fix: batch queries on recall

fix: improve mpfp retrieval (#146 )
2026-01-13 11:54:54 +01:00 · 2026-01-13 11:40:48 +01:00 · 2026-01-12 18:58:05 +01:00 · 2026-01-12 18:50:53 +01:00 · 2026-01-09 16:43:31 +01:00 · 2026-01-09 16:43:00 +01:00
495 changed files with 53937 additions and 36059 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 +1,27 @@
+#!/bin/bash
+# Pre-commit hook - runs all scripts in scripts/hooks/
+
+set -e
+
+REPO_ROOT="$(git rev-parse --show-toplevel)"
+HOOKS_DIR="$REPO_ROOT/scripts/hooks"
+
+if [ ! -d "$HOOKS_DIR" ]; then
+    exit 0
+fi
+
+echo ""
+echo "=== Running pre-commit hooks ==="
+echo ""
+
+# Run all executable scripts in hooks directory
+for hook in "$HOOKS_DIR"/*.sh; do
+    if [ -x "$hook" ]; then
+        echo "[hook] $(basename "$hook")"
+        (cd "$REPO_ROOT" && "$hook")
+    fi
+done
+
+echo ""
+echo "=== Pre-commit hooks completed ==="
+echo ""
@@ -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
@@ -27,7 +27,9 @@ jobs:
          node-version: 20
          cache: npm
          cache-dependency-path: package-lock.json
+      - uses: astral-sh/setup-uv@v4
      - run: npm ci --workspace=hindsight-docs
+      - run: uv run generate-llms-full
      - run: npm run build --workspace=hindsight-docs
      - uses: actions/upload-pages-artifact@v3
        with:
@@ -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
@@ -38,6 +40,29 @@ jobs:
      working-directory: ./${{ matrix.path }}
      run: uv build

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

@@ -57,6 +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

@@ -98,6 +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: ${{ secrets.GITHUB_TOKEN }}
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Download CLI artifact
+      uses: actions/download-artifact@v4
+      with:
+        name: hindsight-cli
+        path: /tmp/cli
+
+    - name: Make CLI executable
+      run: chmod +x /tmp/cli/hindsight
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Build API
+      working-directory: ./hindsight-api
+      run: uv build
+
+    - name: Install API dependencies
+      working-directory: ./hindsight-api
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
+
+    - name: Create .env file
+      run: |
+        cat > .env << EOF
+        HINDSIGHT_API_LLM_PROVIDER=${{ 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

@@ -130,7 +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
@@ -148,6 +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
@@ -157,6 +325,8 @@ jobs:
      GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
      GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
+      HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -182,7 +352,7 @@ jobs:

    - name: Install dependencies
      working-directory: ./hindsight-api
-      run: uv sync --extra test --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --extra test --no-install-project --index-strategy unsafe-best-match

    - name: Cache HuggingFace models
      uses: actions/cache@v4
@@ -243,11 +413,11 @@ jobs:

    - name: Install client test dependencies
      working-directory: ./hindsight-clients/python
-      run: uv sync --extra test --index-strategy unsafe-best-match
+      run: uv sync --frozen --extra test --index-strategy unsafe-best-match

    - name: Install API dependencies
      working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match

    - name: Create .env file
      run: |
@@ -320,7 +490,7 @@ jobs:

    - name: Install API dependencies
      working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match

    - name: Install TypeScript client dependencies
      working-directory: ./hindsight-clients/typescript
@@ -408,7 +578,7 @@ jobs:

    - name: Install API dependencies
      working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match

    - name: Create .env file
      run: |
@@ -445,6 +615,97 @@ 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 --frozen --no-install-project --index-strategy unsafe-best-match
+
+    - name: Install integration test dependencies
+      working-directory: ./hindsight-integration-tests
+      run: uv sync --frozen
+
+    - 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

@@ -468,8 +729,232 @@ jobs:

    - name: Install dependencies
      working-directory: ./hindsight-integrations/litellm
-      run: uv sync --extra dev
+      run: uv sync --frozen --extra dev

    - 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 --frozen --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: ${{ secrets.GITHUB_TOKEN }}
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Download CLI artifact
+      uses: actions/download-artifact@v4
+      with:
+        name: hindsight-cli
+        path: /usr/local/bin
+
+    - name: Make CLI executable
+      run: chmod +x /usr/local/bin/hindsight
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '20'
+        cache: 'npm'
+        cache-dependency-path: package-lock.json
+
+    - name: Build and install API
+      working-directory: ./hindsight-api
+      run: |
+        uv build
+        uv sync --frozen --no-install-project --index-strategy unsafe-best-match
+
+    - name: Install Python client dependencies
+      working-directory: ./hindsight-clients/python
+      run: uv sync --frozen --extra test --index-strategy unsafe-best-match
+
+    - name: Install TypeScript client
+      run: |
+        npm ci --workspace=hindsight-clients/typescript
+        npm run build --workspace=hindsight-clients/typescript
+
+    - name: Create .env file
+      run: |
+        cat > .env << EOF
+        HINDSIGHT_API_LLM_PROVIDER=${{ 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 --frozen --index-strategy unsafe-best-match
+        cd ../hindsight-api && uv sync --frozen --index-strategy unsafe-best-match
+        cd ../hindsight-embed && uv sync --frozen --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"
@@ -5,15 +5,18 @@ build/
 dist/
 wheels/
 *.egg-info
-
+.mcp.json
+.osgrep
 # Virtual environments
 .venv

 # Node
 node_modules/

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

 # IDE
 .idea/
@@ -24,6 +27,10 @@ node_modules/
 # NLTK data (will be downloaded automatically)
 nltk_data/

+# Monitoring stack (Prometheus/Grafana binaries and data)
+.monitoring/
+.pgbouncer
+
 # Large benchmark datasets (will be downloaded automatically)
 **/longmemeval_s_cleaned.json

@@ -32,8 +39,15 @@ logs/

 .DS_Store

+# Generated docs files
+hindsight-docs/static/llms-full.txt
+

 hindsight-dev/benchmarks/locomo/results/
 hindsight-dev/benchmarks/longmemeval/results/
 hindsight-cli/target
-hindsight-clients/rust/target
+hindsight-clients/rust/target
+.claude
+whats-next.md
+TASK.md
+CHANGELOG.md
@@ -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 +1,232 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Hindsight is an agent memory system that provides long-term memory for AI agents using biomimetic data structures. Memories are organized as:
+- **World facts**: General knowledge ("The sky is blue")
+- **Experience facts**: Personal experiences ("I visited Paris in 2023")
+- **Opinion facts**: Beliefs with confidence scores ("Paris is beautiful" - 0.9 confidence)
+- **Observations**: Complex mental models derived from reflection
+
+## Development Commands
+
+### API Server (Python/FastAPI)
+```bash
+# Start API server (loads .env automatically)
+./scripts/dev/start-api.sh
+
+# Run all tests (parallelized with pytest-xdist)
+cd hindsight-api && uv run pytest tests/
+
+# Run specific test file
+cd hindsight-api && uv run pytest tests/test_http_api_integration.py -v
+
+# Run single test function
+cd hindsight-api && uv run pytest tests/test_retain.py::test_retain_simple -v
+
+# Lint and format
+cd hindsight-api && uv run ruff check .
+cd hindsight-api && uv run ruff format .
+
+# Type checking (uses ty - extremely fast type checker from Astral)
+cd hindsight-api && uv run ty check hindsight_api/
+```
+
+### Control Plane (Next.js)
+```bash
+./scripts/dev/start-control-plane.sh
+# Or manually:
+cd hindsight-control-plane && npm run dev
+```
+
+### Documentation Site (Docusaurus)
+```bash
+./scripts/dev/start-docs.sh
+```
+
+### Generating Clients/OpenAPI
+```bash
+# Regenerate OpenAPI spec after API changes (REQUIRED after changing endpoints)
+./scripts/generate-openapi.sh
+
+# Regenerate all client SDKs (Python, TypeScript, Rust)
+./scripts/generate-clients.sh
+```
+
+### Benchmarks
+```bash
+./scripts/benchmarks/run-longmemeval.sh
+./scripts/benchmarks/run-locomo.sh
+./scripts/benchmarks/start-visualizer.sh  # View results at localhost:8001
+```
+
+## Architecture
+
+### Monorepo Structure
+- **hindsight-api/**: Core FastAPI server with memory engine (Python, uv)
+- **hindsight/**: Embedded Python bundle (hindsight-all package)
+- **hindsight-control-plane/**: Admin UI (Next.js, npm)
+- **hindsight-cli/**: CLI tool (Rust, cargo, uses progenitor for API client)
+- **hindsight-clients/**: Generated SDK clients (Python, TypeScript, Rust)
+- **hindsight-docs/**: Docusaurus documentation site
+- **hindsight-integrations/**: Framework integrations (LiteLLM, OpenAI)
+- **hindsight-dev/**: Development tools and benchmarks
+
+### Core Engine (hindsight-api/hindsight_api/engine/)
+- `memory_engine.py`: Main orchestrator (~170KB) for retain/recall/reflect operations
+- `llm_wrapper.py`: LLM abstraction supporting OpenAI, Anthropic, Gemini, Groq, Ollama, LM Studio
+- `embeddings.py`: Embedding generation (local sentence-transformers or TEI)
+- `cross_encoder.py`: Reranking (local or TEI)
+- `entity_resolver.py`: Entity extraction and normalization
+- `query_analyzer.py`: Query intent analysis
+
+**retain/**: Memory ingestion pipeline
+- `orchestrator.py`: Coordinates the retain flow
+- `fact_extraction.py`: LLM-based fact extraction from content
+- `link_utils.py`: Entity link creation and management
+
+**search/**: Multi-strategy retrieval
+- `retrieval.py`: Main retrieval orchestrator
+- `graph_retrieval.py`: Entity/relationship graph traversal
+- `mpfp_retrieval.py`: Multi-Path Fact Propagation retrieval
+- `fusion.py`: Reciprocal rank fusion for combining results
+- `reranking.py`: Cross-encoder reranking
+
+### API Layer (hindsight-api/hindsight_api/api/)
+- `http.py`: FastAPI HTTP routers (~80KB) for all REST endpoints
+- `mcp.py`: Model Context Protocol server implementation
+
+Main operations:
+- **Retain**: Store memories, extracts facts/entities/relationships
+- **Recall**: Retrieve memories via 4 parallel strategies (semantic, BM25, graph, temporal) + reranking
+- **Reflect**: Deep analysis forming new opinions/observations (disposition-aware)
+
+### Database
+PostgreSQL with pgvector. Schema managed via Alembic migrations in `hindsight-api/hindsight_api/alembic/`. Migrations run automatically on API startup.
+
+Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
+
+### Adding Database Migrations
+
+1. **Create a new migration file** in `hindsight-api/hindsight_api/alembic/versions/`:
+   - File name format: `<revision_id>_<description>.py` (e.g., `f1a2b3c4d5e6_add_new_index.py`)
+   - Use a unique hex revision ID (12 chars)
+   - Set `down_revision` to the previous migration's revision ID
+
+2. **Migration template**:
+   ```python
+   """Description of the migration
+
+   Revision ID: f1a2b3c4d5e6
+   Revises: <previous_revision_id>
+   Create Date: YYYY-MM-DD
+   """
+   from collections.abc import Sequence
+   from alembic import context, op
+
+   revision: str = "f1a2b3c4d5e6"
+   down_revision: str | Sequence[str] | None = "<previous_revision_id>"
+   branch_labels: str | Sequence[str] | None = None
+   depends_on: str | Sequence[str] | None = None
+
+   def _get_schema_prefix() -> str:
+       """Get schema prefix for table names (required for multi-tenant support)."""
+       schema = context.config.get_main_option("target_schema")
+       return f'"{schema}".' if schema else ""
+
+   def upgrade() -> None:
+       schema = _get_schema_prefix()
+       op.execute(f"CREATE INDEX ... ON {schema}table_name(...)")
+
+   def downgrade() -> None:
+       schema = _get_schema_prefix()
+       op.execute(f"DROP INDEX IF EXISTS {schema}index_name")
+   ```
+
+3. **Run migrations locally**:
+   ```bash
+   # Set database URL and run migrations
+   uv run hindsight-admin run-db-migration
+
+   # Run on a specific tenant schema
+   uv run hindsight-admin run-db-migration --schema tenant_xyz
+   ```
+
+## Key Conventions
+
+### Code Quality
+**Always run the lint script after making Python or TypeScript/Node changes:**
+```bash
+./scripts/hooks/lint.sh
+```
+This runs the same checks as the pre-commit hook (Ruff for Python, ESLint/Prettier for TypeScript).
+
+### Memory Banks
+- Each bank is an isolated memory store (like a "brain" for one user/agent)
+- Banks have dispositions (skepticism, literalism, empathy traits 1-5) affecting reflect
+- Banks can have background context
+- Bank isolation is strict - no cross-bank data leakage
+
+### API Design
+- All endpoints operate on a single bank per request
+- Multi-bank queries are client responsibility to orchestrate
+- Disposition traits only affect reflect, not recall
+
+### Python Style
+- Python 3.11+, type hints required
+- Async throughout (asyncpg, async FastAPI)
+- Pydantic models for request/response
+- Ruff for linting (line-length 120)
+- No Python files at project root - maintain clean directory structure
+
+### TypeScript Style
+- Next.js App Router for control plane
+- Tailwind CSS with shadcn/ui components
+
+### Adding New API Configuration Flags
+
+When adding a new environment variable configuration:
+
+1. **config.py** (`hindsight-api/hindsight_api/config.py`):
+   - Add `ENV_*` constant for the environment variable name
+   - Add `DEFAULT_*` constant for the default value
+   - Add field to `HindsightConfig` dataclass
+   - Add initialization in `from_env()` method
+
+2. **main.py** (`hindsight-api/hindsight_api/main.py`):
+   - Add field to the manual `HindsightConfig()` constructor call (search for "CLI override")
+
+3. **Use the config** in code:
+   ```python
+   from ...config import get_config
+   config = get_config()
+   value = config.your_new_field
+   ```
+
+4. **Documentation** (`hindsight-docs/docs/developer/configuration.md`):
+   - Add to appropriate section table with Variable, Description, Default
+
+## Environment Setup
+
+```bash
+cp .env.example .env
+# Edit .env with LLM API key
+
+# Python deps
+uv sync --directory hindsight-api/
+
+# Node deps (uses npm workspaces)
+npm install
+```
+
+Required env vars:
+- `HINDSIGHT_API_LLM_PROVIDER`: openai, anthropic, gemini, groq, ollama, lmstudio
+- `HINDSIGHT_API_LLM_API_KEY`: Your API key
+- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., o3-mini, claude-sonnet-4-20250514)
+
+Optional (uses local models by default):
+- `HINDSIGHT_API_EMBEDDINGS_PROVIDER`: local (default) or tei
+- `HINDSIGHT_API_RERANKER_PROVIDER`: local (default) or tei
+- `HINDSIGHT_API_DATABASE_URL`: External PostgreSQL (uses embedded pg0 by default)
@@ -51,7 +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
@@ -2,14 +2,13 @@

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

-[Documentation](https://vectorize-io.github.io/hindsight) • [Paper](#coming-soon) • [Examples](https://github.com/vectorize-io/hindsight-cookbook)
+[Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://vectorize.io/hindsight/cloud)

 [![CI](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml/badge.svg)](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![PyPI - hindsight-api](https://img.shields.io/pypi/v/hindsight-api?label=hindsight-api)](https://pypi.org/project/hindsight-api/)
-[![PyPI - hindsight-client](https://img.shields.io/pypi/v/hindsight-client?label=hindsight-client)](https://pypi.org/project/hindsight-client/)
-[![npm - @vectorize-io/hindsight-client](https://img.shields.io/npm/v/@vectorize-io/hindsight-client)](https://www.npmjs.com/package/@vectorize-io/hindsight-client)
 [![Slack Community](https://img.shields.io/badge/Slack-Join%20Community-4A154B?logo=slack)](https://join.slack.com/t/hindsight-space/shared_invite/zt-3klo21kua-VUCC_zHP5rIcXFB1_5yw6A)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/hindsight-api?label=PyPI)
+![NPM Downloads](https://img.shields.io/npm/dm/%40vectorize-io%2Fhindsight-client?logoColor=orange&label=NPM&color=blue&link=https%3A%2F%2Fwww.npmjs.com%2Fpackage%2F%40vectorize-io%2Fhindsight-client)


 </div>
@@ -18,7 +17,7 @@

 ## What is Hindsight?

-Hindsight™ is an agent memory system built to create smarter agents that learn over time. It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph.
+Hindsight™ is an agent memory system built to create smarter agents that learn over time. It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.

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

@@ -26,27 +25,48 @@ Hindsight addresses common challenges that have frustrated AI engineers building
 - **Hallucinations:** Long term memory can be seeded with external knowledge to ground agent behavior in reliable sources to augment training data.
 - **Cognitive Overload:** As workflows get complex, retrievals, tool calls, user messages and agent responses can grow to fill the context window leading to context rot. Short term memory optimization allows agents to reduce tokens and focus context by removing irrelevant details.

-## How Hindsight Works
+## How is Hindsight Different From Other Memory Systems?

 ![Overview](./hindsight-docs/static/img/hindsight-overview.webp)

-Hindsight organizes memory into four networks to mimic the way human memory works:
+Most agent memory implementation rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:

 - **World:** Facts about the world ("The stove gets hot")
 - **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
 - **Opinion:** Beliefs with confidence scores ("I shouldn't touch the stove again" - .99 confidence)
 - **Observation:** Complex mental models derived by reflecting on facts and experiences ("Curling irons, ovens, and fire are also hot. I shouldn't touch those either.")

+Memories in Hindsight are stored in banks (i.e. memory banks). When memories are added to Hindsight, they are pushed into either the world facts or experiences memory pathway. They are then represented as a combination of entities, relationships, and time series with sparse/dense vector representations to aid in later recall.
+
 Hindsight provides three simple methods to interact with the system:

 - **Retain:** Provide information to Hindsight that you want it to remember
 - **Recall:** Retrieve memories from Hindsight
 - **Reflect:** Reflect on memories and experiences to generate new observations and insights from existing memories.

-Memories in Hindsight are stored in banks (e.g. memory banks). When memories are retained, they are transformed to construct a series of search indexes, time series data, and entity/relationship graphs.
+### Agent Memory That Learns
+
+A key goal of Hindsight is to build agent memory that enables agents to learn and improve over time. This is the role of the `reflect` operation which provides the agent to form broader opinions and observations over time.
+
+For example, imagine a product support agent that is helping a user troubleshoot a problem. It uses a `search-documentation` tool it found on an MCP server. Later in the conversation, the agent discovers that the documentation returned from the tool wasn't for the product the user was asking about. The agent now has an experience in its memory bank. And just like humans, we want that agent to learn from its experience.
+
+As the agent gains more experiences, `reflect` allows the agent to form observations about what worked, what didn't, and what to do differently the next time it encounters a similar task.

 ---

+## Memory Performance & Accuracy
+
+Hindsight has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational
+AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of December 2025 is shown here:
+
+![Overview](./hindsight-docs/static/img/hindsight-bench.jpg)
+
+The benchmark performance data for Hindsight and GPT-4o (full context) have been reproduced by research collaborators at the Virginia Tech [Sanghani Center for Artificial Intelligence and Data Analytics](https://sanghani.cs.vt.edu/) and The Washington Post. Other scores are self-reported by software vendors.
+
+A thorough examination of the techniques implemented in Hindsight and detailed breakdowns of benchmark performance are [available on arXiv](https://arxiv.org/abs/2512.12818). This research is currently being prepared for conference submission and the wider peer review process.
+
+The benchmark results from this research can be inspected in our [visual benchmark explorer](https://hindsight-benchmarks.vercel.app). As additional improvements are made to Hindsight, new benchmark data will be available for review using this same tool.
+
 ## Quick Start

 ### Docker (recommended)
@@ -61,6 +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

@@ -223,6 +245,10 @@ client.reflect(bank_id="my-bank", query="What should I know about Alice?")
 - [Slack](https://join.slack.com/t/hindsight-space/shared_invite/zt-3klo21kua-VUCC_zHP5rIcXFB1_5yw6A)
 - [GitHub Issues](https://github.com/vectorize-io/hindsight/issues)

+---
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=vectorize-io/hindsight&type=date&legend=top-left)](https://www.star-history.com/#vectorize-io/hindsight&type=date&legend=top-left)
 ---

 ## Contributing
@@ -2,16 +2,24 @@
 # 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)
+#   INCLUDE_LOCAL_MODELS=true/false - Include local ML models for embeddings/reranking (default: true)
+#                                     Set to false when using external providers (TEI, OpenAI, Cohere)
+#   PRELOAD_ML_MODELS=true/false   - Pre-download ML models during build (default: true)
+#                                     Only effective when INCLUDE_LOCAL_MODELS=true
 #
 # 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
+#   docker build -t hindsight --build-arg INCLUDE_LOCAL_MODELS=false .   # Skip local ML deps (for external providers)

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

 # =============================================================================
 # Stage: API Builder
@@ -19,6 +27,7 @@ ARG INCLUDE_CP=true
 FROM python:3.11-slim AS api-builder

 ARG INCLUDE_API
+ARG INCLUDE_LOCAL_MODELS
 RUN if [ "$INCLUDE_API" != "true" ]; then echo "Skipping API build" && exit 0; fi

 WORKDIR /app
@@ -37,6 +46,15 @@ COPY hindsight-api/README.md ./api/

 WORKDIR /app/api

+# Remove local ML model dependencies if INCLUDE_LOCAL_MODELS=false
+# This creates a smaller image when using external providers (TEI, OpenAI, Cohere)
+RUN if [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then \
+        echo "Removing local-models dependencies (sentence-transformers, torch, transformers)..." && \
+        sed -i '/"sentence-transformers/d' pyproject.toml && \
+        sed -i '/"transformers/d' pyproject.toml && \
+        sed -i '/"torch/d' pyproject.toml; \
+    fi
+
 # Sync dependencies (will create lock file if needed)
 RUN uv sync

@@ -60,8 +78,8 @@ WORKDIR /app
 COPY package.json package-lock.json ./
 COPY hindsight-clients/typescript/ ./hindsight-clients/typescript/

-# Install and build SDK using workspace
-RUN npm ci -w @vectorize-io/hindsight-client
+# Install and build SDK using workspace (--ignore-scripts skips git hooks setup)
+RUN npm ci --ignore-scripts -w @vectorize-io/hindsight-client
 RUN npm run build -w @vectorize-io/hindsight-client

 # =============================================================================
@@ -72,30 +90,48 @@ 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 +140,18 @@ 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 +161,26 @@ 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)
+# Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
+ARG PRELOAD_ML_MODELS
+ARG INCLUDE_LOCAL_MODELS
+RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_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')"; \
+    elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
+    else echo "Skipping ML model preload"; fi

 EXPOSE 8888

@@ -171,9 +205,9 @@ 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 +234,21 @@ 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 +259,9 @@ 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 +269,26 @@ 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)
+# Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
+ARG PRELOAD_ML_MODELS
+ARG INCLUDE_LOCAL_MODELS
+RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_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')"; \
+    elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
+    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: 0.1.5
-appVersion: "0.1.5"
+version: 0.2.1
+appVersion: "0.2.1"
 keywords:
  - ai
  - memory
@@ -110,3 +110,14 @@ API URL for control plane
 {{- define "hindsight.apiUrl" -}}
 {{- printf "http://%s-api:%d" (include "hindsight.fullname" .) (.Values.api.service.port | int) }}
 {{- end }}
+
+{{/*
+Get the name of the secret to use
+*/}}
+{{- define "hindsight.secretName" -}}
+{{- if .Values.existingSecret }}
+{{- .Values.existingSecret }}
+{{- else }}
+{{- printf "%s-secret" (include "hindsight.fullname" .) }}
+{{- end }}
+{{- end }}
@@ -15,7 +15,9 @@ spec:
  template:
    metadata:
      annotations:
+        {{- if not .Values.existingSecret }}
        checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
+        {{- end }}
        {{- with .Values.podAnnotations }}
        {{- toYaml . | nindent 8 }}
        {{- end }}
@@ -37,27 +39,36 @@ spec:
        - name: http
          containerPort: {{ .Values.api.service.targetPort }}
          protocol: TCP
+        {{- if .Values.existingSecret }}
+        envFrom:
+        - secretRef:
+            name: {{ .Values.existingSecret }}
+        {{- end }}
        env:
-        - name: HINDSIGHT_API_DATABASE_URL
-          value: {{ include "hindsight.databaseUrl" . | quote }}
+        {{- /* POSTGRES_PASSWORD must be defined before DATABASE_URL for $(VAR) interpolation */}}
        {{- if not .Values.postgresql.enabled }}
        - name: POSTGRES_PASSWORD
          valueFrom:
            secretKeyRef:
-              name: {{ include "hindsight.fullname" . }}-secret
+              name: {{ include "hindsight.secretName" . }}
              key: postgres-password
        {{- end }}
+        - name: HINDSIGHT_API_DATABASE_URL
+          value: {{ include "hindsight.databaseUrl" . | quote }}
        {{- range $key, $value := .Values.api.env }}
        - name: {{ $key }}
          value: {{ $value | quote }}
        {{- end }}
+        {{- /* Only use api.secrets when not using existingSecret (for chart-managed secrets) */}}
+        {{- if not .Values.existingSecret }}
        {{- range $key, $value := .Values.api.secrets }}
        - name: {{ $key }}
          valueFrom:
            secretKeyRef:
-              name: {{ include "hindsight.fullname" $ }}-secret
+              name: {{ include "hindsight.secretName" $ }}
              key: {{ $key }}
        {{- end }}
+        {{- end }}
        livenessProbe:
          {{- toYaml .Values.api.livenessProbe | nindent 10 }}
        readinessProbe:
@@ -15,7 +15,9 @@ spec:
  template:
    metadata:
      annotations:
+        {{- if not .Values.existingSecret }}
        checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
+        {{- end }}
        {{- with .Values.podAnnotations }}
        {{- toYaml . | nindent 8 }}
        {{- end }}
@@ -37,6 +39,11 @@ spec:
        - name: http
          containerPort: {{ .Values.controlPlane.service.targetPort }}
          protocol: TCP
+        {{- if .Values.existingSecret }}
+        envFrom:
+        - secretRef:
+            name: {{ .Values.existingSecret }}
+        {{- end }}
        env:
        - name: HINDSIGHT_CP_DATAPLANE_API_URL
          value: {{ include "hindsight.apiUrl" . | quote }}
@@ -44,13 +51,16 @@ spec:
        - name: {{ $key }}
          value: {{ $value | quote }}
        {{- end }}
+        {{- /* Only use controlPlane.secrets when not using existingSecret (for chart-managed secrets) */}}
+        {{- if not .Values.existingSecret }}
        {{- range $key, $value := .Values.controlPlane.secrets }}
        - name: {{ $key }}
          valueFrom:
            secretKeyRef:
-              name: {{ include "hindsight.fullname" $ }}-secret
+              name: {{ include "hindsight.secretName" $ }}
              key: {{ $key }}
        {{- end }}
+        {{- end }}
        livenessProbe:
          {{- toYaml .Values.controlPlane.livenessProbe | nindent 10 }}
        readinessProbe:
@@ -1,7 +1,8 @@
+{{- if not .Values.existingSecret }}
 apiVersion: v1
 kind: Secret
 metadata:
-  name: {{ include "hindsight.fullname" . }}-secret
+  name: {{ include "hindsight.secretName" . }}
  labels:
    {{- include "hindsight.labels" . | nindent 4 }}
 type: Opaque
@@ -15,3 +16,4 @@ data:
  {{- if and (not .Values.postgresql.enabled) .Values.postgresql.external.password }}
  postgres-password: {{ .Values.postgresql.external.password | b64enc | quote }}
  {{- end }}
+{{- end }}
@@ -3,6 +3,15 @@
 # Chart version - use this to set a consistent image tag across all components
 version: "0.1.1"

+# Use an existing secret instead of creating one from values
+# When set, all keys from this secret are injected as environment variables via envFrom
+# Required keys:
+#   - postgres-password: PostgreSQL password (when postgresql.enabled=false)
+# Optional keys (any key becomes an env var):
+#   - HINDSIGHT_API_LLM_API_KEY: API key for LLM provider
+#   - Any other env vars you want to inject
+# existingSecret: "my-hindsight-secret"
+
 # Global settings
 replicaCount: 1

@@ -1 +1,137 @@
-# Memory 
+# Hindsight API
+
+**Memory System for AI Agents** — Temporal + Semantic + Entity Memory Architecture using PostgreSQL with pgvector.
+
+Hindsight gives AI agents persistent memory that works like human memory: it stores facts, tracks entities and relationships, handles temporal reasoning ("what happened last spring?"), and forms opinions based on configurable disposition traits.
+
+## Installation
+
+```bash
+pip install hindsight-api
+```
+
+## Quick Start
+
+### Run the Server
+
+```bash
+# Set your LLM provider
+export HINDSIGHT_API_LLM_PROVIDER=openai
+export HINDSIGHT_API_LLM_API_KEY=sk-xxxxxxxxxxxx
+
+# Start the server (uses embedded PostgreSQL by default)
+hindsight-api
+```
+
+The server starts at http://localhost:8888 with:
+- REST API for memory operations
+- MCP server at `/mcp` for tool-use integration
+
+### Use the Python API
+
+```python
+from hindsight_api import MemoryEngine
+
+# Create and initialize the memory engine
+memory = MemoryEngine()
+await memory.initialize()
+
+# Create a memory bank for your agent
+bank = await memory.create_memory_bank(
+    name="my-assistant",
+    background="A helpful coding assistant"
+)
+
+# Store a memory
+await memory.retain(
+    memory_bank_id=bank.id,
+    content="The user prefers Python for data science projects"
+)
+
+# Recall memories
+results = await memory.recall(
+    memory_bank_id=bank.id,
+    query="What programming language does the user prefer?"
+)
+
+# Reflect with reasoning
+response = await memory.reflect(
+    memory_bank_id=bank.id,
+    query="Should I recommend Python or R for this ML project?"
+)
+```
+
+## CLI Options
+
+```bash
+hindsight-api --help
+
+# Common options
+hindsight-api --port 9000          # Custom port (default: 8888)
+hindsight-api --host 127.0.0.1     # Bind to localhost only
+hindsight-api --workers 4          # Multiple worker processes
+hindsight-api --log-level debug    # Verbose logging
+```
+
+## Configuration
+
+Configure via environment variables:
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `HINDSIGHT_API_DATABASE_URL` | PostgreSQL connection string | `pg0` (embedded) |
+| `HINDSIGHT_API_LLM_PROVIDER` | `openai`, `anthropic`, `gemini`, `groq`, `ollama`, `lmstudio` | `openai` |
+| `HINDSIGHT_API_LLM_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` |
+| `HINDSIGHT_API_PORT` | Server port | `8888` |
+
+### Example with External PostgreSQL
+
+```bash
+export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
+export HINDSIGHT_API_LLM_PROVIDER=groq
+export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
+
+hindsight-api
+```
+
+## Docker
+
+```bash
+docker run --rm -it -p 8888:8888 \
+  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
+  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
+  ghcr.io/vectorize-io/hindsight:latest
+```
+
+## MCP Server
+
+For local MCP integration without running the full API server:
+
+```bash
+hindsight-local-mcp
+```
+
+This runs a stdio-based MCP server that can be used directly with MCP-compatible clients.
+
+## Key Features
+
+- **Multi-Strategy Retrieval (TEMPR)** — Semantic, keyword, graph, and temporal search combined with RRF fusion
+- **Entity Graph** — Automatic entity extraction and relationship tracking
+- **Temporal Reasoning** — Native support for time-based queries
+- **Disposition Traits** — Configurable skepticism, literalism, and empathy influence opinion formation
+- **Three Memory Types** — World facts, bank actions, and formed opinions with confidence scores
+
+## Documentation
+
+Full documentation: [https://hindsight.vectorize.io](https://hindsight.vectorize.io)
+
+- [Installation Guide](https://hindsight.vectorize.io/developer/installation)
+- [Configuration Reference](https://hindsight.vectorize.io/developer/configuration)
+- [API Reference](https://hindsight.vectorize.io/api-reference)
+- [Python SDK](https://hindsight.vectorize.io/sdks/python)
+
+## License
+
+Apache 2.0
@@ -3,26 +3,29 @@ Memory System for AI Agents.

 Temporal + Semantic Memory Architecture using PostgreSQL with pgvector.
 """
+
+from .config import HindsightConfig, get_config
+from .engine.cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
+from .engine.embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
+from .engine.llm_wrapper import LLMConfig
 from .engine.memory_engine import MemoryEngine
 from .engine.search.trace import (
-    SearchTrace,
-    QueryInfo,
    EntryPoint,
-    NodeVisit,
-    WeightComponents,
    LinkInfo,
+    NodeVisit,
    PruningDecision,
-    SearchSummary,
+    QueryInfo,
    SearchPhaseMetrics,
+    SearchSummary,
+    SearchTrace,
+    WeightComponents,
 )
 from .engine.search.tracer import SearchTracer
-from .engine.embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
-from .engine.cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
-from .engine.llm_wrapper import LLMConfig
-from .config import HindsightConfig, get_config
+from .models import RequestContext

 __all__ = [
    "MemoryEngine",
+    "RequestContext",
    "HindsightConfig",
    "get_config",
    "SearchTrace",
@@ -0,0 +1 @@
+# Admin CLI for Hindsight
@@ -0,0 +1,252 @@
+"""
+Hindsight Admin CLI - backup and restore operations.
+"""
+
+import asyncio
+import io
+import json
+import logging
+import zipfile
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+import asyncpg
+import typer
+
+from ..config import HindsightConfig
+from ..pg0 import parse_pg0_url, resolve_database_url
+
+
+def _fq_table(table: str, schema: str) -> str:
+    """Get fully-qualified table name with schema prefix."""
+    return f"{schema}.{table}"
+
+
+# Setup logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(message)s",
+)
+logger = logging.getLogger(__name__)
+
+app = typer.Typer(name="hindsight-admin", help="Hindsight administrative commands")
+
+# Tables to backup/restore in dependency order
+# Import must happen in this order due to foreign key constraints
+BACKUP_TABLES = [
+    "banks",
+    "documents",
+    "entities",
+    "chunks",
+    "memory_units",
+    "unit_entities",
+    "entity_cooccurrences",
+    "memory_links",
+]
+
+MANIFEST_VERSION = "1"
+
+
+async def _backup(database_url: str, output_path: Path, schema: str = "public") -> dict[str, Any]:
+    """Backup all tables to a zip file using binary COPY protocol."""
+    conn = await asyncpg.connect(database_url)
+    try:
+        tables: dict[str, Any] = {}
+        manifest: dict[str, Any] = {
+            "version": MANIFEST_VERSION,
+            "created_at": datetime.now(timezone.utc).isoformat(),
+            "schema": schema,
+            "tables": tables,
+        }
+
+        # Use a transaction with REPEATABLE READ isolation to get a consistent
+        # snapshot across all tables. This prevents race conditions where
+        # entity_cooccurrences could reference entities created after the
+        # entities table was backed up.
+        async with conn.transaction(isolation="repeatable_read"):
+            with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zf:
+                for i, table in enumerate(BACKUP_TABLES, 1):
+                    typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] Backing up {table}...", nl=False)
+
+                    buffer = io.BytesIO()
+
+                    # Use binary COPY for exact type preservation
+                    # asyncpg requires schema_name as separate parameter
+                    await conn.copy_from_table(table, schema_name=schema, output=buffer, format="binary")
+
+                    data = buffer.getvalue()
+                    zf.writestr(f"{table}.bin", data)
+
+                    # Get row count for manifest
+                    qualified_table = _fq_table(table, schema)
+                    row_count = await conn.fetchval(f"SELECT COUNT(*) FROM {qualified_table}")
+                    tables[table] = {
+                        "rows": row_count,
+                        "size_bytes": len(data),
+                    }
+
+                    typer.echo(f" {row_count} rows")
+
+                zf.writestr("manifest.json", json.dumps(manifest, indent=2))
+
+        return manifest
+    finally:
+        await conn.close()
+
+
+async def _restore(database_url: str, input_path: Path, schema: str = "public") -> dict[str, Any]:
+    """Restore all tables from a zip file using binary COPY protocol."""
+    conn = await asyncpg.connect(database_url)
+    try:
+        with zipfile.ZipFile(input_path, "r") as zf:
+            # Read and validate manifest
+            manifest: dict[str, Any] = json.loads(zf.read("manifest.json"))
+            if manifest.get("version") != MANIFEST_VERSION:
+                raise ValueError(f"Unsupported backup version: {manifest.get('version')}")
+
+            # Use a transaction for atomic restore - either all tables are
+            # restored or none are, preventing partial/inconsistent state.
+            async with conn.transaction():
+                typer.echo("  Clearing existing data...")
+                # Truncate tables in reverse order (respects FK constraints)
+                for table in reversed(BACKUP_TABLES):
+                    qualified_table = _fq_table(table, schema)
+                    await conn.execute(f"TRUNCATE TABLE {qualified_table} CASCADE")
+
+                # Restore tables in forward order
+                for i, table in enumerate(BACKUP_TABLES, 1):
+                    filename = f"{table}.bin"
+                    if filename not in zf.namelist():
+                        typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] {table}: skipped (not in backup)")
+                        continue
+
+                    expected_rows = manifest["tables"].get(table, {}).get("rows", "?")
+                    typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] Restoring {table}... {expected_rows} rows")
+
+                    data = zf.read(filename)
+                    buffer = io.BytesIO(data)
+                    # asyncpg requires schema_name as separate parameter
+                    await conn.copy_to_table(table, schema_name=schema, source=buffer, format="binary")
+
+                # Refresh materialized view
+                typer.echo("  Refreshing materialized views...")
+                await conn.execute(f"REFRESH MATERIALIZED VIEW {_fq_table('memory_units_bm25', schema)}")
+
+        return manifest
+    finally:
+        await conn.close()
+
+
+async def _run_backup(db_url: str, output: Path, schema: str = "public") -> dict[str, Any]:
+    """Resolve database URL and run backup."""
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
+    if is_pg0:
+        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
+    resolved_url = await resolve_database_url(db_url)
+    return await _backup(resolved_url, output, schema)
+
+
+async def _run_restore(db_url: str, input_file: Path, schema: str = "public") -> dict[str, Any]:
+    """Resolve database URL and run restore."""
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
+    if is_pg0:
+        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
+    resolved_url = await resolve_database_url(db_url)
+    return await _restore(resolved_url, input_file, schema)
+
+
+@app.command()
+def backup(
+    output: Path = typer.Argument(..., help="Output file path (.zip)"),
+    schema: str = typer.Option("public", "--schema", "-s", help="Database schema to backup"),
+):
+    """Backup the Hindsight database to a zip file."""
+    config = HindsightConfig.from_env()
+
+    if not config.database_url:
+        typer.echo("Error: Database URL not configured.", err=True)
+        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
+        raise typer.Exit(1)
+
+    if output.suffix != ".zip":
+        output = output.with_suffix(".zip")
+
+    typer.echo(f"Backing up database (schema: {schema}) to {output}...")
+
+    manifest = asyncio.run(_run_backup(config.database_url, output, schema))
+
+    total_rows = sum(t["rows"] for t in manifest["tables"].values())
+    typer.echo(f"Backed up {total_rows} rows across {len(BACKUP_TABLES)} tables")
+    typer.echo(f"Backup saved to {output}")
+
+
+@app.command()
+def restore(
+    input_file: Path = typer.Argument(..., help="Input backup file (.zip)"),
+    schema: str = typer.Option("public", "--schema", "-s", help="Database schema to restore to"),
+    yes: bool = typer.Option(False, "--yes", "-y", help="Skip confirmation prompt"),
+):
+    """Restore the database from a backup file. WARNING: This deletes all existing data."""
+    config = HindsightConfig.from_env()
+
+    if not config.database_url:
+        typer.echo("Error: Database URL not configured.", err=True)
+        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
+        raise typer.Exit(1)
+
+    if not input_file.exists():
+        typer.echo(f"Error: File not found: {input_file}", err=True)
+        raise typer.Exit(1)
+
+    if not yes:
+        typer.confirm(
+            "This will DELETE all existing data and replace it with the backup. Continue?",
+            abort=True,
+        )
+
+    typer.echo(f"Restoring database (schema: {schema}) from {input_file}...")
+
+    manifest = asyncio.run(_run_restore(config.database_url, input_file, schema))
+
+    total_rows = sum(t["rows"] for t in manifest["tables"].values())
+    typer.echo(f"Restored {total_rows} rows across {len(BACKUP_TABLES)} tables")
+    typer.echo("Restore complete")
+
+
+async def _run_migration(db_url: str, schema: str = "public") -> None:
+    """Resolve database URL and run migrations."""
+    from ..migrations import run_migrations
+
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
+    if is_pg0:
+        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
+    resolved_url = await resolve_database_url(db_url)
+    run_migrations(resolved_url, schema=schema)
+
+
+@app.command(name="run-db-migration")
+def run_db_migration(
+    schema: str = typer.Option("public", "--schema", "-s", help="Database schema to run migrations on"),
+):
+    """Run database migrations to the latest version."""
+    config = HindsightConfig.from_env()
+
+    if not config.database_url:
+        typer.echo("Error: Database URL not configured.", err=True)
+        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
+        raise typer.Exit(1)
+
+    typer.echo(f"Running database migrations (schema: {schema})...")
+
+    asyncio.run(_run_migration(config.database_url, schema))
+
+    typer.echo("Database migrations completed successfully")
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()
@@ -2,20 +2,19 @@
 Alembic environment configuration for SQLAlchemy with pgvector.
 Uses synchronous psycopg2 driver for migrations to avoid pgbouncer issues.
 """
+
 import logging
 import os
-import sys
 from pathlib import Path

-from sqlalchemy import pool, engine_from_config
-from sqlalchemy.engine import Connection
-
 from alembic import context
 from dotenv import load_dotenv
+from sqlalchemy import engine_from_config, pool

 # Import your models here
 from hindsight_api.models import Base

+
 # Load environment variables based on HINDSIGHT_API_DATABASE_URL env var or default to local
 def load_env():
    """Load environment variables from .env"""
@@ -30,6 +29,7 @@ def load_env():
    if env_file.exists():
        load_dotenv(env_file)

+
 load_env()

 # this is the Alembic Config object, which provides
@@ -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.",
@@ -121,17 +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()
@@ -5,120 +5,150 @@ Revises:
 Create Date: 2025-11-27 11:54:19.228030

 """
-from typing import Sequence, Union

-from alembic import op
+from collections.abc import Sequence
+
 import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
+from alembic import op
 from pgvector.sqlalchemy import Vector
-
+from sqlalchemy.dialects import postgresql

 # revision identifiers, used by Alembic.
-revision: str = '5a366d414dce'
-down_revision: Union[str, Sequence[str], None] = None
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
+revision: str = "5a366d414dce"
+down_revision: str | Sequence[str] | None = None
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None


 def upgrade() -> None:
    """Upgrade schema - create all tables from scratch."""

    # Enable required extensions
-    op.execute('CREATE EXTENSION IF NOT EXISTS vector')
+    op.execute("CREATE EXTENSION IF NOT EXISTS vector")

    # Create banks table
    op.create_table(
-        'banks',
-        sa.Column('bank_id', sa.Text(), nullable=False),
-        sa.Column('name', sa.Text(), nullable=True),
-        sa.Column('personality', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
-        sa.Column('background', sa.Text(), nullable=True),
-        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.PrimaryKeyConstraint('bank_id', name=op.f('pk_banks'))
+        "banks",
+        sa.Column("bank_id", sa.Text(), nullable=False),
+        sa.Column("name", sa.Text(), nullable=True),
+        sa.Column(
+            "personality",
+            postgresql.JSONB(astext_type=sa.Text()),
+            server_default=sa.text("'{}'::jsonb"),
+            nullable=False,
+        ),
+        sa.Column("background", sa.Text(), nullable=True),
+        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.PrimaryKeyConstraint("bank_id", name=op.f("pk_banks")),
    )

    # Create documents table
    op.create_table(
-        'documents',
-        sa.Column('id', sa.Text(), nullable=False),
-        sa.Column('bank_id', sa.Text(), nullable=False),
-        sa.Column('original_text', sa.Text(), nullable=True),
-        sa.Column('content_hash', sa.Text(), nullable=True),
-        sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
-        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.PrimaryKeyConstraint('id', 'bank_id', name=op.f('pk_documents'))
+        "documents",
+        sa.Column("id", sa.Text(), nullable=False),
+        sa.Column("bank_id", sa.Text(), nullable=False),
+        sa.Column("original_text", sa.Text(), nullable=True),
+        sa.Column("content_hash", sa.Text(), nullable=True),
+        sa.Column(
+            "metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
+        ),
+        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.PrimaryKeyConstraint("id", "bank_id", name=op.f("pk_documents")),
    )
-    op.create_index('idx_documents_bank_id', 'documents', ['bank_id'])
-    op.create_index('idx_documents_content_hash', 'documents', ['content_hash'])
+    op.create_index("idx_documents_bank_id", "documents", ["bank_id"])
+    op.create_index("idx_documents_content_hash", "documents", ["content_hash"])

    # Create async_operations table
    op.create_table(
-        'async_operations',
-        sa.Column('operation_id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
-        sa.Column('bank_id', sa.Text(), nullable=False),
-        sa.Column('operation_type', sa.Text(), nullable=False),
-        sa.Column('status', sa.Text(), server_default='pending', nullable=False),
-        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.Column('completed_at', postgresql.TIMESTAMP(timezone=True), nullable=True),
-        sa.Column('error_message', sa.Text(), nullable=True),
-        sa.Column('result_metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
-        sa.PrimaryKeyConstraint('operation_id', name=op.f('pk_async_operations')),
-        sa.CheckConstraint("status IN ('pending', 'processing', 'completed', 'failed')", name='async_operations_status_check')
+        "async_operations",
+        sa.Column(
+            "operation_id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False
+        ),
+        sa.Column("bank_id", sa.Text(), nullable=False),
+        sa.Column("operation_type", sa.Text(), nullable=False),
+        sa.Column("status", sa.Text(), server_default="pending", nullable=False),
+        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.Column("completed_at", postgresql.TIMESTAMP(timezone=True), nullable=True),
+        sa.Column("error_message", sa.Text(), nullable=True),
+        sa.Column(
+            "result_metadata",
+            postgresql.JSONB(astext_type=sa.Text()),
+            server_default=sa.text("'{}'::jsonb"),
+            nullable=False,
+        ),
+        sa.PrimaryKeyConstraint("operation_id", name=op.f("pk_async_operations")),
+        sa.CheckConstraint(
+            "status IN ('pending', 'processing', 'completed', 'failed')", name="async_operations_status_check"
+        ),
    )
-    op.create_index('idx_async_operations_bank_id', 'async_operations', ['bank_id'])
-    op.create_index('idx_async_operations_status', 'async_operations', ['status'])
-    op.create_index('idx_async_operations_bank_status', 'async_operations', ['bank_id', 'status'])
+    op.create_index("idx_async_operations_bank_id", "async_operations", ["bank_id"])
+    op.create_index("idx_async_operations_status", "async_operations", ["status"])
+    op.create_index("idx_async_operations_bank_status", "async_operations", ["bank_id", "status"])

    # Create entities table
    op.create_table(
-        'entities',
-        sa.Column('id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
-        sa.Column('canonical_name', sa.Text(), nullable=False),
-        sa.Column('bank_id', sa.Text(), nullable=False),
-        sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
-        sa.Column('first_seen', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.Column('last_seen', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.Column('mention_count', sa.Integer(), server_default='1', nullable=False),
-        sa.PrimaryKeyConstraint('id', name=op.f('pk_entities'))
+        "entities",
+        sa.Column("id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False),
+        sa.Column("canonical_name", sa.Text(), nullable=False),
+        sa.Column("bank_id", sa.Text(), nullable=False),
+        sa.Column(
+            "metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
+        ),
+        sa.Column("first_seen", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.Column("last_seen", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.Column("mention_count", sa.Integer(), server_default="1", nullable=False),
+        sa.PrimaryKeyConstraint("id", name=op.f("pk_entities")),
    )
-    op.create_index('idx_entities_bank_id', 'entities', ['bank_id'])
-    op.create_index('idx_entities_canonical_name', 'entities', ['canonical_name'])
-    op.create_index('idx_entities_bank_name', 'entities', ['bank_id', 'canonical_name'])
+    op.create_index("idx_entities_bank_id", "entities", ["bank_id"])
+    op.create_index("idx_entities_canonical_name", "entities", ["canonical_name"])
+    op.create_index("idx_entities_bank_name", "entities", ["bank_id", "canonical_name"])
    # Create unique index on (bank_id, LOWER(canonical_name)) for entity resolution
-    op.execute('CREATE UNIQUE INDEX idx_entities_bank_lower_name ON entities (bank_id, LOWER(canonical_name))')
+    op.execute("CREATE UNIQUE INDEX idx_entities_bank_lower_name ON entities (bank_id, LOWER(canonical_name))")

    # Create memory_units table
    op.create_table(
-        'memory_units',
-        sa.Column('id', postgresql.UUID(as_uuid=True), server_default=sa.text('gen_random_uuid()'), nullable=False),
-        sa.Column('bank_id', sa.Text(), nullable=False),
-        sa.Column('document_id', sa.Text(), nullable=True),
-        sa.Column('text', sa.Text(), nullable=False),
-        sa.Column('embedding', Vector(384), nullable=True),
-        sa.Column('context', sa.Text(), nullable=True),
-        sa.Column('event_date', postgresql.TIMESTAMP(timezone=True), nullable=False),
-        sa.Column('occurred_start', postgresql.TIMESTAMP(timezone=True), nullable=True),
-        sa.Column('occurred_end', postgresql.TIMESTAMP(timezone=True), nullable=True),
-        sa.Column('mentioned_at', postgresql.TIMESTAMP(timezone=True), nullable=True),
-        sa.Column('fact_type', sa.Text(), server_default='world', nullable=False),
-        sa.Column('confidence_score', sa.Float(), nullable=True),
-        sa.Column('access_count', sa.Integer(), server_default='0', nullable=False),
-        sa.Column('metadata', postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False),
-        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.Column('updated_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.ForeignKeyConstraint(['document_id', 'bank_id'], ['documents.id', 'documents.bank_id'], name='memory_units_document_fkey', ondelete='CASCADE'),
-        sa.PrimaryKeyConstraint('id', name=op.f('pk_memory_units')),
-        sa.CheckConstraint("fact_type IN ('world', 'bank', 'opinion', 'observation')", name='memory_units_fact_type_check'),
-        sa.CheckConstraint("confidence_score IS NULL OR (confidence_score >= 0.0 AND confidence_score <= 1.0)", name='memory_units_confidence_range_check'),
+        "memory_units",
+        sa.Column("id", postgresql.UUID(as_uuid=True), server_default=sa.text("gen_random_uuid()"), nullable=False),
+        sa.Column("bank_id", sa.Text(), nullable=False),
+        sa.Column("document_id", sa.Text(), nullable=True),
+        sa.Column("text", sa.Text(), nullable=False),
+        sa.Column("embedding", Vector(384), nullable=True),
+        sa.Column("context", sa.Text(), nullable=True),
+        sa.Column("event_date", postgresql.TIMESTAMP(timezone=True), nullable=False),
+        sa.Column("occurred_start", postgresql.TIMESTAMP(timezone=True), nullable=True),
+        sa.Column("occurred_end", postgresql.TIMESTAMP(timezone=True), nullable=True),
+        sa.Column("mentioned_at", postgresql.TIMESTAMP(timezone=True), nullable=True),
+        sa.Column("fact_type", sa.Text(), server_default="world", nullable=False),
+        sa.Column("confidence_score", sa.Float(), nullable=True),
+        sa.Column("access_count", sa.Integer(), server_default="0", nullable=False),
+        sa.Column(
+            "metadata", postgresql.JSONB(astext_type=sa.Text()), server_default=sa.text("'{}'::jsonb"), nullable=False
+        ),
+        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.Column("updated_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["document_id", "bank_id"],
+            ["documents.id", "documents.bank_id"],
+            name="memory_units_document_fkey",
+            ondelete="CASCADE",
+        ),
+        sa.PrimaryKeyConstraint("id", name=op.f("pk_memory_units")),
+        sa.CheckConstraint(
+            "fact_type IN ('world', 'bank', 'opinion', 'observation')", name="memory_units_fact_type_check"
+        ),
+        sa.CheckConstraint(
+            "confidence_score IS NULL OR (confidence_score >= 0.0 AND confidence_score <= 1.0)",
+            name="memory_units_confidence_range_check",
+        ),
        sa.CheckConstraint(
            "(fact_type = 'opinion' AND confidence_score IS NOT NULL) OR "
            "(fact_type = 'observation') OR "
            "(fact_type NOT IN ('opinion', 'observation') AND confidence_score IS NULL)",
-            name='confidence_score_fact_type_check'
-        )
+            name="confidence_score_fact_type_check",
+        ),
    )

    # Add search_vector column for full-text search
@@ -128,18 +158,41 @@ def upgrade() -> None:
        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))) STORED
    """)

-    op.create_index('idx_memory_units_bank_id', 'memory_units', ['bank_id'])
-    op.create_index('idx_memory_units_document_id', 'memory_units', ['document_id'])
-    op.create_index('idx_memory_units_event_date', 'memory_units', [sa.text('event_date DESC')])
-    op.create_index('idx_memory_units_bank_date', 'memory_units', ['bank_id', sa.text('event_date DESC')])
-    op.create_index('idx_memory_units_access_count', 'memory_units', [sa.text('access_count DESC')])
-    op.create_index('idx_memory_units_fact_type', 'memory_units', ['fact_type'])
-    op.create_index('idx_memory_units_bank_fact_type', 'memory_units', ['bank_id', 'fact_type'])
-    op.create_index('idx_memory_units_bank_type_date', 'memory_units', ['bank_id', 'fact_type', sa.text('event_date DESC')])
-    op.create_index('idx_memory_units_opinion_confidence', 'memory_units', ['bank_id', sa.text('confidence_score DESC')], postgresql_where=sa.text("fact_type = 'opinion'"))
-    op.create_index('idx_memory_units_opinion_date', 'memory_units', ['bank_id', sa.text('event_date DESC')], postgresql_where=sa.text("fact_type = 'opinion'"))
-    op.create_index('idx_memory_units_observation_date', 'memory_units', ['bank_id', sa.text('event_date DESC')], postgresql_where=sa.text("fact_type = 'observation'"))
-    op.create_index('idx_memory_units_embedding', 'memory_units', ['embedding'], postgresql_using='hnsw', postgresql_ops={'embedding': 'vector_cosine_ops'})
+    op.create_index("idx_memory_units_bank_id", "memory_units", ["bank_id"])
+    op.create_index("idx_memory_units_document_id", "memory_units", ["document_id"])
+    op.create_index("idx_memory_units_event_date", "memory_units", [sa.text("event_date DESC")])
+    op.create_index("idx_memory_units_bank_date", "memory_units", ["bank_id", sa.text("event_date DESC")])
+    op.create_index("idx_memory_units_access_count", "memory_units", [sa.text("access_count DESC")])
+    op.create_index("idx_memory_units_fact_type", "memory_units", ["fact_type"])
+    op.create_index("idx_memory_units_bank_fact_type", "memory_units", ["bank_id", "fact_type"])
+    op.create_index(
+        "idx_memory_units_bank_type_date", "memory_units", ["bank_id", "fact_type", sa.text("event_date DESC")]
+    )
+    op.create_index(
+        "idx_memory_units_opinion_confidence",
+        "memory_units",
+        ["bank_id", sa.text("confidence_score DESC")],
+        postgresql_where=sa.text("fact_type = 'opinion'"),
+    )
+    op.create_index(
+        "idx_memory_units_opinion_date",
+        "memory_units",
+        ["bank_id", sa.text("event_date DESC")],
+        postgresql_where=sa.text("fact_type = 'opinion'"),
+    )
+    op.create_index(
+        "idx_memory_units_observation_date",
+        "memory_units",
+        ["bank_id", sa.text("event_date DESC")],
+        postgresql_where=sa.text("fact_type = 'observation'"),
+    )
+    op.create_index(
+        "idx_memory_units_embedding",
+        "memory_units",
+        ["embedding"],
+        postgresql_using="hnsw",
+        postgresql_ops={"embedding": "vector_cosine_ops"},
+    )

