect-model"
-
-    def test_memory_engine_with_explicit_params(self):
-        """Test that explicit params override env config."""
-        from hindsight_api import MemoryEngine
-
-        engine = MemoryEngine(
-            memory_llm_provider="mock",
-            memory_llm_model="explicit-default",
-            retain_llm_provider="mock",
-            retain_llm_model="explicit-retain",
-            reflect_llm_provider="mock",
-            reflect_llm_model="explicit-reflect",
-            skip_llm_verification=True,
-            lazy_reranker=True,
-        )
-
-        assert engine._llm_config.model == "explicit-default"
-        assert engine._retain_llm_config.model == "explicit-retain"
-        assert engine._reflect_llm_config.model == "explicit-reflect"
-
-    def test_memory_engine_fallback_when_no_per_operation_config(self):
-        """Test that per-operation configs fall back to default when not set."""
-        from hindsight_api.config import clear_config_cache as clear_cache
-
-        # Temporarily clear per-operation env vars
-        retain_provider = os.environ.pop("HINDSIGHT_API_RETAIN_LLM_PROVIDER", None)
-        retain_model = os.environ.pop("HINDSIGHT_API_RETAIN_LLM_MODEL", None)
-        reflect_provider = os.environ.pop("HINDSIGHT_API_REFLECT_LLM_PROVIDER", None)
-        reflect_model = os.environ.pop("HINDSIGHT_API_REFLECT_LLM_MODEL", None)
-
-        try:
-            clear_cache()
-            from hindsight_api import MemoryEngine
-
-            engine = MemoryEngine(
-                skip_llm_verification=True,
-                lazy_reranker=True,
-            )
-
-            # All should fall back to default
-            assert engine._llm_config.model == "default-model"
-            assert engine._retain_llm_config.model == "default-model"
-            assert engine._reflect_llm_config.model == "default-model"
-        finally:
-            # Restore env vars
-            if retain_provider:
-                os.environ["HINDSIGHT_API_RETAIN_LLM_PROVIDER"] = retain_provider
-            if retain_model:
-                os.environ["HINDSIGHT_API_RETAIN_LLM_MODEL"] = retain_model
-            if reflect_provider:
-                os.environ["HINDSIGHT_API_REFLECT_LLM_PROVIDER"] = reflect_provider
-            if reflect_model:
-                os.environ["HINDSIGHT_API_REFLECT_LLM_MODEL"] = reflect_model
-            clear_cache()
-
-
-class TestMockLLMProvider:
-    """Test the mock LLM provider functionality."""
-
-    def test_mock_provider_records_calls(self):
-        """Test that mock provider records calls."""
-        from hindsight_api.engine.llm_wrapper import LLMProvider
-
-        provider = LLMProvider(
-            provider="mock",
-            api_key="",
-            base_url="",
-            model="test-model",
-        )
-
-        import asyncio
-
-        async def make_call():
-            return await provider.call(
-                messages=[{"role": "user", "content": "test"}],
-                scope="test_scope",
-            )
-
-        result = asyncio.get_event_loop().run_until_complete(make_call())
-
-        # Verify call was recorded
-        calls = provider.get_mock_calls()
-        assert len(calls) == 1
-        assert calls[0]["model"] == "test-model"
-        assert calls[0]["scope"] == "test_scope"
-        assert calls[0]["messages"] == [{"role": "user", "content": "test"}]
-
-    def test_mock_provider_returns_custom_response(self):
-        """Test that mock provider can return custom responses."""
-        from hindsight_api.engine.llm_wrapper import LLMProvider
-
-        provider = LLMProvider(
-            provider="mock",
-            api_key="",
-            base_url="",
-            model="test-model",
-        )
-
-        provider.set_mock_response({"custom": "response"})
-
-        import asyncio
-
-        async def make_call():
-            return await provider.call(
-                messages=[{"role": "user", "content": "test"}],
-            )
-
-        result = asyncio.get_event_loop().run_until_complete(make_call())
-        assert result == {"custom": "response"}
-
-    def test_mock_provider_returns_usage_when_requested(self):
-        """Test that mock provider returns token usage."""
-        from hindsight_api.engine.llm_wrapper import LLMProvider
-
-        provider = LLMProvider(
-            provider="mock",
-            api_key="",
-            base_url="",
-            model="test-model",
-        )
-
-        import asyncio
-
-        async def make_call():
-            return await provider.call(
-                messages=[{"role": "user", "content": "test"}],
-                return_usage=True,
-            )
-
-        result, usage = asyncio.get_event_loop().run_until_complete(make_call())
-        assert usage.input_tokens == 10
-        assert usage.output_tokens == 5
-        assert usage.total_tokens == 15
-
-
-class TestRetainUsesRetainLLMConfig:
-    """Test that retain operations use the retain LLM config."""
-
-    def test_retain_llm_config_is_passed_to_orchestrator(self):
-        """Verify retain operation is configured to use _retain_llm_config."""
-        from hindsight_api import MemoryEngine
-
-        engine = MemoryEngine(
-            memory_llm_provider="mock",
-            memory_llm_model="default-model",
-            retain_llm_provider="mock",
-            retain_llm_model="retain-specific-model",
-            reflect_llm_provider="mock",
-            reflect_llm_model="reflect-specific-model",
-            skip_llm_verification=True,
-            lazy_reranker=True,
-        )
-
-        # Verify the retain LLM config is set correctly
-        assert engine._retain_llm_config.model == "retain-specific-model"
-        assert engine._retain_llm_config.provider == "mock"
-
-        # Verify it's different from the reflect config
-        assert engine._retain_llm_config.model != engine._reflect_llm_config.model
-
-
-class TestReflectUsesReflectLLMConfig:
-    """Test that reflect operations use the reflect LLM config."""
-
-    def test_reflect_llm_config_is_set_correctly(self):
-        """Verify reflect/think operation is configured to use _reflect_llm_config."""
-        from hindsight_api import MemoryEngine
-
-        engine = MemoryEngine(
-            memory_llm_provider="mock",
-            memory_llm_model="default-model",
-            retain_llm_provider="mock",
-            retain_llm_model="retain-specific-model",
-            reflect_llm_provider="mock",
-            reflect_llm_model="reflect-specific-model",
-            skip_llm_verification=True,
-            lazy_reranker=True,
-        )
-
-        # Verify the reflect LLM config is set correctly
-        assert engine._reflect_llm_config.model == "reflect-specific-model"
-        assert engine._reflect_llm_config.provider == "mock"
-
-        # Verify it's different from the retain config
-        assert engine._reflect_llm_config.model != engine._retain_llm_config.model
@@ -5,13 +5,12 @@ import pytest
 import logging
 from datetime import datetime, timezone, timedelta
 from hindsight_api.engine.memory_engine import Budget
-from hindsight_api import RequestContext

 logger = logging.getLogger(__name__)


@pytest.mark.asyncio
-async def test_retain_with_chunks(memory, request_context):
+async def test_retain_with_chunks(memory):
    """
    Test that retain function:
    1. Stores facts with associated chunks
@@ -42,8 +41,7 @@ async def test_retain_with_chunks(memory, request_context):
            content=long_content,
            context="team overview",
            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        print(f"\n=== Retained {len(unit_ids)} facts ===")
@@ -58,8 +56,7 @@ async def test_retain_with_chunks(memory, request_context):
            fact_type=["world"],  # Search for world facts
            include_entities=False,  # Disable entities for simpler test
            include_chunks=True,  # Enable chunks
-            max_chunk_tokens=8192,
-            request_context=request_context,
+            max_chunk_tokens=8192
        )

        print(f"\n=== Recall Results (with chunks) ===")
@@ -91,12 +88,12 @@ async def test_retain_with_chunks(memory, request_context):

    finally:
        # Cleanup - delete the test bank
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)
        print(f"\n=== Cleaned up bank: {bank_id} ===")


