fix

fix(security): fix qs - CVE-2025-15284
feat: backup/restore (#110 )
2026-01-07 14:54:40 +01:00 · 2026-01-07 11:58:07 +01:00 · 2026-01-07 11:29:50 +01:00 · 2026-01-07 11:29:41 +01:00 · 2026-01-07 11:16:24 +01:00 · 2026-01-07 10:26:42 +01:00
371 changed files with 32248 additions and 24368 deletions
@@ -2,11 +2,23 @@
 # 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
@@ -0,0 +1,71 @@
+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
+
@@ -0,0 +1,8 @@
+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.
@@ -0,0 +1,82 @@
+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
@@ -42,6 +42,10 @@ 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
@@ -67,6 +71,12 @@ 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
@@ -77,6 +87,7 @@ jobs:
          hindsight-api/dist/*
          hindsight/dist/*
          hindsight-integrations/litellm/dist/*
+          hindsight-embed/dist/*
        retention-days: 1

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

    - name: Publish to npm
      working-directory: ./hindsight-clients/typescript
-      run: npm publish --access public
+      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 }}

@@ -117,6 +139,65 @@ 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: ${{ matrix.os }}
    strategy:
@@ -181,7 +262,7 @@ jobs:
    - name: Free Disk Space
      uses: jlumbroso/free-disk-space@main
      with:
-        tool-cache: false
+        tool-cache: true
        android: true
        dotnet: true
        haskell: true
@@ -206,7 +287,7 @@ jobs:
      id: get_version
      run: echo "VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT

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

-    - name: Build and push
+    # 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
      uses: docker/build-push-action@v6
      with:
        context: .
@@ -263,7 +366,7 @@ jobs:

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

@@ -286,6 +389,12 @@ 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:
@@ -318,8 +427,11 @@ 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
@@ -20,6 +20,8 @@ jobs:
            path: hindsight-api
          - name: hindsight-client
            path: hindsight-clients/python
+          - name: hindsight-embed
+            path: hindsight-embed

    steps:
    - uses: actions/checkout@v4
@@ -80,6 +82,58 @@ 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

@@ -121,6 +175,90 @@ 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

@@ -153,7 +291,7 @@ jobs:
    - name: Free Disk Space
      uses: jlumbroso/free-disk-space@main
      with:
-        tool-cache: false
+        tool-cache: true
        android: true
        dotnet: true
        haskell: true
@@ -171,6 +309,13 @@ 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
@@ -180,6 +325,7 @@ jobs:
      GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
      GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
      OPENAI_API_KEY: ${{ secrets.OPENAI_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)
@@ -468,6 +614,97 @@ 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

@@ -495,4 +732,228 @@ jobs:

    - name: Run tests
      working-directory: ./hindsight-integrations/litellm
-      run: uv run pytest tests -v
+      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 }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_URL: http://localhost:8888
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Download CLI artifact
+      uses: actions/download-artifact@v4
+      with:
+        name: hindsight-cli
+        path: /usr/local/bin
+
+    - name: Make CLI executable
+      run: chmod +x /usr/local/bin/hindsight
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '20'
+        cache: 'npm'
+        cache-dependency-path: package-lock.json
+
+    - name: Build and install API
+      working-directory: ./hindsight-api
+      run: |
+        uv build
+        uv sync --no-install-project --index-strategy unsafe-best-match
+
+    - name: Install Python client dependencies
+      working-directory: ./hindsight-clients/python
+      run: uv sync --extra test --index-strategy unsafe-best-match
+
+    - name: Install TypeScript client
+      run: |
+        npm ci --workspace=hindsight-clients/typescript
+        npm run build --workspace=hindsight-clients/typescript
+
+    - name: Create .env file
+      run: |
+        cat > .env << EOF
+        HINDSIGHT_API_LLM_PROVIDER=${{ 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 Python doc examples
+      working-directory: ./hindsight-clients/python
+      run: |
+        for f in ../../hindsight-docs/examples/api/*.py; do
+          echo "Running $f..."
+          uv run python "$f"
+        done
+
+    - name: Run Node.js doc examples
+      run: |
+        for f in hindsight-docs/examples/api/*.mjs; do
+          echo "Running $f..."
+          node "$f"
+        done
+
+    - name: Configure CLI
+      run: hindsight configure --api-url http://localhost:8888
+
+    - name: Run 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"
@@ -12,8 +12,10 @@ wheels/
 # Node
 node_modules/