    # Create BM25 full-text search index on search_vector
    op.execute("""
@@ -158,116 +211,149 @@ def upgrade() -> None:
        FROM memory_units
    """)

-    op.create_index('idx_memory_units_bm25_bank', 'memory_units_bm25', ['bank_id'])
-    op.create_index('idx_memory_units_bm25_text_vector', 'memory_units_bm25', ['text_vector'], postgresql_using='gin')
+    op.create_index("idx_memory_units_bm25_bank", "memory_units_bm25", ["bank_id"])
+    op.create_index("idx_memory_units_bm25_text_vector", "memory_units_bm25", ["text_vector"], postgresql_using="gin")

    # Create entity_cooccurrences table
    op.create_table(
-        'entity_cooccurrences',
-        sa.Column('entity_id_1', postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column('entity_id_2', postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column('cooccurrence_count', sa.Integer(), server_default='1', nullable=False),
-        sa.Column('last_cooccurred', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.ForeignKeyConstraint(['entity_id_1'], ['entities.id'], name=op.f('fk_entity_cooccurrences_entity_id_1_entities'), ondelete='CASCADE'),
-        sa.ForeignKeyConstraint(['entity_id_2'], ['entities.id'], name=op.f('fk_entity_cooccurrences_entity_id_2_entities'), ondelete='CASCADE'),
-        sa.PrimaryKeyConstraint('entity_id_1', 'entity_id_2', name=op.f('pk_entity_cooccurrences')),
-        sa.CheckConstraint('entity_id_1 < entity_id_2', name='entity_cooccurrence_order_check')
+        "entity_cooccurrences",
+        sa.Column("entity_id_1", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("entity_id_2", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("cooccurrence_count", sa.Integer(), server_default="1", nullable=False),
+        sa.Column(
+            "last_cooccurred", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False
+        ),
+        sa.ForeignKeyConstraint(
+            ["entity_id_1"],
+            ["entities.id"],
+            name=op.f("fk_entity_cooccurrences_entity_id_1_entities"),
+            ondelete="CASCADE",
+        ),
+        sa.ForeignKeyConstraint(
+            ["entity_id_2"],
+            ["entities.id"],
+            name=op.f("fk_entity_cooccurrences_entity_id_2_entities"),
+            ondelete="CASCADE",
+        ),
+        sa.PrimaryKeyConstraint("entity_id_1", "entity_id_2", name=op.f("pk_entity_cooccurrences")),
+        sa.CheckConstraint("entity_id_1 < entity_id_2", name="entity_cooccurrence_order_check"),
    )
-    op.create_index('idx_entity_cooccurrences_entity1', 'entity_cooccurrences', ['entity_id_1'])
-    op.create_index('idx_entity_cooccurrences_entity2', 'entity_cooccurrences', ['entity_id_2'])
-    op.create_index('idx_entity_cooccurrences_count', 'entity_cooccurrences', [sa.text('cooccurrence_count DESC')])
+    op.create_index("idx_entity_cooccurrences_entity1", "entity_cooccurrences", ["entity_id_1"])
+    op.create_index("idx_entity_cooccurrences_entity2", "entity_cooccurrences", ["entity_id_2"])
+    op.create_index("idx_entity_cooccurrences_count", "entity_cooccurrences", [sa.text("cooccurrence_count DESC")])

    # Create memory_links table
    op.create_table(
-        'memory_links',
-        sa.Column('from_unit_id', postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column('to_unit_id', postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column('link_type', sa.Text(), nullable=False),
-        sa.Column('entity_id', postgresql.UUID(as_uuid=True), nullable=True),
-        sa.Column('weight', sa.Float(), server_default='1.0', nullable=False),
-        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.ForeignKeyConstraint(['entity_id'], ['entities.id'], name=op.f('fk_memory_links_entity_id_entities'), ondelete='CASCADE'),
-        sa.ForeignKeyConstraint(['from_unit_id'], ['memory_units.id'], name=op.f('fk_memory_links_from_unit_id_memory_units'), ondelete='CASCADE'),
-        sa.ForeignKeyConstraint(['to_unit_id'], ['memory_units.id'], name=op.f('fk_memory_links_to_unit_id_memory_units'), ondelete='CASCADE'),
-        sa.CheckConstraint("link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')", name='memory_links_link_type_check'),
-        sa.CheckConstraint('weight >= 0.0 AND weight <= 1.0', name='memory_links_weight_check')
+        "memory_links",
+        sa.Column("from_unit_id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("to_unit_id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("link_type", sa.Text(), nullable=False),
+        sa.Column("entity_id", postgresql.UUID(as_uuid=True), nullable=True),
+        sa.Column("weight", sa.Float(), server_default="1.0", nullable=False),
+        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["entity_id"], ["entities.id"], name=op.f("fk_memory_links_entity_id_entities"), ondelete="CASCADE"
+        ),
+        sa.ForeignKeyConstraint(
+            ["from_unit_id"],
+            ["memory_units.id"],
+            name=op.f("fk_memory_links_from_unit_id_memory_units"),
+            ondelete="CASCADE",
+        ),
+        sa.ForeignKeyConstraint(
+            ["to_unit_id"],
+            ["memory_units.id"],
+            name=op.f("fk_memory_links_to_unit_id_memory_units"),
+            ondelete="CASCADE",
+        ),
+        sa.CheckConstraint(
+            "link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')",
+            name="memory_links_link_type_check",
+        ),
+        sa.CheckConstraint("weight >= 0.0 AND weight <= 1.0", name="memory_links_weight_check"),
    )
    # Create unique constraint using COALESCE for nullable entity_id
-    op.execute("CREATE UNIQUE INDEX idx_memory_links_unique ON memory_links (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid))")
-    op.create_index('idx_memory_links_from_unit', 'memory_links', ['from_unit_id'])
-    op.create_index('idx_memory_links_to_unit', 'memory_links', ['to_unit_id'])
-    op.create_index('idx_memory_links_entity', 'memory_links', ['entity_id'])
-    op.create_index('idx_memory_links_link_type', 'memory_links', ['link_type'])
+    op.execute(
+        "CREATE UNIQUE INDEX idx_memory_links_unique ON memory_links (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid))"
+    )
+    op.create_index("idx_memory_links_from_unit", "memory_links", ["from_unit_id"])
+    op.create_index("idx_memory_links_to_unit", "memory_links", ["to_unit_id"])
+    op.create_index("idx_memory_links_entity", "memory_links", ["entity_id"])
+    op.create_index("idx_memory_links_link_type", "memory_links", ["link_type"])

    # Create unit_entities table
    op.create_table(
-        'unit_entities',
-        sa.Column('unit_id', postgresql.UUID(as_uuid=True), nullable=False),
-        sa.Column('entity_id', postgresql.UUID(as_uuid=True), nullable=False),
-        sa.ForeignKeyConstraint(['entity_id'], ['entities.id'], name=op.f('fk_unit_entities_entity_id_entities'), ondelete='CASCADE'),
-        sa.ForeignKeyConstraint(['unit_id'], ['memory_units.id'], name=op.f('fk_unit_entities_unit_id_memory_units'), ondelete='CASCADE'),
-        sa.PrimaryKeyConstraint('unit_id', 'entity_id', name=op.f('pk_unit_entities'))
+        "unit_entities",
+        sa.Column("unit_id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("entity_id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["entity_id"], ["entities.id"], name=op.f("fk_unit_entities_entity_id_entities"), ondelete="CASCADE"
+        ),
+        sa.ForeignKeyConstraint(
+            ["unit_id"], ["memory_units.id"], name=op.f("fk_unit_entities_unit_id_memory_units"), ondelete="CASCADE"
+        ),
+        sa.PrimaryKeyConstraint("unit_id", "entity_id", name=op.f("pk_unit_entities")),
    )
-    op.create_index('idx_unit_entities_unit', 'unit_entities', ['unit_id'])
-    op.create_index('idx_unit_entities_entity', 'unit_entities', ['entity_id'])
+    op.create_index("idx_unit_entities_unit", "unit_entities", ["unit_id"])
+    op.create_index("idx_unit_entities_entity", "unit_entities", ["entity_id"])


 def downgrade() -> None:
    """Downgrade schema - drop all tables."""

    # Drop tables in reverse dependency order
-    op.drop_index('idx_unit_entities_entity', table_name='unit_entities')
-    op.drop_index('idx_unit_entities_unit', table_name='unit_entities')
-    op.drop_table('unit_entities')
+    op.drop_index("idx_unit_entities_entity", table_name="unit_entities")
+    op.drop_index("idx_unit_entities_unit", table_name="unit_entities")
+    op.drop_table("unit_entities")

-    op.drop_index('idx_memory_links_link_type', table_name='memory_links')
-    op.drop_index('idx_memory_links_entity', table_name='memory_links')
-    op.drop_index('idx_memory_links_to_unit', table_name='memory_links')
-    op.drop_index('idx_memory_links_from_unit', table_name='memory_links')
-    op.execute('DROP INDEX IF EXISTS idx_memory_links_unique')
-    op.drop_table('memory_links')
+    op.drop_index("idx_memory_links_link_type", table_name="memory_links")
+    op.drop_index("idx_memory_links_entity", table_name="memory_links")
+    op.drop_index("idx_memory_links_to_unit", table_name="memory_links")
+    op.drop_index("idx_memory_links_from_unit", table_name="memory_links")
+    op.execute("DROP INDEX IF EXISTS idx_memory_links_unique")
+    op.drop_table("memory_links")

-    op.drop_index('idx_entity_cooccurrences_count', table_name='entity_cooccurrences')
-    op.drop_index('idx_entity_cooccurrences_entity2', table_name='entity_cooccurrences')
-    op.drop_index('idx_entity_cooccurrences_entity1', table_name='entity_cooccurrences')
-    op.drop_table('entity_cooccurrences')
+    op.drop_index("idx_entity_cooccurrences_count", table_name="entity_cooccurrences")
+    op.drop_index("idx_entity_cooccurrences_entity2", table_name="entity_cooccurrences")
+    op.drop_index("idx_entity_cooccurrences_entity1", table_name="entity_cooccurrences")
+    op.drop_table("entity_cooccurrences")

    # Drop BM25 materialized view and index
-    op.drop_index('idx_memory_units_bm25_text_vector', table_name='memory_units_bm25')
-    op.drop_index('idx_memory_units_bm25_bank', table_name='memory_units_bm25')
-    op.execute('DROP MATERIALIZED VIEW IF EXISTS memory_units_bm25')
+    op.drop_index("idx_memory_units_bm25_text_vector", table_name="memory_units_bm25")
+    op.drop_index("idx_memory_units_bm25_bank", table_name="memory_units_bm25")
+    op.execute("DROP MATERIALIZED VIEW IF EXISTS memory_units_bm25")

-    op.drop_index('idx_memory_units_embedding', table_name='memory_units')
-    op.drop_index('idx_memory_units_observation_date', table_name='memory_units')
-    op.drop_index('idx_memory_units_opinion_date', table_name='memory_units')
-    op.drop_index('idx_memory_units_opinion_confidence', table_name='memory_units')
-    op.drop_index('idx_memory_units_bank_type_date', table_name='memory_units')
-    op.drop_index('idx_memory_units_bank_fact_type', table_name='memory_units')
-    op.drop_index('idx_memory_units_fact_type', table_name='memory_units')
-    op.drop_index('idx_memory_units_access_count', table_name='memory_units')
-    op.drop_index('idx_memory_units_bank_date', table_name='memory_units')
-    op.drop_index('idx_memory_units_event_date', table_name='memory_units')
-    op.drop_index('idx_memory_units_document_id', table_name='memory_units')
-    op.drop_index('idx_memory_units_bank_id', table_name='memory_units')
-    op.execute('DROP INDEX IF EXISTS idx_memory_units_text_search')
-    op.drop_table('memory_units')
+    op.drop_index("idx_memory_units_embedding", table_name="memory_units")
+    op.drop_index("idx_memory_units_observation_date", table_name="memory_units")
+    op.drop_index("idx_memory_units_opinion_date", table_name="memory_units")
+    op.drop_index("idx_memory_units_opinion_confidence", table_name="memory_units")
+    op.drop_index("idx_memory_units_bank_type_date", table_name="memory_units")
+    op.drop_index("idx_memory_units_bank_fact_type", table_name="memory_units")
+    op.drop_index("idx_memory_units_fact_type", table_name="memory_units")
+    op.drop_index("idx_memory_units_access_count", table_name="memory_units")
+    op.drop_index("idx_memory_units_bank_date", table_name="memory_units")
+    op.drop_index("idx_memory_units_event_date", table_name="memory_units")
+    op.drop_index("idx_memory_units_document_id", table_name="memory_units")
+    op.drop_index("idx_memory_units_bank_id", table_name="memory_units")
+    op.execute("DROP INDEX IF EXISTS idx_memory_units_text_search")
+    op.drop_table("memory_units")

-    op.execute('DROP INDEX IF EXISTS idx_entities_bank_lower_name')
-    op.drop_index('idx_entities_bank_name', table_name='entities')
-    op.drop_index('idx_entities_canonical_name', table_name='entities')
-    op.drop_index('idx_entities_bank_id', table_name='entities')
-    op.drop_table('entities')
+    op.execute("DROP INDEX IF EXISTS idx_entities_bank_lower_name")
+    op.drop_index("idx_entities_bank_name", table_name="entities")
+    op.drop_index("idx_entities_canonical_name", table_name="entities")
+    op.drop_index("idx_entities_bank_id", table_name="entities")
+    op.drop_table("entities")

-    op.drop_index('idx_async_operations_bank_status', table_name='async_operations')
-    op.drop_index('idx_async_operations_status', table_name='async_operations')
-    op.drop_index('idx_async_operations_bank_id', table_name='async_operations')
-    op.drop_table('async_operations')
+    op.drop_index("idx_async_operations_bank_status", table_name="async_operations")
+    op.drop_index("idx_async_operations_status", table_name="async_operations")
+    op.drop_index("idx_async_operations_bank_id", table_name="async_operations")
+    op.drop_table("async_operations")

-    op.drop_index('idx_documents_content_hash', table_name='documents')
-    op.drop_index('idx_documents_bank_id', table_name='documents')
-    op.drop_table('documents')
+    op.drop_index("idx_documents_content_hash", table_name="documents")
+    op.drop_index("idx_documents_bank_id", table_name="documents")
+    op.drop_table("documents")

-    op.drop_table('banks')
+    op.drop_table("banks")

    # Drop extensions (optional - comment out if you want to keep them)
    # op.execute('DROP EXTENSION IF EXISTS vector')
@@ -5,18 +5,18 @@ Revises: 5a366d414dce
 Create Date: 2025-11-28 00:00:00.000000

 """
-from typing import Sequence, Union

-from alembic import op
+from collections.abc import Sequence
+
 import sqlalchemy as sa
+from alembic import op
 from sqlalchemy.dialects import postgresql

-
 # revision identifiers, used by Alembic.
-revision: str = 'b7c4d8e9f1a2'
-down_revision: Union[str, Sequence[str], None] = '5a366d414dce'
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
+revision: str = "b7c4d8e9f1a2"
+down_revision: str | Sequence[str] | None = "5a366d414dce"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None


 def upgrade() -> None:
@@ -24,47 +24,47 @@ def upgrade() -> None:

    # Create chunks table with single text PK (bank_id_document_id_chunk_index)
    op.create_table(
-        'chunks',
-        sa.Column('chunk_id', sa.Text(), nullable=False),
-        sa.Column('document_id', sa.Text(), nullable=False),
-        sa.Column('bank_id', sa.Text(), nullable=False),
-        sa.Column('chunk_index', sa.Integer(), nullable=False),
-        sa.Column('chunk_text', sa.Text(), nullable=False),
-        sa.Column('created_at', postgresql.TIMESTAMP(timezone=True), server_default=sa.text('now()'), nullable=False),
-        sa.ForeignKeyConstraint(['document_id', 'bank_id'], ['documents.id', 'documents.bank_id'], name='chunks_document_fkey', ondelete='CASCADE'),
-        sa.PrimaryKeyConstraint('chunk_id', name=op.f('pk_chunks'))
+        "chunks",
+        sa.Column("chunk_id", sa.Text(), nullable=False),
+        sa.Column("document_id", sa.Text(), nullable=False),
+        sa.Column("bank_id", sa.Text(), nullable=False),
+        sa.Column("chunk_index", sa.Integer(), nullable=False),
+        sa.Column("chunk_text", sa.Text(), nullable=False),
+        sa.Column("created_at", postgresql.TIMESTAMP(timezone=True), server_default=sa.text("now()"), nullable=False),
+        sa.ForeignKeyConstraint(
+            ["document_id", "bank_id"],
+            ["documents.id", "documents.bank_id"],
+            name="chunks_document_fkey",
+            ondelete="CASCADE",
+        ),
+        sa.PrimaryKeyConstraint("chunk_id", name=op.f("pk_chunks")),
    )

    # Add indexes for efficient queries
-    op.create_index('idx_chunks_document_id', 'chunks', ['document_id'])
-    op.create_index('idx_chunks_bank_id', 'chunks', ['bank_id'])
+    op.create_index("idx_chunks_document_id", "chunks", ["document_id"])
+    op.create_index("idx_chunks_bank_id", "chunks", ["bank_id"])

    # Add chunk_id column to memory_units (nullable, as existing records won't have chunks)
-    op.add_column('memory_units', sa.Column('chunk_id', sa.Text(), nullable=True))
+    op.add_column("memory_units", sa.Column("chunk_id", sa.Text(), nullable=True))

    # Add foreign key constraint to chunks table
    op.create_foreign_key(
-        'memory_units_chunk_fkey',
-        'memory_units',
-        'chunks',
-        ['chunk_id'],
-        ['chunk_id'],
-        ondelete='SET NULL'
+        "memory_units_chunk_fkey", "memory_units", "chunks", ["chunk_id"], ["chunk_id"], ondelete="SET NULL"
    )

    # Add index on chunk_id for efficient lookups
-    op.create_index('idx_memory_units_chunk_id', 'memory_units', ['chunk_id'])
+    op.create_index("idx_memory_units_chunk_id", "memory_units", ["chunk_id"])


 def downgrade() -> None:
    """Remove chunks table and chunk_id from memory_units."""

    # Drop index and foreign key from memory_units
-    op.drop_index('idx_memory_units_chunk_id', table_name='memory_units')
-    op.drop_constraint('memory_units_chunk_fkey', 'memory_units', type_='foreignkey')
-    op.drop_column('memory_units', 'chunk_id')
+    op.drop_index("idx_memory_units_chunk_id", table_name="memory_units")
+    op.drop_constraint("memory_units_chunk_fkey", "memory_units", type_="foreignkey")
+    op.drop_column("memory_units", "chunk_id")

    # Drop chunks table indexes and table
-    op.drop_index('idx_chunks_bank_id', table_name='chunks')
-    op.drop_index('idx_chunks_document_id', table_name='chunks')
-    op.drop_table('chunks')
+    op.drop_index("idx_chunks_bank_id", table_name="chunks")
+    op.drop_index("idx_chunks_document_id", table_name="chunks")
+    op.drop_table("chunks")
@@ -5,35 +5,35 @@ Revises: b7c4d8e9f1a2
 Create Date: 2025-12-02 00:00:00.000000

 """
-from typing import Sequence, Union

-from alembic import op
+from collections.abc import Sequence
+
 import sqlalchemy as sa
+from alembic import op
 from sqlalchemy.dialects import postgresql

-
 # revision identifiers, used by Alembic.
-revision: str = 'c8e5f2a3b4d1'
-down_revision: Union[str, Sequence[str], None] = 'b7c4d8e9f1a2'
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
+revision: str = "c8e5f2a3b4d1"
+down_revision: str | Sequence[str] | None = "b7c4d8e9f1a2"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None


 def upgrade() -> None:
    """Add retain_params JSONB column to documents table."""

    # Add retain_params column to store parameters passed during retain
-    op.add_column('documents', sa.Column('retain_params', postgresql.JSONB(), nullable=True))
+    op.add_column("documents", sa.Column("retain_params", postgresql.JSONB(), nullable=True))

    # Add index for efficient queries on retain_params
-    op.create_index('idx_documents_retain_params', 'documents', ['retain_params'], postgresql_using='gin')
+    op.create_index("idx_documents_retain_params", "documents", ["retain_params"], postgresql_using="gin")


 def downgrade() -> None:
    """Remove retain_params column from documents table."""

    # Drop index
-    op.drop_index('idx_documents_retain_params', table_name='documents')
+    op.drop_index("idx_documents_retain_params", table_name="documents")

    # Drop column
-    op.drop_column('documents', 'retain_params')
+    op.drop_column("documents", "retain_params")
@@ -5,44 +5,49 @@ Revises: c8e5f2a3b4d1
 Create Date: 2024-12-04 15:00:00.000000

 """
-from alembic import op
-import sqlalchemy as sa

+from alembic import context, op

 # revision identifiers, used by Alembic.
-revision = 'd9f6a3b4c5e2'
-down_revision = 'c8e5f2a3b4d1'
+revision = "d9f6a3b4c5e2"
+down_revision = "c8e5f2a3b4d1"
 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')
+    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(
-        'memory_units_fact_type_check',
-        'memory_units',
-        "fact_type IN ('world', 'experience', 'opinion', 'observation')"
+        "memory_units_fact_type_check", "memory_units", "fact_type IN ('world', 'experience', 'opinion', 'observation')"
    )


 def downgrade():
+    schema = _get_schema_prefix()
+
    # Drop new check constraint FIRST
-    op.drop_constraint('memory_units_fact_type_check', 'memory_units', type_='check')
+    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(
-        'memory_units_fact_type_check',
-        'memory_units',
-        "fact_type IN ('world', 'bank', 'opinion', 'observation')"
+        "memory_units_fact_type_check", "memory_units", "fact_type IN ('world', 'bank', 'opinion', 'observation')"
    )
@@ -8,22 +8,49 @@ Migrate disposition traits from Big Five (openness, conscientiousness, extravers
 agreeableness, neuroticism, bias_strength with 0-1 float values) to the new 3-trait
 system (skepticism, literalism, empathy with 1-5 integer values).
 """
-from typing import Sequence, Union

-from alembic import op
+from collections.abc import Sequence
+
 import sqlalchemy as sa
-
+from alembic import context, op

 # revision identifiers, used by Alembic.
-revision: str = 'e0a1b2c3d4e5'
-down_revision: Union[str, Sequence[str], None] = 'rename_personality'
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
+revision: str = "e0a1b2c3d4e5"
+down_revision: str | Sequence[str] | None = "rename_personality"
+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:
@@ -31,32 +58,54 @@ def upgrade() -> None:
    # - literalism: derived from conscientiousness (detail-oriented people are more literal)
    # - 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
+    conn.execute(
+        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
-    """))
+    conn.execute(
+        sa.text(f"""
+        ALTER TABLE {schema}banks
+        ALTER COLUMN disposition SET DEFAULT '{{"skepticism": 3, "literalism": 3, "empathy": 3}}'::jsonb
+    """)
+    )


 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
+    conn.execute(
+        sa.text(f"""
+        UPDATE {schema}banks
+        SET disposition = '{{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}}'::jsonb
        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
-    """))
+    conn.execute(
+        sa.text(f"""
+        ALTER TABLE {schema}banks
+        ALTER COLUMN disposition SET DEFAULT '{{"openness": 0.5, "conscientiousness": 0.5, "extraversion": 0.5, "agreeableness": 0.5, "neuroticism": 0.5, "bias_strength": 0.5}}'::jsonb
+    """)
+    )
@@ -0,0 +1,44 @@
+"""add_memory_links_from_type_weight_index
+
+Revision ID: f1a2b3c4d5e6
+Revises: e0a1b2c3d4e5
+Create Date: 2025-01-12
+
+Add composite index on memory_links (from_unit_id, link_type, weight DESC)
+to optimize MPFP graph traversal queries that need top-k edges per type.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "f1a2b3c4d5e6"
+down_revision: str | Sequence[str] | None = "e0a1b2c3d4e5"
+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 upgrade() -> None:
+    """Add composite index for efficient MPFP edge loading."""
+    schema = _get_schema_prefix()
+    # Create composite index for efficient top-k per (from_node, link_type) queries
+    # This enables LATERAL joins to use index-only scans with early termination
+    # Note: Not using CONCURRENTLY here as it requires running outside a transaction
+    # For production with large tables, consider running this manually with CONCURRENTLY
+    op.execute(
+        f"CREATE INDEX IF NOT EXISTS idx_memory_links_from_type_weight "
+        f"ON {schema}memory_links(from_unit_id, link_type, weight DESC)"
+    )
+
+
+def downgrade() -> None:
+    """Remove the composite index."""
+    schema = _get_schema_prefix()
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_links_from_type_weight")
@@ -5,61 +5,81 @@ Revises: d9f6a3b4c5e2
 Create Date: 2024-12-04

 """
-from typing import Sequence, Union

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

-
 # revision identifiers, used by Alembic.
-revision: str = 'rename_personality'
-down_revision: Union[str, Sequence[str], None] = 'd9f6a3b4c5e2'
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
+revision: str = "rename_personality"
+down_revision: str | Sequence[str] | None = "d9f6a3b4c5e2"
+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("""
+    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

    # Check if 'disposition' column exists (new database)
-    result = conn.execute(sa.text("""
+    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},
+    )
    has_disposition = result.fetchone() is not None

    if has_personality and not has_disposition:
        # Old database: rename personality -> disposition
-        op.alter_column('banks', 'personality', new_column_name='disposition')
+        op.alter_column("banks", "personality", new_column_name="disposition")
    elif not has_personality and not has_disposition:
        # Neither exists (shouldn't happen, but be safe): add disposition column
-        op.add_column('banks', sa.Column(
-            'disposition',
-            postgresql.JSONB(astext_type=sa.Text()),
-            server_default=sa.text("'{}'::jsonb"),
-            nullable=False
-        ))
+        op.add_column(
+            "banks",
+            sa.Column(
+                "disposition",
+                postgresql.JSONB(astext_type=sa.Text()),
+                server_default=sa.text("'{}'::jsonb"),
+                nullable=False,
+            ),
+        )
    # else: disposition already exists, nothing to do


 def downgrade() -> None:
    """Revert disposition column back to personality."""
    conn = op.get_bind()
-    result = conn.execute(sa.text("""
+    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')
+        op.alter_column("banks", "disposition", new_column_name="personality")
@@ -3,8 +3,11 @@ Unified API module for Hindsight.

 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

 from hindsight_api import MemoryEngine
@@ -17,7 +20,7 @@ def create_app(
    http_api_enabled: bool = True,
    mcp_api_enabled: bool = False,
    mcp_mount_path: str = "/mcp",
-    initialize_memory: bool = True
+    initialize_memory: bool = True,
 ) -> FastAPI:
    """
    Create and configure the unified Hindsight API application.
@@ -43,49 +46,70 @@ 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:
        from .http import create_app as create_http_app
-        app = create_http_app(
-            memory=memory,
-            initialize_memory=initialize_memory
-        )
+
+        app = create_http_app(memory=memory, initialize_memory=initialize_memory)
        logger.info("HTTP REST API enabled")
    else:
        # Create minimal FastAPI 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


 # Re-export commonly used items for backwards compatibility
 from .http import (
-    RecallRequest,
-    RecallResult,
-    RecallResponse,
-    MemoryItem,
-    RetainRequest,
-    ReflectRequest,
-    ReflectResponse,
    CreateBankRequest,
    DispositionTraits,
+    MemoryItem,
+    RecallRequest,
+    RecallResponse,
+    RecallResult,
+    ReflectRequest,
+    ReflectResponse,
+    RetainRequest,
 )

 __all__ = [
@@ -4,28 +4,38 @@ import json
 import logging
 import os
 from contextvars import ContextVar
-from typing import Optional

 from fastmcp import FastMCP
+
 from hindsight_api import MemoryEngine
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
+from hindsight_api.models import RequestContext

 # Configure logging from HINDSIGHT_API_LOG_LEVEL environment variable
 _log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
-_log_level_map = {"critical": logging.CRITICAL, "error": logging.ERROR, "warning": logging.WARNING,
-                  "info": logging.INFO, "debug": logging.DEBUG, "trace": logging.DEBUG}
+_log_level_map = {
+    "critical": logging.CRITICAL,
+    "error": logging.ERROR,
+    "warning": logging.WARNING,
+    "info": logging.INFO,
+    "debug": logging.DEBUG,
+    "trace": logging.DEBUG,
+}
 logging.basicConfig(
    level=_log_level_map.get(_log_level_str, logging.INFO),
-    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
 )
 logger = logging.getLogger(__name__)

-# Context variable to hold the current bank_id from the URL path
-_current_bank_id: ContextVar[Optional[str]] = ContextVar("current_bank_id", default=None)
+# 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() -> Optional[str]:
-    """Get the current bank_id from context (set from URL path)."""
+def get_current_bank_id() -> str | None:
+    """Get the current bank_id from context."""
    return _current_bank_id.get()


@@ -37,12 +47,18 @@ 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",
+        async_processing: bool = True,
+        bank_id: str | None = None,
+    ) -> str:
        """
        Store important information to long-term memory.

@@ -58,20 +74,34 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
        Args:
            content: The fact/memory to store (be specific and include relevant details)
            context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
+            async_processing: If True, queue for background processing and return immediately. If False, wait for completion. Default: True
+            bank_id: Optional bank to store in (defaults to session bank). Use for cross-bank operations.
        """
        try:
-            bank_id = get_current_bank_id()
-            await memory.put_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"
+            contents = [{"content": content, "context": context}]
+            if async_processing:
+                # Queue for background processing and return immediately
+                result = await memory.submit_async_retain(
+                    bank_id=target_bank, contents=contents, request_context=RequestContext()
+                )
+                return f"Memory queued for background processing (operation_id: {result.get('operation_id', 'N/A')})"
+            else:
+                # Wait for completion
+                await memory.retain_batch_async(
+                    bank_id=target_bank,
+                    contents=contents,
+                    request_context=RequestContext(),
+                )
+                return f"Memory stored successfully in bank '{target_bank}'"
        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.

@@ -83,45 +113,165 @@ 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,
+
+            recall_result = await memory.recall_async(
+                bank_id=target_bank,
                query=query,
                fact_type=list(VALID_RECALL_FACT_TYPES),
-                budget=Budget.LOW
+                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 tool to discover what memory banks exist in the system.
+        Each bank is an isolated memory store (like a separate "brain").
+
+        Returns:
+            JSON list of banks with their IDs, names, dispositions, and backgrounds.
+        """
+        try:
+            banks = await memory.list_banks(request_context=RequestContext())
+            return json.dumps({"banks": banks}, indent=2)
+        except Exception as e:
+            logger.error(f"Error listing banks: {e}", exc_info=True)
+            return f'{{"error": "{e}", "banks": []}}'
+
+    @mcp.tool()
+    async def create_bank(bank_id: str, name: str | None = None, background: str | None = None) -> str:
+        """
+        Create a new memory bank or get an existing one.
+
+        Memory banks are isolated stores - each one is like a separate "brain" for a user/agent.
+        Banks are auto-created with default settings if they don't exist.
+
+        Args:
+            bank_id: Unique identifier for the bank (e.g., 'user-123', 'agent-alpha')
+            name: Optional human-friendly name for the bank
+            background: Optional background context about the bank's owner/purpose
+        """
+        try:
+            # get_bank_profile auto-creates bank if it doesn't exist
+            profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())
+
+            # Update name/background if provided
+            if name is not None or background is not None:
+                await memory.update_bank(
+                    bank_id,
+                    name=name,
+                    background=background,
+                    request_context=RequestContext(),
+                )
+                # Fetch updated profile
+                profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())
+
+            # Serialize disposition if it's a Pydantic model
+            if "disposition" in profile and hasattr(profile["disposition"], "model_dump"):
+                profile["disposition"] = profile["disposition"].model_dump()
+            return json.dumps(profile, indent=2)
+        except Exception as e:
+            logger.error(f"Error creating bank: {e}", exc_info=True)
+            return f'{{"error": "{e}"}}'

    return 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":
@@ -133,46 +283,50 @@ class MCPMiddleware:
        # Strip any mount prefix (e.g., /mcp) that FastAPI might not have stripped
        root_path = scope.get("root_path", "")
        if root_path and path.startswith(root_path):
-            path = path[len(root_path):] or "/"
+            path = path[len(root_path) :] or "/"

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

-        # Extract 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"")
                    if body and b"/messages" in body:
                        # Rewrite /messages to /{bank_id}/messages in SSE endpoint event
-                        body = body.replace(
-                            b"data: /messages",
-                            f"data: /{bank_id}/messages".encode()
-                        )
+                        body = body.replace(b"data: /messages", f"data: /{bank_id}/messages".encode())
                        message = {**message, "body": body}
                await send(message)

@@ -183,24 +337,29 @@ class MCPMiddleware:
    async def _send_error(self, send, status: int, message: str):
        """Send an error response."""
        body = json.dumps({"error": message}).encode()
-        await send({
-            "type": "http.response.start",
-            "status": status,
-            "headers": [(b"content-type", b"application/json")],
-        })
-        await send({
-            "type": "http.response.body",
-            "body": body,
-        })
+        await send(
+            {
+                "type": "http.response.start",
+                "status": status,
+                "headers": [(b"content-type", b"application/json")],
+            }
+        )
+        await send(
+            {
+                "type": "http.response.body",
+                "body": body,
+            }
+        )


 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
@@ -6,7 +6,7 @@ Shows the logo and tagline with gradient colors.

 # Gradient colors: #0074d9 -> #009296
 GRADIENT_START = (0, 116, 217)  # #0074d9
-GRADIENT_END = (0, 146, 150)    # #009296
+GRADIENT_END = (0, 146, 150)  # #009296

 # Pre-generated logo (generated by test-logo.py)
 LOGO = """\
@@ -31,8 +31,8 @@ def gradient_text(text: str, start: tuple = GRADIENT_START, end: tuple = GRADIEN
    result = []
    length = len(text)
    for i, char in enumerate(text):
-        if char == ' ':
-            result.append(' ')
+        if char == " ":
+            result.append(" ")
        else:
            t = i / max(length - 1, 1)
            r, g, b = _interpolate_color(start, end, t)
@@ -74,9 +74,16 @@ def dim(text: str) -> str:
    return f"\033[38;2;128;128;128m{text}\033[0m"


-def print_startup_info(host: str, port: int, database_url: str, llm_provider: str,
-                       llm_model: str, embeddings_provider: str, reranker_provider: str,
-                       mcp_enabled: bool = False):
+def print_startup_info(
+    host: str,
+    port: int,
+    database_url: str,
+    llm_provider: str,
+    llm_model: str,
+    embeddings_provider: str,
+    reranker_provider: str,
+    mcp_enabled: bool = False,
+):
    """Print styled startup information."""
    print(color_start("Starting Hindsight API..."))
    print(f"  {dim('URL:')} {color(f'http://{host}:{port}', 0.2)}")
@@ -3,10 +3,15 @@ Centralized configuration for Hindsight API.

 All environment variables and their defaults are defined here.
 """
+
+import logging
 import os
 from dataclasses import dataclass
-from typing import Optional
-import logging
+
+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__)

@@ -16,40 +21,177 @@ ENV_LLM_PROVIDER = "HINDSIGHT_API_LLM_PROVIDER"
 ENV_LLM_API_KEY = "HINDSIGHT_API_LLM_API_KEY"
 ENV_LLM_MODEL = "HINDSIGHT_API_LLM_MODEL"
 ENV_LLM_BASE_URL = "HINDSIGHT_API_LLM_BASE_URL"
+ENV_LLM_MAX_CONCURRENT = "HINDSIGHT_API_LLM_MAX_CONCURRENT"
+ENV_LLM_TIMEOUT = "HINDSIGHT_API_LLM_TIMEOUT"
+ENV_LLM_GROQ_SERVICE_TIER = "HINDSIGHT_API_LLM_GROQ_SERVICE_TIER"
+
+# Per-operation LLM configuration (optional, falls back to global LLM config)
+ENV_RETAIN_LLM_PROVIDER = "HINDSIGHT_API_RETAIN_LLM_PROVIDER"
+ENV_RETAIN_LLM_API_KEY = "HINDSIGHT_API_RETAIN_LLM_API_KEY"
+ENV_RETAIN_LLM_MODEL = "HINDSIGHT_API_RETAIN_LLM_MODEL"
+ENV_RETAIN_LLM_BASE_URL = "HINDSIGHT_API_RETAIN_LLM_BASE_URL"
+
+ENV_REFLECT_LLM_PROVIDER = "HINDSIGHT_API_REFLECT_LLM_PROVIDER"
+ENV_REFLECT_LLM_API_KEY = "HINDSIGHT_API_REFLECT_LLM_API_KEY"
+ENV_REFLECT_LLM_MODEL = "HINDSIGHT_API_REFLECT_LLM_MODEL"
+ENV_REFLECT_LLM_BASE_URL = "HINDSIGHT_API_REFLECT_LLM_BASE_URL"

 ENV_EMBEDDINGS_PROVIDER = "HINDSIGHT_API_EMBEDDINGS_PROVIDER"
 ENV_EMBEDDINGS_LOCAL_MODEL = "HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL"
 ENV_EMBEDDINGS_TEI_URL = "HINDSIGHT_API_EMBEDDINGS_TEI_URL"
+ENV_EMBEDDINGS_OPENAI_API_KEY = "HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY"
+ENV_EMBEDDINGS_OPENAI_MODEL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL"
+
+ENV_COHERE_API_KEY = "HINDSIGHT_API_COHERE_API_KEY"
+ENV_EMBEDDINGS_COHERE_MODEL = "HINDSIGHT_API_EMBEDDINGS_COHERE_MODEL"
+ENV_RERANKER_COHERE_MODEL = "HINDSIGHT_API_RERANKER_COHERE_MODEL"

 ENV_RERANKER_PROVIDER = "HINDSIGHT_API_RERANKER_PROVIDER"
 ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
+ENV_RERANKER_LOCAL_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT"
 ENV_RERANKER_TEI_URL = "HINDSIGHT_API_RERANKER_TEI_URL"
+ENV_RERANKER_TEI_BATCH_SIZE = "HINDSIGHT_API_RERANKER_TEI_BATCH_SIZE"
+ENV_RERANKER_TEI_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_TEI_MAX_CONCURRENT"
+ENV_RERANKER_MAX_CANDIDATES = "HINDSIGHT_API_RERANKER_MAX_CANDIDATES"
+ENV_RERANKER_FLASHRANK_MODEL = "HINDSIGHT_API_RERANKER_FLASHRANK_MODEL"
+ENV_RERANKER_FLASHRANK_CACHE_DIR = "HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR"

 ENV_HOST = "HINDSIGHT_API_HOST"
 ENV_PORT = "HINDSIGHT_API_PORT"
 ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
+ENV_WORKERS = "HINDSIGHT_API_WORKERS"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
 ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
+ENV_MPFP_TOP_K_NEIGHBORS = "HINDSIGHT_API_MPFP_TOP_K_NEIGHBORS"
+ENV_RECALL_MAX_CONCURRENT = "HINDSIGHT_API_RECALL_MAX_CONCURRENT"
+ENV_RECALL_CONNECTION_BUDGET = "HINDSIGHT_API_RECALL_CONNECTION_BUDGET"
+ENV_MCP_LOCAL_BANK_ID = "HINDSIGHT_API_MCP_LOCAL_BANK_ID"
+ENV_MCP_INSTRUCTIONS = "HINDSIGHT_API_MCP_INSTRUCTIONS"
+
+# Observation thresholds
+ENV_OBSERVATION_MIN_FACTS = "HINDSIGHT_API_OBSERVATION_MIN_FACTS"
+ENV_OBSERVATION_TOP_ENTITIES = "HINDSIGHT_API_OBSERVATION_TOP_ENTITIES"
+
+# Retain settings
+ENV_RETAIN_MAX_COMPLETION_TOKENS = "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"
+ENV_RETAIN_CHUNK_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_SIZE"
+ENV_RETAIN_EXTRACT_CAUSAL_LINKS = "HINDSIGHT_API_RETAIN_EXTRACT_CAUSAL_LINKS"
+ENV_RETAIN_EXTRACTION_MODE = "HINDSIGHT_API_RETAIN_EXTRACTION_MODE"
+ENV_RETAIN_OBSERVATIONS_ASYNC = "HINDSIGHT_API_RETAIN_OBSERVATIONS_ASYNC"
+
+# Optimization flags
+ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
+ENV_LAZY_RERANKER = "HINDSIGHT_API_LAZY_RERANKER"
+
+# Database migrations
+ENV_RUN_MIGRATIONS_ON_STARTUP = "HINDSIGHT_API_RUN_MIGRATIONS_ON_STARTUP"
+
+# Database connection pool
+ENV_DB_POOL_MIN_SIZE = "HINDSIGHT_API_DB_POOL_MIN_SIZE"
+ENV_DB_POOL_MAX_SIZE = "HINDSIGHT_API_DB_POOL_MAX_SIZE"
+ENV_DB_COMMAND_TIMEOUT = "HINDSIGHT_API_DB_COMMAND_TIMEOUT"
+ENV_DB_ACQUIRE_TIMEOUT = "HINDSIGHT_API_DB_ACQUIRE_TIMEOUT"
+
+# Background task processing
+ENV_TASK_BACKEND = "HINDSIGHT_API_TASK_BACKEND"
+ENV_TASK_BACKEND_MEMORY_BATCH_SIZE = "HINDSIGHT_API_TASK_BACKEND_MEMORY_BATCH_SIZE"
+ENV_TASK_BACKEND_MEMORY_BATCH_INTERVAL = "HINDSIGHT_API_TASK_BACKEND_MEMORY_BATCH_INTERVAL"

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

 DEFAULT_EMBEDDINGS_PROVIDER = "local"
 DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
+DEFAULT_EMBEDDINGS_OPENAI_MODEL = "text-embedding-3-small"
+DEFAULT_EMBEDDING_DIMENSION = 384

 DEFAULT_RERANKER_PROVIDER = "local"
 DEFAULT_RERANKER_LOCAL_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4  # Limit concurrent CPU-bound reranking to prevent thrashing
+DEFAULT_RERANKER_TEI_BATCH_SIZE = 128
+DEFAULT_RERANKER_TEI_MAX_CONCURRENT = 8
+DEFAULT_RERANKER_MAX_CANDIDATES = 300
+DEFAULT_RERANKER_FLASHRANK_MODEL = "ms-marco-MiniLM-L-12-v2"  # Best balance of speed and quality
+DEFAULT_RERANKER_FLASHRANK_CACHE_DIR = None  # Use default cache directory
+
+DEFAULT_EMBEDDINGS_COHERE_MODEL = "embed-english-v3.0"
+DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"

 DEFAULT_HOST = "0.0.0.0"
 DEFAULT_PORT = 8888
 DEFAULT_LOG_LEVEL = "info"
+DEFAULT_WORKERS = 1
 DEFAULT_MCP_ENABLED = True
-DEFAULT_GRAPH_RETRIEVER = "bfs"  # Options: "bfs", "mpfp"
+DEFAULT_GRAPH_RETRIEVER = "link_expansion"  # Options: "link_expansion", "mpfp", "bfs"
+DEFAULT_MPFP_TOP_K_NEIGHBORS = 20  # Fan-out limit per node in MPFP graph traversal
+DEFAULT_RECALL_MAX_CONCURRENT = 32  # Max concurrent recall operations per worker
+DEFAULT_RECALL_CONNECTION_BUDGET = 4  # Max concurrent DB connections per recall operation
+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_RETAIN_CHUNK_SIZE = 3000  # Max chars per chunk for fact extraction
+DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS = True  # Extract causal links between facts
+DEFAULT_RETAIN_EXTRACTION_MODE = "concise"  # Extraction mode: "concise" or "verbose"
+RETAIN_EXTRACTION_MODES = ("concise", "verbose")  # Allowed extraction modes
+DEFAULT_RETAIN_OBSERVATIONS_ASYNC = False  # Run observation generation async (after retain completes)
+
+# Database migrations
+DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
+
+# Database connection pool
+DEFAULT_DB_POOL_MIN_SIZE = 5
+DEFAULT_DB_POOL_MAX_SIZE = 100
+DEFAULT_DB_COMMAND_TIMEOUT = 60  # seconds
+DEFAULT_DB_ACQUIRE_TIMEOUT = 30  # seconds
+
+# Background task processing
+DEFAULT_TASK_BACKEND = "memory"  # Options: "memory", "noop"
+DEFAULT_TASK_BACKEND_MEMORY_BATCH_SIZE = 10
+DEFAULT_TASK_BACKEND_MEMORY_BATCH_INTERVAL = 1.0  # seconds
+
+# 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
+
+
+def _validate_extraction_mode(mode: str) -> str:
+    """Validate and normalize extraction mode."""
+    mode_lower = mode.lower()
+    if mode_lower not in RETAIN_EXTRACTION_MODES:
+        logger.warning(
+            f"Invalid extraction mode '{mode}', must be one of {RETAIN_EXTRACTION_MODES}. "
+            f"Defaulting to '{DEFAULT_RETAIN_EXTRACTION_MODE}'."
+        )
+        return DEFAULT_RETAIN_EXTRACTION_MODE
+    return mode_lower


@dataclass
@@ -59,21 +201,37 @@ class HindsightConfig:
    # Database
    database_url: str

-    # LLM
+    # LLM (default, used as fallback for per-operation config)
    llm_provider: str
-    llm_api_key: Optional[str]
+    llm_api_key: str | None
    llm_model: str
-    llm_base_url: Optional[str]
+    llm_base_url: str | None
+    llm_max_concurrent: int
+    llm_timeout: float
+
+    # Per-operation LLM configuration (None = use default LLM config)
+    retain_llm_provider: str | None
+    retain_llm_api_key: str | None
+    retain_llm_model: str | None
+    retain_llm_base_url: str | None
+
+    reflect_llm_provider: str | None
+    reflect_llm_api_key: str | None
+    reflect_llm_model: str | None
+    reflect_llm_base_url: str | None

    # Embeddings
    embeddings_provider: str
    embeddings_local_model: str
-    embeddings_tei_url: Optional[str]
+    embeddings_tei_url: str | None

    # Reranker
    reranker_provider: str
    reranker_local_model: str
-    reranker_tei_url: Optional[str]
+    reranker_tei_url: str | None
+    reranker_tei_batch_size: int
+    reranker_tei_max_concurrent: int
+    reranker_max_candidates: int

    # Server
    host: str
@@ -83,6 +241,38 @@ class HindsightConfig:

    # Recall
    graph_retriever: str
+    mpfp_top_k_neighbors: int
+    recall_max_concurrent: int
+    recall_connection_budget: int
+
+    # Observation thresholds
+    observation_min_facts: int
+    observation_top_entities: int
+
+    # Retain settings
+    retain_max_completion_tokens: int
+    retain_chunk_size: int
+    retain_extract_causal_links: bool
+    retain_extraction_mode: str
+    retain_observations_async: bool
+
+    # Optimization flags
+    skip_llm_verification: bool
+    lazy_reranker: bool
+
+    # Database migrations
+    run_migrations_on_startup: bool
+
+    # Database connection pool
+    db_pool_min_size: int
+    db_pool_max_size: int
+    db_command_timeout: int
+    db_acquire_timeout: int
+
+    # Background task processing
+    task_backend: str
+    task_backend_memory_batch_size: int
+    task_backend_memory_batch_interval: float

    @classmethod
    def from_env(cls) -> "HindsightConfig":
@@ -90,31 +280,86 @@ class HindsightConfig:
        return cls(
            # Database
            database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
-
            # LLM
            llm_provider=os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER),
            llm_api_key=os.getenv(ENV_LLM_API_KEY),
            llm_model=os.getenv(ENV_LLM_MODEL, DEFAULT_LLM_MODEL),
            llm_base_url=os.getenv(ENV_LLM_BASE_URL) or None,
-
+            llm_max_concurrent=int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_CONCURRENT))),
+            llm_timeout=float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT))),
+            # Per-operation LLM config (None = use default)
+            retain_llm_provider=os.getenv(ENV_RETAIN_LLM_PROVIDER) or None,
+            retain_llm_api_key=os.getenv(ENV_RETAIN_LLM_API_KEY) or None,
+            retain_llm_model=os.getenv(ENV_RETAIN_LLM_MODEL) or None,
+            retain_llm_base_url=os.getenv(ENV_RETAIN_LLM_BASE_URL) or None,
+            reflect_llm_provider=os.getenv(ENV_REFLECT_LLM_PROVIDER) or None,
+            reflect_llm_api_key=os.getenv(ENV_REFLECT_LLM_API_KEY) or None,
+            reflect_llm_model=os.getenv(ENV_REFLECT_LLM_MODEL) or None,
+            reflect_llm_base_url=os.getenv(ENV_REFLECT_LLM_BASE_URL) or None,
            # Embeddings
            embeddings_provider=os.getenv(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER),
            embeddings_local_model=os.getenv(ENV_EMBEDDINGS_LOCAL_MODEL, DEFAULT_EMBEDDINGS_LOCAL_MODEL),
            embeddings_tei_url=os.getenv(ENV_EMBEDDINGS_TEI_URL),
-
            # Reranker
            reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
            reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
            reranker_tei_url=os.getenv(ENV_RERANKER_TEI_URL),
-
+            reranker_tei_batch_size=int(os.getenv(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE))),
+            reranker_tei_max_concurrent=int(
+                os.getenv(ENV_RERANKER_TEI_MAX_CONCURRENT, str(DEFAULT_RERANKER_TEI_MAX_CONCURRENT))
+            ),
+            reranker_max_candidates=int(os.getenv(ENV_RERANKER_MAX_CANDIDATES, str(DEFAULT_RERANKER_MAX_CANDIDATES))),
            # Server
            host=os.getenv(ENV_HOST, DEFAULT_HOST),
            port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
            log_level=os.getenv(ENV_LOG_LEVEL, DEFAULT_LOG_LEVEL),
            mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).lower() == "true",
-
            # Recall
            graph_retriever=os.getenv(ENV_GRAPH_RETRIEVER, DEFAULT_GRAPH_RETRIEVER),
+            mpfp_top_k_neighbors=int(os.getenv(ENV_MPFP_TOP_K_NEIGHBORS, str(DEFAULT_MPFP_TOP_K_NEIGHBORS))),
+            recall_max_concurrent=int(os.getenv(ENV_RECALL_MAX_CONCURRENT, str(DEFAULT_RECALL_MAX_CONCURRENT))),
+            recall_connection_budget=int(
+                os.getenv(ENV_RECALL_CONNECTION_BUDGET, str(DEFAULT_RECALL_CONNECTION_BUDGET))
+            ),
+            # Optimization flags
+            skip_llm_verification=os.getenv(ENV_SKIP_LLM_VERIFICATION, "false").lower() == "true",
+            lazy_reranker=os.getenv(ENV_LAZY_RERANKER, "false").lower() == "true",
+            # Observation thresholds
+            observation_min_facts=int(os.getenv(ENV_OBSERVATION_MIN_FACTS, str(DEFAULT_OBSERVATION_MIN_FACTS))),
+            observation_top_entities=int(
+                os.getenv(ENV_OBSERVATION_TOP_ENTITIES, str(DEFAULT_OBSERVATION_TOP_ENTITIES))
+            ),
+            # Retain settings
+            retain_max_completion_tokens=int(
+                os.getenv(ENV_RETAIN_MAX_COMPLETION_TOKENS, str(DEFAULT_RETAIN_MAX_COMPLETION_TOKENS))
+            ),
+            retain_chunk_size=int(os.getenv(ENV_RETAIN_CHUNK_SIZE, str(DEFAULT_RETAIN_CHUNK_SIZE))),
+            retain_extract_causal_links=os.getenv(
+                ENV_RETAIN_EXTRACT_CAUSAL_LINKS, str(DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS)
+            ).lower()
+            == "true",
+            retain_extraction_mode=_validate_extraction_mode(
+                os.getenv(ENV_RETAIN_EXTRACTION_MODE, DEFAULT_RETAIN_EXTRACTION_MODE)
+            ),
+            retain_observations_async=os.getenv(
+                ENV_RETAIN_OBSERVATIONS_ASYNC, str(DEFAULT_RETAIN_OBSERVATIONS_ASYNC)
+            ).lower()
+            == "true",
+            # Database migrations
+            run_migrations_on_startup=os.getenv(ENV_RUN_MIGRATIONS_ON_STARTUP, "true").lower() == "true",
+            # Database connection pool
+            db_pool_min_size=int(os.getenv(ENV_DB_POOL_MIN_SIZE, str(DEFAULT_DB_POOL_MIN_SIZE))),
+            db_pool_max_size=int(os.getenv(ENV_DB_POOL_MAX_SIZE, str(DEFAULT_DB_POOL_MAX_SIZE))),
+            db_command_timeout=int(os.getenv(ENV_DB_COMMAND_TIMEOUT, str(DEFAULT_DB_COMMAND_TIMEOUT))),
+            db_acquire_timeout=int(os.getenv(ENV_DB_ACQUIRE_TIMEOUT, str(DEFAULT_DB_ACQUIRE_TIMEOUT))),
+            # Background task processing
+            task_backend=os.getenv(ENV_TASK_BACKEND, DEFAULT_TASK_BACKEND),
+            task_backend_memory_batch_size=int(
+                os.getenv(ENV_TASK_BACKEND_MEMORY_BATCH_SIZE, str(DEFAULT_TASK_BACKEND_MEMORY_BATCH_SIZE))
+            ),
+            task_backend_memory_batch_interval=float(
+                os.getenv(ENV_TASK_BACKEND_MEMORY_BATCH_INTERVAL, str(DEFAULT_TASK_BACKEND_MEMORY_BATCH_INTERVAL))
+            ),
        )

    def get_llm_base_url(self) -> str:
@@ -127,6 +372,8 @@ 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 ""

@@ -146,18 +393,40 @@ class HindsightConfig:
        """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"
+            format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+            force=True,  # Override any existing configuration
        )

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


+# Cached config instance
+_config_cache: HindsightConfig | None = None
+
+
 def get_config() -> HindsightConfig:
-    """Get the 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
@@ -7,24 +7,30 @@ This package contains all the implementation details of the memory engine:
 - Supporting modules: embeddings, cross_encoder, entity_resolver, etc.
 """

-from .memory_engine import MemoryEngine
+from .cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
 from .db_utils import acquire_with_retry
 from .embeddings import Embeddings, LocalSTEmbeddings, RemoteTEIEmbeddings
-from .cross_encoder import CrossEncoderModel, LocalSTCrossEncoder, RemoteTEICrossEncoder
+from .llm_wrapper import LLMConfig
+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 (
-    SearchTrace,
-    QueryInfo,
    EntryPoint,
-    NodeVisit,
-    WeightComponents,
    LinkInfo,
+    NodeVisit,
    PruningDecision,
-    SearchSummary,
+    QueryInfo,
    SearchPhaseMetrics,
+    SearchSummary,
+    SearchTrace,
+    WeightComponents,
 )
 from .search.tracer import SearchTracer
-from .llm_wrapper import LLMConfig
-from .response_models import RecallResult, ReflectResult, MemoryFact

 __all__ = [
    "MemoryEngine",
@@ -49,4 +55,9 @@ __all__ = [
    "RecallResult",
    "ReflectResult",
    "MemoryFact",
+    # Schema safety utilities
+    "fq_table",
+    "get_current_schema",
+    "validate_sql_schema",
+    "UnqualifiedTableError",
 ]
@@ -5,19 +5,34 @@ Provides an interface for reranking with different backends.