@pytest.mark.asyncio
-async def test_chunks_and_entities_follow_fact_order(memory, request_context):
+async def test_chunks_and_entities_follow_fact_order(memory):
    """
    Test that chunks and entities in recall results follow the same order as facts.
    This is critical because token limits may truncate later items.
@@ -133,8 +130,7 @@ async def test_chunks_and_entities_follow_fact_order(memory, request_context):
                content=item["content"],
                context=item["context"],
                event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-                document_id=item["document_id"],
-                request_context=request_context,
+                document_id=item["document_id"]
            )

        print("\n=== Stored 3 separate documents ===")
@@ -148,8 +144,7 @@ async def test_chunks_and_entities_follow_fact_order(memory, request_context):
            fact_type=["world"],
            include_entities=True,
            include_chunks=True,
-            max_chunk_tokens=8192,
-            request_context=request_context,
+            max_chunk_tokens=8192
        )

        print(f"\n=== Recall Results ===")
@@ -219,12 +214,12 @@ async def test_chunks_and_entities_follow_fact_order(memory, request_context):

    finally:
        # Cleanup
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)
        print(f"\n=== Cleaned up bank: {bank_id} ===")


@pytest.mark.asyncio
-async def test_event_date_storage(memory, request_context):
+async def test_event_date_storage(memory):
    """
    Test that event_date is correctly stored as occurred_start.
    Verifies that we can track when events actually happened vs when they were stored.
@@ -240,8 +235,7 @@ async def test_event_date_storage(memory, request_context):
            bank_id=bank_id,
            content="Alice completed the Q2 product launch on June 15th, 2023.",
            context="project history",
-            event_date=past_event_date,
-            request_context=request_context,
+            event_date=past_event_date
        )

        assert len(unit_ids) > 0, "Should have created at least one memory unit"
@@ -252,8 +246,7 @@ async def test_event_date_storage(memory, request_context):
            query="When did Alice complete the product launch?",
            budget=Budget.LOW,
            max_tokens=500,
-            fact_type=["world"],
-            request_context=request_context,
+            fact_type=["world"]
        )

        assert len(result.results) > 0, "Should recall the stored fact"
@@ -275,11 +268,11 @@ async def test_event_date_storage(memory, request_context):
        print(f"\n✓ Event date correctly stored: {occurred_dt}")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_temporal_ordering(memory, request_context):
+async def test_temporal_ordering(memory):
    """
    Test that facts can be stored and retrieved with correct temporal ordering.
    Stores facts with different event_dates and verifies temporal relationships.
@@ -312,8 +305,7 @@ async def test_temporal_ordering(memory, request_context):
                bank_id=bank_id,
                content=event["content"],
                context=event["context"],
-                event_date=event["event_date"],
-                request_context=request_context,
+                event_date=event["event_date"]
            )

        print("\n=== Stored 3 events with different temporal dates ===")
@@ -324,8 +316,7 @@ async def test_temporal_ordering(memory, request_context):
            query="Tell me about Alice's career progression",
            budget=Budget.MID,
            max_tokens=1000,
-            fact_type=["world"],
-            request_context=request_context,
+            fact_type=["world"]
        )

        assert len(result.results) >= 3, f"Should recall all 3 events, got {len(result.results)}"
@@ -354,11 +345,11 @@ async def test_temporal_ordering(memory, request_context):
        print(f"\n✓ Temporal ordering preserved: {min_date.date()} to {max_date.date()}")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_mentioned_at_vs_occurred(memory, request_context):
+async def test_mentioned_at_vs_occurred(memory):
    """
    Test distinction between when fact occurred vs when it was mentioned.

@@ -378,8 +369,7 @@ async def test_mentioned_at_vs_occurred(memory, request_context):
            bank_id=bank_id,
            content="Alice graduated from MIT in March 2020.",
            context="education history",
-            event_date=conversation_date,  # When this conversation happened
-            request_context=request_context,
+            event_date=conversation_date  # When this conversation happened
        )

        assert len(unit_ids) > 0, "Should create memory unit"
@@ -390,8 +380,7 @@ async def test_mentioned_at_vs_occurred(memory, request_context):
            query="Where did Alice go to school?",
            budget=Budget.LOW,
            max_tokens=500,
-            fact_type=["world"],
-            request_context=request_context,
+            fact_type=["world"]
        )

        assert len(result.results) > 0, "Should recall the fact"
@@ -426,11 +415,11 @@ async def test_mentioned_at_vs_occurred(memory, request_context):
        print(f"✓ Test passed: Historical conversation correctly ingested with event_date=2020")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_occurred_dates_not_defaulted(memory, request_context):
+async def test_occurred_dates_not_defaulted(memory):
    """
    Test that occurred_start and occurred_end are NOT defaulted to mentioned_at.

@@ -452,8 +441,7 @@ async def test_occurred_dates_not_defaulted(memory, request_context):
            bank_id=bank_id,
            content="Alice likes coffee. The weather is sunny today.",
            context="current observations",
-            event_date=event_date,
-            request_context=request_context,
+            event_date=event_date
        )

        assert len(unit_ids) > 0, "Should create memory unit"
@@ -464,8 +452,7 @@ async def test_occurred_dates_not_defaulted(memory, request_context):
            query="What does Alice like?",
            budget=Budget.LOW,
            max_tokens=500,
-            fact_type=["world", "opinion"],
-            request_context=request_context,
+            fact_type=["world", "opinion"]
        )

        assert len(result.results) > 0, "Should recall the fact"
@@ -517,11 +504,11 @@ async def test_occurred_dates_not_defaulted(memory, request_context):
        print(f"✓ Test passed: occurred dates are not incorrectly defaulted to mentioned_at")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_mentioned_at_from_context_string(memory, request_context):
+async def test_mentioned_at_from_context_string(memory):
    """
    Test that mentioned_at is extracted from context string by LLM.

@@ -540,8 +527,7 @@ async def test_mentioned_at_from_context_string(memory, request_context):
            bank_id=bank_id,
            content="Alice mentioned she loves hiking in the mountains.",
            context=f"Session ABC123 - you are the assistant in this conversation - happened on {session_date.strftime('%Y-%m-%d %H:%M:%S')} UTC.",
-            event_date=None,  # Not providing event_date - should default to now() if LLM doesn't extract
-            request_context=request_context,
+            event_date=None  # Not providing event_date - should default to now() if LLM doesn't extract
        )

        assert len(unit_ids) > 0, "Should create memory unit"
@@ -552,8 +538,7 @@ async def test_mentioned_at_from_context_string(memory, request_context):
            query="What does Alice like?",
            budget=Budget.LOW,
            max_tokens=500,
-            fact_type=["world"],
-            request_context=request_context,
+            fact_type=["world"]
        )

        assert len(result.results) > 0, "Should recall the fact"
@@ -589,7 +574,7 @@ async def test_mentioned_at_from_context_string(memory, request_context):
        print(f"✓ mentioned_at is always set (never None)")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


 # ============================================================
@@ -597,7 +582,7 @@ async def test_mentioned_at_from_context_string(memory, request_context):
 # ============================================================

@pytest.mark.asyncio
-async def test_context_preservation(memory, request_context):
+async def test_context_preservation(memory):
    """
    Test that context is preserved and retrievable.
    Context helps understand why/how memory was formed.
@@ -612,8 +597,7 @@ async def test_context_preservation(memory, request_context):
            bank_id=bank_id,
            content="The team decided to prioritize mobile development for next quarter.",
            context=specific_context,
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc)
        )

        assert len(unit_ids) > 0, "Should create at least one memory unit"
@@ -624,8 +608,7 @@ async def test_context_preservation(memory, request_context):
            query="What did the team decide?",
            budget=Budget.LOW,
            max_tokens=500,
-            fact_type=["world"],
-            request_context=request_context,
+            fact_type=["world"]
        )

        assert len(result.results) > 0, "Should recall the stored fact"
@@ -637,17 +620,13 @@ async def test_context_preservation(memory, request_context):
        print(f"  Retrieved {len(result.results)} facts")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_context_with_batch(memory, request_context):
+async def test_context_with_batch(memory):
    """
    Test that each item in a batch can have different contexts.
-
-    Note: LLM fact extraction is non-deterministic. Simple sentences may
-    not always produce exactly 1 fact each. We verify the batch was
-    processed and at least some facts were extracted.
    """
    bank_id = f"test_batch_context_{datetime.now(timezone.utc).timestamp()}"

@@ -671,20 +650,18 @@ async def test_context_with_batch(memory, request_context):
                    "context": "incident response",
                    "event_date": datetime(2024, 1, 12, tzinfo=timezone.utc)
                }
-            ],
-            request_context=request_context,
+            ]
        )

