docs: update 0.4.15 blog cover image

- Update version to 0.4.15 in all components
- Regenerate OpenAPI spec and client SDKs
- Python packages: hindsight-api, hindsight-dev, hindsight-all, hindsight-litellm, hindsight-crewai, hindsight-pydantic-ai, hindsight-embed
- Python client: hindsight-clients/python
- TypeScript client: hindsight-clients/typescript
- Rust CLI: hindsight-cli
- Control Plane: hindsight-control-plane
- OpenClaw integration: hindsight-integrations/openclaw
- AI SDK integration: hindsight-integrations/ai-sdk
- Chat SDK integration: hindsight-integrations/chat
- Helm chart
- Sync documentation to version-0.4

.env.example

@@ -5,7 +5,7 @@
 # Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai
 HINDSIGHT_API_LLM_PROVIDER=openai
 HINDSIGHT_API_LLM_API_KEY=your-api-key-here
-HINDSIGHT_API_LLM_MODEL=o3-mini
+HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
 HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 
 # Example: Anthropic Claude configuration

.github/workflows/deploy-docs.yml

@@ -31,6 +31,9 @@ jobs:
       - run: npm ci --workspace=hindsight-docs
       - run: uv run generate-llms-full
       - run: npm run build --workspace=hindsight-docs
+        env:
+          UMAMI_URL: https://analytics.hindsight.vectorize.io
+          UMAMI_WEBSITE_ID: ${{ secrets.UMAMI_WEBSITE_ID }}
       - uses: actions/upload-pages-artifact@v3
         with:
           path: hindsight-docs/build

.github/workflows/release.yml

@@ -46,6 +46,14 @@ jobs:
       working-directory: ./hindsight-embed
       run: uv build --out-dir dist
 
+    - name: Build hindsight-crewai
+      working-directory: ./hindsight-integrations/crewai
+      run: uv build --out-dir dist
+
+    - name: Build hindsight-pydantic-ai
+      working-directory: ./hindsight-integrations/pydantic-ai
+      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
@@ -77,6 +85,18 @@ jobs:
         packages-dir: ./hindsight-embed/dist
         skip-existing: true
 
+    - name: Publish hindsight-crewai to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        packages-dir: ./hindsight-integrations/crewai/dist
+        skip-existing: true
+
+    - name: Publish hindsight-pydantic-ai to PyPI
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        packages-dir: ./hindsight-integrations/pydantic-ai/dist
+        skip-existing: true
+
     # Upload artifacts for GitHub release
     - name: Upload artifacts
       uses: actions/upload-artifact@v4
@@ -88,6 +108,8 @@ jobs:
           hindsight/dist/*
           hindsight-integrations/litellm/dist/*
           hindsight-embed/dist/*
+          hindsight-integrations/crewai/dist/*
+          hindsight-integrations/pydantic-ai/dist/*
         retention-days: 1
 
   release-typescript-client:
@@ -237,6 +259,55 @@ jobs:
         path: hindsight-integrations/ai-sdk/*.tgz
         retention-days: 1
 
+  release-chat-integration:
+    runs-on: ubuntu-latest
+    environment: npm
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '22'
+        registry-url: 'https://registry.npmjs.org'
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/chat
+      run: npm ci
+
+    - name: Build
+      working-directory: ./hindsight-integrations/chat
+      run: npm run build
+
+    - name: Publish to npm
+      working-directory: ./hindsight-integrations/chat
+      run: |
+        set +e
+        OUTPUT=$(npm publish --access public 2>&1)
+        EXIT_CODE=$?
+        echo "$OUTPUT"
+        if [ $EXIT_CODE -ne 0 ]; then
+          if echo "$OUTPUT" | grep -q "cannot publish over"; then
+            echo "Package version already published, skipping..."
+            exit 0
+          fi
+          exit $EXIT_CODE
+        fi
+      env:
+        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
+
+    - name: Pack for GitHub release
+      working-directory: ./hindsight-integrations/chat
+      run: npm pack
+
+    - name: Upload artifacts
+      uses: actions/upload-artifact@v4
+      with:
+        name: chat-integration
+        path: hindsight-integrations/chat/*.tgz
+        retention-days: 1
+
   release-control-plane:
     runs-on: ubuntu-latest
     environment: npm
@@ -268,11 +339,14 @@ jobs:
     - name: Build
       run: npm run build --workspace=hindsight-control-plane
 
+    - name: Verify standalone build
+      run: test -f hindsight-control-plane/standalone/server.js || (echo 'standalone/server.js missing - build failed' && exit 1)
+
     - name: Publish to npm
       working-directory: ./hindsight-control-plane
       run: |
         set +e
-        OUTPUT=$(npm publish --access public 2>&1)
+        OUTPUT=$(npm publish --access public --ignore-scripts 2>&1)
         EXIT_CODE=$?
         echo "$OUTPUT"
         if [ $EXIT_CODE -ne 0 ]; then
@@ -487,7 +561,7 @@ jobs:
 
   create-github-release:
     runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-ai-sdk-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
+    needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-ai-sdk-integration, release-chat-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
     permissions:
       contents: write
 
@@ -522,6 +596,12 @@ jobs:
         name: ai-sdk-integration
         path: ./artifacts/ai-sdk-integration
 
+    - name: Download Chat Integration
+      uses: actions/download-artifact@v4
+      with:
+        name: chat-integration
+        path: ./artifacts/chat-integration
+
     - name: Download Control Plane
       uses: actions/download-artifact@v4
       with:
@@ -560,6 +640,7 @@ 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-integrations/pydantic-ai/dist/* release-assets/ || true
         cp artifacts/python-packages/hindsight-embed/dist/* release-assets/ || true
         # TypeScript client
         cp artifacts/typescript-client/*.tgz release-assets/ || true
@@ -567,6 +648,8 @@ jobs:
         cp artifacts/openclaw-integration/*.tgz release-assets/ || true
         # AI SDK Integration
         cp artifacts/ai-sdk-integration/*.tgz release-assets/ || true
+        # Chat Integration
+        cp artifacts/chat-integration/*.tgz release-assets/ || true
         # Control Plane
         cp artifacts/control-plane/*.tgz release-assets/ || true
         # Rust CLI binaries

.github/workflows/test.yml

@@ -97,6 +97,29 @@ jobs:
       working-directory: ./hindsight-integrations/ai-sdk
       run: npm run build
 
+  build-chat-integration:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '22'
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/chat
+      run: npm ci
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/chat
+      run: npm test
+
+    - name: Build
+      working-directory: ./hindsight-integrations/chat
+      run: npm run build
+
   build-control-plane:
     runs-on: ubuntu-latest
 
@@ -171,9 +194,9 @@ jobs:
   test-rust-cli:
     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_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       HINDSIGHT_API_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -181,6 +204,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install Rust
       uses: dtolnay/rust-toolchain@stable
 
@@ -227,25 +256,46 @@ jobs:
       working-directory: ./hindsight-api
       run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
+    - 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 }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         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
+        for i in {1..120}; 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"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -340,12 +390,21 @@ jobs:
 
     # Only test slim variants to save disk space (they're much smaller)
     # Slim variants require external embedding providers
+    - name: Setup GCP credentials for smoke test
+      if: matrix.variant == 'slim'
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Smoke test - verify container starts
       if: matrix.variant == 'slim'
       env:
-        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-        HINDSIGHT_API_EMBEDDINGS_PROVIDER: openai
-        HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        HINDSIGHT_API_LLM_PROVIDER: vertexai
+        HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID: ${{ env.HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID }}
+        HINDSIGHT_API_EMBEDDINGS_PROVIDER: cohere
         HINDSIGHT_API_RERANKER_PROVIDER: cohere
         HINDSIGHT_API_COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
       run: ./docker/test-image.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"
@@ -353,14 +412,13 @@ jobs:
   test-api:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       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)
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -368,6 +426,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -414,9 +478,9 @@ jobs:
   test-python-client:
     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_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       HINDSIGHT_API_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -425,6 +489,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -452,25 +522,46 @@ jobs:
       working-directory: ./hindsight-api
       run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
+    - 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 }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         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
+        for i in {1..120}; 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"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -490,9 +581,9 @@ jobs:
   test-typescript-client:
     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_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       HINDSIGHT_API_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -501,6 +592,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -533,25 +630,46 @@ jobs:
       working-directory: ./hindsight-clients/typescript
       run: npm run build
 
+    - 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 }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         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
+        for i in {1..120}; 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"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -571,9 +689,9 @@ jobs:
   test-rust-client:
     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_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       HINDSIGHT_API_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -582,6 +700,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -613,25 +737,46 @@ jobs:
       working-directory: ./hindsight-api
       run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
+    - 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 }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         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
+        for i in {1..120}; 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"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -651,9 +796,9 @@ jobs:
   test-go-client:
     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_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       HINDSIGHT_API_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -662,6 +807,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -687,31 +838,56 @@ jobs:
       working-directory: ./hindsight-api
       run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
+    - 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 }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         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
+        for i in {1..120}; 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"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
           sleep 1
         done
 
+    - name: Build Go client
+      working-directory: ./hindsight-clients/go
+      run: go build ./...
+
     - name: Run Go client tests
       working-directory: ./hindsight-clients/go
       run: go test -v -tags=integration
@@ -722,12 +898,120 @@ jobs:
         echo "=== API Server Logs ==="
         cat /tmp/api-server.log || echo "No API server log found"
 
+  test-openclaw-integration:
+    runs-on: ubuntu-latest
+    env:
+      HINDSIGHT_API_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
+      HINDSIGHT_API_URL: http://localhost:8888
+      HINDSIGHT_EMBED_PACKAGE_PATH: ${{ github.workspace }}/hindsight-embed
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
+    - 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: '22'
+
+    - 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 embed 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: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading cross-encoder model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
+    - name: Install openclaw integration dependencies
+      working-directory: ./hindsight-integrations/openclaw
+      run: npm ci
+
+    - name: Create .env file
+      run: |
+        cat > .env << EOF
+        HINDSIGHT_API_LLM_PROVIDER=${{ env.HINDSIGHT_API_LLM_PROVIDER }}
+        HINDSIGHT_API_LLM_MODEL=${{ env.HINDSIGHT_API_LLM_MODEL }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
+        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..120}; 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 120 ]; then
+            echo "API server failed to start after 120s"
+            cat /tmp/api-server.log
+            exit 1
+          fi
+          sleep 1
+        done
+
+    - name: Run openclaw integration tests
+      working-directory: ./hindsight-integrations/openclaw
+      run: npm run test:integration
+
+    - name: Show API server logs
+      if: always()
+      run: |
+        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_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       HINDSIGHT_API_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -735,6 +1019,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -782,21 +1072,22 @@ jobs:
       run: |
         cat > .env << EOF
         HINDSIGHT_API_LLM_PROVIDER=${{ 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 }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         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
+        for i in {1..120}; 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"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -813,6 +1104,35 @@ jobs:
         echo "=== API Server Logs ==="
         cat /tmp/api-server.log || echo "No API server log found"
 
+  test-crewai-integration:
+    runs-on: ubuntu-latest
+
+    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 crewai integration
+      working-directory: ./hindsight-integrations/crewai
+      run: uv build
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/crewai
+      run: uv sync --frozen
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/crewai
+      run: uv run pytest tests -v
+
   test-litellm-integration:
     runs-on: ubuntu-latest
 
@@ -842,18 +1162,53 @@ jobs:
       working-directory: ./hindsight-integrations/litellm
       run: uv run pytest tests -v
 
+  test-pydantic-ai-integration:
+    runs-on: ubuntu-latest
+
+    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 pydantic-ai integration
+      working-directory: ./hindsight-integrations/pydantic-ai
+      run: uv build
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/pydantic-ai
+      run: uv sync --frozen
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/pydantic-ai
+      run: uv run pytest tests -v
+
   test-embed:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       # Prefer CPU-only PyTorch in CI
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
 
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -889,19 +1244,25 @@ jobs:
   test-hindsight-all:
     runs-on: ubuntu-latest
     env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       # For test_server_integration.py compatibility
-      HINDSIGHT_LLM_PROVIDER: groq
-      HINDSIGHT_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_LLM_PROVIDER: vertexai
+      HINDSIGHT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_LLM_MODEL: google/gemini-2.5-flash-lite
       # Prefer CPU-only PyTorch in CI
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
 
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -938,9 +1299,9 @@ jobs:
     runs-on: ubuntu-latest
     needs: test-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_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       HINDSIGHT_API_URL: http://localhost:8888
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -948,6 +1309,12 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Download CLI artifact
       uses: actions/download-artifact@v4
       with:
@@ -990,25 +1357,46 @@ jobs:
         npm ci --workspace=hindsight-clients/typescript
         npm run build --workspace=hindsight-clients/typescript
 
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading reranker model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
     - name: Create .env file
       run: |
         cat > .env << EOF
         HINDSIGHT_API_LLM_PROVIDER=${{ 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 }}
+        HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json
+        HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
         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
+        for i in {1..120}; 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"
+          if [ $i -eq 120 ]; then
+            echo "API server failed to start after 120s"
             cat /tmp/api-server.log
             exit 1
           fi
@@ -1030,9 +1418,9 @@ jobs:
   test-upgrade:
     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_LLM_PROVIDER: vertexai
+      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
+      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
       UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
 
@@ -1041,6 +1429,12 @@ jobs:
       with:
         fetch-depth: 0  # Full history needed for git clone of tags
 
+    - name: Setup GCP credentials
+      run: |
+        printf '%s' '${{ secrets.GCP_VERTEXAI_CREDENTIALS }}' > /tmp/gcp-credentials.json
+        PROJECT_ID=$(jq -r '.project_id' /tmp/gcp-credentials.json)
+        echo "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=$PROJECT_ID" >> $GITHUB_ENV
+
     - name: Fetch tags
       run: git fetch --tags
 

CLAUDE.md

@@ -317,10 +317,10 @@ 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)
+- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., gpt-4o-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)
-- `HINDSIGHT_API_ENABLE_BANK_CONFIG_API`: Enable per-bank config API (default: false, disabled for security)
+- `HINDSIGHT_API_ENABLE_BANK_CONFIG_API`: Enable per-bank config API (default: true)

README.md

@@ -36,7 +36,7 @@ Hindsight is being used in production at Fortune 500 enterprises and by a growin
 
 ## Adding Hindsight to Your AI Agents
 
-The easiest way use Hindsight with an existing agent is with the LLM Wrapper. You can add memory to your agent with 2 lines of code. That will swap your current LLM client out with the Hindsight wrapper. After that, memories will be stored and retrieved automatically as you make LLM calls.
+The easiest way to use Hindsight with an existing agent is with the LLM Wrapper. You can add memory to your agent with 2 lines of code. That will swap your current LLM client out with the Hindsight wrapper. After that, memories will be stored and retrieved automatically as you make LLM calls.
 
 If you need more control over how and when your agent stores and recalls memories, there's also a simple API you can integrate with using the SDKs or directly via HTTP.
 
@@ -181,7 +181,7 @@ Satisfying these requirements in Hindsight is straightforward. When new user inp
 
 ![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
 
-Most agent memory implementation rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:
+Most agent memory implementations 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")
@@ -307,3 +307,5 @@ MIT — see [LICENSE](./LICENSE)
 ---
 
 Built by [Vectorize.io](https://vectorize.io)
+
+<img src="https://umami-pixel.chris-latimer.workers.dev/?id=a8b043e6-6964-454d-80df-69b69d3f0d50&host=github.com&url=/vectorize-io/hindsight" width="1" height="1" alt="" />

docker/standalone/Dockerfile

@@ -170,6 +170,11 @@ RUN chown -R hindsight:hindsight /app
 
 USER hindsight
 
+# Create pg0 data directory as hindsight user so that Docker seeds new named
+# volumes with correct ownership (UID 1000) on first use, avoiding the
+# "Permission denied" error when mounting a fresh root-owned volume.
+RUN mkdir -p /home/hindsight/.pg0
+
 ENV PATH="/app/api/.venv/bin:${PATH}"
 
 # Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)
@@ -321,6 +326,11 @@ RUN chown -R hindsight:hindsight /app
 
 USER hindsight
 
+# Create pg0 data directory as hindsight user so that Docker seeds new named
+# volumes with correct ownership (UID 1000) on first use, avoiding the
+# "Permission denied" error when mounting a fresh root-owned volume.
+RUN mkdir -p /home/hindsight/.pg0
+
 ENV PATH="/app/api/.venv/bin:${PATH}"
 
 # Pre-download tiktoken encoding (ALWAYS - required for token counting even in air-gapped envs)

docker/standalone/start-all.sh

@@ -97,7 +97,7 @@ fi
 if [ "$ENABLE_CP" = "true" ]; then
     echo "🎛️  Starting Control Plane..."
     cd /app/control-plane
-    PORT=9999 node server.js &
+    PORT="${HINDSIGHT_CP_PORT:-9999}" node server.js &
     CP_PID=$!
     PIDS+=($CP_PID)
 else
@@ -110,7 +110,7 @@ echo "✅ Hindsight is running!"
 echo ""
 echo "📍 Access:"
 if [ "$ENABLE_CP" = "true" ]; then
-    echo "   Control Plane: http://localhost:9999"
+    echo "   Control Plane: http://localhost:${HINDSIGHT_CP_PORT:-9999}"
 fi
 if [ "$ENABLE_API" = "true" ]; then
     echo "   API:           http://localhost:8888"

docker/test-image.sh

@@ -13,9 +13,9 @@
 #   target  - Optional: 'cp-only' for control plane, otherwise assumes API image (default: api)
 #
 # Environment variables:
-#   GROQ_API_KEY                                - Required for API/standalone images (LLM verification)
-#   HINDSIGHT_API_LLM_PROVIDER                  - LLM provider (default: groq)
-#   HINDSIGHT_API_LLM_MODEL                     - LLM model (default: llama-3.3-70b-versatile)
+#   HINDSIGHT_API_LLM_API_KEY                   - Required for API/standalone images (LLM verification)
+#   HINDSIGHT_API_LLM_PROVIDER                  - LLM provider (default: openai)
+#   HINDSIGHT_API_LLM_MODEL                     - LLM model (default: gpt-4o-mini)
 #   HINDSIGHT_API_EMBEDDINGS_PROVIDER           - Embeddings provider (optional, for slim images: openai, cohere, tei)
 #   HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY     - OpenAI API key for embeddings (optional)
 #   HINDSIGHT_API_RERANKER_PROVIDER             - Reranker provider (optional, for slim images: cohere, tei)
@@ -34,7 +34,7 @@
 #   ./docker/test-image.sh hindsight-control-plane:test cp-only
 #
 #   # Test slim image with external providers
-#   export GROQ_API_KEY=gsk_xxx
+#   export HINDSIGHT_API_LLM_API_KEY=sk_xxx
 #   export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
 #   export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=sk-xxx
 #   export HINDSIGHT_API_RERANKER_PROVIDER=cohere
@@ -60,8 +60,8 @@ IMAGE="${1:-}"
 TARGET="${2:-api}"
 TIMEOUT="${SMOKE_TEST_TIMEOUT:-120}"
 CONTAINER_NAME="${SMOKE_TEST_CONTAINER_NAME:-hindsight-smoke-test}"
-LLM_PROVIDER="${HINDSIGHT_API_LLM_PROVIDER:-groq}"
-LLM_MODEL="${HINDSIGHT_API_LLM_MODEL:-llama-3.3-70b-versatile}"
+LLM_PROVIDER="${HINDSIGHT_API_LLM_PROVIDER:-openai}"
+LLM_MODEL="${HINDSIGHT_API_LLM_MODEL:-gpt-4o-mini}"
 