 Configuration via environment variables - see hindsight_api.config for all env var names.
 """
-from abc import ABC, abstractmethod
-from typing import List, Tuple, Optional
+
+import asyncio
 import logging
 import os
+from abc import ABC, abstractmethod
+from concurrent.futures import ThreadPoolExecutor

 import httpx

 from ..config import (
-    ENV_RERANKER_PROVIDER,
-    ENV_RERANKER_LOCAL_MODEL,
-    ENV_RERANKER_TEI_URL,
-    DEFAULT_RERANKER_PROVIDER,
+    DEFAULT_RERANKER_COHERE_MODEL,
+    DEFAULT_RERANKER_FLASHRANK_CACHE_DIR,
+    DEFAULT_RERANKER_FLASHRANK_MODEL,
+    DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT,
    DEFAULT_RERANKER_LOCAL_MODEL,
+    DEFAULT_RERANKER_PROVIDER,
+    DEFAULT_RERANKER_TEI_BATCH_SIZE,
+    DEFAULT_RERANKER_TEI_MAX_CONCURRENT,
+    ENV_COHERE_API_KEY,
+    ENV_RERANKER_COHERE_MODEL,
+    ENV_RERANKER_FLASHRANK_CACHE_DIR,
+    ENV_RERANKER_FLASHRANK_MODEL,
+    ENV_RERANKER_LOCAL_MAX_CONCURRENT,
+    ENV_RERANKER_LOCAL_MODEL,
+    ENV_RERANKER_PROVIDER,
+    ENV_RERANKER_TEI_BATCH_SIZE,
+    ENV_RERANKER_TEI_MAX_CONCURRENT,
+    ENV_RERANKER_TEI_URL,
 )

 logger = logging.getLogger(__name__)
@@ -47,7 +62,7 @@ class CrossEncoderModel(ABC):
        pass

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

@@ -70,25 +85,34 @@ class LocalSTCrossEncoder(CrossEncoderModel):
    - Fast inference (~80ms for 100 pairs on CPU)
    - Small model (80MB)
    - Trained for passage re-ranking
+
+    Uses a dedicated thread pool to limit concurrent CPU-bound work.
    """

-    def __init__(self, model_name: Optional[str] = None):
+    # Shared executor across all instances (one model loaded anyway)
+    _executor: ThreadPoolExecutor | None = None
+    _max_concurrent: int = 4  # Limit concurrent CPU-bound reranking calls
+
+    def __init__(self, model_name: str | None = None, max_concurrent: int = 4):
        """
        Initialize local SentenceTransformers cross-encoder.

        Args:
            model_name: Name of the CrossEncoder model to use.
                       Default: cross-encoder/ms-marco-MiniLM-L-6-v2
+            max_concurrent: Maximum concurrent reranking calls (default: 2).
+                           Higher values may cause CPU thrashing under load.
        """
        self.model_name = model_name or DEFAULT_RERANKER_LOCAL_MODEL
        self._model = None
+        LocalSTCrossEncoder._max_concurrent = max_concurrent

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

    async def initialize(self) -> None:
-        """Load the cross-encoder model."""
+        """Load the cross-encoder model and initialize the executor."""
        if self._model is not None:
            return

@@ -100,14 +124,30 @@ class LocalSTCrossEncoder(CrossEncoderModel):
                "Install it with: pip install sentence-transformers"
            )

+        # Note: We use CPU even when GPU/MPS is available because:
+        # 1. The reranker model (MiniLM) is tiny (~22M params)
+        # 2. Batch sizes are small (~100-200 pairs)
+        # 3. Data transfer overhead to GPU outweighs compute benefit
+        # 4. CPU inference is actually faster for this workload
        logger.info(f"Reranker: initializing local provider with model {self.model_name}")
        self._model = CrossEncoder(self.model_name)
-        logger.info("Reranker: local provider initialized")

-    def predict(self, pairs: List[Tuple[str, str]]) -> List[float]:
+        # Initialize shared executor (limited workers naturally limits concurrency)
+        if LocalSTCrossEncoder._executor is None:
+            LocalSTCrossEncoder._executor = ThreadPoolExecutor(
+                max_workers=LocalSTCrossEncoder._max_concurrent,
+                thread_name_prefix="reranker",
+            )
+            logger.info(f"Reranker: local provider initialized (max_concurrent={LocalSTCrossEncoder._max_concurrent})")
+        else:
+            logger.info("Reranker: local provider initialized (using existing executor)")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
        """
        Score query-document pairs for relevance.

+        Uses a dedicated thread pool with limited workers to prevent CPU thrashing.
+
        Args:
            pairs: List of (query, document) tuples to score

@@ -116,8 +156,14 @@ class LocalSTCrossEncoder(CrossEncoderModel):
        """
        if self._model is None:
            raise RuntimeError("Reranker not initialized. Call initialize() first.")
-        scores = self._model.predict(pairs, show_progress_bar=False)
-        return scores.tolist() if hasattr(scores, 'tolist') else list(scores)
+
+        # Use dedicated executor - limited workers naturally limits concurrency
+        loop = asyncio.get_event_loop()
+        scores = await loop.run_in_executor(
+            LocalSTCrossEncoder._executor,
+            lambda: self._model.predict(pairs, show_progress_bar=False),
+        )
+        return scores.tolist() if hasattr(scores, "tolist") else list(scores)


 class RemoteTEICrossEncoder(CrossEncoderModel):
@@ -128,13 +174,21 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
    See: https://github.com/huggingface/text-embeddings-inference

    Note: The TEI server must be running a cross-encoder/reranker model.
+
+    Requests are made in parallel with configurable batch size and max concurrency (backpressure).
+    Uses a GLOBAL semaphore to limit concurrent requests across ALL recall operations.
    """

+    # Global semaphore shared across all instances and calls to prevent thundering herd
+    _global_semaphore: asyncio.Semaphore | None = None
+    _global_max_concurrent: int = DEFAULT_RERANKER_TEI_MAX_CONCURRENT
+
    def __init__(
        self,
        base_url: str,
        timeout: float = 30.0,
-        batch_size: int = 32,
+        batch_size: int = DEFAULT_RERANKER_TEI_BATCH_SIZE,
+        max_concurrent: int = DEFAULT_RERANKER_TEI_MAX_CONCURRENT,
        max_retries: int = 3,
        retry_delay: float = 0.5,
    ):
@@ -144,75 +198,237 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
        Args:
            base_url: Base URL of the TEI server (e.g., "http://localhost:8080")
            timeout: Request timeout in seconds (default: 30.0)
-            batch_size: Maximum batch size for rerank requests (default: 32)
+            batch_size: Maximum batch size for rerank requests (default: 128)
+            max_concurrent: Maximum concurrent requests for backpressure (default: 8).
+                           This is a GLOBAL limit across all parallel recall operations.
            max_retries: Maximum number of retries for failed requests (default: 3)
            retry_delay: Initial delay between retries in seconds, doubles each retry (default: 0.5)
        """
        self.base_url = base_url.rstrip("/")
        self.timeout = timeout
        self.batch_size = batch_size
+        self.max_concurrent = max_concurrent
        self.max_retries = max_retries
        self.retry_delay = retry_delay
-        self._client: Optional[httpx.Client] = None
-        self._model_id: Optional[str] = None
+        self._async_client: httpx.AsyncClient | None = None
+        self._model_id: str | None = None
+
+        # Update global semaphore if max_concurrent changed
+        if (
+            RemoteTEICrossEncoder._global_semaphore is None
+            or RemoteTEICrossEncoder._global_max_concurrent != max_concurrent
+        ):
+            RemoteTEICrossEncoder._global_max_concurrent = max_concurrent
+            RemoteTEICrossEncoder._global_semaphore = asyncio.Semaphore(max_concurrent)

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

-    def _request_with_retry(self, method: str, url: str, **kwargs) -> httpx.Response:
-        """Make an HTTP request with automatic retries on transient errors."""
-        import time
+    async def _async_request_with_retry(
+        self,
+        client: httpx.AsyncClient,
+        semaphore: asyncio.Semaphore,
+        method: str,
+        url: str,
+        **kwargs,
+    ) -> httpx.Response:
+        """Make an async HTTP request with automatic retries on transient errors and semaphore for backpressure."""
        last_error = None
        delay = self.retry_delay

-        for attempt in range(self.max_retries + 1):
-            try:
-                if method == "GET":
-                    response = self._client.get(url, **kwargs)
-                else:
-                    response = self._client.post(url, **kwargs)
-                response.raise_for_status()
-                return response
-            except (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout) as e:
-                last_error = e
-                if attempt < self.max_retries:
-                    logger.warning(f"TEI request failed (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s...")
-                    time.sleep(delay)
-                    delay *= 2  # Exponential backoff
-            except httpx.HTTPStatusError as e:
-                # Retry on 5xx server errors
-                if e.response.status_code >= 500 and attempt < self.max_retries:
+        async with semaphore:
+            for attempt in range(self.max_retries + 1):
+                try:
+                    if method == "GET":
+                        response = await client.get(url, **kwargs)
+                    else:
+                        response = await client.post(url, **kwargs)
+                    response.raise_for_status()
+                    return response
+                except (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout) as e:
                    last_error = e
-                    logger.warning(f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s...")
-                    time.sleep(delay)
-                    delay *= 2
-                else:
-                    raise
+                    if attempt < self.max_retries:
+                        logger.warning(
+                            f"TEI request failed (attempt {attempt + 1}/{self.max_retries + 1}): {e}. "
+                            f"Retrying in {delay}s..."
+                        )
+                        await asyncio.sleep(delay)
+                        delay *= 2  # Exponential backoff
+                except httpx.HTTPStatusError as e:
+                    # Retry on 5xx server errors
+                    if e.response.status_code >= 500 and attempt < self.max_retries:
+                        last_error = e
+                        logger.warning(
+                            f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. "
+                            f"Retrying in {delay}s..."
+                        )
+                        await asyncio.sleep(delay)
+                        delay *= 2
+                    else:
+                        raise

        raise last_error

    async def initialize(self) -> None:
        """Initialize the HTTP client and verify server connectivity."""
-        if self._client is not None:
+        if self._async_client is not None:
            return

-        logger.info(f"Reranker: initializing TEI provider at {self.base_url}")
-        self._client = httpx.Client(timeout=self.timeout)
+        logger.info(
+            f"Reranker: initializing TEI provider at {self.base_url} "
+            f"(batch_size={self.batch_size}, max_concurrent={self.max_concurrent})"
+        )
+        self._async_client = httpx.AsyncClient(timeout=self.timeout)

        # Verify server is reachable and get model info
+        # Use a temporary semaphore for initialization
+        init_semaphore = asyncio.Semaphore(1)
        try:
-            response = self._request_with_retry("GET", f"{self.base_url}/info")
+            response = await self._async_request_with_retry(
+                self._async_client, init_semaphore, "GET", f"{self.base_url}/info"
+            )
            info = response.json()
            self._model_id = info.get("model_id", "unknown")
            logger.info(f"Reranker: TEI provider initialized (model: {self._model_id})")
        except httpx.HTTPError as e:
+            self._async_client = None
            raise RuntimeError(f"Failed to connect to TEI server at {self.base_url}: {e}")

-    def predict(self, pairs: List[Tuple[str, str]]) -> List[float]:
+    async def _rerank_query_group(
+        self,
+        client: httpx.AsyncClient,
+        semaphore: asyncio.Semaphore,
+        query: str,
+        texts: list[str],
+    ) -> list[tuple[int, float]]:
+        """Rerank a single query group and return list of (original_index, score) tuples."""
+        try:
+            response = await self._async_request_with_retry(
+                client,
+                semaphore,
+                "POST",
+                f"{self.base_url}/rerank",
+                json={
+                    "query": query,
+                    "texts": texts,
+                    "return_text": False,
+                },
+            )
+            results = response.json()
+            # TEI returns results sorted by score descending, with original index
+            return [(result["index"], result["score"]) for result in results]
+        except httpx.HTTPError as e:
+            raise RuntimeError(f"TEI rerank request failed: {e}")
+
+    async def _predict_async(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """Async implementation of predict that runs requests in parallel with backpressure."""
+        if not pairs:
+            return []
+
+        # Group all pairs by query
+        query_groups: dict[str, list[tuple[int, str]]] = {}
+        for idx, (query, text) in enumerate(pairs):
+            if query not in query_groups:
+                query_groups[query] = []
+            query_groups[query].append((idx, text))
+
+        # Split each query group into batches
+        tasks_info: list[tuple[str, list[int], list[str]]] = []  # (query, indices, texts)
+        for query, indexed_texts in query_groups.items():
+            indices = [idx for idx, _ in indexed_texts]
+            texts = [text for _, text in indexed_texts]
+
+            # Split into batches
+            for i in range(0, len(texts), self.batch_size):
+                batch_indices = indices[i : i + self.batch_size]
+                batch_texts = texts[i : i + self.batch_size]
+                tasks_info.append((query, batch_indices, batch_texts))
+
+        # Run all requests in parallel with GLOBAL semaphore for backpressure
+        # This ensures max_concurrent is respected across ALL parallel recall operations
+        all_scores = [0.0] * len(pairs)
+        semaphore = RemoteTEICrossEncoder._global_semaphore
+
+        tasks = [
+            self._rerank_query_group(self._async_client, semaphore, query, texts) for query, _, texts in tasks_info
+        ]
+        results = await asyncio.gather(*tasks)
+
+        # Map scores back to original positions
+        for (_, indices, _), result_scores in zip(tasks_info, results):
+            for original_idx_in_batch, score in result_scores:
+                global_idx = indices[original_idx_in_batch]
+                all_scores[global_idx] = score
+
+        return all_scores
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
        """
        Score query-document pairs using the remote TEI reranker.

+        Requests are made in parallel with configurable backpressure.
+
+        Args:
+            pairs: List of (query, document) tuples to score
+
+        Returns:
+            List of relevance scores
+        """
+        if self._async_client is None:
+            raise RuntimeError("Reranker not initialized. Call initialize() first.")
+
+        return await self._predict_async(pairs)
+
+
+class CohereCrossEncoder(CrossEncoderModel):
+    """
+    Cohere cross-encoder implementation using the Cohere Rerank API.
+
+    Supports rerank-english-v3.0 and rerank-multilingual-v3.0 models.
+    """
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_RERANKER_COHERE_MODEL,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize Cohere cross-encoder client.
+
+        Args:
+            api_key: Cohere API key
+            model: Cohere rerank model name (default: rerank-english-v3.0)
+            timeout: Request timeout in seconds (default: 60.0)
+        """
+        self.api_key = api_key
+        self.model = model
+        self.timeout = timeout
+        self._client = None
+
+    @property
+    def provider_name(self) -> str:
+        return "cohere"
+
+    async def initialize(self) -> None:
+        """Initialize the Cohere client."""
+        if self._client is not None:
+            return
+
+        try:
+            import cohere
+        except ImportError:
+            raise ImportError("cohere is required for CohereCrossEncoder. Install it with: pip install cohere")
+
+        logger.info(f"Reranker: initializing Cohere provider with model {self.model}")
+        self._client = cohere.Client(api_key=self.api_key, timeout=self.timeout)
+        logger.info("Reranker: Cohere provider initialized")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Score query-document pairs using the Cohere Rerank API.
+
        Args:
            pairs: List of (query, document) tuples to score

@@ -225,54 +441,206 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
        if not pairs:
            return []

-        all_scores = []
+        # Run sync Cohere API calls in thread pool
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(None, self._predict_sync, pairs)

-        # Process in batches
-        for i in range(0, len(pairs), self.batch_size):
-            batch = pairs[i:i + self.batch_size]
+    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """Synchronous predict implementation for Cohere API."""
+        # Group pairs by query for efficient batching
+        # Cohere rerank expects one query with multiple documents
+        query_groups: dict[str, list[tuple[int, str]]] = {}
+        for idx, (query, text) in enumerate(pairs):
+            if query not in query_groups:
+                query_groups[query] = []
+            query_groups[query].append((idx, text))

-            # TEI rerank endpoint expects query and texts separately
-            # All pairs in a batch should have the same query for optimal performance
-            # but we handle mixed queries by making separate requests per unique query
-            query_groups: dict[str, list[tuple[int, str]]] = {}
-            for idx, (query, text) in enumerate(batch):
-                if query not in query_groups:
-                    query_groups[query] = []
-                query_groups[query].append((idx, text))
+        all_scores = [0.0] * len(pairs)

-            batch_scores = [0.0] * len(batch)
+        for query, indexed_texts in query_groups.items():
+            texts = [text for _, text in indexed_texts]
+            indices = [idx for idx, _ in indexed_texts]

-            for query, indexed_texts in query_groups.items():
-                texts = [text for _, text in indexed_texts]
-                indices = [idx for idx, _ in indexed_texts]
+            response = self._client.rerank(
+                query=query,
+                documents=texts,
+                model=self.model,
+                return_documents=False,
+            )

-                try:
-                    response = self._request_with_retry(
-                        "POST",
-                        f"{self.base_url}/rerank",
-                        json={
-                            "query": query,
-                            "texts": texts,
-                            "return_text": False,
-                        },
-                    )
-                    results = response.json()
-
-                    # TEI returns results sorted by score descending, with original index
-                    for result in results:
-                        original_idx = result["index"]
-                        score = result["score"]
-                        # Map back to batch position
-                        batch_scores[indices[original_idx]] = score
-
-                except httpx.HTTPError as e:
-                    raise RuntimeError(f"TEI rerank request failed: {e}")
-
-            all_scores.extend(batch_scores)
+            # Map scores back to original positions
+            for result in response.results:
+                original_idx = result.index
+                score = result.relevance_score
+                all_scores[indices[original_idx]] = score

        return all_scores


+class RRFPassthroughCrossEncoder(CrossEncoderModel):
+    """
+    Passthrough cross-encoder that preserves RRF scores without neural reranking.
+
+    This is useful for:
+    - Testing retrieval quality without reranking overhead
+    - Deployments where reranking latency is unacceptable
+    - Debugging to isolate retrieval vs reranking issues
+    """
+
+    def __init__(self):
+        """Initialize RRF passthrough cross-encoder."""
+        pass
+
+    @property
+    def provider_name(self) -> str:
+        return "rrf"
+
+    async def initialize(self) -> None:
+        """No initialization needed."""
+        logger.info("Reranker: RRF passthrough provider initialized (neural reranking disabled)")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Return neutral scores - actual ranking uses RRF scores from retrieval.
+
+        Args:
+            pairs: List of (query, document) tuples (ignored)
+
+        Returns:
+            List of 0.5 scores (neutral, lets RRF scores dominate)
+        """
+        # Return neutral scores so RRF ranking is preserved
+        return [0.5] * len(pairs)
+
+
+class FlashRankCrossEncoder(CrossEncoderModel):
+    """
+    FlashRank cross-encoder implementation.
+
+    FlashRank is an ultra-lite reranking library that runs on CPU without
+    requiring PyTorch or Transformers. It's ideal for serverless deployments
+    with minimal cold-start overhead.
+
+    Available models:
+    - ms-marco-TinyBERT-L-2-v2: Fastest, ~4MB
+    - ms-marco-MiniLM-L-12-v2: Best quality, ~34MB (default)
+    - rank-T5-flan: Best zero-shot, ~110MB
+    - ms-marco-MultiBERT-L-12: Multi-lingual, ~150MB
+    """
+
+    # Shared executor for CPU-bound reranking
+    _executor: ThreadPoolExecutor | None = None
+    _max_concurrent: int = 4
+
+    def __init__(
+        self,
+        model_name: str | None = None,
+        cache_dir: str | None = None,
+        max_length: int = 512,
+        max_concurrent: int = 4,
+    ):
+        """
+        Initialize FlashRank cross-encoder.
+
+        Args:
+            model_name: FlashRank model name. Default: ms-marco-MiniLM-L-12-v2
+            cache_dir: Directory to cache downloaded models. Default: system cache
+            max_length: Maximum sequence length for reranking. Default: 512
+            max_concurrent: Maximum concurrent reranking calls. Default: 4
+        """
+        self.model_name = model_name or DEFAULT_RERANKER_FLASHRANK_MODEL
+        self.cache_dir = cache_dir or DEFAULT_RERANKER_FLASHRANK_CACHE_DIR
+        self.max_length = max_length
+        self._ranker = None
+        FlashRankCrossEncoder._max_concurrent = max_concurrent
+
+    @property
+    def provider_name(self) -> str:
+        return "flashrank"
+
+    async def initialize(self) -> None:
+        """Load the FlashRank model."""
+        if self._ranker is not None:
+            return
+
+        try:
+            from flashrank import Ranker  # type: ignore[import-untyped]
+        except ImportError:
+            raise ImportError("flashrank is required for FlashRankCrossEncoder. Install it with: pip install flashrank")
+
+        logger.info(f"Reranker: initializing FlashRank provider with model {self.model_name}")
+
+        # Initialize ranker with optional cache directory
+        ranker_kwargs = {"model_name": self.model_name, "max_length": self.max_length}
+        if self.cache_dir:
+            ranker_kwargs["cache_dir"] = self.cache_dir
+
+        self._ranker = Ranker(**ranker_kwargs)
+
+        # Initialize shared executor
+        if FlashRankCrossEncoder._executor is None:
+            FlashRankCrossEncoder._executor = ThreadPoolExecutor(
+                max_workers=FlashRankCrossEncoder._max_concurrent,
+                thread_name_prefix="flashrank",
+            )
+            logger.info(
+                f"Reranker: FlashRank provider initialized (max_concurrent={FlashRankCrossEncoder._max_concurrent})"
+            )
+        else:
+            logger.info("Reranker: FlashRank provider initialized (using existing executor)")
+
+    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """Synchronous predict - processes each query group."""
+        from flashrank import RerankRequest  # type: ignore[import-untyped]
+
+        if not pairs:
+            return []
+
+        # Group pairs by query
+        query_groups: dict[str, list[tuple[int, str]]] = {}
+        for idx, (query, text) in enumerate(pairs):
+            if query not in query_groups:
+                query_groups[query] = []
+            query_groups[query].append((idx, text))
+
+        all_scores = [0.0] * len(pairs)
+
+        for query, indexed_texts in query_groups.items():
+            # Build passages list for FlashRank
+            passages = [{"id": i, "text": text} for i, (_, text) in enumerate(indexed_texts)]
+            global_indices = [idx for idx, _ in indexed_texts]
+
+            # Create rerank request
+            request = RerankRequest(query=query, passages=passages)
+            results = self._ranker.rerank(request)
+
+            # Map scores back to original positions
+            for result in results:
+                local_idx = result["id"]
+                score = result["score"]
+                global_idx = global_indices[local_idx]
+                all_scores[global_idx] = score
+
+        return all_scores
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Score query-document pairs using FlashRank.
+
+        Args:
+            pairs: List of (query, document) tuples to score
+
+        Returns:
+            List of relevance scores (higher = more relevant)
+        """
+        if self._ranker is None:
+            raise RuntimeError("Reranker not initialized. Call initialize() first.")
+
+        # Run in thread pool to avoid blocking event loop
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(FlashRankCrossEncoder._executor, self._predict_sync, pairs)
+
+
 def create_cross_encoder_from_env() -> CrossEncoderModel:
    """
    Create a CrossEncoderModel instance based on environment variables.
@@ -287,15 +655,30 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
    if provider == "tei":
        url = os.environ.get(ENV_RERANKER_TEI_URL)
        if not url:
-            raise ValueError(
-                f"{ENV_RERANKER_TEI_URL} is required when {ENV_RERANKER_PROVIDER} is 'tei'"
-            )
-        return RemoteTEICrossEncoder(base_url=url)
+            raise ValueError(f"{ENV_RERANKER_TEI_URL} is required when {ENV_RERANKER_PROVIDER} is 'tei'")
+        batch_size = int(os.environ.get(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE)))
+        max_concurrent = int(os.environ.get(ENV_RERANKER_TEI_MAX_CONCURRENT, str(DEFAULT_RERANKER_TEI_MAX_CONCURRENT)))
+        return RemoteTEICrossEncoder(base_url=url, batch_size=batch_size, max_concurrent=max_concurrent)
    elif provider == "local":
        model = os.environ.get(ENV_RERANKER_LOCAL_MODEL)
        model_name = model or DEFAULT_RERANKER_LOCAL_MODEL
-        return LocalSTCrossEncoder(model_name=model_name)
+        max_concurrent = int(
+            os.environ.get(ENV_RERANKER_LOCAL_MAX_CONCURRENT, str(DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT))
+        )
+        return LocalSTCrossEncoder(model_name=model_name, max_concurrent=max_concurrent)
+    elif provider == "cohere":
+        api_key = os.environ.get(ENV_COHERE_API_KEY)
+        if not api_key:
+            raise ValueError(f"{ENV_COHERE_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'cohere'")
+        model = os.environ.get(ENV_RERANKER_COHERE_MODEL, DEFAULT_RERANKER_COHERE_MODEL)
+        return CohereCrossEncoder(api_key=api_key, model=model)
+    elif provider == "flashrank":
+        model = os.environ.get(ENV_RERANKER_FLASHRANK_MODEL, DEFAULT_RERANKER_FLASHRANK_MODEL)
+        cache_dir = os.environ.get(ENV_RERANKER_FLASHRANK_CACHE_DIR, DEFAULT_RERANKER_FLASHRANK_CACHE_DIR)
+        return FlashRankCrossEncoder(model_name=model, cache_dir=cache_dir)
+    elif provider == "rrf":
+        return RRFPassthroughCrossEncoder()
    else:
        raise ValueError(
-            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei'"
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'rrf'"
        )
@@ -0,0 +1,284 @@
+"""
+Database connection budget management.
+
+Limits concurrent database connections per operation to prevent
+a single operation (e.g., recall with parallel queries) from
+exhausting the connection pool.
+"""
+
+import asyncio
+import logging
+import uuid
+from contextlib import asynccontextmanager
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, AsyncIterator
+
+if TYPE_CHECKING:
+    import asyncpg
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class OperationBudget:
+    """
+    Tracks connection budget for a single operation.
+
+    Each operation gets a semaphore limiting its concurrent connections.
+    """
+
+    operation_id: str
+    max_connections: int
+    semaphore: asyncio.Semaphore = field(init=False)
+    active_count: int = field(default=0, init=False)
+
+    def __post_init__(self):
+        self.semaphore = asyncio.Semaphore(self.max_connections)
+
+
+class ConnectionBudgetManager:
+    """
+    Manages per-operation connection budgets.
+
+    Usage:
+        manager = ConnectionBudgetManager(default_budget=4)
+
+        # Start an operation
+        async with manager.operation(max_connections=2) as op:
+            # Acquire connections within the budget
+            async with op.acquire(pool) as conn:
+                await conn.fetch(...)
+
+            # Multiple connections respect the budget
+            async with op.acquire(pool) as conn1, op.acquire(pool) as conn2:
+                # At most 2 concurrent connections for this operation
+                ...
+    """
+
+    def __init__(self, default_budget: int = 4):
+        """
+        Initialize the budget manager.
+
+        Args:
+            default_budget: Default max connections per operation
+        """
+        self.default_budget = default_budget
+        self._operations: dict[str, OperationBudget] = {}
+        self._lock = asyncio.Lock()
+
+    @asynccontextmanager
+    async def operation(
+        self,
+        max_connections: int | None = None,
+        operation_id: str | None = None,
+    ) -> AsyncIterator["BudgetedOperation"]:
+        """
+        Create a budgeted operation context.
+
+        Args:
+            max_connections: Max concurrent connections for this operation.
+                           Defaults to manager's default_budget.
+            operation_id: Optional custom operation ID. Auto-generated if not provided.
+
+        Yields:
+            BudgetedOperation context for acquiring connections
+        """
+        op_id = operation_id or f"op-{uuid.uuid4().hex[:12]}"
+        budget = max_connections or self.default_budget
+
+        async with self._lock:
+            if op_id in self._operations:
+                raise ValueError(f"Operation {op_id} already exists")
+            self._operations[op_id] = OperationBudget(op_id, budget)
+
+        try:
+            yield BudgetedOperation(self, op_id)
+        finally:
+            async with self._lock:
+                self._operations.pop(op_id, None)
+
+    def _get_budget(self, operation_id: str) -> OperationBudget:
+        """Get budget for an operation (internal use)."""
+        budget = self._operations.get(operation_id)
+        if not budget:
+            raise ValueError(f"Operation {operation_id} not found")
+        return budget
+
+
+class BudgetedOperation:
+    """
+    A single operation with connection budget.
+
+    Provides methods to acquire connections within the budget.
+    """
+
+    def __init__(self, manager: ConnectionBudgetManager, operation_id: str):
+        self._manager = manager
+        self.operation_id = operation_id
+
+    @property
+    def budget(self) -> OperationBudget:
+        """Get the budget for this operation."""
+        return self._manager._get_budget(self.operation_id)
+
+    @asynccontextmanager
+    async def acquire(self, pool: "asyncpg.Pool") -> AsyncIterator["asyncpg.Connection"]:
+        """
+        Acquire a connection within the operation's budget.
+
+        Blocks if the operation has reached its connection limit.
+
+        Args:
+            pool: asyncpg connection pool
+
+        Yields:
+            Database connection
+        """
+        budget = self.budget
+        async with budget.semaphore:
+            budget.active_count += 1
+            conn = await pool.acquire()
+            try:
+                yield conn
+            finally:
+                budget.active_count -= 1
+                await pool.release(conn)
+
+    def wrap_pool(self, pool: "asyncpg.Pool") -> "BudgetedPool":
+        """
+        Wrap a pool with this operation's budget.
+
+        The returned BudgetedPool can be passed to functions expecting a pool,
+        and all acquire() calls will be limited by this operation's budget.
+
+        Args:
+            pool: asyncpg connection pool to wrap
+
+        Returns:
+            BudgetedPool that limits connections to this operation's budget
+        """
+        return BudgetedPool(pool, self)
+
+    async def acquire_many(
+        self,
+        pool: "asyncpg.Pool",
+        count: int,
+    ) -> AsyncIterator[list["asyncpg.Connection"]]:
+        """
+        Acquire multiple connections within the budget.
+
+        Note: This acquires connections sequentially to respect the budget.
+        For parallel acquisition, use multiple acquire() calls with asyncio.gather().
+
+        Args:
+            pool: asyncpg connection pool
+            count: Number of connections to acquire
+
+        Yields:
+            List of database connections
+        """
+        connections = []
+        try:
+            for _ in range(count):
+                conn = await pool.acquire()
+                connections.append(conn)
+            yield connections
+        finally:
+            for conn in connections:
+                await pool.release(conn)
+
+
+# Global default manager instance
+_default_manager: ConnectionBudgetManager | None = None
+
+
+def get_budget_manager(default_budget: int = 4) -> ConnectionBudgetManager:
+    """
+    Get or create the global budget manager.
+
+    Args:
+        default_budget: Default max connections per operation
+
+    Returns:
+        Global ConnectionBudgetManager instance
+    """
+    global _default_manager
+    if _default_manager is None:
+        _default_manager = ConnectionBudgetManager(default_budget=default_budget)
+    return _default_manager
+
+
+@asynccontextmanager
+async def budgeted_operation(
+    max_connections: int | None = None,
+    operation_id: str | None = None,
+    default_budget: int = 4,
+) -> AsyncIterator[BudgetedOperation]:
+    """
+    Convenience function to create a budgeted operation.
+
+    Args:
+        max_connections: Max concurrent connections for this operation
+        operation_id: Optional custom operation ID
+        default_budget: Default budget if manager not yet created
+
+    Yields:
+        BudgetedOperation context
+
+    Example:
+        async with budgeted_operation(max_connections=2) as op:
+            async with op.acquire(pool) as conn:
+                await conn.fetch(...)
+    """
+    manager = get_budget_manager(default_budget)
+    async with manager.operation(max_connections, operation_id) as op:
+        yield op
+
+
+class BudgetedPool:
+    """
+    A pool wrapper that limits concurrent connection acquisitions.
+
+    This can be passed to functions expecting a pool, and acquire()
+    calls will be limited by the budget semaphore.
+
+    Usage:
+        async with budgeted_operation(max_connections=4) as op:
+            budgeted_pool = op.wrap_pool(pool)
+            # Pass budgeted_pool to functions that expect a pool
+            await some_function(budgeted_pool, ...)
+    """
+
+    def __init__(self, pool: "asyncpg.Pool", operation: BudgetedOperation):
+        self._pool = pool
+        self._operation = operation
+
+    async def acquire(self) -> "asyncpg.Connection":
+        """
+        Acquire a connection within the budget.
+
+        Note: Caller must release the connection when done.
+        Prefer using as context manager via acquire_with_retry or op.acquire().
+        """
+        budget = self._operation.budget
+        await budget.semaphore.acquire()
+        budget.active_count += 1
+        try:
+            return await self._pool.acquire()
+        except Exception:
+            budget.active_count -= 1
+            budget.semaphore.release()
+            raise
+
+    async def release(self, conn: "asyncpg.Connection") -> None:
+        """Release a connection back to the pool."""
+        budget = self._operation.budget
+        try:
+            await self._pool.release(conn)
+        finally:
+            budget.active_count -= 1
+            budget.semaphore.release()
+
+    def __getattr__(self, name):
+        """Proxy other attributes to the underlying pool."""
+        return getattr(self._pool, name)
@@ -1,9 +1,11 @@
 """
 Database utility functions for connection management with retry logic.
 """
+
 import asyncio
 import logging
 from contextlib import asynccontextmanager
+
 import asyncpg

 logger = logging.getLogger(__name__)
@@ -54,16 +56,14 @@ async def retry_with_backoff(
        except retryable_exceptions as e:
            last_exception = e
            if attempt < max_retries:
-                delay = min(base_delay * (2 ** attempt), max_delay)
+                delay = min(base_delay * (2**attempt), max_delay)
                logger.warning(
                    f"Database operation failed (attempt {attempt + 1}/{max_retries + 1}): {e}. "
                    f"Retrying in {delay:.1f}s..."
                )
                await asyncio.sleep(delay)
            else:
-                logger.error(
-                    f"Database operation failed after {max_retries + 1} attempts: {e}"
-                )
+                logger.error(f"Database operation failed after {max_retries + 1} attempts: {e}")
    raise last_exception


@@ -83,10 +83,22 @@ async def acquire_with_retry(pool: asyncpg.Pool, max_retries: int = DEFAULT_MAX_
    Yields:
        An asyncpg connection
    """
+    import time
+
+    start = time.time()
+
    async def acquire():
        return await pool.acquire()

    conn = await retry_with_backoff(acquire, max_retries=max_retries)
+    acquire_time = time.time() - start
+
+    # Log slow connection acquisitions (indicates pool contention)
+    if acquire_time > 0.05:  # 50ms threshold
+        pool_size = pool.get_size()
+        pool_free = pool.get_idle_size()
+        logger.warning(f"[DB POOL] Slow acquire: {acquire_time:.3f}s | size={pool_size}, idle={pool_free}")
+
    try:
        yield conn
    finally:
@@ -3,25 +3,31 @@ 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.
 """
-from abc import ABC, abstractmethod
-from typing import List, Optional
+
 import logging
 import os
+from abc import ABC, abstractmethod

 import httpx

 from ..config import (
-    ENV_EMBEDDINGS_PROVIDER,
-    ENV_EMBEDDINGS_LOCAL_MODEL,
-    ENV_EMBEDDINGS_TEI_URL,
-    DEFAULT_EMBEDDINGS_PROVIDER,
+    DEFAULT_EMBEDDINGS_COHERE_MODEL,
    DEFAULT_EMBEDDINGS_LOCAL_MODEL,
-    EMBEDDING_DIMENSION,
+    DEFAULT_EMBEDDINGS_OPENAI_MODEL,
+    DEFAULT_EMBEDDINGS_PROVIDER,
+    ENV_COHERE_API_KEY,
+    ENV_EMBEDDINGS_COHERE_MODEL,
+    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 +37,8 @@ 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 +47,12 @@ 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:
        """
@@ -52,15 +64,15 @@ class Embeddings(ABC):
        pass

    @abstractmethod
-    def encode(self, texts: List[str]) -> List[List[float]]:
+    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

@@ -70,27 +82,31 @@ 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: Optional[str] = None):
+    def __init__(self, model_name: str | None = None):
        """
        Initialize local SentenceTransformers 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 +128,18 @@ 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."
-            )
+        self._dimension = self._model.get_sentence_embedding_dimension()
+        logger.info(f"Embeddings: local provider initialized (dim: {self._dimension})")

-        logger.info(f"Embeddings: local provider initialized (dim: {model_dim})")
-
-    def encode(self, texts: List[str]) -> List[List[float]]:
+    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 +154,7 @@ 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__(
@@ -172,16 +180,24 @@ class RemoteTEIEmbeddings(Embeddings):
        self.batch_size = batch_size
        self.max_retries = max_retries
        self.retry_delay = retry_delay
-        self._client: Optional[httpx.Client] = None
-        self._model_id: Optional[str] = None
+        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
+
        last_error = None
        delay = self.retry_delay

@@ -196,14 +212,18 @@ class RemoteTEIEmbeddings(Embeddings):
            except (httpx.ConnectError, httpx.ReadTimeout, httpx.WriteTimeout) as e:
                last_error = e
                if attempt < self.max_retries:
-                    logger.warning(f"TEI request failed (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s...")
+                    logger.warning(
+                        f"TEI request failed (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s..."
+                    )
                    time.sleep(delay)
                    delay *= 2  # Exponential backoff
            except httpx.HTTPStatusError as e:
                # Retry on 5xx server errors
                if e.response.status_code >= 500 and attempt < self.max_retries:
                    last_error = e
-                    logger.warning(f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s...")
+                    logger.warning(
+                        f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s..."
+                    )
                    time.sleep(delay)
                    delay *= 2
                else:
@@ -224,11 +244,28 @@ 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}")

-    def encode(self, texts: List[str]) -> List[List[float]]:
+    def encode(self, texts: list[str]) -> list[list[float]]:
        """
        Generate embeddings using the remote TEI server.

@@ -248,7 +285,7 @@ class RemoteTEIEmbeddings(Embeddings):

        # Process in batches
        for i in range(0, len(texts), self.batch_size):
-            batch = texts[i:i + self.batch_size]
+            batch = texts[i : i + self.batch_size]

            try:
                response = self._request_with_retry(
@@ -264,6 +301,234 @@ class RemoteTEIEmbeddings(Embeddings):
        return all_embeddings


+class OpenAIEmbeddings(Embeddings):
+    """
+    OpenAI embeddings implementation using the OpenAI API.
+
+    Supports text-embedding-3-small (1536 dims), text-embedding-3-large (3072 dims),
+    and text-embedding-ada-002 (1536 dims, legacy).
+
+    The embedding dimension is auto-detected from the model at initialization.
+    """
+
+    # Known dimensions for OpenAI embedding models
+    MODEL_DIMENSIONS = {
+        "text-embedding-3-small": 1536,
+        "text-embedding-3-large": 3072,
+        "text-embedding-ada-002": 1536,
+    }
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_EMBEDDINGS_OPENAI_MODEL,
+        batch_size: int = 100,
+        max_retries: int = 3,
+    ):
+        """
+        Initialize OpenAI embeddings client.
+
+        Args:
+            api_key: OpenAI API key
+            model: OpenAI embedding model name (default: text-embedding-3-small)
+            batch_size: Maximum batch size for embedding requests (default: 100)
+            max_retries: Maximum number of retries for failed requests (default: 3)
+        """
+        self.api_key = api_key
+        self.model = model
+        self.batch_size = batch_size
+        self.max_retries = max_retries
+        self._client = None
+        self._dimension: int | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "openai"
+
+    @property
+    def dimension(self) -> int:
+        if self._dimension is None:
+            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+        return self._dimension
+
+    async def initialize(self) -> None:
+        """Initialize the OpenAI client and detect dimension."""
+        if self._client is not None:
+            return
+
+        try:
+            from openai import OpenAI
+        except ImportError:
+            raise ImportError("openai is required for OpenAIEmbeddings. Install it with: pip install openai")
+
+        logger.info(f"Embeddings: initializing OpenAI provider with model {self.model}")
+        self._client = OpenAI(api_key=self.api_key, max_retries=self.max_retries)
+
+        # Try to get dimension from known models, otherwise do a test embedding
+        if self.model in self.MODEL_DIMENSIONS:
+            self._dimension = self.MODEL_DIMENSIONS[self.model]
+        else:
+            # Do a test embedding to detect dimension
+            response = self._client.embeddings.create(
+                model=self.model,
+                input=["test"],
+            )
+            if response.data:
+                self._dimension = len(response.data[0].embedding)
+
+        logger.info(f"Embeddings: OpenAI provider initialized (model: {self.model}, dim: {self._dimension})")
+
+    def encode(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings using the OpenAI API.
+
+        Args:
+            texts: List of text strings to encode
+
+        Returns:
+            List of embedding vectors
+        """
+        if self._client is None:
+            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+
+        if not texts:
+            return []
+
+        all_embeddings = []
+
+        # Process in batches
+        for i in range(0, len(texts), self.batch_size):
+            batch = texts[i : i + self.batch_size]
+
+            response = self._client.embeddings.create(
+                model=self.model,
+                input=batch,
+            )
+
+            # Sort by index to ensure correct order
+            batch_embeddings = sorted(response.data, key=lambda x: x.index)
+            all_embeddings.extend([e.embedding for e in batch_embeddings])
+
+        return all_embeddings
+
+
+class CohereEmbeddings(Embeddings):
+    """
+    Cohere embeddings implementation using the Cohere API.
+
+    Supports embed-english-v3.0 (1024 dims) and embed-multilingual-v3.0 (1024 dims).
+
+    The embedding dimension is auto-detected from the model at initialization.
+    """
+
+    # Known dimensions for Cohere embedding models
+    MODEL_DIMENSIONS = {
+        "embed-english-v3.0": 1024,
+        "embed-multilingual-v3.0": 1024,
+        "embed-english-light-v3.0": 384,
+        "embed-multilingual-light-v3.0": 384,
+        "embed-english-v2.0": 4096,
+        "embed-multilingual-v2.0": 768,
+    }
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_EMBEDDINGS_COHERE_MODEL,
+        batch_size: int = 96,
+        timeout: float = 60.0,
+        input_type: str = "search_document",
+    ):
+        """
+        Initialize Cohere embeddings client.
+
+        Args:
+            api_key: Cohere API key
+            model: Cohere embedding model name (default: embed-english-v3.0)
+            batch_size: Maximum batch size for embedding requests (default: 96, Cohere's limit)
+            timeout: Request timeout in seconds (default: 60.0)
+            input_type: Input type for embeddings (default: search_document).
+                       Options: search_document, search_query, classification, clustering
+        """
+        self.api_key = api_key
+        self.model = model
+        self.batch_size = batch_size
+        self.timeout = timeout
+        self.input_type = input_type
+        self._client = None
+        self._dimension: int | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "cohere"
+
+    @property
+    def dimension(self) -> int:
+        if self._dimension is None:
+            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+        return self._dimension
+
+    async def initialize(self) -> None:
+        """Initialize the Cohere client and detect dimension."""
+        if self._client is not None:
+            return
+
+        try:
+            import cohere
+        except ImportError:
+            raise ImportError("cohere is required for CohereEmbeddings. Install it with: pip install cohere")
+
+        logger.info(f"Embeddings: initializing Cohere provider with model {self.model}")
+        self._client = cohere.Client(api_key=self.api_key, timeout=self.timeout)
+
+        # Try to get dimension from known models, otherwise do a test embedding
+        if self.model in self.MODEL_DIMENSIONS:
+            self._dimension = self.MODEL_DIMENSIONS[self.model]
+        else:
+            # Do a test embedding to detect dimension
+            response = self._client.embed(
+                texts=["test"],
+                model=self.model,
+                input_type=self.input_type,
+            )
+            if response.embeddings:
+                self._dimension = len(response.embeddings[0])
+
+        logger.info(f"Embeddings: Cohere provider initialized (model: {self.model}, dim: {self._dimension})")
+
+    def encode(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings using the Cohere API.
+
+        Args:
+            texts: List of text strings to encode
+
+        Returns:
+            List of embedding vectors
+        """
+        if self._client is None:
+            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+
+        if not texts:
+            return []
+
+        all_embeddings = []
+
+        # Process in batches
+        for i in range(0, len(texts), self.batch_size):
+            batch = texts[i : i + self.batch_size]
+
+            response = self._client.embed(
+                texts=batch,
+                model=self.model,
+                input_type=self.input_type,
+            )
+
+            all_embeddings.extend(response.embeddings)
+
+        return all_embeddings
+
+
 def create_embeddings_from_env() -> Embeddings:
    """
    Create an Embeddings instance based on environment variables.
@@ -278,15 +543,27 @@ def create_embeddings_from_env() -> Embeddings:
    if provider == "tei":
        url = os.environ.get(ENV_EMBEDDINGS_TEI_URL)
        if not url:
-            raise ValueError(
-                f"{ENV_EMBEDDINGS_TEI_URL} is required when {ENV_EMBEDDINGS_PROVIDER} is 'tei'"
-            )
+            raise ValueError(f"{ENV_EMBEDDINGS_TEI_URL} is required when {ENV_EMBEDDINGS_PROVIDER} is 'tei'")
        return RemoteTEIEmbeddings(base_url=url)
    elif provider == "local":
        model = os.environ.get(ENV_EMBEDDINGS_LOCAL_MODEL)
        model_name = model or DEFAULT_EMBEDDINGS_LOCAL_MODEL
        return LocalSTEmbeddings(model_name=model_name)
+    elif provider == "openai":
+        # Use dedicated embeddings API key, or fall back to LLM API key
+        api_key = os.environ.get(ENV_EMBEDDINGS_OPENAI_API_KEY) or os.environ.get(ENV_LLM_API_KEY)
+        if not api_key:
+            raise ValueError(
+                f"{ENV_EMBEDDINGS_OPENAI_API_KEY} or {ENV_LLM_API_KEY} is required "
+                f"when {ENV_EMBEDDINGS_PROVIDER} is 'openai'"
+            )
+        model = os.environ.get(ENV_EMBEDDINGS_OPENAI_MODEL, DEFAULT_EMBEDDINGS_OPENAI_MODEL)
+        return OpenAIEmbeddings(api_key=api_key, model=model)
+    elif provider == "cohere":
+        api_key = os.environ.get(ENV_COHERE_API_KEY)
+        if not api_key:
+            raise ValueError(f"{ENV_COHERE_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'cohere'")
+        model = os.environ.get(ENV_EMBEDDINGS_COHERE_MODEL, DEFAULT_EMBEDDINGS_COHERE_MODEL)
+        return CohereEmbeddings(api_key=api_key, model=model)
    else:
-        raise ValueError(
-            f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei'"
-        )
+        raise ValueError(f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei', 'openai', 'cohere'")
@@ -4,12 +4,14 @@ Entity extraction and resolution for memory system.
 Uses spaCy for entity extraction and implements resolution logic
 to disambiguate entities across memory units.
 """
-import asyncpg
-from typing import List, Dict, Optional, Set, Any
-from difflib import SequenceMatcher
-from datetime import datetime, timezone
-from .db_utils import acquire_with_retry

+from datetime import UTC, datetime
+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
@@ -32,11 +34,11 @@ class EntityResolver:
    async def resolve_entities_batch(
        self,
        bank_id: str,
-        entities_data: List[Dict],
+        entities_data: list[dict],
        context: str,
        unit_event_date,
        conn=None,
-    ) -> List[str]:
+    ) -> list[str]:
        """
        Resolve multiple entities in batch (MUCH faster than sequential).

@@ -62,36 +64,38 @@ class EntityResolver:
        else:
            return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)

-    async def _resolve_entities_batch_impl(self, conn, bank_id: str, entities_data: List[Dict], context: str, unit_event_date) -> List[str]:
+    async def _resolve_entities_batch_impl(
+        self, conn, bank_id: str, entities_data: list[dict], context: str, unit_event_date
+    ) -> 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
+            bank_id,
        )

        # Build entity ID to name mapping for co-occurrence lookups
-        entity_id_to_name = {row['id']: row['canonical_name'].lower() for row in all_entities}
+        entity_id_to_name = {row["id"]: row["canonical_name"].lower() for row in all_entities}

        # 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
+            bank_id,
        )

        # Build co-occurrence map: entity_id -> set of co-occurring entity names (lowercase)
-        cooccurrence_map: Dict[str, Set[str]] = {}
+        cooccurrence_map: dict[str, set[str]] = {}
        for row in all_cooccurrences:
-            eid1, eid2 = row['entity_id_1'], row['entity_id_2']
+            eid1, eid2 = row["entity_id_1"], row["entity_id_2"]
            # Add both directions
            if eid1 not in cooccurrence_map:
                cooccurrence_map[eid1] = set()
@@ -105,22 +109,24 @@ class EntityResolver:

        # Build candidate map for each entity text
        all_candidates = {}  # Maps entity_text -> list of candidates
-        entity_texts = list(set(e['text'] for e in entities_data))
+        entity_texts = list(set(e["text"] for e in entities_data))

        for entity_text in entity_texts:
            matching = []
            entity_text_lower = entity_text.lower()
            for row in all_entities:
-                canonical_name = row['canonical_name']
-                ent_id = row['id']
-                metadata = row['metadata']
-                last_seen = row['last_seen']
-                mention_count = row['mention_count']
+                canonical_name = row["canonical_name"]
+                ent_id = row["id"]
+                metadata = row["metadata"]
+                last_seen = row["last_seen"]
+                mention_count = row["mention_count"]
                canonical_lower = canonical_name.lower()
                # Match if exact or substring match
-                if (entity_text_lower == canonical_lower or
-                    entity_text_lower in canonical_lower or
-                    canonical_lower in entity_text_lower):
+                if (
+                    entity_text_lower == canonical_lower
+                    or entity_text_lower in canonical_lower
+                    or canonical_lower in entity_text_lower
+                ):
                    matching.append((ent_id, canonical_name, metadata, last_seen, mention_count))
            all_candidates[entity_text] = matching

@@ -130,10 +136,10 @@ class EntityResolver:
        entities_to_create = []  # (idx, entity_data, event_date)

        for idx, entity_data in enumerate(entities_data):
-            entity_text = entity_data['text']
-            nearby_entities = entity_data.get('nearby_entities', [])
+            entity_text = entity_data["text"]
+            nearby_entities = entity_data.get("nearby_entities", [])
            # Use per-entity date if available, otherwise fall back to batch-level date
-            entity_event_date = entity_data.get('event_date', unit_event_date)
+            entity_event_date = entity_data.get("event_date", unit_event_date)

            candidates = all_candidates.get(entity_text, [])

@@ -146,17 +152,13 @@ class EntityResolver:
            best_candidate = None
            best_score = 0.0

-            nearby_entity_set = {e['text'].lower() for e in nearby_entities if e['text'] != entity_text}
+            nearby_entity_set = {e["text"].lower() for e in nearby_entities if e["text"] != entity_text}

            for candidate_id, canonical_name, metadata, last_seen, mention_count in candidates:
                score = 0.0

                # 1. Name similarity (0-0.5)
-                name_similarity = SequenceMatcher(
-                    None,
-                    entity_text.lower(),
-                    canonical_name.lower()
-                ).ratio()
+                name_similarity = SequenceMatcher(None, entity_text.lower(), canonical_name.lower()).ratio()
                score += name_similarity * 0.5

                # 2. Co-occurring entities (0-0.3)
@@ -169,8 +171,10 @@ class EntityResolver:
                # 3. Temporal proximity (0-0.2)
                if last_seen and entity_event_date:
                    # Normalize timezone awareness for comparison
-                    event_date_utc = entity_event_date if entity_event_date.tzinfo else entity_event_date.replace(tzinfo=timezone.utc)
-                    last_seen_utc = last_seen if last_seen.tzinfo else last_seen.replace(tzinfo=timezone.utc)
+                    event_date_utc = (
+                        entity_event_date if entity_event_date.tzinfo else entity_event_date.replace(tzinfo=UTC)
+                    )
+                    last_seen_utc = last_seen if last_seen.tzinfo else last_seen.replace(tzinfo=UTC)
                    days_diff = abs((event_date_utc - last_seen_utc).total_seconds() / 86400)
                    if days_diff < 7:
                        temporal_score = max(0, 1.0 - (days_diff / 7))
@@ -192,23 +196,23 @@ 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
                """,
-                entities_to_update
+                entities_to_update,
            )

        # Batch create new entities using COPY + INSERT for maximum speed
        # This handles duplicates via ON CONFLICT and returns all IDs
        if entities_to_create:
            # Group entities by canonical name (lowercase) to handle duplicates within batch
-            # For duplicates, we only insert once and reuse the ID
+            # For duplicates, we only insert once and reuse the ID, but track the count
            unique_entities = {}  # lowercase_name -> (entity_data, event_date, [indices])
            for idx, entity_data, event_date in entities_to_create:
-                name_lower = entity_data['text'].lower()
+                name_lower = entity_data["text"].lower()
                if name_lower not in unique_entities:
                    unique_entities[name_lower] = (entity_data, event_date, [idx])
                else:
@@ -219,34 +223,37 @@ class EntityResolver:
            # Use a single query with unnest for speed
            entity_names = []
            entity_dates = []
+            entity_counts = []  # Track how many times each entity appears in this batch
            indices_map = []  # Maps result index -> list of original indices

            for name_lower, (entity_data, event_date, indices) in unique_entities.items():
-                entity_names.append(entity_data['text'])
+                entity_names.append(entity_data["text"])
                entity_dates.append(event_date)
+                entity_counts.append(len(indices))  # Count of occurrences in this batch
                indices_map.append(indices)

            # Batch INSERT ... ON CONFLICT with RETURNING
-            # This is much faster than individual inserts
+            # Uses the batch count for mention_count instead of always 1
            rows = await conn.fetch(
-                """
-                INSERT INTO entities (bank_id, canonical_name, first_seen, last_seen, mention_count)
-                SELECT $1, name, event_date, event_date, 1
-                FROM unnest($2::text[], $3::timestamptz[]) AS t(name, event_date)
+                f"""
+                INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
+                SELECT $1, name, event_date, event_date, cnt
+                FROM unnest($2::text[], $3::timestamptz[], $4::int[]) AS t(name, event_date, cnt)
                ON CONFLICT (bank_id, LOWER(canonical_name))
                DO UPDATE SET
-                    mention_count = entities.mention_count + 1,
+                    mention_count = {fq_table("entities")}.mention_count + EXCLUDED.mention_count,
                    last_seen = EXCLUDED.last_seen
                RETURNING id
                """,
                bank_id,
                entity_names,
-                entity_dates
+                entity_dates,
+                entity_counts,
            )

            # Map returned IDs back to original indices
            for result_idx, row in enumerate(rows):
-                entity_id = row['id']
+                entity_id = row["id"]
                for original_idx in indices_map[result_idx]:
                    entity_ids[original_idx] = entity_id

@@ -257,7 +264,7 @@ class EntityResolver:
        bank_id: str,
        entity_text: str,
        context: str,
-        nearby_entities: List[Dict],
+        nearby_entities: list[dict],
        unit_event_date,
    ) -> str:
        """
@@ -276,9 +283,9 @@ 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
@@ -287,14 +294,14 @@ class EntityResolver:
                  )
                ORDER BY mention_count DESC
                """,
-                bank_id, entity_text, f"%{entity_text}%"
+                bank_id,
+                entity_text,
+                f"%{entity_text}%",
            )

            if not candidates:
                # New entity - create it
-                return await self._create_entity(
-                    conn, bank_id, entity_text, unit_event_date
-                )
+                return await self._create_entity(conn, bank_id, entity_text, unit_event_date)

            # Score candidates based on:
            # 1. Name similarity
@@ -306,31 +313,27 @@ class EntityResolver:
            best_score = 0.0
            best_name_similarity = 0.0

-            nearby_entity_set = {e['text'].lower() for e in nearby_entities if e['text'] != entity_text}
+            nearby_entity_set = {e["text"].lower() for e in nearby_entities if e["text"] != entity_text}

            for row in candidates:
-                candidate_id = row['id']
-                canonical_name = row['canonical_name']
-                metadata = row['metadata']
-                last_seen = row['last_seen']
+                candidate_id = row["id"]
+                canonical_name = row["canonical_name"]
+                metadata = row["metadata"]
+                last_seen = row["last_seen"]
                score = 0.0

                # 1. Name similarity (0-1)
-                name_similarity = SequenceMatcher(
-                    None,
-                    entity_text.lower(),
-                    canonical_name.lower()
-                ).ratio()
+                name_similarity = SequenceMatcher(None, entity_text.lower(), canonical_name.lower()).ratio()
                score += name_similarity * 0.5

                # 2. Co-occurring entities (0-0.5)
                # 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
@@ -338,9 +341,9 @@ class EntityResolver:
                    )
                    WHERE ec.entity_id_1 = $1 OR ec.entity_id_2 = $1
                    """,
-                    candidate_id
+                    candidate_id,
                )
-                co_entities = {r['canonical_name'].lower() for r in co_entity_rows}
+                co_entities = {r["canonical_name"].lower() for r in co_entity_rows}

                # Check overlap with nearby entities
                overlap = len(nearby_entity_set & co_entities)
@@ -366,20 +369,19 @@ 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
                    """,
-                    unit_event_date, best_candidate
+                    unit_event_date,
+                    best_candidate,
                )
                return best_candidate
            else:
                # Not confident - create new entity
-                return await self._create_entity(
-                    conn, bank_id, entity_text, unit_event_date
-                )
+                return await self._create_entity(conn, bank_id, entity_text, unit_event_date)

    async def _create_entity(
        self,
@@ -404,16 +406,19 @@ 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
            """,
-            bank_id, entity_text, event_date, event_date
+            bank_id,
+            entity_text,
+            event_date,
+            event_date,
        )
        return entity_id

@@ -429,25 +434,27 @@ 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
                """,
-                unit_id, entity_id
+                unit_id,
+                entity_id,
            )

            # 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, entity_id
+                unit_id,
+                entity_id,
            )

-            other_entities = [row['entity_id'] for row in rows]
+            other_entities = [row["entity_id"] for row in rows]

            # Update co-occurrences for each pair
            for other_entity_id in other_entities:
@@ -469,18 +476,19 @@ 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, entity_id_2
+            entity_id_1,
+            entity_id_2,
        )

-    async def link_units_to_entities_batch(self, unit_entity_pairs: List[tuple[str, str]], conn=None):
+    async def link_units_to_entities_batch(self, unit_entity_pairs: list[tuple[str, str]], conn=None):
        """
        Link multiple memory units to entities in batch (MUCH faster than sequential).