-        # Should have created facts from at least some items
-        # LLM extraction is non-deterministic, so we allow some flexibility
+        # Should have created facts from all items
        total_units = sum(len(ids) for ids in unit_ids)
-        assert total_units >= 2, f"Should create at least 2 units from 3 batch items, got {total_units}"
+        assert total_units >= 3, f"Should create at least 3 units, got {total_units}"

        print(f"✓ Stored {len(unit_ids)} batch items with different contexts")
        print(f"  Created {total_units} total memory units")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


 # ============================================================
@@ -692,7 +669,7 @@ async def test_context_with_batch(memory, request_context):
 # ============================================================

@pytest.mark.asyncio
-async def test_metadata_storage_and_retrieval(memory, request_context):
+async def test_metadata_storage_and_retrieval(memory):
    """
    Test that user-defined metadata is preserved.
    Metadata allows arbitrary key-value data to be stored with facts.
@@ -715,8 +692,7 @@ async def test_metadata_storage_and_retrieval(memory, request_context):
            bank_id=bank_id,
            content="The product launch is scheduled for March 1st.",
            context="planning meeting",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc)
        )

        assert len(unit_ids) > 0, "Should create memory units"
@@ -727,8 +703,7 @@ async def test_metadata_storage_and_retrieval(memory, request_context):
            query="When is the product launch?",
            budget=Budget.LOW,
            max_tokens=500,
-            fact_type=["world"],
-            request_context=request_context,
+            fact_type=["world"]
        )

        assert len(result.results) > 0, "Should recall stored facts"
@@ -737,7 +712,7 @@ async def test_metadata_storage_and_retrieval(memory, request_context):
        print(f"  (Note: Metadata support depends on API implementation)")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


 # ============================================================
@@ -745,7 +720,7 @@ async def test_metadata_storage_and_retrieval(memory, request_context):
 # ============================================================

@pytest.mark.asyncio
-async def test_empty_batch(memory, request_context):
+async def test_empty_batch(memory):
    """
    Test that empty batch is handled gracefully without errors.
    """
@@ -755,8 +730,7 @@ async def test_empty_batch(memory, request_context):
        # Attempt to store empty batch
        unit_ids = await memory.retain_batch_async(
            bank_id=bank_id,
-            contents=[],
-            request_context=request_context,
+            contents=[]
        )

        # Should return empty list or handle gracefully
@@ -767,11 +741,11 @@ async def test_empty_batch(memory, request_context):

    finally:
        # Clean up (though nothing should be stored)
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_single_item_batch(memory, request_context):
+async def test_single_item_batch(memory):
    """
    Test that batch with one item works correctly.
    """
@@ -787,8 +761,7 @@ async def test_single_item_batch(memory, request_context):
                    "context": "deployment log",
                    "event_date": datetime(2024, 1, 15, tzinfo=timezone.utc)
                }
-            ],
-            request_context=request_context,
+            ]
        )

        assert len(unit_ids) == 1, "Should return one list of unit IDs"
@@ -797,11 +770,11 @@ async def test_single_item_batch(memory, request_context):
        print(f"✓ Single-item batch created {len(unit_ids[0])} units")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_mixed_content_batch(memory, request_context):
+async def test_mixed_content_batch(memory):
    """
    Test batch with varying content sizes (short and long).
    """
@@ -825,8 +798,7 @@ async def test_mixed_content_batch(memory, request_context):
                {"content": short_content, "context": "onboarding"},
                {"content": long_content, "context": "performance review"},
                {"content": "Charlie is on vacation this week.", "context": "team status"}
-            ],
-            request_context=request_context,
+            ]
        )

        # All items should be processed
@@ -841,11 +813,11 @@ async def test_mixed_content_batch(memory, request_context):
        print(f"  Long content: {long_units} units")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_batch_with_missing_optional_fields(memory, request_context):
+async def test_batch_with_missing_optional_fields(memory):
    """
    Test that batch handles items with missing optional fields.
    """
@@ -870,8 +842,7 @@ async def test_batch_with_missing_optional_fields(memory, request_context):
                    "context": "code review",
                    # No event_date
                }
-            ],
-            request_context=request_context,
+            ]
        )

        # All items should be processed successfully
@@ -881,7 +852,7 @@ async def test_batch_with_missing_optional_fields(memory, request_context):
        print(f"✓ Batch with mixed optional fields created {total_units} total units")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


 # ============================================================
@@ -889,7 +860,7 @@ async def test_batch_with_missing_optional_fields(memory, request_context):
 # ============================================================

@pytest.mark.asyncio
-async def test_single_batch_multiple_documents(memory, request_context):
+async def test_single_batch_multiple_documents(memory):
    """
    Test storing multiple distinct documents in a single batch call.
    Each should be tracked separately.
@@ -905,24 +876,21 @@ async def test_single_batch_multiple_documents(memory, request_context):
            bank_id=bank_id,
            content="Alice's resume: 10 years Python experience, worked at Google.",
            context="resume review",
-            document_id="resume_alice",
-            request_context=request_context,
+            document_id="resume_alice"
        )

        doc2_units = await memory.retain_async(
            bank_id=bank_id,
            content="Bob's resume: 5 years JavaScript experience, worked at Meta.",
            context="resume review",
-            document_id="resume_bob",
-            request_context=request_context,
+            document_id="resume_bob"
        )

        doc3_units = await memory.retain_async(
            bank_id=bank_id,
            content="Charlie's resume: 8 years Go experience, worked at Amazon.",
            context="resume review",
-            document_id="resume_charlie",
-            request_context=request_context,
+            document_id="resume_charlie"
        )

        # All documents should be stored
@@ -939,18 +907,17 @@ async def test_single_batch_multiple_documents(memory, request_context):
            query="Who worked at Google?",
            budget=Budget.MID,
            max_tokens=1000,
-            fact_type=["world"],
-            request_context=request_context,
+            fact_type=["world"]
        )

        assert len(result.results) > 0, "Should find facts about Alice"

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_document_upsert_behavior(memory, request_context):
+async def test_document_upsert_behavior(memory):
    """
    Test that upserting a document replaces the old content.
    """
@@ -963,8 +930,7 @@ async def test_document_upsert_behavior(memory, request_context):
            bank_id=bank_id,
            content="Project is in planning phase. Alice is the lead.",
            context="status update v1",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        assert len(v1_units) > 0, "Should create units for v1"
@@ -974,8 +940,7 @@ async def test_document_upsert_behavior(memory, request_context):
            bank_id=bank_id,
            content="Project is in development phase. Bob has joined as co-lead.",
            context="status update v2",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        assert len(v2_units) > 0, "Should create units for v2"
@@ -986,8 +951,7 @@ async def test_document_upsert_behavior(memory, request_context):
            query="What is the project status?",
            budget=Budget.MID,
            max_tokens=1000,
-            fact_type=["world"],
-            request_context=request_context,
+            fact_type=["world"]
        )

        assert len(result.results) > 0, "Should recall facts"
@@ -995,7 +959,7 @@ async def test_document_upsert_behavior(memory, request_context):
        print(f"✓ Document upsert created v1: {len(v1_units)} units, v2: {len(v2_units)} units")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


 # ============================================================
@@ -1003,7 +967,7 @@ async def test_document_upsert_behavior(memory, request_context):
 # ============================================================