 # Validate arguments
 if [ -z "$IMAGE" ]; then
@@ -88,9 +88,9 @@ else
 fi
 
 # Check for required environment variables
-if [ "$NEEDS_LLM" = true ] && [ -z "${GROQ_API_KEY:-}" ]; then
-    echo -e "${RED}Error: GROQ_API_KEY environment variable is required for API/standalone images${NC}"
-    echo "Set it with: export GROQ_API_KEY=your-api-key"
+if [ "$NEEDS_LLM" = true ] && [ "$LLM_PROVIDER" != "vertexai" ] && [ -z "${HINDSIGHT_API_LLM_API_KEY:-}" ]; then
+    echo -e "${RED}Error: HINDSIGHT_API_LLM_API_KEY environment variable is required for API/standalone images${NC}"
+    echo "Set it with: export HINDSIGHT_API_LLM_API_KEY=your-api-key"
     exit 2
 fi
 
@@ -123,9 +123,25 @@ else
     # Build docker run command with required and optional env vars
     DOCKER_CMD="docker run -d --name $CONTAINER_NAME"
     DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_PROVIDER=$LLM_PROVIDER"
-    DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${GROQ_API_KEY}"
+    if [ -n "${HINDSIGHT_API_LLM_API_KEY:-}" ]; then
+        DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${HINDSIGHT_API_LLM_API_KEY}"
+    fi
     DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_MODEL=$LLM_MODEL"
 
+    # Add Vertex AI config if provider is vertexai
+    if [ "$LLM_PROVIDER" = "vertexai" ]; then
+        if [ -n "${HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY:-}" ]; then
+            DOCKER_CMD="$DOCKER_CMD -v ${HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY}:/tmp/gcp-credentials.json:ro"
+            DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/tmp/gcp-credentials.json"
+        fi
+        if [ -n "${HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID:-}" ]; then
+            DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=${HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID}"
+        fi
+        if [ -n "${HINDSIGHT_API_LLM_VERTEXAI_REGION:-}" ]; then
+            DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_VERTEXAI_REGION=${HINDSIGHT_API_LLM_VERTEXAI_REGION}"
+        fi
+    fi
+
     # Add optional embeddings provider config
     if [ -n "${HINDSIGHT_API_EMBEDDINGS_PROVIDER:-}" ]; then
         DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_EMBEDDINGS_PROVIDER=${HINDSIGHT_API_EMBEDDINGS_PROVIDER}"

docker/test-slim-local.sh

@@ -6,24 +6,17 @@
 # It expects API keys to be set in environment variables.
 #
 # Usage:
-#   export GROQ_API_KEY=gsk_xxx
 #   export OPENAI_API_KEY=sk-xxx
 #   export COHERE_API_KEY=xxx
 #   ./docker/test-slim-local.sh
 #
 # Or inline:
-#   GROQ_API_KEY=gsk_xxx OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
+#   OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
 #
 
 set -euo pipefail
 
 # Check for required API keys
-if [ -z "${GROQ_API_KEY:-}" ]; then
-    echo "❌ Error: GROQ_API_KEY environment variable is required"
-    echo "Set it with: export GROQ_API_KEY=gsk_xxx"
-    exit 1
-fi
-
 if [ -z "${OPENAI_API_KEY:-}" ]; then
     echo "❌ Error: OPENAI_API_KEY environment variable is required"
     echo "Set it with: export OPENAI_API_KEY=sk-xxx"
@@ -41,7 +34,10 @@ IMAGE="${1:-hindsight-slim:test}"
 echo "Testing image: $IMAGE"
 echo ""
 
-# Set up external providers
+# Set up LLM and external providers
+export HINDSIGHT_API_LLM_PROVIDER=openai
+export HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY
+export HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
 export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
 export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=$OPENAI_API_KEY
 export HINDSIGHT_API_RERANKER_PROVIDER=cohere

helm/hindsight/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.11
-appVersion: "0.4.11"
+version: 0.4.15
+appVersion: "0.4.15"
 keywords:
   - ai
   - memory

hindsight-api/hindsight_api/__init__.py

@@ -46,4 +46,4 @@ __all__ = [
     "RemoteTEICrossEncoder",
     "LLMConfig",
 ]
-__version__ = "0.4.11"
+__version__ = "0.4.15"

hindsight-api/hindsight_api/alembic/versions/a2b3c4d5e6f7_add_text_signals_column.py

@@ -0,0 +1,88 @@
+"""Add text_signals column to memory_units for enriched BM25 indexing.
+
+text_signals stores a denormalized space-separated string of entity names
+(and future signals) to improve full-text search recall without polluting
+the stored fact text.
+
+- vchord: text_signals included in tokenize() at insert time
+- native: search_vector GENERATED column regenerated to include text_signals
+- pg_textsearch: no change (index only supports a single base column)
+
+Revision ID: a2b3c4d5e6f7
+Revises: z1u2v3w4x5y6
+Create Date: 2026-02-28
+"""
+
+import os
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "a2b3c4d5e6f7"
+down_revision: str | Sequence[str] | None = "aa2b3c4d5e6f"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def _detect_text_search_extension() -> str:
+    return os.getenv("HINDSIGHT_API_TEXT_SEARCH_EXTENSION", "native").lower()
+
+
+def upgrade() -> None:
+    schema = _get_schema_prefix()
+    table = f"{schema}memory_units"
+    text_search_ext = _detect_text_search_extension()
+
+    # Add text_signals column (nullable TEXT, populated at retain time)
+    op.execute(f"ALTER TABLE {table} ADD COLUMN IF NOT EXISTS text_signals TEXT")
+
+    if text_search_ext == "native":
+        # Native PostgreSQL: drop and recreate the GENERATED tsvector column to include text_signals
+        op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS search_vector")
+        op.execute(f"""
+            ALTER TABLE {table}
+            ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (
+                to_tsvector('english',
+                    COALESCE(text, '') || ' ' ||
+                    COALESCE(context, '') || ' ' ||
+                    COALESCE(text_signals, '')
+                )
+            ) STORED
+        """)
+        # Recreate GIN index (was dropped with the column)
+        op.execute(f"""
+            CREATE INDEX IF NOT EXISTS idx_memory_units_text_search
+            ON {table} USING gin(search_vector)
+        """)
+
+    # vchord: tokenize() call in fact_storage.py is updated to include text_signals at insert time
+    # pg_textsearch: no change — index operates on the base `text` column only
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    table = f"{schema}memory_units"
+    text_search_ext = _detect_text_search_extension()
+
+    if text_search_ext == "native":
+        op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_text_search")
+        op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS search_vector")
+        op.execute(f"""
+            ALTER TABLE {table}
+            ADD COLUMN search_vector tsvector
+            GENERATED ALWAYS AS (
+                to_tsvector('english', COALESCE(text, '') || ' ' || COALESCE(context, ''))
+            ) STORED
+        """)
+        op.execute(f"""
+            CREATE INDEX idx_memory_units_text_search
+            ON {table} USING gin(search_vector)
+        """)
+
+    op.execute(f"ALTER TABLE {table} DROP COLUMN IF EXISTS text_signals")

hindsight-api/hindsight_api/alembic/versions/aa2b3c4d5e6f_nullable_event_date.py

@@ -0,0 +1,36 @@
+"""Make event_date nullable in memory_units to support timestamp-free content
+
+Revision ID: aa2b3c4d5e6f
+Revises: z1u2v3w4x5y6
+Create Date: 2026-03-02
+
+When callers retain content without a timestamp (e.g. fictional documents, static text),
+the event_date column should be allowed to be NULL rather than defaulting to utcnow().
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "aa2b3c4d5e6f"
+down_revision: str | Sequence[str] | None = "z1u2v3w4x5y6"
+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"ALTER TABLE {schema}memory_units ALTER COLUMN event_date DROP NOT NULL")
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    # Backfill NULLs with now() before restoring the NOT NULL constraint
+    op.execute(f"UPDATE {schema}memory_units SET event_date = now() WHERE event_date IS NULL")
+    op.execute(f"ALTER TABLE {schema}memory_units ALTER COLUMN event_date SET NOT NULL")

hindsight-api/hindsight_api/alembic/versions/b3c4d5e6f7g8_add_temporal_date_indexes.py

@@ -0,0 +1,68 @@
+"""Add partial indexes on memory_units temporal date fields for fast temporal retrieval
+
+Revision ID: b3c4d5e6f7g8
+Revises: c1a2b3d4e5f6
+Create Date: 2026-03-02
+
+The temporal retrieval entry-point query filters memory_units by occurred_start,
+occurred_end, and mentioned_at using OR conditions. Without dedicated indexes the
+planner falls back to a sequential scan of all bank rows after applying the
+(bank_id, fact_type) index, then re-checks each date field.
+
+These three partial indexes give the planner bitmap-index scan options for the
+three most common date predicates, dramatically reducing the row set before any
+embedding computation is required.
+
+All indexes are created CONCURRENTLY so the migration does not block writes on
+memory_units during production deployments. CONCURRENTLY requires running outside
+a transaction block; see migrations.py for how this is handled safely.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "b3c4d5e6f7g8"
+down_revision: str | Sequence[str] | None = "c1a2b3d4e5f6"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    schema = _get_schema_prefix()
+    # Partial index on occurred_start (covers "occurred_start BETWEEN $4 AND $5")
+    op.execute("COMMIT")
+    op.execute(
+        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_occurred_start "
+        f"ON {schema}memory_units(bank_id, fact_type, occurred_start) "
+        f"WHERE occurred_start IS NOT NULL"
+    )
+    # Partial index on occurred_end (covers "occurred_end BETWEEN $4 AND $5")
+    op.execute("COMMIT")
+    op.execute(
+        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_occurred_end "
+        f"ON {schema}memory_units(bank_id, fact_type, occurred_end) "
+        f"WHERE occurred_end IS NOT NULL"
+    )
+    # Partial index on mentioned_at (covers "mentioned_at BETWEEN $4 AND $5")
+    op.execute("COMMIT")
+    op.execute(
+        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_units_bank_mentioned_at "
+        f"ON {schema}memory_units(bank_id, fact_type, mentioned_at) "
+        f"WHERE mentioned_at IS NOT NULL"
+    )
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    op.execute("COMMIT")
+    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_mentioned_at")
+    op.execute("COMMIT")
+    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_occurred_end")
+    op.execute("COMMIT")
+    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_units_bank_occurred_start")

hindsight-api/hindsight_api/alembic/versions/b4c5d6e7f8a9_backfill_observation_scopes.py

@@ -0,0 +1,34 @@
+"""Backfill observation_scopes column if missing.
+
+This migration ensures observation_scopes exists even on databases that had
+revision z1u2v3w4x5y6 applied when it referred to the old text_signals migration
+(before it was renamed to a2b3c4d5e6f7). The ADD COLUMN IF NOT EXISTS makes this
+a no-op on databases that already have the column.
+
+Revision ID: b4c5d6e7f8a9
+Revises: a2b3c4d5e6f7
+Create Date: 2026-03-02
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "b4c5d6e7f8a9"
+down_revision: str | Sequence[str] | None = "a2b3c4d5e6f7"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    schema = _get_schema_prefix()
+    op.execute(f"ALTER TABLE {schema}memory_units ADD COLUMN IF NOT EXISTS observation_scopes JSONB")
+
+
+def downgrade() -> None:
+    pass  # intentionally no-op — safe to leave the column in place

hindsight-api/hindsight_api/alembic/versions/c1a2b3d4e5f6_enable_pg_trgm_and_entities_trgm_index.py

@@ -0,0 +1,46 @@
+"""Enable pg_trgm extension and add GIN trigram index on entities.canonical_name
+
+Revision ID: c1a2b3d4e5f6
+Revises: b4c5d6e7f8a9
+Create Date: 2026-03-02
+
+Index is created CONCURRENTLY so the migration does not block writes on entities
+during production deployments. CONCURRENTLY requires running outside a transaction
+block; see migrations.py for how this is handled safely.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "c1a2b3d4e5f6"
+down_revision: str | Sequence[str] | None = "b4c5d6e7f8a9"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    # pg_trgm ships with every standard PostgreSQL installation as a contrib module.
+    # It enables fast similarity lookups via GIN indexes, used for entity name matching.
+    op.execute("CREATE EXTENSION IF NOT EXISTS pg_trgm")
+
+    schema = _get_schema_prefix()
+    # GIN index on canonical_name enables sub-millisecond trigram similarity queries
+    # (% operator, similarity()) instead of full-table scans across all bank entities.
+    op.execute("COMMIT")
+    op.execute(
+        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS entities_canonical_name_trgm_idx "
+        f"ON {schema}entities USING GIN (canonical_name gin_trgm_ops)"
+    )
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    op.execute("COMMIT")
+    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}entities_canonical_name_trgm_idx")
+    # Note: not dropping pg_trgm extension as other indexes may depend on it

hindsight-api/hindsight_api/alembic/versions/d2e3f4a5b6c7_add_memory_links_expansion_indexes.py

@@ -0,0 +1,83 @@
+"""Add covering and composite indexes to speed up link expansion graph retrieval.
+
+Two indexes target the two bottlenecks identified by EXPLAIN ANALYZE on a 17M-row
+memory_links table:
+
+1. idx_memory_links_to_type_weight  (to_unit_id, link_type, weight DESC)
+   The semantic incoming direction — finding facts that consider seeds as their
+   nearest neighbour — currently hits an expensive BitmapAnd of two separate
+   bitmap scans (to_unit_id bitmap ∩ link_type bitmap).  A composite index
+   on (to_unit_id, link_type) turns this into a single index scan and reduces
+   latency from ~36 ms to < 5 ms per query.
+
+2. idx_memory_links_entity_covering  (from_unit_id) INCLUDE (to_unit_id, entity_id)
+   WHERE link_type = 'entity'
+   The entity co-occurrence expansion uses COUNT(DISTINCT ml.entity_id) and
+   joins on ml.to_unit_id.  Without a covering index the planner must read
+   ~2 500 heap pages to fetch entity_id and to_unit_id after the bitmap index
+   scan, adding ~230 ms of random I/O.  INCLUDE adds those two columns to the
+   index leaf pages so the entire query can be served from the index (index-only
+   scan), eliminating the heap reads entirely.
+   Partial index (WHERE link_type = 'entity') keeps index size ~40 % smaller.
+
+Both indexes are created with CONCURRENTLY so the migration does not block
+concurrent reads or writes on memory_links.  CONCURRENTLY requires running
+outside a transaction block, so the migration emits an explicit COMMIT before
+each statement and uses IF NOT EXISTS for idempotency.
+
+Revision ID: d2e3f4a5b6c7
+Revises: b3c4d5e6f7g8
+Create Date: 2026-03-02
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "d2e3f4a5b6c7"
+down_revision: str | Sequence[str] | None = "b3c4d5e6f7g8"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    schema = _get_schema_prefix()
+
+    # CREATE INDEX CONCURRENTLY cannot run inside a transaction block.
+    # Commit the current Alembic transaction, then issue each CONCURRENTLY
+    # statement in its own implicit autocommit transaction.
+    # IF NOT EXISTS makes each statement idempotent if the migration is retried.
+
+    # Index for the semantic *incoming* direction in link_expansion_retrieval.py.
+    # Replaces the BitmapAnd of idx_memory_links_to_unit ∩ idx_memory_links_link_type
+    # with a single composite index scan.
+    op.execute("COMMIT")
+    op.execute(
+        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_to_type_weight "
+        f"ON {schema}memory_links(to_unit_id, link_type, weight DESC)"
+    )
+
+    # Covering index for entity co-occurrence expansion.
+    # Enables an index-only scan: entity_id and to_unit_id are read from the
+    # index leaf pages instead of the heap, eliminating ~2 500 random heap-page
+    # reads per expansion query.
+    op.execute("COMMIT")
+    op.execute(
+        f"CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_memory_links_entity_covering "
+        f"ON {schema}memory_links(from_unit_id) "
+        f"INCLUDE (to_unit_id, entity_id) "
+        f"WHERE link_type = 'entity'"
+    )
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    op.execute("COMMIT")
+    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_entity_covering")
+    op.execute("COMMIT")
+    op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_memory_links_to_type_weight")

hindsight-api/hindsight_api/alembic/versions/z1u2v3w4x5y6_add_observation_tags_to_memory_units.py

@@ -0,0 +1,35 @@
+"""Add observation_scopes column to memory_units table
+
+Revision ID: z1u2v3w4x5y6
+Revises: a1b2c3d4e5f6
+Create Date: 2026-02-25
+
+Adds observation_scopes JSONB column to memory_units to control how observations
+are scoped during consolidation. Accepts "per_tag", "combined", or an explicit
+list of tag-set lists for custom multi-pass consolidation.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "z1u2v3w4x5y6"
+down_revision: str | Sequence[str] | None = "a1b2c3d4e5f6"
+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"ALTER TABLE {schema}memory_units ADD COLUMN IF NOT EXISTS observation_scopes JSONB")
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS observation_scopes")

hindsight-api/hindsight_api/api/http.py

@@ -74,7 +74,7 @@ from hindsight_api.config import get_config
 from hindsight_api.engine.db_utils import acquire_with_retry
 from hindsight_api.engine.memory_engine import Budget, _get_tiktoken_encoding, fq_table
 from hindsight_api.engine.reflect.observations import Observation
-from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, TokenUsage
+from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, MemoryFact, TokenUsage
 from hindsight_api.engine.search.tags import TagsMatch
 from hindsight_api.extensions import HttpExtension, OperationValidationError, load_extension
 from hindsight_api.metrics import create_metrics_collector, get_metrics_collector, initialize_metrics
@@ -97,6 +97,12 @@ class ChunkIncludeOptions(BaseModel):
     max_tokens: int = Field(default=8192, description="Maximum tokens for chunks (chunks may be truncated)")
 
 
+class SourceFactsIncludeOptions(BaseModel):
+    """Options for including source facts for observation-type results."""
+
+    max_tokens: int = Field(default=4096, description="Maximum tokens for source facts")
+
+
 class IncludeOptions(BaseModel):
     """Options for including additional data in recall results."""
 