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

 # IDE
 .idea/
@@ -14,6 +14,7 @@ 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
@@ -148,4 +149,5 @@ 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
+
@@ -0,0 +1,155 @@
+# 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. It stores memories as World facts, Experiences, Opinions, and Observations across memory banks.
+
+## Development Commands
+
+### API Server (Python/FastAPI)
+```bash
+# Start API server (loads .env automatically)
+./scripts/dev/start-api.sh
+
+# Run tests
+cd hindsight-api && uv run pytest tests/
+
+# Run specific test file
+cd hindsight-api && uv run pytest tests/test_http_api_integration.py -v
+
+# Lint
+cd hindsight-api && uv run ruff check .
+```
+
+### 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
+./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)
+- **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 for retain/recall/reflect operations
+- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, Groq, Ollama, LM Studio
+- `embeddings.py`: Embedding generation (local 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
+- `search/`: Multi-strategy retrieval (semantic, BM25, graph, temporal)
+
+### API Layer (hindsight-api/hindsight_api/api/)
+FastAPI routers for all endpoints. Main operations:
+- **Retain**: Store memories, extracts facts/entities/relationships
+- **Recall**: Retrieve memories via parallel search strategies + reranking
+- **Reflect**: Deep analysis forming new opinions/observations
+
+### 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 isolated (no cross-bank data access)
+- Banks have dispositions (skepticism, literalism, empathy traits 1-5) affecting reflect
+- Banks can have background context
+
+### API Design
+- All endpoints operate on a single bank per request
+- Multi-bank queries are client responsibility
+- 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)
+
+### 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 (workspace)
+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)
@@ -51,7 +51,36 @@ cd hindsight-api
 uv run pytest tests/
 ```

-### Code style
+### 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

 - Use Python type hints
 - Follow existing code patterns
@@ -81,6 +81,8 @@ 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

@@ -2,16 +2,19 @@
 # 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)
+#   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)
 #
 # 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 .                                          # 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

 ARG INCLUDE_API=true
 ARG INCLUDE_CP=true
+ARG PRELOAD_ML_MODELS=true

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

-WORKDIR /app
-
-# Copy built SDK
-COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk
+# 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

 # 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
-RUN rm -f package-lock.json
+# 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

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

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

-# Create public directory if it doesn't exist
-RUN mkdir -p public
+# 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)

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

 WORKDIR /app

-# Install pg0 dependencies
+# Note: libicu version varies by Debian version - try common versions in order
 RUN apt-get update && apt-get install -y \
    curl \
+    procps \
    libxml2 \
    libssl3 \
    libgssapi-krb5-2 \
    libossp-uuid16 \
-    && apt-get install -y libicu72 || apt-get install -y libicu74 || apt-get install -y libicu* \
+    && (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) \
    && 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
@@ -125,28 +146,23 @@ 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

-# Create data directory for pg0 and set ownership
-RUN mkdir -p /app/data && chown -R hindsight:hindsight /app
+RUN 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-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 "\
+# 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 "\
 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')"
+print('Models cached successfully')"; \
+    else echo "Skipping ML model preload"; fi

 EXPOSE 8888

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

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

 WORKDIR /app

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

 WORKDIR /app

-# Install Node.js, curl, uv, and pg0 dependencies
+# Install Node.js, curl, uv, and system dependencies
+# Note: libicu version varies by Debian version - try common versions in order
 RUN apt-get update && apt-get install -y \
    curl \
+    procps \
    libxml2 \
    libssl3 \
    libgssapi-krb5-2 \
    libossp-uuid16 \
-    && apt-get install -y libicu72 || apt-get install -y libicu74 || apt-get install -y libicu* \
+    && (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) \
    && 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
@@ -224,9 +241,9 @@ COPY --from=sdk-builder /app/hindsight-clients/typescript /app/sdk

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

 WORKDIR /app

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

-# Create data directory for pg0 and set ownership
-RUN mkdir -p /app/data && chown -R hindsight:hindsight /app
+RUN 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-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 "\
+# 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 "\
 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')"
+print('Models cached successfully')"; \
+    else echo "Skipping ML model preload"; fi

 EXPOSE 8888 9999

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

-# 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"/* "$PG0_HOME"/ 2>/dev/null || true
-        fi
+# =============================================================================
+# Dependency waiting (opt-in via HINDSIGHT_WAIT_FOR_DEPS=true)
+#
+# Problem: When running with LM Studio, the LLM may take time to load models.
+# If Hindsight starts before LM Studio is ready, it fails on LLM verification.
+# This wait loop ensures dependencies are ready before starting.
+# =============================================================================
+if [ "${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|')
    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
@@ -23,7 +77,8 @@ PIDS=()
 # Start API if enabled
 if [ "$ENABLE_API" = "true" ]; then
    cd /app/api
-    hindsight-api 2>&1 | sed -u 's/^/[api] /' &
+    # Run API directly - Python's PYTHONUNBUFFERED=1 handles output buffering
+    hindsight-api &
    API_PID=$!
    PIDS+=($API_PID)

@@ -42,7 +97,7 @@ fi
 if [ "$ENABLE_CP" = "true" ]; then
    echo "🎛️  Starting Control Plane..."
    cd /app/control-plane
-    PORT=9999 node server.js 2>&1 | grep -v -E "^[[:space:]]*(▲|✓|-|$)" | sed -u 's/^/[control-plane] /' &
+    PORT=9999 node server.js &
    CP_PID=$!
    PIDS+=($CP_PID)
 else
@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.1.8
-appVersion: "0.1.8"
+version: 0.2.1
+appVersion: "0.2.1"
 keywords:
  - ai
  - memory
@@ -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`, `groq`, `gemini`, `ollama` | `openai` |
+| `HINDSIGHT_API_LLM_PROVIDER` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio` | `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` |
@@ -21,9 +21,11 @@ from .engine.search.trace import (
    WeightComponents,
 )
 from .engine.search.tracer import SearchTracer
+from .models import RequestContext

 __all__ = [
    "MemoryEngine",
+    "RequestContext",
    "HindsightConfig",
    "get_config",
    "SearchTrace",
@@ -0,0 +1 @@
+# Admin CLI for Hindsight
@@ -0,0 +1,222 @@
+"""
+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")
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()
@@ -109,6 +109,9 @@ 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.",
@@ -121,14 +124,34 @@ 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

-        context.configure(connection=connection, target_metadata=target_metadata)
+        # 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)

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

 """

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

 # revision identifiers, used by Alembic.
 revision = "d9f6a3b4c5e2"
@@ -15,14 +15,22 @@ 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("UPDATE memory_units SET fact_type = 'experience' WHERE fact_type = 'bank'")
+    op.execute(f"UPDATE {schema}memory_units SET fact_type = 'experience' WHERE fact_type = 'bank'")
    # Also update any 'interactions' values (in case of partial migration)
-    op.execute("UPDATE memory_units SET fact_type = 'experience' WHERE fact_type = 'interactions'")
+    op.execute(f"UPDATE {schema}memory_units SET fact_type = 'experience' WHERE fact_type = 'interactions'")

    # Create new check constraint with 'experience' instead of 'bank'
    op.create_check_constraint(
@@ -31,11 +39,13 @@ 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("UPDATE memory_units SET fact_type = 'bank' WHERE fact_type = 'experience'")
+    op.execute(f"UPDATE {schema}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 op
+from alembic import context, op

 # revision identifiers, used by Alembic.
 revision: str = "e0a1b2c3d4e5"
@@ -21,9 +21,36 @@ 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:
@@ -32,18 +59,18 @@ def upgrade() -> None:
    # - empathy: derived from agreeableness + inverse of neuroticism
    # Default all to 3 (neutral) for simplicity
    conn.execute(
-        sa.text("""
-        UPDATE banks
-        SET disposition = '{"skepticism": 3, "literalism": 3, "empathy": 3}'::jsonb
+        sa.text(f"""
+        UPDATE {schema}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("""
-        ALTER TABLE banks
-        ALTER COLUMN disposition SET DEFAULT '{"skepticism": 3, "literalism": 3, "empathy": 3}'::jsonb
+        sa.text(f"""
+        ALTER TABLE {schema}banks
+        ALTER COLUMN disposition SET DEFAULT '{{"skepticism": 3, "literalism": 3, "empathy": 3}}'::jsonb
    """)
    )

@@ -51,20 +78,34 @@ 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("""
-        UPDATE 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(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
        WHERE disposition IS NOT NULL
    """)
    )

    # Update the default for new banks
    conn.execute(
-        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
+        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
    """)
    )
@@ -9,7 +9,7 @@ Create Date: 2024-12-04
 from collections.abc import Sequence

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

 # revision identifiers, used by Alembic.
@@ -19,17 +19,25 @@ 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_name = 'banks' AND column_name = 'personality'
-    """)
+        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'personality'
+    """),
+        {"schema": target_schema},
    )
    has_personality = result.fetchone() is not None

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

@@ -63,12 +72,14 @@ 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_name = 'banks' AND column_name = 'disposition'
-    """)
+        WHERE table_schema = :schema AND table_name = 'banks' AND column_name = 'disposition'
+    """),
+        {"schema": target_schema},
    )
    if result.fetchone():
        op.alter_column("banks", "disposition", new_column_name="personality")
@@ -5,6 +5,7 @@ Provides both HTTP REST API and MCP (Model Context Protocol) server.
 """

 import logging
+from contextlib import asynccontextmanager
 from typing import Optional

 from fastapi import FastAPI
@@ -45,6 +46,18 @@ 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:
@@ -57,20 +70,31 @@ def create_app(
        app = FastAPI(title="Hindsight API", version="0.0.7")
        logger.info("HTTP REST API disabled")

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

-            # 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
+        # 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}/")

    return app

@@ -8,7 +8,9 @@ from contextvars import ContextVar
 from fastmcp import FastMCP

 from hindsight_api import MemoryEngine
+from hindsight_api.api.http import BankListItem, BankListResponse, BankProfileResponse, DispositionTraits
 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()
@@ -26,12 +28,15 @@ logging.basicConfig(
 )
 logger = logging.getLogger(__name__)

-# Context variable to hold the current bank_id from the URL path
+# 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
 _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 (set from URL path)."""
+    """Get the current bank_id from context."""
    return _current_bank_id.get()


@@ -43,12 +48,13 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
        memory: MemoryEngine instance (required)

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

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

@@ -64,17 +70,24 @@ 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'
+            bank_id: Optional bank to store in (defaults to session bank). Use for cross-bank operations.
        """
        try:
-            bank_id = get_current_bank_id()
-            await memory.retain_batch_async(bank_id=bank_id, contents=[{"content": content, "context": context}])
-            return "Memory stored successfully"
+            target_bank = bank_id or get_current_bank_id()
+            if target_bank is None:
+                return "Error: No bank_id configured"
+            await memory.retain_batch_async(
+                bank_id=target_bank,
+                contents=[{"content": content, "context": context}],
+                request_context=RequestContext(),
+            )
+            return f"Memory stored successfully in bank '{target_bank}'"
        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_results: int = 10) -> str:
+    async def recall(query: str, max_tokens: int = 4096, bank_id: str | None = None) -> str:
        """
        Search memories to provide personalized, context-aware responses.

@@ -86,43 +99,184 @@ 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_results: Maximum number of results to return (default: 10)
+            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.
        """
        try:
-            bank_id = get_current_bank_id()
+            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

-            search_result = await memory.recall_async(
-                bank_id=bank_id, query=query, fact_type=list(VALID_RECALL_FACT_TYPES), budget=Budget.LOW
+            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(),
            )

-            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)
+            # Use model's JSON serialization
+            return recall_result.model_dump_json(indent=2)
        except Exception as e:
            logger.error(f"Error searching: {e}", exc_info=True)
-            return json.dumps({"error": str(e), "results": []})
+            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 to discover banks for orchestration or to find
+        the correct bank_id for cross-bank operations.
+
+        Returns:
+            JSON object with banks array containing bank_id, name, disposition, background, and timestamps
+        """
+        try:
+            banks = await memory.list_banks(request_context=RequestContext())
+            bank_items = [
+                BankListItem(
+                    bank_id=b.get("bank_id") or b.get("id"),
+                    name=b.get("name"),
+                    disposition=DispositionTraits(
+                        **b.get("disposition", {"skepticism": 3, "literalism": 3, "empathy": 3})
+                    ),
+                    background=b.get("background"),
+                    created_at=str(b.get("created_at")) if b.get("created_at") else None,
+                    updated_at=str(b.get("updated_at")) if b.get("updated_at") else None,
+                )
+                for b in banks
+            ]
+            return BankListResponse(banks=bank_items).model_dump_json(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 or update a memory bank.
+
+        Use this to create new banks for different agents, sessions, or purposes.
+        Banks are isolated memory stores - each bank has its own memories and personality.
+
+        Args:
+            bank_id: Unique identifier for the bank (e.g., 'orchestrator-memory', 'agent-1')
+            name: Human-readable name for the bank
+            background: Context about what this bank stores or its purpose
+        """
+        try:
+            # Get or create the bank profile (auto-creates with defaults)
+            await memory.get_bank_profile(bank_id, request_context=RequestContext())
+
+            # Update name and/or 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())
+
+            # Get final profile and return using BankProfileResponse model
+            profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())
+            disposition = profile.get("disposition")
+            if hasattr(disposition, "model_dump"):
+                disposition_traits = DispositionTraits(**disposition.model_dump())
+            else:
+                disposition_traits = DispositionTraits(
+                    **dict(disposition or {"skepticism": 3, "literalism": 3, "empathy": 3})
+                )
+
+            response = BankProfileResponse(
+                bank_id=bank_id,
+                name=profile.get("name") or "",
+                disposition=disposition_traits,
+                background=profile.get("background") or "",
+            )
+            return response.model_dump_json(indent=2)
+        except Exception as e:
+            logger.error(f"Error creating bank: {e}", exc_info=True)
+            return json.dumps({"error": str(e)})

    return mcp


 class MCPMiddleware:
-    """ASGI middleware that extracts bank_id from path and sets context."""
+    """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"
+    """

    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()
+        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

    async def __call__(self, scope, receive, send):
        if scope["type"] != "http":
@@ -139,32 +293,39 @@ 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 = "/"

-        # 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
+        # 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 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
+        # MCP endpoint paths that should not be treated as bank_ids
+        MCP_ENDPOINTS = {"sse", "messages"}

-        bank_id = parts[0]
-        new_path = "/" + parts[1] if len(parts) > 1 else "/"
+        # 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}")

        # 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
-            # The SSE app sends "event: endpoint\ndata: /messages\n" but we need
-            # the client to POST to /{bank_id}/messages instead
+            # Wrap send to rewrite the SSE endpoint URL to include bank_id if using path-based routing
            async def send_wrapper(message):
                if message["type"] == "http.response.body":
                    body = message.get("body", b"")
@@ -200,9 +361,10 @@ def create_mcp_app(memory: MemoryEngine):
    """
    Create an ASGI app that handles MCP requests.

-    URL pattern: /mcp/{bank_id}/
-
-    The bank_id is extracted from the URL path and made available to tools.
+    Bank ID can be provided via:
+    1. X-Bank-Id header: claude mcp add --transport http hindsight http://localhost:8888/mcp --header "X-Bank-Id: my-bank"
+    2. URL path: /mcp/{bank_id}/
+    3. Environment variable HINDSIGHT_MCP_BANK_ID (fallback, default: "default")

    Args:
        memory: MemoryEngine instance
@@ -8,6 +8,11 @@ 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
@@ -16,10 +21,15 @@ 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"

 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_RERANKER_PROVIDER = "HINDSIGHT_API_RERANKER_PROVIDER"
 ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
@@ -31,14 +41,30 @@ 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"
+
+# Optimization flags
+ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
+ENV_LAZY_RERANKER = "HINDSIGHT_API_LAZY_RERANKER"

 # 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"
@@ -50,8 +76,35 @@ DEFAULT_MCP_ENABLED = True
 DEFAULT_GRAPH_RETRIEVER = "bfs"  # Options: "bfs", "mpfp"
 DEFAULT_MCP_LOCAL_BANK_ID = "mcp"

-# Required embedding dimension for database schema
-EMBEDDING_DIMENSION = 384
+# 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 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


@dataclass
@@ -66,6 +119,8 @@ class HindsightConfig:
    llm_api_key: str | None
    llm_model: str
    llm_base_url: str | None
+    llm_max_concurrent: int
+    llm_timeout: float

    # Embeddings
    embeddings_provider: str
@@ -86,6 +141,17 @@ class HindsightConfig:
    # Recall
    graph_retriever: str

+    # Observation thresholds
+    observation_min_facts: int
+    observation_top_entities: int
+
+    # Retain settings
+    retain_max_completion_tokens: int
+
+    # Optimization flags
+    skip_llm_verification: bool
+    lazy_reranker: bool
+
    @classmethod
    def from_env(cls) -> "HindsightConfig":
        """Create configuration from environment variables."""
@@ -97,6 +163,8 @@ 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))),
            # Embeddings
            embeddings_provider=os.getenv(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER),
            embeddings_local_model=os.getenv(ENV_EMBEDDINGS_LOCAL_MODEL, DEFAULT_EMBEDDINGS_LOCAL_MODEL),
@@ -112,6 +180,18 @@ 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))
+            ),
        )

    def get_llm_base_url(self) -> str:
@@ -124,6 +204,8 @@ 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 ""

@@ -142,7 +224,9 @@ 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"
+            level=self.get_python_log_level(),
+            format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+            force=True,  # Override any existing configuration
        )

    def log_config(self) -> None:
@@ -154,6 +238,19 @@ class HindsightConfig:
        logger.info(f"Graph retriever: {self.graph_retriever}")


+# Cached config instance
+_config_cache: HindsightConfig | None = None
+
+
 def get_config() -> HindsightConfig:
-    """Get the current configuration from environment variables."""
-    return HindsightConfig.from_env()
+    """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
@@ -0,0 +1,204 @@
+"""
+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
@@ -11,7 +11,13 @@ 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
+from .memory_engine import (
+    MemoryEngine,
+    UnqualifiedTableError,
+    fq_table,
+    get_current_schema,
+    validate_sql_schema,
+)
 from .response_models import MemoryFact, RecallResult, ReflectResult
 from .search.trace import (
    EntryPoint,
@@ -49,4 +55,9 @@ __all__ = [
    "RecallResult",
    "ReflectResult",
    "MemoryFact",
+    # Schema safety utilities
+    "fq_table",
+    "get_current_schema",
+    "validate_sql_schema",
+    "UnqualifiedTableError",
 ]
@@ -3,8 +3,8 @@ Embeddings abstraction for the memory system.

 Provides an interface for generating embeddings with different backends.

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

 Configuration via environment variables - see hindsight_api.config for all env var names.
 """
@@ -17,11 +17,14 @@ import httpx

 from ..config import (
    DEFAULT_EMBEDDINGS_LOCAL_MODEL,
+    DEFAULT_EMBEDDINGS_OPENAI_MODEL,
    DEFAULT_EMBEDDINGS_PROVIDER,
-    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__)
@@ -31,8 +34,8 @@ class Embeddings(ABC):
    """
    Abstract base class for embedding generation.

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

    @property
@@ -41,6 +44,12 @@ 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:
        """
@@ -54,13 +63,13 @@ class Embeddings(ABC):
    @abstractmethod
    def encode(self, texts: list[str]) -> list[list[float]]:
        """
-        Generate 384-dimensional embeddings for a list of texts.
+        Generate embeddings for a list of texts.

        Args:
            texts: List of text strings to encode

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

@@ -70,9 +79,7 @@ class LocalSTEmbeddings(Embeddings):
    Local embeddings implementation using SentenceTransformers.

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

    def __init__(self, model_name: str | None = None):
@@ -81,16 +88,22 @@ 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:
@@ -112,26 +125,18 @@ class LocalSTEmbeddings(Embeddings):
            model_kwargs={"low_cpu_mem_usage": False, "device_map": None},
        )

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

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

        Args:
            texts: List of text strings to encode

        Returns:
-            List of 384-dimensional embedding vectors
+            List of embedding vectors
        """
        if self._model is None:
            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
@@ -146,7 +151,7 @@ class RemoteTEIEmbeddings(Embeddings):
    TEI provides a high-performance inference server for embedding models.
    See: https://github.com/huggingface/text-embeddings-inference

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

    def __init__(
@@ -174,11 +179,18 @@ 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
@@ -229,7 +241,24 @@ 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")
-            logger.info(f"Embeddings: TEI provider initialized (model: {self._model_id})")
+
+            # 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})")
        except httpx.HTTPError as e:
            raise RuntimeError(f"Failed to connect to TEI server at {self.base_url}: {e}")

@@ -269,6 +298,117 @@ 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
+
+
 def create_embeddings_from_env() -> Embeddings:
    """
    Create an Embeddings instance based on environment variables.
@@ -289,5 +429,15 @@ 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)