@@ -499,15 +507,15 @@ class EntityResolver:
        else:
            return await self._link_units_to_entities_batch_impl(conn, unit_entity_pairs)

-    async def _link_units_to_entities_batch_impl(self, conn, unit_entity_pairs: List[tuple[str, str]]):
+    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
            """,
-            unit_entity_pairs
+            unit_entity_pairs,
        )

        # Build map of unit -> entities for co-occurrence calculation
@@ -524,7 +532,7 @@ class EntityResolver:
            entity_list = list(entity_ids)  # Convert set to list for iteration
            # For each pair of entities in this unit, create co-occurrence
            for i, entity_id_1 in enumerate(entity_list):
-                for entity_id_2 in entity_list[i+1:]:
+                for entity_id_2 in entity_list[i + 1 :]:
                    # Skip if same entity (shouldn't happen with set, but be safe)
                    if entity_id_1 == entity_id_2:
                        continue
@@ -535,20 +543,20 @@ class EntityResolver:

        # Batch update co-occurrences
        if cooccurrence_pairs:
-            now = datetime.now(timezone.utc)
+            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]
+                [(e1, e2, 1, now) for e1, e2 in cooccurrence_pairs],
            )

-    async def get_units_by_entity(self, entity_id: str, limit: int = 100) -> List[str]:
+    async def get_units_by_entity(self, entity_id: str, limit: int = 100) -> list[str]:
        """
        Get all units that mention an entity.

@@ -561,22 +569,23 @@ 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
                """,
-                entity_id, limit
+                entity_id,
+                limit,
            )
-            return [row['unit_id'] for row in rows]
+            return [row["unit_id"] for row in rows]

    async def get_entity_by_text(
        self,
        bank_id: str,
        entity_text: str,
-    ) -> Optional[str]:
+    ) -> str | None:
        """
        Find an entity by text (for query resolution).

@@ -589,14 +598,15 @@ 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
                LIMIT 1
                """,
-                bank_id, entity_text
+                bank_id,
+                entity_text,
            )

-            return row['id'] if row else None
+            return row["id"] if row else None
@@ -0,0 +1,600 @@
+"""Abstract interface for MemoryEngine public methods.
+
+This module defines the public API that HTTP endpoints and extensions should use
+to interact with the memory system. All methods require a RequestContext for
+authentication when a TenantExtension is configured.
+"""
+
+from abc import ABC, abstractmethod
+from datetime import datetime
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from hindsight_api.engine.memory_engine import Budget
+    from hindsight_api.engine.response_models import RecallResult, ReflectResult
+    from hindsight_api.models import RequestContext
+
+
+class MemoryEngineInterface(ABC):
+    """
+    Abstract interface for the Memory Engine.
+
+    This defines the public API that should be used by HTTP endpoints and extensions.
+    All methods require a RequestContext for authentication.
+    """
+
+    # =========================================================================
+    # Health & Status
+    # =========================================================================
+
+    @abstractmethod
+    async def health_check(self) -> dict:
+        """
+        Check the health of the memory system.
+
+        Returns:
+            Dict with 'status' key ('healthy' or 'unhealthy') and additional info.
+        """
+        ...
+
+    # =========================================================================
+    # Core Memory Operations
+    # =========================================================================
+
+    @abstractmethod
+    async def retain_batch_async(
+        self,
+        bank_id: str,
+        contents: list[dict[str, Any]],
+        *,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        Retain a batch of memory items.
+
+        Args:
+            bank_id: The memory bank ID.
+            contents: List of content dicts with 'content', optional 'event_date',
+                     'context', 'metadata', 'document_id'.
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with processing results.
+        """
+        ...
+
+    @abstractmethod
+    async def recall_async(
+        self,
+        bank_id: str,
+        query: str,
+        *,
+        budget: "Budget | None" = None,
+        max_tokens: int = 4096,
+        enable_trace: bool = False,
+        fact_type: list[str] | None = None,
+        question_date: datetime | None = None,
+        include_entities: bool = False,
+        max_entity_tokens: int = 500,
+        include_chunks: bool = False,
+        max_chunk_tokens: int = 8192,
+        request_context: "RequestContext",
+    ) -> "RecallResult":
+        """
+        Recall memories relevant to a query.
+
+        Args:
+            bank_id: The memory bank ID.
+            query: The search query.
+            budget: Search budget (LOW, MID, HIGH).
+            max_tokens: Maximum tokens in response.
+            enable_trace: Include trace information.
+            fact_type: Filter by fact types.
+            question_date: Context date for temporal relevance.
+            include_entities: Include entity observations.
+            max_entity_tokens: Max tokens for entity observations.
+            include_chunks: Include raw chunks.
+            max_chunk_tokens: Max tokens for chunks.
+            request_context: Request context for authentication.
+
+        Returns:
+            RecallResult with matching memories.
+        """
+        ...
+
+    @abstractmethod
+    async def reflect_async(
+        self,
+        bank_id: str,
+        query: str,
+        *,
+        budget: "Budget | None" = None,
+        context: str | None = None,
+        max_tokens: int = 4096,
+        response_schema: dict | None = None,
+        request_context: "RequestContext",
+    ) -> "ReflectResult":
+        """
+        Reflect on a query and generate a thoughtful response.
+
+        Args:
+            bank_id: The memory bank ID.
+            query: The question to reflect on.
+            budget: Search budget for retrieving context.
+            context: Additional context for the reflection.
+            max_tokens: Maximum tokens for the response.
+            response_schema: Optional JSON Schema for structured output.
+            request_context: Request context for authentication.
+
+        Returns:
+            ReflectResult with generated response and supporting facts.
+        """
+        ...
+
+    # =========================================================================
+    # Bank Management
+    # =========================================================================
+
+    @abstractmethod
+    async def list_banks(
+        self,
+        *,
+        request_context: "RequestContext",
+    ) -> list[dict[str, Any]]:
+        """
+        List all memory banks.
+
+        Args:
+            request_context: Request context for authentication.
+
+        Returns:
+            List of bank info dicts.
+        """
+        ...
+
+    @abstractmethod
+    async def get_bank_profile(
+        self,
+        bank_id: str,
+        *,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        Get bank profile including disposition and background.
+
+        Args:
+            bank_id: The memory bank ID.
+            request_context: Request context for authentication.
+
+        Returns:
+            Bank profile dict.
+        """
+        ...
+
+    @abstractmethod
+    async def update_bank_disposition(
+        self,
+        bank_id: str,
+        disposition: dict[str, int],
+        *,
+        request_context: "RequestContext",
+    ) -> None:
+        """
+        Update bank disposition traits.
+
+        Args:
+            bank_id: The memory bank ID.
+            disposition: Dict with trait values.
+            request_context: Request context for authentication.
+        """
+        ...
+
+    @abstractmethod
+    async def merge_bank_background(
+        self,
+        bank_id: str,
+        new_info: str,
+        *,
+        update_disposition: bool = True,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        Merge new background information into bank profile.
+
+        Args:
+            bank_id: The memory bank ID.
+            new_info: New background information to merge.
+            update_disposition: Whether to infer disposition from background.
+            request_context: Request context for authentication.
+
+        Returns:
+            Updated background info.
+        """
+        ...
+
+    @abstractmethod
+    async def delete_bank(
+        self,
+        bank_id: str,
+        *,
+        fact_type: str | None = None,
+        request_context: "RequestContext",
+    ) -> dict[str, int]:
+        """
+        Delete a bank or its memories.
+
+        Args:
+            bank_id: The memory bank ID.
+            fact_type: If specified, only delete memories of this type.
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with deletion counts.
+        """
+        ...
+
+    # =========================================================================
+    # Memory Units
+    # =========================================================================
+
+    @abstractmethod
+    async def list_memory_units(
+        self,
+        bank_id: str,
+        *,
+        fact_type: str | None = None,
+        search_query: str | None = None,
+        limit: int = 100,
+        offset: int = 0,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        List memory units with pagination.
+
+        Args:
+            bank_id: The memory bank ID.
+            fact_type: Filter by fact type.
+            search_query: Full-text search query.
+            limit: Maximum results.
+            offset: Pagination offset.
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with 'items', 'total', 'limit', 'offset'.
+        """
+        ...
+
+    @abstractmethod
+    async def delete_memory_unit(
+        self,
+        unit_id: str,
+        *,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        Delete a specific memory unit.
+
+        Args:
+            unit_id: The memory unit ID.
+            request_context: Request context for authentication.
+
+        Returns:
+            Deletion result.
+        """
+        ...
+
+    @abstractmethod
+    async def get_graph_data(
+        self,
+        bank_id: str,
+        *,
+        fact_type: str | None = None,
+        limit: int = 1000,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        Get graph data for visualization.
+
+        Args:
+            bank_id: The memory bank ID.
+            fact_type: Filter by fact type.
+            limit: Maximum number of items to return (default: 1000).
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with nodes, edges, table_rows, total_units, limit.
+        """
+        ...
+
+    # =========================================================================
+    # Documents
+    # =========================================================================
+
+    @abstractmethod
+    async def list_documents(
+        self,
+        bank_id: str,
+        *,
+        search_query: str | None = None,
+        limit: int = 100,
+        offset: int = 0,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        List documents with pagination.
+
+        Args:
+            bank_id: The memory bank ID.
+            search_query: Search query.
+            limit: Maximum results.
+            offset: Pagination offset.
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with 'items', 'total', 'limit', 'offset'.
+        """
+        ...
+
+    @abstractmethod
+    async def get_document(
+        self,
+        document_id: str,
+        bank_id: str,
+        *,
+        request_context: "RequestContext",
+    ) -> dict[str, Any] | None:
+        """
+        Get a specific document.
+
+        Args:
+            document_id: The document ID.
+            bank_id: The memory bank ID.
+            request_context: Request context for authentication.
+
+        Returns:
+            Document dict or None if not found.
+        """
+        ...
+
+    @abstractmethod
+    async def delete_document(
+        self,
+        document_id: str,
+        bank_id: str,
+        *,
+        request_context: "RequestContext",
+    ) -> dict[str, int]:
+        """
+        Delete a document and its memory units.
+
+        Args:
+            document_id: The document ID.
+            bank_id: The memory bank ID.
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with deletion counts.
+        """
+        ...
+
+    @abstractmethod
+    async def get_chunk(
+        self,
+        chunk_id: str,
+        *,
+        request_context: "RequestContext",
+    ) -> dict[str, Any] | None:
+        """
+        Get a specific chunk.
+
+        Args:
+            chunk_id: The chunk ID.
+            request_context: Request context for authentication.
+
+        Returns:
+            Chunk dict or None if not found.
+        """
+        ...
+
+    # =========================================================================
+    # Entities
+    # =========================================================================
+
+    @abstractmethod
+    async def list_entities(
+        self,
+        bank_id: str,
+        *,
+        limit: int = 100,
+        offset: int = 0,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        List entities for a bank with pagination.
+
+        Args:
+            bank_id: The memory bank ID.
+            limit: Maximum results.
+            offset: Offset for pagination.
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with items, total, limit, offset.
+        """
+        ...
+
+    @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.
+        """
+        ...
@@ -4,11 +4,12 @@ Query analysis abstraction for the memory system.
 Provides an interface for analyzing natural language queries to extract
 structured information like temporal constraints.
 """
-from abc import ABC, abstractmethod
-from typing import Optional
-from datetime import datetime, timedelta
+
 import logging
 import re
+from abc import ABC, abstractmethod
+from datetime import datetime, timedelta
+
 from pydantic import BaseModel, Field

 logger = logging.getLogger(__name__)
@@ -20,6 +21,7 @@ class TemporalConstraint(BaseModel):

    Represents a time range with start and end dates.
    """
+
    start_date: datetime = Field(description="Start of the time range (inclusive)")
    end_date: datetime = Field(description="End of the time range (inclusive)")

@@ -33,9 +35,9 @@ class QueryAnalysis(BaseModel):

    Contains extracted structured information like temporal constraints.
    """
-    temporal_constraint: Optional[TemporalConstraint] = Field(
-        default=None,
-        description="Extracted temporal constraint, if any"
+
+    temporal_constraint: TemporalConstraint | None = Field(
+        default=None, description="Extracted temporal constraint, if any"
    )


@@ -58,9 +60,7 @@ class QueryAnalyzer(ABC):
        pass

    @abstractmethod
-    def analyze(
-        self, query: str, reference_date: Optional[datetime] = None
-    ) -> QueryAnalysis:
+    def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
        """
        Analyze a natural language query.

@@ -84,7 +84,7 @@ class DateparserQueryAnalyzer(QueryAnalyzer):

    Performance:
    - ~10-50ms per query
-    - No model loading required
+    - No model loading required (lazy import on first use)
    """

    def __init__(self):
@@ -95,11 +95,10 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
        """Load dateparser (lazy import)."""
        if self._search_dates is None:
            from dateparser.search import search_dates
+
            self._search_dates = search_dates

-    def analyze(
-        self, query: str, reference_date: Optional[datetime] = None
-    ) -> QueryAnalysis:
+    def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
        """
        Analyze query using dateparser.

@@ -113,8 +112,6 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
        Returns:
            QueryAnalysis with temporal_constraint if found
        """
-        self.load()
-
        if reference_date is None:
            reference_date = datetime.now()

@@ -124,11 +121,14 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
        if period_result is not None:
            return QueryAnalysis(temporal_constraint=period_result)

+        # Lazy load dateparser (only imports on first call, then cached)
+        self.load()
+
        # Use dateparser's search_dates to find temporal expressions
        settings = {
-            'RELATIVE_BASE': reference_date,
-            'PREFER_DATES_FROM': 'past',
-            'RETURN_AS_TIMEZONE_AWARE': False,
+            "RELATIVE_BASE": reference_date,
+            "PREFER_DATES_FROM": "past",
+            "RETURN_AS_TIMEZONE_AWARE": False,
        }

        results = self._search_dates(query, settings=settings)
@@ -137,11 +137,8 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
            return QueryAnalysis(temporal_constraint=None)

        # Filter out false positives (common words parsed as dates)
-        false_positives = {'do', 'may', 'march', 'will', 'can', 'sat', 'sun', 'mon', 'tue', 'wed', 'thu', 'fri'}
-        valid_results = [
-            (text, date) for text, date in results
-            if text.lower() not in false_positives or len(text) > 3
-        ]
+        false_positives = {"do", "may", "march", "will", "can", "sat", "sun", "mon", "tue", "wed", "thu", "fri"}
+        valid_results = [(text, date) for text, date in results if text.lower() not in false_positives or len(text) > 3]

        if not valid_results:
            return QueryAnalysis(temporal_constraint=None)
@@ -153,84 +150,94 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
        start_date = parsed_date.replace(hour=0, minute=0, second=0, microsecond=0)
        end_date = parsed_date.replace(hour=23, minute=59, second=59, microsecond=999999)

-        return QueryAnalysis(
-            temporal_constraint=TemporalConstraint(
-                start_date=start_date,
-                end_date=end_date
-            )
-        )
+        return QueryAnalysis(temporal_constraint=TemporalConstraint(start_date=start_date, end_date=end_date))

-    def _extract_period(
-        self, query: str, reference_date: datetime
-    ) -> Optional[TemporalConstraint]:
+    def _extract_period(self, query: str, reference_date: datetime) -> TemporalConstraint | None:
        """
        Extract period-based temporal expressions (week, month, year, weekend).

        These need special handling as they represent date ranges, not single dates.
        Supports multiple languages.
        """
+
        def constraint(start: datetime, end: datetime) -> TemporalConstraint:
            return TemporalConstraint(
                start_date=start.replace(hour=0, minute=0, second=0, microsecond=0),
-                end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999)
+                end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999),
            )

        # Yesterday patterns (English, Spanish, Italian, French, German)
-        if re.search(r'\b(yesterday|ayer|ieri|hier|gestern)\b', query, re.IGNORECASE):
+        if re.search(r"\b(yesterday|ayer|ieri|hier|gestern)\b", query, re.IGNORECASE):
            d = reference_date - timedelta(days=1)
            return constraint(d, d)

        # Today patterns
-        if re.search(r'\b(today|hoy|oggi|aujourd\'?hui|heute)\b', query, re.IGNORECASE):
+        if re.search(r"\b(today|hoy|oggi|aujourd\'?hui|heute)\b", query, re.IGNORECASE):
            return constraint(reference_date, reference_date)

        # "a couple of days ago" / "a few days ago" patterns
        # These are imprecise so we create a range
-        if re.search(r'\b(a\s+)?couple\s+(of\s+)?days?\s+ago\b', query, re.IGNORECASE):
+        if re.search(r"\b(a\s+)?couple\s+(of\s+)?days?\s+ago\b", query, re.IGNORECASE):
            # "a couple of days" = approximately 2 days, give range of 1-3 days
            return constraint(reference_date - timedelta(days=3), reference_date - timedelta(days=1))

-        if re.search(r'\b(a\s+)?few\s+days?\s+ago\b', query, re.IGNORECASE):
+        if re.search(r"\b(a\s+)?few\s+days?\s+ago\b", query, re.IGNORECASE):
            # "a few days" = approximately 3-4 days, give range of 2-5 days
            return constraint(reference_date - timedelta(days=5), reference_date - timedelta(days=2))

        # "a couple of weeks ago" / "a few weeks ago" patterns
-        if re.search(r'\b(a\s+)?couple\s+(of\s+)?weeks?\s+ago\b', query, re.IGNORECASE):
+        if re.search(r"\b(a\s+)?couple\s+(of\s+)?weeks?\s+ago\b", query, re.IGNORECASE):
            # "a couple of weeks" = approximately 2 weeks, give range of 1-3 weeks
            return constraint(reference_date - timedelta(weeks=3), reference_date - timedelta(weeks=1))

-        if re.search(r'\b(a\s+)?few\s+weeks?\s+ago\b', query, re.IGNORECASE):
+        if re.search(r"\b(a\s+)?few\s+weeks?\s+ago\b", query, re.IGNORECASE):
            # "a few weeks" = approximately 3-4 weeks, give range of 2-5 weeks
            return constraint(reference_date - timedelta(weeks=5), reference_date - timedelta(weeks=2))

        # "a couple of months ago" / "a few months ago" patterns
-        if re.search(r'\b(a\s+)?couple\s+(of\s+)?months?\s+ago\b', query, re.IGNORECASE):
+        if re.search(r"\b(a\s+)?couple\s+(of\s+)?months?\s+ago\b", query, re.IGNORECASE):
            # "a couple of months" = approximately 2 months, give range of 1-3 months
            return constraint(reference_date - timedelta(days=90), reference_date - timedelta(days=30))

-        if re.search(r'\b(a\s+)?few\s+months?\s+ago\b', query, re.IGNORECASE):
+        if re.search(r"\b(a\s+)?few\s+months?\s+ago\b", query, re.IGNORECASE):
            # "a few months" = approximately 3-4 months, give range of 2-5 months
            return constraint(reference_date - timedelta(days=150), reference_date - timedelta(days=60))

        # Last week patterns (English, Spanish, Italian, French, German)
-        if re.search(r'\b(last\s+week|la\s+semana\s+pasada|la\s+settimana\s+scorsa|la\s+semaine\s+derni[eè]re|letzte\s+woche)\b', query, re.IGNORECASE):
+        if re.search(
+            r"\b(last\s+week|la\s+semana\s+pasada|la\s+settimana\s+scorsa|la\s+semaine\s+derni[eè]re|letzte\s+woche)\b",
+            query,
+            re.IGNORECASE,
+        ):
            start = reference_date - timedelta(days=reference_date.weekday() + 7)
            return constraint(start, start + timedelta(days=6))

        # Last month patterns
-        if re.search(r'\b(last\s+month|el\s+mes\s+pasado|il\s+mese\s+scorso|le\s+mois\s+dernier|letzten?\s+monat)\b', query, re.IGNORECASE):
+        if re.search(
+            r"\b(last\s+month|el\s+mes\s+pasado|il\s+mese\s+scorso|le\s+mois\s+dernier|letzten?\s+monat)\b",
+            query,
+            re.IGNORECASE,
+        ):
            first = reference_date.replace(day=1)
            end = first - timedelta(days=1)
            start = end.replace(day=1)
            return constraint(start, end)

        # Last year patterns
-        if re.search(r'\b(last\s+year|el\s+a[ñn]o\s+pasado|l\'anno\s+scorso|l\'ann[ée]e\s+derni[eè]re|letztes?\s+jahr)\b', query, re.IGNORECASE):
+        if re.search(
+            r"\b(last\s+year|el\s+a[ñn]o\s+pasado|l\'anno\s+scorso|l\'ann[ée]e\s+derni[eè]re|letztes?\s+jahr)\b",
+            query,
+            re.IGNORECASE,
+        ):
            year = reference_date.year - 1
            return constraint(datetime(year, 1, 1), datetime(year, 12, 31))

        # Last weekend patterns
-        if re.search(r'\b(last\s+weekend|el\s+fin\s+de\s+semana\s+pasado|lo\s+scorso\s+fine\s+settimana|le\s+week-?end\s+dernier|letztes?\s+wochenende)\b', query, re.IGNORECASE):
+        if re.search(
+            r"\b(last\s+weekend|el\s+fin\s+de\s+semana\s+pasado|lo\s+scorso\s+fine\s+settimana|le\s+week-?end\s+dernier|letztes?\s+wochenende)\b",
+            query,
+            re.IGNORECASE,
+        ):
            days_since_sat = (reference_date.weekday() + 2) % 7
            if days_since_sat == 0:
                days_since_sat = 7
@@ -239,22 +246,22 @@ class DateparserQueryAnalyzer(QueryAnalyzer):

        # Month + Year patterns (e.g., "June 2024", "junio 2024", "giugno 2024")
        month_patterns = {
-            'january|enero|gennaio|janvier|januar': 1,
-            'february|febrero|febbraio|f[ée]vrier|februar': 2,
-            'march|marzo|mars|m[äa]rz': 3,
-            'april|abril|aprile|avril': 4,
-            'may|mayo|maggio|mai': 5,
-            'june|junio|giugno|juin|juni': 6,
-            'july|julio|luglio|juillet|juli': 7,
-            'august|agosto|ao[uû]t': 8,
-            'september|septiembre|settembre|septembre': 9,
-            'october|octubre|ottobre|octobre|oktober': 10,
-            'november|noviembre|novembre': 11,
-            'december|diciembre|dicembre|d[ée]cembre|dezember': 12,
+            "january|enero|gennaio|janvier|januar": 1,
+            "february|febrero|febbraio|f[ée]vrier|februar": 2,
+            "march|marzo|mars|m[äa]rz": 3,
+            "april|abril|aprile|avril": 4,
+            "may|mayo|maggio|mai": 5,
+            "june|junio|giugno|juin|juni": 6,
+            "july|julio|luglio|juillet|juli": 7,
+            "august|agosto|ao[uû]t": 8,
+            "september|septiembre|settembre|septembre": 9,
+            "october|octubre|ottobre|octobre|oktober": 10,
+            "november|noviembre|novembre": 11,
+            "december|diciembre|dicembre|d[ée]cembre|dezember": 12,
        }

        for pattern, month_num in month_patterns.items():
-            match = re.search(rf'\b({pattern})\s+(\d{{4}})\b', query, re.IGNORECASE)
+            match = re.search(rf"\b({pattern})\s+(\d{{4}})\b", query, re.IGNORECASE)
            if match:
                year = int(match.group(2))
                start = datetime(year, month_num, 1)
@@ -279,11 +286,7 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
    - Model size: ~80M params (~300MB download)
    """

-    def __init__(
-        self,
-        model_name: str = "google/flan-t5-small",
-        device: str = "cpu"
-    ):
+    def __init__(self, model_name: str = "google/flan-t5-small", device: str = "cpu"):
        """
        Initialize T5 query analyzer.

@@ -304,11 +307,10 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
            return

        try:
-            from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+            from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
        except ImportError:
            raise ImportError(
-                "transformers is required for TransformerQueryAnalyzer. "
-                "Install it with: pip install transformers"
+                "transformers is required for TransformerQueryAnalyzer. Install it with: pip install transformers"
            )

        logger.info(f"Loading query analyzer model: {self.model_name}...")
@@ -322,9 +324,7 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
        """Lazy load the T5 model for temporal extraction (calls load())."""
        self.load()

-    def _extract_with_rules(
-        self, query: str, reference_date: datetime
-    ) -> Optional[TemporalConstraint]:
+    def _extract_with_rules(self, query: str, reference_date: datetime) -> TemporalConstraint | None:
        """
        Extract temporal expressions using rule-based patterns.

@@ -332,6 +332,7 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
        patterns that need model-based extraction.
        """
        import re
+
        query_lower = query.lower()

        def get_last_weekday(weekday: int) -> datetime:
@@ -343,50 +344,60 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
        def constraint(start: datetime, end: datetime) -> TemporalConstraint:
            return TemporalConstraint(
                start_date=start.replace(hour=0, minute=0, second=0, microsecond=0),
-                end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999)
+                end_date=end.replace(hour=23, minute=59, second=59, microsecond=999999),
            )

        # Yesterday
-        if re.search(r'\byesterday\b', query_lower):
+        if re.search(r"\byesterday\b", query_lower):
            d = reference_date - timedelta(days=1)
            return constraint(d, d)

        # Last week
-        if re.search(r'\blast\s+week\b', query_lower):
+        if re.search(r"\blast\s+week\b", query_lower):
            start = reference_date - timedelta(days=reference_date.weekday() + 7)
            return constraint(start, start + timedelta(days=6))

        # Last month
-        if re.search(r'\blast\s+month\b', query_lower):
+        if re.search(r"\blast\s+month\b", query_lower):
            first = reference_date.replace(day=1)
            end = first - timedelta(days=1)
            start = end.replace(day=1)
            return constraint(start, end)

        # Last year
-        if re.search(r'\blast\s+year\b', query_lower):
+        if re.search(r"\blast\s+year\b", query_lower):
            y = reference_date.year - 1
            return constraint(datetime(y, 1, 1), datetime(y, 12, 31))

        # Last weekend
-        if re.search(r'\blast\s+weekend\b', query_lower):
+        if re.search(r"\blast\s+weekend\b", query_lower):
            sat = get_last_weekday(5)
            return constraint(sat, sat + timedelta(days=1))

        # Last <weekday>
-        weekdays = {'monday': 0, 'tuesday': 1, 'wednesday': 2, 'thursday': 3,
-                    'friday': 4, 'saturday': 5, 'sunday': 6}
+        weekdays = {"monday": 0, "tuesday": 1, "wednesday": 2, "thursday": 3, "friday": 4, "saturday": 5, "sunday": 6}
        for name, num in weekdays.items():
-            if re.search(rf'\blast\s+{name}\b', query_lower):
+            if re.search(rf"\blast\s+{name}\b", query_lower):
                d = get_last_weekday(num)
                return constraint(d, d)

        # Month + Year: "June 2024", "in March 2023"
-        months = {'january': 1, 'february': 2, 'march': 3, 'april': 4, 'may': 5,
-                  'june': 6, 'july': 7, 'august': 8, 'september': 9, 'october': 10,
-                  'november': 11, 'december': 12}
+        months = {
+            "january": 1,
+            "february": 2,
+            "march": 3,
+            "april": 4,
+            "may": 5,
+            "june": 6,
+            "july": 7,
+            "august": 8,
+            "september": 9,
+            "october": 10,
+            "november": 11,
+            "december": 12,
+        }
        for name, num in months.items():
-            match = re.search(rf'\b{name}\s+(\d{{4}})\b', query_lower)
+            match = re.search(rf"\b{name}\s+(\d{{4}})\b", query_lower)
            if match:
                year = int(match.group(1))
                if num == 12:
@@ -397,9 +408,7 @@ class TransformerQueryAnalyzer(QueryAnalyzer):

        return None

-    def analyze(
-        self, query: str, reference_date: Optional[datetime] = None
-    ) -> QueryAnalysis:
+    def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
        """
        Analyze query for temporal expressions.

@@ -435,11 +444,11 @@ class TransformerQueryAnalyzer(QueryAnalyzer):
        last_saturday = get_last_weekday(5)

        # Build prompt for T5
-        prompt = f"""Today is {reference_date.strftime('%Y-%m-%d')}. Extract date range or "none".
+        prompt = f"""Today is {reference_date.strftime("%Y-%m-%d")}. Extract date range or "none".

 June 2024 = 2024-06-01 to 2024-06-30
-yesterday = {yesterday.strftime('%Y-%m-%d')} to {yesterday.strftime('%Y-%m-%d')}
-last Saturday = {last_saturday.strftime('%Y-%m-%d')} to {last_saturday.strftime('%Y-%m-%d')}
+yesterday = {yesterday.strftime("%Y-%m-%d")} to {yesterday.strftime("%Y-%m-%d")}
+last Saturday = {last_saturday.strftime("%Y-%m-%d")} to {last_saturday.strftime("%Y-%m-%d")}
 what is the weather = none
 {query} ="""

@@ -448,13 +457,7 @@ what is the weather = none
        inputs = {k: v.to(self.device) for k, v in inputs.items()}

        with self._no_grad():
-            outputs = self._model.generate(
-                **inputs,
-                max_new_tokens=30,
-                num_beams=3,
-                do_sample=False,
-                temperature=1.0
-            )
+            outputs = self._model.generate(**inputs, max_new_tokens=30, num_beams=3, do_sample=False, temperature=1.0)

        result = self._tokenizer.decode(outputs[0], skip_special_tokens=True).strip()

@@ -466,14 +469,14 @@ what is the weather = none
        """Get torch.no_grad context manager."""
        try:
            import torch
+
            return torch.no_grad()
        except ImportError:
            from contextlib import nullcontext
+
            return nullcontext()

-    def _parse_generated_output(
-        self, result: str, reference_date: datetime
-    ) -> Optional[TemporalConstraint]:
+    def _parse_generated_output(self, result: str, reference_date: datetime) -> TemporalConstraint | None:
        """
        Parse T5 generated output into TemporalConstraint.

@@ -492,7 +495,8 @@ what is the weather = none
        try:
            # Parse "YYYY-MM-DD to YYYY-MM-DD"
            import re
-            pattern = r'(\d{4}-\d{2}-\d{2})\s+to\s+(\d{4}-\d{2}-\d{2})'
+
+            pattern = r"(\d{4}-\d{2}-\d{2})\s+to\s+(\d{4}-\d{2}-\d{2})"
            match = re.search(pattern, result, re.IGNORECASE)

            if match:
@@ -513,7 +517,7 @@ what is the weather = none

                return TemporalConstraint(start_date=start_date, end_date=end_date)

-        except (ValueError, AttributeError) as e:
+        except (ValueError, AttributeError):
            return None

        return None
@@ -6,14 +6,45 @@ API response models should be kept separate and convert from these core models t
 API stability even if internal models change.
 """

-from typing import Optional, List, Dict, Any
-from pydantic import BaseModel, Field, ConfigDict
+from typing import Any

+from pydantic import BaseModel, ConfigDict, Field

 # Valid fact types for recall operations (excludes 'observation' which is internal)
 VALID_RECALL_FACT_TYPES = frozenset(["world", "experience", "opinion"])


+class TokenUsage(BaseModel):
+    """
+    Token usage metrics for LLM calls.
+
+    Tracks input/output tokens for a single request to enable
+    per-request cost tracking and monitoring.
+    """
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "input_tokens": 1500,
+                "output_tokens": 500,
+                "total_tokens": 2000,
+            }
+        }
+    )
+
+    input_tokens: int = Field(default=0, description="Number of input/prompt tokens consumed")
+    output_tokens: int = Field(default=0, description="Number of output/completion tokens generated")
+    total_tokens: int = Field(default=0, description="Total tokens (input + output)")
+
+    def __add__(self, other: "TokenUsage") -> "TokenUsage":
+        """Allow aggregating token usage from multiple calls."""
+        return TokenUsage(
+            input_tokens=self.input_tokens + other.input_tokens,
+            output_tokens=self.output_tokens + other.output_tokens,
+            total_tokens=self.total_tokens + other.total_tokens,
+        )
+
+
 class DispositionTraits(BaseModel):
    """
    Disposition traits for a memory bank.
@@ -23,17 +54,12 @@ class DispositionTraits(BaseModel):
    - literalism: 1=flexible interpretation, 5=literal interpretation (how strictly to interpret information)
    - empathy: 1=detached, 5=empathetic (how much to consider emotional context)
    """
+
    skepticism: int = Field(ge=1, le=5, description="How skeptical vs trusting (1=trusting, 5=skeptical)")
    literalism: int = Field(ge=1, le=5, description="How literally to interpret information (1=flexible, 5=literal)")
    empathy: int = Field(ge=1, le=5, description="How much to consider emotional context (1=detached, 5=empathetic)")

-    model_config = ConfigDict(json_schema_extra={
-        "example": {
-            "skepticism": 3,
-            "literalism": 3,
-            "empathy": 3
-        }
-    })
+    model_config = ConfigDict(json_schema_extra={"example": {"skepticism": 3, "literalism": 3, "empathy": 3}})


 class MemoryFact(BaseModel):
@@ -43,38 +69,44 @@ class MemoryFact(BaseModel):
    This represents a unit of information stored in the memory system,
    including both the content and metadata.
    """
-    model_config = ConfigDict(json_schema_extra={
-        "example": {
-            "id": "123e4567-e89b-12d3-a456-426614174000",
-            "text": "Alice works at Google on the AI team",
-            "fact_type": "world",
-            "entities": ["Alice", "Google"],
-            "context": "work info",
-            "occurred_start": "2024-01-15T10:30:00Z",
-            "occurred_end": "2024-01-15T10:30:00Z",
-            "mentioned_at": "2024-01-15T10:30:00Z",
-            "document_id": "session_abc123",
-            "metadata": {"source": "slack"},
-            "chunk_id": "bank123_session_abc123_0",
-            "activation": 0.95
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "id": "123e4567-e89b-12d3-a456-426614174000",
+                "text": "Alice works at Google on the AI team",
+                "fact_type": "world",
+                "entities": ["Alice", "Google"],
+                "context": "work info",
+                "occurred_start": "2024-01-15T10:30:00Z",
+                "occurred_end": "2024-01-15T10:30:00Z",
+                "mentioned_at": "2024-01-15T10:30:00Z",
+                "document_id": "session_abc123",
+                "metadata": {"source": "slack"},
+                "chunk_id": "bank123_session_abc123_0",
+                "activation": 0.95,
+            }
        }
-    })
+    )

    id: str = Field(description="Unique identifier for the memory fact")
    text: str = Field(description="The actual text content of the memory")
    fact_type: str = Field(description="Type of fact: 'world', 'experience', 'opinion', or 'observation'")
-    entities: Optional[List[str]] = Field(None, description="Entity names mentioned in this fact")
-    context: Optional[str] = Field(None, description="Additional context for the memory")
-    occurred_start: Optional[str] = Field(None, description="ISO format date when the event started occurring")
-    occurred_end: Optional[str] = Field(None, description="ISO format date when the event ended occurring")
-    mentioned_at: Optional[str] = Field(None, description="ISO format date when the fact was mentioned/learned")
-    document_id: Optional[str] = Field(None, description="ID of the document this memory belongs to")
-    metadata: Optional[Dict[str, str]] = Field(None, description="User-defined metadata")
-    chunk_id: Optional[str] = Field(None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)")
+    entities: list[str] | None = Field(None, description="Entity names mentioned in this fact")
+    context: str | None = Field(None, description="Additional context for the memory")
+    occurred_start: str | None = Field(None, description="ISO format date when the event started occurring")
+    occurred_end: str | None = Field(None, description="ISO format date when the event ended occurring")
+    mentioned_at: str | None = Field(None, description="ISO format date when the fact was mentioned/learned")
+    document_id: str | None = Field(None, description="ID of the document this memory belongs to")
+    metadata: dict[str, str] | None = Field(None, description="User-defined metadata")
+    chunk_id: str | None = Field(
+        None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)"
+    )


 class ChunkInfo(BaseModel):
    """Information about a chunk."""
+
    chunk_text: str = Field(description="The raw chunk text")
    chunk_index: int = Field(description="Index of the chunk within the document")
    truncated: bool = Field(default=False, description="Whether the chunk was truncated due to token limits")
@@ -87,35 +119,33 @@ class RecallResult(BaseModel):
    Contains a list of matching memory facts and optional trace information
    for debugging and transparency.
    """
-    model_config = ConfigDict(json_schema_extra={
-        "example": {
-            "results": [
-                {
-                    "id": "123e4567-e89b-12d3-a456-426614174000",
-                    "text": "Alice works at Google on the AI team",
-                    "fact_type": "world",
-                    "context": "work info",
-                    "occurred_start": "2024-01-15T10:30:00Z",
-                    "occurred_end": "2024-01-15T10:30:00Z",
-                    "activation": 0.95
-                }
-            ],
-            "trace": {
-                "query": "What did Alice say about machine learning?",
-                "num_results": 1
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "results": [
+                    {
+                        "id": "123e4567-e89b-12d3-a456-426614174000",
+                        "text": "Alice works at Google on the AI team",
+                        "fact_type": "world",
+                        "context": "work info",
+                        "occurred_start": "2024-01-15T10:30:00Z",
+                        "occurred_end": "2024-01-15T10:30:00Z",
+                        "activation": 0.95,
+                    }
+                ],
+                "trace": {"query": "What did Alice say about machine learning?", "num_results": 1},
            }
        }
-    })
-
-    results: List[MemoryFact] = Field(description="List of memory facts matching the query")
-    trace: Optional[Dict[str, Any]] = Field(None, description="Trace information for debugging")
-    entities: Optional[Dict[str, "EntityState"]] = Field(
-        None,
-        description="Entity states for entities mentioned in results (keyed by canonical name)"
    )
-    chunks: Optional[Dict[str, ChunkInfo]] = Field(
-        None,
-        description="Chunks for facts, keyed by '{document_id}_{chunk_index}'"
+
+    results: list[MemoryFact] = Field(description="List of memory facts matching the query")
+    trace: dict[str, Any] | None = Field(None, description="Trace information for debugging")
+    entities: dict[str, "EntityState"] | None = Field(
+        None, description="Entity states for entities mentioned in results (keyed by canonical name)"
+    )
+    chunks: dict[str, ChunkInfo] | None = Field(
+        None, description="Chunks for facts, keyed by '{document_id}_{chunk_index}'"
    )


@@ -124,38 +154,47 @@ 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(json_schema_extra={
-        "example": {
-            "text": "Based on my knowledge, machine learning is being actively used in healthcare...",
-            "based_on": {
-                "world": [
-                    {
-                        "id": "123e4567-e89b-12d3-a456-426614174000",
-                        "text": "Machine learning is used in medical diagnosis",
-                        "fact_type": "world",
-                        "context": "healthcare",
-                        "occurred_start": "2024-01-15T10:30:00Z",
-                        "occurred_end": "2024-01-15T10:30:00Z"
-                    }
-                ],
-                "experience": [],
-                "opinion": []
-            },
-            "new_opinions": [
-                "Machine learning has great potential in healthcare"
-            ]
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "text": "Based on my knowledge, machine learning is being actively used in healthcare...",
+                "based_on": {
+                    "world": [
+                        {
+                            "id": "123e4567-e89b-12d3-a456-426614174000",
+                            "text": "Machine learning is used in medical diagnosis",
+                            "fact_type": "world",
+                            "context": "healthcare",
+                            "occurred_start": "2024-01-15T10:30:00Z",
+                            "occurred_end": "2024-01-15T10:30:00Z",
+                        }
+                    ],
+                    "experience": [],
+                    "opinion": [],
+                },
+                "new_opinions": ["Machine learning has great potential in healthcare"],
+                "structured_output": {"summary": "ML in healthcare", "confidence": 0.9},
+                "usage": {"input_tokens": 1500, "output_tokens": 500, "total_tokens": 2000},
+            }
        }
-    })
+    )

    text: str = Field(description="The formulated answer text")
-    based_on: Dict[str, List[MemoryFact]] = Field(
+    based_on: dict[str, list[MemoryFact]] = Field(
        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"
+    new_opinions: list[str] = Field(default_factory=list, description="List of newly formed opinions during reflection")
+    structured_output: dict[str, Any] | None = Field(
+        default=None,
+        description="Structured output parsed according to the provided response schema. Only present when response_schema was provided.",
+    )
+    usage: TokenUsage | None = Field(
+        default=None,
+        description="Token usage metrics for the LLM calls made during this reflect operation.",
    )


@@ -166,12 +205,12 @@ class Opinion(BaseModel):
    Opinions represent the bank's formed perspectives on topics,
    with a confidence level indicating strength of belief.
    """
-    model_config = ConfigDict(json_schema_extra={
-        "example": {
-            "text": "Machine learning has great potential in healthcare",
-            "confidence": 0.85
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {"text": "Machine learning has great potential in healthcare", "confidence": 0.85}
        }
-    })
+    )

    text: str = Field(description="The opinion text")
    confidence: float = Field(description="Confidence score between 0.0 and 1.0")
@@ -184,15 +223,15 @@ class EntityObservation(BaseModel):
    Observations are objective facts synthesized from multiple memory facts
    about an entity, without personality influence.
    """
-    model_config = ConfigDict(json_schema_extra={
-        "example": {
-            "text": "John is detail-oriented and works at Google",
-            "mentioned_at": "2024-01-15T10:30:00Z"
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {"text": "John is detail-oriented and works at Google", "mentioned_at": "2024-01-15T10:30:00Z"}
        }
-    })
+    )

    text: str = Field(description="The observation text")
-    mentioned_at: Optional[str] = Field(None, description="ISO format date when this observation was created")
+    mentioned_at: str | None = Field(None, description="ISO format date when this observation was created")


 class EntityState(BaseModel):
@@ -201,20 +240,22 @@ class EntityState(BaseModel):

    Contains observations synthesized from facts about the entity.
    """
-    model_config = ConfigDict(json_schema_extra={
-        "example": {
-            "entity_id": "123e4567-e89b-12d3-a456-426614174000",
-            "canonical_name": "John",
-            "observations": [
-                {"text": "John is detail-oriented", "mentioned_at": "2024-01-15T10:30:00Z"},
-                {"text": "John works at Google on the AI team", "mentioned_at": "2024-01-14T09:00:00Z"}
-            ]
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "entity_id": "123e4567-e89b-12d3-a456-426614174000",
+                "canonical_name": "John",
+                "observations": [
+                    {"text": "John is detail-oriented", "mentioned_at": "2024-01-15T10:30:00Z"},
+                    {"text": "John works at Google on the AI team", "mentioned_at": "2024-01-14T09:00:00Z"},
+                ],
+            }
        }
-    })
+    )

    entity_id: str = Field(description="Unique identifier for the entity")
    canonical_name: str = Field(description="Canonical name of the entity")
-    observations: List[EntityObservation] = Field(
-        default_factory=list,
-        description="List of observations about this entity"
+    observations: list[EntityObservation] = Field(
+        default_factory=list, description="List of observations about this entity"
    )
@@ -12,23 +12,16 @@ This package contains modular components for the retain operation:
 - fact_storage: Handle fact insertion into database
 """

-from .types import (
-    RetainContent,
-    ExtractedFact,
-    ProcessedFact,
-    ChunkMetadata,
-    EntityRef,
-    CausalRelation,
-    RetainBatch
+from . import (
+    chunk_storage,
+    deduplication,
+    embedding_processing,
+    entity_processing,
+    fact_extraction,
+    fact_storage,
+    link_creation,
 )
-
-from . import fact_extraction
-from . import embedding_processing
-from . import deduplication
-from . import entity_processing
-from . import link_creation
-from . import chunk_storage
-from . import fact_storage
+from .types import CausalRelation, ChunkMetadata, EntityRef, ExtractedFact, ProcessedFact, RetainBatch, RetainContent

 __all__ = [
    # Types
@@ -5,9 +5,12 @@ bank profile utilities for disposition and background management.
 import json
 import logging
 import re
-from typing import Dict, Optional, TypedDict
+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__)
@@ -21,6 +24,7 @@ DEFAULT_DISPOSITION = {

 class BankProfile(TypedDict):
    """Type for bank profile data."""
+
    name: str
    disposition: DispositionTraits
    background: str
@@ -28,6 +32,7 @@ class BankProfile(TypedDict):

 class BackgroundMergeResponse(BaseModel):
    """LLM response for background merge with disposition inference."""
+
    background: str = Field(description="Merged background in first person perspective")
    disposition: DispositionTraits = Field(description="Inferred disposition traits (skepticism, literalism, empathy)")

@@ -47,11 +52,11 @@ 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
+            bank_id,
        )

        if row:
@@ -61,36 +66,26 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
                disposition_data = json.loads(disposition_data)

            return BankProfile(
-                name=row["name"],
-                disposition=DispositionTraits(**disposition_data),
-                background=row["background"]
+                name=row["name"], disposition=DispositionTraits(**disposition_data), background=row["background"]
            )

        # 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
            """,
            bank_id,
            bank_id,  # Default name is the bank_id
            json.dumps(DEFAULT_DISPOSITION),
-            ""
+            "",
        )

-        return BankProfile(
-            name=bank_id,
-            disposition=DispositionTraits(**DEFAULT_DISPOSITION),
-            background=""
-        )
+        return BankProfile(name=bank_id, disposition=DispositionTraits(**DEFAULT_DISPOSITION), background="")


-async def update_bank_disposition(
-    pool,
-    bank_id: str,
-    disposition: Dict[str, int]
-) -> None:
+async def update_bank_disposition(pool, bank_id: str, disposition: dict[str, int]) -> None:
    """
    Update bank disposition traits.

@@ -104,24 +99,18 @@ async def update_bank_disposition(

    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
            """,
            bank_id,
-            json.dumps(disposition)
+            json.dumps(disposition),
        )


-async def merge_bank_background(
-    pool,
-    llm_config,
-    bank_id: str,
-    new_info: str,
-    update_disposition: bool = True
-) -> dict:
+async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, update_disposition: bool = True) -> dict:
    """
    Merge new background information with existing background using LLM.
    Normalizes to first person ("I") and resolves conflicts.
@@ -142,12 +131,7 @@ async def merge_bank_background(
    current_background = profile["background"]

    # Use LLM to merge backgrounds and optionally infer disposition
-    result = await _llm_merge_background(
-        llm_config,
-        current_background,
-        new_info,
-        infer_disposition=update_disposition
-    )
+    result = await _llm_merge_background(llm_config, current_background, new_info, infer_disposition=update_disposition)

    merged_background = result["background"]
    inferred_disposition = result.get("disposition")
@@ -157,8 +141,8 @@ async def merge_bank_background(
        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()
@@ -166,19 +150,19 @@ async def merge_bank_background(
                """,
                bank_id,
                merged_background,
-                json.dumps(inferred_disposition)
+                json.dumps(inferred_disposition),
            )
        else:
            # Update only background
            await conn.execute(
-                """
-                UPDATE banks
+                f"""
+                UPDATE {fq_table("banks")}
                SET background = $2,
                    updated_at = NOW()
                WHERE bank_id = $1
                """,
                bank_id,
-                merged_background
+                merged_background,
            )

    response = {"background": merged_background}
@@ -188,12 +172,7 @@ async def merge_bank_background(
    return response


-async def _llm_merge_background(
-    llm_config,
-    current: str,
-    new_info: str,
-    infer_disposition: bool = False
-) -> dict:
+async def _llm_merge_background(llm_config, current: str, new_info: str, infer_disposition: bool = False) -> dict:
    """
    Use LLM to intelligently merge background information.
    Optionally infer Big Five disposition traits from the merged background.
@@ -273,25 +252,19 @@ Merged background:"""
                    response_format=BackgroundMergeResponse,
                    scope="bank_background",
                    temperature=0.3,
-                    max_completion_tokens=8192
+                    max_completion_tokens=8192,
                )
                logger.info(f"Successfully got structured response: background={parsed.background[:100]}")

                # Convert Pydantic model to dict format
-                return {
-                    "background": parsed.background,
-                    "disposition": parsed.disposition.model_dump()
-                }
+                return {"background": parsed.background, "disposition": parsed.disposition.model_dump()}
            except Exception as e:
                logger.warning(f"Structured output failed, falling back to manual parsing: {e}")
                # Fall through to manual parsing below

        # Manual parsing fallback or non-disposition merge
        content = await llm_config.call(
-            messages=messages,
-            scope="bank_background",
-            temperature=0.3,
-            max_completion_tokens=8192
+            messages=messages, scope="bank_background", temperature=0.3, max_completion_tokens=8192
        )

        logger.info(f"LLM response for background merge (first 500 chars): {content[:500]}")
@@ -310,7 +283,7 @@ Merged background:"""
            # Method 2: Extract from markdown code blocks
            if result is None:
                # Remove markdown code blocks
-                code_block_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', content, re.DOTALL)
+                code_block_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", content, re.DOTALL)
                if code_block_match:
                    try:
                        result = json.loads(code_block_match.group(1))
@@ -321,7 +294,9 @@ Merged background:"""
            # Method 3: Find nested JSON structure
            if result is None:
                # Look for JSON object with nested structure
-                json_match = re.search(r'\{[^{}]*"background"[^{}]*"disposition"[^{}]*\{[^{}]*\}[^{}]*\}', content, re.DOTALL)
+                json_match = re.search(
+                    r'\{[^{}]*"background"[^{}]*"disposition"[^{}]*\{[^{}]*\}[^{}]*\}', content, re.DOTALL
+                )
                if json_match:
                    try:
                        result = json.loads(json_match.group())
@@ -335,7 +310,7 @@ Merged background:"""
                # Fallback: use new_info as background with default disposition
                return {
                    "background": new_info if new_info else current if current else "",
-                    "disposition": DEFAULT_DISPOSITION.copy()
+                    "disposition": DEFAULT_DISPOSITION.copy(),
                }

            # Validate disposition values
@@ -387,9 +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
            """
        )
@@ -401,13 +376,15 @@ async def list_banks(pool) -> list:
            if isinstance(disposition_data, str):
                disposition_data = json.loads(disposition_data)

-            result.append({
-                "bank_id": row["bank_id"],
-                "name": row["name"],
-                "disposition": disposition_data,
-                "background": row["background"],
-                "created_at": row["created_at"].isoformat() if row["created_at"] else None,
-                "updated_at": row["updated_at"].isoformat() if row["updated_at"] else None,
-            })
+            result.append(
+                {
+                    "bank_id": row["bank_id"],
+                    "name": row["name"],
+                    "disposition": disposition_data,
+                    "background": row["background"],
+                    "created_at": row["created_at"].isoformat() if row["created_at"] else None,
+                    "updated_at": row["updated_at"].isoformat() if row["updated_at"] else None,
+                }
+            )

        return result
@@ -3,20 +3,16 @@ Chunk storage for retain pipeline.