@@ -107,6 +113,10 @@ class IncludeOptions(BaseModel):
     chunks: ChunkIncludeOptions | None = Field(
         default=None, description="Include raw chunks. Set to {} to enable, null to disable (default: disabled)."
     )
+    source_facts: SourceFactsIncludeOptions | None = Field(
+        default=None,
+        description="Include source facts for observation-type results. Set to {} to enable, null to disable (default: disabled).",
+    )
 
 
 class RecallRequest(BaseModel):
@@ -189,6 +199,9 @@ class RecallResult(BaseModel):
     metadata: dict[str, str] | None = None  # User-defined metadata
     chunk_id: str | None = None  # Chunk this fact was extracted from
     tags: list[str] | None = None  # Visibility scope tags
+    source_fact_ids: list[str] | None = (
+        None  # IDs of source facts (observation type only, when source_facts is enabled)
+    )
 
 
 class EntityObservationResponse(BaseModel):
@@ -340,6 +353,9 @@ class RecallResponse(BaseModel):
         default=None, description="Entity states for entities mentioned in results"
     )
     chunks: dict[str, ChunkData] | None = Field(default=None, description="Chunks for facts, keyed by chunk_id")
+    source_facts: dict[str, RecallResult] | None = Field(
+        default=None, description="Source facts for observation-type results, keyed by fact ID"
+    )
 
 
 class EntityInput(BaseModel):
@@ -367,7 +383,15 @@ class MemoryItem(BaseModel):
     )
 
     content: str
-    timestamp: datetime | None = None
+    timestamp: datetime | str | None = Field(
+        default=None,
+        description=(
+            "When the content occurred. "
+            "Accepts an ISO 8601 datetime string (e.g. '2024-01-15T10:30:00Z'), null/omitted (defaults to now), "
+            "or the special string 'unset' to explicitly store without any timestamp "
+            "(use this for timeless content such as fictional documents or static reference material)."
+        ),
+    )
     context: str | None = None
     metadata: dict[str, str] | None = None
     document_id: str | None = Field(default=None, description="Optional document ID for this memory item.")
@@ -379,6 +403,16 @@ class MemoryItem(BaseModel):
         default=None,
         description="Optional tags for visibility scoping. Memories with tags can be filtered during recall.",
     )
+    observation_scopes: Literal["per_tag", "combined", "all_combinations"] | list[list[str]] | None = Field(
+        default=None,
+        title="ObservationScopes",
+        description=(
+            "How to scope observations during consolidation. "
+            "'per_tag' runs one consolidation pass per individual tag, creating separate observations for each tag. "
+            "'combined' (default) runs a single pass with all tags together. "
+            "A list of tag lists runs one pass per inner list, giving full control over which combinations to use."
+        ),
+    )
 
     @field_validator("timestamp", mode="before")
     @classmethod
@@ -388,12 +422,14 @@ class MemoryItem(BaseModel):
         if isinstance(v, datetime):
             return v
         if isinstance(v, str):
+            if v.lower() == "unset":
+                return "unset"
             try:
                 # Try parsing as ISO format
                 return datetime.fromisoformat(v.replace("Z", "+00:00"))
             except ValueError as e:
                 raise ValueError(
-                    f"Invalid timestamp/event_date format: '{v}'. Expected ISO format like '2024-01-15T10:30:00' or '2024-01-15T10:30:00Z'"
+                    f"Invalid timestamp/event_date format: '{v}'. Expected ISO format like '2024-01-15T10:30:00' or '2024-01-15T10:30:00Z', or the special value 'unset' to store without a timestamp."
                 ) from e
         raise ValueError(f"timestamp must be a string or datetime, got {type(v).__name__}")
 
@@ -413,7 +449,6 @@ class RetainRequest(BaseModel):
                     },
                 ],
                 "async": False,
-                "document_tags": ["user_a", "user_b"],
             }
         }
     )
@@ -426,7 +461,8 @@ class RetainRequest(BaseModel):
     )
     document_tags: list[str] | None = Field(
         default=None,
-        description="Tags applied to all items in this request. These are merged with any item-level tags.",
+        description="Deprecated. Use item-level tags instead.",
+        deprecated=True,
     )
 
 
@@ -863,18 +899,103 @@ class CreateBankRequest(BaseModel):
     model_config = ConfigDict(
         json_schema_extra={
             "example": {
-                "name": "Alice",
-                "disposition": {"skepticism": 3, "literalism": 3, "empathy": 3},
-                "mission": "I am a PM helping my engineering team stay organized",
+                "retain_mission": "Always include technical decisions and architectural trade-offs. Ignore meeting logistics.",
+                "observations_mission": "Observations are stable facts about people and projects. Always include preferences and skills.",
             }
         }
     )
 
-    name: str | None = None
-    disposition: DispositionTraits | None = None
-    mission: str | None = Field(default=None, description="The agent's mission")
-    # Deprecated: use mission instead
-    background: str | None = Field(default=None, description="Deprecated: use mission instead")
+    # Deprecated fields — kept for backwards compatibility only
+    name: str | None = Field(default=None, description="Deprecated: display label only, not advertised")
+    disposition: DispositionTraits | None = Field(
+        default=None, description="Deprecated: use update_bank_config instead"
+    )
+    disposition_skepticism: int | None = Field(
+        default=None, ge=1, le=5, description="Deprecated: use update_bank_config instead"
+    )
+    disposition_literalism: int | None = Field(
+        default=None, ge=1, le=5, description="Deprecated: use update_bank_config instead"
+    )
+    disposition_empathy: int | None = Field(
+        default=None, ge=1, le=5, description="Deprecated: use update_bank_config instead"
+    )
+    # Deprecated: use update_bank_config with reflect_mission instead
+    mission: str | None = Field(
+        default=None, description="Deprecated: use update_bank_config with reflect_mission instead"
+    )
+    # Deprecated alias for mission
+    background: str | None = Field(
+        default=None, description="Deprecated: use update_bank_config with reflect_mission instead"
+    )
+
+    # Reflect configuration
+    reflect_mission: str | None = Field(
+        default=None,
+        description="Mission/context for Reflect operations. Guides how Reflect interprets and uses memories.",
+    )
+
+    # Operational configuration (applied via config resolver)
+    retain_mission: str | None = Field(
+        default=None,
+        description="Steers what gets extracted during retain(). Injected alongside built-in extraction rules.",
+    )
+    retain_extraction_mode: str | None = Field(
+        default=None,
+        description="Fact extraction mode: 'concise' (default), 'verbose', or 'custom'.",
+    )
+    retain_custom_instructions: str | None = Field(
+        default=None,
+        description="Custom extraction prompt. Only active when retain_extraction_mode is 'custom'.",
+    )
+    retain_chunk_size: int | None = Field(
+        default=None,
+        description="Maximum token size for each content chunk during retain.",
+    )
+    enable_observations: bool | None = Field(
+        default=None,
+        description="Toggle automatic observation consolidation after retain().",
+    )
+    observations_mission: str | None = Field(
+        default=None,
+        description="Controls what gets synthesised into observations. Replaces built-in consolidation rules entirely.",
+    )
+
+    def get_config_updates(self) -> dict[str, Any]:
+        """Return only the config fields that were explicitly set.
+
+        reflect_mission takes precedence over deprecated mission/background aliases.
+        Individual disposition_* fields take priority over the deprecated disposition dict.
+        """
+        updates: dict[str, Any] = {}
+        # Resolve reflect mission: reflect_mission (new) > mission (deprecated) > background (deprecated)
+        resolved_reflect_mission = self.reflect_mission or self.mission or self.background
+        if resolved_reflect_mission is not None:
+            updates["reflect_mission"] = resolved_reflect_mission
+        # Disposition: individual fields take priority over legacy disposition dict
+        if self.disposition_skepticism is not None:
+            updates["disposition_skepticism"] = self.disposition_skepticism
+        elif self.disposition is not None:
+            updates["disposition_skepticism"] = self.disposition.skepticism
+        if self.disposition_literalism is not None:
+            updates["disposition_literalism"] = self.disposition_literalism
+        elif self.disposition is not None:
+            updates["disposition_literalism"] = self.disposition.literalism
+        if self.disposition_empathy is not None:
+            updates["disposition_empathy"] = self.disposition_empathy
+        elif self.disposition is not None:
+            updates["disposition_empathy"] = self.disposition.empathy
+        for field_name in (
+            "retain_mission",
+            "retain_extraction_mode",
+            "retain_custom_instructions",
+            "retain_chunk_size",
+            "enable_observations",
+            "observations_mission",
+        ):
+            value = getattr(self, field_name)
+            if value is not None:
+                updates[field_name] = value
+        return updates
 
 
 class BankConfigUpdate(BaseModel):
@@ -1134,6 +1255,14 @@ class DeleteResponse(BaseModel):
     deleted_count: int | None = None
 
 
+class ClearMemoryObservationsResponse(BaseModel):
+    """Response model for clearing observations for a specific memory."""
+
+    model_config = ConfigDict(json_schema_extra={"example": {"deleted_count": 3}})
+
+    deleted_count: int
+
+
 class BankStatsResponse(BaseModel):
     """Response model for bank statistics endpoint."""
 
@@ -1701,6 +1830,12 @@ def create_app(
         app.include_router(extension_router, prefix="/ext", tags=["Extension"])
         logging.info("HTTP extension router mounted at /ext/")
 
+        # Mount root router if provided (for well-known endpoints, etc.)
+        root_router = http_extension.get_root_router(memory)
+        if root_router:
+            app.include_router(root_router)
+            logging.info("HTTP extension root router mounted")
+
     return app
 
 
@@ -1813,12 +1948,19 @@ def _register_routes(app: FastAPI):
         bank_id: str,
         type: str | None = None,
         limit: int = 1000,
+        q: str | None = None,
+        tags: list[str] | None = Query(None),
+        tags_match: str = "all_strict",
         request_context: RequestContext = Depends(get_request_context),
     ):
         """Get graph data from database, filtered by bank_id and optionally by type."""
         try:
-            data = await app.state.memory.get_graph_data(bank_id, type, limit=limit, request_context=request_context)
+            data = await app.state.memory.get_graph_data(
+                bank_id, type, limit=limit, q=q, tags=tags, tags_match=tags_match, request_context=request_context
+            )
             return data
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -1867,6 +2009,8 @@ def _register_routes(app: FastAPI):
                 request_context=request_context,
             )
             return data
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -1898,6 +2042,8 @@ def _register_routes(app: FastAPI):
             if data is None:
                 raise HTTPException(status_code=404, detail=f"Memory unit '{memory_id}' not found")
             return data
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -1959,6 +2105,10 @@ def _register_routes(app: FastAPI):
             include_chunks = request.include.chunks is not None
             max_chunk_tokens = request.include.chunks.max_tokens if include_chunks else 8192
 
+            # Determine source facts inclusion settings
+            include_source_facts = request.include.source_facts is not None
+            max_source_facts_tokens = request.include.source_facts.max_tokens if include_source_facts else 4096
+
             pre_recall = time.time() - handler_start
             # Run recall with tracing (record metrics)
             with metrics.record_operation(
@@ -1977,14 +2127,16 @@ def _register_routes(app: FastAPI):
                     max_entity_tokens=max_entity_tokens,
                     include_chunks=include_chunks,
                     max_chunk_tokens=max_chunk_tokens,
+                    include_source_facts=include_source_facts,
+                    max_source_facts_tokens=max_source_facts_tokens,
                     request_context=request_context,
                     tags=request.tags,
                     tags_match=request.tags_match,
                 )
 
             # Convert core MemoryFact objects to API RecallResult objects (excluding internal metrics)
-            recall_results = [
-                RecallResult(
+            def _fact_to_result(fact: "MemoryFact") -> RecallResult:
+                return RecallResult(
                     id=fact.id,
                     text=fact.text,
                     type=fact.fact_type,
@@ -1996,9 +2148,10 @@ def _register_routes(app: FastAPI):
                     document_id=fact.document_id,
                     chunk_id=fact.chunk_id,
                     tags=fact.tags,
+                    source_fact_ids=fact.source_fact_ids,
                 )
-                for fact in core_result.results
-            ]
+
+            recall_results = [_fact_to_result(fact) for fact in core_result.results]
 
             # Convert chunks from engine to HTTP API format
             chunks_response = None
@@ -2026,11 +2179,19 @@ def _register_routes(app: FastAPI):
                         ],
                     )
 
+            # Convert source facts dict to API format
+            source_facts_response = None
+            if core_result.source_facts:
+                source_facts_response = {
+                    fact_id: _fact_to_result(fact) for fact_id, fact in core_result.source_facts.items()
+                }
+
             response = RecallResponse(
                 results=recall_results,
                 trace=core_result.trace,
                 entities=entities_response,
                 chunks=chunks_response,
+                source_facts=source_facts_response,
             )
 
             handler_duration = time.time() - handler_start
@@ -2224,141 +2385,34 @@ def _register_routes(app: FastAPI):
     ):
         """Get statistics about memory nodes and links for a memory bank."""
         try:
-            # Authenticate and set tenant schema
-            await app.state.memory._authenticate_tenant(request_context)
-            pool = await app.state.memory._get_pool()
-            async with acquire_with_retry(pool) as conn:
-                # Get node counts by fact_type
-                node_stats = await conn.fetch(
-                    f"""
-                    SELECT fact_type, COUNT(*) as count
-                    FROM {fq_table("memory_units")}
-                    WHERE bank_id = $1
-                    GROUP BY fact_type
-                    """,
-                    bank_id,
-                )
-
-                # Get link counts by link_type
-                link_stats = await conn.fetch(
-                    f"""
-                    SELECT ml.link_type, COUNT(*) as count
-                    FROM {fq_table("memory_links")} ml
-                    JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
-                    WHERE mu.bank_id = $1
-                    GROUP BY ml.link_type
-                    """,
-                    bank_id,
-                )
-
-                # Get link counts by fact_type (from nodes)
-                link_fact_type_stats = await conn.fetch(
-                    f"""
-                    SELECT mu.fact_type, COUNT(*) as count
-                    FROM {fq_table("memory_links")} ml
-                    JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
-                    WHERE mu.bank_id = $1
-                    GROUP BY mu.fact_type
-                    """,
-                    bank_id,
-                )
-
-                # Get link counts by fact_type AND link_type
-                link_breakdown_stats = await conn.fetch(
-                    f"""
-                    SELECT mu.fact_type, ml.link_type, COUNT(*) as count
-                    FROM {fq_table("memory_links")} ml
-                    JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
-                    WHERE mu.bank_id = $1
-                    GROUP BY mu.fact_type, ml.link_type
-                    """,
-                    bank_id,
-                )
-
-                # Get pending and failed operations counts
-                ops_stats = await conn.fetch(
-                    f"""
-                    SELECT status, COUNT(*) as count
-                    FROM {fq_table("async_operations")}
-                    WHERE bank_id = $1
-                    GROUP BY status
-                    """,
-                    bank_id,
-                )
-                ops_by_status = {row["status"]: row["count"] for row in ops_stats}
-                pending_operations = ops_by_status.get("pending", 0)
-                failed_operations = ops_by_status.get("failed", 0)
-
-                # Get document count
-                doc_count_result = await conn.fetchrow(
-                    f"""
-                    SELECT COUNT(*) as count
-                    FROM {fq_table("documents")}
-                    WHERE bank_id = $1
-                    """,
-                    bank_id,
-                )
-                total_documents = doc_count_result["count"] if doc_count_result else 0
-
-                # Get consolidation stats from memory-level tracking
-                consolidation_stats = await conn.fetchrow(
-                    f"""
-                    SELECT
-                        MAX(consolidated_at) as last_consolidated_at,
-                        COUNT(*) FILTER (WHERE consolidated_at IS NULL AND fact_type IN ('experience', 'world')) as pending
-                    FROM {fq_table("memory_units")}
-                    WHERE bank_id = $1
-                    """,
-                    bank_id,
-                )
-                last_consolidated_at = consolidation_stats["last_consolidated_at"] if consolidation_stats else None
-                pending_consolidation = consolidation_stats["pending"] if consolidation_stats else 0
-
-                # Count total observations (consolidated knowledge)
-                observation_count_result = await conn.fetchrow(
-                    f"""
-                    SELECT COUNT(*) as count
-                    FROM {fq_table("memory_units")}
-                    WHERE bank_id = $1 AND fact_type = 'observation'
-                    """,
-                    bank_id,
-                )
-                total_observations = observation_count_result["count"] if observation_count_result else 0
-
-                # Format results
-                nodes_by_type = {row["fact_type"]: row["count"] for row in node_stats}
-                links_by_type = {row["link_type"]: row["count"] for row in link_stats}
-                links_by_fact_type = {row["fact_type"]: row["count"] for row in link_fact_type_stats}
-
-                # Build detailed breakdown: {fact_type: {link_type: count}}
-                links_breakdown = {}
-                for row in link_breakdown_stats:
-                    fact_type = row["fact_type"]
-                    link_type = row["link_type"]
-                    count = row["count"]
-                    if fact_type not in links_breakdown:
-                        links_breakdown[fact_type] = {}
-                    links_breakdown[fact_type][link_type] = count
-
-                total_nodes = sum(nodes_by_type.values())
-                total_links = sum(links_by_type.values())
-
-                return BankStatsResponse(
-                    bank_id=bank_id,
-                    total_nodes=total_nodes,
-                    total_links=total_links,
-                    total_documents=total_documents,
-                    nodes_by_fact_type=nodes_by_type,
-                    links_by_link_type=links_by_type,
-                    links_by_fact_type=links_by_fact_type,
-                    links_breakdown=links_breakdown,
-                    pending_operations=pending_operations,
-                    failed_operations=failed_operations,
-                    last_consolidated_at=(last_consolidated_at.isoformat() if last_consolidated_at else None),
-                    pending_consolidation=pending_consolidation,
-                    total_observations=total_observations,
-                )
-
+            stats = await app.state.memory.get_bank_stats(bank_id, request_context=request_context)
+            nodes_by_type = stats["node_counts"]
+            links_by_type = stats["link_counts"]
+            links_by_fact_type = stats["link_counts_by_fact_type"]
+            links_breakdown: dict[str, dict[str, int]] = {}
+            for row in stats["link_breakdown"]:
+                ft = row["fact_type"]
+                if ft not in links_breakdown:
+                    links_breakdown[ft] = {}
+                links_breakdown[ft][row["link_type"]] = row["count"]
+            ops = stats["operations"]
+            return BankStatsResponse(
+                bank_id=bank_id,
+                total_nodes=sum(nodes_by_type.values()),
+                total_links=sum(links_by_type.values()),
+                total_documents=stats["total_documents"],
+                nodes_by_fact_type=nodes_by_type,
+                links_by_link_type=links_by_type,
+                links_by_fact_type=links_by_fact_type,
+                links_breakdown=links_breakdown,
+                pending_operations=ops.get("pending", 0),
+                failed_operations=ops.get("failed", 0),
+                last_consolidated_at=stats["last_consolidated_at"],
+                pending_consolidation=stats["pending_consolidation"],
+                total_observations=stats["total_observations"],
+            )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2393,6 +2447,8 @@ def _register_routes(app: FastAPI):
                 limit=data["limit"],
                 offset=data["offset"],
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2432,6 +2488,8 @@ def _register_routes(app: FastAPI):
                     for obs in entity["observations"]
                 ],
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2493,6 +2551,8 @@ def _register_routes(app: FastAPI):
                 request_context=request_context,
             )
             return MentalModelListResponse(items=[MentalModelResponse(**m) for m in mental_models])
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2651,6 +2711,8 @@ def _register_routes(app: FastAPI):
             if mental_model is None:
                 raise HTTPException(status_code=404, detail=f"Mental model '{mental_model_id}' not found")
             return MentalModelResponse(**mental_model)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2682,6 +2744,8 @@ def _register_routes(app: FastAPI):
             if not deleted:
                 raise HTTPException(status_code=404, detail=f"Mental model '{mental_model_id}' not found")
             return {"status": "deleted"}
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2724,6 +2788,8 @@ def _register_routes(app: FastAPI):
                 request_context=request_context,
             )
             return DirectiveListResponse(items=[DirectiveResponse(**d) for d in directives])
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2756,6 +2822,8 @@ def _register_routes(app: FastAPI):
             if directive is None:
                 raise HTTPException(status_code=404, detail=f"Directive '{directive_id}' not found")
             return DirectiveResponse(**directive)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2792,6 +2860,8 @@ def _register_routes(app: FastAPI):
             return DirectiveResponse(**directive)
         except ValueError as e:
             raise HTTPException(status_code=400, detail=str(e))
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2830,6 +2900,8 @@ def _register_routes(app: FastAPI):
             if directive is None:
                 raise HTTPException(status_code=404, detail=f"Directive '{directive_id}' not found")
             return DirectiveResponse(**directive)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2861,6 +2933,8 @@ def _register_routes(app: FastAPI):
             if not deleted:
                 raise HTTPException(status_code=404, detail=f"Directive '{directive_id}' not found")
             return {"status": "deleted"}
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2880,7 +2954,13 @@ def _register_routes(app: FastAPI):
     )
     async def api_list_documents(
         bank_id: str,
-        q: str | None = None,
+        q: str | None = Query(
+            None, description="Case-insensitive substring filter on document ID (e.g. 'report' matches 'report-2024')"
+        ),
+        tags: list[str] | None = Query(None, description="Filter documents by tags"),
+        tags_match: str = Query(
+            "any_strict", description="How to match tags: 'any', 'all', 'any_strict', 'all_strict'"
+        ),
         limit: int = 100,
         offset: int = 0,
         request_context: RequestContext = Depends(get_request_context),
@@ -2890,15 +2970,25 @@ def _register_routes(app: FastAPI):
 