    else:
-        raise ValueError(f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei'")
+        raise ValueError(f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei', 'openai'")
@@ -11,6 +11,7 @@ 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
@@ -68,9 +69,9 @@ 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 entities
+            FROM {fq_table("entities")}
            WHERE bank_id = $1
            """,
            bank_id,
@@ -82,11 +83,11 @@ 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 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)
+            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)
            """,
            bank_id,
        )
@@ -195,8 +196,8 @@ class EntityResolver:
        # Batch update existing entities
        if entities_to_update:
            await conn.executemany(
-                """
-                UPDATE entities SET
+                f"""
+                UPDATE {fq_table("entities")} SET
                    mention_count = mention_count + 1,
                    last_seen = $2
                WHERE id = $1::uuid
@@ -232,13 +233,13 @@ class EntityResolver:
            # Batch INSERT ... ON CONFLICT with RETURNING
            # This is much faster than individual inserts
            rows = await conn.fetch(
-                """
-                INSERT INTO entities (bank_id, canonical_name, first_seen, last_seen, mention_count)
+                f"""
+                INSERT INTO {fq_table("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 = entities.mention_count + 1,
+                    mention_count = {fq_table("entities")}.mention_count + 1,
                    last_seen = EXCLUDED.last_seen
                RETURNING id
                """,
@@ -279,9 +280,9 @@ 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 entities
+                FROM {fq_table("entities")}
                WHERE bank_id = $1
                  AND (
                    canonical_name ILIKE $2
@@ -326,10 +327,10 @@ 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 entity_cooccurrences ec
-                    JOIN entities e ON (
+                    FROM {fq_table("entity_cooccurrences")} ec
+                    JOIN {fq_table("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
@@ -365,8 +366,8 @@ class EntityResolver:
            if best_score > threshold:
                # Update entity
                await conn.execute(
-                    """
-                    UPDATE entities
+                    f"""
+                    UPDATE {fq_table("entities")}
                    SET mention_count = mention_count + 1,
                        last_seen = $1
                    WHERE id = $2
@@ -402,12 +403,12 @@ class EntityResolver:
            Entity ID
        """
        entity_id = await conn.fetchval(
-            """
-            INSERT INTO entities (bank_id, canonical_name, first_seen, last_seen, mention_count)
+            f"""
+            INSERT INTO {fq_table("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 = entities.mention_count + 1,
+                mention_count = {fq_table("entities")}.mention_count + 1,
                last_seen = EXCLUDED.last_seen
            RETURNING id
            """,
@@ -430,8 +431,8 @@ class EntityResolver:
        async with acquire_with_retry(self.pool) as conn:
            # Insert unit-entity link
            await conn.execute(
-                """
-                INSERT INTO unit_entities (unit_id, entity_id)
+                f"""
+                INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
                VALUES ($1, $2)
                ON CONFLICT DO NOTHING
                """,
@@ -441,9 +442,9 @@ class EntityResolver:

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

        await conn.execute(
-            """
-            INSERT INTO entity_cooccurrences (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
+            f"""
+            INSERT INTO {fq_table("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 = entity_cooccurrences.cooccurrence_count + 1,
+                cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
                last_cooccurred = NOW()
            """,
            entity_id_1,
@@ -506,8 +507,8 @@ 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(
-            """
-            INSERT INTO unit_entities (unit_id, entity_id)
+            f"""
+            INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
            VALUES ($1, $2)
            ON CONFLICT DO NOTHING
            """,
@@ -541,12 +542,12 @@ class EntityResolver:
        if cooccurrence_pairs:
            now = datetime.now(UTC)
            await conn.executemany(
-                """
-                INSERT INTO entity_cooccurrences (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
+                f"""
+                INSERT INTO {fq_table("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 = entity_cooccurrences.cooccurrence_count + 1,
+                    cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
                    last_cooccurred = EXCLUDED.last_cooccurred
                """,
                [(e1, e2, 1, now) for e1, e2 in cooccurrence_pairs],
@@ -565,9 +566,9 @@ class EntityResolver:
        """
        async with acquire_with_retry(self.pool) as conn:
            rows = await conn.fetch(
-                """
+                f"""
                SELECT unit_id
-                FROM unit_entities
+                FROM {fq_table("unit_entities")}
                WHERE entity_id = $1
                ORDER BY unit_id
                LIMIT $2
@@ -594,8 +595,8 @@ class EntityResolver:
        """
        async with acquire_with_retry(self.pool) as conn:
            row = await conn.fetchrow(
-                """
-                SELECT id FROM entities
+                f"""
+                SELECT id FROM {fq_table("entities")}
                WHERE bank_id = $1
                  AND canonical_name ILIKE $2
                ORDER BY mention_count DESC
@@ -0,0 +1,596 @@
+"""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,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        Get graph data for visualization.
+
+        Args:
+            bank_id: The memory bank ID.
+            fact_type: Filter by fact type.
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with nodes, edges, table_rows, total_units.
+        """
+        ...
+
+    # =========================================================================
+    # 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.
+        """
+        ...
@@ -3,16 +3,27 @@ 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,
+)
+
 # Seed applied to every Groq request for deterministic behavior.
 DEFAULT_LLM_SEED = 4242

@@ -22,7 +33,9 @@ logger = logging.getLogger(__name__)
 logging.getLogger("httpx").setLevel(logging.WARNING)

 # Global semaphore to limit concurrent LLM requests across all instances
-_global_llm_semaphore = asyncio.Semaphore(32)
+# 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)


 class OutputTooLongError(Exception):
@@ -51,25 +64,29 @@ 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").
+            provider: Provider name ("openai", "groq", "ollama", "gemini", "anthropic", "lmstudio").
            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"]
+        valid_providers = ["openai", "groq", "ollama", "gemini", "anthropic", "lmstudio"]
        if self.provider not in valid_providers:
            raise ValueError(f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}")

@@ -79,25 +96,48 @@ class LLMProvider:
                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)
-        if self.provider != "ollama" and not self.api_key:
+        # Validate API key (not needed for ollama or lmstudio)
+        if self.provider not in ("ollama", "lmstudio") 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 == "gemini":
            self._gemini_client = genai.Client(api_key=self.api_key)
-            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
+        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)
        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:
        """
@@ -112,7 +152,7 @@ class LLMProvider:
            )
            await self.call(
                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=10,
+                max_completion_tokens=100,
                max_retries=2,
                initial_backoff=0.5,
                max_backoff=2.0,
@@ -133,6 +173,7 @@ class LLMProvider:
        initial_backoff: float = 1.0,
        max_backoff: float = 60.0,
        skip_validation: bool = False,
+        strict_schema: bool = False,
    ) -> Any:
        """
        Make an LLM API call with retry logic.
@@ -147,6 +188,7 @@ 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.

        Returns:
            Parsed response if response_format is provided, otherwise text content.
@@ -157,7 +199,6 @@ class LLMProvider:
        """
        async with _global_llm_semaphore:
            start_time = time.time()
-            import json

            # Handle Gemini provider separately
            if self.provider == "gemini":
@@ -165,6 +206,33 @@ class LLMProvider:
                    messages, response_format, max_retries, initial_backoff, max_backoff, skip_validation, start_time
                )

+            # 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,
+                )
+
+            # 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,
+                )
+
            call_params = {
                "model": self.model,
                "messages": messages,
@@ -172,7 +240,7 @@ 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"])
+            is_reasoning_model = any(x in model_lower for x in ["gpt-5", "o1", "o3", "deepseek"])

            # For GPT-4 and GPT-4.1 models, cap max_completion_tokens to 32000
            # For GPT-4o models, cap to 16384
@@ -194,41 +262,114 @@ class LLMProvider:
                call_params["temperature"] = temperature

            # Set reasoning_effort for reasoning models (OpenAI gpt-5, o1, o3)
-            if is_reasoning_model and self.provider == "openai":
+            if is_reasoning_model:
                call_params["reasoning_effort"] = self.reasoning_effort

            # Provider-specific parameters
            if self.provider == "groq":
                call_params["seed"] = DEFAULT_LLM_SEED
-                extra_body = {"service_tier": "auto"}
-                # Only add reasoning parameters for reasoning models
+                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
                if is_reasoning_model:
-                    extra_body["reasoning_effort"] = self.reasoning_effort
                    extra_body["include_reasoning"] = False
-                call_params["extra_body"] = extra_body
+                if 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:
-                        # Add schema to system message for JSON mode
+                        schema = None
                        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 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 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)}"

-                        call_params["response_format"] = {"type": "json_object"}
+                                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})")
                        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
-                        json_data = json.loads(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

                        if skip_validation:
                            result = json_data
@@ -301,6 +442,265 @@ 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,
+    ) -> 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
+
+                # Log slow calls
+                duration = time.time() - start_time
+                if duration > 10.0:
+                    input_tokens = response.usage.input_tokens
+                    output_tokens = response.usage.output_tokens
+                    logger.info(
+                        f"slow llm call: model={self.provider}/{self.model}, "
+                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
+                        f"time={duration:.3f}s"
+                    )
+
+                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,
+    ) -> 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
+
+                    # Validate against Pydantic model or return raw JSON
+                    if skip_validation:
+                        return json_data
+                    else:
+                        return response_format.model_validate(json_data)
+
+                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]],
@@ -312,8 +712,6 @@ class LLMProvider:
        start_time: float,
    ) -> Any:
        """Handle Gemini-specific API calls."""
-        import json
-
        # Convert OpenAI-style messages to Gemini format
        system_instruction = None
        gemini_contents = []
@@ -444,6 +842,8 @@ class 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")

@@ -454,6 +854,10 @@ 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"))

@@ -464,6 +868,10 @@ 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"))

@@ -123,7 +123,8 @@ class ReflectResult(BaseModel):
    Result from a reflect operation.

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

    model_config = ConfigDict(
@@ -145,6 +146,7 @@ class ReflectResult(BaseModel):
                    "opinion": [],
                },
                "new_opinions": ["Machine learning has great potential in healthcare"],
+                "structured_output": {"summary": "ML in healthcare", "confidence": 0.9},
            }
        }
    )
@@ -154,6 +156,10 @@ 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.",
+    )


 class Opinion(BaseModel):
@@ -10,6 +10,7 @@ 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__)
@@ -51,9 +52,9 @@ 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 banks WHERE bank_id = $1
+            FROM {fq_table("banks")} WHERE bank_id = $1
            """,
            bank_id,
        )
@@ -70,8 +71,8 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:

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

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

 import logging

+from ..memory_engine import fq_table
 from .types import ChunkMetadata

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

    # Batch insert all chunks
    await conn.execute(
-        """
-        INSERT INTO chunks (chunk_id, document_id, bank_id, chunk_text, chunk_index)
+        f"""
+        INSERT INTO {fq_table("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,
@@ -13,16 +13,23 @@ 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
+    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,
 ) -> list[EntityLink]:
    """
    Process entities for all facts and create entity links.

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

    Args:
        entity_resolver: EntityResolver instance for entity resolution
@@ -31,6 +38,7 @@ 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
@@ -41,14 +49,35 @@ 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 expected by link_utils
-    entities_per_fact = [
-        [{"text": entity.name, "type": "CONCEPT"} for entity in (fact.entities or [])] 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)

    # Use existing link_utils function for entity processing
    entity_links = await link_utils.extract_entities_batch_optimized(
@@ -14,9 +14,48 @@ from typing import Literal

 from pydantic import BaseModel, ConfigDict, Field, field_validator

+from ...config import get_config
 from ..llm_wrapper import LLMConfig, OutputTooLongError


+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:
    """
    Sanitize text by removing invalid Unicode surrogate characters.
@@ -71,7 +110,7 @@ class Fact(BaseModel):


 class CausalRelation(BaseModel):
-    """Causal relationship between facts."""
+    """Causal relationship between facts (legacy - embedded in each fact)."""

    target_fact_index: int = Field(
        description="Index of the related fact in the facts array (0-based). "
@@ -93,6 +132,36 @@ class CausalRelation(BaseModel):
    )


+class TopLevelCausalRelation(BaseModel):
+    """
+    Causal relationship between two facts (top-level schema).
+
+    This is the preferred format - defined AFTER all facts are extracted,
+    allowing the LLM to see the full list of facts before specifying relationships.
+    """
+
+    from_fact_index: int = Field(
+        description="Index of the source fact (0-based). The fact that causes/enables/prevents."
+    )
+    to_fact_index: int = Field(
+        description="Index of the target fact (0-based). The fact that is caused/enabled/prevented."
+    )
+    relation_type: Literal["causes", "caused_by", "enables", "prevents"] = Field(
+        description="Type of causal relationship: "
+        "'causes' = source fact directly causes the target fact, "
+        "'caused_by' = source fact was caused by the target fact, "
+        "'enables' = source fact enables/allows the target fact, "
+        "'prevents' = source fact prevents/blocks the target fact"
+    )
+    strength: float = Field(
+        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,
+    )
+
+
 class ExtractedFact(BaseModel):
    """A single extracted fact with 5 required dimensions for comprehensive capture."""

@@ -215,9 +284,15 @@ class ExtractedFact(BaseModel):


 class FactExtractionResponse(BaseModel):
-    """Response containing all extracted facts."""
+    """Response containing all extracted facts and their causal relationships."""

    facts: list[ExtractedFact] = Field(description="List of extracted factual statements")
+    causal_relationships: list[TopLevelCausalRelation] | None = Field(
+        default=None,
+        description="Causal relationships between facts. Define these AFTER listing all facts. "
+        "Each relationship specifies from_fact_index -> to_fact_index with a relation type. "
+        "Indices must be valid (0 to N-1 where N is the number of facts).",
+    )


 def chunk_text(text: str, max_chars: int) -> list[str]:
@@ -534,7 +609,53 @@ 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"""
+❌ SKIP: Greetings, filler ("thanks", "cool"), purely structural statements
+
+══════════════════════════════════════════════════════════════════════════
+CAUSAL RELATIONSHIPS (CRITICAL - DEFINE AFTER ALL FACTS)
+══════════════════════════════════════════════════════════════════════════
+
+⚠️ IMPORTANT: Causal relationships are defined at the TOP LEVEL, AFTER listing all facts!
+
+The `causal_relationships` array goes at the root of your response (NOT inside each fact).
+This allows you to see all facts first before defining how they relate.
+
+Format:
+```json
+{{
+  "facts": [...all your extracted facts...],
+  "causal_relationships": [
+    {{"from_fact_index": 0, "to_fact_index": 1, "relation_type": "causes", "strength": 0.9}},
+    {{"from_fact_index": 1, "to_fact_index": 2, "relation_type": "enables", "strength": 0.7}}
+  ]
+}}
+```
+
+Relationship types:
+- "causes": Fact A directly causes Fact B (A → B)
+- "caused_by": Fact A was caused by Fact B (A ← B)
+- "enables": Fact A enables/allows Fact B to happen
+- "prevents": Fact A prevents/blocks Fact B from happening
+
+⚠️ INDEX VALIDATION: If you extract N facts (indices 0 to N-1), both from_fact_index and to_fact_index MUST be in range [0, N-1].
+
+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."
+
+Facts extracted:
+- Fact 0: "User lost their job in January due to layoffs"
+- Fact 1: "User couldn't pay rent because of job loss"
+- Fact 2: "User moved to a cheaper apartment"
+
+Causal relationships (at root level):
+```json
+"causal_relationships": [
+  {{"from_fact_index": 0, "to_fact_index": 1, "relation_type": "causes", "strength": 1.0}},
+  {{"from_fact_index": 1, "to_fact_index": 2, "relation_type": "causes", "strength": 0.9}}
+]
+```
+
+This creates a chain: Job loss (0) → Can't pay rent (1) → Moved to cheaper apartment (2)"""

    import logging

@@ -545,6 +666,7 @@ WHAT TO EXTRACT vs SKIP
    # 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)
@@ -570,7 +692,7 @@ Text:
                response_format=FactExtractionResponse,
                scope="memory_extract_facts",
                temperature=0.1,
-                max_completion_tokens=65000,
+                max_completion_tokens=config.retain_max_completion_tokens,
                skip_validation=True,  # Get raw JSON, we'll validate leniently
            )

@@ -593,6 +715,9 @@ Text:
                    return []

            raw_facts = extraction_response_json.get("facts", [])
+            # Get top-level causal relationships (new schema)
+            top_level_causal_relations = extraction_response_json.get("causal_relationships", [])
+
            if not raw_facts:
                logger.debug(
                    f"LLM response missing 'facts' field or returned empty list. "
@@ -603,6 +728,47 @@ Text:
                    f"text: {chunk}"
                )

+            # Build a map from fact index to causal relations (from top-level field)
+            # This converts from_fact_index -> [{target_fact_index, relation_type, strength}]
+            causal_relations_by_fact: dict[int, list[dict]] = {}
+            if top_level_causal_relations:
+                num_facts = len(raw_facts)
+                for rel in top_level_causal_relations:
+                    if not isinstance(rel, dict):
+                        continue
+                    from_idx = rel.get("from_fact_index")
+                    to_idx = rel.get("to_fact_index")
+                    relation_type = rel.get("relation_type")
+                    strength = rel.get("strength", 1.0)
+
+                    # Validate indices
+                    if from_idx is None or to_idx is None or relation_type is None:
+                        logger.warning(f"Skipping malformed top-level causal relation: {rel}")
+                        continue
+                    if from_idx < 0 or from_idx >= num_facts:
+                        logger.warning(
+                            f"Invalid from_fact_index {from_idx} in top-level causal relation "
+                            f"(valid range: 0-{num_facts - 1}). Skipping."
+                        )
+                        continue
+                    if to_idx < 0 or to_idx >= num_facts:
+                        logger.warning(
+                            f"Invalid to_fact_index {to_idx} in top-level causal relation "
+                            f"(valid range: 0-{num_facts - 1}). Skipping."
+                        )
+                        continue
+
+                    # Add to the map for the from_fact_index
+                    if from_idx not in causal_relations_by_fact:
+                        causal_relations_by_fact[from_idx] = []
+                    causal_relations_by_fact[from_idx].append(
+                        {
+                            "target_fact_index": to_idx,
+                            "relation_type": relation_type,
+                            "strength": strength,
+                        }
+                    )
+
            for i, llm_fact in enumerate(raw_facts):
                # Skip non-dict entries but track them for retry
                if not isinstance(llm_fact, dict):
@@ -676,13 +842,18 @@ Text:
                if fact_kind == "event":
                    occurred_start = get_value("occurred_start")
                    occurred_end = get_value("occurred_end")
-                    if occurred_start:
+
+                    # 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:
                        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
-                        else:
-                            fact_data["occurred_end"] = 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"]

                # Add entities if present (validate as Entity objects)
                # LLM sometimes returns strings instead of {"text": "..."} format
@@ -702,19 +873,40 @@ Text:
                    if validated_entities:
                        fact_data["entities"] = validated_entities

-                # 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:
+                # Add causal relations from both sources:
+                # 1. Top-level causal_relationships (preferred, new schema)
+                # 2. Per-fact causal_relations (legacy, for backward compatibility)
+                validated_relations = []
+
+                # First, add relations from top-level (already validated above)
+                if i in causal_relations_by_fact:
+                    for rel in causal_relations_by_fact[i]:
+                        try:
+                            validated_relations.append(CausalRelation.model_validate(rel))
+                        except Exception as e:
+                            logger.warning(f"Invalid top-level causal relation for fact {i}: {rel}: {e}")
+
+                # Then, add any legacy per-fact relations (with index validation)
+                legacy_causal_relations = get_value("causal_relations")
+                if legacy_causal_relations:
+                    num_facts = len(raw_facts)
+                    for rel in legacy_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
+                            target_idx = rel.get("target_fact_index")
+                            # Validate target index for legacy format too
+                            if target_idx is not None and 0 <= target_idx < num_facts:
+                                try:
+                                    validated_relations.append(CausalRelation.model_validate(rel))
+                                except Exception as e:
+                                    logger.warning(f"Invalid causal relation {rel}: {e}")
+                            else:
+                                logger.warning(
+                                    f"Invalid target_fact_index {target_idx} in per-fact causal relation "
+                                    f"from fact {i} (valid range: 0-{num_facts - 1}). Skipping."
+                                )
+
+                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()