@pytest.mark.asyncio
-async def test_chunk_fact_mapping(memory, request_context):
+async def test_chunk_fact_mapping(memory):
    """
    Test that facts correctly reference their source chunks via chunk_id.
    """
@@ -1026,8 +990,7 @@ async def test_chunk_fact_mapping(memory, request_context):
            bank_id=bank_id,
            content=content,
            context="technical documentation",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        assert len(unit_ids) > 0, "Should create memory units"
@@ -1040,8 +1003,7 @@ async def test_chunk_fact_mapping(memory, request_context):
            max_tokens=1000,
            fact_type=["world"],
            include_chunks=True,
-            max_chunk_tokens=8192,
-            request_context=request_context,
+            max_chunk_tokens=8192
        )

        assert len(result.results) > 0, "Should recall facts"
@@ -1064,11 +1026,11 @@ async def test_chunk_fact_mapping(memory, request_context):
            print(f"  Returned {len(result.chunks)} chunks matching fact references")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_chunk_ordering_preservation(memory, request_context):
+async def test_chunk_ordering_preservation(memory):
    """
    Test that chunk_index reflects the correct order within a document.
    """
@@ -1108,8 +1070,7 @@ async def test_chunk_ordering_preservation(memory, request_context):
            bank_id=bank_id,
            content=content,
            context="multi-section document",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        assert len(unit_ids) > 0, "Should create units"
@@ -1122,8 +1083,7 @@ async def test_chunk_ordering_preservation(memory, request_context):
            max_tokens=2000,
            fact_type=["world"],
            include_chunks=True,
-            max_chunk_tokens=8192,
-            request_context=request_context,
+            max_chunk_tokens=8192
        )

        if result.chunks:
@@ -1143,23 +1103,19 @@ async def test_chunk_ordering_preservation(memory, request_context):
            print("✓ Content stored (may have created single chunk or no chunks returned)")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-@pytest.mark.timeout(180)  # Allow up to 3 minutes for this test
-async def test_chunks_truncation_behavior(memory, request_context):
+async def test_chunks_truncation_behavior(memory):
    """
    Test that when chunks exceed max_chunk_tokens, truncation is indicated.
-
-    Note: This test processes larger content and may take longer than typical tests.
    """
    bank_id = f"test_chunk_truncation_{datetime.now(timezone.utc).timestamp()}"
    document_id = "large_doc"

    try:
-        # Create a moderately large document with meaningful content
-        # Reduced from * 5 to * 2 for faster execution while still testing truncation
+        # Create a large document with meaningful content
        large_content = """
        The company's product roadmap for 2024 includes several major initiatives.
        The engineering team is expanding to support these efforts.
@@ -1203,14 +1159,13 @@ async def test_chunks_truncation_behavior(memory, request_context):
        The finance team is implementing new budgeting tools for better forecasting.
        They are also working on automated expense reporting and approval workflows.
        This will save approximately 100 hours per month in manual work.
-        """ * 2  # Repeat to create enough content for truncation testing
+        """ * 5  # Repeat to make it very large

        unit_ids = await memory.retain_async(
            bank_id=bank_id,
            content=large_content,
            context="large document test",
-            document_id=document_id,
-            request_context=request_context,
+            document_id=document_id
        )

        assert len(unit_ids) > 0, "Should create units"
@@ -1223,8 +1178,7 @@ async def test_chunks_truncation_behavior(memory, request_context):
            max_tokens=1000,
            fact_type=["world"],
            include_chunks=True,
-            max_chunk_tokens=500,  # Small limit to test truncation
-            request_context=request_context,
+            max_chunk_tokens=500  # Small limit to test truncation
        )

        if result.chunks:
@@ -1244,7 +1198,7 @@ async def test_chunks_truncation_behavior(memory, request_context):
            print("✓ No chunks returned (may be under token limit)")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


 # ============================================================
@@ -1252,7 +1206,7 @@ async def test_chunks_truncation_behavior(memory, request_context):
 # ============================================================

@pytest.mark.asyncio
-async def test_temporal_links_creation(memory, request_context):
+async def test_temporal_links_creation(memory):
    """
    Test that temporal links are created between facts with nearby event dates.

@@ -1269,8 +1223,7 @@ async def test_temporal_links_creation(memory, request_context):
            bank_id=bank_id,
            content="Alice started working on the authentication module.",
            context="daily standup",
-            event_date=base_date,
-            request_context=request_context,
+            event_date=base_date
        )

        # Fact 2 at 2:00 PM same day (4 hours later)
@@ -1278,8 +1231,7 @@ async def test_temporal_links_creation(memory, request_context):
            bank_id=bank_id,
            content="Bob reviewed the API design document.",
            context="daily standup",
-            event_date=base_date.replace(hour=14),
-            request_context=request_context,
+            event_date=base_date.replace(hour=14)
        )

        # Fact 3 at 9:00 AM next day (23 hours later)
@@ -1287,8 +1239,7 @@ async def test_temporal_links_creation(memory, request_context):
            bank_id=bank_id,
            content="Charlie deployed the new database schema.",
            context="daily standup",
-            event_date=base_date.replace(day=16, hour=9),
-            request_context=request_context,
+            event_date=base_date.replace(day=16, hour=9)
        )

        assert len(unit_ids_1) > 0 and len(unit_ids_2) > 0 and len(unit_ids_3) > 0
@@ -1327,11 +1278,11 @@ async def test_temporal_links_creation(memory, request_context):
            logger.info("Temporal links created successfully with proper weights")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_semantic_links_creation(memory, request_context):
+async def test_semantic_links_creation(memory):
    """
    Test that semantic links are created between facts with similar content.

@@ -1344,24 +1295,21 @@ async def test_semantic_links_creation(memory, request_context):
        unit_ids_1 = await memory.retain_async(
            bank_id=bank_id,
            content="Alice is an expert in Python programming and has built many web applications.",
-            context="team skills",
-            request_context=request_context,
+            context="team skills"
        )

        # Similar content - should create semantic link
        unit_ids_2 = await memory.retain_async(
            bank_id=bank_id,
            content="Bob is proficient in Python development and specializes in building APIs.",
-            context="team skills",
-            request_context=request_context,
+            context="team skills"
        )

        # Different content - less likely to create strong semantic link
        unit_ids_3 = await memory.retain_async(
            bank_id=bank_id,
            content="The quarterly sales meeting is scheduled for next Tuesday at 3 PM.",
-            context="calendar events",
-            request_context=request_context,
+            context="calendar events"
        )

        assert len(unit_ids_1) > 0 and len(unit_ids_2) > 0 and len(unit_ids_3) > 0
@@ -1401,11 +1349,11 @@ async def test_semantic_links_creation(memory, request_context):
            logger.info("Semantic links created successfully between similar content")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_entity_links_creation(memory, request_context):
+async def test_entity_links_creation(memory):
    """
    Test that entity links are created between facts that mention the same entities.

@@ -1419,32 +1367,28 @@ async def test_entity_links_creation(memory, request_context):
        unit_ids_1 = await memory.retain_async(
            bank_id=bank_id,
            content="Alice joined Google as a software engineer in 2020.",
-            context="career history",
-            request_context=request_context,
+            context="career history"
        )

        # Mentions same entity (Alice) - should create entity link
        unit_ids_2 = await memory.retain_async(
            bank_id=bank_id,
            content="Alice led the development of the new authentication system.",
-            context="project updates",
-            request_context=request_context,
+            context="project updates"
        )

        # Mentions same entity (Google) - should create entity link
        unit_ids_3 = await memory.retain_async(
            bank_id=bank_id,
            content="Google announced new cloud services at their annual conference.",
-            context="tech news",
-            request_context=request_context,
+            context="tech news"
        )

        # Different entities - no entity link expected
        unit_ids_4 = await memory.retain_async(
            bank_id=bank_id,
            content="Bob works at Meta on machine learning infrastructure.",
-            context="career history",
-            request_context=request_context,
+            context="career history"
        )

        assert len(unit_ids_1) > 0 and len(unit_ids_2) > 0 and len(unit_ids_3) > 0 and len(unit_ids_4) > 0
@@ -1501,11 +1445,11 @@ async def test_entity_links_creation(memory, request_context):
            logger.info("Entity links are properly bidirectional")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_causal_links_creation(memory, request_context):
+async def test_causal_links_creation(memory):
    """
    Test that causal links are created between facts with causal relationships.

@@ -1527,8 +1471,7 @@ async def test_causal_links_creation(memory, request_context):
        unit_ids = await memory.retain_async(
            bank_id=bank_id,
            content=content,
-            context="project timeline",
-            request_context=request_context,
+            context="project timeline"
        )

        assert len(unit_ids) > 0, "Should have created facts"
@@ -1574,11 +1517,11 @@ async def test_causal_links_creation(memory, request_context):
        logger.info("Test completed (causal link extraction is LLM-dependent)")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_all_link_types_together(memory, request_context):
+async def test_all_link_types_together(memory):
    """
    Integration test: Verify all link types can be created in a single retain operation.