         Args:
             bank_id: Memory Bank ID (from path)
-            q: Search query (searches document ID and metadata)
+            q: Case-insensitive substring filter on document ID
+            tags: Filter documents by tags
+            tags_match: How to match tags (any, all, any_strict, all_strict)
             limit: Maximum number of results (default: 100)
             offset: Offset for pagination (default: 0)
         """
         try:
             data = await app.state.memory.list_documents(
-                bank_id=bank_id, search_query=q, limit=limit, offset=offset, request_context=request_context
+                bank_id=bank_id,
+                search_query=q,
+                tags=tags,
+                tags_match=tags_match,
+                limit=limit,
+                offset=offset,
+                request_context=request_context,
             )
             return data
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2931,6 +3021,8 @@ def _register_routes(app: FastAPI):
             if not document:
                 raise HTTPException(status_code=404, detail="Document not found")
             return document
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -2984,6 +3076,8 @@ def _register_routes(app: FastAPI):
                 request_context=request_context,
             )
             return data
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3013,6 +3107,8 @@ def _register_routes(app: FastAPI):
             if not chunk:
                 raise HTTPException(status_code=404, detail="Chunk not found")
             return chunk
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3057,6 +3153,8 @@ def _register_routes(app: FastAPI):
                 document_id=document_id,
                 memory_units_deleted=result["memory_units_deleted"],
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3093,6 +3191,8 @@ def _register_routes(app: FastAPI):
                 offset=offset,
                 operations=[OperationResponse(**op) for op in result["operations"]],
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3124,6 +3224,8 @@ def _register_routes(app: FastAPI):
 
             result = await app.state.memory.get_operation_status(bank_id, operation_id, request_context=request_context)
             return OperationStatusResponse(**result)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3156,6 +3258,8 @@ def _register_routes(app: FastAPI):
             return CancelOperationResponse(**result)
         except ValueError as e:
             raise HTTPException(status_code=404, detail=str(e))
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3172,6 +3276,7 @@ def _register_routes(app: FastAPI):
         description="Get disposition traits and mission for a memory bank. Auto-creates agent with defaults if not exists.",
         operation_id="get_bank_profile",
         tags=["Banks"],
+        deprecated=True,
     )
     async def api_get_bank_profile(bank_id: str, request_context: RequestContext = Depends(get_request_context)):
         """Get memory bank profile (disposition + mission)."""
@@ -3191,6 +3296,8 @@ def _register_routes(app: FastAPI):
                 mission=mission,
                 background=mission,  # Backwards compat
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3207,6 +3314,7 @@ def _register_routes(app: FastAPI):
         description="Update bank's disposition traits (skepticism, literalism, empathy)",
         operation_id="update_bank_disposition",
         tags=["Banks"],
+        deprecated=True,
     )
     async def api_update_bank_disposition(
         bank_id: str, request: UpdateDispositionRequest, request_context: RequestContext = Depends(get_request_context)
@@ -3233,6 +3341,8 @@ def _register_routes(app: FastAPI):
                 mission=mission,
                 background=mission,  # Backwards compat
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3261,6 +3371,8 @@ def _register_routes(app: FastAPI):
             )
             mission = result.get("mission") or ""
             return BackgroundResponse(mission=mission, background=mission)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3286,21 +3398,18 @@ def _register_routes(app: FastAPI):
             # Ensure bank exists by getting profile (auto-creates with defaults)
             await app.state.memory.get_bank_profile(bank_id, request_context=request_context)
 
-            # Update name and/or mission if provided (support both mission and deprecated background)
-            mission_value = request.mission or request.background
-            if request.name is not None or mission_value is not None:
+            # Update name if provided (stored in DB for display only, deprecated)
+            if request.name is not None:
                 await app.state.memory.update_bank(
                     bank_id,
                     name=request.name,
-                    mission=mission_value,
                     request_context=request_context,
                 )
 
-            # Update disposition if provided
-            if request.disposition is not None:
-                await app.state.memory.update_bank_disposition(
-                    bank_id, request.disposition.model_dump(), request_context=request_context
-                )
+            # Apply all config overrides (includes reflect_mission, disposition, retain settings)
+            config_updates = request.get_config_updates()
+            if config_updates:
+                await app.state.memory._config_resolver.update_bank_config(bank_id, config_updates, request_context)
 
             # Get final profile
             final_profile = await app.state.memory.get_bank_profile(bank_id, request_context=request_context)
@@ -3317,6 +3426,8 @@ def _register_routes(app: FastAPI):
                 mission=mission,
                 background=mission,  # Backwards compat
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3342,21 +3453,18 @@ def _register_routes(app: FastAPI):
             # Ensure bank exists
             await app.state.memory.get_bank_profile(bank_id, request_context=request_context)
 
-            # Update name and/or mission if provided
-            mission_value = request.mission or request.background
-            if request.name is not None or mission_value is not None:
+            # Update name if provided (stored in DB for display only, deprecated)
+            if request.name is not None:
                 await app.state.memory.update_bank(
                     bank_id,
                     name=request.name,
-                    mission=mission_value,
                     request_context=request_context,
                 )
 
-            # Update disposition if provided
-            if request.disposition is not None:
-                await app.state.memory.update_bank_disposition(
-                    bank_id, request.disposition.model_dump(), request_context=request_context
-                )
+            # Apply all config overrides (includes reflect_mission, disposition, retain settings)
+            config_updates = request.get_config_updates()
+            if config_updates:
+                await app.state.memory._config_resolver.update_bank_config(bank_id, config_updates, request_context)
 
             # Get final profile
             final_profile = await app.state.memory.get_bank_profile(bank_id, request_context=request_context)
@@ -3373,6 +3481,8 @@ def _register_routes(app: FastAPI):
                 mission=mission,
                 background=mission,  # Backwards compat
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3402,6 +3512,8 @@ def _register_routes(app: FastAPI):
                 + result.get("entities_deleted", 0)
                 + result.get("documents_deleted", 0),
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3428,6 +3540,8 @@ def _register_routes(app: FastAPI):
                 message=f"Cleared {result.get('deleted_count', 0)} observations",
                 deleted_count=result.get("deleted_count", 0),
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3437,6 +3551,42 @@ def _register_routes(app: FastAPI):
             logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/observations: {error_detail}")
             raise HTTPException(status_code=500, detail=str(e))
 
+    @app.delete(
+        "/v1/default/banks/{bank_id}/memories/{memory_id}/observations",
+        response_model=ClearMemoryObservationsResponse,
+        summary="Clear observations for a memory",
+        description="Delete all observations derived from a specific memory and reset it for re-consolidation. "
+        "The memory itself is not deleted. A consolidation job is triggered automatically so the memory "
+        "will produce fresh observations on the next consolidation run.",
+        operation_id="clear_memory_observations",
+        tags=["Memory"],
+    )
+    async def api_clear_memory_observations(
+        bank_id: str,
+        memory_id: str,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Clear all observations derived from a specific memory."""
+        try:
+            result = await app.state.memory.clear_observations_for_memory(
+                bank_id=bank_id,
+                memory_id=memory_id,
+                request_context=request_context,
+            )
+            return ClearMemoryObservationsResponse(deleted_count=result["deleted_count"])
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(
+                f"Error in DELETE /v1/default/banks/{bank_id}/memories/{memory_id}/observations: {error_detail}"
+            )
+            raise HTTPException(status_code=500, detail=str(e))
+
     @app.get(
         "/v1/default/banks/{bank_id}/config",
         response_model=BankConfigResponse,
@@ -3451,9 +3601,19 @@ def _register_routes(app: FastAPI):
         if not get_config().enable_bank_config_api:
             raise HTTPException(
                 status_code=404,
-                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to re-enable.",
             )
         try:
+            # Authenticate and set schema context for multi-tenant DB queries
+            await app.state.memory._authenticate_tenant(request_context)
+            if app.state.memory._operation_validator:
+                from hindsight_api.extensions import BankReadContext
+
+                ctx = BankReadContext(bank_id=bank_id, operation="get_bank_config", request_context=request_context)
+                await app.state.memory._validate_operation(
+                    app.state.memory._operation_validator.validate_bank_read(ctx)
+                )
+
             # Get resolved config from config resolver
             config_dict = await app.state.memory._config_resolver.get_bank_config(bank_id, request_context)
 
@@ -3461,6 +3621,8 @@ def _register_routes(app: FastAPI):
             bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
 
             return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3486,9 +3648,19 @@ def _register_routes(app: FastAPI):
         if not get_config().enable_bank_config_api:
             raise HTTPException(
                 status_code=404,
-                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to re-enable.",
             )
         try:
+            # Authenticate and set schema context for multi-tenant DB queries
+            await app.state.memory._authenticate_tenant(request_context)
+            if app.state.memory._operation_validator:
+                from hindsight_api.extensions import BankWriteContext
+
+                ctx = BankWriteContext(bank_id=bank_id, operation="update_bank_config", request_context=request_context)
+                await app.state.memory._validate_operation(
+                    app.state.memory._operation_validator.validate_bank_write(ctx)
+                )
+
             # Update config via config resolver (validates configurable fields and permissions)
             await app.state.memory._config_resolver.update_bank_config(bank_id, request.updates, request_context)
 
@@ -3497,6 +3669,8 @@ def _register_routes(app: FastAPI):
             bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
 
             return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except ValueError as e:
             # Validation error (e.g., trying to override static field)
             raise HTTPException(status_code=400, detail=str(e))
@@ -3523,9 +3697,19 @@ def _register_routes(app: FastAPI):
         if not get_config().enable_bank_config_api:
             raise HTTPException(
                 status_code=404,
-                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to enable.",
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to re-enable.",
             )
         try:
+            # Authenticate and set schema context for multi-tenant DB queries
+            await app.state.memory._authenticate_tenant(request_context)
+            if app.state.memory._operation_validator:
+                from hindsight_api.extensions import BankWriteContext
+
+                ctx = BankWriteContext(bank_id=bank_id, operation="reset_bank_config", request_context=request_context)
+                await app.state.memory._validate_operation(
+                    app.state.memory._operation_validator.validate_bank_write(ctx)
+                )
+
             # Reset config via config resolver
             await app.state.memory._config_resolver.reset_bank_config(bank_id)
 
@@ -3534,6 +3718,8 @@ def _register_routes(app: FastAPI):
             bank_overrides = await app.state.memory._config_resolver._load_bank_config(bank_id)
 
             return BankConfigResponse(bank_id=bank_id, config=config_dict, overrides=bank_overrides)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3559,6 +3745,8 @@ def _register_routes(app: FastAPI):
                 operation_id=result["operation_id"],
                 deduplicated=result.get("deduplicated", False),
             )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:
@@ -3604,7 +3792,9 @@ def _register_routes(app: FastAPI):
             contents = []
             for item in request.items:
                 content_dict = {"content": item.content}
-                if item.timestamp:
+                if item.timestamp == "unset":
+                    content_dict["event_date"] = None
+                elif item.timestamp:
                     content_dict["event_date"] = item.timestamp
                 if item.context:
                     content_dict["context"] = item.context
@@ -3616,6 +3806,8 @@ def _register_routes(app: FastAPI):
                     content_dict["entities"] = [{"text": e.text, "type": e.type or "CONCEPT"} for e in item.entities]
                 if item.tags:
                     content_dict["tags"] = item.tags
+                if item.observation_scopes is not None:
+                    content_dict["observation_scopes"] = item.observation_scopes
                 contents.append(content_dict)
 
             if request.async_:
@@ -3844,6 +4036,8 @@ def _register_routes(app: FastAPI):
             await app.state.memory.delete_bank(bank_id, fact_type=type, request_context=request_context)
 
             return DeleteResponse(success=True)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
         except Exception as e:

hindsight-api/hindsight_api/api/mcp.py

@@ -8,12 +8,48 @@ from contextvars import ContextVar
 from fastmcp import FastMCP
 
 from hindsight_api import MemoryEngine
+from hindsight_api.config import _get_raw_config
 from hindsight_api.engine.memory_engine import _current_schema
 from hindsight_api.extensions import MCPExtension, load_extension
 from hindsight_api.extensions.tenant import AuthenticationError
 from hindsight_api.mcp_tools import MCPToolsConfig, register_mcp_tools
 from hindsight_api.models import RequestContext
 
+# All tools available in the system (explicit list — no wildcards)
+_ALL_TOOLS: frozenset[str] = frozenset(
+    {
+        "retain",
+        "recall",
+        "reflect",
+        "list_banks",
+        "create_bank",
+        "list_mental_models",
+        "get_mental_model",
+        "create_mental_model",
+        "update_mental_model",
+        "delete_mental_model",
+        "refresh_mental_model",
+        "list_directives",
+        "create_directive",
+        "delete_directive",
+        "list_memories",
+        "get_memory",
+        "delete_memory",
+        "list_documents",
+        "get_document",
+        "delete_document",
+        "list_operations",
+        "get_operation",
+        "cancel_operation",
+        "list_tags",
+        "get_bank",
+        "get_bank_stats",
+        "update_bank",
+        "delete_bank",
+        "clear_memories",
+    }
+)
+
 # Configure logging from HINDSIGHT_API_LOG_LEVEL environment variable
 _log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
 _log_level_map = {
@@ -78,21 +114,15 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
                    If False, only expose bank-scoped tools without bank_id parameters.
 