@@ -7,6 +7,7 @@ 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__)
@@ -67,8 +68,8 @@ async def insert_facts_batch(

    # Batch insert all facts
    results = await conn.fetch(
-        """
-        INSERT INTO memory_units (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+        f"""
+        INSERT INTO {fq_table("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[],
@@ -107,8 +108,8 @@ async def ensure_bank_exists(conn, bank_id: str) -> None:
        bank_id: Bank identifier
    """
    await conn.execute(
-        """
-        INSERT INTO banks (bank_id, disposition, background)
+        f"""
+        INSERT INTO {fq_table("banks")} (bank_id, disposition, background)
        VALUES ($1, $2::jsonb, $3)
        ON CONFLICT (bank_id) DO UPDATE
        SET updated_at = NOW()
@@ -141,12 +142,14 @@ 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("DELETE FROM documents WHERE id = $1 AND bank_id = $2 RETURNING id", document_id, bank_id)
+        await conn.fetchval(
+            f"DELETE FROM {fq_table('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(
-        """
-        INSERT INTO documents (id, bank_id, original_text, content_hash, metadata, retain_params)
+        f"""
+        INSERT INTO {fq_table("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,
@@ -7,6 +7,7 @@ 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__)
@@ -290,9 +291,9 @@ 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 unit_entities
+                FROM {fq_table("unit_entities")}
                WHERE entity_id = ANY($1::uuid[])
                """,
                entity_id_list,
@@ -413,9 +414,9 @@ 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 memory_units
+            FROM {fq_table("memory_units")}
            WHERE id::text = ANY($1)
            """,
            unit_ids,
@@ -432,9 +433,9 @@ 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 memory_units
+            FROM {fq_table("memory_units")}
            WHERE bank_id = $1
              AND event_date BETWEEN $2 AND $3
              AND id::text != ALL($4)
@@ -479,8 +480,8 @@ async def create_temporal_links_batch_per_fact(
        if links:
            insert_start = time_mod.time()
            await conn.executemany(
-                """
-                INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                f"""
+                INSERT INTO {fq_table("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
                """,
@@ -535,9 +536,9 @@ 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 memory_units
+            FROM {fq_table("memory_units")}
            WHERE bank_id = $1
              AND embedding IS NOT NULL
              AND id::text != ALL($2)
@@ -644,8 +645,8 @@ async def create_semantic_links_batch(
        if all_links:
            insert_start = time_mod.time()
            await conn.executemany(
-                """
-                INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                f"""
+                INSERT INTO {fq_table("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
                """,
@@ -721,8 +722,8 @@ 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("""
-        INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
+    await conn.execute(f"""
+        INSERT INTO {fq_table("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
@@ -808,8 +809,8 @@ async def create_causal_links_batch(
            insert_start = time_mod.time()
            try:
                await conn.executemany(
-                    """
-                    INSERT INTO memory_links (from_unit_id, to_unit_id, link_type, weight, entity_id)
+                    f"""
+                    INSERT INTO {fq_table("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
                    """,
@@ -9,6 +9,8 @@ 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
@@ -48,8 +50,9 @@ async def regenerate_observations_batch(
        entity_links: Entity links from this batch
        log_buffer: Optional log buffer for timing
    """
-    TOP_N_ENTITIES = 5
-    MIN_FACTS_THRESHOLD = 5
+    config = get_config()
+    TOP_N_ENTITIES = config.observation_top_entities
+    MIN_FACTS_THRESHOLD = config.observation_min_facts

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

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

    # Batch query for fact counts
    fact_counts = await conn.fetch(
-        """
+        f"""
        SELECT ue.entity_id, COUNT(*) as cnt
-        FROM unit_entities ue
-        JOIN memory_units mu ON ue.unit_id = mu.id
+        FROM {fq_table("unit_entities")} ue
+        JOIN {fq_table("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
        """,
@@ -154,10 +157,10 @@ 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 memory_units mu
-        JOIN unit_entities ue ON mu.id = ue.unit_id
+        FROM {fq_table("memory_units")} mu
+        JOIN {fq_table("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')
@@ -193,12 +196,12 @@ async def _regenerate_entity_observations(

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

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

        # Link observation to entity
        await conn.execute(
-            """
-            INSERT INTO unit_entities (unit_id, entity_id)
+            f"""
+            INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
            VALUES ($1, $2)
            """,
            uuid.UUID(obs_id),
@@ -8,7 +8,6 @@ import logging
 import time
 import uuid
 from datetime import UTC, datetime
-from typing import Any

 from ..db_utils import acquire_with_retry
 from . import bank_utils
@@ -29,7 +28,7 @@ from . import (
    link_creation,
    observation_regeneration,
 )
-from .types import ExtractedFact, ProcessedFact, RetainContent
+from .types import ExtractedFact, ProcessedFact, RetainContent, RetainContentDict

 logger = logging.getLogger(__name__)

@@ -43,7 +42,7 @@ async def retain_batch(
    format_date_fn,
    duplicate_checker_fn,
    bank_id: str,
-    contents_dicts: list[dict[str, Any]],
+    contents_dicts: list[RetainContentDict],
    document_id: str | None = None,
    is_first_batch: bool = True,
    fact_type_override: str | None = None,
@@ -92,6 +91,7 @@ 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)

@@ -107,6 +107,63 @@ 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]

    # Apply fact_type_override if provided
@@ -296,8 +353,18 @@ 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
+                entity_resolver,
+                conn,
+                bank_id,
+                unit_ids,
+                non_duplicate_facts,
+                log_buffer,
+                user_entities_per_content=user_entities_per_content,
            )
            log_buffer.append(f"[6] Process entities: {len(entity_links)} links in {time.time() - step_start:.3f}s")

@@ -7,9 +7,35 @@ 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:
    """
@@ -20,15 +46,9 @@ class RetainContent:

    content: str
    context: str = ""
-    event_date: datetime | None = None
+    event_date: datetime = field(default_factory=_now_utc)
    metadata: dict[str, str] = field(default_factory=dict)
-
-    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)
+    entities: list[dict[str, str]] = field(default_factory=list)  # User-provided entities


@dataclass
@@ -135,6 +155,9 @@ 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."""
@@ -177,6 +200,7 @@ class ProcessedFact:
            entities=entities,
            causal_relations=extracted_fact.causal_relations,
            chunk_id=chunk_id,
+            content_index=extracted_fact.content_index,
        )


@@ -10,6 +10,7 @@ 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__)
@@ -139,11 +140,11 @@ 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 memory_units
+            FROM {fq_table("memory_units")}
            WHERE bank_id = $2
              AND embedding IS NOT NULL
              AND fact_type = $3
@@ -188,13 +189,13 @@ 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 memory_links ml
-                    JOIN memory_units mu ON ml.to_unit_id = mu.id
+                    FROM {fq_table("memory_links")} ml
+                    JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
                    WHERE ml.from_unit_id = ANY($1::uuid[])
                      AND ml.weight >= $2
                      AND mu.fact_type = $3
@@ -20,6 +20,7 @@ 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

@@ -217,10 +218,10 @@ 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 memory_links ml
-            JOIN memory_units mu ON ml.from_unit_id = mu.id
+            FROM {fq_table("memory_links")} ml
+            JOIN {fq_table("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
@@ -252,10 +253,10 @@ 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 memory_units
+            FROM {fq_table("memory_units")}
            WHERE id = ANY($1::uuid[])
              AND fact_type = $2
            """,
@@ -418,9 +419,9 @@ 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 memory_units
+                FROM {fq_table("memory_units")}
                WHERE bank_id = $2
                  AND embedding IS NOT NULL
                  AND fact_type = $3
@@ -26,6 +26,23 @@ 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]:
        """
@@ -16,6 +16,7 @@ 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
@@ -80,10 +81,10 @@ 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 memory_units
+        FROM {fq_table("memory_units")}
        WHERE bank_id = $2
          AND embedding IS NOT NULL
          AND fact_type = $3
@@ -131,10 +132,10 @@ 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 memory_units
+        FROM {fq_table("memory_units")}
        WHERE bank_id = $2
          AND fact_type = $3
          AND search_vector @@ to_tsquery('english', $1)
@@ -188,10 +189,10 @@ 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 memory_units
+        FROM {fq_table("memory_units")}
        WHERE bank_id = $2
          AND fact_type = $3
          AND embedding IS NOT NULL
@@ -272,12 +273,12 @@ 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 memory_links ml
-                JOIN memory_units mu ON ml.to_unit_id = mu.id
+                FROM {fq_table("memory_links")} ml
+                JOIN {fq_table("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
@@ -546,11 +547,11 @@ 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 memory_units
+        FROM {fq_table("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,
+    context: str | None = None,
 ) -> str:
    """Build the think prompt for the LLM."""
    disposition_desc = build_disposition_description(disposition)
@@ -115,7 +115,7 @@ class SearchTracer:
        node_id: str,
        text: str,
        context: str,
-        event_date: datetime,
+        event_date: datetime | None,
        access_count: int,
        is_entry_point: bool,
        parent_node_id: str | None,
@@ -89,6 +89,38 @@ 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.
@@ -0,0 +1,66 @@
+"""
+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",
+]
@@ -0,0 +1,81 @@
+"""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
@@ -0,0 +1,18 @@
+"""
+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",
+]
@@ -0,0 +1,33 @@
+"""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")
@@ -0,0 +1,117 @@
+"""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
@@ -0,0 +1,89 @@
+"""
+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
@@ -0,0 +1,125 @@
+"""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
@@ -0,0 +1,327 @@
+"""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
@@ -0,0 +1,63 @@
+"""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.
+        """
+        ...
@@ -4,6 +4,9 @@ Command-line interface for Hindsight API.
 Run the server with:
    hindsight-api

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

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

 # Filter deprecation warnings from third-party libraries
 warnings.filterwarnings("ignore", message="websockets.legacy is deprecated")
@@ -106,8 +114,52 @@ 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:
@@ -117,6 +169,8 @@ 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,
            embeddings_provider=config.embeddings_provider,
            embeddings_local_model=config.embeddings_local_model,
            embeddings_tei_url=config.embeddings_tei_url,
@@ -127,16 +181,47 @@ 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,
+            skip_llm_verification=config.skip_llm_verification,
+            lazy_reranker=config.lazy_reranker,
        )
    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()
+    _memory = MemoryEngine(operation_validator=operation_validator, tenant_extension=tenant_extension)
+
+    # 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")

    # Create FastAPI app
    app = create_app(
@@ -147,6 +232,12 @@ 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,
@@ -170,20 +261,40 @@ def main():
    if args.ssl_certfile:
        uvicorn_config["ssl_certfile"] = args.ssl_certfile

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

-    uvicorn.run(**uvicorn_config)
+    # 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


 if __name__ == "__main__":
@@ -28,7 +28,15 @@ 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: "info").
+    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."
 """

 import logging
@@ -36,14 +44,19 @@ 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 info
-_log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
+# 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()
 _log_level_map = {
    "critical": logging.CRITICAL,
    "error": logging.ERROR,
@@ -74,27 +87,27 @@ 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()
+    @mcp.tool(description=retain_description)
    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'
@@ -103,7 +116,11 @@ 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}])
+                await memory.retain_batch_async(
+                    bank_id=bank_id,
+                    contents=[{"content": content, "context": context}],
+                    request_context=RequestContext(),
+                )
            except Exception as e:
                logger.error(f"Error storing memory: {e}", exc_info=True)

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

-    @mcp.tool()
+    @mcp.tool(description=recall_description)
    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)
@@ -138,6 +147,7 @@ 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()
@@ -153,10 +163,9 @@ async def _initialize_and_run(bank_id: str):
    from hindsight_api import MemoryEngine

    # Create and initialize memory engine with pg0 embedded database
-    print("Initializing memory engine...", file=sys.stderr)
+    # Note: We avoid printing to stderr during init as MCP clients show it as "errors"
    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)
@@ -179,8 +188,8 @@ 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)

-    # Print startup message to stderr (stdout is reserved for MCP protocol)
-    print(f"Hindsight MCP server starting (bank_id={bank_id})...", file=sys.stderr)
+    # 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

    # Run the async initialization and server
    asyncio.run(_initialize_and_run(bank_id))
@@ -6,12 +6,16 @@ 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
@@ -26,11 +30,29 @@ logger = logging.getLogger(__name__)
 MIGRATION_LOCK_ID = 123456789


-def _run_migrations_internal(database_url: str, script_location: str) -> None:
+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:
    """
    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)
    """
-    logger.info("Running database migrations to head...")
+    schema_name = schema or "public"
+    logger.info(f"Running database migrations to head for schema '{schema_name}'...")
    logger.info(f"Database URL: {database_url}")
    logger.info(f"Script location: {script_location}")

@@ -50,13 +72,22 @@ def _run_migrations_internal(database_url: str, script_location: str) -> None:
    # Set path_separator to avoid deprecation warning
    alembic_cfg.set_main_option("path_separator", "os")

-    # Run migrations to head (latest version)
+    # 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
    command.upgrade(alembic_cfg, "head")

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


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

@@ -65,19 +96,28 @@ def run_migrations(database_url: str, script_location: str | None = None) -> Non
    - 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 (hindsight_api package)
+        # Using default location and public schema
        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",
@@ -99,21 +139,25 @@ def run_migrations(database_url: str, script_location: str | None = None) -> Non
                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 (id={MIGRATION_LOCK_ID})...")
-            conn.execute(text(f"SELECT pg_advisory_lock({MIGRATION_LOCK_ID})"))
+            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("Migration advisory lock acquired")

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

    except FileNotFoundError:
@@ -185,3 +229,131 @@ 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}")
@@ -2,9 +2,27 @@
 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,
@@ -23,6 +41,8 @@ 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."""
@@ -63,7 +83,7 @@ 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(384))  # pgvector type
+    embedding = mapped_column(Vector(EMBEDDING_DIMENSION))  # 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)
+            self._pg0 = Pg0(**kwargs)  # type: ignore[invalid-argument-type] - dict kwargs
        return self._pg0

    async def start(self, max_retries: int = 5, retry_delay: float = 4.0) -> str:
@@ -132,3 +132,56 @@ 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 = "0.1.8"
+version = "0.2.1"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -37,6 +37,8 @@ dependencies = [
    "opentelemetry-exporter-prometheus>=0.41b0",
    "dateparser>=1.2.2",
    "google-genai>=1.0.0",
+    "anthropic>=0.40.0",
+    "typer>=0.9.0",
 ]

 [project.optional-dependencies]
@@ -51,6 +53,7 @@ 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"]
@@ -74,7 +77,7 @@ 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 --durations=10 -v"
+addopts = "--timeout 120 -n 8 --dist loadgroup --durations=10 -v"
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "function"
 log_auto_indent = true
@@ -92,6 +95,7 @@ dev = [
    "python-dotenv>=1.2.1",
    "filelock>=3.0.0",
    "ruff>=0.8.0",
+    "ty>=0.0.1",
 ]

 [tool.ruff]
@@ -121,3 +125,28 @@ 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
+from hindsight_api import MemoryEngine, LLMConfig, LocalSTEmbeddings, RequestContext

 from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder
 from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
@@ -99,6 +99,12 @@ 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():
    """
@@ -0,0 +1,208 @@
+"""
+Tests for admin backup and restore functionality.
+
+Note: These tests run sequentially (not in parallel) because they all
+manipulate the same database and do full backup/restore operations.
+"""
+
+import tempfile
+import uuid
+import zipfile
+from pathlib import Path
+
+import pytest
+
+from hindsight_api import RequestContext
+from hindsight_api.admin.cli import _backup, _restore, BACKUP_TABLES
+
+
+# Run these tests sequentially since they do full DB backup/restore
+pytestmark = pytest.mark.xdist_group(name="backup_restore")
+
+
+@pytest.mark.asyncio
+async def test_backup_restore_roundtrip(memory, pg0_db_url, request_context):
+    """Test that backup and restore preserves all data correctly."""
+    # Use unique bank ID to avoid conflicts
+    bank_id = f"test-backup-{uuid.uuid4().hex[:8]}"
+
+    # Create some test data
+    await memory.retain_batch_async(
+        bank_id=bank_id,
+        contents=[
+            {"content": "Alice is a software engineer who loves Python."},
+            {"content": "Bob works with Alice on the backend team."},
+            {"content": "The team uses PostgreSQL for their database."},
+        ],
+        request_context=request_context,
+    )
+
+    # Get counts before backup
+    async with memory._pool.acquire() as conn:
+        counts_before = {}
+        for table in BACKUP_TABLES:
+            counts_before[table] = await conn.fetchval(f"SELECT COUNT(*) FROM {table}")
+
+    # Verify we have data
+    assert counts_before["banks"] > 0
+    assert counts_before["memory_units"] > 0
+
+    # Backup to a temp file
+    with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as f:
+        backup_path = Path(f.name)
+
+    try:
+        manifest = await _backup(pg0_db_url, backup_path)
+
+        # 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
+        async with memory._pool.acquire() as conn:
+            for table in reversed(BACKUP_TABLES):
+                await conn.execute(f"TRUNCATE TABLE {table} CASCADE")
+
+        # Verify data is gone
+        async with memory._pool.acquire() as conn:
+            for table in BACKUP_TABLES:
+                count = await conn.fetchval(f"SELECT COUNT(*) FROM {table}")
+                assert count == 0, f"Table {table} should be empty after truncate"
+
+        # Restore from backup
+        await _restore(pg0_db_url, backup_path)
+
+        # Verify counts match original
+        async with memory._pool.acquire() as conn:
+            for table in BACKUP_TABLES:
+                count = await conn.fetchval(f"SELECT COUNT(*) FROM {table}")
+                assert count == counts_before[table], f"Table {table} count mismatch after restore"
+
+        # Verify data content is preserved
+        async with memory._pool.acquire() as conn:
+            texts = await conn.fetch(
+                "SELECT text FROM 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:
+        # Cleanup
+        if backup_path.exists():
+            backup_path.unlink()
+
+
+@pytest.mark.asyncio
+async def test_backup_restore_preserves_all_column_types(memory, pg0_db_url, request_context):
+    """Test that all column types are preserved: vectors, UUIDs, timestamps, JSONB."""
+    # Use unique bank ID
+    bank_id = f"test-types-{uuid.uuid4().hex[:8]}"
+
+    # Create data with meaningful content that will produce facts
+    await memory.retain_batch_async(
+        bank_id=bank_id,
+        contents=[
+            {"content": "John Smith is a senior engineer at Acme Corp since 2020."},
+            {"content": "The project deadline is December 15th 2024."},
+        ],
+        request_context=request_context,
+    )
+
+    # Get original data with all important column types
+    async with memory._pool.acquire() as conn:
+        # memory_units: UUID (id), Vector (embedding), Timestamp (event_date, created_at), JSONB (metadata)
+        original_unit = await conn.fetchrow(
+            """SELECT id, embedding, event_date, created_at, metadata, text
+               FROM memory_units WHERE bank_id = $1 LIMIT 1""",
+            bank_id,
+        )
+
+        # entities: UUID (id), Timestamp (first_seen, last_seen), JSONB (metadata)
+        original_entity = await conn.fetchrow(
+            """SELECT id, first_seen, last_seen, metadata, canonical_name
+               FROM entities WHERE bank_id = $1 LIMIT 1""",
+            bank_id,
+        )
+
+        # banks: JSONB (personality/disposition)
+        original_bank = await conn.fetchrow(
+            "SELECT bank_id, created_at, updated_at FROM banks WHERE bank_id = $1",
+            bank_id,
+        )
+
+    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(pg0_db_url, backup_path)
+
+        # Clear all data
+        async with memory._pool.acquire() as conn:
+            for table in reversed(BACKUP_TABLES):
+                await conn.execute(f"TRUNCATE TABLE {table} CASCADE")
+
+        await _restore(pg0_db_url, backup_path)
+
+        # Verify all column types are preserved exactly
+        async with memory._pool.acquire() as conn:
+            restored_unit = await conn.fetchrow(
+                """SELECT id, embedding, event_date, created_at, metadata, text
+                   FROM memory_units WHERE bank_id = $1 LIMIT 1""",
+                bank_id,
+            )
+
+            restored_entity = await conn.fetchrow(
+                """SELECT id, first_seen, last_seen, metadata, canonical_name
+                   FROM entities WHERE bank_id = $1 LIMIT 1""",
+                bank_id,
+            )
+
+            restored_bank = await conn.fetchrow(
+                "SELECT bank_id, created_at, updated_at FROM banks WHERE bank_id = $1",
+                bank_id,
+            )
+
+        # 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
+from hindsight_api import MemoryEngine, RequestContext
 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):
+    async def test_get_agent_profile_creates_default(self, memory: MemoryEngine, request_context):
        """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)
+        profile = await memory.get_bank_profile(bank_id, request_context=request_context)

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

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

-        updated_profile = await memory.get_bank_profile(bank_id)
+        updated_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
        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):
+    async def test_list_agents(self, memory: MemoryEngine, request_context):
        """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)
-        await memory.get_bank_profile(agent_id_2)
-        await memory.get_bank_profile(agent_id_3)
+        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)

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

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

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

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

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

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

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

    @pytest.mark.asyncio
-    async def test_merge_background_handles_conflicts(self, memory: MemoryEngine):
+    async def test_merge_background_handles_conflicts(self, memory: MemoryEngine, request_context):