 Handles storage of document chunks in the database.
 """
-import logging
-from typing import List, Dict, Optional

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

 logger = logging.getLogger(__name__)


-async def store_chunks_batch(
-    conn,
-    bank_id: str,
-    document_id: str,
-    chunks: List[ChunkMetadata]
-) -> Dict[int, str]:
+async def store_chunks_batch(conn, bank_id: str, document_id: str, chunks: list[ChunkMetadata]) -> dict[int, str]:
    """
    Store document chunks in the database.

@@ -47,24 +43,21 @@ async def store_chunks_batch(

    # 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,
        [document_id] * len(chunk_texts),
        [bank_id] * len(chunk_texts),
        chunk_texts,
-        chunk_indices
+        chunk_indices,
    )

    return chunk_id_map


-def map_facts_to_chunks(
-    facts_chunk_indices: List[int],
-    chunk_id_map: Dict[int, str]
-) -> List[Optional[str]]:
+def map_facts_to_chunks(facts_chunk_indices: list[int], chunk_id_map: dict[int, str]) -> list[str | None]:
    """
    Map fact chunk indices to chunk IDs.

@@ -3,22 +3,17 @@ Deduplication logic for retain pipeline.

 Checks for duplicate facts using semantic similarity and temporal proximity.
 """
+
 import logging
-from datetime import datetime
-from typing import List
 from collections import defaultdict
+from datetime import UTC

 from .types import ProcessedFact

 logger = logging.getLogger(__name__)


-async def check_duplicates_batch(
-    conn,
-    bank_id: str,
-    facts: List[ProcessedFact],
-    duplicate_checker_fn
-) -> List[bool]:
+async def check_duplicates_batch(conn, bank_id: str, facts: list[ProcessedFact], duplicate_checker_fn) -> list[bool]:
    """
    Check which facts are duplicates using batched time-window queries.

@@ -47,16 +42,12 @@ async def check_duplicates_batch(

        # Defensive: if both are None (shouldn't happen), use now()
        if fact_date is None:
-            from datetime import datetime, timezone
-            fact_date = datetime.now(timezone.utc)
+            from datetime import datetime
+
+            fact_date = datetime.now(UTC)

        # Round to 12-hour bucket to group similar times
-        bucket_key = fact_date.replace(
-            hour=(fact_date.hour // 12) * 12,
-            minute=0,
-            second=0,
-            microsecond=0
-        )
+        bucket_key = fact_date.replace(hour=(fact_date.hour // 12) * 12, minute=0, second=0, microsecond=0)
        time_buckets[bucket_key].append((idx, fact))

    # Process each bucket in batch
@@ -68,14 +59,7 @@ async def check_duplicates_batch(
        embeddings = [item[1].embedding for item in bucket_items]

        # Check duplicates for this time bucket
-        dup_flags = await duplicate_checker_fn(
-            conn,
-            bank_id,
-            texts,
-            embeddings,
-            bucket_date,
-            time_window_hours=24
-        )
+        dup_flags = await duplicate_checker_fn(conn, bank_id, texts, embeddings, bucket_date, time_window_hours=24)

        # Map results back to original indices
        for idx, is_dup in zip(indices, dup_flags):
@@ -84,10 +68,7 @@ async def check_duplicates_batch(
    return all_is_duplicate


-def filter_duplicates(
-    facts: List[ProcessedFact],
-    is_duplicate_flags: List[bool]
-) -> List[ProcessedFact]:
+def filter_duplicates(facts: list[ProcessedFact], is_duplicate_flags: list[bool]) -> list[ProcessedFact]:
    """
    Filter out duplicate facts based on duplicate flags.

@@ -3,9 +3,8 @@ Embedding processing for retain pipeline.

 Handles augmenting fact texts with temporal information and generating embeddings.
 """
+
 import logging
-from typing import List
-from datetime import datetime

 from . import embedding_utils
 from .types import ExtractedFact
@@ -13,7 +12,7 @@ from .types import ExtractedFact
 logger = logging.getLogger(__name__)


-def augment_texts_with_dates(facts: List[ExtractedFact], format_date_fn) -> List[str]:
+def augment_texts_with_dates(facts: list[ExtractedFact], format_date_fn) -> list[str]:
    """
    Augment fact texts with readable dates for better temporal matching.

@@ -37,10 +36,7 @@ def augment_texts_with_dates(facts: List[ExtractedFact], format_date_fn) -> List
    return augmented_texts


-async def generate_embeddings_batch(
-    embeddings_model,
-    texts: List[str]
-) -> List[List[float]]:
+async def generate_embeddings_batch(embeddings_model, texts: list[str]) -> list[list[float]]:
    """
    Generate embeddings for a batch of texts.

@@ -54,9 +50,6 @@ async def generate_embeddings_batch(
    if not texts:
        return []

-    embeddings = await embedding_utils.generate_embeddings_batch(
-        embeddings_model,
-        texts
-    )
+    embeddings = await embedding_utils.generate_embeddings_batch(embeddings_model, texts)

    return embeddings
@@ -4,12 +4,11 @@ Embedding generation utilities for memory units.

 import asyncio
 import logging
-from typing import List

 logger = logging.getLogger(__name__)


-def generate_embedding(embeddings_backend, text: str) -> List[float]:
+def generate_embedding(embeddings_backend, text: str) -> list[float]:
    """
    Generate embedding for text using the provided embeddings backend.

@@ -27,7 +26,7 @@ def generate_embedding(embeddings_backend, text: str) -> List[float]:
        raise Exception(f"Failed to generate embedding: {str(e)}")


-async def generate_embeddings_batch(embeddings_backend, texts: List[str]) -> List[List[float]]:
+async def generate_embeddings_batch(embeddings_backend, texts: list[str]) -> list[list[float]]:
    """
    Generate embeddings for multiple texts using the provided embeddings backend.

@@ -47,7 +46,7 @@ async def generate_embeddings_batch(embeddings_backend, texts: List[str]) -> Lis
        embeddings = await loop.run_in_executor(
            None,  # Use default thread pool
            embeddings_backend.encode,
-            texts
+            texts,
        )
        return embeddings
    except Exception as e:
@@ -3,12 +3,11 @@ Entity processing for retain pipeline.

 Handles entity extraction, resolution, and link creation for stored facts.
 """
-import logging
-from typing import List, Tuple, Dict, Any
-from uuid import UUID

-from .types import ProcessedFact, EntityRef, EntityLink
+import logging
+
 from . import link_utils
+from .types import EntityLink, ProcessedFact

 logger = logging.getLogger(__name__)

@@ -17,18 +16,20 @@ async def process_entities_batch(
    entity_resolver,
    conn,
    bank_id: str,
-    unit_ids: List[str],
-    facts: List[ProcessedFact],
-    log_buffer: List[str] = None
-) -> List[EntityLink]:
+    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
@@ -37,6 +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
@@ -47,15 +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(
@@ -67,16 +89,13 @@ async def process_entities_batch(
        "",  # context (not used in current implementation)
        fact_dates,
        entities_per_fact,
-        log_buffer  # Pass log_buffer for detailed logging
+        log_buffer,  # Pass log_buffer for detailed logging
    )

    return entity_links


-async def insert_entity_links_batch(
-    conn,
-    entity_links: List[EntityLink]
-) -> None:
+async def insert_entity_links_batch(conn, entity_links: list[EntityLink]) -> None:
    """
    Insert entity links in batch.

@@ -3,22 +3,19 @@ Fact storage for retain pipeline.

 Handles insertion of facts into the database.
 """
-import logging
-import json
-from typing import List, Optional
-from uuid import UUID

+import json
+import logging
+
+from ..memory_engine import fq_table
 from .types import ProcessedFact

 logger = logging.getLogger(__name__)


 async def insert_facts_batch(
-    conn,
-    bank_id: str,
-    facts: List[ProcessedFact],
-    document_id: Optional[str] = None
-) -> List[str]:
+    conn, bank_id: str, facts: list[ProcessedFact], document_id: str | None = None
+) -> list[str]:
    """
    Insert facts into the database in batch.

@@ -62,7 +59,7 @@ async def insert_facts_batch(
        contexts.append(fact.context)
        fact_types.append(fact.fact_type)
        # confidence_score is only for opinion facts
-        confidence_scores.append(1.0 if fact.fact_type == 'opinion' else None)
+        confidence_scores.append(1.0 if fact.fact_type == "opinion" else None)
        access_counts.append(0)  # Initial access count
        metadata_jsons.append(json.dumps(fact.metadata))
        chunk_ids.append(fact.chunk_id)
@@ -71,8 +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[],
@@ -93,10 +90,10 @@ async def insert_facts_batch(
        access_counts,
        metadata_jsons,
        chunk_ids,
-        document_ids
+        document_ids,
    )

-    unit_ids = [str(row['id']) for row in results]
+    unit_ids = [str(row["id"]) for row in results]
    return unit_ids


@@ -111,25 +108,20 @@ 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()
        """,
        bank_id,
        '{"skepticism": 3, "literalism": 3, "empathy": 3}',
-        ""
+        "",
    )


 async def handle_document_tracking(
-    conn,
-    bank_id: str,
-    document_id: str,
-    combined_content: str,
-    is_first_batch: bool,
-    retain_params: Optional[dict] = None
+    conn, bank_id: str, document_id: str, combined_content: str, is_first_batch: bool, retain_params: dict | None = None
 ) -> None:
    """
    Handle document tracking in the database.
@@ -151,14 +143,13 @@ async def handle_document_tracking(
    # 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
+            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,
@@ -172,5 +163,5 @@ async def handle_document_tracking(
        combined_content,
        content_hash,
        json.dumps({}),  # Empty metadata dict
-        json.dumps(retain_params) if retain_params else None
+        json.dumps(retain_params) if retain_params else None,
    )
@@ -3,20 +3,16 @@ Link creation for retain pipeline.

 Handles creation of temporal, semantic, and causal links between facts.
 """
-import logging
-from typing import List

-from .types import ProcessedFact, CausalRelation
+import logging
+
 from . import link_utils
+from .types import ProcessedFact

 logger = logging.getLogger(__name__)


-async def create_temporal_links_batch(
-    conn,
-    bank_id: str,
-    unit_ids: List[str]
-) -> int:
+async def create_temporal_links_batch(conn, bank_id: str, unit_ids: list[str]) -> int:
    """
    Create temporal links between facts.

@@ -33,20 +29,10 @@ async def create_temporal_links_batch(
    if not unit_ids:
        return 0

-    return await link_utils.create_temporal_links_batch_per_fact(
-        conn,
-        bank_id,
-        unit_ids,
-        log_buffer=[]
-    )
+    return await link_utils.create_temporal_links_batch_per_fact(conn, bank_id, unit_ids, log_buffer=[])


-async def create_semantic_links_batch(
-    conn,
-    bank_id: str,
-    unit_ids: List[str],
-    embeddings: List[List[float]]
-) -> int:
+async def create_semantic_links_batch(conn, bank_id: str, unit_ids: list[str], embeddings: list[list[float]]) -> int:
    """
    Create semantic links between facts.

@@ -67,20 +53,10 @@ async def create_semantic_links_batch(
    if len(unit_ids) != len(embeddings):
        raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and embeddings ({len(embeddings)})")

-    return await link_utils.create_semantic_links_batch(
-        conn,
-        bank_id,
-        unit_ids,
-        embeddings,
-        log_buffer=[]
-    )
+    return await link_utils.create_semantic_links_batch(conn, bank_id, unit_ids, embeddings, log_buffer=[])


-async def create_causal_links_batch(
-    conn,
-    unit_ids: List[str],
-    facts: List[ProcessedFact]
-) -> int:
+async def create_causal_links_batch(conn, unit_ids: list[str], facts: list[ProcessedFact]) -> int:
    """
    Create causal links between facts.

@@ -108,9 +84,9 @@ async def create_causal_links_batch(
            # Convert CausalRelation objects to dicts
            relations_dicts = [
                {
-                    'relation_type': rel.relation_type,
-                    'target_fact_index': rel.target_fact_index,
-                    'strength': rel.strength
+                    "relation_type": rel.relation_type,
+                    "target_fact_index": rel.target_fact_index,
+                    "strength": rel.strength,
                }
                for rel in fact.causal_relations
            ]
@@ -118,10 +94,6 @@ async def create_causal_links_batch(
        else:
            causal_relations_per_fact.append([])

-    link_count = await link_utils.create_causal_links_batch(
-        conn,
-        unit_ids,
-        causal_relations_per_fact
-    )
+    link_count = await link_utils.create_causal_links_batch(conn, unit_ids, causal_relations_per_fact)

    return link_count
@@ -2,12 +2,12 @@
 Link creation utilities for temporal, semantic, and entity links.
 """

-import time
 import logging
-from typing import List
-from datetime import timedelta, datetime, timezone
+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__)
@@ -19,7 +19,7 @@ def _normalize_datetime(dt):
        return None
    if dt.tzinfo is None:
        # Naive datetime - assume UTC
-        return dt.replace(tzinfo=timezone.utc)
+        return dt.replace(tzinfo=UTC)
    return dt


@@ -54,24 +54,26 @@ def compute_temporal_links(
        try:
            time_lower = unit_event_date_norm - timedelta(hours=time_window_hours)
        except OverflowError:
-            time_lower = datetime.min.replace(tzinfo=timezone.utc)
+            time_lower = datetime.min.replace(tzinfo=UTC)
        try:
            time_upper = unit_event_date_norm + timedelta(hours=time_window_hours)
        except OverflowError:
-            time_upper = datetime.max.replace(tzinfo=timezone.utc)
+            time_upper = datetime.max.replace(tzinfo=UTC)

        # Filter candidates within this unit's time window
        matching_neighbors = [
-            (row['id'], row['event_date'])
+            (row["id"], row["event_date"])
            for row in candidates
-            if time_lower <= _normalize_datetime(row['event_date']) <= time_upper
+            if time_lower <= _normalize_datetime(row["event_date"]) <= time_upper
        ][:10]  # Limit to top 10

        for recent_id, recent_event_date in matching_neighbors:
            # Calculate temporal proximity weight
-            time_diff_hours = abs((unit_event_date_norm - _normalize_datetime(recent_event_date)).total_seconds() / 3600)
+            time_diff_hours = abs(
+                (unit_event_date_norm - _normalize_datetime(recent_event_date)).total_seconds() / 3600
+            )
            weight = max(0.3, 1.0 - (time_diff_hours / time_window_hours))
-            links.append((unit_id, str(recent_id), 'temporal', weight, None))
+            links.append((unit_id, str(recent_id), "temporal", weight, None))

    return links

@@ -99,17 +101,17 @@ def compute_temporal_query_bounds(
    try:
        min_date = min(all_dates) - timedelta(hours=time_window_hours)
    except OverflowError:
-        min_date = datetime.min.replace(tzinfo=timezone.utc)
+        min_date = datetime.min.replace(tzinfo=UTC)

    try:
        max_date = max(all_dates) + timedelta(hours=time_window_hours)
    except OverflowError:
-        max_date = datetime.max.replace(tzinfo=timezone.utc)
+        max_date = datetime.max.replace(tzinfo=UTC)

    return min_date, max_date


-def _log(log_buffer, message, level='info'):
+def _log(log_buffer, message, level="info"):
    """Helper to log to buffer if available, otherwise use logger.

    Args:
@@ -117,7 +119,7 @@ def _log(log_buffer, message, level='info'):
        message: The log message
        level: 'info', 'debug', 'warning', or 'error'. Debug messages are not added to buffer.
    """
-    if level == 'debug':
+    if level == "debug":
        # Debug messages only go to logger, not to buffer
        logger.debug(message)
        return
@@ -125,23 +127,23 @@ def _log(log_buffer, message, level='info'):
    if log_buffer is not None:
        log_buffer.append(message)
    else:
-        if level == 'info':
+        if level == "info":
            logger.info(message)
        else:
-            logger.log(logging.WARNING if level == 'warning' else logging.ERROR, message)
+            logger.log(logging.WARNING if level == "warning" else logging.ERROR, message)


 async def extract_entities_batch_optimized(
    entity_resolver,
    conn,
    bank_id: str,
-    unit_ids: List[str],
-    sentences: List[str],
+    unit_ids: list[str],
+    sentences: list[str],
    context: str,
-    fact_dates: List,
-    llm_entities: List[List[dict]],
-    log_buffer: List[str] = None,
-) -> List[tuple]:
+    fact_dates: list,
+    llm_entities: list[list[dict]],
+    log_buffer: list[str] = None,
+) -> list[tuple]:
    """
    Process LLM-extracted entities for ALL facts in batch.

@@ -171,15 +173,19 @@ async def extract_entities_batch_optimized(
            formatted_entities = []
            for ent in entity_list:
                # Handle both Entity objects and dicts
-                if hasattr(ent, 'text'):
+                if hasattr(ent, "text"):
                    # Entity objects only have 'text', default type to 'CONCEPT'
-                    formatted_entities.append({'text': ent.text, 'type': 'CONCEPT'})
+                    formatted_entities.append({"text": ent.text, "type": "CONCEPT"})
                elif isinstance(ent, dict):
-                    formatted_entities.append({'text': ent.get('text', ''), 'type': ent.get('type', 'CONCEPT')})
+                    formatted_entities.append({"text": ent.get("text", ""), "type": ent.get("type", "CONCEPT")})
            all_entities.append(formatted_entities)

        total_entities = sum(len(ents) for ents in all_entities)
-        _log(log_buffer, f"  [6.1] Process LLM entities: {total_entities} entities from {len(sentences)} facts in {time.time() - substep_start:.3f}s", level='debug')
+        _log(
+            log_buffer,
+            f"  [6.1] Process LLM entities: {total_entities} entities from {len(sentences)} facts in {time.time() - substep_start:.3f}s",
+            level="debug",
+        )

        # Step 2: Resolve entities in BATCH (much faster!)
        substep_start = time.time()
@@ -195,13 +201,19 @@ async def extract_entities_batch_optimized(
                continue

            for local_idx, entity in enumerate(entities):
-                all_entities_flat.append({
-                    'text': entity['text'],
-                    'type': entity['type'],
-                    'nearby_entities': entities,
-                })
+                all_entities_flat.append(
+                    {
+                        "text": entity["text"],
+                        "type": entity["type"],
+                        "nearby_entities": entities,
+                    }
+                )
                entity_to_unit.append((unit_id, local_idx, fact_date))
-        _log(log_buffer, f"    [6.2.1] Prepare entities: {len(all_entities_flat)} entities in {time.time() - substep_6_2_1_start:.3f}s", level='debug')
+        _log(
+            log_buffer,
+            f"    [6.2.1] Prepare entities: {len(all_entities_flat)} entities in {time.time() - substep_6_2_1_start:.3f}s",
+            level="debug",
+        )

        # Resolve ALL entities in one batch call
        if all_entities_flat:
@@ -210,7 +222,7 @@ async def extract_entities_batch_optimized(

            # Add per-entity dates to entity data for batch resolution
            for idx, (unit_id, local_idx, fact_date) in enumerate(entity_to_unit):
-                all_entities_flat[idx]['event_date'] = fact_date
+                all_entities_flat[idx]["event_date"] = fact_date

            # Resolve ALL entities in ONE batch call (much faster than sequential buckets)
            # INSERT ... ON CONFLICT handles any race conditions at the DB level
@@ -219,10 +231,14 @@ async def extract_entities_batch_optimized(
                entities_data=all_entities_flat,
                context=context,
                unit_event_date=None,  # Not used when per-entity dates provided
-                conn=conn  # Use main transaction connection
+                conn=conn,  # Use main transaction connection
            )

-            _log(log_buffer, f"    [6.2.2] Resolve entities: {len(all_entities_flat)} entities in single batch in {time.time() - substep_6_2_2_start:.3f}s", level='debug')
+            _log(
+                log_buffer,
+                f"    [6.2.2] Resolve entities: {len(all_entities_flat)} entities in single batch in {time.time() - substep_6_2_2_start:.3f}s",
+                level="debug",
+            )

            # [6.2.3] Create unit-entity links in BATCH
            substep_6_2_3_start = time.time()
@@ -239,12 +255,24 @@ async def extract_entities_batch_optimized(

            # Batch insert all unit-entity links (MUCH faster!)
            await entity_resolver.link_units_to_entities_batch(unit_entity_pairs, conn=conn)
-            _log(log_buffer, f"    [6.2.3] Create unit-entity links (batched): {len(unit_entity_pairs)} links in {time.time() - substep_6_2_3_start:.3f}s", level='debug')
+            _log(
+                log_buffer,
+                f"    [6.2.3] Create unit-entity links (batched): {len(unit_entity_pairs)} links in {time.time() - substep_6_2_3_start:.3f}s",
+                level="debug",
+            )

-            _log(log_buffer, f"  [6.2] Entity resolution (batched): {len(all_entities_flat)} entities resolved in {time.time() - step_6_2_start:.3f}s", level='debug')
+            _log(
+                log_buffer,
+                f"  [6.2] Entity resolution (batched): {len(all_entities_flat)} entities resolved in {time.time() - step_6_2_start:.3f}s",
+                level="debug",
+            )
        else:
            unit_to_entity_ids = {}
-            _log(log_buffer, f"  [6.2] Entity resolution (batched): 0 entities in {time.time() - step_6_2_start:.3f}s", level='debug')
+            _log(
+                log_buffer,
+                f"  [6.2] Entity resolution (batched): 0 entities in {time.time() - step_6_2_start:.3f}s",
+                level="debug",
+            )

        # Step 3: Create entity links between units that share entities
        substep_start = time.time()
@@ -253,39 +281,44 @@ async def extract_entities_batch_optimized(
        for entity_ids in unit_to_entity_ids.values():
            all_entity_ids.update(entity_ids)

-        _log(log_buffer, f"  [6.3] Creating entity links for {len(all_entity_ids)} unique entities...", level='debug')
+        _log(log_buffer, f"  [6.3] Creating entity links for {len(all_entity_ids)} unique entities...", level="debug")

        # Find all units that reference these entities (ONE batched query)
        entity_to_units = {}
        if all_entity_ids:
            query_start = time.time()
            import uuid
+
            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
+                entity_id_list,
+            )
+            _log(
+                log_buffer,
+                f"      [6.3.1] Query unit_entities: {len(rows)} rows in {time.time() - query_start:.3f}s",
+                level="debug",
            )
-            _log(log_buffer, f"      [6.3.1] Query unit_entities: {len(rows)} rows in {time.time() - query_start:.3f}s", level='debug')

            # Group by entity_id
            group_start = time.time()
            for row in rows:
-                entity_id = row['entity_id']
+                entity_id = row["entity_id"]
                if entity_id not in entity_to_units:
                    entity_to_units[entity_id] = []
-                entity_to_units[entity_id].append(row['unit_id'])
-            _log(log_buffer, f"      [6.3.2] Group by entity_id: {time.time() - group_start:.3f}s", level='debug')
+                entity_to_units[entity_id].append(row["unit_id"])
+            _log(log_buffer, f"      [6.3.2] Group by entity_id: {time.time() - group_start:.3f}s", level="debug")

        # Create bidirectional links between units that share entities
        # OPTIMIZATION: Limit links per entity to avoid N² explosion
        # Only link each new unit to the most recent MAX_LINKS_PER_ENTITY units
        MAX_LINKS_PER_ENTITY = 50  # Limit to prevent explosion when entity appears in many facts
        link_gen_start = time.time()
-        links: List[EntityLink] = []
+        links: list[EntityLink] = []
        new_unit_set = set(unit_ids)  # Units from this batch

        def to_uuid(val) -> UUID:
@@ -299,27 +332,52 @@ async def extract_entities_batch_optimized(

            # Link new units to each other (within batch) - also limited
            # For very common entities, limit within-batch links too
-            new_units_to_link = new_units[-MAX_LINKS_PER_ENTITY:] if len(new_units) > MAX_LINKS_PER_ENTITY else new_units
+            new_units_to_link = (
+                new_units[-MAX_LINKS_PER_ENTITY:] if len(new_units) > MAX_LINKS_PER_ENTITY else new_units
+            )
            for i, unit_id_1 in enumerate(new_units_to_link):
-                for unit_id_2 in new_units_to_link[i+1:]:
-                    links.append(EntityLink(from_unit_id=to_uuid(unit_id_1), to_unit_id=to_uuid(unit_id_2), entity_id=entity_uuid))
-                    links.append(EntityLink(from_unit_id=to_uuid(unit_id_2), to_unit_id=to_uuid(unit_id_1), entity_id=entity_uuid))
+                for unit_id_2 in new_units_to_link[i + 1 :]:
+                    links.append(
+                        EntityLink(
+                            from_unit_id=to_uuid(unit_id_1), to_unit_id=to_uuid(unit_id_2), entity_id=entity_uuid
+                        )
+                    )
+                    links.append(
+                        EntityLink(
+                            from_unit_id=to_uuid(unit_id_2), to_unit_id=to_uuid(unit_id_1), entity_id=entity_uuid
+                        )
+                    )

            # Link new units to LIMITED existing units (most recent)
            existing_to_link = existing_units[-MAX_LINKS_PER_ENTITY:]  # Take most recent
            for new_unit in new_units:
                for existing_unit in existing_to_link:
-                    links.append(EntityLink(from_unit_id=to_uuid(new_unit), to_unit_id=to_uuid(existing_unit), entity_id=entity_uuid))
-                    links.append(EntityLink(from_unit_id=to_uuid(existing_unit), to_unit_id=to_uuid(new_unit), entity_id=entity_uuid))
+                    links.append(
+                        EntityLink(
+                            from_unit_id=to_uuid(new_unit), to_unit_id=to_uuid(existing_unit), entity_id=entity_uuid
+                        )
+                    )
+                    links.append(
+                        EntityLink(
+                            from_unit_id=to_uuid(existing_unit), to_unit_id=to_uuid(new_unit), entity_id=entity_uuid
+                        )
+                    )

-        _log(log_buffer, f"      [6.3.3] Generate {len(links)} links: {time.time() - link_gen_start:.3f}s", level='debug')
-        _log(log_buffer, f"  [6.3] Entity link creation: {len(links)} links for {len(all_entity_ids)} unique entities in {time.time() - substep_start:.3f}s", level='debug')
+        _log(
+            log_buffer, f"      [6.3.3] Generate {len(links)} links: {time.time() - link_gen_start:.3f}s", level="debug"
+        )
+        _log(
+            log_buffer,
+            f"  [6.3] Entity link creation: {len(links)} links for {len(all_entity_ids)} unique entities in {time.time() - substep_start:.3f}s",
+            level="debug",
+        )

        return links

    except Exception as e:
        logger.error(f"Failed to extract entities in batch: {str(e)}")
        import traceback
+
        traceback.print_exc()
        raise

@@ -327,9 +385,9 @@ async def extract_entities_batch_optimized(
 async def create_temporal_links_batch_per_fact(
    conn,
    bank_id: str,
-    unit_ids: List[str],
+    unit_ids: list[str],
    time_window_hours: int = 24,
-    log_buffer: List[str] = None,
+    log_buffer: list[str] = None,
 ) -> int:
    """
    Create temporal links for multiple units, each with their own event_date.
@@ -356,15 +414,18 @@ 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
+            unit_ids,
+        )
+        new_units = {str(row["id"]): row["event_date"] for row in rows}
+        _log(
+            log_buffer,
+            f"      [7.1] Fetch event_dates for {len(unit_ids)} units: {time_mod.time() - fetch_dates_start:.3f}s",
        )
-        new_units = {str(row['id']): row['event_date'] for row in rows}
-        _log(log_buffer, f"      [7.1] Fetch event_dates for {len(unit_ids)} units: {time_mod.time() - fetch_dates_start:.3f}s")

        # Fetch ALL potential temporal neighbors in ONE query (much faster!)
        # Get time range across all units with overflow protection
@@ -372,9 +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)
@@ -383,9 +444,12 @@ async def create_temporal_links_batch_per_fact(
            bank_id,
            min_date,
            max_date,
-            unit_ids
+            unit_ids,
+        )
+        _log(
+            log_buffer,
+            f"      [7.2] Fetch {len(all_candidates)} candidate neighbors (1 query): {time_mod.time() - fetch_neighbors_start:.3f}s",
        )
-        _log(log_buffer, f"      [7.2] Fetch {len(all_candidates)} candidate neighbors (1 query): {time_mod.time() - fetch_neighbors_start:.3f}s")

        # Filter and create links in memory (much faster than N queries)
        link_gen_start = time_mod.time()
@@ -408,21 +472,25 @@ async def create_temporal_links_batch_per_fact(
                    if time_diff_hours <= time_window_hours:
                        weight = max(0.3, 1.0 - (time_diff_hours / time_window_hours))
                        # Create bidirectional links
-                        links.append((unit_id, other_id, 'temporal', weight, None))
-                        links.append((other_id, unit_id, 'temporal', weight, None))
+                        links.append((unit_id, other_id, "temporal", weight, None))
+                        links.append((other_id, unit_id, "temporal", weight, None))

        _log(log_buffer, f"      [7.3] Generate {len(links)} temporal links: {time_mod.time() - link_gen_start:.3f}s")

        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)
-                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
-                """,
-                links
-            )
+            # Batch inserts to avoid timeout on large batches
+            BATCH_SIZE = 1000
+            for batch_start in range(0, len(links), BATCH_SIZE):
+                batch = links[batch_start : batch_start + BATCH_SIZE]
+                await conn.executemany(
+                    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
+                    """,
+                    batch,
+                )
            _log(log_buffer, f"      [7.4] Insert {len(links)} temporal links: {time_mod.time() - insert_start:.3f}s")

        return len(links)
@@ -430,6 +498,7 @@ async def create_temporal_links_batch_per_fact(
    except Exception as e:
        logger.error(f"Failed to create temporal links: {str(e)}")
        import traceback
+
        traceback.print_exc()
        raise

@@ -437,11 +506,11 @@ async def create_temporal_links_batch_per_fact(
 async def create_semantic_links_batch(
    conn,
    bank_id: str,
-    unit_ids: List[str],
-    embeddings: List[List[float]],
+    unit_ids: list[str],
+    embeddings: list[list[float]],
    top_k: int = 5,
    threshold: float = 0.7,
-    log_buffer: List[str] = None,
+    log_buffer: list[str] = None,
 ) -> int:
    """
    Create semantic links for multiple units efficiently.
@@ -465,22 +534,26 @@ async def create_semantic_links_batch(

    try:
        import time as time_mod
+
        import numpy as np

        # 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)
            """,
            bank_id,
-            unit_ids
+            unit_ids,
+        )
+        _log(
+            log_buffer,
+            f"      [8.1] Fetch {len(all_existing)} existing embeddings (1 query): {time_mod.time() - fetch_start:.3f}s",
        )
-        _log(log_buffer, f"      [8.1] Fetch {len(all_existing)} existing embeddings (1 query): {time_mod.time() - fetch_start:.3f}s")

        # Convert to numpy for vectorized similarity computation
        compute_start = time_mod.time()
@@ -488,15 +561,16 @@ async def create_semantic_links_batch(

        if all_existing:
            # Convert existing embeddings to numpy array
-            existing_ids = [str(row['id']) for row in all_existing]
+            existing_ids = [str(row["id"]) for row in all_existing]
            # Stack embeddings as 2D array: (num_embeddings, embedding_dim)
            embedding_arrays = []
            for row in all_existing:
-                raw_emb = row['embedding']
+                raw_emb = row["embedding"]
                # Handle different pgvector formats
                if isinstance(raw_emb, str):
                    # Parse string format: "[1.0, 2.0, ...]"
                    import json
+
                    emb = np.array(json.loads(raw_emb), dtype=np.float32)
                elif isinstance(raw_emb, (list, tuple)):
                    emb = np.array(raw_emb, dtype=np.float32)
@@ -537,7 +611,7 @@ async def create_semantic_links_batch(
                        similar_id = existing_ids[idx]
                        # Clamp to [0, 1] to handle floating point precision issues
                        similarity = float(min(1.0, max(0.0, similarities[idx])))
-                        all_links.append((unit_id, similar_id, 'semantic', similarity, None))
+                        all_links.append((unit_id, similar_id, "semantic", similarity, None))

        # Also compute similarities WITHIN the new batch (new units to each other)
        # Apply the same top_k limit per unit as we do for existing units
@@ -565,32 +639,42 @@ async def create_semantic_links_batch(
                        other_id = unit_ids[other_idx]
                        # Clamp to [0, 1] to handle floating point precision issues
                        similarity = float(min(1.0, max(0.0, similarities[local_idx])))
-                        all_links.append((unit_id, other_id, 'semantic', similarity, None))
+                        all_links.append((unit_id, other_id, "semantic", similarity, None))

-        _log(log_buffer, f"      [8.2] Compute similarities & generate {len(all_links)} semantic links: {time_mod.time() - compute_start:.3f}s")
+        _log(
+            log_buffer,
+            f"      [8.2] Compute similarities & generate {len(all_links)} semantic links: {time_mod.time() - compute_start:.3f}s",
+        )

        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)
-                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
-                """,
-                all_links
+            # Batch inserts to avoid timeout on large batches
+            BATCH_SIZE = 1000
+            for batch_start in range(0, len(all_links), BATCH_SIZE):
+                batch = all_links[batch_start : batch_start + BATCH_SIZE]
+                await conn.executemany(
+                    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
+                    """,
+                    batch,
+                )
+            _log(
+                log_buffer, f"      [8.3] Insert {len(all_links)} semantic links: {time_mod.time() - insert_start:.3f}s"
            )
-            _log(log_buffer, f"      [8.3] Insert {len(all_links)} semantic links: {time_mod.time() - insert_start:.3f}s")

        return len(all_links)

    except Exception as e:
        logger.error(f"Failed to create semantic links: {str(e)}")
        import traceback
+
        traceback.print_exc()
        raise


-async def insert_entity_links_batch(conn, links: List[EntityLink], chunk_size: int = 50000):
+async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: int = 50000):
    """
    Insert all entity links using COPY to temp table + INSERT for maximum speed.

@@ -606,7 +690,6 @@ async def insert_entity_links_batch(conn, links: List[EntityLink], chunk_size: i
    if not links:
        return

-    import uuid as uuid_mod
    import time as time_mod

    total_start = time_mod.time()
@@ -633,28 +716,22 @@ async def insert_entity_links_batch(conn, links: List[EntityLink], chunk_size: i
    convert_start = time_mod.time()
    records = []
    for link in links:
-        records.append((
-            link.from_unit_id,
-            link.to_unit_id,
-            link.link_type,
-            link.weight,
-            link.entity_id
-        ))
+        records.append((link.from_unit_id, link.to_unit_id, link.link_type, link.weight, link.entity_id))
    logger.debug(f"      [9.3] Convert {len(records)} records: {time_mod.time() - convert_start:.3f}s")

    # Bulk load using COPY (fastest method)
    copy_start = time_mod.time()
    await conn.copy_records_to_table(
-        '_temp_entity_links',
+        "_temp_entity_links",
        records=records,
-        columns=['from_unit_id', 'to_unit_id', 'link_type', 'weight', 'entity_id']
+        columns=["from_unit_id", "to_unit_id", "link_type", "weight", "entity_id"],
    )
    logger.debug(f"      [9.4] COPY {len(records)} records to temp table: {time_mod.time() - copy_start:.3f}s")

    # 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
@@ -665,8 +742,8 @@ async def insert_entity_links_batch(conn, links: List[EntityLink], chunk_size: i

 async def create_causal_links_batch(
    conn,
-    unit_ids: List[str],
-    causal_relations_per_fact: List[List[dict]],
+    unit_ids: list[str],
+    causal_relations_per_fact: list[list[dict]],
 ) -> int:
    """
    Create causal links between facts based on LLM-extracted causal relationships.
@@ -694,6 +771,7 @@ async def create_causal_links_batch(

    try:
        import time as time_mod
+
        create_start = time_mod.time()

        # Build links list
@@ -705,12 +783,12 @@ async def create_causal_links_batch(
            from_unit_id = unit_ids[fact_idx]

            for relation in causal_relations:
-                target_idx = relation['target_fact_index']
-                relation_type = relation['relation_type']
-                strength = relation.get('strength', 1.0)
+                target_idx = relation["target_fact_index"]
+                relation_type = relation["relation_type"]
+                strength = relation.get("strength", 1.0)

                # Validate relation_type - must match database constraint
-                valid_types = {'causes', 'caused_by', 'enables', 'prevents'}
+                valid_types = {"causes", "caused_by", "enables", "prevents"}
                if relation_type not in valid_types:
                    logger.error(
                        f"Invalid relation_type '{relation_type}' (type: {type(relation_type).__name__}) "
@@ -735,24 +813,25 @@ async def create_causal_links_batch(
                # weight is the strength of the relationship
                links.append((from_unit_id, to_unit_id, relation_type, strength, None))

-
        if links:
            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
                    """,
-                    links
+                    links,
                )
            except Exception as db_error:
                # Log the actual data being inserted for debugging
                logger.error(f"Database insert failed for causal links. Error: {db_error}")
                logger.error(f"Attempted to insert {len(links)} links. First few:")
                for i, link in enumerate(links[:3]):
-                    logger.error(f"  Link {i}: from={link[0]}, to={link[1]}, type='{link[2]}' (repr={repr(link[2])}), weight={link[3]}, entity={link[4]}")
+                    logger.error(
+                        f"  Link {i}: from={link[0]}, to={link[1]}, type='{link[2]}' (repr={repr(link[2])}), weight={link[3]}, entity={link[4]}"
+                    )
                raise

        return len(links)
@@ -760,5 +839,6 @@ async def create_causal_links_batch(
    except Exception as e:
        logger.error(f"Failed to create causal links: {str(e)}")
        import traceback
+
        traceback.print_exc()
        raise
@@ -3,15 +3,16 @@ Observation regeneration for retain pipeline.

 Regenerates entity observations as part of the retain transaction.
 """
+
 import logging
 import time
 import uuid
-from datetime import datetime, timezone
-from typing import List, Dict, Optional
+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 ..db_utils import acquire_with_retry
 from .types import EntityLink

 logger = logging.getLogger(__name__)
@@ -19,12 +20,12 @@ logger = logging.getLogger(__name__)

 def utcnow():
    """Get current UTC time."""
-    return datetime.now(timezone.utc)
+    return datetime.now(UTC)


 # Simple dataclass-like container for facts (avoid importing from memory_engine)
 class MemoryFactForObservation:
-    def __init__(self, id: str, text: str, fact_type: str, context: str, occurred_start: Optional[str]):
+    def __init__(self, id: str, text: str, fact_type: str, context: str, occurred_start: str | None):
        self.id = id
        self.text = text
        self.fact_type = fact_type
@@ -33,12 +34,7 @@ class MemoryFactForObservation:


 async def regenerate_observations_batch(
-    conn,
-    embeddings_model,
-    llm_config,
-    bank_id: str,
-    entity_links: List[EntityLink],
-    log_buffer: List[str] = None
+    conn, embeddings_model, llm_config, bank_id: str, entity_links: list[EntityLink], log_buffer: list[str] = None
 ) -> None:
    """
    Regenerate observations for top entities in this batch.
@@ -54,14 +50,15 @@ 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

    # Count mentions per entity in this batch
-    entity_mention_counts: Dict[str, int] = {}
+    entity_mention_counts: dict[str, int] = {}
    for link in entity_links:
        if link.entity_id:
            entity_id = str(link.entity_id)
@@ -71,11 +68,7 @@ async def regenerate_observations_batch(
        return

    # Sort by mention count descending and take top N
-    sorted_entities = sorted(
-        entity_mention_counts.items(),
-        key=lambda x: x[1],
-        reverse=True
-    )
+    sorted_entities = sorted(entity_mention_counts.items(), key=lambda x: x[1], reverse=True)
    entities_to_process = [e[0] for e in sorted_entities[:TOP_N_ENTITIES]]

    obs_start = time.time()
@@ -85,26 +78,28 @@ 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, bank_id
+        entity_uuids,
+        bank_id,
    )
-    entity_names = {row['id']: row['canonical_name'] for row in entity_rows}
+    entity_names = {row["id"]: row["canonical_name"] for row in entity_rows}

    # 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
        """,
-        entity_uuids, bank_id
+        entity_uuids,
+        bank_id,
    )
-    entity_fact_counts = {row['entity_id']: row['cnt'] for row in fact_counts}
+    entity_fact_counts = {row["entity_id"]: row["cnt"] for row in fact_counts}

    # Filter entities that meet the threshold
    entities_with_names = []
@@ -126,8 +121,7 @@ async def regenerate_observations_batch(
    for entity_id, entity_name in entities_with_names:
        try:
            obs_ids = await _regenerate_entity_observations(
-                conn, embeddings_model, llm_config,
-                bank_id, entity_id, entity_name
+                conn, embeddings_model, llm_config, bank_id, entity_id, entity_name
            )
            total_observations += len(obs_ids)
        except Exception as e:
@@ -135,17 +129,14 @@ async def regenerate_observations_batch(

    obs_time = time.time() - obs_start
    if log_buffer is not None:
-        log_buffer.append(f"[11] Observations: {total_observations} observations for {len(entities_with_names)} entities in {obs_time:.3f}s")
+        log_buffer.append(
+            f"[11] Observations: {total_observations} observations for {len(entities_with_names)} entities in {obs_time:.3f}s"
+        )


 async def _regenerate_entity_observations(
-    conn,
-    embeddings_model,
-    llm_config,
-    bank_id: str,
-    entity_id: str,
-    entity_name: str
-) -> List[str]:
+    conn, embeddings_model, llm_config, bank_id: str, entity_id: str, entity_name: str
+) -> list[str]:
    """
    Regenerate observations for a single entity.

@@ -166,17 +157,18 @@ 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')
        ORDER BY mu.occurred_start DESC
        LIMIT 50
        """,
-        bank_id, entity_uuid
+        bank_id,
+        entity_uuid,
    )

    if not rows:
@@ -185,45 +177,42 @@ async def _regenerate_entity_observations(
    # Convert to fact objects for observation extraction
    facts = []
    for row in rows:
-        occurred_start = row['occurred_start'].isoformat() if row['occurred_start'] else None
-        facts.append(MemoryFactForObservation(
-            id=str(row['id']),
-            text=row['text'],
-            fact_type=row['fact_type'],
-            context=row['context'],
-            occurred_start=occurred_start
-        ))
+        occurred_start = row["occurred_start"].isoformat() if row["occurred_start"] else None
+        facts.append(
+            MemoryFactForObservation(
+                id=str(row["id"]),
+                text=row["text"],
+                fact_type=row["fact_type"],
+                context=row["context"],
+                occurred_start=occurred_start,
+            )
+        )

    # Extract observations using LLM
-    observations = await observation_utils.extract_observations_from_facts(
-        llm_config,
-        entity_name,
-        facts
-    )
+    observations = await observation_utils.extract_observations_from_facts(llm_config, entity_name, facts)

    if not observations:
        return []

    # 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
        )
        """,
-        bank_id, entity_uuid
+        bank_id,
+        entity_uuid,
    )

    # Generate embeddings for new observations
-    embeddings = await embedding_utils.generate_embeddings_batch(
-        embeddings_model, observations
-    )
+    embeddings = await embedding_utils.generate_embeddings_batch(embeddings_model, observations)

    # Insert new observations
    current_time = utcnow()
@@ -231,8 +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
@@ -247,18 +236,19 @@ async def _regenerate_entity_observations(
            current_time,
            current_time,
            current_time,
-            current_time
+            current_time,
        )
-        obs_id = str(result['id'])
+        obs_id = str(result["id"])
        created_ids.append(obs_id)

        # 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), entity_uuid
+            uuid.UUID(obs_id),
+            entity_uuid,
        )

    return created_ids
@@ -3,31 +3,34 @@ Main orchestrator for the retain pipeline.

 Coordinates all retain pipeline modules to store memories efficiently.
 """
+
 import logging
 import time
 import uuid
-from datetime import datetime, timezone
-from typing import List, Dict, Any, Optional
+from datetime import UTC, datetime

-from . import bank_utils
+from ...config import get_config
 from ..db_utils import acquire_with_retry
+from . import bank_utils


 def utcnow():
    """Get current UTC time."""
-    return datetime.now(timezone.utc)
+    return datetime.now(UTC)

-from .types import RetainContent, ExtractedFact, ProcessedFact, EntityLink
+
+from ..response_models import TokenUsage
 from . import (
-    fact_extraction,
-    embedding_processing,
-    deduplication,
    chunk_storage,
-    fact_storage,
+    deduplication,
+    embedding_processing,
    entity_processing,
+    fact_extraction,
+    fact_storage,
    link_creation,
-    observation_regeneration
+    observation_regeneration,
 )
+from .types import ExtractedFact, ProcessedFact, RetainContent, RetainContentDict

 logger = logging.getLogger(__name__)

@@ -41,12 +44,12 @@ async def retain_batch(
    format_date_fn,
    duplicate_checker_fn,
    bank_id: str,
-    contents_dicts: List[Dict[str, Any]],
-    document_id: Optional[str] = None,
+    contents_dicts: list[RetainContentDict],
+    document_id: str | None = None,
    is_first_batch: bool = True,
-    fact_type_override: Optional[str] = None,
-    confidence_score: Optional[float] = None,
-) -> List[List[str]]:
+    fact_type_override: str | None = None,
+    confidence_score: float | None = None,
+) -> tuple[list[list[str]], TokenUsage]:
    """
    Process a batch of content through the retain pipeline.

@@ -66,17 +69,17 @@ async def retain_batch(
        confidence_score: Confidence score for opinions

    Returns:
-        List of unit ID lists (one list per content item)
+        Tuple of (unit ID lists, token usage for fact extraction)
    """
    start_time = time.time()
    total_chars = sum(len(item.get("content", "")) for item in contents_dicts)

    # Buffer all logs
    log_buffer = []
-    log_buffer.append(f"{'='*60}")
+    log_buffer.append(f"{'=' * 60}")
    log_buffer.append(f"RETAIN_BATCH START: {bank_id}")
    log_buffer.append(f"Batch size: {len(contents_dicts)} content items, {total_chars:,} chars")
-    log_buffer.append(f"{'='*60}")
+    log_buffer.append(f"{'=' * 60}")

    # Get bank profile
    profile = await bank_utils.get_bank_profile(pool, bank_id)
@@ -89,24 +92,81 @@ async def retain_batch(
            content=item["content"],
            context=item.get("context", ""),
            event_date=item.get("event_date") or utcnow(),
-            metadata=item.get("metadata", {})
+            metadata=item.get("metadata", {}),
+            entities=item.get("entities", []),
        )
        contents.append(content)

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

-    extracted_facts, chunks = await fact_extraction.extract_facts_from_contents(
-        contents,
-        llm_config,
-        agent_name,
-        extract_opinions
+    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(
+        contents, llm_config, agent_name, extract_opinions
+    )
+    log_buffer.append(
+        f"[1] Extract facts: {len(extracted_facts)} facts, {len(chunks)} chunks from {len(contents)} contents in {time.time() - step_start:.3f}s"
    )
-    log_buffer.append(f"[1] Extract facts: {len(extracted_facts)} facts, {len(chunks)} chunks from {len(contents)} contents in {time.time() - step_start:.3f}s")

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

    # Apply fact_type_override if provided
    if fact_type_override:
@@ -130,6 +190,7 @@ async def retain_batch(

    # Group contents by document_id for document tracking and chunk storage
    from collections import defaultdict
+
    contents_by_doc = defaultdict(list)
    for idx, content_dict in enumerate(contents_dicts):
        doc_id = content_dict.get("document_id")
@@ -155,7 +216,11 @@ async def retain_batch(
                    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"])
+                        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"]

@@ -195,7 +260,11 @@ async def retain_batch(
                                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"])
+                                    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"]

@@ -205,7 +274,9 @@ async def retain_batch(
                            document_ids_added.append(actual_doc_id)

            if document_ids_added:
-                log_buffer.append(f"[2.5] Document tracking: {len(document_ids_added)} documents in {time.time() - step_start:.3f}s")
+                log_buffer.append(
+                    f"[2.5] Document tracking: {len(document_ids_added)} documents in {time.time() - step_start:.3f}s"
+                )

            # Store chunks and map to facts for all documents
            step_start = time.time()
@@ -230,7 +301,9 @@ async def retain_batch(
                    for chunk_idx, chunk_id in chunk_id_map.items():
                        chunk_id_map_by_doc[(doc_id, chunk_idx)] = chunk_id

-                log_buffer.append(f"[3] Store chunks: {len(chunks)} chunks for {len(chunks_by_doc)} documents in {time.time() - step_start:.3f}s")
+                log_buffer.append(
+                    f"[3] Store chunks: {len(chunks)} chunks for {len(chunks_by_doc)} documents in {time.time() - step_start:.3f}s"
+                )

                # Map chunk_ids and document_ids to facts
                for fact, processed_fact in zip(extracted_facts, processed_facts):
@@ -265,13 +338,15 @@ async def retain_batch(
            is_duplicate_flags = await deduplication.check_duplicates_batch(
                conn, bank_id, processed_facts, duplicate_checker_fn
            )
-            log_buffer.append(f"[4] Deduplication: {sum(is_duplicate_flags)} duplicates in {time.time() - step_start:.3f}s")
+            log_buffer.append(
+                f"[4] Deduplication: {sum(is_duplicate_flags)} duplicates in {time.time() - step_start:.3f}s"
+            )

            # Filter out duplicates
            non_duplicate_facts = deduplication.filter_duplicates(processed_facts, is_duplicate_flags)

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

            # Insert facts (document_id is now stored per-fact)
            step_start = time.time()
@@ -280,8 +355,18 @@ 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")

@@ -293,62 +378,64 @@ async def retain_batch(
            # Create semantic links
            step_start = time.time()
            embeddings_for_links = [fact.embedding for fact in non_duplicate_facts]
-            semantic_link_count = await link_creation.create_semantic_links_batch(conn, bank_id, unit_ids, embeddings_for_links)
+            semantic_link_count = await link_creation.create_semantic_links_batch(
+                conn, bank_id, unit_ids, embeddings_for_links
+            )
            log_buffer.append(f"[8] Semantic links: {semantic_link_count} links in {time.time() - step_start:.3f}s")

            # Insert entity links
            step_start = time.time()
            if entity_links:
                await entity_processing.insert_entity_links_batch(conn, entity_links)
-            log_buffer.append(f"[9] Entity links: {len(entity_links) if entity_links else 0} links in {time.time() - step_start:.3f}s")
+            log_buffer.append(
+                f"[9] Entity links: {len(entity_links) if entity_links else 0} links in {time.time() - step_start:.3f}s"
+            )

            # Create causal links
            step_start = time.time()
            causal_link_count = await link_creation.create_causal_links_batch(conn, unit_ids, non_duplicate_facts)
            log_buffer.append(f"[10] Causal links: {causal_link_count} links in {time.time() - step_start:.3f}s")

-            # Regenerate observations INSIDE transaction for atomicity
-            await observation_regeneration.regenerate_observations_batch(
-                conn,
-                embeddings_model,
-                llm_config,
-                bank_id,
-                entity_links,
-                log_buffer
-            )
+            # Regenerate observations - sync (in transaction) or async (background task)
+            config = get_config()
+            if config.retain_observations_async:
+                # Queue for async processing after transaction commits
+                entity_ids_for_async = list(set(link.entity_id for link in entity_links)) if entity_links else []
+                log_buffer.append(
+                    f"[11] Observations: queued {len(entity_ids_for_async)} entities for async processing"
+                )
+            else:
+                # Run synchronously inside transaction for atomicity
+                await observation_regeneration.regenerate_observations_batch(
+                    conn, embeddings_model, llm_config, bank_id, entity_links, log_buffer
+                )
+                entity_ids_for_async = []

            # Map results back to original content items
-            result_unit_ids = _map_results_to_contents(
-                contents, extracted_facts, is_duplicate_flags, unit_ids
-            )
+            result_unit_ids = _map_results_to_contents(contents, extracted_facts, is_duplicate_flags, unit_ids)

-        # Trigger background tasks AFTER transaction commits (opinion reinforcement only)
-        await _trigger_background_tasks(
-            task_backend,
-            bank_id,
-            unit_ids,
-            non_duplicate_facts
-        )
+        # Trigger background tasks AFTER transaction commits
+        await _trigger_background_tasks(task_backend, bank_id, unit_ids, non_duplicate_facts, entity_ids_for_async)

        # Log final summary
        total_time = time.time() - start_time
-        log_buffer.append(f"{'='*60}")
+        log_buffer.append(f"{'=' * 60}")
        log_buffer.append(f"RETAIN_BATCH COMPLETE: {len(unit_ids)} units in {total_time:.3f}s")
        if document_ids_added:
            log_buffer.append(f"Documents: {', '.join(document_ids_added)}")
-        log_buffer.append(f"{'='*60}")
+        log_buffer.append(f"{'=' * 60}")

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

-        return result_unit_ids
+        return result_unit_ids, usage


 def _map_results_to_contents(
-    contents: List[RetainContent],
-    extracted_facts: List[ExtractedFact],
-    is_duplicate_flags: List[bool],
-    unit_ids: List[str]
-) -> List[List[str]]:
+    contents: list[RetainContent],
+    extracted_facts: list[ExtractedFact],
+    is_duplicate_flags: list[bool],
+    unit_ids: list[str],
+) -> list[list[str]]:
    """
    Map created unit IDs back to original content items.