     Returns:
-        Configured FastMCP server instance with stateless_http enabled
+        Configured FastMCP server instance
     """
-    # Use stateless_http=True for Claude Code compatibility
-    mcp = FastMCP("hindsight-mcp-server", stateless_http=True)
+    mcp = FastMCP("hindsight-mcp-server")
 
-    # Configure and register tools using shared module
-    config = MCPToolsConfig(
-        bank_id_resolver=get_current_bank_id,
-        api_key_resolver=get_current_api_key,  # Propagate API key for tenant auth
-        tenant_id_resolver=get_current_tenant_id,  # Propagate tenant_id for usage metering
-        api_key_id_resolver=get_current_api_key_id,  # Propagate api_key_id for usage metering
-        include_bank_id_param=multi_bank,
-        tools=None
-        if multi_bank
-        else {
+    global_config = _get_raw_config()
+
+    # Tools available for this mode (multi-bank exposes all tools; single-bank excludes bank-management tools)
+    _SINGLE_BANK_TOOLS: frozenset[str] = frozenset(
+        {
             "retain",
             "recall",
             "reflect",
@@ -102,8 +132,40 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
             "update_mental_model",
             "delete_mental_model",
             "refresh_mental_model",
-        },  # Scoped tools for single-bank mode (excludes bank management: list_banks, create_bank)
-        retain_fire_and_forget=False,  # HTTP MCP supports sync/async modes
+            "list_directives",
+            "create_directive",
+            "delete_directive",
+            "list_memories",
+            "get_memory",
+            "delete_memory",
+            "list_documents",
+            "get_document",
+            "delete_document",
+            "list_operations",
+            "get_operation",
+            "cancel_operation",
+            "list_tags",
+            "get_bank",
+            "update_bank",
+            "delete_bank",
+            "clear_memories",
+        }
+    )
+    base_tools: frozenset[str] | None = None if multi_bank else _SINGLE_BANK_TOOLS
+
+    # Apply global mcp_enabled_tools filter (env-level allowlist)
+    if global_config.mcp_enabled_tools is not None:
+        allowed = frozenset(global_config.mcp_enabled_tools)
+        base_tools = (base_tools if base_tools is not None else _ALL_TOOLS) & allowed
+
+    # Configure and register tools using shared module
+    config = MCPToolsConfig(
+        bank_id_resolver=get_current_bank_id,
+        api_key_resolver=get_current_api_key,  # Propagate API key for tenant auth
+        tenant_id_resolver=get_current_tenant_id,  # Propagate tenant_id for usage metering
+        api_key_id_resolver=get_current_api_key_id,  # Propagate api_key_id for usage metering
+        include_bank_id_param=multi_bank,
+        tools=base_tools,
     )
 
     register_mcp_tools(mcp, memory, config)
@@ -211,9 +273,9 @@ class MCPMiddleware:
         else:
             # Create servers internally (for direct construction / tests)
             self.multi_bank_server = create_mcp_server(memory, multi_bank=True)
-            self.multi_bank_app = self.multi_bank_server.http_app(path="/")
+            self.multi_bank_app = self.multi_bank_server.http_app(path="/", stateless_http=True)
             self.single_bank_server = create_mcp_server(memory, multi_bank=False)
-            self.single_bank_app = self.single_bank_server.http_app(path="/")
+            self.single_bank_app = self.single_bank_server.http_app(path="/", stateless_http=True)
 
     def _get_header(self, scope: dict, name: str) -> str | None:
         """Extract a header value from ASGI scope."""
@@ -269,7 +331,7 @@ class MCPMiddleware:
                 auth_tenant_id = auth_context.tenant_id
                 auth_api_key_id = auth_context.api_key_id
             except AuthenticationError as e:
-                await self._send_error(send, 401, str(e))
+                await self._send_error(send, 401, str(e), extra_headers=e.headers)
                 return
 
         # Set schema from tenant context so downstream DB queries use the correct schema
@@ -351,14 +413,17 @@ class MCPMiddleware:
             if schema_token is not None:
                 _current_schema.reset(schema_token)
 
-    async def _send_error(self, send, status: int, message: str):
+    async def _send_error(self, send, status: int, message: str, extra_headers: dict[str, str] | None = None):
         """Send an error response."""
         body = json.dumps({"error": message}).encode()
+        headers = [(b"content-type", b"application/json")]
+        for key, value in (extra_headers or {}).items():
+            headers.append((key.encode(), value.encode()))
         await send(
             {
                 "type": "http.response.start",
                 "status": status,
-                "headers": [(b"content-type", b"application/json")],
+                "headers": headers,
             }
         )
         await send(
@@ -379,9 +444,9 @@ def create_mcp_servers(memory: MemoryEngine):
         Tuple of (multi_bank_server, single_bank_server, multi_bank_app, single_bank_app)
     """
     multi_bank_server = create_mcp_server(memory, multi_bank=True)
-    multi_bank_app = multi_bank_server.http_app(path="/")
+    multi_bank_app = multi_bank_server.http_app(path="/", stateless_http=True)
 
     single_bank_server = create_mcp_server(memory, multi_bank=False)
-    single_bank_app = single_bank_server.http_app(path="/")
+    single_bank_app = single_bank_server.http_app(path="/", stateless_http=True)
 
     return multi_bank_server, single_bank_server, multi_bank_app, single_bank_app

hindsight-api/hindsight_api/config.py

@@ -218,6 +218,10 @@ 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"
 
+# ZeroEntropy configuration (reranker only)
+ENV_RERANKER_ZEROENTROPY_API_KEY = "HINDSIGHT_API_RERANKER_ZEROENTROPY_API_KEY"
+ENV_RERANKER_ZEROENTROPY_MODEL = "HINDSIGHT_API_RERANKER_ZEROENTROPY_MODEL"
+
 ENV_VECTOR_EXTENSION = "HINDSIGHT_API_VECTOR_EXTENSION"
 ENV_TEXT_SEARCH_EXTENSION = "HINDSIGHT_API_TEXT_SEARCH_EXTENSION"
 
@@ -228,13 +232,12 @@ ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
 ENV_LOG_FORMAT = "HINDSIGHT_API_LOG_FORMAT"
 ENV_WORKERS = "HINDSIGHT_API_WORKERS"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
+ENV_MCP_ENABLED_TOOLS = "HINDSIGHT_API_MCP_ENABLED_TOOLS"
 ENV_ENABLE_BANK_CONFIG_API = "HINDSIGHT_API_ENABLE_BANK_CONFIG_API"
 ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
 ENV_MPFP_TOP_K_NEIGHBORS = "HINDSIGHT_API_MPFP_TOP_K_NEIGHBORS"
 ENV_RECALL_MAX_CONCURRENT = "HINDSIGHT_API_RECALL_MAX_CONCURRENT"
 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"
 ENV_MENTAL_MODEL_REFRESH_CONCURRENCY = "HINDSIGHT_API_MENTAL_MODEL_REFRESH_CONCURRENCY"
 
 # OpenTelemetry tracing configuration
@@ -249,13 +252,18 @@ ENV_LLM_VERTEXAI_PROJECT_ID = "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID"
 ENV_LLM_VERTEXAI_REGION = "HINDSIGHT_API_LLM_VERTEXAI_REGION"
 ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY = "HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY"
 
+# Gemini safety settings
+ENV_LLM_GEMINI_SAFETY_SETTINGS = "HINDSIGHT_API_LLM_GEMINI_SAFETY_SETTINGS"
+
 # 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_MISSION = "HINDSIGHT_API_RETAIN_MISSION"
 ENV_RETAIN_CUSTOM_INSTRUCTIONS = "HINDSIGHT_API_RETAIN_CUSTOM_INSTRUCTIONS"
 ENV_RETAIN_BATCH_TOKENS = "HINDSIGHT_API_RETAIN_BATCH_TOKENS"
+ENV_RETAIN_ENTITY_LOOKUP = "HINDSIGHT_API_RETAIN_ENTITY_LOOKUP"
 ENV_RETAIN_BATCH_ENABLED = "HINDSIGHT_API_RETAIN_BATCH_ENABLED"
 ENV_RETAIN_BATCH_POLL_INTERVAL_SECONDS = "HINDSIGHT_API_RETAIN_BATCH_POLL_INTERVAL_SECONDS"
 
@@ -272,6 +280,8 @@ ENV_FILE_STORAGE_AZURE_CONTAINER = "HINDSIGHT_API_FILE_STORAGE_AZURE_CONTAINER"
 ENV_FILE_STORAGE_AZURE_ACCOUNT_NAME = "HINDSIGHT_API_FILE_STORAGE_AZURE_ACCOUNT_NAME"
 ENV_FILE_STORAGE_AZURE_ACCOUNT_KEY = "HINDSIGHT_API_FILE_STORAGE_AZURE_ACCOUNT_KEY"
 ENV_FILE_PARSER = "HINDSIGHT_API_FILE_PARSER"
+ENV_FILE_PARSER_IRIS_TOKEN = "HINDSIGHT_API_FILE_PARSER_IRIS_TOKEN"
+ENV_FILE_PARSER_IRIS_ORG_ID = "HINDSIGHT_API_FILE_PARSER_IRIS_ORG_ID"
 ENV_FILE_CONVERSION_MAX_BATCH_SIZE_MB = "HINDSIGHT_API_FILE_CONVERSION_MAX_BATCH_SIZE_MB"
 ENV_FILE_CONVERSION_MAX_BATCH_SIZE = "HINDSIGHT_API_FILE_CONVERSION_MAX_BATCH_SIZE"
 ENV_ENABLE_FILE_UPLOAD_API = "HINDSIGHT_API_ENABLE_FILE_UPLOAD_API"
@@ -280,7 +290,9 @@ ENV_FILE_DELETE_AFTER_RETAIN = "HINDSIGHT_API_FILE_DELETE_AFTER_RETAIN"
 # Observations settings (consolidated knowledge from facts)
 ENV_ENABLE_OBSERVATIONS = "HINDSIGHT_API_ENABLE_OBSERVATIONS"
 ENV_CONSOLIDATION_BATCH_SIZE = "HINDSIGHT_API_CONSOLIDATION_BATCH_SIZE"
+ENV_CONSOLIDATION_LLM_BATCH_SIZE = "HINDSIGHT_API_CONSOLIDATION_LLM_BATCH_SIZE"
 ENV_CONSOLIDATION_MAX_TOKENS = "HINDSIGHT_API_CONSOLIDATION_MAX_TOKENS"
+ENV_OBSERVATIONS_MISSION = "HINDSIGHT_API_OBSERVATIONS_MISSION"
 
 # Optimization flags
 ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
@@ -306,6 +318,13 @@ ENV_WORKER_CONSOLIDATION_MAX_SLOTS = "HINDSIGHT_API_WORKER_CONSOLIDATION_MAX_SLO
 
 # Reflect agent settings
 ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"
+ENV_REFLECT_MAX_CONTEXT_TOKENS = "HINDSIGHT_API_REFLECT_MAX_CONTEXT_TOKENS"
+ENV_REFLECT_MISSION = "HINDSIGHT_API_REFLECT_MISSION"
+
+# Disposition settings
+ENV_DISPOSITION_SKEPTICISM = "HINDSIGHT_API_DISPOSITION_SKEPTICISM"
+ENV_DISPOSITION_LITERALISM = "HINDSIGHT_API_DISPOSITION_LITERALISM"
+ENV_DISPOSITION_EMPATHY = "HINDSIGHT_API_DISPOSITION_EMPATHY"
 
 # Default values
 DEFAULT_DATABASE_URL = "pg0"
@@ -314,18 +333,18 @@ DEFAULT_LLM_PROVIDER = "openai"
 
 # Provider-specific default models
 PROVIDER_DEFAULT_MODELS = {
-    "openai": "o3-mini",
+    "openai": "gpt-4o-mini",
     "anthropic": "claude-haiku-4-5-20251001",
     "gemini": "gemini-2.5-flash",
     "groq": "openai/gpt-oss-120b",
     "ollama": "gemma3:12b",
     "lmstudio": "local-model",
-    "vertexai": "gemini-2.0-flash-001",
+    "vertexai": "google/gemini-2.5-flash-lite",
     "openai-codex": "gpt-5.2-codex",
     "claude-code": "claude-sonnet-4-5-20250929",
     "mock": "mock-model",
 }
-DEFAULT_LLM_MODEL = "o3-mini"  # Fallback if provider not in table
+DEFAULT_LLM_MODEL = "gpt-4o-mini"  # Fallback if provider not in table
 DEFAULT_LLM_MAX_CONCURRENT = 32
 DEFAULT_LLM_MAX_RETRIES = 10  # Max retry attempts for LLM API calls
 DEFAULT_LLM_INITIAL_BACKOFF = 1.0  # Initial backoff in seconds for retry exponential backoff
@@ -337,6 +356,9 @@ DEFAULT_LLM_VERTEXAI_PROJECT_ID = None  # Required for Vertex AI
 DEFAULT_LLM_VERTEXAI_REGION = "us-central1"
 DEFAULT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY = None  # Optional, uses ADC if not set
 
+# Gemini safety settings defaults
+DEFAULT_LLM_GEMINI_SAFETY_SETTINGS = None  # None = use Gemini default safety settings
+
 DEFAULT_EMBEDDINGS_PROVIDER = "local"
 DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
 DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU = False  # Force CPU mode for local embeddings (avoids MPS/XPC issues on macOS)
@@ -360,6 +382,8 @@ 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_RERANKER_ZEROENTROPY_MODEL = "zerank-2"
+
 # Vector extension (pgvector, vchord, or pgvectorscale)
 DEFAULT_VECTOR_EXTENSION = "pgvector"  # Options: "pgvector", "vchord", "pgvectorscale"
 
@@ -382,12 +406,12 @@ DEFAULT_LOG_LEVEL = "info"
 DEFAULT_LOG_FORMAT = "text"  # Options: "text", "json"
 DEFAULT_WORKERS = 1
 DEFAULT_MCP_ENABLED = True
-DEFAULT_ENABLE_BANK_CONFIG_API = False  # Disabled by default for security
+DEFAULT_MCP_ENABLED_TOOLS: list[str] | None = None  # None = all tools enabled
+DEFAULT_ENABLE_BANK_CONFIG_API = True
 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"
 DEFAULT_MENTAL_MODEL_REFRESH_CONCURRENCY = 8  # Max concurrent mental model refreshes
 
 # Retain settings
@@ -396,8 +420,10 @@ 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", "verbose", or "custom"
 RETAIN_EXTRACTION_MODES = ("concise", "verbose", "custom")  # Allowed extraction modes
+DEFAULT_RETAIN_MISSION = None  # Declarative spec of what to retain (injected into any extraction mode)
 DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS = None  # Custom extraction guidelines (only used when mode="custom")
 DEFAULT_RETAIN_BATCH_TOKENS = 10_000  # ~40KB of text  # Max chars per sub-batch for async retain auto-splitting
+DEFAULT_RETAIN_ENTITY_LOOKUP = "trigram"  # "full" or "trigram"
 DEFAULT_RETAIN_BATCH_ENABLED = False  # Use LLM Batch API for fact extraction (only when async=True)
 DEFAULT_RETAIN_BATCH_POLL_INTERVAL_SECONDS = 60  # Batch API polling interval in seconds
 
@@ -412,7 +438,9 @@ DEFAULT_FILE_DELETE_AFTER_RETAIN = True  # Delete file bytes after retain (saves
 # Observations defaults (consolidated knowledge from facts)
 DEFAULT_ENABLE_OBSERVATIONS = True  # Observations enabled by default
 DEFAULT_CONSOLIDATION_BATCH_SIZE = 50  # Memories to load per batch (internal memory optimization)
-DEFAULT_CONSOLIDATION_MAX_TOKENS = 1024  # Max tokens for recall when finding related observations
+DEFAULT_CONSOLIDATION_LLM_BATCH_SIZE = 8  # Facts per LLM call (1 = no batching; >1 = batch mode)
+DEFAULT_CONSOLIDATION_MAX_TOKENS = 512  # Max tokens for recall when finding related observations
+DEFAULT_OBSERVATIONS_MISSION = None  # Declarative spec of what observations are for this bank
 
 # Database migrations
 DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
@@ -434,6 +462,12 @@ DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS = 2  # Max concurrent consolidation tasks
 
 # Reflect agent settings
 DEFAULT_REFLECT_MAX_ITERATIONS = 10  # Max tool call iterations before forcing response
+DEFAULT_REFLECT_MAX_CONTEXT_TOKENS = 100_000  # Max accumulated context tokens before forcing final prompt
+
+# Disposition defaults (None = not set, fall back to bank DB value or 3)
+DEFAULT_DISPOSITION_SKEPTICISM = None
+DEFAULT_DISPOSITION_LITERALISM = None
+DEFAULT_DISPOSITION_EMPATHY = None
 
 # OpenTelemetry tracing configuration
 DEFAULT_OTEL_TRACES_ENABLED = False  # Disabled by default for backward compatibility
@@ -539,6 +573,9 @@ class HindsightConfig:
     llm_vertexai_region: str
     llm_vertexai_service_account_key: str | None
 
+    # Gemini safety settings (None = use Gemini defaults; list of dicts with category/threshold)
+    llm_gemini_safety_settings: list | None
+
     # Per-operation LLM configuration (None = use default LLM config)
     retain_llm_provider: str | None
     retain_llm_api_key: str | None
@@ -606,6 +643,8 @@ class HindsightConfig:
     reranker_litellm_sdk_api_key: str | None
     reranker_litellm_sdk_model: str
     reranker_litellm_sdk_api_base: str | None
+    reranker_zeroentropy_api_key: str | None
+    reranker_zeroentropy_model: str
 
     # Server
     host: str
@@ -614,6 +653,7 @@ class HindsightConfig:
     log_level: str
     log_format: str
     mcp_enabled: bool
+    mcp_enabled_tools: list[str] | None  # None = all tools; explicit list = allowlist
     enable_bank_config_api: bool
 
     # Recall
@@ -628,10 +668,12 @@ class HindsightConfig:
     retain_chunk_size: int
     retain_extract_causal_links: bool
     retain_extraction_mode: str
+    retain_mission: str | None
     retain_custom_instructions: str | None
     retain_batch_tokens: int
     retain_batch_enabled: bool
     retain_batch_poll_interval_seconds: int
+    retain_entity_lookup: str  # "full" or "trigram"
 