        """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
+            update_disposition=False,
+            request_context=request_context,
        )
        assert "Colorado" in result1["background"]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


@pytest.mark.asyncio
-async def test_large_batch_auto_chunks(memory):
+async def test_large_batch_auto_chunks(memory, request_context):
    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)
@@ -24,7 +24,8 @@ async def test_large_batch_auto_chunks(memory):
    # Ingest the large batch (should auto-chunk)
    result = await memory.retain_batch_async(
        bank_id=bank_id,
-        contents=contents
+        contents=contents,
+        request_context=request_context,
    )

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


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

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

    # Verify we got results back
@@ -0,0 +1,222 @@
+"""
+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
+        num_facts = len(facts)
+        invalid_relations = []
+        for rel in all_causal_relations:
+            if rel["to_fact_index"] < 0 or rel["to_fact_index"] >= num_facts:
+                invalid_relations.append(rel)
+
+        assert len(invalid_relations) == 0, (
+            f"Found {len(invalid_relations)} causal relations with invalid indices! "
+            f"Valid range is 0-{num_facts - 1}. "
+            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
+        valid_types = {"causes", "caused_by", "enables", "prevents"}
+        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
+        num_facts = len(facts)
+        for i, fact in enumerate(facts):
+            if fact.causal_relations:
+                for rel in fact.causal_relations:
+                    assert 0 <= rel.target_fact_index < num_facts, (
+                        f"Fact {i} has causal relation to invalid index {rel.target_fact_index}. "
+                        f"Valid range is 0-{num_facts - 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! "
+                        f"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"
+        )
+
+        num_facts = len(facts)
+
+        # Validate all indices
+        for i, fact in enumerate(facts):
+            if fact.causal_relations:
+                for rel in fact.causal_relations:
+                    assert 0 <= rel.target_fact_index < num_facts, (
+                        f"Invalid target_fact_index {rel.target_fact_index} in fact {i}. "
+                        f"Valid range: 0-{num_facts - 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]}..."
+                    )
@@ -10,6 +10,7 @@ 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:
@@ -125,7 +126,7 @@ class TestCombinedScoringFormula:


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

@@ -135,21 +136,25 @@ async def test_trace_has_normalized_rrf(memory):
            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
@@ -160,6 +165,7 @@ async def test_trace_has_normalized_rrf(memory):
            budget=Budget.LOW,
            max_tokens=1024,
            enable_trace=True,
+            request_context=request_context,
        )

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

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


@pytest.mark.asyncio
-async def test_rrf_normalized_not_raw_in_trace(memory):
+async def test_rrf_normalized_not_raw_in_trace(memory, request_context):
    """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()}"

@@ -225,6 +231,7 @@ async def test_rrf_normalized_not_raw_in_trace(memory):
                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(
@@ -234,6 +241,7 @@ async def test_rrf_normalized_not_raw_in_trace(memory):
            budget=Budget.LOW,
            max_tokens=512,
            enable_trace=True,
+            request_context=request_context,
        )

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

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


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

@@ -281,11 +289,13 @@ async def test_combined_score_matches_components(memory):
            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(
@@ -295,6 +305,7 @@ async def test_combined_score_matches_components(memory):
            budget=Budget.LOW,
            max_tokens=512,
            enable_trace=True,
+            request_context=request_context,
        )

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

    finally:
-        await memory.delete_bank(bank_id)
+        await memory.delete_bank(bank_id, request_context=request_context)
@@ -0,0 +1,428 @@
+"""
+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
+from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder
+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
@@ -4,10 +4,11 @@ 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):
+async def test_document_creation_and_retrieval(memory, request_context):
    """Test that documents are created and can be retrieved."""
    bank_id = f"test_doc_{datetime.now(timezone.utc).timestamp()}"

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

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

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

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


@pytest.mark.asyncio
-async def test_document_upsert(memory):
+async def test_document_upsert(memory, request_context):
    """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()}"

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

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

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

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

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

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


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

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

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

        # Delete document
-        result = await memory.delete_document(document_id, bank_id)
+        result = await memory.delete_document(document_id, bank_id, request_context=request_context)
        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)
+        doc_after = await memory.get_document(document_id, bank_id, request_context=request_context)
        assert doc_after is None

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


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

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

        assert len(units) > 0

    finally:
-        await memory.delete_bank(bank_id)
+        await memory.delete_bank(bank_id, request_context=request_context)
@@ -0,0 +1,796 @@
+"""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
@@ -402,6 +402,8 @@ 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
@@ -410,41 +412,69 @@ 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)

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

-        assert len(facts) > 0, "Should extract at least one fact"
+        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"
+                )

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

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

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

-        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)
+                fact_date_str = birthday_fact.occurred_start
+                assert fact_date_str is not None, "occurred_start should not be None for temporal events"

-        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 '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

    @pytest.mark.asyncio
    async def test_date_field_calculation_yesterday(self):
@@ -897,7 +927,7 @@ class TestDispositionInference:
    """Tests for LLM-based disposition trait inference from background."""

    @pytest.mark.asyncio
-    async def test_background_merge_with_disposition_inference(self, memory):
+    async def test_background_merge_with_disposition_inference(self, memory, request_context):
        """Test that background merge infers disposition traits by default."""
        import uuid
        bank_id = f"test_infer_{uuid.uuid4().hex[:8]}"
@@ -905,7 +935,8 @@ 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
+            update_disposition=True,
+            request_context=request_context,
        )

        assert "background" in result
@@ -923,30 +954,31 @@ class TestDispositionInference:
            assert 1 <= disposition[trait] <= 5

    @pytest.mark.asyncio
-    async def test_background_merge_without_disposition_inference(self, memory):
+    async def test_background_merge_without_disposition_inference(self, memory, request_context):
        """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)
+        initial_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
        initial_disposition = initial_profile["disposition"]

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

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

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

        assert initial_disposition == final_disposition

    @pytest.mark.asyncio
-    async def test_disposition_inference_for_lawyer(self, memory):
+    async def test_disposition_inference_for_lawyer(self, memory, request_context):
        """Test disposition inference for lawyer profile (high skepticism, high literalism)."""
        import uuid
        bank_id = f"test_lawyer_{uuid.uuid4().hex[:8]}"
@@ -954,7 +986,8 @@ 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
+            update_disposition=True,
+            request_context=request_context,
        )

        disposition = result["disposition"]
@@ -964,7 +997,7 @@ class TestDispositionInference:
        assert disposition["literalism"] >= 3

    @pytest.mark.asyncio
-    async def test_disposition_inference_for_therapist(self, memory):
+    async def test_disposition_inference_for_therapist(self, memory, request_context):
        """Test disposition inference for therapist profile (high empathy)."""
        import uuid
        bank_id = f"test_therapist_{uuid.uuid4().hex[:8]}"
@@ -972,7 +1005,8 @@ 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
+            update_disposition=True,
+            request_context=request_context,
        )

        disposition = result["disposition"]
@@ -981,7 +1015,7 @@ class TestDispositionInference:
        assert disposition["empathy"] >= 3

    @pytest.mark.asyncio
-    async def test_disposition_updates_in_database(self, memory):
+    async def test_disposition_updates_in_database(self, memory, request_context):
        """Test that inferred disposition is actually stored in database."""
        import uuid
        bank_id = f"test_db_update_{uuid.uuid4().hex[:8]}"
@@ -989,12 +1023,13 @@ class TestDispositionInference:
        result = await memory.merge_bank_background(
            bank_id,
            "I am an innovative designer",
-            update_disposition=True
+            update_disposition=True,
+            request_context=request_context,
        )

        inferred_disposition = result["disposition"]

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

        # Compare values (db_disposition is a Pydantic model)
@@ -1003,7 +1038,7 @@ class TestDispositionInference:
        assert db_disposition.empathy == inferred_disposition["empathy"]

    @pytest.mark.asyncio
-    async def test_multiple_background_merges_update_disposition(self, memory):
+    async def test_multiple_background_merges_update_disposition(self, memory, request_context):
        """Test that each background merge can update disposition."""
        import uuid
        bank_id = f"test_multi_merge_{uuid.uuid4().hex[:8]}"
@@ -1011,14 +1046,16 @@ class TestDispositionInference:
        result1 = await memory.merge_bank_background(
            bank_id,
            "I am a software engineer",
-            update_disposition=True
+            update_disposition=True,
+            request_context=request_context,
        )
        disposition1 = result1["disposition"]

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

@@ -1026,7 +1063,7 @@ 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):
+    async def test_background_merge_conflict_resolution_with_disposition(self, memory, request_context):
        """Test that conflicts are resolved and disposition reflects final background."""
        import uuid
        bank_id = f"test_conflict_{uuid.uuid4().hex[:8]}"
@@ -1034,13 +1071,15 @@ class TestDispositionInference:
        await memory.merge_bank_background(
            bank_id,
            "I was born in Colorado and prefer stability",
-            update_disposition=True
+            update_disposition=True,
+            request_context=request_context,
        )

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

        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
+from hindsight_api import MemoryEngine, RequestContext
 from hindsight_api.engine.memory_engine import Budget
 import os


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

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

    # 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 = """
@@ -43,7 +43,8 @@ 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"
+        document_id="test_conv_1",
+        request_context=request_context,
    )

    # Search for all facts about Marcus's predictions
@@ -52,7 +53,8 @@ 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
+        max_tokens=8192,
+        request_context=request_context,
    )

    print(f"\n=== Retrieved {len(results.results)} facts ===")
@@ -113,17 +115,17 @@ 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)
+    await memory.delete_bank(bank_id, request_context=request_context)

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


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

    bank_id = "test_multi_doc_agent"

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

    # Two separate conversations with same base time
    base_time = datetime(2024, 11, 14, 10, 0, 0, tzinfo=timezone.utc)
@@ -146,7 +148,8 @@ 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
@@ -155,7 +158,8 @@ Alice: I reconsidered the team's experience level.
        query="Alice preference React Vue",
        fact_type=['opinion', 'experience'],
        budget=Budget.LOW,
-        max_tokens=8192
+        max_tokens=8192,
+        request_context=request_context,
    )

    print(f"\n=== Retrieved {len(results.results)} agent facts ===")
@@ -175,6 +179,6 @@ 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)
+    await memory.delete_bank(bank_id, request_context=request_context)

    print(f"\n✅ Test passed: Multiple documents maintain separate ordering")
@@ -428,6 +428,66 @@ 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.
@@ -608,3 +668,167 @@ 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
@@ -26,6 +26,9 @@ 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"),
 ]


@@ -48,12 +51,18 @@ 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)
-    if not api_key:
+
+    # 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:
        pytest.skip(f"Skipping {provider}/{model}: no API key available")

    llm = LLMProvider(
        provider=provider,
-        api_key=api_key,
+        api_key=api_key or "",
        base_url="",
        model=model,
    )
@@ -1,177 +0,0 @@
-"""
-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"])
@@ -3,11 +3,12 @@ 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):
+async def test_observation_generation_on_put(memory, request_context):
    """
    Test that observations are generated SYNCHRONOUSLY when new facts are added.

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

        # Observations are generated SYNCHRONOUSLY during retain,
@@ -75,7 +77,7 @@ async def test_observation_generation_on_put(memory):
        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)
+        observations = await memory.get_entity_observations(bank_id, entity_id, limit=10, request_context=request_context)

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


@pytest.mark.asyncio
-async def test_regenerate_entity_observations(memory):
+async def test_regenerate_entity_observations(memory, request_context):
    """
    Test explicit regeneration of observations for an entity.
    """
@@ -114,7 +116,8 @@ async def test_regenerate_entity_observations(memory):
            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)
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            request_context=request_context,
        )

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

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

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


@pytest.mark.asyncio
-async def test_search_with_include_entities(memory):
+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):
    """
    Test that search with include_entities=True returns entity observations.

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

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

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


@pytest.mark.asyncio
-async def test_get_entity_state(memory):
+async def test_get_entity_state(memory, request_context):
    """
    Test getting the full state of an entity.
    """
@@ -275,7 +382,8 @@ async def test_get_entity_state(memory):
            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)
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            request_context=request_context,
        )

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

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


@pytest.mark.asyncio
-async def test_observation_fact_type_in_database(memory):
+async def test_observation_fact_type_in_database(memory, request_context):
    """
    Test that observations are stored with correct fact_type in database.
    """
@@ -336,7 +445,8 @@ async def test_observation_fact_type_in_database(memory):
            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)
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            request_context=request_context,
        )

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


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

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

        # Observations are generated synchronously during retain
@@ -466,7 +577,7 @@ async def test_user_entity_prioritized_for_observations(memory):
            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)
+        observations = await memory.get_entity_observations(bank_id, user_entity_id, limit=10, request_context=request_context)

        print(f"\n=== User Entity Observations ===")
        print(f"Total observations: {len(observations)}")
@@ -5,12 +5,13 @@ 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):
+async def test_retain_with_chunks(memory, request_context):
    """
    Test that retain function:
    1. Stores facts with associated chunks
@@ -41,7 +42,8 @@ async def test_retain_with_chunks(memory):
            content=long_content,
            context="team overview",
            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            document_id=document_id
+            document_id=document_id,
+            request_context=request_context,
        )

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

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

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


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

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

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

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


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

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

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

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


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

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

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

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


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

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

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

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

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


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

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

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

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

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


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

@@ -527,7 +540,8 @@ async def test_mentioned_at_from_context_string(memory):
            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
+            event_date=None,  # Not providing event_date - should default to now() if LLM doesn't extract
+            request_context=request_context,
        )

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

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

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


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

@pytest.mark.asyncio
-async def test_context_preservation(memory):
+async def test_context_preservation(memory, request_context):
    """
    Test that context is preserved and retrievable.
    Context helps understand why/how memory was formed.
@@ -597,7 +612,8 @@ async def test_context_preservation(memory):
            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)
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            request_context=request_context,
        )

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

        assert len(result.results) > 0, "Should recall the stored fact"
@@ -620,11 +637,11 @@ async def test_context_preservation(memory):
        print(f"  Retrieved {len(result.results)} facts")

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


@pytest.mark.asyncio
-async def test_context_with_batch(memory):
+async def test_context_with_batch(memory, request_context):
    """
    Test that each item in a batch can have different contexts.
    """
@@ -650,7 +667,8 @@ async def test_context_with_batch(memory):
                    "context": "incident response",
                    "event_date": datetime(2024, 1, 12, tzinfo=timezone.utc)
                }
-            ]
+            ],
+            request_context=request_context,
        )

        # Should have created facts from all items
@@ -661,7 +679,7 @@ async def test_context_with_batch(memory):
        print(f"  Created {total_units} total memory units")

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


 # ============================================================
@@ -669,7 +687,7 @@ async def test_context_with_batch(memory):
 # ============================================================

@pytest.mark.asyncio
-async def test_metadata_storage_and_retrieval(memory):
+async def test_metadata_storage_and_retrieval(memory, request_context):
    """
    Test that user-defined metadata is preserved.
    Metadata allows arbitrary key-value data to be stored with facts.
@@ -692,7 +710,8 @@ async def test_metadata_storage_and_retrieval(memory):
            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)
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            request_context=request_context,
        )

        assert len(unit_ids) > 0, "Should create memory units"
@@ -703,7 +722,8 @@ async def test_metadata_storage_and_retrieval(memory):
            query="When is the product launch?",
            budget=Budget.LOW,
            max_tokens=500,
-            fact_type=["world"]
+            fact_type=["world"],
+            request_context=request_context,
        )

        assert len(result.results) > 0, "Should recall stored facts"
@@ -712,7 +732,7 @@ async def test_metadata_storage_and_retrieval(memory):
        print(f"  (Note: Metadata support depends on API implementation)")

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


 # ============================================================
@@ -720,7 +740,7 @@ async def test_metadata_storage_and_retrieval(memory):
 # ============================================================

@pytest.mark.asyncio
-async def test_empty_batch(memory):
+async def test_empty_batch(memory, request_context):
    """
    Test that empty batch is handled gracefully without errors.
    """
@@ -730,7 +750,8 @@ async def test_empty_batch(memory):
        # Attempt to store empty batch
        unit_ids = await memory.retain_batch_async(
            bank_id=bank_id,
-            contents=[]
+            contents=[],
+            request_context=request_context,
        )

        # Should return empty list or handle gracefully
@@ -741,11 +762,11 @@ async def test_empty_batch(memory):

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


@pytest.mark.asyncio
-async def test_single_item_batch(memory):
+async def test_single_item_batch(memory, request_context):
    """
    Test that batch with one item works correctly.
    """
@@ -761,7 +782,8 @@ async def test_single_item_batch(memory):
                    "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"
@@ -770,11 +792,11 @@ async def test_single_item_batch(memory):
        print(f"✓ Single-item batch created {len(unit_ids[0])} units")

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


@pytest.mark.asyncio
-async def test_mixed_content_batch(memory):
+async def test_mixed_content_batch(memory, request_context):
    """
    Test batch with varying content sizes (short and long).
    """
@@ -798,7 +820,8 @@ async def test_mixed_content_batch(memory):
                {"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
@@ -813,11 +836,11 @@ async def test_mixed_content_batch(memory):
        print(f"  Long content: {long_units} units")

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


@pytest.mark.asyncio
-async def test_batch_with_missing_optional_fields(memory):
+async def test_batch_with_missing_optional_fields(memory, request_context):
    """
    Test that batch handles items with missing optional fields.
    """