@@ -1596,8 +1539,7 @@ async def test_all_link_types_together(memory, request_context):
            bank_id=bank_id,
            content="Alice completed the Python backend service for the authentication system.",
            context="sprint review",
-            event_date=base_date,
-            request_context=request_context,
+            event_date=base_date
        )

        # Fact 2: Related to Alice, similar topic (Python), close in time
@@ -1605,8 +1547,7 @@ async def test_all_link_types_together(memory, request_context):
            bank_id=bank_id,
            content="Alice optimized the Python code and improved the authentication performance by 40%.",
            context="sprint review",
-            event_date=base_date.replace(hour=14),  # Same day, 4 hours later
-            request_context=request_context,
+            event_date=base_date.replace(hour=14)  # Same day, 4 hours later
        )

        # Fact 3: Related to Alice, different topic but same entity
@@ -1614,8 +1555,7 @@ async def test_all_link_types_together(memory, request_context):
            bank_id=bank_id,
            content="Alice presented the security architecture at the team meeting.",
            context="team meeting",
-            event_date=base_date.replace(day=16),  # Next day
-            request_context=request_context,
+            event_date=base_date.replace(day=16)  # Next day
        )

        assert len(unit_ids_1) > 0 and len(unit_ids_2) > 0 and len(unit_ids_3) > 0
@@ -1654,11 +1594,11 @@ async def test_all_link_types_together(memory, request_context):
            logger.info("All major link types (temporal, semantic, entity) are working correctly")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_semantic_links_within_same_batch(memory, request_context):
+async def test_semantic_links_within_same_batch(memory):
    """
    Test that semantic links are created between facts retained in the SAME batch.

@@ -1677,8 +1617,7 @@ async def test_semantic_links_within_same_batch(memory, request_context):

        result = await memory.retain_batch_async(
            bank_id=bank_id,
-            contents=contents,
-            request_context=request_context,
+            contents=contents
        )

        # Flatten the list of lists
@@ -1713,11 +1652,11 @@ async def test_semantic_links_within_same_batch(memory, request_context):
                logger.info(f"  Semantic link: {str(link['from_unit_id'])[:8]}... -> {str(link['to_unit_id'])[:8]}... (weight: {link['weight']:.3f})")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_temporal_links_within_same_batch(memory, request_context):
+async def test_temporal_links_within_same_batch(memory):
    """
    Test that temporal links are created between facts retained in the SAME batch.

@@ -1750,8 +1689,7 @@ async def test_temporal_links_within_same_batch(memory, request_context):

        result = await memory.retain_batch_async(
            bank_id=bank_id,
-            contents=contents,
-            request_context=request_context,
+            contents=contents
        )

        # Flatten the list of lists
@@ -1786,72 +1724,4 @@ async def test_temporal_links_within_same_batch(memory, request_context):
                logger.info(f"  Temporal link: {str(link['from_unit_id'])[:8]}... -> {str(link['to_unit_id'])[:8]}... (weight: {link['weight']:.3f})")