     # File storage (static - server-level only)
     file_storage_type: str  # "native" (PostgreSQL) or "s3" (S3-compatible)
@@ -645,7 +687,9 @@ class HindsightConfig:
     file_storage_azure_container: str | None  # Azure container name (required for azure storage)
     file_storage_azure_account_name: str | None  # Azure storage account name
     file_storage_azure_account_key: str | None  # Azure storage account key
-    file_parser: str  # File parser to use (e.g., "markitdown")
+    file_parser: str  # File parser to use (e.g., "markitdown", "iris")
+    file_parser_iris_token: str | None  # Vectorize API token for iris parser (VECTORIZE_TOKEN)
+    file_parser_iris_org_id: str | None  # Vectorize org ID for iris parser (VECTORIZE_ORG_ID)
     file_conversion_max_batch_size_mb: int  # Max total batch size in MB (all files combined)
     file_conversion_max_batch_size: int  # Max files per request
     enable_file_upload_api: bool
@@ -654,7 +698,24 @@ class HindsightConfig:
     # Observations settings (consolidated knowledge from facts)
     enable_observations: bool
     consolidation_batch_size: int
+    consolidation_llm_batch_size: int
     consolidation_max_tokens: int
+    observations_mission: str | None
+
+    # Entity labels (controlled vocabulary of key:value classification labels extracted at retain time)
+    # List of label group dicts: [{key, description, type, optional, values: [{value, description}]}]
+    entity_labels: list | None
+    # Whether to extract regular named entities alongside entity labels (default: True)
+    # When False: only label entities are extracted (or no entities at all if no labels configured)
+    entities_allow_free_form: bool
+
+    # Reflect agent settings
+    reflect_mission: str | None
+
+    # Disposition settings (hierarchical - can be overridden per bank; None = fall back to DB)
+    disposition_skepticism: int | None
+    disposition_literalism: int | None
+    disposition_empathy: int | None
 
     # Optimization flags
     skip_llm_verification: bool
@@ -680,6 +741,7 @@ class HindsightConfig:
 
     # Reflect agent settings
     reflect_max_iterations: int
+    reflect_max_context_tokens: int
 
     # OpenTelemetry tracing configuration
     otel_traces_enabled: bool
@@ -712,18 +774,35 @@ class HindsightConfig:
         "file_storage_s3_secret_access_key",
         "file_storage_gcs_service_account_key",
         "file_storage_azure_account_key",
+        # File parser credentials
+        "file_parser_iris_token",
     }
 
     # CONFIGURABLE_FIELDS: Safe behavioral settings that can be customized per-tenant/bank
     # These fields are manually tagged as safe to expose and modify.
     # Excludes credentials, infrastructure config, provider/model selection, and performance tuning.
     _CONFIGURABLE_FIELDS = {
+        # MCP tool access control
+        "mcp_enabled_tools",
         # Retention settings (behavioral)
         "retain_chunk_size",
         "retain_extraction_mode",
+        "retain_mission",
         "retain_custom_instructions",
+        # Entity labels (controlled vocabulary for entity classification)
+        "entity_labels",
+        "entities_allow_free_form",
         # Consolidation settings
         "enable_observations",
+        "observations_mission",
+        # Reflect settings
+        "reflect_mission",
+        # Disposition settings
+        "disposition_skepticism",
+        "disposition_literalism",
+        "disposition_empathy",
+        # Gemini safety settings (controls content filtering for Gemini/VertexAI providers)
+        "llm_gemini_safety_settings",
     }
 
     @property
@@ -844,6 +923,8 @@ class HindsightConfig:
             llm_vertexai_region=os.getenv(ENV_LLM_VERTEXAI_REGION, DEFAULT_LLM_VERTEXAI_REGION),
             llm_vertexai_service_account_key=os.getenv(ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY)
             or DEFAULT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY,
+            # Gemini safety settings (JSON-encoded list of {category, threshold} dicts)
+            llm_gemini_safety_settings=json.loads(os.getenv(ENV_LLM_GEMINI_SAFETY_SETTINGS, "null")),
             # 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,
@@ -976,6 +1057,9 @@ class HindsightConfig:
             reranker_litellm_sdk_api_key=os.getenv(ENV_RERANKER_LITELLM_SDK_API_KEY),
             reranker_litellm_sdk_model=os.getenv(ENV_RERANKER_LITELLM_SDK_MODEL, DEFAULT_RERANKER_LITELLM_SDK_MODEL),
             reranker_litellm_sdk_api_base=os.getenv(ENV_RERANKER_LITELLM_SDK_API_BASE) or None,
+            # ZeroEntropy reranker
+            reranker_zeroentropy_api_key=os.getenv(ENV_RERANKER_ZEROENTROPY_API_KEY),
+            reranker_zeroentropy_model=os.getenv(ENV_RERANKER_ZEROENTROPY_MODEL, DEFAULT_RERANKER_ZEROENTROPY_MODEL),
             # Server
             host=os.getenv(ENV_HOST, DEFAULT_HOST),
             port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
@@ -983,6 +1067,9 @@ class HindsightConfig:
             log_level=os.getenv(ENV_LOG_LEVEL, DEFAULT_LOG_LEVEL),
             log_format=os.getenv(ENV_LOG_FORMAT, DEFAULT_LOG_FORMAT).lower(),
             mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).lower() == "true",
+            mcp_enabled_tools=[t.strip() for t in os.getenv(ENV_MCP_ENABLED_TOOLS).split(",") if t.strip()]
+            if os.getenv(ENV_MCP_ENABLED_TOOLS)
+            else DEFAULT_MCP_ENABLED_TOOLS,
             enable_bank_config_api=os.getenv(ENV_ENABLE_BANK_CONFIG_API, str(DEFAULT_ENABLE_BANK_CONFIG_API)).lower()
             == "true",
             # Recall
@@ -1010,8 +1097,10 @@ class HindsightConfig:
             retain_extraction_mode=_validate_extraction_mode(
                 os.getenv(ENV_RETAIN_EXTRACTION_MODE, DEFAULT_RETAIN_EXTRACTION_MODE)
             ),
+            retain_mission=os.getenv(ENV_RETAIN_MISSION) or DEFAULT_RETAIN_MISSION,
             retain_custom_instructions=os.getenv(ENV_RETAIN_CUSTOM_INSTRUCTIONS) or DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS,
             retain_batch_tokens=int(os.getenv(ENV_RETAIN_BATCH_TOKENS, str(DEFAULT_RETAIN_BATCH_TOKENS))),
+            retain_entity_lookup=os.getenv(ENV_RETAIN_ENTITY_LOOKUP, DEFAULT_RETAIN_ENTITY_LOOKUP),
             retain_batch_enabled=os.getenv(ENV_RETAIN_BATCH_ENABLED, str(DEFAULT_RETAIN_BATCH_ENABLED)).lower()
             == "true",
             retain_batch_poll_interval_seconds=int(
@@ -1030,6 +1119,8 @@ class HindsightConfig:
             file_storage_azure_account_name=os.getenv(ENV_FILE_STORAGE_AZURE_ACCOUNT_NAME) or None,
             file_storage_azure_account_key=os.getenv(ENV_FILE_STORAGE_AZURE_ACCOUNT_KEY) or None,
             file_parser=os.getenv(ENV_FILE_PARSER, DEFAULT_FILE_PARSER),
+            file_parser_iris_token=os.getenv(ENV_FILE_PARSER_IRIS_TOKEN) or None,
+            file_parser_iris_org_id=os.getenv(ENV_FILE_PARSER_IRIS_ORG_ID) or None,
             file_conversion_max_batch_size_mb=int(
                 os.getenv(ENV_FILE_CONVERSION_MAX_BATCH_SIZE_MB, str(DEFAULT_FILE_CONVERSION_MAX_BATCH_SIZE_MB))
             ),
@@ -1047,9 +1138,15 @@ class HindsightConfig:
             consolidation_batch_size=int(
                 os.getenv(ENV_CONSOLIDATION_BATCH_SIZE, str(DEFAULT_CONSOLIDATION_BATCH_SIZE))
             ),
+            consolidation_llm_batch_size=int(
+                os.getenv(ENV_CONSOLIDATION_LLM_BATCH_SIZE, str(DEFAULT_CONSOLIDATION_LLM_BATCH_SIZE))
+            ),
             consolidation_max_tokens=int(
                 os.getenv(ENV_CONSOLIDATION_MAX_TOKENS, str(DEFAULT_CONSOLIDATION_MAX_TOKENS))
             ),
+            observations_mission=os.getenv(ENV_OBSERVATIONS_MISSION) or DEFAULT_OBSERVATIONS_MISSION,
+            entity_labels=None,
+            entities_allow_free_form=True,
             # Database migrations
             run_migrations_on_startup=os.getenv(ENV_RUN_MIGRATIONS_ON_STARTUP, "true").lower() == "true",
             # Database connection pool
@@ -1069,6 +1166,20 @@ class HindsightConfig:
             ),
             # Reflect agent settings
             reflect_max_iterations=int(os.getenv(ENV_REFLECT_MAX_ITERATIONS, str(DEFAULT_REFLECT_MAX_ITERATIONS))),
+            reflect_max_context_tokens=int(
+                os.getenv(ENV_REFLECT_MAX_CONTEXT_TOKENS, str(DEFAULT_REFLECT_MAX_CONTEXT_TOKENS))
+            ),
+            reflect_mission=os.getenv(ENV_REFLECT_MISSION) or None,
+            # Disposition settings (None = fall back to DB value)
+            disposition_skepticism=int(os.getenv(ENV_DISPOSITION_SKEPTICISM))
+            if os.getenv(ENV_DISPOSITION_SKEPTICISM)
+            else DEFAULT_DISPOSITION_SKEPTICISM,
+            disposition_literalism=int(os.getenv(ENV_DISPOSITION_LITERALISM))
+            if os.getenv(ENV_DISPOSITION_LITERALISM)
+            else DEFAULT_DISPOSITION_LITERALISM,
+            disposition_empathy=int(os.getenv(ENV_DISPOSITION_EMPATHY))
+            if os.getenv(ENV_DISPOSITION_EMPATHY)
+            else DEFAULT_DISPOSITION_EMPATHY,
             # OpenTelemetry tracing configuration
             otel_traces_enabled=os.getenv(ENV_OTEL_TRACES_ENABLED, str(DEFAULT_OTEL_TRACES_ENABLED)).lower()
             in ("true", "1", "yes"),

hindsight-api/hindsight_api/config_resolver.py

@@ -16,6 +16,7 @@ from typing import Any
 import asyncpg
 
 from hindsight_api.config import HindsightConfig, _get_raw_config, normalize_config_dict
+from hindsight_api.engine.memory_engine import fq_table
 from hindsight_api.extensions.tenant import TenantExtension
 from hindsight_api.models import RequestContext
 
@@ -149,8 +150,8 @@ class ConfigResolver:
         try:
             async with self.pool.acquire() as conn:
                 row = await conn.fetchrow(
-                    """
-                    SELECT config FROM banks WHERE bank_id = $1
+                    f"""
+                    SELECT config FROM {fq_table("banks")} WHERE bank_id = $1
                     """,
                     bank_id,
                 )
@@ -241,8 +242,8 @@ class ConfigResolver:
         # Merge with existing config (JSONB || operator)
         async with self.pool.acquire() as conn:
             await conn.execute(
-                """
-                UPDATE banks
+                f"""
+                UPDATE {fq_table("banks")}
                 SET config = config || $1::jsonb,
                     updated_at = now()
                 WHERE bank_id = $2
@@ -262,9 +263,9 @@ class ConfigResolver:
         """
         async with self.pool.acquire() as conn:
             await conn.execute(
-                """
-                UPDATE banks
-                SET config = '{}'::jsonb,
+                f"""
+                UPDATE {fq_table("banks")}
+                SET config = '{{}}'::jsonb,
                     updated_at = now()
                 WHERE bank_id = $1
                 """,

hindsight-api/hindsight_api/engine/consolidation/prompts.py

@@ -1,85 +1,66 @@
 """Prompts for the consolidation engine."""
 
-CONSOLIDATION_SYSTEM_PROMPT = """You are a memory consolidation system. Your job is to convert facts into durable knowledge (observations) and merge with existing knowledge when appropriate.
+# Default mission when no bank-specific mission is set
+_DEFAULT_MISSION = "Track every detail: names, numbers, dates, places, and relationships. Prefer specifics over abstractions, never generalise."
 
-You must output ONLY valid JSON with no markdown code blocks or additional text. However, the "text" field within each observation should use markdown formatting (headers, lists, bold, etc.) for clarity and readability.
+# Processing rules — always present regardless of mission
+_PROCESSING_RULES = """Processing rules (always apply):
+- REDUNDANT: same info worded differently → UPDATE the existing observation.
+- CONTRADICTION/UPDATE: capture both states with temporal markers ("used to X, now Y").
+- RESOLVE REFERENCES: when a new fact provides a concrete value resolving a vague placeholder in an existing observation (e.g. "home country", "hometown", "birthplace", "native language", "her ex", "that city"), UPDATE the observation to embed the resolved value explicitly. Example: new fact says "grandma in Sweden" + existing observation says "moved from her home country" → update to "home country is Sweden".
+- NEVER merge observations about different people or unrelated topics."""
 
-## EXTRACT DURABLE KNOWLEDGE, NOT EPHEMERAL STATE
-Facts often describe events or actions. Extract the DURABLE KNOWLEDGE implied by the fact, not the transient state.
+# Data section — format placeholders {facts_text} and {observations_text} are substituted at call time
+_BATCH_DATA_SECTION = """
+NEW FACTS:
+{facts_text}
 
-Examples of extracting durable knowledge:
-- "User moved to Room 203" -> "Room 203 exists" (location exists, not where user is now)
-- "User visited Acme Corp at Room 105" -> "Acme Corp is located in Room 105"
-- "User took the elevator to floor 3" -> "Floor 3 is accessible by elevator"
-- "User met Sarah at the lobby" -> "Sarah can be found at the lobby"
-
-DO NOT track current user position/state as knowledge - that changes constantly.
-DO track permanent facts learned from the user's actions.
-
-## PRESERVE SPECIFIC DETAILS
-Keep names, locations, numbers, and other specifics. Do NOT:
-- Abstract into general principles
-- Generate business insights
-- Make knowledge generic
-
-GOOD examples:
-- Fact: "John likes pizza" -> "John likes pizza"
-- Fact: "Alice works at Google" -> "Alice works at Google"
-
-BAD examples:
-- "John likes pizza" -> "Understanding dietary preferences helps..." (TOO ABSTRACT)
-- "User is at Room 203" -> "User is currently at Room 203" (EPHEMERAL STATE)
-
-## MERGE RULES (when comparing to existing observations):
-1. REDUNDANT: Same information worded differently → update existing
-2. CONTRADICTION: Opposite information about same topic → update with temporal markers showing change
-   Example: "Alex used to love pizza but now hates it" OR "Alex's pizza preference changed from love to hate"
-3. UPDATE: New state replacing old state → update showing the transition with "used to", "now", "changed from X to Y"
-
-## CRITICAL RULES:
-- NEVER merge facts about DIFFERENT people
-- NEVER merge unrelated topics (food preferences vs work vs hobbies)
-- When merging contradictions, the "text" field MUST capture BOTH states with temporal markers:
-  * Use "used to X, now Y" OR "changed from X to Y" OR "X but now Y"
-  * DO NOT just state the new fact - you MUST show the change
-- Keep observations focused on ONE specific topic per person
-- The "text" field MUST contain durable knowledge, not ephemeral state
-- Do NOT include "tags" in output - tags are handled automatically"""
-
-CONSOLIDATION_USER_PROMPT = """Analyze this new fact and consolidate into knowledge.
-{mission_section}
-NEW FACT: {fact_text}
-
-EXISTING OBSERVATIONS (JSON array with source memories and dates):
+EXISTING OBSERVATIONS (JSON array, pooled from recalls across all facts above):
 {observations_text}
 
 Each observation includes:
 - id: unique identifier for updating
 - text: the observation content
 - proof_count: number of supporting memories
-- tags: visibility scope (handled automatically)
-- created_at/updated_at: when observation was created/modified
 - occurred_start/occurred_end: temporal range of source facts
 - source_memories: array of supporting facts with their text and dates
 
-Instructions:
-1. Extract DURABLE KNOWLEDGE from the new fact (not ephemeral state)
-2. Review source_memories in existing observations to understand evidence
-3. Check dates to detect contradictions or updates
-4. Compare with observations:
-   - Same topic → UPDATE with learning_id
-   - New topic → CREATE new observation
-   - Purely ephemeral → return []
+Compare the facts against existing observations:
+- Same topic as an existing observation → UPDATE it (observation_id + source_fact_ids)
+- New topic with durable knowledge → CREATE a new observation (source_fact_ids)
+- Cross-reference facts within the batch: a later fact may resolve a vague reference in an earlier one
+- Purely ephemeral facts → omit them (no create/update needed)"""
 
-Output JSON array of actions (the "text" field should use markdown formatting for structure):
-[
-  {{"action": "update", "learning_id": "uuid-from-observations", "text": "## Updated Knowledge\n\n**Key point**: details here\n\n- Supporting detail 1\n- Supporting detail 2", "reason": "..."}},
-  {{"action": "create", "text": "## New Durable Knowledge\n\nDescription with **emphasis** and proper structure", "reason": "..."}}
-]
+# Output format — JSON braces escaped as {{ }} so .format() leaves them literal
+_BATCH_OUTPUT_FORMAT = """
+Output a JSON object with three arrays.
 