@@ -842,7 +865,8 @@ async def test_batch_with_missing_optional_fields(memory):
                    "context": "code review",
                    # No event_date
                }
-            ]
+            ],
+            request_context=request_context,
        )

        # All items should be processed successfully
@@ -852,7 +876,7 @@ async def test_batch_with_missing_optional_fields(memory):
        print(f"✓ Batch with mixed optional fields created {total_units} total units")

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


 # ============================================================
@@ -860,7 +884,7 @@ async def test_batch_with_missing_optional_fields(memory):
 # ============================================================

@pytest.mark.asyncio
-async def test_single_batch_multiple_documents(memory):
+async def test_single_batch_multiple_documents(memory, request_context):
    """
    Test storing multiple distinct documents in a single batch call.
    Each should be tracked separately.
@@ -876,21 +900,24 @@ async def test_single_batch_multiple_documents(memory):
            bank_id=bank_id,
            content="Alice's resume: 10 years Python experience, worked at Google.",
            context="resume review",
-            document_id="resume_alice"
+            document_id="resume_alice",
+            request_context=request_context,
        )

        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"
+            document_id="resume_bob",
+            request_context=request_context,
        )

        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"
+            document_id="resume_charlie",
+            request_context=request_context,
        )

        # All documents should be stored
@@ -907,17 +934,18 @@ async def test_single_batch_multiple_documents(memory):
            query="Who worked at Google?",
            budget=Budget.MID,
            max_tokens=1000,
-            fact_type=["world"]
+            fact_type=["world"],
+            request_context=request_context,
        )

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

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


@pytest.mark.asyncio
-async def test_document_upsert_behavior(memory):
+async def test_document_upsert_behavior(memory, request_context):
    """
    Test that upserting a document replaces the old content.
    """
@@ -930,7 +958,8 @@ async def test_document_upsert_behavior(memory):
            bank_id=bank_id,
            content="Project is in planning phase. Alice is the lead.",
            context="status update v1",
-            document_id=document_id
+            document_id=document_id,
+            request_context=request_context,
        )

        assert len(v1_units) > 0, "Should create units for v1"
@@ -940,7 +969,8 @@ async def test_document_upsert_behavior(memory):
            bank_id=bank_id,
            content="Project is in development phase. Bob has joined as co-lead.",
            context="status update v2",
-            document_id=document_id
+            document_id=document_id,
+            request_context=request_context,
        )

        assert len(v2_units) > 0, "Should create units for v2"
@@ -951,7 +981,8 @@ async def test_document_upsert_behavior(memory):
            query="What is the project status?",
            budget=Budget.MID,
            max_tokens=1000,
-            fact_type=["world"]
+            fact_type=["world"],
+            request_context=request_context,
        )

        assert len(result.results) > 0, "Should recall facts"
@@ -959,7 +990,7 @@ async def test_document_upsert_behavior(memory):
        print(f"✓ Document upsert created v1: {len(v1_units)} units, v2: {len(v2_units)} units")

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


 # ============================================================
@@ -967,7 +998,7 @@ async def test_document_upsert_behavior(memory):
 # ============================================================

@pytest.mark.asyncio
-async def test_chunk_fact_mapping(memory):
+async def test_chunk_fact_mapping(memory, request_context):
    """
    Test that facts correctly reference their source chunks via chunk_id.
    """
@@ -990,7 +1021,8 @@ async def test_chunk_fact_mapping(memory):
            bank_id=bank_id,
            content=content,
            context="technical documentation",
-            document_id=document_id
+            document_id=document_id,
+            request_context=request_context,
        )

        assert len(unit_ids) > 0, "Should create memory units"
@@ -1003,7 +1035,8 @@ async def test_chunk_fact_mapping(memory):
            max_tokens=1000,
            fact_type=["world"],
            include_chunks=True,
-            max_chunk_tokens=8192
+            max_chunk_tokens=8192,
+            request_context=request_context,
        )

        assert len(result.results) > 0, "Should recall facts"
@@ -1026,11 +1059,11 @@ async def test_chunk_fact_mapping(memory):
            print(f"  Returned {len(result.chunks)} chunks matching fact references")

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


@pytest.mark.asyncio
-async def test_chunk_ordering_preservation(memory):
+async def test_chunk_ordering_preservation(memory, request_context):
    """
    Test that chunk_index reflects the correct order within a document.
    """
@@ -1070,7 +1103,8 @@ async def test_chunk_ordering_preservation(memory):
            bank_id=bank_id,
            content=content,
            context="multi-section document",
-            document_id=document_id
+            document_id=document_id,
+            request_context=request_context,
        )

        assert len(unit_ids) > 0, "Should create units"
@@ -1083,7 +1117,8 @@ async def test_chunk_ordering_preservation(memory):
            max_tokens=2000,
            fact_type=["world"],
            include_chunks=True,
-            max_chunk_tokens=8192
+            max_chunk_tokens=8192,
+            request_context=request_context,
        )

        if result.chunks:
@@ -1103,11 +1138,11 @@ async def test_chunk_ordering_preservation(memory):
            print("✓ Content stored (may have created single chunk or no chunks returned)")

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


@pytest.mark.asyncio
-async def test_chunks_truncation_behavior(memory):
+async def test_chunks_truncation_behavior(memory, request_context):
    """
    Test that when chunks exceed max_chunk_tokens, truncation is indicated.
    """
@@ -1165,7 +1200,8 @@ async def test_chunks_truncation_behavior(memory):
            bank_id=bank_id,
            content=large_content,
            context="large document test",
-            document_id=document_id
+            document_id=document_id,
+            request_context=request_context,
        )

        assert len(unit_ids) > 0, "Should create units"
@@ -1178,7 +1214,8 @@ async def test_chunks_truncation_behavior(memory):
            max_tokens=1000,
            fact_type=["world"],
            include_chunks=True,
-            max_chunk_tokens=500  # Small limit to test truncation
+            max_chunk_tokens=500,  # Small limit to test truncation
+            request_context=request_context,
        )

        if result.chunks:
@@ -1198,7 +1235,7 @@ async def test_chunks_truncation_behavior(memory):
            print("✓ No chunks returned (may be under token limit)")

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


 # ============================================================
@@ -1206,7 +1243,7 @@ async def test_chunks_truncation_behavior(memory):
 # ============================================================

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

@@ -1223,7 +1260,8 @@ async def test_temporal_links_creation(memory):
            bank_id=bank_id,
            content="Alice started working on the authentication module.",
            context="daily standup",
-            event_date=base_date
+            event_date=base_date,
+            request_context=request_context,
        )

        # Fact 2 at 2:00 PM same day (4 hours later)
@@ -1231,7 +1269,8 @@ async def test_temporal_links_creation(memory):
            bank_id=bank_id,
            content="Bob reviewed the API design document.",
            context="daily standup",
-            event_date=base_date.replace(hour=14)
+            event_date=base_date.replace(hour=14),
+            request_context=request_context,
        )

        # Fact 3 at 9:00 AM next day (23 hours later)
@@ -1239,7 +1278,8 @@ async def test_temporal_links_creation(memory):
            bank_id=bank_id,
            content="Charlie deployed the new database schema.",
            context="daily standup",
-            event_date=base_date.replace(day=16, hour=9)
+            event_date=base_date.replace(day=16, hour=9),
+            request_context=request_context,
        )

        assert len(unit_ids_1) > 0 and len(unit_ids_2) > 0 and len(unit_ids_3) > 0
@@ -1278,11 +1318,11 @@ async def test_temporal_links_creation(memory):
            logger.info("Temporal links created successfully with proper weights")

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


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

@@ -1295,21 +1335,24 @@ async def test_semantic_links_creation(memory):
        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"
+            context="team skills",
+            request_context=request_context,
        )

        # 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"
+            context="team skills",
+            request_context=request_context,
        )

        # 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"
+            context="calendar events",
+            request_context=request_context,
        )

        assert len(unit_ids_1) > 0 and len(unit_ids_2) > 0 and len(unit_ids_3) > 0
@@ -1349,11 +1392,11 @@ async def test_semantic_links_creation(memory):
            logger.info("Semantic links created successfully between similar content")

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


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

@@ -1367,28 +1410,32 @@ async def test_entity_links_creation(memory):
        unit_ids_1 = await memory.retain_async(
            bank_id=bank_id,
            content="Alice joined Google as a software engineer in 2020.",
-            context="career history"
+            context="career history",
+            request_context=request_context,
        )

        # 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"
+            context="project updates",
+            request_context=request_context,
        )

        # 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"
+            context="tech news",
+            request_context=request_context,
        )

        # 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"
+            context="career history",
+            request_context=request_context,
        )

        assert len(unit_ids_1) > 0 and len(unit_ids_2) > 0 and len(unit_ids_3) > 0 and len(unit_ids_4) > 0
@@ -1445,11 +1492,11 @@ async def test_entity_links_creation(memory):
            logger.info("Entity links are properly bidirectional")

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


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

@@ -1471,7 +1518,8 @@ async def test_causal_links_creation(memory):
        unit_ids = await memory.retain_async(
            bank_id=bank_id,
            content=content,
-            context="project timeline"
+            context="project timeline",
+            request_context=request_context,
        )

        assert len(unit_ids) > 0, "Should have created facts"
@@ -1517,11 +1565,11 @@ async def test_causal_links_creation(memory):
        logger.info("Test completed (causal link extraction is LLM-dependent)")

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


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

@@ -1539,7 +1587,8 @@ async def test_all_link_types_together(memory):
            bank_id=bank_id,
            content="Alice completed the Python backend service for the authentication system.",
            context="sprint review",
-            event_date=base_date
+            event_date=base_date,
+            request_context=request_context,
        )

        # Fact 2: Related to Alice, similar topic (Python), close in time
@@ -1547,7 +1596,8 @@ async def test_all_link_types_together(memory):
            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
+            event_date=base_date.replace(hour=14),  # Same day, 4 hours later
+            request_context=request_context,
        )

        # Fact 3: Related to Alice, different topic but same entity
@@ -1555,7 +1605,8 @@ async def test_all_link_types_together(memory):
            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
+            event_date=base_date.replace(day=16),  # Next day
+            request_context=request_context,
        )

        assert len(unit_ids_1) > 0 and len(unit_ids_2) > 0 and len(unit_ids_3) > 0
@@ -1594,11 +1645,11 @@ async def test_all_link_types_together(memory):
            logger.info("All major link types (temporal, semantic, entity) are working correctly")

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


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

@@ -1617,7 +1668,8 @@ async def test_semantic_links_within_same_batch(memory):

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

        # Flatten the list of lists
@@ -1652,11 +1704,11 @@ async def test_semantic_links_within_same_batch(memory):
                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)
+        await memory.delete_bank(bank_id, request_context=request_context)


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

@@ -1689,7 +1741,8 @@ async def test_temporal_links_within_same_batch(memory):

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

        # Flatten the list of lists
@@ -1724,4 +1777,72 @@ async def test_temporal_links_within_same_batch(memory):
                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)
+        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)
@@ -0,0 +1,406 @@
+"""
+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
+from hindsight_api import SearchTrace, RequestContext
 from datetime import datetime, timezone


@pytest.mark.asyncio
-async def test_search_with_trace(memory):
+async def test_search_with_trace(memory, request_context):
    """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()}"
@@ -20,16 +20,19 @@ async def test_search_with_trace(memory):
            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
@@ -40,6 +43,7 @@ async def test_search_with_trace(memory):
            budget=Budget.LOW, # 20,
            max_tokens=512,
            enable_trace=True,
+            request_context=request_context,
        )

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

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


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

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

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

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

    finally:
        # Cleanup
-        await memory.delete_bank(bank_id)
+        await memory.delete_bank(bank_id, request_context=request_context)
@@ -0,0 +1,136 @@
+"""
+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"
@@ -3,16 +3,17 @@ 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):
+async def test_temporal_ranges_are_written(memory, request_context):
    """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)
+        await memory.delete_bank(bank_id, request_context=request_context)
    except Exception:
        pass

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

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

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

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

    # Clean up
-    await memory.delete_bank(bank_id)
+    await memory.delete_bank(bank_id, request_context=request_context)
@@ -4,10 +4,11 @@ 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):
+async def test_think_opinion_consistency(memory, request_context):
    """
    Test that think function:
    1. Generates an opinion
@@ -23,14 +24,16 @@ async def test_think_opinion_consistency(memory):
            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)
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            request_context=request_context,
        )

        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)
+            event_date=datetime(2024, 2, 1, tzinfo=timezone.utc),
+            request_context=request_context,
        )

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

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

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


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

    print(f"\n=== Think Without Context ===")
@@ -1,102 +0,0 @@
-name: Release
-
-on:
-  push:
-    tags:
-      - 'v*'
-  workflow_dispatch:
-
-jobs:
-  build:
-    name: Build ${{ matrix.target }}
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        include:
-          - os: ubuntu-latest
-            target: x86_64-unknown-linux-gnu
-            artifact_name: memora
-            release_name: memora-linux-x86_64
-          - os: ubuntu-latest
-            target: aarch64-unknown-linux-gnu
-            artifact_name: memora
-            release_name: memora-linux-arm64
-          - os: macos-latest
-            target: x86_64-apple-darwin
-            artifact_name: memora
-            release_name: memora-macos-x86_64
-          - os: macos-latest
-            target: aarch64-apple-darwin
-            artifact_name: memora
-            release_name: memora-macos-arm64
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Install Rust
-        uses: dtolnay/rust-toolchain@stable
-        with:
-          targets: ${{ matrix.target }}
-
-      - name: Install cross-compilation tools (Linux ARM64)
-        if: matrix.target == 'aarch64-unknown-linux-gnu'
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y gcc-aarch64-linux-gnu
-
-      - name: Build
-        run: cargo build --release --target ${{ matrix.target }}
-
-      - name: Strip binary (Linux)
-        if: runner.os == 'Linux'
-        run: strip target/${{ matrix.target }}/release/${{ matrix.artifact_name }}
-
-      - name: Strip binary (macOS)
-        if: runner.os == 'macOS'
-        run: strip target/${{ matrix.target }}/release/${{ matrix.artifact_name }}
-
-      - name: Upload artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: ${{ matrix.release_name }}
-          path: target/${{ matrix.target }}/release/${{ matrix.artifact_name }}
-
-  release:
-    name: Create Release
-    needs: build
-    runs-on: ubuntu-latest
-    if: startsWith(github.ref, 'refs/tags/')
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Download all artifacts
-        uses: actions/download-artifact@v4
-        with:
-          path: artifacts
-
-      - name: Create checksums
-        run: |
-          cd artifacts
-          for dir in */; do
-            cd "$dir"
-            sha256sum * > SHA256SUMS
-            cd ..
-          done
-
-      - name: Create Release
-        uses: softprops/action-gh-release@v1
-        with:
-          files: |
-            artifacts/memora-linux-x86_64/memora
-            artifacts/memora-linux-arm64/memora
-            artifacts/memora-macos-x86_64/memora
-            artifacts/memora-macos-arm64/memora
-            artifacts/*/SHA256SUMS
-          draft: false
-          prerelease: false
-          generate_release_notes: true
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -1,6 +1,6 @@
 [package]
 name = "hindsight-cli"
-version = "0.1.8"
+version = "0.2.1"
 edition = "2021"
 authors = ["Hindsight Team"]
 description = "A beautiful CLI for Hindsight - semantic memory system"
@@ -0,0 +1,128 @@
+#!/bin/bash
+# CLI smoke test - verifies basic CLI functionality against a running API server
+#
+# Prerequisites:
+#   - hindsight CLI must be in PATH or HINDSIGHT_CLI env var set
+#   - API server must be running at HINDSIGHT_API_URL (default: http://localhost:8888)
+#
+# Usage:
+#   ./hindsight-cli/smoke-test.sh
+#   HINDSIGHT_CLI=/path/to/hindsight ./hindsight-cli/smoke-test.sh
+
+set -e
+
+# Configuration
+HINDSIGHT_CLI="${HINDSIGHT_CLI:-hindsight}"
+export HINDSIGHT_API_URL="${HINDSIGHT_API_URL:-http://localhost:8888}"
+TEST_BANK="cli-smoke-test-$(date +%s)"
+
+echo "=== Hindsight CLI Smoke Test ==="
+echo "CLI: $HINDSIGHT_CLI"
+echo "API URL: $HINDSIGHT_API_URL"
+echo "Test bank: $TEST_BANK"
+echo ""
+
+# Helper function
+run_test() {
+    local name="$1"
+    shift
+    echo -n "Testing: $name... "
+    if "$@" > /tmp/cli-test-output.txt 2>&1; then
+        echo "OK"
+        return 0
+    else
+        echo "FAILED"
+        echo "  Command: $*"
+        echo "  Output:"
+        cat /tmp/cli-test-output.txt | sed 's/^/    /'
+        return 1
+    fi
+}
+
+run_test_output() {
+    local name="$1"
+    local expected="$2"
+    shift 2
+    echo -n "Testing: $name... "
+    if "$@" > /tmp/cli-test-output.txt 2>&1; then
+        if grep -qi "$expected" /tmp/cli-test-output.txt; then
+            echo "OK"
+            return 0
+        else
+            echo "FAILED (expected '$expected' not found)"
+            echo "  Command: $*"
+            echo "  Output:"
+            cat /tmp/cli-test-output.txt | sed 's/^/    /'
+            return 1
+        fi
+    else
+        echo "FAILED"
+        echo "  Command: $*"
+        echo "  Output:"
+        cat /tmp/cli-test-output.txt | sed 's/^/    /'
+        return 1
+    fi
+}
+
+cleanup() {
+    echo ""
+    echo "Cleaning up test bank..."
+    "$HINDSIGHT_CLI" bank delete "$TEST_BANK" 2>/dev/null || true
+}
+trap cleanup EXIT
+
+FAILED=0
+
+# Test 1: Version
+run_test "version" "$HINDSIGHT_CLI" --version || FAILED=1
+
+# Test 2: Help
+run_test "help" "$HINDSIGHT_CLI" --help || FAILED=1
+
+# Test 3: Configure help
+run_test "configure help" "$HINDSIGHT_CLI" configure --help || FAILED=1
+
+# Test 4: List banks (JSON output)
+run_test "list banks" "$HINDSIGHT_CLI" bank list -o json || FAILED=1
+
+# Test 5: Set bank name (creates the bank)
+run_test "set bank name" "$HINDSIGHT_CLI" bank name "$TEST_BANK" "CLI Smoke Test Bank" || FAILED=1
+
+# Test 6: Get bank disposition
+run_test_output "get bank disposition" "CLI Smoke Test Bank" "$HINDSIGHT_CLI" bank disposition "$TEST_BANK" || FAILED=1
+
+# Test 7: Retain memory
+run_test "retain memory" "$HINDSIGHT_CLI" memory retain "$TEST_BANK" "Alice is a software engineer who loves Rust programming" || FAILED=1
+
+# Test 8: Retain more memories
+run_test "retain more memories" "$HINDSIGHT_CLI" memory retain "$TEST_BANK" "Bob is Alice's colleague who prefers Python" || FAILED=1
+
+# Test 9: Recall memories
+run_test_output "recall memories" "Alice" "$HINDSIGHT_CLI" memory recall "$TEST_BANK" "Who is Alice?" || FAILED=1
+
+# Test 10: Reflect on memories
+run_test_output "reflect" "Alice" "$HINDSIGHT_CLI" memory reflect "$TEST_BANK" "What do you know about Alice?" || FAILED=1
+
+# Test 11: Get bank stats
+run_test "bank stats" "$HINDSIGHT_CLI" bank stats "$TEST_BANK" || FAILED=1
+
+# Test 12: List entities
+run_test "list entities" "$HINDSIGHT_CLI" entity list "$TEST_BANK" || FAILED=1
+
+# Test 13: List documents
+run_test "list documents" "$HINDSIGHT_CLI" document list "$TEST_BANK" || FAILED=1
+
+# Test 14: Clear memories
+run_test "clear memories" "$HINDSIGHT_CLI" memory clear "$TEST_BANK" || FAILED=1
+
+# Test 15: Delete bank
+run_test "delete bank" "$HINDSIGHT_CLI" bank delete "$TEST_BANK" || FAILED=1
+
+echo ""
+if [ $FAILED -eq 0 ]; then
+    echo "=== All smoke tests passed! ==="
+    exit 0
+else
+    echo "=== Some smoke tests failed ==="
+    exit 1
+fi
@@ -64,13 +64,24 @@ pub struct ApiClient {
 }

 impl ApiClient {
-    pub fn new(base_url: String) -> Result<Self> {
+    pub fn new(base_url: String, api_key: Option<String>) -> Result<Self> {
        let runtime = std::sync::Arc::new(tokio::runtime::Runtime::new()?);

-        // Create HTTP client with 2-minute timeout
-        let http_client = reqwest::Client::builder()
-            .timeout(std::time::Duration::from_secs(120))
-            .build()?;
+        // Create HTTP client with 2-minute timeout and optional auth header
+        let mut client_builder = reqwest::Client::builder()
+            .timeout(std::time::Duration::from_secs(120));
+
+        if let Some(key) = api_key {
+            let mut headers = reqwest::header::HeaderMap::new();
+            let auth_value = format!("Bearer {}", key);
+            headers.insert(
+                reqwest::header::AUTHORIZATION,
+                reqwest::header::HeaderValue::from_str(&auth_value)?,
+            );
+            client_builder = client_builder.default_headers(headers);
+        }
+
+        let http_client = client_builder.build()?;

        let client = AsyncClient::new_with_client(&base_url, http_client);
        Ok(ApiClient { client, runtime })
@@ -78,14 +89,14 @@ impl ApiClient {

    pub fn list_agents(&self, _verbose: bool) -> Result<Vec<types::BankListItem>> {
        self.runtime.block_on(async {
-            let response = self.client.list_banks().await?;
+            let response = self.client.list_banks(None).await?;
            Ok(response.into_inner().banks)
        })
    }

    pub fn get_profile(&self, agent_id: &str, _verbose: bool) -> Result<types::BankProfileResponse> {
        self.runtime.block_on(async {
-            let response = self.client.get_bank_profile(agent_id).await?;
+            let response = self.client.get_bank_profile(agent_id, None).await?;
            Ok(response.into_inner())
        })
    }
@@ -94,7 +105,9 @@ impl ApiClient {
        self.runtime.block_on(async {
            let response = self.client.get_agent_stats(agent_id).await?;
            let value = response.into_inner();
-            let stats: AgentStats = serde_json::from_value(value)?;
+            // Convert to JSON Value first, then parse into our type
+            let json_value = serde_json::to_value(&value)?;
+            let stats: AgentStats = serde_json::from_value(json_value)?;
            Ok(stats)
        })
    }
@@ -106,7 +119,7 @@ impl ApiClient {
                background: None,