    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_user_provided_entities(memory, request_context):
-    """
-    Test that user-provided entities are merged with auto-extracted entities.
-
-    This tests the feature added in PR #91 where users can provide entities
-    via the 'entities' field in the retain request. These should be combined
-    with LLM-extracted entities, with case-insensitive deduplication.
-    """
-    bank_id = f"test_user_entities_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Store content with user-provided entities
-        # The content mentions "Alice" which LLM might extract,
-        # but we also provide "ProjectX" and "ACME Corp" which may not be in the text
-        contents = [
-            {
-                "content": "Alice completed the quarterly report.",
-                "context": "work update",
-                "entities": [
-                    {"text": "ProjectX", "type": "PROJECT"},
-                    {"text": "ACME Corp", "type": "ORG"},
-                    {"text": "Alice"},  # May also be extracted by LLM (dedup test)
-                ],
-            }
-        ]
-
-        result = await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=contents,
-            request_context=request_context,
-        )
-
-        # Flatten the list of lists
-        unit_ids = [uid for sublist in result for uid in sublist]
-        assert len(unit_ids) > 0, "Should have created at least one fact"
-
-        logger.info(f"Created {len(unit_ids)} facts with user-provided entities")
-
-        # Query entity links to verify user-provided entities were stored
-        async with memory._pool.acquire() as conn:
-            # Get all entities linked to our facts via the unit_entities junction table
-            entity_rows = await conn.fetch(
-                """
-                SELECT DISTINCT e.canonical_name
-                FROM entities e
-                JOIN unit_entities ue ON e.id = ue.entity_id
-                WHERE ue.unit_id::text = ANY($1)
-                """,
-                unit_ids
-            )
-
-            entity_names = {row['canonical_name'].lower() for row in entity_rows}
-            logger.info(f"Found entities linked to facts: {[row['canonical_name'] for row in entity_rows]}")
-
-            # Verify user-provided entities are present
-            assert "projectx" in entity_names, "User-provided entity 'ProjectX' should be linked"
-            assert "acme corp" in entity_names, "User-provided entity 'ACME Corp' should be linked"
-
-            # Alice should be present (either from LLM extraction or user-provided)
-            assert "alice" in entity_names, "Entity 'Alice' should be linked"
-
-            logger.info("✓ User-provided entities successfully merged with extracted entities")
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)
@@ -1,406 +0,0 @@
-"""
-Tests for multi-tenant schema isolation.
-
-Verifies that concurrent retain operations from different tenants
-are properly isolated in their respective PostgreSQL schemas.
-"""
-
-import asyncio
-import uuid
-
-import pytest
-import pytest_asyncio
-
-from hindsight_api.extensions import RequestContext, TenantContext, TenantExtension
-from hindsight_api.engine.memory_engine import _current_schema, fq_table
-from hindsight_api.migrations import run_migrations
-
-
-class MultiSchemaTestTenantExtension(TenantExtension):
-    """
-    Test tenant extension that maps API keys to schema names.
-
-    API keys are in format: "key-{schema_name}"
-    Provisions schemas on first access using run_migrations(schema=name).
-    """
-
-    def __init__(self, config: dict):
-        super().__init__(config)
-        self.db_url = config.get("db_url")
-        # Pre-configured valid schemas for test
-        self.valid_schemas = config.get("valid_schemas", set())
-        # Track provisioned schemas
-        self._provisioned: set[str] = set()
-
-    async def authenticate(self, context: RequestContext) -> TenantContext:
-        if not context.api_key:
-            from hindsight_api.extensions import AuthenticationError
-
-            raise AuthenticationError("API key required")
-
-        # Parse schema from API key (format: "key-{schema}")
-        if context.api_key.startswith("key-"):
-            schema = context.api_key[4:]  # Remove "key-" prefix
-            if schema in self.valid_schemas:
-                # Provision schema on first access
-                if schema not in self._provisioned and self.db_url:
-                    run_migrations(self.db_url, schema=schema)
-                    self._provisioned.add(schema)
-                return TenantContext(schema_name=schema)
-
-        from hindsight_api.extensions import AuthenticationError
-
-        raise AuthenticationError(f"Unknown API key: {context.api_key}")
-
-
-async def drop_schema(conn, schema_name: str) -> None:
-    """Drop a schema and all its contents."""
-    await conn.execute(f'DROP SCHEMA IF EXISTS "{schema_name}" CASCADE')
-
-
-async def count_memories_in_schema(conn, schema_name: str, bank_id: str) -> int:
-    """Count memory units in a specific schema for a bank."""
-    result = await conn.fetchval(
-        f'SELECT COUNT(*) FROM "{schema_name}".memory_units WHERE bank_id = $1',
-        bank_id,
-    )
-    return result or 0
-
-
-async def get_memory_texts_in_schema(conn, schema_name: str, bank_id: str) -> list[str]:
-    """Get all memory texts in a specific schema for a bank."""
-    rows = await conn.fetch(
-        f'SELECT text FROM "{schema_name}".memory_units WHERE bank_id = $1 ORDER BY text',
-        bank_id,
-    )
-    return [row["text"] for row in rows]
-
-
-class TestSchemaIsolation:
-    """Tests for multi-tenant schema isolation."""
-
-    @pytest.mark.asyncio
-    async def test_concurrent_inserts_isolated_by_schema(self, memory, pg0_db_url):
-        """
-        Multiple concurrent database operations from different tenants
-        should store data in their respective schemas without cross-contamination.
-
-        Uses run_migrations(schema=x) to provision schemas like a real extension.
-        """
-        import asyncpg
-
-        # Test schemas
-        schemas = ["tenant_alpha", "tenant_beta", "tenant_gamma"]
-        bank_id = f"test-isolation-{uuid.uuid4().hex[:8]}"
-
-        # Clean up any existing schemas
-        conn = await asyncpg.connect(pg0_db_url)
-        try:
-            for schema in schemas:
-                await drop_schema(conn, schema)
-        finally:
-            await conn.close()
-
-        # Configure tenant extension that provisions schemas via run_migrations
-        tenant_ext = MultiSchemaTestTenantExtension({
-            "db_url": pg0_db_url,
-            "valid_schemas": set(schemas),
-        })
-        memory._tenant_extension = tenant_ext
-
-        # Define concurrent insert tasks for each tenant
-        async def insert_for_tenant(schema_name: str, content_prefix: str):
-            """Insert memories for a specific tenant using schema context."""
-            # Authenticate to set the schema context
-            tenant_request = RequestContext(api_key=f"key-{schema_name}")
-            await memory._authenticate_tenant(tenant_request)
-
-            # Now fq_table will use the correct schema
-            pool = await memory._get_pool()
-            from hindsight_api.engine.db_utils import acquire_with_retry
-
-            async with acquire_with_retry(pool) as conn:
-                # Insert 3 memories for this tenant
-                for i in range(3):
-                    await conn.execute(
-                        f"""
-                        INSERT INTO {fq_table('memory_units')} (bank_id, text, event_date, fact_type)
-                        VALUES ($1, $2, now(), 'world')
-                        """,
-                        bank_id,
-                        f"MARKER_{content_prefix}_DOC{i}: Memory for {schema_name}",
-                    )
-
-        # Run concurrent inserts for all tenants
-        await asyncio.gather(
-            insert_for_tenant("tenant_alpha", "ALPHA"),
-            insert_for_tenant("tenant_beta", "BETA"),
-            insert_for_tenant("tenant_gamma", "GAMMA"),
-        )
-
-        # Verify isolation - each schema should only have its own data
-        conn = await asyncpg.connect(pg0_db_url)
-        try:
-            for schema in schemas:
-                texts = await get_memory_texts_in_schema(conn, schema, bank_id)
-                prefix = schema.replace("tenant_", "").upper()
-
-                # Should have exactly 3 memories
-                assert len(texts) == 3, f"Schema {schema} should have 3 memories, got {len(texts)}"
-
-                # All texts should contain the schema's marker
-                for text in texts:
-                    assert f"MARKER_{prefix}" in text, (
-                        f"Memory in {schema} missing its marker: {text}"
-                    )
-
-                # Should NOT contain other tenants' markers
-                other_prefixes = ["ALPHA", "BETA", "GAMMA"]
-                other_prefixes.remove(prefix)
-                for other in other_prefixes:
-                    for text in texts:
-                        assert f"MARKER_{other}" not in text, (
-                            f"Cross-contamination! Schema {schema} has {other}'s marker: {text}"
-                        )
-
-        finally:
-            # Cleanup
-            for schema in schemas:
-                await drop_schema(conn, schema)
-            await conn.close()
-
-        # Reset tenant extension
-        memory._tenant_extension = None
-        _current_schema.set("public")
-
-    @pytest.mark.asyncio
-    async def test_schema_context_isolation_in_concurrent_tasks(self, pg0_db_url):
-        """
-        Verify that _current_schema contextvar is properly isolated
-        between concurrent async tasks.
-        """
-        results = {}
-        errors = []
-
-        async def check_schema_context(schema_name: str, delay: float):
-            """Set schema context, wait, then verify it's still correct."""
-            try:
-                # Set the schema
-                _current_schema.set(schema_name)
-
-                # Small delay to allow interleaving
-                await asyncio.sleep(delay)
-
-                # Verify schema is still correct
-                current = _current_schema.get()
-                if current != schema_name:
-                    errors.append(f"Expected {schema_name}, got {current}")
-
-                # Verify fq_table uses correct schema
-                table = fq_table("memory_units")
-                expected = f"{schema_name}.memory_units"
-                if table != expected:
-                    errors.append(f"Expected {expected}, got {table}")
-
-                results[schema_name] = current
-
-            except Exception as e:
-                errors.append(f"Error in {schema_name}: {e}")
-
-        # Run many concurrent tasks with different schemas
-        tasks = []
-        for i in range(10):
-            for schema in ["schema_a", "schema_b", "schema_c"]:
-                # Vary delays to create interleaving
-                delay = 0.01 * (i % 3)
-                tasks.append(check_schema_context(f"{schema}_{i}", delay))
-
-        await asyncio.gather(*tasks)
-
-        # No errors should have occurred
-        assert not errors, f"Schema context isolation errors: {errors}"
-
-    @pytest.mark.asyncio
-    async def test_list_memories_respects_schema(self, memory, pg0_db_url):
-        """
-        list_memory_units should only return memories from the current schema.
-
-        Uses run_migrations(schema=x) to provision schemas.
-        """
-        import asyncpg
-
-        schemas = ["tenant_list_a", "tenant_list_b"]
-        bank_id = f"test-list-{uuid.uuid4().hex[:8]}"
-
-        # Clean up any existing schemas and provision via migrations
-        conn = await asyncpg.connect(pg0_db_url)
-        try:
-            for schema in schemas:
-                await drop_schema(conn, schema)
-        finally:
-            await conn.close()
-
-        # Provision schemas using run_migrations
-        for schema in schemas:
-            run_migrations(pg0_db_url, schema=schema)
-
-        # Insert test data directly into each schema
-        conn = await asyncpg.connect(pg0_db_url)
-        try:
-            for schema in schemas:
-                await conn.execute(
-                    f"""
-                    INSERT INTO "{schema}".memory_units (bank_id, text, event_date, fact_type)
-                    VALUES ($1, $2, now(), 'world')
-                    """,
-                    bank_id,
-                    f"Direct insert for {schema}",
-                )
-        finally:
-            await conn.close()
-
-        # Configure tenant extension
-        tenant_ext = MultiSchemaTestTenantExtension({
-            "db_url": pg0_db_url,
-            "valid_schemas": set(schemas),
-        })
-        memory._tenant_extension = tenant_ext
-
-        try:
-            # Query as tenant_list_a - should only see tenant_list_a's data
-            tenant_a_request = RequestContext(api_key="key-tenant_list_a")
-            await memory._authenticate_tenant(tenant_a_request)
-
-            result_a = await memory.list_memory_units(bank_id=bank_id, request_context=tenant_a_request)
-            texts_a = [item["text"] for item in result_a.get("items", [])]
-
-            assert len(texts_a) == 1, f"Expected 1 memory for tenant_list_a, got {len(texts_a)}"
-            assert "tenant_list_a" in texts_a[0], f"Wrong content: {texts_a[0]}"
-
-            # Query as tenant_list_b - should only see tenant_list_b's data
-            tenant_b_request = RequestContext(api_key="key-tenant_list_b")
-            await memory._authenticate_tenant(tenant_b_request)
-
-            result_b = await memory.list_memory_units(bank_id=bank_id, request_context=tenant_b_request)
-            texts_b = [item["text"] for item in result_b.get("items", [])]
-
-            assert len(texts_b) == 1, f"Expected 1 memory for tenant_list_b, got {len(texts_b)}"
-            assert "tenant_list_b" in texts_b[0], f"Wrong content: {texts_b[0]}"
-
-        finally:
-            # Cleanup
-            conn = await asyncpg.connect(pg0_db_url)
-            try:
-                for schema in schemas:
-                    await drop_schema(conn, schema)
-            finally:
-                await conn.close()
-
-            memory._tenant_extension = None
-            _current_schema.set("public")
-
-    @pytest.mark.asyncio
-    async def test_high_concurrency_schema_isolation(self, memory, pg0_db_url):
-        """
-        Stress test: Many concurrent operations across multiple schemas
-        should maintain perfect isolation.
-
-        Uses run_migrations(schema=x) to provision schemas like a real extension.
-        """
-        import asyncpg
-
-        # Create more schemas for stress test
-        num_schemas = 5
-        ops_per_schema = 10
-        schemas = [f"stress_tenant_{i}" for i in range(num_schemas)]
-        bank_id = f"test-stress-{uuid.uuid4().hex[:8]}"
-
-        # Clean up any existing schemas first
-        conn = await asyncpg.connect(pg0_db_url)
-        try:
-            for schema in schemas:
-                await drop_schema(conn, schema)
-        finally:
-            await conn.close()
-
-        # Provision schemas using run_migrations
-        for schema in schemas:
-            run_migrations(pg0_db_url, schema=schema)
-
-        # Configure tenant extension (schemas already provisioned)
-        tenant_ext = MultiSchemaTestTenantExtension({
-            "db_url": pg0_db_url,
-            "valid_schemas": set(schemas),
-        })
-        # Mark schemas as already provisioned so extension doesn't re-run migrations
-        tenant_ext._provisioned = set(schemas)
-        memory._tenant_extension = tenant_ext
-
-        errors = []
-
-        async def insert_one(schema: str, item_id: int):
-            """Single insert operation for tracking."""
-            try:
-                # Authenticate to set the schema context
-                tenant_request = RequestContext(api_key=f"key-{schema}")
-                await memory._authenticate_tenant(tenant_request)
-
-                # Insert using fq_table
-                pool = await memory._get_pool()
-                from hindsight_api.engine.db_utils import acquire_with_retry
-
-                async with acquire_with_retry(pool) as conn:
-                    await conn.execute(
-                        f"""
-                        INSERT INTO {fq_table('memory_units')} (bank_id, text, event_date, fact_type)
-                        VALUES ($1, $2, now(), 'world')
-                        """,
-                        bank_id,
-                        f"STRESS_MARKER_{schema}_ITEM{item_id}: Memory for {schema}",
-                    )
-            except Exception as e:
-                errors.append(f"Insert error for {schema}: {e}")
-
-        # Run many concurrent operations
-        tasks = []
-        for i in range(ops_per_schema):
-            for schema in schemas:
-                tasks.append(insert_one(schema, i))
-
-        await asyncio.gather(*tasks)
-
-        # Check for errors during insert
-        assert not errors, f"Errors during insert: {errors}"
-
-        # Verify no cross-contamination
-        conn = await asyncpg.connect(pg0_db_url)
-        try:
-            for schema in schemas:
-                texts = await get_memory_texts_in_schema(conn, schema, bank_id)
-
-                # Should have exactly ops_per_schema memories
-                assert len(texts) == ops_per_schema, (
-                    f"Schema {schema} should have {ops_per_schema} memories, got {len(texts)}"
-                )
-
-                # All memories should reference this schema only
-                for text in texts:
-                    # Check it contains our schema marker
-                    assert f"STRESS_MARKER_{schema}" in text, (
-                        f"Memory in {schema} doesn't contain schema marker: {text}"
-                    )
-
-                    # Check it doesn't contain other schema markers
-                    for other_schema in schemas:
-                        if other_schema != schema:
-                            assert f"STRESS_MARKER_{other_schema}" not in text, (
-                                f"Cross-contamination! {schema} has {other_schema}'s data: {text}"
-                            )
-        finally:
-            # Cleanup
-            for schema in schemas:
-                await drop_schema(conn, schema)
-            await conn.close()
-
-        memory._tenant_extension = None
-        _current_schema.set("public")
@@ -3,12 +3,12 @@ Test search tracing functionality.
 """
 import pytest
 from hindsight_api.engine.memory_engine import Budget
-from hindsight_api import SearchTrace, RequestContext
+from hindsight_api import SearchTrace
 from datetime import datetime, timezone