-Return [] if fact contains no durable knowledge.
+Example (showing the required UUID format for all IDs):
+{{"creates": [{{"text": "Alice lives in Berlin", "source_fact_ids": ["a1b2c3d4-e5f6-7890-abcd-ef1234567890", "b2c3d4e5-f6a7-8901-bcde-f12345678901"]}}],
+  "updates": [{{"text": "Alice works at Acme Corp as a senior engineer", "observation_id": "c3d4e5f6-a7b8-9012-cdef-123456789012", "source_fact_ids": ["d4e5f6a7-b8c9-0123-defa-234567890123"]}}],
+  "deletes": [{{"observation_id": "e5f6a7b8-c9d0-1234-efab-345678901234"}}]}}
 
-IMPORTANT: Format the "text" field with markdown for better readability:
-- Use headers, lists, bold/italic, tables where appropriate
-- CRITICAL: Add blank lines before and after block elements (tables, code blocks, lists)
-- Ensure proper spacing for markdown to render correctly"""
+Rules:
+- "source_fact_ids": copy the EXACT UUID strings shown in brackets [uuid] from NEW FACTS — never use integers or positions.
+- "observation_id": copy the EXACT "id" UUID string from EXISTING OBSERVATIONS.
+- One create/update may reference multiple facts when they jointly support the observation.
+- "deletes": only when an observation is directly superseded or contradicted by new facts.
+- Do NOT include "tags" — handled automatically.
+- Return {{"creates": [], "updates": [], "deletes": []}} if nothing durable is found."""
+
+
+def build_batch_consolidation_prompt(observations_mission: str | None = None) -> str:
+    """
+    Build the consolidation prompt for batch mode (multiple facts per LLM call).
+
+    The mission defines *what* to track (customisable per bank).
+    Processing rules and output format are always present regardless of mission.
+    """
+    mission = observations_mission or _DEFAULT_MISSION
+
+    return (
+        "You are a memory consolidation system. Synthesize facts into observations "
+        "and merge with existing observations when appropriate.\n\n"
+        f"## MISSION\n{mission}\n\n"
+        f"{_PROCESSING_RULES}" + _BATCH_DATA_SECTION + _BATCH_OUTPUT_FORMAT
+    )

hindsight-api/hindsight_api/engine/cross_encoder.py

@@ -29,6 +29,7 @@ from ..config import (
     DEFAULT_RERANKER_PROVIDER,
     DEFAULT_RERANKER_TEI_BATCH_SIZE,
     DEFAULT_RERANKER_TEI_MAX_CONCURRENT,
+    DEFAULT_RERANKER_ZEROENTROPY_MODEL,
     ENV_RERANKER_COHERE_API_KEY,
     ENV_RERANKER_COHERE_MODEL,
     ENV_RERANKER_FLASHRANK_CACHE_DIR,
@@ -42,6 +43,7 @@ from ..config import (
     ENV_RERANKER_TEI_BATCH_SIZE,
     ENV_RERANKER_TEI_MAX_CONCURRENT,
     ENV_RERANKER_TEI_URL,
+    ENV_RERANKER_ZEROENTROPY_API_KEY,
 )
 
 logger = logging.getLogger(__name__)
@@ -556,6 +558,104 @@ class CohereCrossEncoder(CrossEncoderModel):
         return all_scores
 
 
+class ZeroEntropyCrossEncoder(CrossEncoderModel):
+    """
+    ZeroEntropy cross-encoder implementation using the ZeroEntropy Rerank API.
+
+    Supports zerank-2 (flagship) and zerank-2-small models.
+    See: https://docs.zeroentropy.dev/models
+    """
+
+    RERANK_URL = "https://api.zeroentropy.dev/v1/models/rerank"
+
+    def __init__(
+        self,
+        api_key: str,
+        model: str = DEFAULT_RERANKER_ZEROENTROPY_MODEL,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize ZeroEntropy cross-encoder client.
+
+        Args:
+            api_key: ZeroEntropy API key
+            model: ZeroEntropy rerank model name (default: zerank-2)
+            timeout: Request timeout in seconds (default: 60.0)
+        """
+        self.api_key = api_key
+        self.model = model
+        self.timeout = timeout
+        self._async_client: httpx.AsyncClient | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "zeroentropy"
+
+    async def initialize(self) -> None:
+        """Initialize the async HTTP client."""
+        if self._async_client is not None:
+            return
+
+        logger.info(f"Reranker: initializing ZeroEntropy provider with model {self.model}")
+        self._async_client = httpx.AsyncClient(
+            timeout=self.timeout,
+            headers={
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json",
+            },
+        )
+        logger.info("Reranker: ZeroEntropy provider initialized")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Score query-document pairs using the ZeroEntropy Rerank API.
+
+        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.")
+
+        if not pairs:
+            return []
+
+        # Group pairs by query for efficient batching
+        query_groups: dict[str, list[tuple[int, str]]] = {}
+        for idx, (query, text) in enumerate(pairs):
+            if query not in query_groups:
+                query_groups[query] = []
+            query_groups[query].append((idx, text))
+
+        all_scores = [0.0] * len(pairs)
+
+        for query, indexed_texts in query_groups.items():
+            texts = [text for _, text in indexed_texts]
+            indices = [idx for idx, _ in indexed_texts]
+
+            response = await self._async_client.post(
+                self.RERANK_URL,
+                json={
+                    "model": self.model,
+                    "query": query,
+                    "documents": texts,
+                    "top_n": len(texts),
+                },
+            )
+            response.raise_for_status()
+            result = response.json()
+
+            # Map scores back to original positions
+            for item in result.get("results", []):
+                original_idx = item["index"]
+                score = item["relevance_score"]
+                all_scores[indices[original_idx]] = score
+
+        return all_scores
+
+
 class RRFPassthroughCrossEncoder(CrossEncoderModel):
     """
     Passthrough cross-encoder that preserves RRF scores without neural reranking.
@@ -1010,9 +1110,19 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
             model=config.reranker_litellm_sdk_model,
             api_base=config.reranker_litellm_sdk_api_base,
         )
+    elif provider == "zeroentropy":
+        api_key = config.reranker_zeroentropy_api_key
+        if not api_key:
+            raise ValueError(
+                f"{ENV_RERANKER_ZEROENTROPY_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'zeroentropy'"
+            )
+        return ZeroEntropyCrossEncoder(
+            api_key=api_key,
+            model=config.reranker_zeroentropy_model,
+        )
     elif provider == "rrf":
         return RRFPassthroughCrossEncoder()
     else:
         raise ValueError(
-            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'zeroentropy', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
         )

hindsight-api/hindsight_api/engine/db_utils.py

@@ -20,6 +20,7 @@ RETRYABLE_EXCEPTIONS = (
     asyncpg.exceptions.InterfaceError,
     asyncpg.exceptions.ConnectionDoesNotExistError,
     asyncpg.exceptions.TooManyConnectionsError,
+    asyncpg.exceptions.DeadlockDetectedError,
     OSError,
     ConnectionError,
     asyncio.TimeoutError,

hindsight-api/hindsight_api/engine/embeddings.py

@@ -794,6 +794,7 @@ class LiteLLMSDKEmbeddings(Embeddings):
                 "model": self.model,
                 "input": ["test"],
                 "api_key": self.api_key,
+                "encoding_format": "float",
             }
             if self.api_base:
                 embed_kwargs["api_base"] = self.api_base
@@ -840,6 +841,7 @@ class LiteLLMSDKEmbeddings(Embeddings):
                     "model": self.model,
                     "input": batch,
                     "api_key": self.api_key,
+                    "encoding_format": "float",
                 }
                 if self.api_base:
                     embed_kwargs["api_base"] = self.api_base

hindsight-api/hindsight_api/engine/entity_resolver.py

@@ -5,6 +5,10 @@ Uses spaCy for entity extraction and implements resolution logic
 to disambiguate entities across memory units.
 """
 
+import asyncio
+import logging
+from collections import defaultdict
+from dataclasses import dataclass, field
 from datetime import UTC, datetime
 from difflib import SequenceMatcher
 
@@ -12,6 +16,43 @@ import asyncpg
 
 from .db_utils import acquire_with_retry
 from .memory_engine import fq_table
+from .retain.entity_labels import build_labels_lookup as _build_labels_lookup_from_config
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class _EntityToCreate:
+    """An entity that needs to be inserted (no matching candidate found)."""
+
+    idx: int
+    name: str
+    event_date: datetime | None
+
+
+@dataclass
+class _EntityStat:
+    """Stat accumulation entry for a resolved entity (post-transaction update)."""
+
+    entity_id: str
+    event_date: datetime | None
+
+
+@dataclass
+class _EntityStatAgg:
+    """Aggregated stats used when flushing pending updates."""
+
+    count: int = 0
+    max_date: datetime | None = None
+
+
+@dataclass
+class _CooccurrencePair:
+    """A (entity_id_1, entity_id_2) pair observed in a retain batch (for post-txn flush)."""
+
+    entity_id_1: str
+    entity_id_2: str
+
 
 # Load spaCy model (singleton)
 _nlp = None
@@ -22,14 +63,95 @@ class EntityResolver:
     Resolves entities to canonical IDs with disambiguation.
     """
 
-    def __init__(self, pool: asyncpg.Pool):
+    def __init__(self, pool: asyncpg.Pool, entity_lookup: str = "full"):
         """
         Initialize entity resolver.
 
         Args:
             pool: asyncpg connection pool
+            entity_lookup: Lookup strategy — "full" loads all bank entities then
+                matches in Python; "trigram" uses pg_trgm GIN index to fetch only
+                similar candidates per entity name (much faster for large banks).
         """
         self.pool = pool
+        self.entity_lookup = entity_lookup
+        # Keyed by asyncio task id so concurrent retain batches never mix their
+        # pending updates.  flush_pending_stats() pops only the calling task's items.
+        self._pending_stats: dict[int, list[_EntityStat]] = {}
+        self._pending_cooccurrences: dict[int, list[_CooccurrencePair]] = {}
+
+    def _task_key(self) -> int:
+        """Return a unique key for the current asyncio task (or 0 for non-task context)."""
+        task = asyncio.current_task()
+        return id(task) if task is not None else 0
+
+    async def flush_pending_stats(self) -> None:
+        """
+        Flush accumulated entity stats and co-occurrence counts for the current task.
+
+        Must be called AFTER the retain transaction commits.  Pops only the items
+        accumulated by the calling asyncio task so concurrent retain batches never
+        flush each other's uncommitted entity IDs.
+        """
+        if self.pool is None:
+            return
+
+        key = self._task_key()
+        stats = self._pending_stats.pop(key, [])
+        cooccurrences = self._pending_cooccurrences.pop(key, [])
+
+        if not stats and not cooccurrences:
+            return
+
+        async with acquire_with_retry(self.pool) as conn:
+            if stats:
+                # Aggregate: sum counts and find max date per entity_id.
+                agg: dict[str, _EntityStatAgg] = defaultdict(_EntityStatAgg)
+                for s in stats:
+                    entry = agg[s.entity_id]
+                    entry.count += 1
+                    if s.event_date is not None:
+                        entry.max_date = s.event_date if entry.max_date is None else max(entry.max_date, s.event_date)
+
+                # Sort by entity_id so all concurrent workers acquire row locks in
+                # the same order — prevents circular lock dependencies (deadlocks).
+                rows = sorted((eid, a.count, a.max_date) for eid, a in agg.items())
+                await conn.executemany(
+                    f"""
+                    UPDATE {fq_table("entities")} SET
+                        mention_count = mention_count + $2,
+                        last_seen     = GREATEST(last_seen, $3)
+                    WHERE id = $1::uuid
+                    """,
+                    rows,
+                )
+
+            if cooccurrences:
+                # Aggregate: count occurrences per (entity_id_1, entity_id_2) pair.
+                coo_agg: dict[tuple[str, str], int] = {}
+                for c in cooccurrences:
+                    pair = (c.entity_id_1, c.entity_id_2)
+                    coo_agg[pair] = coo_agg.get(pair, 0) + 1
+
+                now = datetime.now(UTC)
+                # Sort by (entity_id_1, entity_id_2) for consistent lock ordering.
+                await conn.executemany(
+                    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 = {fq_table("entity_cooccurrences")}.cooccurrence_count + EXCLUDED.cooccurrence_count,
+                        last_cooccurred    = GREATEST({fq_table("entity_cooccurrences")}.last_cooccurred, EXCLUDED.last_cooccurred)
+                    """,
+                    sorted((e1, e2, count, now) for (e1, e2), count in coo_agg.items()),
+                )
+
+    @staticmethod
+    def _build_labels_lookup(entity_labels: list | None) -> set[str]:
+        """Build a set of valid 'key:value' entity label strings for fast lookup."""
+        return _build_labels_lookup_from_config(entity_labels)
 
     async def resolve_entities_batch(
         self,
@@ -38,6 +160,7 @@ class EntityResolver:
         context: str,
         unit_event_date,
         conn=None,
+        entity_labels: list | None = None,
     ) -> list[str]:
         """
         Resolve multiple entities in batch (MUCH faster than sequential).
@@ -58,15 +181,34 @@ class EntityResolver:
         if not entities_data:
             return []
 
+        taxonomy_lookup = self._build_labels_lookup(entity_labels)
         if conn is None:
             async with acquire_with_retry(self.pool) as conn:
-                return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
+                return await self._resolve_entities_batch_impl(
+                    conn, bank_id, entities_data, context, unit_event_date, taxonomy_lookup
+                )
         else:
-            return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
+            return await self._resolve_entities_batch_impl(
+                conn, bank_id, entities_data, context, unit_event_date, taxonomy_lookup
+            )
 
     async def _resolve_entities_batch_impl(
-        self, conn, bank_id: str, entities_data: list[dict], context: str, unit_event_date
+        self,
+        conn,
+        bank_id: str,
+        entities_data: list[dict],
+        context: str,
+        unit_event_date,
+        taxonomy_lookup: set[str] | None = None,
     ) -> list[str]:
+        if self.entity_lookup == "trigram":
+            return await self._resolve_entities_batch_trigram(conn, bank_id, entities_data, unit_event_date)
+        return await self._resolve_entities_batch_full(conn, bank_id, entities_data, unit_event_date)
+
+    async def _resolve_entities_batch_full(
+        self, conn, bank_id: str, entities_data: list[dict], unit_event_date
+    ) -> list[str]:
+        """Original strategy: load all bank entities then match in Python."""
         # Query ALL candidates for this bank
         all_entities = await conn.fetch(
             f"""
@@ -130,10 +272,103 @@ class EntityResolver:
                     matching.append((ent_id, canonical_name, metadata, last_seen, mention_count))
             all_candidates[entity_text] = matching
 
+        return await self._resolve_from_candidates(
+            conn, bank_id, entities_data, unit_event_date, all_candidates, cooccurrence_map
+        )
+
+    async def _resolve_entities_batch_trigram(
+        self, conn, bank_id: str, entities_data: list[dict], unit_event_date
+    ) -> list[str]:
+        """
+        Trigram strategy: fetch only similar candidates per entity name using pg_trgm.
+
+        Instead of loading all bank entities (O(N)), uses a GIN trigram index to fetch
+        only the small set of candidates that are textually similar to each input name.
+        Reduces DB data transfer from 165K rows to ~5-20 rows per entity.
+        """
+        entity_texts = list(set(e["text"] for e in entities_data))
+
+        # Fetch candidates for all unique entity texts in a single batched query.
+        # The trigram % operator uses the GIN index; the substring conditions cover
+        # exact prefix/suffix matches that trigrams might miss at low similarity.
+        rows = await conn.fetch(
+            f"""
+            SELECT DISTINCT ON (e.id)
+                e.id, e.canonical_name, e.metadata, e.last_seen, e.mention_count,
+                q.query_text
+            FROM unnest($2::text[]) AS q(query_text)
+            JOIN {fq_table("entities")} e ON (
+                e.bank_id = $1
+                AND (
+                    e.canonical_name % q.query_text
+                    OR LOWER(e.canonical_name) LIKE '%' || LOWER(q.query_text) || '%'
+                    OR LOWER(q.query_text) LIKE '%' || LOWER(e.canonical_name) || '%'
+                )
+            )
+            """,
+            bank_id,
+            entity_texts,
+        )
+
+        # Group candidates by query_text
+        all_candidates: dict[str, list] = {t: [] for t in entity_texts}
+        candidate_ids: set = set()
+        for row in rows:
+            query_text = row["query_text"]
+            all_candidates[query_text].append(
+                (row["id"], row["canonical_name"], row["metadata"], row["last_seen"], row["mention_count"])
+            )
+            candidate_ids.add(row["id"])
+
+        # Fetch co-occurrences only for the candidate entities (not all bank entities)
+        cooccurrence_map: dict[str, set[str]] = {}
+        if candidate_ids:
+            candidate_id_list = list(candidate_ids)
+            cooc_rows = await conn.fetch(
+                f"""
+                SELECT ec.entity_id_1, ec.entity_id_2
+                FROM {fq_table("entity_cooccurrences")} ec
+                WHERE ec.entity_id_1 = ANY($1::uuid[])
+                   OR ec.entity_id_2 = ANY($1::uuid[])
+                """,
+                candidate_id_list,
+            )
+            # Build name lookup for co-occurrence mapping
+            id_to_name = {
+                row["id"]: row["canonical_name"].lower()
+                for cands in all_candidates.values()
+                for row in [{"id": c[0], "canonical_name": c[1]} for c in cands]
+            }
+            for row in cooc_rows:
+                eid1, eid2 = row["entity_id_1"], row["entity_id_2"]
+                if eid1 not in cooccurrence_map:
+                    cooccurrence_map[eid1] = set()
+                if eid2 not in cooccurrence_map:
+                    cooccurrence_map[eid2] = set()
+                if eid2 in id_to_name:
+                    cooccurrence_map[eid1].add(id_to_name[eid2])
+                if eid1 in id_to_name:
+                    cooccurrence_map[eid2].add(id_to_name[eid1])
+
+        return await self._resolve_from_candidates(
+            conn, bank_id, entities_data, unit_event_date, all_candidates, cooccurrence_map
+        )
+
+    async def _resolve_from_candidates(
+        self,
+        conn,
+        bank_id: str,
+        entities_data: list[dict],
+        unit_event_date,
+        all_candidates: dict[str, list],
+        cooccurrence_map: dict[str, set[str]],
+    ) -> list[str]:
+        """Shared scoring + upsert logic used by both lookup strategies."""
+
         # Resolve each entity using pre-fetched candidates
         entity_ids = [None] * len(entities_data)
-        entities_to_update = []  # (entity_id, event_date)
-        entities_to_create = []  # (idx, entity_data, event_date)
+        entities_to_update: list[_EntityStat] = []
+        entities_to_create: list[_EntityToCreate] = []
 
         for idx, entity_data in enumerate(entities_data):
             entity_text = entity_data["text"]
@@ -145,7 +380,7 @@ class EntityResolver:
 
             if not candidates:
                 # Will create new entity
-                entities_to_create.append((idx, entity_data, entity_event_date))
+                entities_to_create.append(_EntityToCreate(idx=idx, name=entity_text, event_date=entity_event_date))
                 continue
 
             # Score candidates
@@ -189,73 +424,83 @@ class EntityResolver:
 
             if best_score > threshold:
                 entity_ids[idx] = best_candidate
-                entities_to_update.append((best_candidate, entity_event_date))
+                entities_to_update.append(_EntityStat(entity_id=best_candidate, event_date=entity_event_date))
             else:
-                entities_to_create.append((idx, entity_data, entity_event_date))
+                entities_to_create.append(
+                    _EntityToCreate(idx=idx, name=entity_data["text"], event_date=entity_event_date)
+                )
 
-        # Batch update existing entities
-        if entities_to_update:
-            await conn.executemany(
-                f"""
-                UPDATE {fq_table("entities")} SET
-                    mention_count = mention_count + 1,
-                    last_seen = $2
-                WHERE id = $1::uuid
-                """,
-                entities_to_update,
-            )
+        # Existing entities: IDs already known from the candidate SELECT above.
+        # No in-transaction UPDATE — mention_count/last_seen are stats deferred to
+        # flush_pending_stats() which the orchestrator calls after the transaction.
+        pending: list[_EntityStat] = list(entities_to_update)
 