@@ -376,17 +463,30 @@ def _map_results_to_contents(
 async def _trigger_background_tasks(
    task_backend,
    bank_id: str,
-    unit_ids: List[str],
-    facts: List[ProcessedFact],
+    unit_ids: list[str],
+    facts: list[ProcessedFact],
+    entity_ids_for_observations: list[str] | None = None,
 ) -> None:
-    """Trigger opinion reinforcement as background task (after transaction commits)."""
+    """Trigger background tasks after transaction commits."""
    # Trigger opinion reinforcement if there are entities
    fact_entities = [[e.name for e in fact.entities] for fact in facts]
    if any(fact_entities):
-        await task_backend.submit_task({
-            'type': 'reinforce_opinion',
-            'bank_id': bank_id,
-            'created_unit_ids': unit_ids,
-            'unit_texts': [fact.fact_text for fact in facts],
-            'unit_entities': fact_entities
-        })
+        await task_backend.submit_task(
+            {
+                "type": "reinforce_opinion",
+                "bank_id": bank_id,
+                "created_unit_ids": unit_ids,
+                "unit_texts": [fact.fact_text for fact in facts],
+                "unit_entities": fact_entities,
+            }
+        )
+
+    # Trigger observation regeneration if async mode is enabled
+    if entity_ids_for_observations:
+        await task_backend.submit_task(
+            {
+                "type": "regenerate_observations",
+                "bank_id": bank_id,
+                "entity_ids": entity_ids_for_observations,
+            }
+        )
@@ -6,11 +6,36 @@ from content input to fact storage.
 """

 from dataclasses import dataclass, field
-from typing import List, Optional, Dict, Any
-from datetime import datetime
+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:
    """
@@ -18,16 +43,12 @@ class RetainContent:

    Represents a single piece of content to extract facts from.
    """
+
    content: str
    context: str = ""
-    event_date: Optional[datetime] = None
-    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, timezone
-            self.event_date = datetime.now(timezone.utc)
+    event_date: datetime = field(default_factory=_now_utc)
+    metadata: dict[str, str] = field(default_factory=dict)
+    entities: list[dict[str, str]] = field(default_factory=list)  # User-provided entities


@dataclass
@@ -37,6 +58,7 @@ class ChunkMetadata:

    Used to track which facts were extracted from which chunks.
    """
+
    chunk_text: str
    fact_count: int
    content_index: int  # Index of the source content
@@ -50,9 +72,10 @@ class EntityRef:

    Entities are extracted by the LLM during fact extraction.
    """
+
    name: str
-    canonical_name: Optional[str] = None  # Resolved canonical name
-    entity_id: Optional[UUID] = None  # Resolved entity ID
+    canonical_name: str | None = None  # Resolved canonical name
+    entity_id: UUID | None = None  # Resolved entity ID


@dataclass
@@ -62,6 +85,7 @@ class CausalRelation:

    Represents how one fact causes, enables, or prevents another.
    """
+
    relation_type: str  # "causes", "enables", "prevents", "caused_by"
    target_fact_index: int  # Index of the target fact in the batch
    strength: float = 1.0  # Strength of the causal relationship
@@ -74,20 +98,21 @@ class ExtractedFact:

    This is the raw output from fact extraction before processing.
    """
+
    fact_text: str
    fact_type: str  # "world", "experience", "opinion", "observation"
-    entities: List[str] = field(default_factory=list)
-    occurred_start: Optional[datetime] = None
-    occurred_end: Optional[datetime] = None
-    where: Optional[str] = None  # WHERE the fact occurred or is about
-    causal_relations: List[CausalRelation] = field(default_factory=list)
+    entities: list[str] = field(default_factory=list)
+    occurred_start: datetime | None = None
+    occurred_end: datetime | None = None
+    where: str | None = None  # WHERE the fact occurred or is about
+    causal_relations: list[CausalRelation] = field(default_factory=list)

    # Context from the content item
    content_index: int = 0  # Which content this fact came from
    chunk_index: int = 0  # Which chunk this fact came from
    context: str = ""
-    mentioned_at: Optional[datetime] = None
-    metadata: Dict[str, str] = field(default_factory=dict)
+    mentioned_at: datetime | None = None
+    metadata: dict[str, str] = field(default_factory=dict)


@dataclass
@@ -97,37 +122,41 @@ class ProcessedFact:

    Includes resolved entities, embeddings, and all necessary fields.
    """
+
    # Core fact data
    fact_text: str
    fact_type: str
-    embedding: List[float]
+    embedding: list[float]

    # Temporal data
-    occurred_start: Optional[datetime]
-    occurred_end: Optional[datetime]
+    occurred_start: datetime | None
+    occurred_end: datetime | None
    mentioned_at: datetime

    # Context and metadata
    context: str
-    metadata: Dict[str, str]
+    metadata: dict[str, str]

    # Location data
-    where: Optional[str] = None
+    where: str | None = None

    # Entities
-    entities: List[EntityRef] = field(default_factory=list)
+    entities: list[EntityRef] = field(default_factory=list)

    # Causal relations
-    causal_relations: List[CausalRelation] = field(default_factory=list)
+    causal_relations: list[CausalRelation] = field(default_factory=list)

    # Chunk reference
-    chunk_id: Optional[str] = None
+    chunk_id: str | None = None

    # Document reference (denormalized for query performance)
-    document_id: Optional[str] = None
+    document_id: str | None = None

    # DB fields (set after insertion)
-    unit_id: Optional[UUID] = None
+    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:
@@ -136,10 +165,8 @@ class ProcessedFact:

    @staticmethod
    def from_extracted_fact(
-        extracted_fact: 'ExtractedFact',
-        embedding: List[float],
-        chunk_id: Optional[str] = None
-    ) -> 'ProcessedFact':
+        extracted_fact: "ExtractedFact", embedding: list[float], chunk_id: str | None = None
+    ) -> "ProcessedFact":
        """
        Create ProcessedFact from ExtractedFact.

@@ -151,12 +178,12 @@ class ProcessedFact:
        Returns:
            ProcessedFact ready for storage
        """
-        from datetime import datetime, timezone
+        from datetime import datetime

        # Use occurred dates only if explicitly provided by LLM
        occurred_start = extracted_fact.occurred_start
        occurred_end = extracted_fact.occurred_end
-        mentioned_at = extracted_fact.mentioned_at or datetime.now(timezone.utc)
+        mentioned_at = extracted_fact.mentioned_at or datetime.now(UTC)

        # Convert entity strings to EntityRef objects
        entities = [EntityRef(name=name) for name in extracted_fact.entities]
@@ -172,7 +199,8 @@ class ProcessedFact:
            metadata=extracted_fact.metadata,
            entities=entities,
            causal_relations=extracted_fact.causal_relations,
-            chunk_id=chunk_id
+            chunk_id=chunk_id,
+            content_index=extracted_fact.content_index,
        )


@@ -183,10 +211,11 @@ class EntityLink:

    Used for entity-based graph connections in the memory graph.
    """
+
    from_unit_id: UUID
    to_unit_id: UUID
    entity_id: UUID
-    link_type: str = 'entity'
+    link_type: str = "entity"
    weight: float = 1.0


@@ -197,24 +226,25 @@ class RetainBatch:

    Tracks all facts, chunks, and metadata for a batch operation.
    """
+
    bank_id: str
-    contents: List[RetainContent]
-    document_id: Optional[str] = None
-    fact_type_override: Optional[str] = None
-    confidence_score: Optional[float] = None
+    contents: list[RetainContent]
+    document_id: str | None = None
+    fact_type_override: str | None = None
+    confidence_score: float | None = None

    # Extracted data (populated during processing)
-    extracted_facts: List[ExtractedFact] = field(default_factory=list)
-    processed_facts: List[ProcessedFact] = field(default_factory=list)
-    chunks: List[ChunkMetadata] = field(default_factory=list)
+    extracted_facts: list[ExtractedFact] = field(default_factory=list)
+    processed_facts: list[ProcessedFact] = field(default_factory=list)
+    chunks: list[ChunkMetadata] = field(default_factory=list)

    # Results (populated after storage)
-    unit_ids_by_content: List[List[str]] = field(default_factory=list)
+    unit_ids_by_content: list[list[str]] = field(default_factory=list)

-    def get_facts_for_content(self, content_index: int) -> List[ExtractedFact]:
+    def get_facts_for_content(self, content_index: int) -> list[ExtractedFact]:
        """Get all extracted facts for a specific content item."""
        return [f for f in self.extracted_facts if f.content_index == content_index]

-    def get_chunks_for_content(self, content_index: int) -> List[ChunkMetadata]:
+    def get_chunks_for_content(self, content_index: int) -> list[ChunkMetadata]:
        """Get all chunks for a specific content item."""
        return [c for c in self.chunks if c.content_index == content_index]
@@ -7,15 +7,15 @@ Provides modular search architecture:
 - Reranking: Pluggable strategies (heuristic, cross-encoder)
 """

-from .retrieval import (
-    retrieve_parallel,
-    get_default_graph_retriever,
-    set_default_graph_retriever,
-    ParallelRetrievalResult,
-)
-from .graph_retrieval import GraphRetriever, BFSGraphRetriever
+from .graph_retrieval import BFSGraphRetriever, GraphRetriever
 from .mpfp_retrieval import MPFPGraphRetriever
 from .reranking import CrossEncoderReranker
+from .retrieval import (
+    ParallelRetrievalResult,
+    get_default_graph_retriever,
+    retrieve_parallel,
+    set_default_graph_retriever,
+)

 __all__ = [
    "retrieve_parallel",
@@ -2,15 +2,12 @@
 Helper functions for hybrid search (semantic + BM25 + graph).
 """

-from typing import List, Dict, Any, Tuple
-import asyncio
-from .types import RetrievalResult, MergedCandidate
+from typing import Any
+
+from .types import MergedCandidate, RetrievalResult


-def reciprocal_rank_fusion(
-    result_lists: List[List[RetrievalResult]],
-    k: int = 60
-) -> List[MergedCandidate]:
+def reciprocal_rank_fusion(result_lists: list[list[RetrievalResult]], k: int = 60) -> list[MergedCandidate]:
    """
    Merge multiple ranked result lists using Reciprocal Rank Fusion.

@@ -73,20 +70,14 @@ def reciprocal_rank_fusion(
        sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True), start=1
    ):
        merged_candidate = MergedCandidate(
-            retrieval=all_retrievals[doc_id],
-            rrf_score=rrf_score,
-            rrf_rank=rrf_rank,
-            source_ranks=source_ranks[doc_id]
+            retrieval=all_retrievals[doc_id], rrf_score=rrf_score, rrf_rank=rrf_rank, source_ranks=source_ranks[doc_id]
        )
        merged_results.append(merged_candidate)

    return merged_results


-def normalize_scores_on_deltas(
-    results: List[Dict[str, Any]],
-    score_keys: List[str]
-) -> List[Dict[str, Any]]:
+def normalize_scores_on_deltas(results: list[dict[str, Any]], score_keys: list[str]) -> list[dict[str, Any]]:
    """
    Normalize scores based on deltas (min-max normalization within result set).

@@ -6,13 +6,12 @@ allowing different algorithms (BFS spreading activation, PPR, etc.) to be
 swapped without changing the rest of the recall pipeline.
 """

-from abc import ABC, abstractmethod
-from typing import List, Optional
-from datetime import datetime
 import logging
+from abc import ABC, abstractmethod

-from .types import RetrievalResult
 from ..db_utils import acquire_with_retry
+from ..memory_engine import fq_table
+from .types import MPFPTimings, RetrievalResult

 logger = logging.getLogger(__name__)

@@ -40,10 +39,11 @@ class GraphRetriever(ABC):
        bank_id: str,
        fact_type: str,
        budget: int,
-        query_text: Optional[str] = None,
-        semantic_seeds: Optional[List[RetrievalResult]] = None,
-        temporal_seeds: Optional[List[RetrievalResult]] = None,
-    ) -> List[RetrievalResult]:
+        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
+        adjacency=None,  # TypedAdjacency, optional pre-loaded graph
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
        """
        Retrieve relevant facts via graph traversal.

@@ -56,9 +56,10 @@ class GraphRetriever(ABC):
            query_text: Original query text (optional, for some strategies)
            semantic_seeds: Pre-computed semantic entry points (from semantic retrieval)
            temporal_seeds: Pre-computed temporal entry points (from temporal retrieval)
+            adjacency: Pre-loaded typed adjacency graph (optional, for MPFP)

        Returns:
-            List of RetrievalResult objects with activation scores set
+            Tuple of (List of RetrievalResult with activation scores, optional timing info)
        """
        pass

@@ -109,10 +110,11 @@ class BFSGraphRetriever(GraphRetriever):
        bank_id: str,
        fact_type: str,
        budget: int,
-        query_text: Optional[str] = None,
-        semantic_seeds: Optional[List[RetrievalResult]] = None,
-        temporal_seeds: Optional[List[RetrievalResult]] = None,
-    ) -> List[RetrievalResult]:
+        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
+        adjacency=None,  # Not used by BFS
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
        """
        Retrieve facts using BFS spreading activation.

@@ -123,13 +125,12 @@ class BFSGraphRetriever(GraphRetriever):
        4. Return visited nodes up to budget

        Note: BFS finds its own entry points via embedding search.
-        The semantic_seeds and temporal_seeds parameters are accepted
+        The semantic_seeds, temporal_seeds, and adjacency parameters are accepted
        for interface compatibility but not used.
        """
        async with acquire_with_retry(pool) as conn:
-            return await self._retrieve_with_conn(
-                conn, query_embedding_str, bank_id, fact_type, budget
-            )
+            results = await self._retrieve_with_conn(conn, query_embedding_str, bank_id, fact_type, budget)
+            return results, None

    async def _retrieve_with_conn(
        self,
@@ -138,16 +139,16 @@ class BFSGraphRetriever(GraphRetriever):
        bank_id: str,
        fact_type: str,
        budget: int,
-    ) -> List[RetrievalResult]:
+    ) -> list[RetrievalResult]:
        """Internal implementation with connection."""

        # 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
@@ -155,8 +156,11 @@ class BFSGraphRetriever(GraphRetriever):
            ORDER BY embedding <=> $1::vector
            LIMIT $5
            """,
-            query_embedding_str, bank_id, fact_type,
-            self.entry_point_threshold, self.entry_point_limit
+            query_embedding_str,
+            bank_id,
+            fact_type,
+            self.entry_point_threshold,
+            self.entry_point_limit,
        )

        if not entry_points:
@@ -165,10 +169,7 @@ class BFSGraphRetriever(GraphRetriever):
        # Step 2: BFS spreading activation
        visited = set()
        results = []
-        queue = [
-            (RetrievalResult.from_db_row(dict(r)), r["similarity"])
-            for r in entry_points
-        ]
+        queue = [(RetrievalResult.from_db_row(dict(r)), r["similarity"]) for r in entry_points]
        budget_remaining = budget

        while queue and budget_remaining > 0:
@@ -192,20 +193,23 @@ 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
                    ORDER BY ml.weight DESC
                    LIMIT $4
                    """,
-                    batch_nodes, self.min_activation, fact_type, max_neighbors
+                    batch_nodes,
+                    self.min_activation,
+                    fact_type,
+                    max_neighbors,
                )

                for n in neighbors:
@@ -0,0 +1,230 @@
+"""
+Link Expansion graph retrieval.
+
+A simple, fast graph retrieval that expands from seeds via:
+1. Entity links: Find facts sharing entities with seeds (filtered by entity frequency)
+2. Causal links: Find facts causally linked to seeds (top-k by weight)
+
+Characteristics:
+- 2-3 DB queries (seed finding + parallel entity/causal expansion)
+- Sublinear: only touches connected facts via indexes
+- No iteration, no propagation, no normalization
+- Target: <100ms
+"""
+
+import logging
+import time
+
+from ..db_utils import acquire_with_retry
+from ..memory_engine import fq_table
+from .graph_retrieval import GraphRetriever
+from .types import MPFPTimings, RetrievalResult
+
+logger = logging.getLogger(__name__)
+
+
+async def _find_semantic_seeds(
+    conn,
+    query_embedding_str: str,
+    bank_id: str,
+    fact_type: str,
+    limit: int = 20,
+    threshold: float = 0.3,
+) -> list[RetrievalResult]:
+    """Find semantic seeds via embedding search."""
+    rows = await conn.fetch(
+        f"""
+        SELECT id, text, context, event_date, occurred_start, occurred_end,
+               mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
+               1 - (embedding <=> $1::vector) AS similarity
+        FROM {fq_table("memory_units")}
+        WHERE bank_id = $2
+          AND embedding IS NOT NULL
+          AND fact_type = $3
+          AND (1 - (embedding <=> $1::vector)) >= $4
+        ORDER BY embedding <=> $1::vector
+        LIMIT $5
+        """,
+        query_embedding_str,
+        bank_id,
+        fact_type,
+        threshold,
+        limit,
+    )
+    return [RetrievalResult.from_db_row(dict(r)) for r in rows]
+
+
+class LinkExpansionRetriever(GraphRetriever):
+    """
+    Graph retrieval via direct link expansion from seeds.
+
+    Expands through entity co-occurrence and causal links in a single query.
+    Fast and simple alternative to MPFP.
+    """
+
+    def __init__(
+        self,
+        max_entity_frequency: int = 500,
+        causal_weight_threshold: float = 0.3,
+        causal_limit_per_seed: int = 10,
+    ):
+        """
+        Initialize link expansion retriever.
+
+        Args:
+            max_entity_frequency: Skip entities appearing in more than this many facts
+            causal_weight_threshold: Minimum weight for causal links
+            causal_limit_per_seed: Max causal links to follow per seed
+        """
+        self.max_entity_frequency = max_entity_frequency
+        self.causal_weight_threshold = causal_weight_threshold
+        self.causal_limit_per_seed = causal_limit_per_seed
+
+    @property
+    def name(self) -> str:
+        return "link_expansion"
+
+    async def retrieve(
+        self,
+        pool,
+        query_embedding_str: str,
+        bank_id: str,
+        fact_type: str,
+        budget: int,
+        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
+        adjacency=None,
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
+        """
+        Retrieve facts by expanding links from seeds.
+
+        Args:
+            pool: Database connection pool
+            query_embedding_str: Query embedding (unused, kept for interface)
+            bank_id: Memory bank ID
+            fact_type: Fact type to filter
+            budget: Maximum results to return
+            query_text: Original query text (unused)
+            semantic_seeds: Pre-computed semantic entry points
+            temporal_seeds: Pre-computed temporal entry points
+            adjacency: Unused, kept for interface compatibility
+
+        Returns:
+            Tuple of (results, timings)
+        """
+        start_time = time.time()
+        timings = MPFPTimings(fact_type=fact_type)
+
+        # Use single connection for all queries to reduce pool pressure
+        # (queries are fast ~50ms each, connection acquisition is the bottleneck)
+        async with acquire_with_retry(pool) as conn:
+            # Find seeds if not provided
+            if semantic_seeds:
+                all_seeds = list(semantic_seeds)
+            else:
+                seeds_start = time.time()
+                all_seeds = await _find_semantic_seeds(
+                    conn, query_embedding_str, bank_id, fact_type, limit=20, threshold=0.3
+                )
+                timings.seeds_time = time.time() - seeds_start
+
+            # Add temporal seeds if provided
+            if temporal_seeds:
+                all_seeds.extend(temporal_seeds)
+
+            if not all_seeds:
+                return [], timings
+
+            seed_ids = list({s.id for s in all_seeds})
+            timings.pattern_count = len(seed_ids)
+
+            # Run entity and causal expansion sequentially on same connection
+            query_start = time.time()
+
+            entity_rows = 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,
+                    COUNT(*)::float AS score
+                FROM {fq_table("unit_entities")} seed_ue
+                JOIN {fq_table("entities")} e ON seed_ue.entity_id = e.id
+                JOIN {fq_table("unit_entities")} other_ue ON seed_ue.entity_id = other_ue.entity_id
+                JOIN {fq_table("memory_units")} mu ON other_ue.unit_id = mu.id
+                WHERE seed_ue.unit_id = ANY($1::uuid[])
+                  AND e.mention_count < $2
+                  AND mu.id != ALL($1::uuid[])
+                  AND mu.fact_type = $3
+                GROUP BY mu.id
+                ORDER BY score DESC
+                LIMIT $4
+                """,
+                seed_ids,
+                self.max_entity_frequency,
+                fact_type,
+                budget,
+            )
+
+            causal_rows = await conn.fetch(
+                f"""
+                SELECT DISTINCT ON (mu.id)
+                    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 + 1.0 AS score
+                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.link_type IN ('causes', 'caused_by', 'enables', 'prevents')
+                  AND ml.weight >= $2
+                  AND mu.fact_type = $3
+                ORDER BY mu.id, ml.weight DESC
+                LIMIT $4
+                """,
+                seed_ids,
+                self.causal_weight_threshold,
+                fact_type,
+                budget,
+            )
+
+            timings.edge_load_time = time.time() - query_start
+            timings.db_queries = 2
+            timings.edge_count = len(entity_rows) + len(causal_rows)
+
+        # Merge results, taking max score per fact
+        score_map: dict[str, float] = {}
+        row_map: dict[str, dict] = {}
+
+        for row in entity_rows:
+            fact_id = str(row["id"])
+            score_map[fact_id] = max(score_map.get(fact_id, 0), row["score"])
+            row_map[fact_id] = dict(row)
+
+        for row in causal_rows:
+            fact_id = str(row["id"])
+            score_map[fact_id] = max(score_map.get(fact_id, 0), row["score"])
+            if fact_id not in row_map:
+                row_map[fact_id] = dict(row)
+
+        # Sort by score and limit
+        sorted_ids = sorted(score_map.keys(), key=lambda x: score_map[x], reverse=True)[:budget]
+        rows = [row_map[fact_id] for fact_id in sorted_ids]
+
+        # Convert to results
+        results = []
+        for row in rows:
+            result = RetrievalResult.from_db_row(dict(row))
+            result.activation = row["score"]
+            results.append(result)
+
+        timings.result_count = len(results)
+        timings.traverse = time.time() - start_time
+
+        logger.debug(
+            f"LinkExpansion: {len(results)} results from {len(seed_ids)} seeds "
+            f"in {timings.traverse * 1000:.1f}ms (query: {timings.edge_load_time * 1000:.1f}ms)"
+        )
+
+        return results, timings
@@ -9,6 +9,7 @@ propagation from Approximate PPR.

 Key properties:
 - Sublinear in graph size (threshold pruning bounds active nodes)
+- Lazy edge loading: only loads edges for frontier nodes, not entire graph
 - Predefined patterns capture different retrieval intents
 - All patterns run in parallel, results fused via RRF
 - No LLM in the loop during traversal
@@ -16,13 +17,13 @@ Key properties:

 import asyncio
 import logging
-from dataclasses import dataclass, field
-from typing import List, Dict, Optional, Tuple
 from collections import defaultdict
+from dataclasses import dataclass, field

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

 logger = logging.getLogger(__name__)

@@ -31,29 +32,43 @@ logger = logging.getLogger(__name__)
 # Data Classes
 # -----------------------------------------------------------------------------

+
@dataclass
 class EdgeTarget:
    """A neighbor node with its edge weight."""
+
    node_id: str
    weight: float


@dataclass
-class TypedAdjacency:
-    """Adjacency lists split by edge type."""
-    # edge_type -> from_node_id -> list of (to_node_id, weight)
-    graphs: Dict[str, Dict[str, List[EdgeTarget]]] = field(default_factory=dict)
+class EdgeCache:
+    """
+    Cache for lazily-loaded edges.

-    def get_neighbors(self, edge_type: str, node_id: str) -> List[EdgeTarget]:
+    Grows per-hop as edges are loaded for frontier nodes.
+    Shared across patterns to avoid redundant loads.
+    Loads ALL edge types at once to minimize DB queries.
+    Thread-safe via asyncio lock to prevent redundant concurrent loads.
+    """
+
+    # edge_type -> from_node_id -> list of EdgeTarget
+    graphs: dict[str, dict[str, list[EdgeTarget]]] = field(default_factory=dict)
+    # Track which nodes have been fully loaded (all edge types)
+    _fully_loaded: set[str] = field(default_factory=set)
+    # Timing stats
+    db_queries: int = 0
+    edge_load_time: float = 0.0
+    # Detailed hop timing for debugging
+    hop_details: list[dict] = field(default_factory=list)
+    # Lock to prevent redundant concurrent loads
+    _lock: asyncio.Lock = field(default_factory=asyncio.Lock)
+
+    def get_neighbors(self, edge_type: str, node_id: str) -> list[EdgeTarget]:
        """Get neighbors for a node via a specific edge type."""
        return self.graphs.get(edge_type, {}).get(node_id, [])

-    def get_normalized_neighbors(
-        self,
-        edge_type: str,
-        node_id: str,
-        top_k: int
-    ) -> List[EdgeTarget]:
+    def get_normalized_neighbors(self, edge_type: str, node_id: str, top_k: int) -> list[EdgeTarget]:
        """Get top-k neighbors with weights normalized to sum to 1."""
        neighbors = self.get_neighbors(edge_type, node_id)[:top_k]
        if not neighbors:
@@ -63,123 +78,329 @@ class TypedAdjacency:
        if total == 0:
            return []

-        return [
-            EdgeTarget(node_id=n.node_id, weight=n.weight / total)
-            for n in neighbors
-        ]
+        return [EdgeTarget(node_id=n.node_id, weight=n.weight / total) for n in neighbors]
+
+    def is_fully_loaded(self, node_id: str) -> bool:
+        """Check if all edges for this node have been loaded."""
+        return node_id in self._fully_loaded
+
+    def get_uncached(self, node_ids: list[str]) -> list[str]:
+        """Get node IDs that haven't been fully loaded yet."""
+        return [n for n in node_ids if not self.is_fully_loaded(n)]
+
+    def add_all_edges(self, edges_by_type: dict[str, dict[str, list[EdgeTarget]]], all_queried: list[str]):
+        """
+        Add loaded edges to the cache (all edge types at once).
+
+        Args:
+            edges_by_type: Dict mapping edge_type -> from_node_id -> list of EdgeTarget
+            all_queried: All node IDs that were queried (marks them as fully loaded)
+        """
+        for edge_type, edges in edges_by_type.items():
+            if edge_type not in self.graphs:
+                self.graphs[edge_type] = {}
+            for node_id, neighbors in edges.items():
+                self.graphs[edge_type][node_id] = neighbors
+
+        # Mark all queried nodes as fully loaded (even if they have no edges)
+        self._fully_loaded.update(all_queried)


@dataclass
 class PatternResult:
    """Result from a single pattern traversal."""
-    pattern: List[str]
-    scores: Dict[str, float]  # node_id -> accumulated mass
+
+    pattern: list[str]
+    scores: dict[str, float]  # node_id -> accumulated mass


@dataclass
 class MPFPConfig:
    """Configuration for MPFP algorithm."""
-    alpha: float = 0.15           # teleport/keep probability
-    threshold: float = 1e-6       # mass pruning threshold (lower = explore more)
-    top_k_neighbors: int = 20     # fan-out limit per node
+
+    alpha: float = 0.15  # teleport/keep probability
+    threshold: float = 1e-6  # mass pruning threshold (lower = explore more)
+    top_k_neighbors: int = 20  # fan-out limit per node

    # Patterns from semantic seeds
-    patterns_semantic: List[List[str]] = field(default_factory=lambda: [
-        ['semantic', 'semantic'],   # topic expansion
-        ['entity', 'temporal'],     # entity timeline
-        ['semantic', 'causes'],     # reasoning chains (forward)
-        ['semantic', 'caused_by'],  # reasoning chains (backward)
-        ['entity', 'semantic'],     # entity context
-    ])
+    patterns_semantic: list[list[str]] = field(
+        default_factory=lambda: [
+            ["semantic", "semantic"],  # topic expansion
+            ["entity", "temporal"],  # entity timeline
+            ["semantic", "causes"],  # reasoning chains (forward)
+            ["semantic", "caused_by"],  # reasoning chains (backward)
+            ["entity", "semantic"],  # entity context
+        ]
+    )

    # Patterns from temporal seeds
-    patterns_temporal: List[List[str]] = field(default_factory=lambda: [
-        ['temporal', 'semantic'],   # what was happening then
-        ['temporal', 'entity'],     # who was involved then
-    ])
+    patterns_temporal: list[list[str]] = field(
+        default_factory=lambda: [
+            ["temporal", "semantic"],  # what was happening then
+            ["temporal", "entity"],  # who was involved then
+        ]
+    )


@dataclass
 class SeedNode:
    """An entry point node with its initial score."""
+
    node_id: str
    score: float  # initial mass (e.g., similarity score)


 # -----------------------------------------------------------------------------
-# Core Algorithm
+# Lazy Edge Loading
 # -----------------------------------------------------------------------------

-def mpfp_traverse(
-    seeds: List[SeedNode],
-    pattern: List[str],
-    adjacency: TypedAdjacency,
-    config: MPFPConfig,
-) -> PatternResult:
+
+async def load_all_edges_for_frontier(
+    pool,
+    node_ids: list[str],
+    top_k_per_type: int = 20,
+) -> dict[str, dict[str, list[EdgeTarget]]]:
    """
-    Forward Push traversal following a meta-path pattern.
+    Load top-k edges per (node, edge_type) for frontier nodes.
+
+    Uses a LATERAL join to efficiently fetch only the top-k edges per type,
+    avoiding loading hundreds of entity edges when only 20 are needed.
+
+    Requires composite index: (from_unit_id, link_type, weight DESC)

    Args:
-        seeds: Entry point nodes with initial scores
-        pattern: Sequence of edge types to follow
-        adjacency: Typed adjacency structure
-        config: Algorithm parameters
+        pool: Database connection pool
+        node_ids: Frontier node IDs to load edges for
+        top_k_per_type: Max edges to load per (node, link_type) pair

    Returns:
-        PatternResult with accumulated scores per node
+        Dict mapping edge_type -> from_node_id -> list of EdgeTarget
+    """
+    if not node_ids:
+        return {}
+
+    async with acquire_with_retry(pool) as conn:
+        # Use LATERAL join to get top-k per (from_node, link_type)
+        # This leverages the composite index for efficient early termination
+        rows = await conn.fetch(
+            f"""
+            WITH frontier(node_id) AS (SELECT unnest($1::uuid[]))
+            SELECT f.node_id as from_unit_id, lt.link_type, edges.to_unit_id, edges.weight
+            FROM frontier f
+            CROSS JOIN (VALUES ('semantic'), ('temporal'), ('entity'), ('causes'), ('caused_by')) AS lt(link_type)
+            CROSS JOIN LATERAL (
+                SELECT ml.to_unit_id, ml.weight
+                FROM {fq_table("memory_links")} ml
+                WHERE ml.from_unit_id = f.node_id
+                  AND ml.link_type = lt.link_type
+                  AND ml.weight >= 0.1
+                ORDER BY ml.weight DESC
+                LIMIT $2
+            ) edges
+            """,
+            node_ids,
+            top_k_per_type,
+        )
+
+    # Group by edge_type -> from_node -> neighbors
+    result: dict[str, dict[str, list[EdgeTarget]]] = defaultdict(lambda: defaultdict(list))
+    for row in rows:
+        edge_type = row["link_type"]
+        from_id = str(row["from_unit_id"])
+        to_id = str(row["to_unit_id"])
+        weight = row["weight"]
+        result[edge_type][from_id].append(EdgeTarget(node_id=to_id, weight=weight))
+
+    # Convert nested defaultdicts to regular dicts
+    return {edge_type: dict(edges) for edge_type, edges in result.items()}
+
+
+# -----------------------------------------------------------------------------
+# Core Algorithm (Async with Lazy Loading)
+# -----------------------------------------------------------------------------
+
+
+@dataclass
+class PatternState:
+    """State for a pattern traversal between hops."""
+
+    pattern: list[str]
+    hop_index: int
+    scores: dict[str, float]
+    frontier: dict[str, float]
+
+
+def _init_pattern_state(seeds: list[SeedNode], pattern: list[str]) -> PatternState:
+    """Initialize pattern state from seeds."""
+    if not seeds:
+        return PatternState(pattern=pattern, hop_index=0, scores={}, frontier={})
+
+    total_seed_score = sum(s.score for s in seeds)
+    if total_seed_score == 0:
+        total_seed_score = len(seeds)
+
+    frontier = {s.node_id: s.score / total_seed_score for s in seeds}
+    return PatternState(pattern=pattern, hop_index=0, scores={}, frontier=frontier)
+
+
+def _execute_hop(state: PatternState, cache: EdgeCache, config: MPFPConfig) -> set[str]:
+    """
+    Execute ONE hop of traversal, return frontier nodes for next hop.
+
+    This is a pure function that uses cached edges (no DB access).
+    Returns set of uncached nodes needed for next hop.
+    """
+    if state.hop_index >= len(state.pattern):
+        return set()
+
+    edge_type = state.pattern[state.hop_index]
+
+    # Collect active nodes above threshold
+    active_nodes = [node_id for node_id, mass in state.frontier.items() if mass >= config.threshold]
+    if not active_nodes:
+        state.frontier = {}
+        return set()
+
+    # Propagate mass using cached edges
+    next_frontier: dict[str, float] = {}
+    uncached_for_next: set[str] = set()
+
+    for node_id, mass in state.frontier.items():
+        if mass < config.threshold:
+            continue
+
+        # Keep α portion for this node
+        state.scores[node_id] = state.scores.get(node_id, 0) + config.alpha * mass
+
+        # Push (1-α) to neighbors
+        push_mass = (1 - config.alpha) * mass
+        neighbors = cache.get_normalized_neighbors(edge_type, node_id, config.top_k_neighbors)
+
+        for neighbor in neighbors:
+            next_frontier[neighbor.node_id] = next_frontier.get(neighbor.node_id, 0) + push_mass * neighbor.weight
+            # Track if we'll need edges for this node in the next hop
+            if not cache.is_fully_loaded(neighbor.node_id):
+                uncached_for_next.add(neighbor.node_id)
+
+    state.frontier = next_frontier
+    state.hop_index += 1
+
+    return uncached_for_next
+
+
+def _finalize_pattern(state: PatternState, config: MPFPConfig) -> PatternResult:
+    """Finalize pattern by adding remaining frontier mass to scores."""
+    for node_id, mass in state.frontier.items():
+        if mass >= config.threshold:
+            state.scores[node_id] = state.scores.get(node_id, 0) + mass
+
+    return PatternResult(pattern=state.pattern, scores=state.scores)
+
+
+async def mpfp_traverse_hop_synchronized(
+    pool,
+    pattern_jobs: list[tuple[list[SeedNode], list[str]]],
+    config: MPFPConfig,
+    cache: EdgeCache,
+) -> list[PatternResult]:
+    """
+    Execute ALL patterns with hop-synchronized edge loading.
+
+    Instead of running each pattern independently (causing multiple DB queries),
+    this function:
+    1. Runs hop 1 for ALL patterns (using pre-warmed seed edges)
+    2. Collects ALL unique hop-2 frontier nodes across patterns
+    3. Pre-warms hop-2 edges in ONE query
+    4. Runs hop 2 for ALL patterns
+
+    This reduces DB queries from O(patterns * hops) to O(hops).
+
+    Args:
+        pool: Database connection pool
+        pattern_jobs: List of (seeds, pattern) tuples
+        config: Algorithm parameters
+        cache: Shared edge cache (should be pre-warmed with seed edges)
+
+    Returns:
+        List of PatternResult for each pattern
+    """
+    import time
+
+    # Initialize all pattern states
+    states = [_init_pattern_state(seeds, pattern) for seeds, pattern in pattern_jobs]
+
+    # Determine max hops (all patterns should be same length, but be safe)
+    max_hops = max((len(p) for _, p in pattern_jobs), default=0)
+
+    # Detailed timing for debugging
+    hop_times: list[dict] = []
+
+    # Execute hop-by-hop across ALL patterns
+    for hop in range(max_hops):
+        hop_start = time.time()
+        hop_timing = {"hop": hop, "patterns_executed": 0, "uncached_count": 0, "load_time": 0.0}
+
+        # Execute this hop for all patterns, collect uncached nodes for next hop
+        all_uncached: set[str] = set()
+        exec_start = time.time()
+        for state in states:
+            if state.hop_index < len(state.pattern):
+                uncached = _execute_hop(state, cache, config)
+                all_uncached.update(uncached)
+                hop_timing["patterns_executed"] += 1
+        hop_timing["exec_time"] = time.time() - exec_start
+
+        # Pre-warm edges for ALL uncached nodes before next hop
+        hop_timing["uncached_count"] = len(all_uncached)
+        if all_uncached:
+            uncached_list = list(all_uncached - cache._fully_loaded)
+            hop_timing["uncached_after_filter"] = len(uncached_list)
+            if uncached_list:
+                load_start = time.time()
+                edges_by_type = await load_all_edges_for_frontier(pool, uncached_list, config.top_k_neighbors)
+                hop_timing["load_time"] = time.time() - load_start
+                cache.edge_load_time += hop_timing["load_time"]
+                cache.db_queries += 1
+                cache.add_all_edges(edges_by_type, uncached_list)
+                hop_timing["edges_loaded"] = sum(
+                    len(neighbors) for edges in edges_by_type.values() for neighbors in edges.values()
+                )
+
+        hop_timing["total_time"] = time.time() - hop_start
+        hop_times.append(hop_timing)
+
+    # Store hop timing details in cache for logging
+    cache.hop_details = hop_times
+
+    # Finalize all patterns
+    return [_finalize_pattern(state, config) for state in states]
+
+
+async def mpfp_traverse_async(
+    pool,
+    seeds: list[SeedNode],
+    pattern: list[str],
+    config: MPFPConfig,
+    cache: EdgeCache,
+) -> PatternResult:
+    """
+    Async Forward Push traversal with lazy edge loading.
+
+    NOTE: For better performance with multiple patterns, use mpfp_traverse_hop_synchronized().
+    This function is kept for single-pattern use cases.
    """
    if not seeds:
        return PatternResult(pattern=pattern, scores={})

-    scores: Dict[str, float] = {}
-
-    # Initialize frontier with seed masses (normalized)
-    total_seed_score = sum(s.score for s in seeds)
-    if total_seed_score == 0:
-        total_seed_score = len(seeds)  # fallback to uniform
-
-    frontier: Dict[str, float] = {
-        s.node_id: s.score / total_seed_score for s in seeds
-    }
-
-    # Follow pattern hop by hop
-    for edge_type in pattern:
-        next_frontier: Dict[str, float] = {}
-
-        for node_id, mass in frontier.items():
-            if mass < config.threshold:
-                continue
-
-            # Keep α portion for this node
-            scores[node_id] = scores.get(node_id, 0) + config.alpha * mass
-
-            # Push (1-α) to neighbors
-            push_mass = (1 - config.alpha) * mass
-            neighbors = adjacency.get_normalized_neighbors(
-                edge_type, node_id, config.top_k_neighbors
-            )
-
-            for neighbor in neighbors:
-                next_frontier[neighbor.node_id] = (
-                    next_frontier.get(neighbor.node_id, 0) +
-                    push_mass * neighbor.weight
-                )
-
-        frontier = next_frontier
-
-    # Final frontier nodes get their remaining mass
-    for node_id, mass in frontier.items():
-        if mass >= config.threshold:
-            scores[node_id] = scores.get(node_id, 0) + mass
-
-    return PatternResult(pattern=pattern, scores=scores)
+    results = await mpfp_traverse_hop_synchronized(pool, [(seeds, pattern)], config, cache)
+    return results[0] if results else PatternResult(pattern=pattern, scores={})


 def rrf_fusion(
-    results: List[PatternResult],
+    results: list[PatternResult],
    k: int = 60,
    top_k: int = 50,
-) -> List[Tuple[str, float]]:
+) -> list[tuple[str, float]]:
    """
    Reciprocal Rank Fusion to combine pattern results.

@@ -191,28 +412,20 @@ def rrf_fusion(
    Returns:
        List of (node_id, fused_score) tuples, sorted by score descending
    """
-    fused: Dict[str, float] = {}
+    fused: dict[str, float] = {}

    for result in results:
        if not result.scores:
            continue

        # Rank nodes by their score in this pattern
-        ranked = sorted(
-            result.scores.keys(),
-            key=lambda n: result.scores[n],
-            reverse=True
-        )
+        ranked = sorted(result.scores.keys(), key=lambda n: result.scores[n], reverse=True)

        for rank, node_id in enumerate(ranked):
            fused[node_id] = fused.get(node_id, 0) + 1.0 / (k + rank + 1)

    # Sort by fused score and return top-k
-    sorted_results = sorted(
-        fused.items(),
-        key=lambda x: x[1],
-        reverse=True
-    )
+    sorted_results = sorted(fused.items(), key=lambda x: x[1], reverse=True)

    return sorted_results[:top_k]

@@ -221,62 +434,27 @@ def rrf_fusion(
 # Database Loading
 # -----------------------------------------------------------------------------

-async def load_typed_adjacency(pool, bank_id: str) -> TypedAdjacency:
-    """
-    Load all edges for a bank, split by edge type.
-
-    Single query, then organize in-memory for fast traversal.
-    """
-    async with acquire_with_retry(pool) as conn:
-        rows = await conn.fetch(
-            """
-            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
-            WHERE mu.bank_id = $1
-              AND ml.weight >= 0.1
-            ORDER BY ml.from_unit_id, ml.weight DESC
-            """,
-            bank_id
-        )
-
-    graphs: Dict[str, Dict[str, List[EdgeTarget]]] = defaultdict(
-        lambda: defaultdict(list)
-    )
-
-    for row in rows:
-        from_id = str(row['from_unit_id'])
-        to_id = str(row['to_unit_id'])
-        link_type = row['link_type']
-        weight = row['weight']
-
-        graphs[link_type][from_id].append(
-            EdgeTarget(node_id=to_id, weight=weight)
-        )
-
-    return TypedAdjacency(graphs=dict(graphs))
-

 async def fetch_memory_units_by_ids(
    pool,
-    node_ids: List[str],
+    node_ids: list[str],
    fact_type: str,
-) -> List[RetrievalResult]:
+) -> list[RetrievalResult]:
    """Fetch full memory unit details for a list of node IDs."""
    if not node_ids:
        return []

    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
            """,
            node_ids,
-            fact_type
+            fact_type,
        )

    return [RetrievalResult.from_db_row(dict(r)) for r in rows]
@@ -286,23 +464,29 @@ async def fetch_memory_units_by_ids(
 # Graph Retriever Implementation
 # -----------------------------------------------------------------------------

+
 class MPFPGraphRetriever(GraphRetriever):
    """
-    Graph retrieval using Meta-Path Forward Push.
+    Graph retrieval using Meta-Path Forward Push with lazy edge loading.

    Runs predefined patterns in parallel from semantic and temporal seeds,
-    then fuses results via RRF.
+    loading edges on-demand per hop instead of loading entire graph upfront.
    """

-    def __init__(self, config: Optional[MPFPConfig] = None):
+    def __init__(self, config: MPFPConfig | None = None):
        """
        Initialize MPFP retriever.

        Args:
            config: Algorithm configuration (uses defaults if None)
        """
-        self.config = config or MPFPConfig()
-        self._adjacency_cache: Dict[str, TypedAdjacency] = {}
+        if config is None:
+            # Read top_k_neighbors from global config
+            from ...config import get_config
+
+            global_config = get_config()
+            config = MPFPConfig(top_k_neighbors=global_config.mpfp_top_k_neighbors)
+        self.config = config

    @property
    def name(self) -> str:
@@ -315,12 +499,13 @@ class MPFPGraphRetriever(GraphRetriever):
        bank_id: str,
        fact_type: str,
        budget: int,
-        query_text: Optional[str] = None,
-        semantic_seeds: Optional[List[RetrievalResult]] = None,
-        temporal_seeds: Optional[List[RetrievalResult]] = None,
-    ) -> List[RetrievalResult]:
+        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
+        adjacency=None,  # Ignored - kept for interface compatibility
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
        """
-        Retrieve facts using MPFP algorithm.
+        Retrieve facts using MPFP algorithm with lazy edge loading.

        Args:
            pool: Database connection pool
@@ -331,69 +516,87 @@ class MPFPGraphRetriever(GraphRetriever):
            query_text: Original query text (optional)
            semantic_seeds: Pre-computed semantic entry points
            temporal_seeds: Pre-computed temporal entry points
+            adjacency: Ignored (kept for interface compatibility)

        Returns:
-            List of RetrievalResult with activation scores
+            Tuple of (List of RetrievalResult with activation scores, MPFPTimings)
        """
-        # Load typed adjacency (could cache per bank_id with TTL)
-        adjacency = await load_typed_adjacency(pool, bank_id)
+        import time
+
+        timings = MPFPTimings(fact_type=fact_type)

        # Convert seeds to SeedNode format
-        semantic_seed_nodes = self._convert_seeds(semantic_seeds, 'similarity')
-        temporal_seed_nodes = self._convert_seeds(temporal_seeds, 'temporal_score')
+        semantic_seed_nodes = self._convert_seeds(semantic_seeds, "similarity")
+        temporal_seed_nodes = self._convert_seeds(temporal_seeds, "temporal_score")

        # If no semantic seeds provided, fall back to finding our own
        if not semantic_seed_nodes:
-            semantic_seed_nodes = await self._find_semantic_seeds(
-                pool, query_embedding_str, bank_id, fact_type
-            )
+            seeds_start = time.time()
+            semantic_seed_nodes = await self._find_semantic_seeds(pool, query_embedding_str, bank_id, fact_type)
+            timings.seeds_time = time.time() - seeds_start

-        # Run all patterns in parallel
-        tasks = []
+        # Collect all pattern jobs
+        pattern_jobs = []

        # Patterns from semantic seeds
        for pattern in self.config.patterns_semantic:
            if semantic_seed_nodes:
-                tasks.append(
-                    asyncio.to_thread(
-                        mpfp_traverse,
-                        semantic_seed_nodes,
-                        pattern,
-                        adjacency,
-                        self.config,
-                    )
-                )
+                pattern_jobs.append((semantic_seed_nodes, pattern))

        # Patterns from temporal seeds
        for pattern in self.config.patterns_temporal:
            if temporal_seed_nodes:
-                tasks.append(
-                    asyncio.to_thread(
-                        mpfp_traverse,
-                        temporal_seed_nodes,
-                        pattern,
-                        adjacency,
-                        self.config,
-                    )
-                )
+                pattern_jobs.append((temporal_seed_nodes, pattern))

-        if not tasks:
-            return []
+        if not pattern_jobs:
+            return [], timings

-        # Gather pattern results
-        pattern_results = await asyncio.gather(*tasks)
+        timings.pattern_count = len(pattern_jobs)
+
+        # Shared edge cache across all patterns
+        cache = EdgeCache()
+
+        # Pre-warm cache with ALL seed node edges BEFORE running patterns
+        # This prevents redundant DB queries at hop 1
+        all_seed_ids = list({s.node_id for seeds, _ in pattern_jobs for s in seeds})
+        if all_seed_ids:
+            import time as time_module
+
+            prewarm_start = time_module.time()
+            edges_by_type = await load_all_edges_for_frontier(pool, all_seed_ids, self.config.top_k_neighbors)
+            cache.edge_load_time += time_module.time() - prewarm_start
+            cache.db_queries += 1
+            cache.add_all_edges(edges_by_type, all_seed_ids)
+
+        # Run all patterns with HOP-SYNCHRONIZED edge loading
+        # This batches hop-2 edge loads across ALL patterns into ONE query
+        # Reduces DB queries from O(patterns * hops) to O(hops)
+        step_start = time.time()
+        pattern_results = await mpfp_traverse_hop_synchronized(pool, pattern_jobs, self.config, cache)
+        timings.traverse = time.time() - step_start
+
+        # Record edge loading stats from cache
+        timings.edge_count = sum(len(neighbors) for g in cache.graphs.values() for neighbors in g.values())
+        timings.db_queries = cache.db_queries
+        timings.edge_load_time = cache.edge_load_time
+        timings.hop_details = cache.hop_details

        # Fuse results
+        step_start = time.time()
        fused = rrf_fusion(pattern_results, top_k=budget)
+        timings.fusion = time.time() - step_start

        if not fused:
-            return []
+            return [], timings

-        # Get top result IDs (don't exclude seeds - they may be highly relevant)
+        # Get top result IDs
        result_ids = [node_id for node_id, score in fused][:budget]

        # Fetch full details
+        step_start = time.time()
        results = await fetch_memory_units_by_ids(pool, result_ids, fact_type)
+        timings.fetch = time.time() - step_start
+        timings.result_count = len(results)

        # Add activation scores from fusion
        score_map = {node_id: score for node_id, score in fused}
@@ -403,13 +606,13 @@ class MPFPGraphRetriever(GraphRetriever):
        # Sort by activation
        results.sort(key=lambda r: r.activation or 0, reverse=True)

-        return results
+        return results, timings

    def _convert_seeds(
        self,
-        seeds: Optional[List[RetrievalResult]],
+        seeds: list[RetrievalResult] | None,
        score_attr: str,
-    ) -> List[SeedNode]:
+    ) -> list[SeedNode]:
        """Convert RetrievalResult seeds to SeedNode format."""
        if not seeds:
            return []
@@ -431,13 +634,13 @@ class MPFPGraphRetriever(GraphRetriever):
        fact_type: str,
        limit: int = 20,
        threshold: float = 0.3,
-    ) -> List[SeedNode]:
+    ) -> list[SeedNode]:
        """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
@@ -445,10 +648,11 @@ class MPFPGraphRetriever(GraphRetriever):
                ORDER BY embedding <=> $1::vector
                LIMIT $5
                """,
-                query_embedding_str, bank_id, fact_type, threshold, limit
+                query_embedding_str,
+                bank_id,
+                fact_type,
+                threshold,
+                limit,
            )

-        return [
-            SeedNode(node_id=str(r['id']), score=r['similarity'])
-            for r in rows
-        ]
+        return [SeedNode(node_id=str(r["id"]), score=r["similarity"]) for r in rows]
@@ -6,7 +6,7 @@ about an entity, without personality influence.
 """

 import logging
-from typing import List, Dict, Any
+
 from pydantic import BaseModel, Field

 from ..response_models import MemoryFact
@@ -16,18 +16,17 @@ logger = logging.getLogger(__name__)

 class Observation(BaseModel):
    """An observation about an entity."""
+
    observation: str = Field(description="The observation text - a factual statement about the entity")


 class ObservationExtractionResponse(BaseModel):
    """Response containing extracted observations."""
-    observations: List[Observation] = Field(
-        default_factory=list,
-        description="List of observations about the entity"
-    )
+
+    observations: list[Observation] = Field(default_factory=list, description="List of observations about the entity")


-def format_facts_for_observation_prompt(facts: List[MemoryFact]) -> str:
+def format_facts_for_observation_prompt(facts: list[MemoryFact]) -> str:
    """Format facts as text for observation extraction prompt."""
    import json

@@ -35,9 +34,7 @@ def format_facts_for_observation_prompt(facts: List[MemoryFact]) -> str:
        return "[]"
    formatted = []
    for fact in facts:
-        fact_obj = {
-            "text": fact.text
-        }
+        fact_obj = {"text": fact.text}

        # Add context if available
        if fact.context:
@@ -92,11 +89,7 @@ def get_observation_system_message() -> str:
    return "You are an objective observer synthesizing facts about an entity. Generate clear, factual observations without opinions or personality influence. Be concise and accurate."


-async def extract_observations_from_facts(
-    llm_config,
-    entity_name: str,
-    facts: List[MemoryFact]
-) -> List[str]:
+async def extract_observations_from_facts(llm_config, entity_name: str, facts: list[MemoryFact]) -> list[str]:
    """
    Extract observations from facts about an entity using LLM.

@@ -118,10 +111,10 @@ async def extract_observations_from_facts(
        result = await llm_config.call(
            messages=[
                {"role": "system", "content": get_observation_system_message()},
-                {"role": "user", "content": prompt}
+                {"role": "user", "content": prompt},
            ],
            response_format=ObservationExtractionResponse,
-            scope="memory_extract_observation"
+            scope="memory_extract_observation",
        )

        observations = [op.observation for op in result.observations]
@@ -2,7 +2,6 @@
 Cross-encoder neural reranking for search results.
 """

-from typing import List
 from .types import MergedCandidate, ScoredResult


@@ -24,14 +23,28 @@ class CrossEncoderReranker:
        """
        if cross_encoder is None:
            from hindsight_api.engine.cross_encoder import create_cross_encoder_from_env
+
            cross_encoder = create_cross_encoder_from_env()
        self.cross_encoder = cross_encoder
+        self._initialized = False

-    def rerank(
-        self,
-        query: str,
-        candidates: List[MergedCandidate]
-    ) -> List[ScoredResult]:
+    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
+
+    async def rerank(self, query: str, candidates: list[MergedCandidate]) -> list[ScoredResult]:
        """
        Rerank candidates using cross-encoder scores.

@@ -72,11 +85,12 @@ class CrossEncoderReranker:
            pairs.append([query, doc_text])

        # Get cross-encoder scores
-        scores = self.cross_encoder.predict(pairs)
+        scores = await self.cross_encoder.predict(pairs)

        # Normalize scores using sigmoid to [0, 1] range
        # Cross-encoder returns logits which can be negative
        import numpy as np
+
        def sigmoid(x):
            return 1 / (1 + np.exp(-x))

@@ -89,7 +103,7 @@ class CrossEncoderReranker:
                candidate=candidate,
                cross_encoder_score=float(raw_score),
                cross_encoder_score_normalized=float(norm_score),
-                weight=float(norm_score)  # Initial weight is just cross-encoder score
+                weight=float(norm_score),  # Initial weight is just cross-encoder score
            )
            scored_results.append(scored_result)

@@ -4,11 +4,11 @@ Scoring functions for memory search and retrieval.
 Includes recency weighting, frequency weighting, temporal proximity,
 and similarity calculations used in memory activation and ranking.
 """
+
 from datetime import datetime
-from typing import List


-def cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
+def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
    """
    Calculate cosine similarity between two vectors.

@@ -58,6 +58,7 @@ def calculate_recency_weight(days_since: float, half_life_days: float = 365.0) -
        Weight between 0 and 1
    """
    import math
+
    # Logarithmic decay: 1 / (1 + log(1 + days_since/half_life))
    # This decays much slower than exponential, giving better long-term differentiation
    normalized_age = days_since / half_life_days
@@ -79,6 +80,7 @@ def calculate_frequency_weight(access_count: int, max_boost: float = 2.0) -> flo
        Weight between 1.0 and max_boost
    """
    import math
+
    if access_count <= 0:
        return 1.0

@@ -116,11 +118,7 @@ def calculate_temporal_anchor(occurred_start: datetime, occurred_end: datetime)
    return midpoint


-def calculate_temporal_proximity(
-    anchor_a: datetime,
-    anchor_b: datetime,
-    half_life_days: float = 30.0
-) -> float:
+def calculate_temporal_proximity(anchor_a: datetime, anchor_b: datetime, half_life_days: float = 30.0) -> float:
    """
    Calculate temporal proximity between two temporal anchors.

@@ -4,16 +4,16 @@ Temporal extraction for time-aware search queries.
 Handles natural language temporal expressions using transformer-based query analysis.
 """

-from typing import Optional, Tuple
-from datetime import datetime
 import logging
-from hindsight_api.engine.query_analyzer import QueryAnalyzer, DateparserQueryAnalyzer
+from datetime import datetime
+
+from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer, QueryAnalyzer

 logger = logging.getLogger(__name__)

 # Global default analyzer instance
 # Can be overridden by passing a custom analyzer to extract_temporal_constraint
-_default_analyzer: Optional[QueryAnalyzer] = None
+_default_analyzer: QueryAnalyzer | None = None


 def get_default_analyzer() -> QueryAnalyzer:
@@ -33,9 +33,9 @@ def get_default_analyzer() -> QueryAnalyzer:

 def extract_temporal_constraint(
    query: str,
-    reference_date: Optional[datetime] = None,
-    analyzer: Optional[QueryAnalyzer] = None,
-) -> Optional[Tuple[datetime, datetime]]:
+    reference_date: datetime | None = None,
+    analyzer: QueryAnalyzer | None = None,
+) -> tuple[datetime, datetime] | None:
    """
    Extract temporal constraint from query.