@pytest.mark.asyncio
-async def test_search_with_trace(memory, request_context):
+async def test_search_with_trace(memory):
    """Test that search with enable_trace=True returns a valid SearchTrace."""
    # Generate a unique agent ID for this test
    bank_id = f"test_trace_{datetime.now(timezone.utc).timestamp()}"
@@ -20,19 +20,16 @@ async def test_search_with_trace(memory, request_context):
            bank_id=bank_id,
            content="Alice works at Google in Mountain View",
            context="test context",
-            request_context=request_context,
        )
        await memory.retain_async(
            bank_id=bank_id,
            content="Bob also works at Google but in New York",
            context="test context",
-            request_context=request_context,
        )
        await memory.retain_async(
            bank_id=bank_id,
            content="Charlie founded a startup called TechCorp",
            context="test context",
-            request_context=request_context,
        )

        # Search with tracing enabled
@@ -43,7 +40,6 @@ async def test_search_with_trace(memory, request_context):
            budget=Budget.LOW, # 20,
            max_tokens=512,
            enable_trace=True,
-            request_context=request_context,
        )

        # Verify results
@@ -106,11 +102,11 @@ async def test_search_with_trace(memory, request_context):

    finally:
        # Cleanup
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)


@pytest.mark.asyncio
-async def test_search_without_trace(memory, request_context):
+async def test_search_without_trace(memory):
    """Test that search with enable_trace=False returns None for trace."""
    bank_id = f"test_no_trace_{datetime.now(timezone.utc).timestamp()}"

@@ -121,7 +117,6 @@ async def test_search_without_trace(memory, request_context):
            bank_id=bank_id,
            content="Test memory without trace",
            context="test",
-            request_context=request_context,
        )

        # Search without tracing
@@ -132,7 +127,6 @@ async def test_search_without_trace(memory, request_context):
            budget=Budget.LOW, # 10,
            max_tokens=512,
            enable_trace=False,
-            request_context=request_context,
        )

        # Verify trace is None
@@ -143,4 +137,4 @@ async def test_search_without_trace(memory, request_context):

    finally:
        # Cleanup
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)
@@ -1,136 +0,0 @@
-"""
-Safety tests to ensure all SQL queries use fully-qualified table names.
-
-This prevents cross-tenant data access by ensuring every table reference
-includes the schema prefix (e.g., public.memory_units instead of just memory_units).
-"""
-
-import re
-from pathlib import Path
-
-import pytest
-
-# All tables that MUST be schema-qualified in SQL queries
-TABLES = [
-    "memory_units",
-    "memory_links",
-    "unit_entities",
-    "entities",
-    "entity_cooccurrences",
-    "banks",
-    "documents",
-    "chunks",
-    "async_operations",
-]
-
-# Files to scan for SQL queries
-SCAN_PATHS = [
-    "hindsight_api/engine",
-    "hindsight_api/api",
-]
-
-# Files to exclude (e.g., migrations, tests)
-EXCLUDE_PATTERNS = [
-    "alembic",
-    "__pycache__",
-    "test_",
-]
-
-
-def get_python_files() -> list[Path]:
-    """Get all Python files to scan."""
-    root = Path(__file__).parent.parent
-    files = []
-    for scan_path in SCAN_PATHS:
-        path = root / scan_path
-        if path.exists():
-            for py_file in path.rglob("*.py"):
-                # Check exclusions
-                if any(excl in str(py_file) for excl in EXCLUDE_PATTERNS):
-                    continue
-                files.append(py_file)
-    return files
-
-
-def find_unqualified_table_refs(content: str, filename: str) -> list[tuple[int, str, str]]:
-    """
-    Find SQL statements with unqualified table references.
-
-    Returns list of (line_number, table_name, line_content).
-    """
-    violations = []
-
-    # Patterns that indicate SQL context
-    sql_keywords = r"(?:FROM|JOIN|INTO|UPDATE|DELETE\s+FROM)\s+"
-
-    # Additional SQL indicators to confirm this is actually SQL, not prose
-    sql_indicators = re.compile(
-        r"(SELECT|INSERT|DELETE|UPDATE|CREATE|ALTER|DROP|WHERE|SET|VALUES|"
-        r'f"""|f\'\'\'|""".*SELECT|\'\'\'.*SELECT)',
-        re.IGNORECASE,
-    )
-
-    lines = content.split("\n")
-    for line_num, line in enumerate(lines, 1):
-        # Skip comments and strings that are clearly not SQL
-        stripped = line.strip()
-        if stripped.startswith("#"):
-            continue
-
-        for table in TABLES:
-            # Pattern: SQL keyword followed by unqualified table name
-            # Should match: FROM memory_units, JOIN memory_units, INTO memory_units
-            # Should NOT match: FROM public.memory_units, FROM {schema}.memory_units
-            # Should NOT match: fq_table("memory_units")
-
-            # Check for unqualified table after SQL keyword
-            pattern = rf"{sql_keywords}{table}(?:\s|$|,|\))"
-
-            if re.search(pattern, line, re.IGNORECASE):
-                # Check if it's actually qualified (has schema prefix)
-                qualified_pattern = rf"\.\s*{table}(?:\s|$|,|\))"
-                fq_table_pattern = rf'fq_table\s*\(\s*["\']?{table}'
-
-                if not re.search(qualified_pattern, line) and not re.search(
-                    fq_table_pattern, line
-                ):
-                    # Additional check: line must have SQL indicators
-                    # This avoids false positives in docstrings like "split into chunks"
-                    if sql_indicators.search(line):
-                        violations.append((line_num, table, stripped))
-
-    return violations
-
-
-class TestSQLSchemaSafety:
-    """Ensure all SQL uses schema-qualified table names."""
-
-    def test_no_unqualified_table_references(self):
-        """All SQL queries must use fq_table() or schema.table format."""
-        all_violations = []
-
-        for py_file in get_python_files():
-            content = py_file.read_text()
-            violations = find_unqualified_table_refs(content, py_file.name)
-
-            for line_num, table, line in violations:
-                all_violations.append(
-                    f"{py_file.relative_to(py_file.parent.parent)}:{line_num} - "
-                    f"unqualified '{table}': {line[:80]}..."
-                )
-
-        if all_violations:
-            msg = (
-                f"Found {len(all_violations)} unqualified table references!\n"
-                "These could cause cross-tenant data access.\n"
-                "Use fq_table('table_name') for all table references.\n\n"
-                + "\n".join(all_violations[:20])  # Show first 20
-            )
-            if len(all_violations) > 20:
-                msg += f"\n... and {len(all_violations) - 20} more"
-            pytest.fail(msg)
-
-    def test_tables_list_is_complete(self):
-        """Verify we're checking for all tables (sanity check)."""
-        # This is a sanity check - if you add a new table, add it to TABLES
-        assert len(TABLES) >= 9, "Update TABLES list if you added new tables"
@@ -3,17 +3,16 @@ import asyncio
 from datetime import datetime, timezone, timedelta
 import pytest
 from hindsight_api.engine.memory_engine import Budget
-from hindsight_api import RequestContext