                disposition: None,
            };
-            let response = self.client.create_or_update_bank(agent_id, &request).await?;
+            let response = self.client.create_or_update_bank(agent_id, None, &request).await?;
            Ok(response.into_inner())
        })
    }
@@ -117,7 +130,7 @@ impl ApiClient {
                content: content.to_string(),
                update_disposition,
            };
-            let response = self.client.add_bank_background(agent_id, &request).await?;
+            let response = self.client.add_bank_background(agent_id, None, &request).await?;
            Ok(response.into_inner())
        })
    }
@@ -127,21 +140,21 @@ impl ApiClient {
            eprintln!("Request body: {}", serde_json::to_string_pretty(request).unwrap_or_default());
        }
        self.runtime.block_on(async {
-            let response = self.client.recall_memories(agent_id, request).await?;
+            let response = self.client.recall_memories(agent_id, None, request).await?;
            Ok(response.into_inner())
        })
    }

    pub fn reflect(&self, agent_id: &str, request: &types::ReflectRequest, _verbose: bool) -> Result<types::ReflectResponse> {
        self.runtime.block_on(async {
-            let response = self.client.reflect(agent_id, request).await?;
+            let response = self.client.reflect(agent_id, None, request).await?;
            Ok(response.into_inner())
        })
    }

    pub fn retain(&self, agent_id: &str, request: &types::RetainRequest, _async_mode: bool, _verbose: bool) -> Result<MemoryPutResult> {
        self.runtime.block_on(async {
-            let response = self.client.retain_memories(agent_id, request).await?;
+            let response = self.client.retain_memories(agent_id, None, request).await?;
            let result = response.into_inner();
            Ok(MemoryPutResult {
                success: result.success,
@@ -159,7 +172,7 @@ impl ApiClient {

    pub fn clear_memories(&self, agent_id: &str, fact_type: Option<&str>, _verbose: bool) -> Result<types::DeleteResponse> {
        self.runtime.block_on(async {
-            let response = self.client.clear_bank_memories(agent_id, fact_type).await?;
+            let response = self.client.clear_bank_memories(agent_id, None, Some(fact_type)).await?;
            Ok(response.into_inner())
        })
    }
@@ -170,7 +183,8 @@ impl ApiClient {
                agent_id,
                limit.map(|l| l as i64),
                offset.map(|o| o as i64),
-                q
+                q,
+                None,
            ).await?;
            Ok(response.into_inner())
        })
@@ -178,69 +192,79 @@ impl ApiClient {

    pub fn get_document(&self, agent_id: &str, document_id: &str, _verbose: bool) -> Result<types::DocumentResponse> {
        self.runtime.block_on(async {
-            let response = self.client.get_document(agent_id, document_id).await?;
+            let response = self.client.get_document(agent_id, document_id, None).await?;
            Ok(response.into_inner())
        })
    }

    pub fn delete_document(&self, agent_id: &str, document_id: &str, _verbose: bool) -> Result<types::DeleteResponse> {
        self.runtime.block_on(async {
-            let response = self.client.delete_document(agent_id, document_id).await?;
+            let response = self.client.delete_document(agent_id, document_id, None).await?;
            let value = response.into_inner();
-            let result: types::DeleteResponse = serde_json::from_value(value)?;
-            Ok(result)
+            // Convert typed response to DeleteResponse
+            Ok(types::DeleteResponse {
+                deleted_count: Some(value.memory_units_deleted),
+                message: Some(value.message),
+                success: value.success,
+            })
        })
    }

    pub fn list_operations(&self, agent_id: &str, _verbose: bool) -> Result<OperationsResponse> {
        self.runtime.block_on(async {
-            let response = self.client.list_operations(agent_id).await?;
+            let response = self.client.list_operations(agent_id, None).await?;
            let value = response.into_inner();
-            let ops: OperationsResponse = serde_json::from_value(value)?;
+            // Convert to JSON Value first, then parse into our type
+            let json_value = serde_json::to_value(&value)?;
+            let ops: OperationsResponse = serde_json::from_value(json_value)?;
            Ok(ops)
        })
    }

    pub fn cancel_operation(&self, agent_id: &str, operation_id: &str, _verbose: bool) -> Result<types::DeleteResponse> {
        self.runtime.block_on(async {
-            let response = self.client.cancel_operation(agent_id, operation_id).await?;
+            let response = self.client.cancel_operation(agent_id, operation_id, None).await?;
            let value = response.into_inner();
-            let result: types::DeleteResponse = serde_json::from_value(value)?;
-            Ok(result)
+            // Convert typed response to DeleteResponse
+            Ok(types::DeleteResponse {
+                deleted_count: None,
+                message: Some(value.message),
+                success: value.success,
+            })
        })
    }

    pub fn list_memories(&self, bank_id: &str, type_filter: Option<&str>, q: Option<&str>, limit: Option<i64>, offset: Option<i64>, _verbose: bool) -> Result<types::ListMemoryUnitsResponse> {
        self.runtime.block_on(async {
-            let response = self.client.list_memories(bank_id, limit, offset, q, type_filter).await?;
+            let response = self.client.list_memories(bank_id, limit, offset, q, type_filter, None).await?;
            Ok(response.into_inner())
        })
    }

    pub fn list_entities(&self, bank_id: &str, limit: Option<i64>, _verbose: bool) -> Result<types::EntityListResponse> {
        self.runtime.block_on(async {
-            let response = self.client.list_entities(bank_id, limit).await?;
+            let response = self.client.list_entities(bank_id, limit, None).await?;
            Ok(response.into_inner())
        })
    }

    pub fn get_entity(&self, bank_id: &str, entity_id: &str, _verbose: bool) -> Result<types::EntityDetailResponse> {
        self.runtime.block_on(async {
-            let response = self.client.get_entity(bank_id, entity_id).await?;
+            let response = self.client.get_entity(bank_id, entity_id, None).await?;
            Ok(response.into_inner())
        })
    }

    pub fn regenerate_entity(&self, bank_id: &str, entity_id: &str, _verbose: bool) -> Result<types::EntityDetailResponse> {
        self.runtime.block_on(async {
-            let response = self.client.regenerate_entity_observations(bank_id, entity_id).await?;
+            let response = self.client.regenerate_entity_observations(bank_id, entity_id, None).await?;
            Ok(response.into_inner())
        })
    }

    pub fn delete_bank(&self, bank_id: &str, _verbose: bool) -> Result<types::DeleteResponse> {
        self.runtime.block_on(async {
-            let response = self.client.delete_bank(bank_id).await?;
+            let response = self.client.delete_bank(bank_id, None).await?;
            Ok(response.into_inner())
        })
    }
@@ -354,7 +354,9 @@ impl App {
                            query: query_text,
                            budget: Some(query_budget),
                            context: None,
+                            max_tokens: 4096,
                            include: None,
+                            response_schema: None,
                        };

                        let result = client.reflect(&bank_id, &request, false)
@@ -944,7 +946,7 @@ fn render_banks(f: &mut Frame, app: &mut App, area: Rect) {
        .banks
        .iter()
        .map(|bank| {
-            let name = if bank.name.is_empty() { "Unnamed" } else { &bank.name };
+            let name = bank.name.as_deref().filter(|s| !s.is_empty()).unwrap_or("Unnamed");
            let content = format!("{} - {}", bank.bank_id, name);
            ListItem::new(content).style(Style::default().fg(Color::White))
        })
@@ -10,6 +10,7 @@ use crate::ui;

 // Import types from generated client
 use hindsight_client::types::{Budget, ChunkIncludeOptions, IncludeOptions};
+use serde_json;

 // Helper function to parse budget string to Budget enum
 fn parse_budget(budget: &str) -> Budget {
@@ -86,6 +87,8 @@ pub fn reflect(
    query: String,
    budget: String,
    context: Option<String>,
+    max_tokens: Option<i64>,
+    schema_path: Option<PathBuf>,
    verbose: bool,
    output_format: OutputFormat,
 ) -> Result<()> {
@@ -95,11 +98,24 @@ pub fn reflect(
        None
    };

+    // Load and parse schema if provided
+    let response_schema = if let Some(path) = schema_path {
+        let schema_content = fs::read_to_string(&path)
+            .with_context(|| format!("Failed to read schema file: {}", path.display()))?;
+        let schema: serde_json::Map<String, serde_json::Value> = serde_json::from_str(&schema_content)
+            .with_context(|| format!("Failed to parse JSON schema from: {}", path.display()))?;
+        Some(schema)
+    } else {
+        None
+    };
+
    let request = ReflectRequest {
        query,
        budget: Some(parse_budget(&budget)),
        context,
+        max_tokens: max_tokens.unwrap_or(4096),
        include: None,
+        response_schema,
    };

    let response = client.reflect(agent_id, &request, verbose);
@@ -145,6 +161,7 @@ pub fn retain(
        metadata: None,
        timestamp: None,
        document_id: Some(doc_id.clone()),
+        entities: None,
    };

    let request = RetainRequest {
@@ -254,6 +271,7 @@ pub fn retain_files(
            metadata: None,
            timestamp: None,
            document_id: Some(doc_id),
+            entities: None,
        });

        pb.inc(1);
@@ -10,6 +10,7 @@ const CONFIG_DIR_NAME: &str = ".hindsight";

 pub struct Config {
    pub api_url: String,
+    pub api_key: Option<String>,
    pub source: ConfigSource,
 }

@@ -32,22 +33,27 @@ impl std::fmt::Display for ConfigSource {

 impl Config {
    /// Load configuration with the following priority:
-    /// 1. Environment variable (HINDSIGHT_API_URL) - highest priority, for overrides
+    /// 1. Environment variable (HINDSIGHT_API_URL, HINDSIGHT_API_KEY) - highest priority, for overrides
    /// 2. Local config file (~/.hindsight/config.toml)
    /// 3. Default (http://localhost:8888)
    pub fn load() -> Result<Self> {
+        // Load API key from environment (highest priority)
+        let env_api_key = env::var("HINDSIGHT_API_KEY").ok();
+
        // 1. Environment variable takes highest priority (for overrides)
        if let Ok(api_url) = env::var("HINDSIGHT_API_URL") {
-            return Self::validate_and_create(api_url, ConfigSource::Environment);
+            return Self::validate_and_create(api_url, env_api_key, ConfigSource::Environment);
        }

        // 2. Try local config file
-        if let Some(api_url) = Self::load_from_file()? {
-            return Self::validate_and_create(api_url, ConfigSource::LocalFile);
+        if let Some((api_url, file_api_key)) = Self::load_from_file()? {
+            // Environment api_key takes precedence over file api_key
+            let api_key = env_api_key.or(file_api_key);
+            return Self::validate_and_create(api_url, api_key, ConfigSource::LocalFile);
        }

        // 3. Fall back to default
-        Self::validate_and_create(DEFAULT_API_URL.to_string(), ConfigSource::Default)
+        Self::validate_and_create(DEFAULT_API_URL.to_string(), env_api_key, ConfigSource::Default)
    }

    /// Legacy method for backwards compatibility
@@ -55,14 +61,14 @@ impl Config {
        Self::load()
    }

-    fn validate_and_create(api_url: String, source: ConfigSource) -> Result<Self> {
+    fn validate_and_create(api_url: String, api_key: Option<String>, source: ConfigSource) -> Result<Self> {
        if !api_url.starts_with("http://") && !api_url.starts_with("https://") {
            anyhow::bail!(
                "Invalid API URL: {}. Must start with http:// or https://",
                api_url
            );
        }
-        Ok(Config { api_url, source })
+        Ok(Config { api_url, api_key, source })
    }

    fn config_dir() -> Option<PathBuf> {
@@ -73,7 +79,7 @@ impl Config {
        Self::config_dir().map(|dir| dir.join(CONFIG_FILE_NAME))
    }

-    fn load_from_file() -> Result<Option<String>> {
+    fn load_from_file() -> Result<Option<(String, Option<String>)>> {
        let config_path = match Self::config_file_path() {
            Some(path) => path,
            None => return Ok(None),
@@ -86,23 +92,40 @@ impl Config {
        let content = fs::read_to_string(&config_path)
            .with_context(|| format!("Failed to read config file: {}", config_path.display()))?;

-        // Simple TOML parsing for api_url
+        let mut api_url: Option<String> = None;
+        let mut api_key: Option<String> = None;
+
+        // Simple TOML parsing for api_url and api_key
        for line in content.lines() {
            let line = line.trim();
            if line.starts_with("api_url") {
                if let Some(value) = line.split('=').nth(1) {
                    let value = value.trim().trim_matches('"').trim_matches('\'');
                    if !value.is_empty() {
-                        return Ok(Some(value.to_string()));
+                        api_url = Some(value.to_string());
+                    }
+                }
+            } else if line.starts_with("api_key") {
+                if let Some(value) = line.split('=').nth(1) {
+                    let value = value.trim().trim_matches('"').trim_matches('\'');
+                    if !value.is_empty() {
+                        api_key = Some(value.to_string());
                    }
                }
            }
        }

-        Ok(None)
+        match api_url {
+            Some(url) => Ok(Some((url, api_key))),
+            None => Ok(None),
+        }
    }

    pub fn save_api_url(api_url: &str) -> Result<PathBuf> {
+        Self::save_config(api_url, None)
+    }
+
+    pub fn save_config(api_url: &str, api_key: Option<&str>) -> Result<PathBuf> {
        let config_dir = Self::config_dir()
            .ok_or_else(|| anyhow::anyhow!("Could not determine home directory"))?;

@@ -113,7 +136,10 @@ impl Config {
        }

        let config_path = config_dir.join(CONFIG_FILE_NAME);
-        let content = format!("api_url = \"{}\"\n", api_url);
+        let mut content = format!("api_url = \"{}\"\n", api_url);
+        if let Some(key) = api_key {
+            content.push_str(&format!("api_key = \"{}\"\n", key));
+        }

        fs::write(&config_path, content)
            .with_context(|| format!("Failed to write config file: {}", config_path.display()))?;
@@ -91,12 +91,18 @@ enum Commands {
    #[command(alias = "tui")]
    Explore,

-    /// Configure the CLI (API URL, etc.)
-    #[command(after_help = "Configuration priority:\n  1. Environment variable (HINDSIGHT_API_URL) - highest priority\n  2. Config file (~/.hindsight/config)\n  3. Default (http://localhost:8888)")]
+    /// Launch the web-based control plane UI
+    Ui,
+
+    /// Configure the CLI (API URL, API key, etc.)
+    #[command(after_help = "Configuration priority:\n  1. Environment variables (HINDSIGHT_API_URL, HINDSIGHT_API_KEY) - highest priority\n  2. Config file (~/.hindsight/config)\n  3. Default (http://localhost:8888)")]
    Configure {
        /// API URL to connect to (interactive prompt if not provided)
        #[arg(long)]
        api_url: Option<String>,
+        /// API key for authentication (sent as Bearer token)
+        #[arg(long)]
+        api_key: Option<String>,
    },
 }

@@ -200,6 +206,14 @@ enum MemoryCommands {
        /// Additional context
        #[arg(short = 'c', long)]
        context: Option<String>,
+
+        /// Maximum tokens for the response (server default: 4096)
+        #[arg(short = 'm', long)]
+        max_tokens: Option<i64>,
+
+        /// Path to JSON schema file for structured output
+        #[arg(short = 's', long)]
+        schema: Option<PathBuf>,
    },

    /// Store (retain) a single memory
@@ -369,8 +383,13 @@ fn run() -> Result<()> {
    let verbose = cli.verbose;

    // Handle configure command before loading full config (it doesn't need API client)
-    if let Commands::Configure { api_url } = cli.command {
-        return handle_configure(api_url, output_format);
+    if let Commands::Configure { api_url, api_key } = cli.command {
+        return handle_configure(api_url, api_key, output_format);
+    }
+
+    // Handle ui command - needs config but not API client
+    if let Commands::Ui = cli.command {
+        return handle_ui(output_format);
    }

    // Load configuration
@@ -381,15 +400,17 @@ fn run() -> Result<()> {
    });

    let api_url = config.api_url().to_string();
+    let api_key = config.api_key.clone();

    // Create API client
-    let client = ApiClient::new(api_url.clone()).unwrap_or_else(|e| {
+    let client = ApiClient::new(api_url.clone(), api_key).unwrap_or_else(|e| {
        errors::handle_api_error(e, &api_url);
    });

    // Execute command and handle errors
    let result: Result<()> = match cli.command {
        Commands::Configure { .. } => unreachable!(), // Handled above
+        Commands::Ui => unreachable!(), // Handled above
        Commands::Explore => commands::explore::run(&client),
        Commands::Bank(bank_cmd) => match bank_cmd {
            BankCommands::List => commands::bank::list(&client, verbose, output_format),
@@ -408,8 +429,8 @@ fn run() -> Result<()> {
            MemoryCommands::Recall { bank_id, query, fact_type, budget, max_tokens, trace, include_chunks, chunk_max_tokens } => {
                commands::memory::recall(&client, &bank_id, query, fact_type, budget, max_tokens, trace, include_chunks, chunk_max_tokens, verbose, output_format)
            }
-            MemoryCommands::Reflect { bank_id, query, budget, context } => {
-                commands::memory::reflect(&client, &bank_id, query, budget, context, verbose, output_format)
+            MemoryCommands::Reflect { bank_id, query, budget, context, max_tokens, schema } => {
+                commands::memory::reflect(&client, &bank_id, query, budget, context, max_tokens, schema, verbose, output_format)
            }
            MemoryCommands::Retain { bank_id, content, doc_id, context, r#async } => {
                commands::memory::retain(&client, &bank_id, content, doc_id, context, r#async, verbose, output_format)
@@ -467,7 +488,7 @@ fn run() -> Result<()> {
    Ok(())
 }

-fn handle_configure(api_url: Option<String>, output_format: OutputFormat) -> Result<()> {
+fn handle_configure(api_url: Option<String>, api_key: Option<String>, output_format: OutputFormat) -> Result<()> {
    // Load current config to show current state
    let current_config = Config::load().ok();

@@ -478,6 +499,15 @@ fn handle_configure(api_url: Option<String>, output_format: OutputFormat) -> Res
        // Show current configuration
        if let Some(ref config) = current_config {
            println!("  Current API URL: {}", config.api_url);
+            if let Some(ref key) = config.api_key {
+                // Mask the API key for display
+                let masked = if key.len() > 8 {
+                    format!("{}...{}", &key[..4], &key[key.len()-4..])
+                } else {
+                    "****".to_string()
+                };
+                println!("  Current API Key: {}", masked);
+            }
            println!("  Source: {}", config.source);
            println!();
        }
@@ -502,18 +532,30 @@ fn handle_configure(api_url: Option<String>, output_format: OutputFormat) -> Res
        return Ok(());
    }

+    // Use provided api_key, or keep existing one if not provided
+    let new_api_key = api_key.or_else(|| current_config.as_ref().and_then(|c| c.api_key.clone()));
+
    // Save to config file
-    let config_path = Config::save_api_url(&new_api_url)?;
+    let config_path = Config::save_config(&new_api_url, new_api_key.as_deref())?;

    if output_format == OutputFormat::Pretty {
        ui::print_success(&format!("Configuration saved to {}", config_path.display()));
        println!();
        println!("  API URL: {}", new_api_url);
+        if let Some(ref key) = new_api_key {
+            let masked = if key.len() > 8 {
+                format!("{}...{}", &key[..4], &key[key.len()-4..])
+            } else {
+                "****".to_string()
+            };
+            println!("  API Key: {}", masked);
+        }
        println!();
-        println!("Note: Environment variable HINDSIGHT_API_URL will override this setting.");
+        println!("Note: Environment variables HINDSIGHT_API_URL and HINDSIGHT_API_KEY will override these settings.");
    } else {
        let result = serde_json::json!({
            "api_url": new_api_url,
+            "api_key_set": new_api_key.is_some(),
            "config_path": config_path.display().to_string(),
        });
        output::print_output(&result, output_format)?;
@@ -521,3 +563,50 @@ fn handle_configure(api_url: Option<String>, output_format: OutputFormat) -> Res

    Ok(())
 }
+
+fn handle_ui(output_format: OutputFormat) -> Result<()> {
+    use std::process::Command;
+
+    // Load configuration to get the API URL
+    let config = Config::load().unwrap_or_else(|e| {
+        ui::print_error(&format!("Configuration error: {}", e));
+        errors::print_config_help();
+        std::process::exit(1);
+    });
+
+    let api_url = config.api_url();
+
+    if output_format == OutputFormat::Pretty {
+        ui::print_info("Launching Hindsight Control Plane UI...");
+        println!();
+        println!("  API URL: {}", api_url);
+        println!();
+    }
+
+    // Run npx @vectorize-io/hindsight-control-plane --api-url {api_url}
+    let status = Command::new("npx")
+        .arg("@vectorize-io/hindsight-control-plane")
+        .arg("--api-url")
+        .arg(api_url)
+        .status();
+
+    match status {
+        Ok(exit_status) => {
+            if !exit_status.success() {
+                if let Some(code) = exit_status.code() {
+                    std::process::exit(code);
+                } else {
+                    std::process::exit(1);
+                }
+            }
+        }
+        Err(e) => {
+            ui::print_error(&format!("Failed to launch control plane UI: {}", e));
+            ui::print_info("Make sure you have Node.js and npm installed.");
+            ui::print_info("You can also install the control plane globally: npm install -g @vectorize-io/hindsight-control-plane");
+            std::process::exit(1);
+        }
+    }
+
+    Ok(())
+}
@@ -175,6 +175,16 @@ pub fn print_think_response(response: &ReflectResponse) {
    if !response.based_on.is_empty() {
        println!("{}", dim(&format!("Based on {} memory units", response.based_on.len())));
    }
+
+    // Display structured output if present
+    if let Some(structured) = &response.structured_output {
+        println!();
+        println!("{}", gradient_text("─── Structured Output ───"));
+        println!();
+        if let Ok(json) = serde_json::to_string_pretty(structured) {
+            println!("{}", json);
+        }
+    }
 }

 pub fn print_trace_info(trace: &serde_json::Map<String, serde_json::Value>) {
@@ -5,7 +5,7 @@ use crate::output::OutputFormat;

 /// Get API client from config
 pub fn get_client(config: &Config) -> Result<ApiClient> {
-    ApiClient::new(config.api_url.clone())
+    ApiClient::new(config.api_url.clone(), config.api_key.clone())
        .context("Failed to create API client")
 }

@@ -1,49 +1,14 @@
 hindsight_client_api/__init__.py
 hindsight_client_api/api/__init__.py
-hindsight_client_api/api/default_api.py
+hindsight_client_api/api/banks_api.py
+hindsight_client_api/api/documents_api.py
+hindsight_client_api/api/entities_api.py
+hindsight_client_api/api/memory_api.py
 hindsight_client_api/api/monitoring_api.py
+hindsight_client_api/api/operations_api.py
 hindsight_client_api/api_client.py
 hindsight_client_api/api_response.py
 hindsight_client_api/configuration.py
-hindsight_client_api/docs/AddBackgroundRequest.md
-hindsight_client_api/docs/BackgroundResponse.md
-hindsight_client_api/docs/BankListItem.md
-hindsight_client_api/docs/BankListResponse.md
-hindsight_client_api/docs/BankProfileResponse.md
-hindsight_client_api/docs/Budget.md
-hindsight_client_api/docs/ChunkData.md
-hindsight_client_api/docs/ChunkIncludeOptions.md
-hindsight_client_api/docs/ChunkResponse.md
-hindsight_client_api/docs/CreateBankRequest.md
-hindsight_client_api/docs/DefaultApi.md
-hindsight_client_api/docs/DeleteResponse.md
-hindsight_client_api/docs/DispositionTraits.md
-hindsight_client_api/docs/DocumentResponse.md
-hindsight_client_api/docs/EntityDetailResponse.md
-hindsight_client_api/docs/EntityIncludeOptions.md
-hindsight_client_api/docs/EntityListItem.md
-hindsight_client_api/docs/EntityListResponse.md
-hindsight_client_api/docs/EntityObservationResponse.md
-hindsight_client_api/docs/EntityStateResponse.md
-hindsight_client_api/docs/GraphDataResponse.md
-hindsight_client_api/docs/HTTPValidationError.md
-hindsight_client_api/docs/IncludeOptions.md
-hindsight_client_api/docs/ListDocumentsResponse.md
-hindsight_client_api/docs/ListMemoryUnitsResponse.md
-hindsight_client_api/docs/MemoryItem.md
-hindsight_client_api/docs/MonitoringApi.md
-hindsight_client_api/docs/RecallRequest.md
-hindsight_client_api/docs/RecallResponse.md
-hindsight_client_api/docs/RecallResult.md
-hindsight_client_api/docs/ReflectFact.md
-hindsight_client_api/docs/ReflectIncludeOptions.md
-hindsight_client_api/docs/ReflectRequest.md
-hindsight_client_api/docs/ReflectResponse.md
-hindsight_client_api/docs/RetainRequest.md
-hindsight_client_api/docs/RetainResponse.md
-hindsight_client_api/docs/UpdateDispositionRequest.md
-hindsight_client_api/docs/ValidationError.md
-hindsight_client_api/docs/ValidationErrorLocInner.md
 hindsight_client_api/exceptions.py
 hindsight_client_api/models/__init__.py
 hindsight_client_api/models/add_background_request.py
@@ -51,16 +16,20 @@ hindsight_client_api/models/background_response.py
 hindsight_client_api/models/bank_list_item.py
 hindsight_client_api/models/bank_list_response.py