@@ -55,10 +55,7 @@ def extract_temporal_constraint(
    analysis = analyzer.analyze(query, reference_date)

    if analysis.temporal_constraint:
-        result = (
-            analysis.temporal_constraint.start_date,
-            analysis.temporal_constraint.end_date
-        )
+        result = (analysis.temporal_constraint.start_date, analysis.temporal_constraint.end_date)
        return result

    return None
@@ -2,41 +2,35 @@
 Think operation utilities for formulating answers based on agent and world facts.
 """

-import asyncio
 import logging
 import re
-from datetime import datetime, timezone
-from typing import Dict, List, Any
+from datetime import datetime
+
 from pydantic import BaseModel, Field

-from ..response_models import ReflectResult, MemoryFact, DispositionTraits
+from ..response_models import DispositionTraits, MemoryFact

 logger = logging.getLogger(__name__)


 class Opinion(BaseModel):
    """An opinion formed by the bank."""
+
    opinion: str = Field(description="The opinion or perspective with reasoning included")
    confidence: float = Field(description="Confidence score for this opinion (0.0 to 1.0, where 1.0 is very confident)")


 class OpinionExtractionResponse(BaseModel):
    """Response containing extracted opinions."""
-    opinions: List[Opinion] = Field(
-        default_factory=list,
-        description="List of opinions formed with their supporting reasons and confidence scores"
+
+    opinions: list[Opinion] = Field(
+        default_factory=list, description="List of opinions formed with their supporting reasons and confidence scores"
    )


 def describe_trait_level(value: int) -> str:
    """Convert trait value (1-5) to descriptive text."""
-    levels = {
-        1: "very low",
-        2: "low",
-        3: "moderate",
-        4: "high",
-        5: "very high"
-    }
+    levels = {1: "very low", 2: "low", 3: "moderate", 4: "high", 5: "very high"}
    return levels.get(value, "moderate")


@@ -47,7 +41,7 @@ def build_disposition_description(disposition: DispositionTraits) -> str:
        2: "You tend to trust information but may question obvious inconsistencies.",
        3: "You have a balanced approach to information, neither too trusting nor too skeptical.",
        4: "You are somewhat skeptical and often question the reliability of information.",
-        5: "You are highly skeptical and critically examine all information for accuracy and hidden motives."
+        5: "You are highly skeptical and critically examine all information for accuracy and hidden motives.",
    }

    literalism_desc = {
@@ -55,7 +49,7 @@ def build_disposition_description(disposition: DispositionTraits) -> str:
        2: "You tend to consider context and implied meaning alongside literal statements.",
        3: "You balance literal interpretation with contextual understanding.",
        4: "You prefer to interpret information more literally and precisely.",
-        5: "You interpret information very literally and focus on exact wording and commitments."
+        5: "You interpret information very literally and focus on exact wording and commitments.",
    }

    empathy_desc = {
@@ -63,7 +57,7 @@ def build_disposition_description(disposition: DispositionTraits) -> str:
        2: "You consider facts first but acknowledge emotional factors exist.",
        3: "You balance factual analysis with emotional understanding.",
        4: "You give significant weight to emotional context and human factors.",
-        5: "You strongly consider the emotional state and circumstances of others when forming memories."
+        5: "You strongly consider the emotional state and circumstances of others when forming memories.",
    }

    return f"""Your disposition traits:
@@ -72,7 +66,7 @@ def build_disposition_description(disposition: DispositionTraits) -> str:
 - Empathy ({describe_trait_level(disposition.empathy)}): {empathy_desc.get(disposition.empathy, empathy_desc[3])}"""


-def format_facts_for_prompt(facts: List[MemoryFact]) -> str:
+def format_facts_for_prompt(facts: list[MemoryFact]) -> str:
    """Format facts as JSON for LLM prompt."""
    import json

@@ -80,9 +74,7 @@ def format_facts_for_prompt(facts: List[MemoryFact]) -> str:
        return "[]"
    formatted = []
    for fact in facts:
-        fact_obj = {
-            "text": fact.text
-        }
+        fact_obj = {"text": fact.text}

        # Add context if available
        if fact.context:
@@ -94,7 +86,7 @@ def format_facts_for_prompt(facts: List[MemoryFact]) -> str:
            if isinstance(occurred_start, str):
                fact_obj["occurred_start"] = occurred_start
            elif isinstance(occurred_start, datetime):
-                fact_obj["occurred_start"] = occurred_start.strftime('%Y-%m-%d %H:%M:%S')
+                fact_obj["occurred_start"] = occurred_start.strftime("%Y-%m-%d %H:%M:%S")

        formatted.append(fact_obj)

@@ -109,7 +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)
@@ -176,16 +168,14 @@ def get_system_message(disposition: DispositionTraits) -> str:
    elif disposition.empathy <= 2:
        instructions.append("Focus on facts and outcomes rather than emotional context.")

-    disposition_instruction = " ".join(instructions) if instructions else "Balance your disposition traits when interpreting information."
+    disposition_instruction = (
+        " ".join(instructions) if instructions else "Balance your disposition traits when interpreting information."
+    )

-    return f"You are a person with your own thoughts, experiences, opinions, and disposition. When asked a question, respond naturally like a human would - share what you genuinely think based on what you know and have experienced. {disposition_instruction} Be direct, express your views confidently, and use 'I think', 'I believe', 'in my view', etc. Respond in plain text without markdown formatting."
+    return f"You are a person with your own thoughts, experiences, opinions, and disposition. When asked a question, respond naturally like a human would - share what you genuinely think based on what you know and have experienced. {disposition_instruction} Be direct, express your views confidently, and use 'I think', 'I believe', 'in my view', etc. Respond in plain text without markdown formatting. IMPORTANT: Detect the language of the question and respond in the SAME language. Do not translate to English if the question is in another language."


-async def extract_opinions_from_text(
-    llm_config,
-    text: str,
-    query: str
-) -> List[Opinion]:
+async def extract_opinions_from_text(llm_config, text: str, query: str) -> list[Opinion]:
    """
    Extract opinions with reasons and confidence from text using LLM.

@@ -238,11 +228,14 @@ If no genuine opinions are expressed (e.g., the response just says "I don't know
    try:
        result = await llm_config.call(
            messages=[
-                {"role": "system", "content": "You are converting opinions from text into first-person statements. Always use 'I think', 'I believe', 'I feel', etc. NEVER use third-person like 'The speaker' or 'They'."},
-                {"role": "user", "content": extraction_prompt}
+                {
+                    "role": "system",
+                    "content": "You are converting opinions from text into first-person statements. Always use 'I think', 'I believe', 'I feel', etc. NEVER use third-person like 'The speaker' or 'They'.",
+                },
+                {"role": "user", "content": extraction_prompt},
            ],
            response_format=OpinionExtractionResponse,
-            scope="memory_extract_opinion"
+            scope="memory_extract_opinion",
        )

        # Format opinions with confidence score and convert to first-person
@@ -253,14 +246,18 @@ If no genuine opinions are expressed (e.g., the response just says "I don't know

            # Replace common third-person patterns with first-person
            def singularize_verb(verb):
-                if verb.endswith('es'):
+                if verb.endswith("es"):
                    return verb[:-1]  # believes -> believe
-                elif verb.endswith('s'):
+                elif verb.endswith("s"):
                    return verb[:-1]  # thinks -> think
                return verb

            # Pattern: "The speaker/user [verb]..." -> "I [verb]..."
-            match = re.match(r'^(The speaker|The user|They|It is believed) (believes?|thinks?|feels?|says|asserts?|considers?)(\s+that)?(.*)$', opinion_text, re.IGNORECASE)
+            match = re.match(
+                r"^(The speaker|The user|They|It is believed) (believes?|thinks?|feels?|says|asserts?|considers?)(\s+that)?(.*)$",
+                opinion_text,
+                re.IGNORECASE,
+            )
            if match:
                verb = singularize_verb(match.group(2))
                that_part = match.group(3) or ""  # Keep " that" if present
@@ -268,17 +265,96 @@ If no genuine opinions are expressed (e.g., the response just says "I don't know
                opinion_text = f"I {verb}{that_part}{rest}"

            # If still doesn't start with first-person, prepend "I believe that "
-            first_person_starters = ["I think", "I believe", "I feel", "In my view", "I've come to believe", "Previously I"]
+            first_person_starters = [
+                "I think",
+                "I believe",
+                "I feel",
+                "In my view",
+                "I've come to believe",
+                "Previously I",
+            ]
            if not any(opinion_text.startswith(starter) for starter in first_person_starters):
                opinion_text = "I believe that " + opinion_text[0].lower() + opinion_text[1:]

-            formatted_opinions.append(Opinion(
-                opinion=opinion_text,
-                confidence=op.confidence
-            ))
+            formatted_opinions.append(Opinion(opinion=opinion_text, confidence=op.confidence))

        return formatted_opinions

    except Exception as e:
        logger.warning(f"Failed to extract opinions: {str(e)}")
        return []
+
+
+async def reflect(
+    llm_config,
+    query: str,
+    experience_facts: list[str] = None,
+    world_facts: list[str] = None,
+    opinion_facts: list[str] = None,
+    name: str = "Assistant",
+    disposition: DispositionTraits = None,
+    background: str = "",
+    context: str = None,
+) -> str:
+    """
+    Standalone reflect function for generating answers based on facts.
+
+    This is a static version of the reflect operation that can be called
+    without a MemoryEngine instance, useful for testing.
+
+    Args:
+        llm_config: LLM provider instance
+        query: Question to answer
+        experience_facts: List of experience/agent fact strings
+        world_facts: List of world fact strings
+        opinion_facts: List of opinion fact strings
+        name: Name of the agent/persona
+        disposition: Disposition traits (defaults to neutral)
+        background: Background information
+        context: Additional context for the prompt
+
+    Returns:
+        Generated answer text
+    """
+    # Default disposition if not provided
+    if disposition is None:
+        disposition = DispositionTraits(skepticism=3, literalism=3, empathy=3)
+
+    # Convert string lists to MemoryFact format for formatting
+    def to_memory_facts(facts: list[str], fact_type: str) -> list[MemoryFact]:
+        if not facts:
+            return []
+        return [MemoryFact(id=f"test-{i}", text=f, fact_type=fact_type) for i, f in enumerate(facts)]
+
+    agent_results = to_memory_facts(experience_facts or [], "experience")
+    world_results = to_memory_facts(world_facts or [], "world")
+    opinion_results = to_memory_facts(opinion_facts or [], "opinion")
+
+    # Format facts for prompt
+    agent_facts_text = format_facts_for_prompt(agent_results)
+    world_facts_text = format_facts_for_prompt(world_results)
+    opinion_facts_text = format_facts_for_prompt(opinion_results)
+
+    # Build prompt
+    prompt = build_think_prompt(
+        agent_facts_text=agent_facts_text,
+        world_facts_text=world_facts_text,
+        opinion_facts_text=opinion_facts_text,
+        query=query,
+        name=name,
+        disposition=disposition,
+        background=background,
+        context=context,
+    )
+
+    system_message = get_system_message(disposition)
+
+    # Call LLM
+    answer_text = await llm_config.call(
+        messages=[{"role": "system", "content": system_message}, {"role": "user", "content": prompt}],
+        scope="memory_think",
+        temperature=0.9,
+        max_completion_tokens=1000,
+    )
+
+    return answer_text.strip()
@@ -4,15 +4,18 @@ Search trace models for debugging and visualization.
 These Pydantic models define the structure of search traces, capturing
 every step of the spreading activation search process for analysis.
 """
+
 from datetime import datetime
-from typing import List, Optional, Dict, Any, Literal
+from typing import Any, Literal
+
 from pydantic import BaseModel, Field


 class QueryInfo(BaseModel):
    """Information about the search query."""
+
    query_text: str = Field(description="Original query text")
-    query_embedding: List[float] = Field(description="Generated query embedding vector")
+    query_embedding: list[float] = Field(description="Generated query embedding vector")
    timestamp: datetime = Field(description="When the query was executed")
    budget: int = Field(description="Maximum nodes to explore")
    max_tokens: int = Field(description="Maximum tokens to return in results")
@@ -20,6 +23,7 @@ class QueryInfo(BaseModel):

 class EntryPoint(BaseModel):
    """An entry point node selected for search."""
+
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    similarity_score: float = Field(description="Cosine similarity to query", ge=0.0, le=1.0)
@@ -28,6 +32,7 @@ class EntryPoint(BaseModel):

 class WeightComponents(BaseModel):
    """Breakdown of weight calculation components."""
+
    activation: float = Field(description="Activation from spreading (can exceed 1.0 through accumulation)", ge=0.0)
    semantic_similarity: float = Field(description="Semantic similarity to query", ge=0.0, le=1.0)
    recency: float = Field(description="Recency weight", ge=0.0, le=1.0)
@@ -43,99 +48,120 @@ class WeightComponents(BaseModel):

 class LinkInfo(BaseModel):
    """Information about a link to a neighbor."""
+
    to_node_id: str = Field(description="Target node ID")
    link_type: Literal["temporal", "semantic", "entity"] = Field(description="Type of link")
-    link_weight: float = Field(description="Weight of the link (can exceed 1.0 when aggregating multiple connections)", ge=0.0)
-    entity_id: Optional[str] = Field(default=None, description="Entity ID if link_type is 'entity'")
-    new_activation: Optional[float] = Field(default=None, description="Activation that would be passed to neighbor (None for supplementary links)")
+    link_weight: float = Field(
+        description="Weight of the link (can exceed 1.0 when aggregating multiple connections)", ge=0.0
+    )
+    entity_id: str | None = Field(default=None, description="Entity ID if link_type is 'entity'")
+    new_activation: float | None = Field(
+        default=None, description="Activation that would be passed to neighbor (None for supplementary links)"
+    )
    followed: bool = Field(description="Whether this link was followed (or pruned)")
-    prune_reason: Optional[str] = Field(default=None, description="Why link was not followed (if not followed)")
-    is_supplementary: bool = Field(default=False, description="Whether this is a supplementary link (multiple connections to same node)")
+    prune_reason: str | None = Field(default=None, description="Why link was not followed (if not followed)")
+    is_supplementary: bool = Field(
+        default=False, description="Whether this is a supplementary link (multiple connections to same node)"
+    )


 class NodeVisit(BaseModel):
    """Information about visiting a node during search."""
+
    step: int = Field(description="Step number in search (1-based)")
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    context: str = Field(description="Memory unit context")
-    event_date: Optional[datetime] = Field(default=None, description="When the memory occurred")
+    event_date: datetime | None = Field(default=None, description="When the memory occurred")
    access_count: int = Field(description="Number of times accessed before this search")

    # How this node was reached
    is_entry_point: bool = Field(description="Whether this is an entry point")
-    parent_node_id: Optional[str] = Field(default=None, description="Node that led to this one")
-    link_type: Optional[Literal["temporal", "semantic", "entity"]] = Field(default=None, description="Type of link from parent")
-    link_weight: Optional[float] = Field(default=None, description="Weight of link from parent")
+    parent_node_id: str | None = Field(default=None, description="Node that led to this one")
+    link_type: Literal["temporal", "semantic", "entity"] | None = Field(
+        default=None, description="Type of link from parent"
+    )
+    link_weight: float | None = Field(default=None, description="Weight of link from parent")

    # Weights
    weights: WeightComponents = Field(description="Weight calculation breakdown")

    # Neighbors discovered from this node
-    neighbors_explored: List[LinkInfo] = Field(default_factory=list, description="Links explored from this node")
+    neighbors_explored: list[LinkInfo] = Field(default_factory=list, description="Links explored from this node")

    # Ranking
-    final_rank: Optional[int] = Field(default=None, description="Final rank in results (1-based, None if not in top-k)")
+    final_rank: int | None = Field(default=None, description="Final rank in results (1-based, None if not in top-k)")


 class PruningDecision(BaseModel):
    """Records when a node was considered but not visited."""
+
    node_id: str = Field(description="Node that was pruned")
-    reason: Literal["already_visited", "activation_too_low", "budget_exhausted"] = Field(description="Why it was pruned")
+    reason: Literal["already_visited", "activation_too_low", "budget_exhausted"] = Field(
+        description="Why it was pruned"
+    )
    activation: float = Field(description="Activation value when pruned")
    would_have_been_step: int = Field(description="What step it would have been if visited")


 class SearchPhaseMetrics(BaseModel):
    """Performance metrics for a search phase."""
+
    phase_name: str = Field(description="Name of the phase")
    duration_seconds: float = Field(description="Time taken in seconds")
-    details: Dict[str, Any] = Field(default_factory=dict, description="Additional phase-specific metrics")
+    details: dict[str, Any] = Field(default_factory=dict, description="Additional phase-specific metrics")


 class RetrievalResult(BaseModel):
    """A single result from a retrieval method."""
+
    rank: int = Field(description="Rank in this retrieval method (1-based)")
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    context: str = Field(default="", description="Memory unit context")
-    event_date: Optional[datetime] = Field(default=None, description="When the memory occurred")
-    fact_type: Optional[str] = Field(default=None, description="Fact type (world, experience, opinion)")
+    event_date: datetime | None = Field(default=None, description="When the memory occurred")
+    fact_type: str | None = Field(default=None, description="Fact type (world, experience, opinion)")
    score: float = Field(description="Score from this retrieval method")
    score_name: str = Field(description="Name of the score (e.g., 'similarity', 'bm25_score', 'activation')")


 class RetrievalMethodResults(BaseModel):
    """Results from a single retrieval method."""
+
    method_name: Literal["semantic", "bm25", "graph", "temporal"] = Field(description="Name of retrieval method")
-    fact_type: Optional[str] = Field(default=None, description="Fact type this retrieval was for (world, experience, opinion)")
-    results: List[RetrievalResult] = Field(description="Retrieved results with ranks")
+    fact_type: str | None = Field(
+        default=None, description="Fact type this retrieval was for (world, experience, opinion)"
+    )
+    results: list[RetrievalResult] = Field(description="Retrieved results with ranks")
    duration_seconds: float = Field(description="Time taken for this retrieval")
-    metadata: Dict[str, Any] = Field(default_factory=dict, description="Method-specific metadata")
+    metadata: dict[str, Any] = Field(default_factory=dict, description="Method-specific metadata")


 class RRFMergeResult(BaseModel):
    """A result after RRF merging."""
+
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    rrf_score: float = Field(description="Reciprocal Rank Fusion score")
-    source_ranks: Dict[str, int] = Field(description="Rank in each source that contributed (method_name -> rank)")
+    source_ranks: dict[str, int] = Field(description="Rank in each source that contributed (method_name -> rank)")
    final_rrf_rank: int = Field(description="Rank after RRF merge (1-based)")


 class RerankedResult(BaseModel):
    """A result after reranking."""
+
    node_id: str = Field(description="Memory unit ID")
    text: str = Field(description="Memory unit text content")
    rerank_score: float = Field(description="Final reranking score")
    rerank_rank: int = Field(description="Rank after reranking (1-based)")
    rrf_rank: int = Field(description="Original RRF rank before reranking")
    rank_change: int = Field(description="Change in rank (positive = moved up)")
-    score_components: Dict[str, float] = Field(default_factory=dict, description="Score breakdown")
+    score_components: dict[str, float] = Field(default_factory=dict, description="Score breakdown")


 class SearchSummary(BaseModel):
    """Summary statistics about the search."""
+
    total_nodes_visited: int = Field(description="Total nodes visited")
    total_nodes_pruned: int = Field(description="Total nodes pruned")
    entry_points_found: int = Field(description="Number of entry points")
@@ -150,33 +176,36 @@ class SearchSummary(BaseModel):
    entity_links_followed: int = Field(default=0, description="Entity links followed")

    # Phase timings
-    phase_metrics: List[SearchPhaseMetrics] = Field(default_factory=list, description="Metrics for each phase")
+    phase_metrics: list[SearchPhaseMetrics] = Field(default_factory=list, description="Metrics for each phase")


 class SearchTrace(BaseModel):
    """Complete trace of a search operation."""
+
    query: QueryInfo = Field(description="Query information")

    # New 4-way retrieval architecture
-    retrieval_results: List[RetrievalMethodResults] = Field(default_factory=list, description="Results from each retrieval method")
-    rrf_merged: List[RRFMergeResult] = Field(default_factory=list, description="Results after RRF merging")
-    reranked: List[RerankedResult] = Field(default_factory=list, description="Results after reranking")
+    retrieval_results: list[RetrievalMethodResults] = Field(
+        default_factory=list, description="Results from each retrieval method"
+    )
+    rrf_merged: list[RRFMergeResult] = Field(default_factory=list, description="Results after RRF merging")
+    reranked: list[RerankedResult] = Field(default_factory=list, description="Results after reranking")

    # Legacy fields (kept for backward compatibility with graph/temporal visualizations)
-    entry_points: List[EntryPoint] = Field(default_factory=list, description="Entry points selected for search (legacy)")
-    visits: List[NodeVisit] = Field(default_factory=list, description="All nodes visited during search (legacy, for graph viz)")
-    pruned: List[PruningDecision] = Field(default_factory=list, description="Nodes that were pruned (legacy)")
+    entry_points: list[EntryPoint] = Field(
+        default_factory=list, description="Entry points selected for search (legacy)"
+    )
+    visits: list[NodeVisit] = Field(
+        default_factory=list, description="All nodes visited during search (legacy, for graph viz)"
+    )
+    pruned: list[PruningDecision] = Field(default_factory=list, description="Nodes that were pruned (legacy)")

    summary: SearchSummary = Field(description="Summary statistics")

    # Final results (for comparison with visits)
-    final_results: List[Dict[str, Any]] = Field(description="Final ranked results returned to user")
+    final_results: list[dict[str, Any]] = Field(description="Final ranked results returned to user")

-    model_config = {
-        "json_encoders": {
-            datetime: lambda v: v.isoformat()
-        }
-    }
+    model_config = {"json_encoders": {datetime: lambda v: v.isoformat()}}

    def to_json(self, **kwargs) -> str:
        """Export trace as JSON string."""
@@ -186,14 +215,14 @@ class SearchTrace(BaseModel):
        """Export trace as dictionary."""
        return self.model_dump()

-    def get_visit_by_node_id(self, node_id: str) -> Optional[NodeVisit]:
+    def get_visit_by_node_id(self, node_id: str) -> NodeVisit | None:
        """Find a visit by node ID."""
        for visit in self.visits:
            if visit.node_id == node_id:
                return visit
        return None

-    def get_search_path_to_node(self, node_id: str) -> List[NodeVisit]:
+    def get_search_path_to_node(self, node_id: str) -> list[NodeVisit]:
        """Get the path from entry point to a specific node."""
        path = []
        current_visit = self.get_visit_by_node_id(node_id)
@@ -207,10 +236,10 @@ class SearchTrace(BaseModel):

        return path

-    def get_nodes_by_link_type(self, link_type: Literal["temporal", "semantic", "entity"]) -> List[NodeVisit]:
+    def get_nodes_by_link_type(self, link_type: Literal["temporal", "semantic", "entity"]) -> list[NodeVisit]:
        """Get all nodes reached via a specific link type."""
        return [v for v in self.visits if v.link_type == link_type]

-    def get_entry_point_nodes(self) -> List[NodeVisit]:
+    def get_entry_point_nodes(self) -> list[NodeVisit]:
        """Get all entry point visits."""
        return [v for v in self.visits if v.is_entry_point]
@@ -4,24 +4,25 @@ Search tracer for collecting detailed search execution traces.
 The SearchTracer collects comprehensive information about each step
 of the spreading activation search process for debugging and visualization.
 """
+
 import time
-from datetime import datetime, timezone
-from typing import List, Optional, Dict, Any, Literal
+from datetime import UTC, datetime
+from typing import Any, Literal

 from .trace import (
-    SearchTrace,
-    QueryInfo,
    EntryPoint,
-    NodeVisit,
-    WeightComponents,
    LinkInfo,
+    NodeVisit,
    PruningDecision,
-    SearchSummary,
-    SearchPhaseMetrics,
-    RetrievalResult,
-    RetrievalMethodResults,
-    RRFMergeResult,
+    QueryInfo,
    RerankedResult,
+    RetrievalMethodResults,
+    RetrievalResult,
+    RRFMergeResult,
+    SearchPhaseMetrics,
+    SearchSummary,
+    SearchTrace,
+    WeightComponents,
 )


@@ -58,17 +59,17 @@ class SearchTracer:
        self.max_tokens = max_tokens

        # Trace data
-        self.query_embedding: Optional[List[float]] = None
-        self.start_time: Optional[float] = None
-        self.entry_points: List[EntryPoint] = []
-        self.visits: List[NodeVisit] = []
-        self.pruned: List[PruningDecision] = []
-        self.phase_metrics: List[SearchPhaseMetrics] = []
+        self.query_embedding: list[float] | None = None
+        self.start_time: float | None = None
+        self.entry_points: list[EntryPoint] = []
+        self.visits: list[NodeVisit] = []
+        self.pruned: list[PruningDecision] = []
+        self.phase_metrics: list[SearchPhaseMetrics] = []

        # New 4-way retrieval tracking
-        self.retrieval_results: List[RetrievalMethodResults] = []
-        self.rrf_merged: List[RRFMergeResult] = []
-        self.reranked: List[RerankedResult] = []
+        self.retrieval_results: list[RetrievalMethodResults] = []
+        self.rrf_merged: list[RRFMergeResult] = []
+        self.reranked: list[RerankedResult] = []

        # Tracking state
        self.current_step = 0
@@ -83,7 +84,7 @@ class SearchTracer:
        """Start timing the search."""
        self.start_time = time.time()

-    def record_query_embedding(self, embedding: List[float]):
+    def record_query_embedding(self, embedding: list[float]):
        """Record the query embedding."""
        self.query_embedding = embedding

@@ -114,12 +115,12 @@ 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: Optional[str],
-        link_type: Optional[Literal["temporal", "semantic", "entity"]],
-        link_weight: Optional[float],
+        parent_node_id: str | None,
+        link_type: Literal["temporal", "semantic", "entity"] | None,
+        link_weight: float | None,
        activation: float,
        semantic_similarity: float,
        recency: float,
@@ -199,10 +200,10 @@ class SearchTracer:
        to_node_id: str,
        link_type: Literal["temporal", "semantic", "entity"],
        link_weight: float,
-        entity_id: Optional[str],
-        new_activation: Optional[float],
+        entity_id: str | None,
+        new_activation: float | None,
        followed: bool,
-        prune_reason: Optional[str] = None,
+        prune_reason: str | None = None,
        is_supplementary: bool = False,
    ):
        """
@@ -266,7 +267,7 @@ class SearchTracer:
            )
        )

-    def add_phase_metric(self, phase_name: str, duration_seconds: float, details: Optional[Dict[str, Any]] = None):
+    def add_phase_metric(self, phase_name: str, duration_seconds: float, details: dict[str, Any] | None = None):
        """
        Record metrics for a search phase.

@@ -286,11 +287,11 @@ class SearchTracer:
    def add_retrieval_results(
        self,
        method_name: Literal["semantic", "bm25", "graph", "temporal"],
-        results: List[tuple],  # List of (doc_id, data) tuples
+        results: list[tuple],  # List of (doc_id, data) tuples
        duration_seconds: float,
        score_field: str,  # e.g., "similarity", "bm25_score"
-        metadata: Optional[Dict[str, Any]] = None,
-        fact_type: Optional[str] = None
+        metadata: dict[str, Any] | None = None,
+        fact_type: str | None = None,
    ):
        """
        Record results from a single retrieval method.
@@ -331,7 +332,7 @@ class SearchTracer:
            )
        )

-    def add_rrf_merged(self, merged_results: List[tuple]):
+    def add_rrf_merged(self, merged_results: list[tuple]):
        """
        Record RRF merged results.

@@ -350,7 +351,7 @@ class SearchTracer:
                )
            )

-    def add_reranked(self, reranked_results: List[Dict[str, Any]], rrf_merged: List):
+    def add_reranked(self, reranked_results: list[dict[str, Any]], rrf_merged: list):
        """
        Record reranked results.

@@ -373,7 +374,15 @@ class SearchTracer:
            # Keys from ScoredResult.to_dict(): cross_encoder_score, cross_encoder_score_normalized,
            # rrf_normalized, temporal, recency, combined_score, weight
            score_components = {}
-            for key in ["cross_encoder_score", "cross_encoder_score_normalized", "rrf_score", "rrf_normalized", "temporal", "recency", "combined_score"]:
+            for key in [
+                "cross_encoder_score",
+                "cross_encoder_score_normalized",
+                "rrf_score",
+                "rrf_normalized",
+                "temporal",
+                "recency",
+                "combined_score",
+            ]:
                if key in result and result[key] is not None:
                    score_components[key] = result[key]

@@ -389,7 +398,7 @@ class SearchTracer:
                )
            )

-    def finalize(self, final_results: List[Dict[str, Any]]) -> SearchTrace:
+    def finalize(self, final_results: list[dict[str, Any]]) -> SearchTrace:
        """
        Finalize the trace and return the complete SearchTrace object.

@@ -416,7 +425,7 @@ class SearchTracer:
        query_info = QueryInfo(
            query_text=self.query_text,
            query_embedding=self.query_embedding or [],
-            timestamp=datetime.now(timezone.utc),
+            timestamp=datetime.now(UTC),
            budget=self.budget,
            max_tokens=self.max_tokens,
        )
@@ -6,8 +6,26 @@ providing type safety and making data flow explicit.
 """

 from dataclasses import dataclass, field
-from typing import Optional, List, Dict, Any
 from datetime import datetime
+from typing import Any
+
+
+@dataclass
+class MPFPTimings:
+    """Timing breakdown for a single MPFP retrieval call."""
+
+    fact_type: str
+    edge_count: int = 0  # Total edges loaded
+    db_queries: int = 0  # Number of DB queries for edge loading
+    edge_load_time: float = 0.0  # Time spent loading edges from DB
+    traverse: float = 0.0  # Total traversal time (includes edge loading)
+    pattern_count: int = 0  # Number of patterns executed
+    fusion: float = 0.0  # Time for RRF fusion
+    fetch: float = 0.0  # Time to fetch memory unit details
+    seeds_time: float = 0.0  # Time to find semantic seeds (if fallback used)
+    result_count: int = 0  # Number of results returned
+    # Detailed per-hop timing: list of {hop, exec_time, uncached, load_time, edges_loaded, total_time}
+    hop_details: list[dict] = field(default_factory=list)


@dataclass
@@ -17,28 +35,29 @@ class RetrievalResult:

    This represents a raw result from the database query, before merging or reranking.
    """
+
    id: str
    text: str
    fact_type: str
-    context: Optional[str] = None
-    event_date: Optional[datetime] = None
-    occurred_start: Optional[datetime] = None
-    occurred_end: Optional[datetime] = None
-    mentioned_at: Optional[datetime] = None
-    document_id: Optional[str] = None
-    chunk_id: Optional[str] = None
+    context: str | None = None
+    event_date: datetime | None = None
+    occurred_start: datetime | None = None
+    occurred_end: datetime | None = None
+    mentioned_at: datetime | None = None
+    document_id: str | None = None
+    chunk_id: str | None = None
    access_count: int = 0
-    embedding: Optional[List[float]] = None
+    embedding: list[float] | None = None

    # Retrieval-specific scores (only one will be set depending on retrieval method)
-    similarity: Optional[float] = None  # Semantic retrieval
-    bm25_score: Optional[float] = None  # BM25 retrieval
-    activation: Optional[float] = None  # Graph retrieval (spreading activation)
-    temporal_score: Optional[float] = None  # Temporal retrieval
-    temporal_proximity: Optional[float] = None  # Temporal retrieval
+    similarity: float | None = None  # Semantic retrieval
+    bm25_score: float | None = None  # BM25 retrieval
+    activation: float | None = None  # Graph retrieval (spreading activation)
+    temporal_score: float | None = None  # Temporal retrieval
+    temporal_proximity: float | None = None  # Temporal retrieval

    @classmethod
-    def from_db_row(cls, row: Dict[str, Any]) -> "RetrievalResult":
+    def from_db_row(cls, row: dict[str, Any]) -> "RetrievalResult":
        """Create from a database row (asyncpg Record converted to dict)."""
        return cls(
            id=str(row["id"]),
@@ -68,13 +87,14 @@ class MergedCandidate:

    Contains the original retrieval data plus RRF metadata.
    """
+
    # Original retrieval data
    retrieval: RetrievalResult

    # RRF metadata
    rrf_score: float
    rrf_rank: int = 0
-    source_ranks: Dict[str, int] = field(default_factory=dict)  # method_name -> rank
+    source_ranks: dict[str, int] = field(default_factory=dict)  # method_name -> rank

    @property
    def id(self) -> str:
@@ -89,6 +109,7 @@ class ScoredResult:

    Contains all retrieval/merge data plus reranking scores and combined score.
    """
+
    # Original merged candidate
    candidate: MergedCandidate

@@ -115,7 +136,7 @@ class ScoredResult:
        """Convenience property to access retrieval data."""
        return self.candidate.retrieval

-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
        """
        Convert to dict for backwards compatibility.

@@ -6,10 +6,12 @@ This provides an abstraction that can be adapted to different execution models:
 - Pub/Sub architectures (future)
 - Message brokers (future)
 """
-from abc import ABC, abstractmethod
-from typing import Any, Dict, Optional, Callable, Awaitable
+
 import asyncio
 import logging
+from abc import ABC, abstractmethod
+from collections.abc import Awaitable, Callable
+from typing import Any

 logger = logging.getLogger(__name__)

@@ -29,10 +31,10 @@ class TaskBackend(ABC):

    def __init__(self):
        """Initialize the task backend."""
-        self._executor: Optional[Callable[[Dict[str, Any]], Awaitable[None]]] = None
+        self._executor: Callable[[dict[str, Any]], Awaitable[None]] | None = None
        self._initialized = False

-    def set_executor(self, executor: Callable[[Dict[str, Any]], Awaitable[None]]):
+    def set_executor(self, executor: Callable[[dict[str, Any]], Awaitable[None]]):
        """
        Set the executor callback for processing tasks.

@@ -49,7 +51,7 @@ class TaskBackend(ABC):
        pass

    @abstractmethod
-    async def submit_task(self, task_dict: Dict[str, Any]):
+    async def submit_task(self, task_dict: dict[str, Any]):
        """
        Submit a task for execution.

@@ -65,7 +67,7 @@ class TaskBackend(ABC):
        """
        pass

-    async def _execute_task(self, task_dict: Dict[str, Any]):
+    async def _execute_task(self, task_dict: dict[str, Any]):
        """
        Execute a task through the registered executor.

@@ -73,19 +75,75 @@ class TaskBackend(ABC):
            task_dict: Task dictionary to execute
        """
        if self._executor is None:
-            task_type = task_dict.get('type', 'unknown')
+            task_type = task_dict.get("type", "unknown")
            logger.warning(f"No executor registered, skipping task {task_type}")
            return

        try:
            await self._executor(task_dict)
        except Exception as e:
-            task_type = task_dict.get('type', 'unknown')
+            task_type = task_dict.get("type", "unknown")
            logger.error(f"Error executing task {task_type}: {e}")
            import traceback
+
            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 NoopTaskBackend(TaskBackend):
+    """
+    No-op task backend that discards all tasks.
+
+    This is useful for tests where background task execution is not needed
+    and would only slow down the test suite.
+    """
+
+    async def initialize(self):
+        """No-op."""
+        self._initialized = True
+        logger.debug("NoopTaskBackend initialized")
+
+    async def submit_task(self, task_dict: dict[str, Any]):
+        """Discard the task (do nothing)."""
+        pass
+
+    async def shutdown(self):
+        """No-op."""
+        self._initialized = False
+        logger.debug("NoopTaskBackend shutdown")
+
+
 class AsyncIOQueueBackend(TaskBackend):
    """
    Task backend implementation using asyncio queues.
@@ -94,11 +152,7 @@ class AsyncIOQueueBackend(TaskBackend):
    and a periodic consumer worker.
    """

-    def __init__(
-        self,
-        batch_size: int = 100,
-        batch_interval: float = 1.0
-    ):
+    def __init__(self, batch_size: int = 10, batch_interval: float = 1.0):
        """
        Initialize AsyncIO queue backend.

@@ -107,11 +161,13 @@ class AsyncIOQueueBackend(TaskBackend):
            batch_interval: Maximum time (seconds) to wait before processing batch
        """
        super().__init__()
-        self._queue: Optional[asyncio.Queue] = None
-        self._worker_task: Optional[asyncio.Task] = None
-        self._shutdown_event: Optional[asyncio.Event] = None
+        self._queue: asyncio.Queue | None = None
+        self._worker_task: asyncio.Task | None = None
+        self._shutdown_event: asyncio.Event | None = None
        self._batch_size = batch_size
        self._batch_interval = batch_interval
+        self._in_flight_count = 0
+        self._in_flight_lock = asyncio.Lock()

    async def initialize(self):
        """Initialize the queue and start the worker."""
@@ -124,7 +180,7 @@ class AsyncIOQueueBackend(TaskBackend):
        self._initialized = True
        logger.info("AsyncIOQueueBackend initialized")

-    async def submit_task(self, task_dict: Dict[str, Any]):
+    async def submit_task(self, task_dict: dict[str, Any]):
        """
        Submit a task by putting it in the queue.

@@ -135,33 +191,31 @@ class AsyncIOQueueBackend(TaskBackend):
            await self.initialize()

        await self._queue.put(task_dict)
-        task_type = task_dict.get('type', 'unknown')
-        task_id = task_dict.get('id')

-    async def wait_for_pending_tasks(self, timeout: float = 5.0):
+    async def wait_for_pending_tasks(self, timeout: float = 120.0):
        """
-        Wait for all pending tasks in the queue to be processed.
+        Wait for all pending tasks in the queue and in-flight tasks to complete.

        This is useful in tests to ensure background tasks complete before assertions.

        Args:
-            timeout: Maximum time to wait in seconds
+            timeout: Maximum time to wait in seconds (default 120s for long-running tasks)
        """
        if not self._initialized or self._queue is None:
            return

-        # Wait for queue to be empty and give worker time to process
+        # Wait for queue to be empty AND no in-flight tasks
        start_time = asyncio.get_event_loop().time()
        while asyncio.get_event_loop().time() - start_time < timeout:
-            if self._queue.empty():
-                # Queue is empty, give worker a bit more time to finish any in-flight task
-                await asyncio.sleep(0.3)
-                # Check again - if still empty, we're done
-                if self._queue.empty():
-                    return
-            else:
-                # Queue not empty, wait a bit
-                await asyncio.sleep(0.1)
+            async with self._in_flight_lock:
+                in_flight = self._in_flight_count
+
+            if self._queue.empty() and in_flight == 0:
+                # Queue is empty and no tasks in flight, we're done
+                return
+
+            # Wait a bit before checking again
+            await asyncio.sleep(0.5)

    async def shutdown(self):
        """Shutdown the worker and drain the queue."""
@@ -184,6 +238,39 @@ class AsyncIOQueueBackend(TaskBackend):
        self._initialized = False
        logger.info("AsyncIOQueueBackend shutdown complete")

+    async def _execute_task_with_tracking(self, task_dict: dict[str, Any]):
+        """Execute a task and track its in-flight status."""
+        async with self._in_flight_lock:
+            self._in_flight_count += 1
+        try:
+            await self._execute_task(task_dict)
+        finally:
+            async with self._in_flight_lock:
+                self._in_flight_count -= 1
+
+    async def _execute_task_no_tracking(self, task_dict: dict[str, Any]):
+        """Execute a task without in-flight tracking (tracking done at batch level)."""
+        await self._execute_task(task_dict)
+
+    def _get_queue_stats(self) -> tuple[int, dict[str, int]]:
+        """Get current queue size and bank_id distribution."""
+        queue_size = self._queue.qsize() if self._queue else 0
+        bank_distribution: dict[str, int] = {}
+
+        if queue_size > 0 and self._queue:
+            # Peek at queue items without removing them
+            # Note: This is a snapshot and may not be perfectly accurate due to concurrency
+            try:
+                # Access internal deque for logging purposes only
+                items = list(self._queue._queue)  # type: ignore[attr-defined]
+                for item in items:
+                    bank_id = item.get("bank_id", "unknown")
+                    bank_distribution[bank_id] = bank_distribution.get(bank_id, 0) + 1
+            except Exception:
+                pass  # Queue access failed, return empty distribution
+
+        return queue_size, bank_distribution
+
    async def _worker(self):
        """
        Background worker that processes tasks in batches.
@@ -200,22 +287,53 @@ class AsyncIOQueueBackend(TaskBackend):
                while len(tasks) < self._batch_size and asyncio.get_event_loop().time() < deadline:
                    try:
                        remaining_time = max(0.1, deadline - asyncio.get_event_loop().time())
-                        task_dict = await asyncio.wait_for(
-                            self._queue.get(),
-                            timeout=remaining_time
-                        )
+                        task_dict = await asyncio.wait_for(self._queue.get(), timeout=remaining_time)
+                        # Track task as in-flight immediately when picked up from queue
+                        # This prevents wait_for_pending_tasks from returning too early
+                        async with self._in_flight_lock:
+                            self._in_flight_count += 1
                        tasks.append(task_dict)
-                    except asyncio.TimeoutError:
+                    except TimeoutError:
                        break

                # Process batch
                if tasks:
-                    # Execute tasks concurrently
+                    # Log batch start with queue stats
+                    queue_size, bank_distribution = self._get_queue_stats()
+
+                    # Summarize batch by task type and bank
+                    batch_summary: dict[str, dict[str, int]] = {}
+                    for task_dict in tasks:
+                        task_type = task_dict.get("type", "unknown")
+                        bank_id = task_dict.get("bank_id", "unknown")
+                        if task_type not in batch_summary:
+                            batch_summary[task_type] = {}
+                        batch_summary[task_type][bank_id] = batch_summary[task_type].get(bank_id, 0) + 1
+
+                    # Build log message
+                    batch_parts = []
+                    for task_type, banks in sorted(batch_summary.items()):
+                        bank_str = ", ".join(f"{b}:{c}" for b, c in sorted(banks.items()))
+                        batch_parts.append(f"{task_type}[{bank_str}]")
+                    batch_str = ", ".join(batch_parts)
+
+                    if queue_size > 0:
+                        pending_str = ", ".join(f"{k}:{v}" for k, v in sorted(bank_distribution.items()))
+                        logger.info(
+                            f"Processing {len(tasks)} tasks: {batch_str} (pending={queue_size} [{pending_str}])"
+                        )
+                    else:
+                        logger.info(f"Processing {len(tasks)} tasks: {batch_str}")
+
+                    # Execute tasks concurrently (in_flight already tracked when picked up)
                    await asyncio.gather(
-                        *[self._execute_task(task_dict) for task_dict in tasks],
-                        return_exceptions=True
+                        *[self._execute_task_no_tracking(task_dict) for task_dict in tasks], return_exceptions=True
                    )

+                    # Decrement in_flight count after all tasks complete
+                    async with self._in_flight_lock:
+                        self._in_flight_count -= len(tasks)
+
            except asyncio.CancelledError:
                break
            except Exception as e:
@@ -1,9 +1,10 @@
 """
 Utility functions for memory system.
 """
+
 import logging
 from datetime import datetime
-from typing import List, Dict, TYPE_CHECKING
+from typing import TYPE_CHECKING

 if TYPE_CHECKING:
    from .llm_wrapper import LLMConfig
@@ -12,7 +13,14 @@ if TYPE_CHECKING:
 from .retain.fact_extraction import extract_facts_from_text


-async def extract_facts(text: str, event_date: datetime, context: str = "", llm_config: 'LLMConfig' = None, agent_name: str = None, extract_opinions: bool = False) -> tuple[List['Fact'], List[tuple[str, int]]]:
+async def extract_facts(
+    text: str,
+    event_date: datetime,
+    context: str = "",
+    llm_config: "LLMConfig" = None,
+    agent_name: str = None,
+    extract_opinions: bool = False,
+) -> tuple[list["Fact"], list[tuple[str, int]]]:
    """
    Extract semantic facts from text using LLM.

@@ -41,16 +49,25 @@ async def extract_facts(text: str, event_date: datetime, context: str = "", llm_
    if not text or not text.strip():
        return [], []

-    facts, chunks = await extract_facts_from_text(text, event_date, context=context, llm_config=llm_config, agent_name=agent_name, extract_opinions=extract_opinions)
+    facts, chunks, _ = await extract_facts_from_text(
+        text,
+        event_date,
+        context=context,
+        llm_config=llm_config,
+        agent_name=agent_name,
+        extract_opinions=extract_opinions,
+    )

    if not facts:
-        logging.warning(f"LLM extracted 0 facts from text of length {len(text)}. This may indicate the text contains no meaningful information, or the LLM failed to extract facts. Full text: {text}")
+        logging.warning(
+            f"LLM extracted 0 facts from text of length {len(text)}. This may indicate the text contains no meaningful information, or the LLM failed to extract facts. Full text: {text}"
+        )
        return [], chunks

    return facts, chunks


-def cosine_similarity(vec1: List[float], vec2: List[float]) -> float:
+def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
    """
    Calculate cosine similarity between two vectors.

@@ -100,6 +117,7 @@ def calculate_recency_weight(days_since: float, half_life_days: float = 365.0) -
        Weight between 0 and 1
    """
    import math
+
    # Logarithmic decay: 1 / (1 + log(1 + days_since/half_life))
    # This decays much slower than exponential, giving better long-term differentiation
    normalized_age = days_since / half_life_days
@@ -121,6 +139,7 @@ def calculate_frequency_weight(access_count: int, max_boost: float = 2.0) -> flo
        Weight between 1.0 and max_boost
    """
    import math
+
    if access_count <= 0:
        return 1.0

@@ -158,11 +177,7 @@ def calculate_temporal_anchor(occurred_start: datetime, occurred_end: datetime)
    return midpoint


-def calculate_temporal_proximity(
-    anchor_a: datetime,
-    anchor_b: datetime,
-    half_life_days: float = 30.0
-) -> float:
+def calculate_temporal_proximity(anchor_a: datetime, anchor_b: datetime, half_life_days: float = 30.0) -> float:
    """
    Calculate temporal proximity between two temporal anchors.

@@ -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 +1,126 @@
+"""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 ensure_embedding_dimension, run_migrations
+
+        # Prefer getting URL from memory engine (handles pg0 case where URL is set after init)
+        db_url = self._database_url
+        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)
+
+        # Ensure embedding column dimension matches the model's dimension
+        # This is needed because migrations create columns with default dimension
+        if self._memory_engine is not None:
+            embeddings = getattr(self._memory_engine, "embeddings", None)
+            if embeddings is not None:
+                dimension = getattr(embeddings, "dimension", None)
+                if dimension is not None:
+                    ensure_embedding_dimension(db_url, dimension, 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.
+        """
+        ...
@@ -4,8 +4,12 @@ Command-line interface for Hindsight API.
 Run the server with:
    hindsight-api

+Run as background daemon:
+    hindsight-api --daemon
+
 Stop with Ctrl+C.
 """
+
 import argparse
 import asyncio
 import atexit
@@ -13,17 +17,21 @@ import os
 import signal
 import sys
 import warnings
-from typing import Optional

 import uvicorn

 from . import MemoryEngine
 from .api import create_app
-from .config import get_config, HindsightConfig
-
 from .banner import print_banner
-print()
-print_banner()
+from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, get_config
+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")
@@ -33,7 +41,7 @@ warnings.filterwarnings("ignore", message="websockets.server.WebSocketServerProt
 os.environ["TOKENIZERS_PARALLELISM"] = "false"

 # Global reference for cleanup
-_memory: Optional[MemoryEngine] = None
+_memory: MemoryEngine | None = None


 def _cleanup():
@@ -70,62 +78,93 @@ def main():

    # Server options
    parser.add_argument(
-        "--host", default=config.host,
-        help=f"Host to bind to (default: {config.host}, env: HINDSIGHT_API_HOST)"
+        "--host", default=config.host, help=f"Host to bind to (default: {config.host}, env: HINDSIGHT_API_HOST)"
    )
    parser.add_argument(
-        "--port", type=int, default=config.port,
-        help=f"Port to bind to (default: {config.port}, env: HINDSIGHT_API_PORT)"
+        "--port",
+        type=int,
+        default=config.port,
+        help=f"Port to bind to (default: {config.port}, env: HINDSIGHT_API_PORT)",
    )
    parser.add_argument(
-        "--log-level", default=config.log_level,
+        "--log-level",
+        default=config.log_level,
        choices=["critical", "error", "warning", "info", "debug", "trace"],
-        help=f"Log level (default: {config.log_level}, env: HINDSIGHT_API_LOG_LEVEL)"
+        help=f"Log level (default: {config.log_level}, env: HINDSIGHT_API_LOG_LEVEL)",
    )

    # Development options
+    parser.add_argument("--reload", action="store_true", help="Enable auto-reload on code changes (development only)")
    parser.add_argument(
-        "--reload", action="store_true",
-        help="Enable auto-reload on code changes (development only)"
-    )
-    parser.add_argument(
-        "--workers", type=int, default=1,
-        help="Number of worker processes (default: 1)"
+        "--workers",
+        type=int,
+        default=int(os.getenv(ENV_WORKERS, str(DEFAULT_WORKERS))),
+        help=f"Number of worker processes (env: {ENV_WORKERS}, default: {DEFAULT_WORKERS})",
    )

    # Access log options
-    parser.add_argument(
-        "--access-log", action="store_true",
-        help="Enable access log"
-    )
-    parser.add_argument(
-        "--no-access-log", dest="access_log", action="store_false",
-        help="Disable access log (default)"
-    )
+    parser.add_argument("--access-log", action="store_true", help="Enable access log")
+    parser.add_argument("--no-access-log", dest="access_log", action="store_false", help="Disable access log (default)")
    parser.set_defaults(access_log=False)

    # Proxy options
    parser.add_argument(
-        "--proxy-headers", action="store_true",
-        help="Enable X-Forwarded-Proto, X-Forwarded-For headers"
+        "--proxy-headers", action="store_true", help="Enable X-Forwarded-Proto, X-Forwarded-For headers"
    )
    parser.add_argument(
-        "--forwarded-allow-ips", default=None,
-        help="Comma separated list of IPs to trust with proxy headers"
+        "--forwarded-allow-ips", default=None, help="Comma separated list of IPs to trust with proxy headers"
    )

    # SSL options
+    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(
-        "--ssl-keyfile", default=None,
-        help="SSL key file"
+        "--daemon",
+        action="store_true",
+        help=f"Run as background daemon (uses port {DEFAULT_DAEMON_PORT}, auto-exits after idle)",
    )
    parser.add_argument(
-        "--ssl-certfile", default=None,
-        help="SSL certificate file"
+        "--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:
@@ -135,26 +174,89 @@ def main():
            llm_api_key=config.llm_api_key,
            llm_model=config.llm_model,
            llm_base_url=config.llm_base_url,
+            llm_max_concurrent=config.llm_max_concurrent,
+            llm_timeout=config.llm_timeout,
+            retain_llm_provider=config.retain_llm_provider,
+            retain_llm_api_key=config.retain_llm_api_key,
+            retain_llm_model=config.retain_llm_model,
+            retain_llm_base_url=config.retain_llm_base_url,
+            reflect_llm_provider=config.reflect_llm_provider,
+            reflect_llm_api_key=config.reflect_llm_api_key,
+            reflect_llm_model=config.reflect_llm_model,
+            reflect_llm_base_url=config.reflect_llm_base_url,
            embeddings_provider=config.embeddings_provider,
            embeddings_local_model=config.embeddings_local_model,
            embeddings_tei_url=config.embeddings_tei_url,
            reranker_provider=config.reranker_provider,
            reranker_local_model=config.reranker_local_model,
            reranker_tei_url=config.reranker_tei_url,
+            reranker_tei_batch_size=config.reranker_tei_batch_size,
+            reranker_tei_max_concurrent=config.reranker_tei_max_concurrent,
+            reranker_max_candidates=config.reranker_max_candidates,
            host=args.host,
            port=args.port,
            log_level=args.log_level,
            mcp_enabled=config.mcp_enabled,
+            graph_retriever=config.graph_retriever,
+            mpfp_top_k_neighbors=config.mpfp_top_k_neighbors,
+            recall_max_concurrent=config.recall_max_concurrent,
+            recall_connection_budget=config.recall_connection_budget,
+            observation_min_facts=config.observation_min_facts,
+            observation_top_entities=config.observation_top_entities,
+            retain_max_completion_tokens=config.retain_max_completion_tokens,
+            retain_chunk_size=config.retain_chunk_size,
+            retain_extract_causal_links=config.retain_extract_causal_links,
+            retain_extraction_mode=config.retain_extraction_mode,
+            retain_observations_async=config.retain_observations_async,
+            skip_llm_verification=config.skip_llm_verification,
+            lazy_reranker=config.lazy_reranker,
+            run_migrations_on_startup=config.run_migrations_on_startup,
+            db_pool_min_size=config.db_pool_min_size,
+            db_pool_max_size=config.db_pool_max_size,
+            db_command_timeout=config.db_command_timeout,
+            db_acquire_timeout=config.db_acquire_timeout,
+            task_backend=config.task_backend,
+            task_backend_memory_batch_size=config.task_backend_memory_batch_size,
+            task_backend_memory_batch_interval=config.task_backend_memory_batch_interval,
        )
    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,
+        run_migrations=config.run_migrations_on_startup,
+    )
+
+    # Set extension context on tenant extension (needed for schema provisioning)
+    if tenant_extension:
+        extension_context = DefaultExtensionContext(
+            database_url=config.database_url,
+            memory_engine=_memory,
+        )
+        tenant_extension.set_context(extension_context)
+        logging.info("Extension context set on tenant extension")

    # Create FastAPI app
    app = create_app(
@@ -165,15 +267,34 @@ 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
+    # When using workers or reload, we must use import string so each worker can import the app
+    use_import_string = args.workers > 1 or args.reload
+    # Check for uvloop availability
+    try:
+        import uvloop  # noqa: F401
+
+        loop_impl = "uvloop"
+        print("uvloop available, will use for event loop")
+    except ImportError:
+        loop_impl = "asyncio"
+        print("uvloop not installed, using default asyncio event loop")
+
    uvicorn_config = {
-        "app": app,
+        "app": "hindsight_api.server:app" if use_import_string else app,
        "host": args.host,
        "port": args.port,
        "log_level": args.log_level,
        "access_log": args.access_log,
        "proxy_headers": args.proxy_headers,
        "ws": "wsproto",  # Use wsproto instead of websockets to avoid deprecation warnings
+        "loop": loop_impl,  # Explicitly set event loop implementation
    }