@pytest.mark.asyncio
-async def test_temporal_ranges_are_written(memory, request_context):
+async def test_temporal_ranges_are_written(memory):
    """Test that occurred_start, occurred_end, and mentioned_at are actually written to database."""
    bank_id = "test_temporal_ranges"

    # Clean up any existing data
    try:
-        await memory.delete_bank(bank_id, request_context=request_context)
+        await memory.delete_bank(bank_id)
    except Exception:
        pass

@@ -24,8 +23,7 @@ async def test_temporal_ranges_are_written(memory, request_context):
    await memory.retain_async(
        bank_id=bank_id,
        content=text1,
-        event_date=conversation_date,
-        request_context=request_context,
+        event_date=conversation_date
    )

    # Test 2: Period event (month range)
@@ -34,8 +32,7 @@ async def test_temporal_ranges_are_written(memory, request_context):
    await memory.retain_async(
        bank_id=bank_id,
        content=text2,
-        event_date=conversation_date,
-        request_context=request_context,
+        event_date=conversation_date
    )

    # Give it a moment for async processing
@@ -117,8 +114,7 @@ async def test_temporal_ranges_are_written(memory, request_context):
        query="pottery workshop",
        fact_type=["world", "experience"],
        budget=Budget.LOW,
-        max_tokens=4096,
-        request_context=request_context,
+        max_tokens=4096
    )

    print(f"Found {len(search_result.results)} search results")
@@ -136,4 +132,4 @@ async def test_temporal_ranges_are_written(memory, request_context):
            print("⚠ Temporal fields not yet populated in search results (known issue)")

    # Clean up
-    await memory.delete_bank(bank_id, request_context=request_context)
+    await memory.delete_bank(bank_id)
@@ -4,11 +4,10 @@ Test think function for opinion generation and consistency.
 import pytest
 from datetime import datetime, timezone
 from hindsight_api.engine.memory_engine import Budget
-from hindsight_api import RequestContext


@pytest.mark.asyncio
-async def test_think_opinion_consistency(memory, request_context):
+async def test_think_opinion_consistency(memory):
    """
    Test that think function:
    1. Generates an opinion
@@ -24,16 +23,14 @@ async def test_think_opinion_consistency(memory, request_context):
            bank_id=bank_id,
            content="Alice is a software engineer who has worked on 5 major projects. She always delivers on time and writes clean, well-documented code.",
            context="performance review",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc)
        )

        await memory.retain_async(
            bank_id=bank_id,
            content="Bob recently joined the team. He missed his first deadline and his code had many bugs.",
            context="performance review",
-            event_date=datetime(2024, 2, 1, tzinfo=timezone.utc),
-            request_context=request_context,
+            event_date=datetime(2024, 2, 1, tzinfo=timezone.utc)
        )

        # First think call - should generate opinions
@@ -42,7 +39,6 @@ async def test_think_opinion_consistency(memory, request_context):
            bank_id=bank_id,
            query=query,
            budget=Budget.LOW,
-            request_context=request_context,
        )

        print(f"\n=== First Think Call ===")
@@ -86,7 +82,6 @@ async def test_think_opinion_consistency(memory, request_context):
            bank_id=bank_id,
            query=query,
            budget=Budget.LOW,
-            request_context=request_context,
        )

        print(f"\n=== Second Think Call ===")
@@ -127,13 +122,13 @@ async def test_think_opinion_consistency(memory, request_context):
    finally:
        # Clean up agent data
        try:
-            await memory.delete_bank(bank_id, request_context=request_context)
+            await memory.delete_bank(bank_id)
        except Exception as e:
            print(f"Warning: Error during cleanup: {e}")


@pytest.mark.asyncio
-async def test_think_without_prior_context(memory, request_context):
+async def test_think_without_prior_context(memory):
    """
    Test that think function handles queries when there's no relevant context.
    """
@@ -144,7 +139,6 @@ async def test_think_without_prior_context(memory, request_context):
        bank_id=bank_id,
        query="What is the capital of France?",
        budget=Budget.LOW,
-        request_context=request_context,
    )

    print(f"\n=== Think Without Context ===")
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
DK09876	e521914f0f	Fix example scripts: remove non-existent API attributes - recall.py: remove .weight, fix entities iteration (dict not list) - retain.mjs: remove result.async check	2025-12-17 12:14:43 -07:00
DK09876	7cb469ff75	Fix opinions.py: use actual API attributes instead of non-existent ones	2025-12-17 11:40:55 -07:00
DK09876	9118e7b4cb	Fix main-methods.py: RecallResult and ReflectFact don't have weight attribute	2025-12-17 11:18:09 -07:00
DK09876	841a66f375	Fix async API client usage in documents.py example	2025-12-17 11:11:46 -07:00
DK09876	eb06adb2be	Add documentation code validation CI job - Use uv sync + uv run pattern (matches existing CI) - Add requests to test dependencies for cleanup scripts	2025-12-17 10:48:38 -07:00
DK09876	3913788fd8	Fix: run cd in subshell so install runs from repo root	2025-12-17 10:40:10 -07:00
DK09876	6ea02eb023	Fix: use explicit shell expansion for wheel install	2025-12-17 10:35:01 -07:00
DK09876	55154384f6	Fix wheel path - uv build outputs to repo root dist/	2025-12-17 10:24:52 -07:00
DK09876	19e4e2d635	Fix CI issue	2025-12-17 10:20:15 -07:00
DK09876	8f2396f04a	Fix wheel glob expansion in test-doc-examples CI job	2025-12-17 10:15:13 -07:00
DK09876	bffc0ee0d0	Add documentation code validation system - Create runnable example scripts in examples/api/ (19 files) - Add CodeSnippet component for extracting marked sections - Add raw-loader dependency for importing source files - Create sample retain-new.mdx showing new approach - Add README documenting coverage and gaps	2025-12-17 10:06:53 -07:00