 hindsight_client_api/models/bank_profile_response.py
+hindsight_client_api/models/bank_stats_response.py
 hindsight_client_api/models/budget.py
+hindsight_client_api/models/cancel_operation_response.py
 hindsight_client_api/models/chunk_data.py
 hindsight_client_api/models/chunk_include_options.py
 hindsight_client_api/models/chunk_response.py
 hindsight_client_api/models/create_bank_request.py
+hindsight_client_api/models/delete_document_response.py
 hindsight_client_api/models/delete_response.py
 hindsight_client_api/models/disposition_traits.py
 hindsight_client_api/models/document_response.py
 hindsight_client_api/models/entity_detail_response.py
 hindsight_client_api/models/entity_include_options.py
+hindsight_client_api/models/entity_input.py
 hindsight_client_api/models/entity_list_item.py
 hindsight_client_api/models/entity_list_response.py
 hindsight_client_api/models/entity_observation_response.py
@@ -71,6 +40,8 @@ hindsight_client_api/models/include_options.py
 hindsight_client_api/models/list_documents_response.py
 hindsight_client_api/models/list_memory_units_response.py
 hindsight_client_api/models/memory_item.py
+hindsight_client_api/models/operation_response.py
+hindsight_client_api/models/operations_list_response.py
 hindsight_client_api/models/recall_request.py
 hindsight_client_api/models/recall_response.py
 hindsight_client_api/models/recall_result.py
@@ -84,44 +55,4 @@ hindsight_client_api/models/update_disposition_request.py
 hindsight_client_api/models/validation_error.py
 hindsight_client_api/models/validation_error_loc_inner.py
 hindsight_client_api/rest.py
-hindsight_client_api/test/__init__.py
-hindsight_client_api/test/test_add_background_request.py
-hindsight_client_api/test/test_background_response.py
-hindsight_client_api/test/test_bank_list_item.py
-hindsight_client_api/test/test_bank_list_response.py
-hindsight_client_api/test/test_bank_profile_response.py
-hindsight_client_api/test/test_budget.py
-hindsight_client_api/test/test_chunk_data.py
-hindsight_client_api/test/test_chunk_include_options.py
-hindsight_client_api/test/test_chunk_response.py
-hindsight_client_api/test/test_create_bank_request.py
-hindsight_client_api/test/test_default_api.py
-hindsight_client_api/test/test_delete_response.py
-hindsight_client_api/test/test_disposition_traits.py
-hindsight_client_api/test/test_document_response.py
-hindsight_client_api/test/test_entity_detail_response.py
-hindsight_client_api/test/test_entity_include_options.py
-hindsight_client_api/test/test_entity_list_item.py
-hindsight_client_api/test/test_entity_list_response.py
-hindsight_client_api/test/test_entity_observation_response.py
-hindsight_client_api/test/test_entity_state_response.py
-hindsight_client_api/test/test_graph_data_response.py
-hindsight_client_api/test/test_http_validation_error.py
-hindsight_client_api/test/test_include_options.py
-hindsight_client_api/test/test_list_documents_response.py
-hindsight_client_api/test/test_list_memory_units_response.py
-hindsight_client_api/test/test_memory_item.py
-hindsight_client_api/test/test_monitoring_api.py
-hindsight_client_api/test/test_recall_request.py
-hindsight_client_api/test/test_recall_response.py
-hindsight_client_api/test/test_recall_result.py
-hindsight_client_api/test/test_reflect_fact.py
-hindsight_client_api/test/test_reflect_include_options.py
-hindsight_client_api/test/test_reflect_request.py
-hindsight_client_api/test/test_reflect_response.py
-hindsight_client_api/test/test_retain_request.py
-hindsight_client_api/test/test_retain_response.py
-hindsight_client_api/test/test_update_disposition_request.py
-hindsight_client_api/test/test_validation_error.py
-hindsight_client_api/test/test_validation_error_loc_inner.py
 hindsight_client_api_README.md
@@ -1 +1 @@
-7.18.0-SNAPSHOT
+7.10.0
@@ -10,7 +10,7 @@ from typing import Optional, List, Dict, Any
 from datetime import datetime

 import hindsight_client_api
-from hindsight_client_api.api import default_api
+from hindsight_client_api.api import memory_api, banks_api
 from hindsight_client_api.models import (
    recall_request,
    retain_request,
@@ -44,8 +44,12 @@ class Hindsight:
        ```python
        from hindsight_client import Hindsight

+        # Without authentication
        client = Hindsight(base_url="http://localhost:8888")

+        # With API key authentication
+        client = Hindsight(base_url="http://localhost:8888", api_key="your-api-key")
+
        # Store a memory
        client.retain(bank_id="alice", content="Alice loves AI")

@@ -59,17 +63,21 @@ class Hindsight:
        ```
    """

-    def __init__(self, base_url: str, timeout: float = 30.0):
+    def __init__(self, base_url: str, api_key: Optional[str] = None, timeout: float = 30.0):
        """
        Initialize the Hindsight client.

        Args:
            base_url: The base URL of the Hindsight API server
+            api_key: Optional API key for authentication (sent as Bearer token)
            timeout: Request timeout in seconds (default: 30.0)
        """
-        config = hindsight_client_api.Configuration(host=base_url)
+        config = hindsight_client_api.Configuration(host=base_url, access_token=api_key)
        self._api_client = hindsight_client_api.ApiClient(config)
-        self._api = default_api.DefaultApi(self._api_client)
+        if api_key:
+            self._api_client.set_default_header("Authorization", f"Bearer {api_key}")
+        self._memory_api = memory_api.MemoryApi(self._api_client)
+        self._banks_api = banks_api.BanksApi(self._api_client)

    def __enter__(self):
        """Context manager entry."""
@@ -80,9 +88,21 @@ class Hindsight:
        self.close()

    def close(self):
-        """Close the API client."""
+        """Close the API client (sync version - use aclose() in async code)."""
        if self._api_client:
-            _run_async(self._api_client.close())
+            try:
+                loop = asyncio.get_running_loop()
+                # We're in an async context - schedule but don't wait
+                # The caller should use aclose() instead
+                loop.create_task(self._api_client.close())
+            except RuntimeError:
+                # No running loop - safe to run synchronously
+                _run_async(self._api_client.close())
+
+    async def aclose(self):
+        """Close the API client (async version)."""
+        if self._api_client:
+            await self._api_client.close()

    # Simplified methods for main operations

@@ -94,6 +114,7 @@ class Hindsight:
        context: Optional[str] = None,
        document_id: Optional[str] = None,
        metadata: Optional[Dict[str, str]] = None,
+        entities: Optional[List[Dict[str, str]]] = None,
    ) -> RetainResponse:
        """
        Store a single memory (simplified interface).
@@ -105,13 +126,14 @@ class Hindsight:
            context: Optional context description
            document_id: Optional document ID for grouping
            metadata: Optional user-defined metadata
+            entities: Optional list of entities [{"text": "...", "type": "..."}]

        Returns:
            RetainResponse with success status
        """
        return self.retain_batch(
            bank_id=bank_id,
-            items=[{"content": content, "timestamp": timestamp, "context": context, "metadata": metadata}],
+            items=[{"content": content, "timestamp": timestamp, "context": context, "metadata": metadata, "entities": entities}],
            document_id=document_id,
        )

@@ -127,31 +149,41 @@ class Hindsight:

        Args:
            bank_id: The memory bank ID
-            items: List of memory items with 'content' and optional 'timestamp', 'context', 'metadata', 'document_id'
+            items: List of memory items with 'content' and optional 'timestamp', 'context', 'metadata', 'document_id', 'entities'
            document_id: Optional document ID for grouping memories (applied to items that don't have their own)
            retain_async: If True, process asynchronously in background (default: False)

        Returns:
            RetainResponse with success status and item count
        """
-        memory_items = [
-            memory_item.MemoryItem(
-                content=item["content"],
-                timestamp=item.get("timestamp"),
-                context=item.get("context"),
-                metadata=item.get("metadata"),
-                # Use item's document_id if provided, otherwise fall back to batch-level document_id
-                document_id=item.get("document_id") or document_id,
+        from hindsight_client_api.models.entity_input import EntityInput
+
+        memory_items = []
+        for item in items:
+            entities = None
+            if item.get("entities"):
+                entities = [
+                    EntityInput(text=e["text"], type=e.get("type"))
+                    for e in item["entities"]
+                ]
+            memory_items.append(
+                memory_item.MemoryItem(
+                    content=item["content"],
+                    timestamp=item.get("timestamp"),
+                    context=item.get("context"),
+                    metadata=item.get("metadata"),
+                    # Use item's document_id if provided, otherwise fall back to batch-level document_id
+                    document_id=item.get("document_id") or document_id,
+                    entities=entities,
+                )
            )
-            for item in items
-        ]

        request_obj = retain_request.RetainRequest(
            items=memory_items,
            async_=retain_async,
        )

-        return _run_async(self._api.retain_memories(bank_id, request_obj))
+        return _run_async(self._memory_api.retain_memories(bank_id, request_obj))

    def recall(
        self,
@@ -203,7 +235,7 @@ class Hindsight:
            include=include_opts,
        )

-        return _run_async(self._api.recall_memories(bank_id, request_obj))
+        return _run_async(self._memory_api.recall_memories(bank_id, request_obj))

    def reflect(
        self,
@@ -211,6 +243,8 @@ class Hindsight:
        query: str,
        budget: str = "low",
        context: Optional[str] = None,
+        max_tokens: Optional[int] = None,
+        response_schema: Optional[Dict[str, Any]] = None,
    ) -> ReflectResponse:
        """
        Generate a contextual answer based on bank identity and memories.
@@ -220,17 +254,24 @@ class Hindsight:
            query: The question or prompt
            budget: Budget level for reflection - "low", "mid", or "high" (default: "low")
            context: Optional additional context
+            max_tokens: Maximum tokens for the response (server default: 4096)
+            response_schema: Optional JSON Schema for structured output. When provided,
+                the response will include a 'structured_output' field with the LLM
+                response parsed according to this schema.

        Returns:
-            ReflectResponse with answer text and optionally facts used
+            ReflectResponse with answer text, optionally facts used, and optionally
+            structured_output if response_schema was provided
        """
        request_obj = reflect_request.ReflectRequest(
            query=query,
            budget=budget,
            context=context,
+            max_tokens=max_tokens,
+            response_schema=response_schema,
        )

-        return _run_async(self._api.reflect(bank_id, request_obj))
+        return _run_async(self._memory_api.reflect(bank_id, request_obj))

    def list_memories(
        self,
@@ -241,7 +282,7 @@ class Hindsight:
        offset: int = 0,
    ) -> ListMemoryUnitsResponse:
        """List memory units with pagination."""
-        return _run_async(self._api.list_memories(
+        return _run_async(self._memory_api.list_memories(
            bank_id=bank_id,
            type=type,
            q=search_query,
@@ -269,7 +310,7 @@ class Hindsight:
            disposition=disposition_obj,
        )

-        return _run_async(self._api.create_or_update_bank(bank_id, request_obj))
+        return _run_async(self._banks_api.create_or_update_bank(bank_id, request_obj))

    # Async methods (native async, no _run_async wrapper)

@@ -285,31 +326,41 @@ class Hindsight:

        Args:
            bank_id: The memory bank ID
-            items: List of memory items with 'content' and optional 'timestamp', 'context', 'metadata', 'document_id'
+            items: List of memory items with 'content' and optional 'timestamp', 'context', 'metadata', 'document_id', 'entities'
            document_id: Optional document ID for grouping memories (applied to items that don't have their own)
            retain_async: If True, process asynchronously in background (default: False)

        Returns:
            RetainResponse with success status and item count
        """
-        memory_items = [
-            memory_item.MemoryItem(
-                content=item["content"],
-                timestamp=item.get("timestamp"),
-                context=item.get("context"),
-                metadata=item.get("metadata"),
-                # Use item's document_id if provided, otherwise fall back to batch-level document_id
-                document_id=item.get("document_id") or document_id,
+        from hindsight_client_api.models.entity_input import EntityInput
+
+        memory_items = []
+        for item in items:
+            entities = None
+            if item.get("entities"):
+                entities = [
+                    EntityInput(text=e["text"], type=e.get("type"))
+                    for e in item["entities"]
+                ]
+            memory_items.append(
+                memory_item.MemoryItem(
+                    content=item["content"],
+                    timestamp=item.get("timestamp"),
+                    context=item.get("context"),
+                    metadata=item.get("metadata"),
+                    # Use item's document_id if provided, otherwise fall back to batch-level document_id
+                    document_id=item.get("document_id") or document_id,
+                    entities=entities,
+                )
            )
-            for item in items
-        ]

        request_obj = retain_request.RetainRequest(
            items=memory_items,
            async_=retain_async,
        )

-        return await self._api.retain_memories(bank_id, request_obj)
+        return await self._memory_api.retain_memories(bank_id, request_obj)

    async def aretain(
        self,
@@ -319,6 +370,7 @@ class Hindsight:
        context: Optional[str] = None,
        document_id: Optional[str] = None,
        metadata: Optional[Dict[str, str]] = None,
+        entities: Optional[List[Dict[str, str]]] = None,
    ) -> RetainResponse:
        """
        Store a single memory (async).
@@ -330,13 +382,14 @@ class Hindsight:
            context: Optional context description
            document_id: Optional document ID for grouping
            metadata: Optional user-defined metadata
+            entities: Optional list of entities [{"text": "...", "type": "..."}]

        Returns:
            RetainResponse with success status
        """
        return await self.aretain_batch(
            bank_id=bank_id,
-            items=[{"content": content, "timestamp": timestamp, "context": context, "metadata": metadata}],
+            items=[{"content": content, "timestamp": timestamp, "context": context, "metadata": metadata, "entities": entities}],
            document_id=document_id,
        )

@@ -369,7 +422,7 @@ class Hindsight:
            trace=False,
        )

-        response = await self._api.recall_memories(bank_id, request_obj)
+        response = await self._memory_api.recall_memories(bank_id, request_obj)
        return response.results if hasattr(response, 'results') else []

    async def areflect(
@@ -397,4 +450,4 @@ class Hindsight:
            context=context,
        )

-        return await self._api.reflect(bank_id, request_obj)
+        return await self._memory_api.reflect(bank_id, request_obj)
@@ -7,7 +7,7 @@

    HTTP API for Hindsight

-    The version of the OpenAPI document: 1.0.0
+    The version of the OpenAPI document: 0.1.0
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
@@ -16,109 +16,66 @@

 __version__ = "0.0.7"

-# Define package exports
-__all__ = [
-    "MonitoringApi",
-    "DefaultApi",
-    "ApiResponse",
-    "ApiClient",
-    "Configuration",
-    "OpenApiException",
-    "ApiTypeError",
-    "ApiValueError",
-    "ApiKeyError",
-    "ApiAttributeError",
-    "ApiException",
-    "AddBackgroundRequest",
-    "BackgroundResponse",
-    "BankListItem",
-    "BankListResponse",
-    "BankProfileResponse",
-    "Budget",
-    "ChunkData",
-    "ChunkIncludeOptions",
-    "ChunkResponse",
-    "CreateBankRequest",
-    "DeleteResponse",
-    "DispositionTraits",
-    "DocumentResponse",
-    "EntityDetailResponse",
-    "EntityIncludeOptions",
-    "EntityListItem",
-    "EntityListResponse",
-    "EntityObservationResponse",
-    "EntityStateResponse",
-    "GraphDataResponse",
-    "HTTPValidationError",
-    "IncludeOptions",
-    "ListDocumentsResponse",
-    "ListMemoryUnitsResponse",
-    "MemoryItem",
-    "RecallRequest",
-    "RecallResponse",
-    "RecallResult",
-    "ReflectFact",
-    "ReflectIncludeOptions",
-    "ReflectRequest",
-    "ReflectResponse",
-    "RetainRequest",
-    "RetainResponse",
-    "UpdateDispositionRequest",
-    "ValidationError",
-    "ValidationErrorLocInner",
-]
-
 # import apis into sdk package
-from hindsight_client_api.api.monitoring_api import MonitoringApi as MonitoringApi
-from hindsight_client_api.api.default_api import DefaultApi as DefaultApi
+from hindsight_client_api.api.banks_api import BanksApi
+from hindsight_client_api.api.documents_api import DocumentsApi
+from hindsight_client_api.api.entities_api import EntitiesApi
+from hindsight_client_api.api.memory_api import MemoryApi
+from hindsight_client_api.api.monitoring_api import MonitoringApi
+from hindsight_client_api.api.operations_api import OperationsApi

 # import ApiClient
-from hindsight_client_api.api_response import ApiResponse as ApiResponse
-from hindsight_client_api.api_client import ApiClient as ApiClient
-from hindsight_client_api.configuration import Configuration as Configuration
-from hindsight_client_api.exceptions import OpenApiException as OpenApiException
-from hindsight_client_api.exceptions import ApiTypeError as ApiTypeError
-from hindsight_client_api.exceptions import ApiValueError as ApiValueError
-from hindsight_client_api.exceptions import ApiKeyError as ApiKeyError
-from hindsight_client_api.exceptions import ApiAttributeError as ApiAttributeError
-from hindsight_client_api.exceptions import ApiException as ApiException
+from hindsight_client_api.api_response import ApiResponse
+from hindsight_client_api.api_client import ApiClient
+from hindsight_client_api.configuration import Configuration
+from hindsight_client_api.exceptions import OpenApiException
+from hindsight_client_api.exceptions import ApiTypeError
+from hindsight_client_api.exceptions import ApiValueError
+from hindsight_client_api.exceptions import ApiKeyError
+from hindsight_client_api.exceptions import ApiAttributeError
+from hindsight_client_api.exceptions import ApiException

 # import models into sdk package
-from hindsight_client_api.models.add_background_request import AddBackgroundRequest as AddBackgroundRequest
-from hindsight_client_api.models.background_response import BackgroundResponse as BackgroundResponse
-from hindsight_client_api.models.bank_list_item import BankListItem as BankListItem
-from hindsight_client_api.models.bank_list_response import BankListResponse as BankListResponse
-from hindsight_client_api.models.bank_profile_response import BankProfileResponse as BankProfileResponse
-from hindsight_client_api.models.budget import Budget as Budget
-from hindsight_client_api.models.chunk_data import ChunkData as ChunkData
-from hindsight_client_api.models.chunk_include_options import ChunkIncludeOptions as ChunkIncludeOptions
-from hindsight_client_api.models.chunk_response import ChunkResponse as ChunkResponse
-from hindsight_client_api.models.create_bank_request import CreateBankRequest as CreateBankRequest
-from hindsight_client_api.models.delete_response import DeleteResponse as DeleteResponse
-from hindsight_client_api.models.disposition_traits import DispositionTraits as DispositionTraits
-from hindsight_client_api.models.document_response import DocumentResponse as DocumentResponse
-from hindsight_client_api.models.entity_detail_response import EntityDetailResponse as EntityDetailResponse
-from hindsight_client_api.models.entity_include_options import EntityIncludeOptions as EntityIncludeOptions
-from hindsight_client_api.models.entity_list_item import EntityListItem as EntityListItem
-from hindsight_client_api.models.entity_list_response import EntityListResponse as EntityListResponse
-from hindsight_client_api.models.entity_observation_response import EntityObservationResponse as EntityObservationResponse
-from hindsight_client_api.models.entity_state_response import EntityStateResponse as EntityStateResponse
-from hindsight_client_api.models.graph_data_response import GraphDataResponse as GraphDataResponse
-from hindsight_client_api.models.http_validation_error import HTTPValidationError as HTTPValidationError
-from hindsight_client_api.models.include_options import IncludeOptions as IncludeOptions
-from hindsight_client_api.models.list_documents_response import ListDocumentsResponse as ListDocumentsResponse
-from hindsight_client_api.models.list_memory_units_response import ListMemoryUnitsResponse as ListMemoryUnitsResponse
-from hindsight_client_api.models.memory_item import MemoryItem as MemoryItem
-from hindsight_client_api.models.recall_request import RecallRequest as RecallRequest
-from hindsight_client_api.models.recall_response import RecallResponse as RecallResponse
-from hindsight_client_api.models.recall_result import RecallResult as RecallResult
-from hindsight_client_api.models.reflect_fact import ReflectFact as ReflectFact
-from hindsight_client_api.models.reflect_include_options import ReflectIncludeOptions as ReflectIncludeOptions
-from hindsight_client_api.models.reflect_request import ReflectRequest as ReflectRequest
-from hindsight_client_api.models.reflect_response import ReflectResponse as ReflectResponse
-from hindsight_client_api.models.retain_request import RetainRequest as RetainRequest
-from hindsight_client_api.models.retain_response import RetainResponse as RetainResponse
-from hindsight_client_api.models.update_disposition_request import UpdateDispositionRequest as UpdateDispositionRequest
-from hindsight_client_api.models.validation_error import ValidationError as ValidationError
-from hindsight_client_api.models.validation_error_loc_inner import ValidationErrorLocInner as ValidationErrorLocInner
-
+from hindsight_client_api.models.add_background_request import AddBackgroundRequest
+from hindsight_client_api.models.background_response import BackgroundResponse
+from hindsight_client_api.models.bank_list_item import BankListItem
+from hindsight_client_api.models.bank_list_response import BankListResponse
+from hindsight_client_api.models.bank_profile_response import BankProfileResponse
+from hindsight_client_api.models.bank_stats_response import BankStatsResponse
+from hindsight_client_api.models.budget import Budget
+from hindsight_client_api.models.cancel_operation_response import CancelOperationResponse
+from hindsight_client_api.models.chunk_data import ChunkData
+from hindsight_client_api.models.chunk_include_options import ChunkIncludeOptions
+from hindsight_client_api.models.chunk_response import ChunkResponse
+from hindsight_client_api.models.create_bank_request import CreateBankRequest
+from hindsight_client_api.models.delete_document_response import DeleteDocumentResponse
+from hindsight_client_api.models.delete_response import DeleteResponse
+from hindsight_client_api.models.disposition_traits import DispositionTraits
+from hindsight_client_api.models.document_response import DocumentResponse
+from hindsight_client_api.models.entity_detail_response import EntityDetailResponse
+from hindsight_client_api.models.entity_include_options import EntityIncludeOptions
+from hindsight_client_api.models.entity_input import EntityInput
+from hindsight_client_api.models.entity_list_item import EntityListItem
+from hindsight_client_api.models.entity_list_response import EntityListResponse
+from hindsight_client_api.models.entity_observation_response import EntityObservationResponse
+from hindsight_client_api.models.entity_state_response import EntityStateResponse
+from hindsight_client_api.models.graph_data_response import GraphDataResponse
+from hindsight_client_api.models.http_validation_error import HTTPValidationError
+from hindsight_client_api.models.include_options import IncludeOptions
+from hindsight_client_api.models.list_documents_response import ListDocumentsResponse
+from hindsight_client_api.models.list_memory_units_response import ListMemoryUnitsResponse
+from hindsight_client_api.models.memory_item import MemoryItem
+from hindsight_client_api.models.operation_response import OperationResponse
+from hindsight_client_api.models.operations_list_response import OperationsListResponse
+from hindsight_client_api.models.recall_request import RecallRequest
+from hindsight_client_api.models.recall_response import RecallResponse
+from hindsight_client_api.models.recall_result import RecallResult
+from hindsight_client_api.models.reflect_fact import ReflectFact
+from hindsight_client_api.models.reflect_include_options import ReflectIncludeOptions
+from hindsight_client_api.models.reflect_request import ReflectRequest
+from hindsight_client_api.models.reflect_response import ReflectResponse
+from hindsight_client_api.models.retain_request import RetainRequest
+from hindsight_client_api.models.retain_response import RetainResponse
+from hindsight_client_api.models.update_disposition_request import UpdateDispositionRequest
+from hindsight_client_api.models.validation_error import ValidationError
+from hindsight_client_api.models.validation_error_loc_inner import ValidationErrorLocInner
--- a/Show More
+++ b/Show More