    # Add optional parameters if provided
@@ -188,21 +309,40 @@ def main():
    if args.ssl_certfile:
        uvicorn_config["ssl_certfile"] = args.ssl_certfile

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

+        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,
+        )

-    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,
-    )
+    # 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

-    uvicorn.run(**uvicorn_config)
+        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__":
@@ -0,0 +1,199 @@
+"""
+Local MCP server for use with Claude Code (stdio transport).
+
+This runs a fully local Hindsight instance with embedded PostgreSQL (pg0).
+No external database or server required.
+
+Run with:
+    hindsight-local-mcp
+
+Or with uvx:
+    uvx hindsight-api@latest hindsight-local-mcp
+
+Configure in Claude Code's MCP settings:
+    {
+        "mcpServers": {
+            "hindsight": {
+                "command": "uvx",
+                "args": ["hindsight-api@latest", "hindsight-local-mcp"],
+                "env": {
+                    "HINDSIGHT_API_LLM_API_KEY": "your-openai-key"
+                }
+            }
+        }
+    }
+
+Environment variables:
+    HINDSIGHT_API_LLM_API_KEY: Required. API key for LLM provider.
+    HINDSIGHT_API_LLM_PROVIDER: Optional. LLM provider (default: "openai").
+    HINDSIGHT_API_LLM_MODEL: Optional. LLM model (default: "gpt-4o-mini").
+    HINDSIGHT_API_MCP_LOCAL_BANK_ID: Optional. Memory bank ID (default: "mcp").
+    HINDSIGHT_API_LOG_LEVEL: Optional. Log level (default: "warning").
+    HINDSIGHT_API_MCP_INSTRUCTIONS: Optional. Additional instructions appended to both retain and recall tools.
+
+Example custom instructions (these are ADDED to the default behavior):
+    To also store assistant actions:
+        HINDSIGHT_API_MCP_INSTRUCTIONS="Also store every action you take, including tool calls, code written, and decisions made."
+
+    To also store conversation summaries:
+        HINDSIGHT_API_MCP_INSTRUCTIONS="Also store summaries of important conversations and their outcomes."
+"""
+
+import logging
+import os
+import sys
+
+from mcp.server.fastmcp import FastMCP
+from mcp.types import Icon
+
+from hindsight_api.config import (
+    DEFAULT_MCP_LOCAL_BANK_ID,
+    DEFAULT_MCP_RECALL_DESCRIPTION,
+    DEFAULT_MCP_RETAIN_DESCRIPTION,
+    ENV_MCP_INSTRUCTIONS,
+    ENV_MCP_LOCAL_BANK_ID,
+)
+
+# Configure logging - default to warning to avoid polluting stderr during MCP init
+# MCP clients interpret stderr output as errors, so we suppress INFO logs by default
+_log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "warning").lower()
+_log_level_map = {
+    "critical": logging.CRITICAL,
+    "error": logging.ERROR,
+    "warning": logging.WARNING,
+    "info": logging.INFO,
+    "debug": logging.DEBUG,
+}
+logging.basicConfig(
+    level=_log_level_map.get(_log_level_str, logging.WARNING),
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    stream=sys.stderr,  # MCP uses stdout for protocol, logs go to stderr
+)
+logger = logging.getLogger(__name__)
+
+
+def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
+    """
+    Create a stdio MCP server with retain/recall tools.
+
+    Args:
+        bank_id: The memory bank ID to use for all operations.
+        memory: Optional MemoryEngine instance. If not provided, creates one with pg0.
+
+    Returns:
+        Configured FastMCP server instance.
+    """
+    # Import here to avoid slow startup if just checking --help
+    from hindsight_api import MemoryEngine
+    from hindsight_api.engine.memory_engine import Budget
+    from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
+    from hindsight_api.models import RequestContext
+
+    # Create memory engine with pg0 embedded database if not provided
+    if memory is None:
+        memory = MemoryEngine(db_url="pg0://hindsight-mcp")
+
+    # Get custom instructions from environment variable (appended to both tools)
+    extra_instructions = os.environ.get(ENV_MCP_INSTRUCTIONS, "")
+
+    retain_description = DEFAULT_MCP_RETAIN_DESCRIPTION
+    recall_description = DEFAULT_MCP_RECALL_DESCRIPTION
+
+    if extra_instructions:
+        retain_description = f"{DEFAULT_MCP_RETAIN_DESCRIPTION}\n\nAdditional instructions: {extra_instructions}"
+        recall_description = f"{DEFAULT_MCP_RECALL_DESCRIPTION}\n\nAdditional instructions: {extra_instructions}"
+
+    mcp = FastMCP("hindsight")
+
+    @mcp.tool(description=retain_description)
+    async def retain(content: str, context: str = "general") -> dict:
+        """
+        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'
+        """
+        import asyncio
+
+        async def _retain():
+            try:
+                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)
+
+        # Fire and forget - don't block on memory storage
+        asyncio.create_task(_retain())
+        return {"status": "accepted", "message": "Memory storage initiated"}
+
+    @mcp.tool(description=recall_description)
+    async def recall(query: str, max_tokens: int = 4096, budget: str = "low") -> dict:
+        """
+        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)
+            budget: Search budget level - "low", "mid", or "high" (default: "low")
+        """
+        try:
+            # 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)
+
+            search_result = await memory.recall_async(
+                bank_id=bank_id,
+                query=query,
+                fact_type=list(VALID_RECALL_FACT_TYPES),
+                budget=budget_enum,
+                max_tokens=max_tokens,
+                request_context=RequestContext(),
+            )
+
+            return search_result.model_dump()
+        except Exception as e:
+            logger.error(f"Error searching: {e}", exc_info=True)
+            return {"error": str(e), "results": []}
+
+    return mcp
+
+
+async def _initialize_and_run(bank_id: str):
+    """Initialize memory and run the MCP server."""
+    from hindsight_api import MemoryEngine
+
+    # Create and initialize memory engine with pg0 embedded database
+    # Note: We avoid printing to stderr during init as MCP clients show it as "errors"
+    memory = MemoryEngine(db_url="pg0://hindsight-mcp")
+    await memory.initialize()
+
+    # Create and run the server
+    mcp = create_local_mcp_server(bank_id, memory=memory)
+    await mcp.run_stdio_async()
+
+
+def main():
+    """Main entry point for the stdio MCP server."""
+    import asyncio
+
+    from hindsight_api.config import ENV_LLM_API_KEY, get_config
+
+    # Check for required environment variables
+    config = get_config()
+    if not config.llm_api_key:
+        print(f"Error: {ENV_LLM_API_KEY} environment variable is required", file=sys.stderr)
+        print("Set it in your MCP configuration or shell environment", file=sys.stderr)
+        sys.exit(1)
+
+    # Get bank ID from environment, default to "mcp"
+    bank_id = os.environ.get(ENV_MCP_LOCAL_BANK_ID, DEFAULT_MCP_LOCAL_BANK_ID)
+
+    # Note: We don't print to stderr as MCP clients display it as "error output"
+    # Use HINDSIGHT_API_LOG_LEVEL=debug for verbose startup logging
+
+    # Run the async initialization and server
+    asyncio.run(_initialize_and_run(bank_id))
+
+
+if __name__ == "__main__":
+    main()
@@ -5,17 +5,76 @@ This module provides metrics for:
 - Operation latency (retain, recall, reflect) with percentiles
 - Token usage (input/output) per operation
 - Per-bank granularity via labels
+- LLM call latency and token usage with scope dimension
+- HTTP request metrics (latency, count by endpoint/method/status)
+- Process metrics (CPU, memory, file descriptors, threads)
+- Database connection pool metrics
 """
+
 import logging
-from typing import Dict, Any, Optional
-from contextlib import contextmanager
+import os
+import resource
+import threading
 import time
+from contextlib import contextmanager
+from typing import TYPE_CHECKING, Callable

 from opentelemetry import metrics
-from opentelemetry.sdk.metrics import MeterProvider
-from opentelemetry.sdk.resources import Resource
 from opentelemetry.exporter.prometheus import PrometheusMetricReader
-from prometheus_client import REGISTRY
+from opentelemetry.sdk.metrics import MeterProvider
+from opentelemetry.sdk.metrics.view import ExplicitBucketHistogramAggregation, View
+from opentelemetry.sdk.resources import Resource
+
+if TYPE_CHECKING:
+    import asyncpg
+
+# Custom bucket boundaries for operation duration (in seconds)
+# Fine granularity in 0-30s range where most operations complete
+DURATION_BUCKETS = (0.1, 0.25, 0.5, 0.75, 1.0, 2.0, 3.0, 5.0, 7.5, 10.0, 15.0, 20.0, 30.0, 60.0, 120.0)
+
+# LLM duration buckets (finer granularity for faster LLM calls)
+LLM_DURATION_BUCKETS = (0.1, 0.25, 0.5, 1.0, 2.0, 3.0, 5.0, 10.0, 15.0, 30.0, 60.0, 120.0)
+
+# HTTP request duration buckets (millisecond-level for fast endpoints)
+HTTP_DURATION_BUCKETS = (0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0, 30.0)
+
+
+def get_token_bucket(token_count: int) -> str:
+    """
+    Convert a token count to a bucket label for use as a dimension.
+
+    This allows analyzing token usage patterns without high-cardinality issues.
+
+    Buckets:
+    - "0-100": Very small requests/responses
+    - "100-500": Small requests/responses
+    - "500-1k": Medium requests/responses
+    - "1k-5k": Large requests/responses
+    - "5k-10k": Very large requests/responses
+    - "10k-50k": Huge requests/responses
+    - "50k+": Extremely large requests/responses
+
+    Args:
+        token_count: Number of tokens
+
+    Returns:
+        Bucket label string
+    """
+    if token_count < 100:
+        return "0-100"
+    elif token_count < 500:
+        return "100-500"
+    elif token_count < 1000:
+        return "500-1k"
+    elif token_count < 5000:
+        return "1k-5k"
+    elif token_count < 10000:
+        return "5k-10k"
+    elif token_count < 50000:
+        return "10k-50k"
+    else:
+        return "50k+"
+

 logger = logging.getLogger(__name__)

@@ -39,18 +98,39 @@ def initialize_metrics(service_name: str = "hindsight-api", service_version: str
    global _meter

    # Create resource with service information
-    resource = Resource.create({
-        "service.name": service_name,
-        "service.version": service_version,
-    })
+    resource = Resource.create(
+        {
+            "service.name": service_name,
+            "service.version": service_version,
+        }
+    )

    # Create Prometheus metric reader
    prometheus_reader = PrometheusMetricReader()

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

    # Set the global meter provider
@@ -73,27 +153,84 @@ class MetricsCollectorBase:
    """Base class for metrics collectors."""

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

-    def record_tokens(self, operation: str, bank_id: str, input_tokens: int = 0, output_tokens: int = 0, budget: Optional[str] = None, max_tokens: Optional[int] = None):
-        """Record token usage for an operation."""
+    def record_llm_call(
+        self,
+        provider: str,
+        model: str,
+        scope: str,
+        duration: float,
+        input_tokens: int = 0,
+        output_tokens: int = 0,
+        success: bool = True,
+    ):
+        """
+        Record metrics for an LLM call.
+
+        Args:
+            provider: LLM provider name (openai, anthropic, gemini, groq, ollama, lmstudio)
+            model: Model name
+            scope: Scope identifier (e.g., "memory", "reflect", "entity_observation")
+            duration: Call duration in seconds
+            input_tokens: Number of input/prompt tokens
+            output_tokens: Number of output/completion tokens
+            success: Whether the call was successful
+        """
        raise NotImplementedError

+    @contextmanager
+    def record_http_request(self, method: str, endpoint: str, status_code_getter: Callable[[], int]):
+        """Context manager to record HTTP request metrics."""
+        raise NotImplementedError
+
+    def set_db_pool(self, pool: "asyncpg.Pool"):
+        """Set the database pool for metrics collection."""
+        pass
+

 class NoOpMetricsCollector(MetricsCollectorBase):
    """No-op metrics collector that does nothing. Used when metrics are disabled."""

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

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

+    @contextmanager
+    def record_http_request(self, method: str, endpoint: str, status_code_getter: Callable[[], int]):
+        """No-op HTTP request recording."""
+        yield
+

 class MetricsCollector(MetricsCollectorBase):
    """
@@ -108,44 +245,76 @@ class MetricsCollector(MetricsCollectorBase):
        # Operation latency histogram (in seconds)
        # Records duration of retain, recall, reflect operations
        self.operation_duration = self.meter.create_histogram(
-            name="hindsight.operation.duration",
-            description="Duration of Hindsight operations in seconds",
-            unit="s"
-        )
-
-        # Token usage counters
-        self.tokens_input = self.meter.create_counter(
-            name="hindsight.tokens.input",
-            description="Number of input tokens consumed",
-            unit="tokens"
-        )
-
-        self.tokens_output = self.meter.create_counter(
-            name="hindsight.tokens.output",
-            description="Number of output tokens generated",
-            unit="tokens"
+            name="hindsight.operation.duration", description="Duration of Hindsight operations in seconds", unit="s"
        )

        # Operation counter (success/failure)
        self.operation_total = self.meter.create_counter(
-            name="hindsight.operation.total",
-            description="Total number of operations executed",
-            unit="operations"
+            name="hindsight.operation.total", description="Total number of operations executed", unit="operations"
        )

+        # LLM call latency histogram (in seconds)
+        # Records duration of LLM API calls with provider, model, and scope dimensions
+        self.llm_duration = self.meter.create_histogram(
+            name="hindsight.llm.duration", description="Duration of LLM API calls in seconds", unit="s"
+        )
+
+        # LLM token usage counters with bucket labels
+        self.llm_tokens_input = self.meter.create_counter(
+            name="hindsight.llm.tokens.input", description="Number of input tokens for LLM calls", unit="tokens"
+        )
+
+        self.llm_tokens_output = self.meter.create_counter(
+            name="hindsight.llm.tokens.output", description="Number of output tokens from LLM calls", unit="tokens"
+        )
+
+        # LLM call counter (success/failure)
+        self.llm_calls_total = self.meter.create_counter(
+            name="hindsight.llm.calls.total", description="Total number of LLM API calls", unit="calls"
+        )
+
+        # HTTP request metrics
+        self.http_request_duration = self.meter.create_histogram(
+            name="hindsight.http.duration", description="Duration of HTTP requests in seconds", unit="s"
+        )
+
+        self.http_requests_total = self.meter.create_counter(
+            name="hindsight.http.requests.total", description="Total number of HTTP requests", unit="requests"
+        )
+
+        self.http_requests_in_progress = self.meter.create_up_down_counter(
+            name="hindsight.http.requests.in_progress",
+            description="Number of HTTP requests in progress",
+            unit="requests",
+        )
+
+        # Process metrics (observable gauges - collected on scrape)
+        self._setup_process_metrics()
+
+        # DB pool metrics holder (set via set_db_pool)
+        self._db_pool: "asyncpg.Pool | None" = None
+
    @contextmanager
-    def record_operation(self, operation: str, bank_id: str, budget: Optional[str] = None, max_tokens: Optional[int] = None):
+    def record_operation(
+        self,
+        operation: str,
+        bank_id: str,
+        source: str = "api",
+        budget: str | None = None,
+        max_tokens: int | None = None,
+    ):
        """
        Context manager to record operation duration and status.

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

        Args:
-            operation: Operation name (retain, recall, reflect)
+            operation: Operation name (retain, recall, reflect, entity_observation)
            bank_id: Memory bank ID
+            source: Source of the operation (api, reflect, internal)
            budget: Optional budget level (low, mid, high)
            max_tokens: Optional max tokens for the operation
        """
@@ -153,6 +322,7 @@ class MetricsCollector(MetricsCollectorBase):
        attributes = {
            "operation": operation,
            "bank_id": bank_id,
+            "source": source,
        }
        if budget:
            attributes["budget"] = budget
@@ -175,32 +345,246 @@ class MetricsCollector(MetricsCollectorBase):
            # Record operation count
            self.operation_total.add(1, attributes)

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

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

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

        if output_tokens > 0:
-            self.tokens_output.add(output_tokens, attributes)
+            output_attributes = {
+                **base_attributes,
+                "token_bucket": get_token_bucket(output_tokens),
+            }
+            self.llm_tokens_output.add(output_tokens, output_attributes)
+
+    @contextmanager
+    def record_http_request(self, method: str, endpoint: str, status_code_getter: Callable[[], int]):
+        """
+        Context manager to record HTTP request metrics.
+
+        Usage:
+            status_code = [200]  # Use list for mutability
+            with metrics.record_http_request("GET", "/api/banks", lambda: status_code[0]):
+                # ... handle request
+                status_code[0] = response.status_code
+
+        Args:
+            method: HTTP method (GET, POST, etc.)
+            endpoint: Request endpoint path
+            status_code_getter: Callable that returns the status code after request completes
+        """
+        start_time = time.time()
+        base_attributes = {"method": method, "endpoint": endpoint}
+
+        # Track in-progress
+        self.http_requests_in_progress.add(1, base_attributes)
+
+        try:
+            yield
+        finally:
+            duration = time.time() - start_time
+            status_code = status_code_getter()
+            status_class = f"{status_code // 100}xx"
+
+            attributes = {
+                **base_attributes,
+                "status_code": str(status_code),
+                "status_class": status_class,
+            }
+
+            # Record duration and count
+            self.http_request_duration.record(duration, attributes)
+            self.http_requests_total.add(1, attributes)
+
+            # Decrement in-progress
+            self.http_requests_in_progress.add(-1, base_attributes)
+
+    def _setup_process_metrics(self):
+        """Set up observable gauges for process metrics."""
+
+        def get_cpu_times(_options):
+            """Get process CPU times."""
+            try:
+                rusage = resource.getrusage(resource.RUSAGE_SELF)
+                yield metrics.Observation(rusage.ru_utime, {"type": "user"})
+                yield metrics.Observation(rusage.ru_stime, {"type": "system"})
+            except Exception:
+                pass
+
+        def get_memory_usage(_options):
+            """Get process memory usage in bytes."""
+            try:
+                rusage = resource.getrusage(resource.RUSAGE_SELF)
+                # ru_maxrss is in kilobytes on Linux, bytes on macOS
+                max_rss = rusage.ru_maxrss
+                if os.uname().sysname == "Linux":
+                    max_rss *= 1024  # Convert KB to bytes
+                yield metrics.Observation(max_rss, {"type": "rss_max"})
+            except Exception:
+                pass
+
+        def get_open_file_descriptors(_options):
+            """Get number of open file descriptors."""
+            try:
+                # Try to count open FDs by checking /proc on Linux
+                if os.path.exists("/proc/self/fd"):
+                    count = len(os.listdir("/proc/self/fd"))
+                    yield metrics.Observation(count)
+                else:
+                    # Fallback: use resource limits
+                    soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
+                    yield metrics.Observation(soft, {"limit": "soft"})
+            except Exception:
+                pass
+
+        def get_thread_count(_options):
+            """Get number of active threads."""
+            try:
+                yield metrics.Observation(threading.active_count())
+            except Exception:
+                pass
+
+        # Create observable gauges
+        self.meter.create_observable_gauge(
+            name="hindsight.process.cpu.seconds",
+            callbacks=[get_cpu_times],
+            description="Process CPU time in seconds",
+            unit="s",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.process.memory.bytes",
+            callbacks=[get_memory_usage],
+            description="Process memory usage in bytes",
+            unit="By",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.process.open_fds",
+            callbacks=[get_open_file_descriptors],
+            description="Number of open file descriptors",
+            unit="{fds}",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.process.threads",
+            callbacks=[get_thread_count],
+            description="Number of active threads",
+            unit="{threads}",
+        )
+
+    def set_db_pool(self, pool: "asyncpg.Pool"):
+        """
+        Set the database pool for metrics collection.
+
+        Args:
+            pool: asyncpg connection pool instance
+        """
+        self._db_pool = pool
+        self._setup_db_pool_metrics()
+
+    def _setup_db_pool_metrics(self):
+        """Set up observable gauges for database pool metrics."""
+
+        def get_pool_size(_options):
+            """Get current pool size."""
+            if self._db_pool is not None:
+                try:
+                    yield metrics.Observation(self._db_pool.get_size())
+                except Exception:
+                    pass
+
+        def get_pool_free_size(_options):
+            """Get number of free connections in pool."""
+            if self._db_pool is not None:
+                try:
+                    yield metrics.Observation(self._db_pool.get_idle_size())
+                except Exception:
+                    pass
+
+        def get_pool_min_size(_options):
+            """Get pool minimum size."""
+            if self._db_pool is not None:
+                try:
+                    yield metrics.Observation(self._db_pool.get_min_size())
+                except Exception:
+                    pass
+
+        def get_pool_max_size(_options):
+            """Get pool maximum size."""
+            if self._db_pool is not None:
+                try:
+                    yield metrics.Observation(self._db_pool.get_max_size())
+                except Exception:
+                    pass
+
+        # Create observable gauges for pool metrics
+        self.meter.create_observable_gauge(
+            name="hindsight.db.pool.size",
+            callbacks=[get_pool_size],
+            description="Current number of connections in the pool",
+            unit="{connections}",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.db.pool.idle",
+            callbacks=[get_pool_free_size],
+            description="Number of idle connections in the pool",
+            unit="{connections}",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.db.pool.min",
+            callbacks=[get_pool_min_size],
+            description="Minimum pool size",
+            unit="{connections}",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.db.pool.max",
+            callbacks=[get_pool_max_size],
+            description="Maximum pool size",
+            unit="{connections}",
+        )


 # Global metrics collector instance (defaults to no-op)
@@ -6,19 +6,23 @@ 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
-import shutil
 from pathlib import Path
-from typing import Optional

 from alembic import command
 from alembic.config import Config
+from alembic.script.revision import ResolutionError
 from sqlalchemy import create_engine, text

 logger = logging.getLogger(__name__)
@@ -27,11 +31,29 @@ 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(f"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}")

@@ -51,13 +73,33 @@ 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)
-    command.upgrade(alembic_cfg, "head")
+    # 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)

-    logger.info("Database migrations completed successfully")
+    # Run migrations
+    try:
+        command.upgrade(alembic_cfg, "head")
+    except ResolutionError as e:
+        # This happens during rolling deployments when a newer version of the code
+        # has already run migrations, and this older replica doesn't have the new
+        # migration files. The database is already at a newer revision than we know.
+        # This is safe to ignore - the newer code has already applied its migrations.
+        logger.warning(
+            f"Database is at a newer migration revision than this code version knows about. "
+            f"This is expected during rolling deployments. Skipping migrations. Error: {e}"
+        )
+        return
+
+    logger.info(f"Database migrations completed successfully for schema '{schema_name}'")


-def run_migrations(database_url: str, script_location: Optional[str] = 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.

@@ -66,19 +108,28 @@ def run_migrations(database_url: str, script_location: Optional[str] = None) ->
    - 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",
@@ -97,25 +148,28 @@ def run_migrations(database_url: str, script_location: Optional[str] = None) ->
        script_path = Path(script_location)
        if not script_path.exists():
            raise FileNotFoundError(
-                f"Alembic script location not found at {script_location}. "
-                "Database migrations cannot be run."
+                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:
@@ -130,7 +184,9 @@ def run_migrations(database_url: str, script_location: Optional[str] = None) ->
        raise RuntimeError("Database migration failed") from e


-def check_migration_status(database_url: Optional[str] = None, script_location: Optional[str] = None) -> tuple[str | None, str | None]:
+def check_migration_status(
+    database_url: str | None = None, script_location: str | None = None
+) -> tuple[str | None, str | None]:
    """
    Check current database schema version and latest available version.

@@ -151,7 +207,9 @@ def check_migration_status(database_url: Optional[str] = None, script_location:
        if database_url is None:
            database_url = os.getenv("HINDSIGHT_API_DATABASE_URL")
        if not database_url:
-            logger.warning("Database URL not provided and HINDSIGHT_API_DATABASE_URL not set, cannot check migration status")
+            logger.warning(
+                "Database URL not provided and HINDSIGHT_API_DATABASE_URL not set, cannot check migration status"
+            )
            return None, None

        # Get current revision from database
@@ -183,3 +241,131 @@ def check_migration_status(database_url: Optional[str] = None, script_location:
    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}")
@@ -1,49 +1,67 @@
 """
 SQLAlchemy models for the memory system.
 """
-from datetime import datetime
-from typing import Optional
-from uuid import UUID as PyUUID, uuid4

+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,
-    Column,
    Float,
    ForeignKey,
    ForeignKeyConstraint,
    Index,
    Integer,
-    PrimaryKeyConstraint,
    Text,
    func,
+)
+from sqlalchemy import (
    text as sql_text,
 )
 from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP, UUID
 from sqlalchemy.ext.asyncio import AsyncAttrs
 from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship
-from pgvector.sqlalchemy import Vector
+
+from .config import EMBEDDING_DIMENSION


 class Base(AsyncAttrs, DeclarativeBase):
    """Base class for all models."""
+
    pass


 class Document(Base):
    """Source documents for memory units."""
+
    __tablename__ = "documents"

    id: Mapped[str] = mapped_column(Text, primary_key=True)
    bank_id: Mapped[str] = mapped_column(Text, primary_key=True)
-    original_text: Mapped[Optional[str]] = mapped_column(Text)
-    content_hash: Mapped[Optional[str]] = mapped_column(Text)
+    original_text: Mapped[str | None] = mapped_column(Text)
+    content_hash: Mapped[str | None] = mapped_column(Text)
    doc_metadata: Mapped[dict] = mapped_column("metadata", JSONB, server_default=sql_text("'{}'::jsonb"))
-    created_at: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
-    updated_at: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
+    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    updated_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())

    # Relationships
    memory_units = relationship("MemoryUnit", back_populates="document", cascade="all, delete-orphan")
@@ -56,45 +74,42 @@ class Document(Base):

 class MemoryUnit(Base):
    """Individual sentence-level memories."""
+
    __tablename__ = "memory_units"

    id: Mapped[PyUUID] = mapped_column(
        UUID(as_uuid=True), primary_key=True, server_default=sql_text("gen_random_uuid()")
    )
    bank_id: Mapped[str] = mapped_column(Text, nullable=False)
-    document_id: Mapped[Optional[str]] = mapped_column(Text)
+    document_id: Mapped[str | None] = mapped_column(Text)
    text: Mapped[str] = mapped_column(Text, nullable=False)
-    embedding = mapped_column(Vector(384))  # pgvector type
-    context: Mapped[Optional[str]] = mapped_column(Text)
-    event_date: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), nullable=False)  # Kept for backward compatibility
-    occurred_start: Mapped[Optional[datetime]] = mapped_column(TIMESTAMP(timezone=True))  # When fact occurred (range start)
-    occurred_end: Mapped[Optional[datetime]] = mapped_column(TIMESTAMP(timezone=True))    # When fact occurred (range end)
-    mentioned_at: Mapped[Optional[datetime]] = mapped_column(TIMESTAMP(timezone=True))    # When fact was mentioned
+    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
+    )  # Kept for backward compatibility
+    occurred_start: Mapped[datetime | None] = mapped_column(
+        TIMESTAMP(timezone=True)
+    )  # When fact occurred (range start)
+    occurred_end: Mapped[datetime | None] = mapped_column(TIMESTAMP(timezone=True))  # When fact occurred (range end)
+    mentioned_at: Mapped[datetime | None] = mapped_column(TIMESTAMP(timezone=True))  # When fact was mentioned
    fact_type: Mapped[str] = mapped_column(Text, nullable=False, server_default="world")
-    confidence_score: Mapped[Optional[float]] = mapped_column(Float)
+    confidence_score: Mapped[float | None] = mapped_column(Float)
    access_count: Mapped[int] = mapped_column(Integer, server_default="0")
-    unit_metadata: Mapped[dict] = mapped_column("metadata", JSONB, server_default=sql_text("'{}'::jsonb"))  # User-defined metadata (str->str)
-    created_at: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
-    updated_at: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
+    unit_metadata: Mapped[dict] = mapped_column(
+        "metadata", JSONB, server_default=sql_text("'{}'::jsonb")
+    )  # User-defined metadata (str->str)
+    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    updated_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())

    # Relationships
    document = relationship("Document", back_populates="memory_units")
    unit_entities = relationship("UnitEntity", back_populates="memory_unit", cascade="all, delete-orphan")
    outgoing_links = relationship(
-        "MemoryLink",
-        foreign_keys="MemoryLink.from_unit_id",
-        back_populates="from_unit",
-        cascade="all, delete-orphan"
+        "MemoryLink", foreign_keys="MemoryLink.from_unit_id", back_populates="from_unit", cascade="all, delete-orphan"
    )
    incoming_links = relationship(
-        "MemoryLink",
-        foreign_keys="MemoryLink.to_unit_id",
-        back_populates="to_unit",
-        cascade="all, delete-orphan"
+        "MemoryLink", foreign_keys="MemoryLink.to_unit_id", back_populates="to_unit", cascade="all, delete-orphan"
    )

    __table_args__ = (
@@ -110,7 +125,7 @@ class MemoryUnit(Base):
            "(fact_type = 'opinion' AND confidence_score IS NOT NULL) OR "
            "(fact_type = 'observation') OR "
            "(fact_type NOT IN ('opinion', 'observation') AND confidence_score IS NULL)",
-            name="confidence_score_fact_type_check"
+            name="confidence_score_fact_type_check",
        ),
        Index("idx_memory_units_bank_id", "bank_id"),
        Index("idx_memory_units_document_id", "document_id"),
@@ -119,39 +134,46 @@ class MemoryUnit(Base):
        Index("idx_memory_units_access_count", "access_count", postgresql_ops={"access_count": "DESC"}),
        Index("idx_memory_units_fact_type", "fact_type"),
        Index("idx_memory_units_bank_fact_type", "bank_id", "fact_type"),
-        Index("idx_memory_units_bank_type_date", "bank_id", "fact_type", "event_date", postgresql_ops={"event_date": "DESC"}),
+        Index(
+            "idx_memory_units_bank_type_date",
+            "bank_id",
+            "fact_type",
+            "event_date",
+            postgresql_ops={"event_date": "DESC"},
+        ),
        Index(
            "idx_memory_units_opinion_confidence",
            "bank_id",
            "confidence_score",
            postgresql_where=sql_text("fact_type = 'opinion'"),
-            postgresql_ops={"confidence_score": "DESC"}
+            postgresql_ops={"confidence_score": "DESC"},
        ),
        Index(
            "idx_memory_units_opinion_date",
            "bank_id",
            "event_date",
            postgresql_where=sql_text("fact_type = 'opinion'"),
-            postgresql_ops={"event_date": "DESC"}
+            postgresql_ops={"event_date": "DESC"},
        ),
        Index(
            "idx_memory_units_observation_date",
            "bank_id",
            "event_date",
            postgresql_where=sql_text("fact_type = 'observation'"),
-            postgresql_ops={"event_date": "DESC"}
+            postgresql_ops={"event_date": "DESC"},
        ),
        Index(
            "idx_memory_units_embedding",
            "embedding",
            postgresql_using="hnsw",
-            postgresql_ops={"embedding": "vector_cosine_ops"}
+            postgresql_ops={"embedding": "vector_cosine_ops"},
        ),
    )


 class Entity(Base):
    """Resolved entities (people, organizations, locations, etc.)."""
+
    __tablename__ = "entities"

    id: Mapped[PyUUID] = mapped_column(
@@ -160,12 +182,8 @@ class Entity(Base):
    canonical_name: Mapped[str] = mapped_column(Text, nullable=False)
    bank_id: Mapped[str] = mapped_column(Text, nullable=False)
    entity_metadata: Mapped[dict] = mapped_column("metadata", JSONB, server_default=sql_text("'{}'::jsonb"))
-    first_seen: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
-    last_seen: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
+    first_seen: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    last_seen: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
    mention_count: Mapped[int] = mapped_column(Integer, server_default="1")

    # Relationships
@@ -175,13 +193,13 @@ class Entity(Base):
        "EntityCooccurrence",
        foreign_keys="EntityCooccurrence.entity_id_1",
        back_populates="entity_1",
-        cascade="all, delete-orphan"
+        cascade="all, delete-orphan",
    )
    cooccurrences_2 = relationship(
        "EntityCooccurrence",
        foreign_keys="EntityCooccurrence.entity_id_2",
        back_populates="entity_2",
-        cascade="all, delete-orphan"
+        cascade="all, delete-orphan",
    )

    __table_args__ = (
@@ -193,6 +211,7 @@ class Entity(Base):

 class UnitEntity(Base):
    """Association between memory units and entities."""
+
    __tablename__ = "unit_entities"

    unit_id: Mapped[PyUUID] = mapped_column(
@@ -214,6 +233,7 @@ class UnitEntity(Base):

 class EntityCooccurrence(Base):
    """Materialized cache of entity co-occurrences."""
+
    __tablename__ = "entity_cooccurrences"

    entity_id_1: Mapped[PyUUID] = mapped_column(
@@ -223,9 +243,7 @@ class EntityCooccurrence(Base):
        UUID(as_uuid=True), ForeignKey("entities.id", ondelete="CASCADE"), primary_key=True
    )
    cooccurrence_count: Mapped[int] = mapped_column(Integer, server_default="1")
-    last_cooccurred: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
+    last_cooccurred: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())

    # Relationships
    entity_1 = relationship("Entity", foreign_keys=[entity_id_1], back_populates="cooccurrences_1")
@@ -241,6 +259,7 @@ class EntityCooccurrence(Base):

 class MemoryLink(Base):
    """Links between memory units (temporal, semantic, entity)."""
+
    __tablename__ = "memory_links"

    from_unit_id: Mapped[PyUUID] = mapped_column(
@@ -250,13 +269,11 @@ class MemoryLink(Base):
        UUID(as_uuid=True), ForeignKey("memory_units.id", ondelete="CASCADE"), primary_key=True
    )
    link_type: Mapped[str] = mapped_column(Text, primary_key=True)
-    entity_id: Mapped[Optional[PyUUID]] = mapped_column(
+    entity_id: Mapped[PyUUID | None] = mapped_column(
        UUID(as_uuid=True), ForeignKey("entities.id", ondelete="CASCADE"), primary_key=True
    )
    weight: Mapped[float] = mapped_column(Float, nullable=False, server_default="1.0")
-    created_at: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
+    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())

    # Relationships
    from_unit = relationship("MemoryUnit", foreign_keys=[from_unit_id], back_populates="outgoing_links")
@@ -266,7 +283,7 @@ class MemoryLink(Base):
    __table_args__ = (
        CheckConstraint(
            "link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')",
-            name="memory_links_link_type_check"
+            name="memory_links_link_type_check",
        ),
        CheckConstraint("weight >= 0.0 AND weight <= 1.0", name="memory_links_weight_check"),
        Index("idx_memory_links_from", "from_unit_id"),
@@ -278,31 +295,22 @@ class MemoryLink(Base):
            "from_unit_id",
            "weight",
            postgresql_where=sql_text("weight >= 0.1"),
-            postgresql_ops={"weight": "DESC"}
+            postgresql_ops={"weight": "DESC"},
        ),
    )


 class Bank(Base):
    """Memory bank profiles with disposition traits and background."""
+
    __tablename__ = "banks"

    bank_id: Mapped[str] = mapped_column(Text, primary_key=True)
    disposition: Mapped[dict] = mapped_column(
-        JSONB,
-        nullable=False,
-        server_default=sql_text(
-            '\'{"skepticism": 3, "literalism": 3, "empathy": 3}\'::jsonb'
-        )
+        JSONB, nullable=False, server_default=sql_text('\'{"skepticism": 3, "literalism": 3, "empathy": 3}\'::jsonb')
    )
    background: Mapped[str] = mapped_column(Text, nullable=False, server_default="")
-    created_at: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
-    updated_at: Mapped[datetime] = mapped_column(
-        TIMESTAMP(timezone=True), server_default=func.now()
-    )
+    created_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())
+    updated_at: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), server_default=func.now())

-    __table_args__ = (
-        Index("idx_banks_bank_id", "bank_id"),
-    )
+    __table_args__ = (Index("idx_banks_bank_id", "bank_id"),)
@@ -1,12 +1,10 @@
 import asyncio
 import logging
-from typing import Optional

 from pg0 import Pg0

 logger = logging.getLogger(__name__)

-DEFAULT_PORT = 5555
 DEFAULT_USERNAME = "hindsight"
 DEFAULT_PASSWORD = "hindsight"
 DEFAULT_DATABASE = "hindsight"
@@ -17,34 +15,38 @@ class EmbeddedPostgres:

    def __init__(
        self,
-        port: int = DEFAULT_PORT,
+        port: int | None = None,
        username: str = DEFAULT_USERNAME,
        password: str = DEFAULT_PASSWORD,
        database: str = DEFAULT_DATABASE,
        name: str = "hindsight",
        **kwargs,
    ):
-        self.port = port
+        self.port = port  # None means pg0 will auto-assign
        self.username = username
        self.password = password
        self.database = database
        self.name = name
-        self._pg0: Optional[Pg0] = None
+        self._pg0: Pg0 | None = None

    def _get_pg0(self) -> Pg0:
        if self._pg0 is None:
-            self._pg0 = Pg0(
-                name=self.name,
-                port=self.port,
-                username=self.username,
-                password=self.password,
-                database=self.database,
-            )
+            kwargs = {
+                "name": self.name,
+                "username": self.username,
+                "password": self.password,
+                "database": self.database,
+            }
+            # Only set port if explicitly specified
+            if self.port is not None:
+                kwargs["port"] = self.port
+            self._pg0 = Pg0(**kwargs)  # type: ignore[invalid-argument-type] - dict kwargs
        return self._pg0

-    async def start(self, max_retries: int = 3, retry_delay: float = 2.0) -> str:
+    async def start(self, max_retries: int = 5, retry_delay: float = 4.0) -> str:
        """Start the PostgreSQL server with retry logic."""
-        logger.info(f"Starting embedded PostgreSQL (name: {self.name}, port: {self.port})...")
+        port_info = f"port={self.port}" if self.port else "port=auto"
+        logger.info(f"Starting embedded PostgreSQL (name={self.name}, {port_info})...")

        pg0 = self._get_pg0()
        last_error = None
@@ -53,9 +55,9 @@ class EmbeddedPostgres:
            try:
                loop = asyncio.get_event_loop()
                info = await loop.run_in_executor(None, pg0.start)
-                logger.info(f"PostgreSQL started on port {self.port}")
-                # Construct URI manually since pg0-embedded may return None
-                uri = info.uri if info and info.uri else f"postgresql://{self.username}:{self.password}@localhost:{self.port}/{self.database}"
+                # Get URI from pg0 (includes auto-assigned port)
+                uri = info.uri
+                logger.info(f"PostgreSQL started: {uri}")
                return uri
            except Exception as e:
                last_error = str(e)
@@ -68,8 +70,7 @@ class EmbeddedPostgres:
                    logger.debug(f"pg0 start attempt {attempt}/{max_retries} failed: {last_error}")

        raise RuntimeError(
-            f"Failed to start embedded PostgreSQL after {max_retries} attempts. "
-            f"Last error: {last_error}"
+            f"Failed to start embedded PostgreSQL after {max_retries} attempts. Last error: {last_error}"
        )

    async def stop(self) -> None:
@@ -91,9 +92,7 @@ class EmbeddedPostgres:
        pg0 = self._get_pg0()
        loop = asyncio.get_event_loop()
        info = await loop.run_in_executor(None, pg0.info)
-        # Construct URI manually since pg0-embedded may return None
-        uri = info.uri if info and info.uri else f"postgresql://{self.username}:{self.password}@localhost:{self.port}/{self.database}"
-        return uri
+        return info.uri

    async def is_running(self) -> bool:
        """Check if the PostgreSQL server is currently running."""
@@ -112,7 +111,7 @@ class EmbeddedPostgres:
        return await self.start()


-_default_instance: Optional[EmbeddedPostgres] = None
+_default_instance: EmbeddedPostgres | None = None


 def get_embedded_postgres() -> EmbeddedPostgres:
@@ -133,3 +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
@@ -6,6 +6,7 @@ This module provides the ASGI app for uvicorn import string usage:

 For CLI usage, use the hindsight-api command instead.
 """
+
 import os
 import warnings

@@ -26,18 +27,21 @@ config.configure_logging()

 # Create app at module level (required for uvicorn import string)
 # MemoryEngine reads configuration from environment variables automatically
-_memory = MemoryEngine()
+# Note: run_migrations=True by default, but migrations are idempotent so safe with workers
+_memory = MemoryEngine(run_migrations=config.run_migrations_on_startup)

 # Create unified app with both HTTP and optionally MCP
 app = create_app(
    memory=_memory,
    http_api_enabled=True,
    mcp_api_enabled=config.mcp_enabled,
-    mcp_mount_path="/mcp"
+    mcp_mount_path="/mcp",
+    initialize_memory=True,
 )


 if __name__ == "__main__":
    # When run directly, delegate to the CLI
    from hindsight_api.main import main
+
    main()
@@ -4,8 +4,8 @@ build-backend = "hatchling.build"

 [project]
 name = "hindsight-api"
-version = "0.1.5"
-description = "Temporal + Semantic + Entity Memory System for AI agents using PostgreSQL"
+version = "0.2.1"
+description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
@@ -14,7 +14,6 @@ dependencies = [
    "openai>=1.0.0",
    "pydantic>=2.0.0",
    "rich>=13.0.0",
-    "sentence-transformers>=3.0.0,<3.3.0",
    "langchain-text-splitters>=0.3.0",
    "fastapi[standard]>=0.120.3",
    "uvicorn>=0.38.0",
@@ -24,12 +23,10 @@ dependencies = [
    "pgvector>=0.4.1",
    "greenlet>=3.2.4",
    "psycopg2-binary>=2.9.11",
-    "transformers>=4.30.0,<4.46.0",
-    "torch>=2.0.0,<2.6.0",
    "tiktoken>=0.12.0",
    "httpx>=0.27.0",
    "fastmcp>=2.3.0",
-    "pg0-embedded>=0.1.0",
+    "pg0-embedded>=0.11.0",
    "python-dateutil>=2.8.0",
    "opentelemetry-api>=1.20.0",
    "opentelemetry-sdk>=1.20.0",
@@ -37,6 +34,15 @@ dependencies = [
    "opentelemetry-exporter-prometheus>=0.41b0",
    "dateparser>=1.2.2",
    "google-genai>=1.0.0",
+    "anthropic>=0.40.0",
+    "typer>=0.9.0",
+    "cohere>=5.0.0",
+    "flashrank>=0.2.0",
+    # Local ML models for embeddings/reranking - can be excluded in Docker with INCLUDE_LOCAL_MODELS=false
+    "sentence-transformers>=3.0.0,<3.3.0",
+    "transformers>=4.30.0,<4.46.0",
+    "torch>=2.0.0",
+    "uvloop>=0.22.1",
 ]

 [project.optional-dependencies]
@@ -50,6 +56,8 @@ 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"]
@@ -73,7 +81,7 @@ 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
@@ -90,4 +98,62 @@ dev = [
    "pytest-xdist>=3.8.0",
    "python-dotenv>=1.2.1",
    "filelock>=3.0.0",
+    "ruff>=0.8.0",
+    "ty>=0.0.1",
 ]
+
+[tool.ruff]
+line-length = 120
+target-version = "py311"
+exclude = [
+    "tests/",
+    "**/tests/",
+]
+
+[tool.ruff.lint]
+select = [
+    "E",      # pycodestyle errors
+    "W",      # pycodestyle warnings
+    "F",      # Pyflakes
+    "I",      # isort
+]
+ignore = [
+    "E501",   # line too long (handled by formatter)
+    "E402",   # module import not at top of file
+    "F401",   # unused import (too noisy during development)
+    "F841",   # unused variable (too noisy during development)
+    "F811",   # redefined while unused
+    "F821",   # undefined name (forward references in type hints)
+]
+
+[tool.ruff.lint.isort]
+known-third-party = ["alembic"]
+
+[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 +1,292 @@
+"""
+Tests for admin backup and restore functionality.
+
+These tests use an isolated schema to avoid interfering with other tests.
+The backup/restore operations truncate tables, which would cause deadlocks
+and race conditions if run against the shared public schema.
+"""
+
+import tempfile
+import uuid
+import zipfile
+from pathlib import Path
+
+import asyncpg
+import pytest
+import pytest_asyncio
+
+from hindsight_api.admin.cli import _backup, _restore, BACKUP_TABLES
+from hindsight_api.migrations import run_migrations
+
+
+# Run these tests sequentially since they do full DB backup/restore
+pytestmark = pytest.mark.xdist_group(name="backup_restore")
+
+
+@pytest_asyncio.fixture(scope="function")
+async def backup_test_schema(pg0_db_url, embeddings):
+    """Create an isolated schema for backup/restore tests.
+
+    Uses a unique schema name per test invocation to avoid conflicts with
+    parallel test runs or leftover state from interrupted runs.
+
+    Returns a tuple of (db_url, schema_name, fq_helper, embeddings).
+    """
+    # Initialize embeddings if not already done
+    await embeddings.initialize()
+
+    # Use unique schema name to avoid conflicts
+    schema_name = f"backup_test_{uuid.uuid4().hex[:8]}"
+
+    def _fq(table: str) -> str:
+        """Get fully-qualified table name in test schema."""
+        return f"{schema_name}.{table}"
+
+    conn = await asyncpg.connect(pg0_db_url)
+    try:
+        await conn.execute(f"CREATE SCHEMA {schema_name}")
+    finally:
+        await conn.close()
+
+    # Run migrations on the isolated schema
+    run_migrations(pg0_db_url, schema=schema_name)
+
+    yield pg0_db_url, schema_name, _fq, embeddings
+
+    # Cleanup after test
+    conn = await asyncpg.connect(pg0_db_url)
+    try:
+        await conn.execute(f"DROP SCHEMA IF EXISTS {schema_name} CASCADE")
+    finally:
+        await conn.close()
+
+
+@pytest.mark.asyncio
+async def test_backup_restore_roundtrip(backup_test_schema):
+    """Test that backup and restore preserves all data correctly."""
+    db_url, schema_name, _fq, embeddings = backup_test_schema
+    bank_id = f"test-backup-{uuid.uuid4().hex[:8]}"
+    conn = await asyncpg.connect(db_url)
+
+    try:
+        # Create a bank
+        await conn.execute(
+            f"INSERT INTO {_fq('banks')} (bank_id) VALUES ($1) ON CONFLICT DO NOTHING",
+            bank_id,
+        )
+
+        # Create some test memory units with embeddings
+        # Convert embedding list to pgvector format string
+        embedding_list = embeddings.encode(["Test content about Alice"])[0]
+        embedding_str = "[" + ",".join(str(x) for x in embedding_list) + "]"
+        for text in [
+            "Alice is a software engineer who loves Python.",
+            "Bob works with Alice on the backend team.",
+            "The team uses PostgreSQL for their database.",
+        ]:
+            await conn.execute(
+                f"""INSERT INTO {_fq('memory_units')}
+                    (bank_id, text, fact_type, embedding, event_date)
+                    VALUES ($1, $2, 'world', $3::vector, NOW())""",
+                bank_id,
+                text,
+                embedding_str,
+            )
+
+        # Get counts before backup
+        counts_before = {}
+        for table in BACKUP_TABLES:
+            counts_before[table] = await conn.fetchval(f"SELECT COUNT(*) FROM {_fq(table)}")
+
+        # Verify we have data
+        assert counts_before["banks"] > 0
+        assert counts_before["memory_units"] > 0
+
+    finally:
+        await conn.close()
+
+    # Backup to a temp file
+    with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as f:
+        backup_path = Path(f.name)
+
+    try:
+        manifest = await _backup(db_url, backup_path, schema=schema_name)
+
+        # Verify backup file exists and is valid
+        assert backup_path.exists()
+        assert backup_path.stat().st_size > 0
+
+        # Verify manifest
+        assert manifest["version"] == "1"
+        assert "created_at" in manifest
+        for table in BACKUP_TABLES:
+            assert table in manifest["tables"]
+            assert manifest["tables"][table]["rows"] == counts_before[table]
+
+        # Verify zip contents
+        with zipfile.ZipFile(backup_path, "r") as zf:
+            assert "manifest.json" in zf.namelist()
+            for table in BACKUP_TABLES:
+                assert f"{table}.bin" in zf.namelist()
+
+        # Clear all data
+        conn = await asyncpg.connect(db_url)
+        try:
+            for table in reversed(BACKUP_TABLES):
+                await conn.execute(f"TRUNCATE TABLE {_fq(table)} CASCADE")
+
+            # Verify data is gone
+            for table in BACKUP_TABLES:
+                count = await conn.fetchval(f"SELECT COUNT(*) FROM {_fq(table)}")
+                assert count == 0, f"Table {table} should be empty after truncate"
+        finally:
+            await conn.close()
+
+        # Restore from backup
+        await _restore(db_url, backup_path, schema=schema_name)
+
+        # Verify counts match original
+        conn = await asyncpg.connect(db_url)
+        try:
+            for table in BACKUP_TABLES:
+                count = await conn.fetchval(f"SELECT COUNT(*) FROM {_fq(table)}")
+                assert count == counts_before[table], f"Table {table} count mismatch after restore"
+
+            # Verify data content is preserved
+            texts = await conn.fetch(
+                f"SELECT text FROM {_fq('memory_units')} WHERE bank_id = $1",
+                bank_id,
+            )
+            text_content = " ".join(r["text"] for r in texts)
+            assert "Alice" in text_content or "software" in text_content
+        finally:
+            await conn.close()
+
+    finally:
+        # Cleanup
+        if backup_path.exists():
+            backup_path.unlink()
+
+
+@pytest.mark.asyncio
+async def test_backup_restore_preserves_all_column_types(backup_test_schema):
+    """Test that all column types are preserved: vectors, UUIDs, timestamps, JSONB."""
+    db_url, schema_name, _fq, embeddings = backup_test_schema
+    bank_id = f"test-types-{uuid.uuid4().hex[:8]}"
+    conn = await asyncpg.connect(db_url)
+
+    try:
+        # Create a bank
+        await conn.execute(
+            f"INSERT INTO {_fq('banks')} (bank_id) VALUES ($1) ON CONFLICT DO NOTHING",
+            bank_id,
+        )
+
+        # Create a memory unit with all column types
+        # Convert embedding list to pgvector format string
+        embedding_list = embeddings.encode(["John Smith engineer"])[0]
+        embedding_str = "[" + ",".join(str(x) for x in embedding_list) + "]"
+        await conn.execute(
+            f"""INSERT INTO {_fq('memory_units')}
+                (bank_id, text, fact_type, embedding, event_date, metadata)
+                VALUES ($1, $2, 'world', $3::vector, NOW(), $4)""",
+            bank_id,
+            "John Smith is a senior engineer at Acme Corp since 2020.",
+            embedding_str,
+            '{"key": "value"}',
+        )
+
+        # Create an entity
+        await conn.execute(
+            f"""INSERT INTO {_fq('entities')}
+                (bank_id, canonical_name, metadata)
+                VALUES ($1, $2, $3)""",
+            bank_id,
+            "John Smith",
+            '{"role": "engineer"}',
+        )
+
+        # Get original data
+        original_unit = await conn.fetchrow(
+            f"""SELECT id, embedding, event_date, created_at, metadata, text
+               FROM {_fq('memory_units')} WHERE bank_id = $1 LIMIT 1""",
+            bank_id,
+        )
+        original_entity = await conn.fetchrow(
+            f"""SELECT id, first_seen, last_seen, metadata, canonical_name
+               FROM {_fq('entities')} WHERE bank_id = $1 LIMIT 1""",
+            bank_id,
+        )
+        original_bank = await conn.fetchrow(
+            f"SELECT bank_id, created_at, updated_at FROM {_fq('banks')} WHERE bank_id = $1",
+            bank_id,
+        )
+    finally:
+        await conn.close()
+
+    assert original_unit is not None, "Should have created memory units"
+    assert original_unit["embedding"] is not None, "Should have embedding"
+    assert original_unit["id"] is not None, "Should have UUID"
+    assert original_entity is not None, "Should have created entities"
+
+    with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as f:
+        backup_path = Path(f.name)
+
+    try:
+        await _backup(db_url, backup_path, schema=schema_name)
+
+        # Clear all data
+        conn = await asyncpg.connect(db_url)
+        try:
+            for table in reversed(BACKUP_TABLES):
+                await conn.execute(f"TRUNCATE TABLE {_fq(table)} CASCADE")
+        finally:
+            await conn.close()
+
+        await _restore(db_url, backup_path, schema=schema_name)
+
+        # Verify all column types are preserved exactly
+        conn = await asyncpg.connect(db_url)
+        try:
+            restored_unit = await conn.fetchrow(
+                f"""SELECT id, embedding, event_date, created_at, metadata, text
+                   FROM {_fq('memory_units')} WHERE bank_id = $1 LIMIT 1""",
+                bank_id,
+            )
+            restored_entity = await conn.fetchrow(
+                f"""SELECT id, first_seen, last_seen, metadata, canonical_name
+                   FROM {_fq('entities')} WHERE bank_id = $1 LIMIT 1""",
+                bank_id,
+            )
+            restored_bank = await conn.fetchrow(
+                f"SELECT bank_id, created_at, updated_at FROM {_fq('banks')} WHERE bank_id = $1",
+                bank_id,
+            )
+        finally:
+            await conn.close()
+
+        # Verify memory_units
+        assert restored_unit is not None, "Should have restored memory unit"
+        assert restored_unit["id"] == original_unit["id"], "UUID should match exactly"
+        assert restored_unit["text"] == original_unit["text"], "Text should match"
+        assert list(restored_unit["embedding"]) == list(original_unit["embedding"]), "Vector embedding should match exactly"
+        assert restored_unit["event_date"] == original_unit["event_date"], "Timestamp should match exactly"
+        assert restored_unit["created_at"] == original_unit["created_at"], "Created timestamp should match"
+        assert restored_unit["metadata"] == original_unit["metadata"], "JSONB metadata should match"
+
+        # Verify entities
+        assert restored_entity is not None, "Should have restored entity"
+        assert restored_entity["id"] == original_entity["id"], "Entity UUID should match"
+        assert restored_entity["canonical_name"] == original_entity["canonical_name"], "Entity name should match"
+        assert restored_entity["first_seen"] == original_entity["first_seen"], "Entity first_seen should match"
+        assert restored_entity["last_seen"] == original_entity["last_seen"], "Entity last_seen should match"
+        assert restored_entity["metadata"] == original_entity["metadata"], "Entity metadata should match"
+
+        # Verify banks
+        assert restored_bank is not None, "Should have restored bank"
+        assert restored_bank["bank_id"] == original_bank["bank_id"], "Bank ID should match"
+        assert restored_bank["created_at"] == original_bank["created_at"], "Bank created_at should match"
+
+    finally:
+        if backup_path.exists():
+            backup_path.unlink()
@@ -3,7 +3,7 @@ Tests for agent management API (profile, disposition, background).
 """
 import pytest
 import uuid
-from hindsight_api import MemoryEngine
+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
--- a/Show More
+++ b/Show More