-        # Batch create new entities using COPY + INSERT for maximum speed
-        # This handles duplicates via ON CONFLICT and returns all IDs
+        # New entities: INSERT with DO NOTHING to avoid row locks on concurrent races.
+        # ON CONFLICT DO NOTHING returns nothing for rows that conflicted; we handle
+        # that rare case with a fallback SELECT.
         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, 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()
-                if name_lower not in unique_entities:
-                    unique_entities[name_lower] = (entity_data, event_date, [idx])
-                else:
-                    # Same entity appears multiple times - add index to list
-                    unique_entities[name_lower][2].append(idx)
+            # Group by lowercase name — deduplicate within the batch.
+            @dataclass
+            class _NameGroup:
+                name: str
+                event_date: datetime | None
+                indices: list[int] = field(default_factory=list)
 
-            # Batch insert unique entities and get their IDs
-            # 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
+            groups: dict[str, _NameGroup] = {}
+            for e in entities_to_create:
+                name_lower = e.name.lower()
+                if name_lower not in groups:
+                    groups[name_lower] = _NameGroup(name=e.name, event_date=e.event_date)
+                groups[name_lower].indices.append(e.idx)
 
-            for name_lower, (entity_data, event_date, indices) in unique_entities.items():
-                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)
+            # Sort by lowercase name for deterministic ordering.
+            sorted_groups = sorted(groups.items())
+            entity_names = [g.name for _, g in sorted_groups]
+            entity_dates = [g.event_date for _, g in sorted_groups]
 
-            # Batch INSERT ... ON CONFLICT with RETURNING
-            # Uses the batch count for mention_count instead of always 1
-            rows = await conn.fetch(
+            # INSERT ... ON CONFLICT DO NOTHING — no row lock on already-existing entities.
+            inserted_rows = await conn.fetch(
                 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)
+                SELECT $1, name, COALESCE(event_date, now()), COALESCE(event_date, now()), 1
+                FROM unnest($2::text[], $3::timestamptz[]) AS t(name, event_date)
                 ON CONFLICT (bank_id, LOWER(canonical_name))
-                DO UPDATE SET
-                    mention_count = {fq_table("entities")}.mention_count + EXCLUDED.mention_count,
-                    last_seen = EXCLUDED.last_seen
-                RETURNING id
+                DO NOTHING
+                RETURNING id, LOWER(canonical_name) AS name_lower
                 """,
                 bank_id,
                 entity_names,
                 entity_dates,
-                entity_counts,
             )
+            id_by_name: dict[str, str] = {row["name_lower"]: row["id"] for row in inserted_rows}
 
-            # Map returned IDs back to original indices
-            for result_idx, row in enumerate(rows):
-                entity_id = row["id"]
-                for original_idx in indices_map[result_idx]:
-                    entity_ids[original_idx] = entity_id
+            # Fallback SELECT for names that conflicted (another worker won the race).
+            missing = [n for n, _ in sorted_groups if n not in id_by_name]
+            if missing:
+                existing_rows = await conn.fetch(
+                    f"""
+                    SELECT id, LOWER(canonical_name) AS name_lower
+                    FROM {fq_table("entities")}
+                    WHERE bank_id = $1 AND LOWER(canonical_name) = ANY($2::text[])
+                    """,
+                    bank_id,
+                    missing,
+                )
+                for row in existing_rows:
+                    id_by_name[row["name_lower"]] = row["id"]
+
+            # Assign entity IDs back and queue for post-txn stats flush.
+            for name_lower, g in sorted_groups:
+                entity_id = id_by_name.get(name_lower)
+                if entity_id:
+                    for original_idx in g.indices:
+                        entity_ids[original_idx] = entity_id
+                    pending.append(_EntityStat(entity_id=entity_id, event_date=g.event_date))
+
+        # Accumulate into the resolver's pending list; the orchestrator flushes
+        # these with await entity_resolver.flush_pending_stats() after the txn.
+        key = self._task_key()
+        self._pending_stats.setdefault(key, []).extend(pending)
 
         return entity_ids
 
@@ -408,7 +653,7 @@ class EntityResolver:
         entity_id = await conn.fetchval(
             f"""
             INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
-            VALUES ($1, $2, $3, $4, 1)
+            VALUES ($1, $2, COALESCE($3, now()), COALESCE($4, now()), 1)
             ON CONFLICT (bank_id, LOWER(canonical_name))
             DO UPDATE SET
                 mention_count = {fq_table("entities")}.mention_count + 1,
@@ -541,19 +786,14 @@ class EntityResolver:
                         entity_id_1, entity_id_2 = entity_id_2, entity_id_1
                     cooccurrence_pairs.add((entity_id_1, entity_id_2))
 
-        # Batch update co-occurrences
+        # Accumulate co-occurrence pairs for post-transaction flush.
+        # The actual INSERT/UPDATE is deferred to flush_pending_stats() to avoid
+        # row-level lock contention (ON CONFLICT DO UPDATE inside a long transaction
+        # serialises concurrent writers on popular entity pairs).
         if cooccurrence_pairs:
-            now = datetime.now(UTC)
-            await conn.executemany(
-                f"""
-                INSERT INTO {fq_table("entity_cooccurrences")} (entity_id_1, entity_id_2, cooccurrence_count, last_cooccurred)
-                VALUES ($1, $2, $3, $4)
-                ON CONFLICT (entity_id_1, entity_id_2)
-                DO UPDATE SET
-                    cooccurrence_count = {fq_table("entity_cooccurrences")}.cooccurrence_count + 1,
-                    last_cooccurred = EXCLUDED.last_cooccurred
-                """,
-                [(e1, e2, 1, now) for e1, e2 in cooccurrence_pairs],
+            key = self._task_key()
+            self._pending_cooccurrences.setdefault(key, []).extend(
+                _CooccurrencePair(entity_id_1=e1, entity_id_2=e2) for e1, e2 in cooccurrence_pairs
             )
 
     async def get_units_by_entity(self, entity_id: str, limit: int = 100) -> list[str]:

hindsight-api/hindsight_api/engine/interface.py

@@ -12,6 +12,7 @@ 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.engine.search.tags import TagsMatch
     from hindsight_api.models import RequestContext
 
 
@@ -337,6 +338,8 @@ class MemoryEngineInterface(ABC):
         bank_id: str,
         *,
         search_query: str | None = None,
+        tags: list[str] | None = None,
+        tags_match: "TagsMatch" = "any_strict",
         limit: int = 100,
         offset: int = 0,
         request_context: "RequestContext",
@@ -346,7 +349,9 @@ class MemoryEngineInterface(ABC):
 
         Args:
             bank_id: The memory bank ID.
-            search_query: Search query.
+            search_query: Case-insensitive substring filter on document ID.
+            tags: Filter by tags.
+            tags_match: How to match tags (any, all, any_strict, all_strict).
             limit: Maximum results.
             offset: Pagination offset.
             request_context: Request context for authentication.

hindsight-api/hindsight_api/engine/llm_wrapper.py

@@ -60,6 +60,59 @@ class OutputTooLongError(Exception):
     pass
 
 
+def parse_llm_json(raw: str) -> Any:
+    """
+    Robustly parse JSON returned by an LLM.
+
+    Handles common LLM output quirks:
+    1. Markdown code fences (```json ... ```) — strip them before parsing.
+    2. Embedded control characters (\\x00-\\x1f, \\x7f) — replace with space
+       and retry if the initial parse fails.
+
+    Args:
+        raw: Raw text returned by the LLM.
+
+    Returns:
+        Parsed Python object (dict, list, etc.).
+
+    Raises:
+        json.JSONDecodeError: If the text cannot be parsed even after cleanup.
+    """
+    text = raw.strip()
+
+    # Strip markdown code fences (some models wrap JSON in ```json ... ```)
+    if text.startswith("```"):
+        text = text.split("\n", 1)[1] if "\n" in text else text[3:]
+        if text.endswith("```"):
+            text = text[:-3]
+        text = text.strip()
+
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        # Some models (e.g. Gemini) embed raw control characters inside JSON
+        # string values. Replacing them with a space usually produces valid JSON.
+        cleaned = re.sub(r"[\x00-\x1f\x7f]", " ", text)
+        return json.loads(cleaned)
+
+
+_PROVIDERS_WITHOUT_API_KEY = frozenset(
+    {
+        "ollama",
+        "lmstudio",
+        "openai-codex",
+        "claude-code",
+        "mock",
+        "vertexai",
+    }
+)
+
+
+def requires_api_key(provider: str) -> bool:
+    """Return True if the given provider requires an API key to operate."""
+    return provider.lower() not in _PROVIDERS_WITHOUT_API_KEY
+
+
 def create_llm_provider(
     provider: str,
     api_key: str,
@@ -71,6 +124,7 @@ def create_llm_provider(
     vertexai_project_id: str | None = None,
     vertexai_region: str | None = None,
     vertexai_credentials: Any = None,
+    gemini_safety_settings: list | None = None,
 ) -> Any:  # Returns LLMInterface
     """
     Factory function to create the appropriate LLM provider implementation.
@@ -139,6 +193,7 @@ def create_llm_provider(
             vertexai_project_id=vertexai_project_id,
             vertexai_region=vertexai_region,
             vertexai_credentials=vertexai_credentials,
+            gemini_safety_settings=gemini_safety_settings,
         )
 
     elif provider_lower == "anthropic":
@@ -181,6 +236,7 @@ class LLMProvider:
         reasoning_effort: str = "low",
         groq_service_tier: str | None = None,
         openai_service_tier: str | None = None,
+        gemini_safety_settings: list | None = None,
     ):
         """
         Initialize LLM provider.
@@ -193,6 +249,7 @@ class LLMProvider:
             reasoning_effort: Reasoning effort level for supported providers.
             groq_service_tier: Groq service tier ("on_demand", "flex", "auto") - from config.
             openai_service_tier: OpenAI service tier (None or "flex") - from config.
+            gemini_safety_settings: Safety settings for Gemini/VertexAI providers.
         """
         self.provider = provider.lower()
         self.api_key = api_key
@@ -202,6 +259,8 @@ class LLMProvider:
         # Service tiers from hierarchical config (not env vars)
         self.groq_service_tier = groq_service_tier
         self.openai_service_tier = openai_service_tier
+        # Gemini safety settings (instance default; can be overridden per-request via context var)
+        self.gemini_safety_settings = gemini_safety_settings
 
         # Validate provider
         valid_providers = [
@@ -270,6 +329,18 @@ class LLMProvider:
                 f"model={self.model}, auth={'service_account' if service_account_key else 'ADC'}"
             )
 
+        # For Gemini/VertexAI providers: read safety settings from global config if not explicitly provided
+        # Use _get_raw_config() to bypass StaticConfigProxy (which blocks configurable fields),
+        # since LLMProvider initialization legitimately needs the server-level default.
+        if self.provider in ("gemini", "vertexai") and self.gemini_safety_settings is None:
+            from ..config import _get_raw_config
+
+            try:
+                raw_config = _get_raw_config()
+                self.gemini_safety_settings = raw_config.llm_gemini_safety_settings
+            except Exception:
+                pass  # Config may not be initialized in test environments
+
         # Create provider implementation using factory
         self._provider_impl = create_llm_provider(
             provider=self.provider,
@@ -282,6 +353,7 @@ class LLMProvider:
             vertexai_project_id=vertexai_project_id,
             vertexai_region=vertexai_region,
             vertexai_credentials=vertexai_credentials,
+            gemini_safety_settings=self.gemini_safety_settings,
         )
 
         # Backward compatibility: Keep mock provider properties
@@ -450,6 +522,14 @@ class LLMProvider:
 
             return result
 
+    def set_response_callback(self, fn: Any) -> None:
+        """Set a callback invoked on each call() instead of the fixed mock response."""
+        if self.provider == "mock":
+            from .providers.mock_llm import MockLLM
+
+            if isinstance(self._provider_impl, MockLLM):
+                self._provider_impl.set_response_callback(fn)
+
     def set_mock_response(self, response: Any) -> None:
         """Set the response to return from mock calls."""
         # Backward compatibility: Store in both wrapper and provider implementation
@@ -542,6 +622,23 @@ class LLMProvider:
         # SDK will automatically check for authentication when first used
         # No need to verify here - let it fail gracefully on first call with helpful error
 
+    def with_config(self, config: Any) -> "ConfiguredLLMProvider":
+        """
+        Return a configured wrapper for a specific bank operation.
+
+        The wrapper applies per-bank overrides (e.g. Gemini safety settings)
+        to every ``call()`` / ``call_with_tools()`` invocation without
+        changing the underlying provider or its long-lived client connection.
+
+        Args:
+            config: Resolved ``HindsightConfig`` for the current bank/request.
+
+        Returns:
+            A ``ConfiguredLLMProvider`` that delegates to this provider with
+            the supplied config applied.
+        """
+        return ConfiguredLLMProvider(self, config.llm_gemini_safety_settings)
+
     async def cleanup(self) -> None:
         """Clean up resources."""
         pass
@@ -552,8 +649,9 @@ class LLMProvider:
         provider = os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq")
         api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY", "")
 
-        # API key not needed for openai-codex (uses OAuth) or claude-code (uses Keychain OAuth)
-        if not api_key and provider not in ("openai-codex", "claude-code"):
+        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
+        # ollama (local), or vertexai (uses GCP service account credentials)
+        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
             raise ValueError(
                 "HINDSIGHT_API_LLM_API_KEY environment variable is required (unless using openai-codex or claude-code)"
             )
@@ -569,8 +667,9 @@ class LLMProvider:
         provider = os.getenv("HINDSIGHT_API_ANSWER_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
         api_key = os.getenv("HINDSIGHT_API_ANSWER_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", ""))
 
-        # API key not needed for openai-codex (uses OAuth) or claude-code (uses Keychain OAuth)
-        if not api_key and provider not in ("openai-codex", "claude-code"):
+        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
+        # ollama (local), or vertexai (uses GCP service account credentials)
+        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
             raise ValueError(
                 "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_ANSWER_LLM_API_KEY environment variable is required "
                 "(unless using openai-codex or claude-code)"
@@ -587,8 +686,9 @@ class LLMProvider:
         provider = os.getenv("HINDSIGHT_API_JUDGE_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
         api_key = os.getenv("HINDSIGHT_API_JUDGE_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", ""))
 
-        # API key not needed for openai-codex (uses OAuth) or claude-code (uses Keychain OAuth)
-        if not api_key and provider not in ("openai-codex", "claude-code"):
+        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
+        # ollama (local), or vertexai (uses GCP service account credentials)
+        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
             raise ValueError(
                 "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_JUDGE_LLM_API_KEY environment variable is required "
                 "(unless using openai-codex or claude-code)"
@@ -600,5 +700,58 @@ class LLMProvider:
         return cls(provider=provider, api_key=api_key, base_url=base_url, model=model, reasoning_effort="high")
 
 
+class ConfiguredLLMProvider:
+    """
+    Thin wrapper around LLMProvider that applies bank-specific config to every call.
+
+    Obtained via ``LLMProvider.with_config(resolved_config)``.  The wrapper
+    sets any provider-specific overrides (currently Gemini safety settings)
+    immediately before each call using a ContextVar token, then resets it
+    afterwards — so nesting is safe and the configuration cannot leak across
+    operations.
+
+    All attribute access falls through to the underlying provider so callers
+    that read ``llm.provider``, ``llm.model``, etc. continue to work without
+    any changes.
+    """
+
+    def __init__(self, provider: "LLMProvider", gemini_safety_settings: list | None) -> None:
+        # Use object.__setattr__ to avoid triggering __getattr__
+        object.__setattr__(self, "_provider", provider)
+        object.__setattr__(self, "_gemini_safety_settings", gemini_safety_settings)
+
+    # ── attribute passthrough ──────────────────────────────────────────────────
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(object.__getattribute__(self, "_provider"), name)
+
+    # ── overridden call methods ────────────────────────────────────────────────
+
+    async def call(self, messages: list[dict[str, Any]], **kwargs: Any) -> Any:
+        from .providers.gemini_llm import _safety_settings_ctx
+
+        token = _safety_settings_ctx.set(object.__getattribute__(self, "_gemini_safety_settings"))
+        try:
+            return await object.__getattribute__(self, "_provider").call(messages=messages, **kwargs)
+        finally:
+            _safety_settings_ctx.reset(token)
+
+    async def call_with_tools(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        **kwargs: Any,
+    ) -> "LLMToolCallResult":
+        from .providers.gemini_llm import _safety_settings_ctx
+
+        token = _safety_settings_ctx.set(object.__getattribute__(self, "_gemini_safety_settings"))
+        try:
+            return await object.__getattribute__(self, "_provider").call_with_tools(
+                messages=messages, tools=tools, **kwargs
+            )
+        finally:
+            _safety_settings_ctx.reset(token)
+
+
 # Backwards compatibility alias
 LLMConfig = LLMProvider

hindsight-api/hindsight_api/engine/parsers/__init__.py

@@ -1,9 +1,10 @@
 """File parser implementations."""
 
-from .base import FileParser
+from .base import FileParser, UnsupportedFileTypeError
+from .iris import IrisParser
 from .markitdown import MarkitdownParser
 
-__all__ = ["FileParser", "MarkitdownParser", "FileParserRegistry"]
+__all__ = ["FileParser", "UnsupportedFileTypeError", "IrisParser", "MarkitdownParser", "FileParserRegistry"]
 
 
 class FileParserRegistry:
@@ -43,7 +44,8 @@ class FileParserRegistry:
             ValueError: If no suitable parser found
         """
         if name:
-            # Explicit parser requested
+            # Explicit parser requested — return it directly, let the parser
+            # raise UnsupportedFileTypeError from convert() if needed
             if name not in self._parsers:
                 raise ValueError(f"Parser '{name}' not found. Available: {list(self._parsers.keys())}")
             return self._parsers[name]

hindsight-api/hindsight_api/engine/parsers/base.py

@@ -3,6 +3,12 @@
 from abc import ABC, abstractmethod
 
 
+class UnsupportedFileTypeError(Exception):
+    """Raised by a parser when it does not support the given file type."""
+
+    pass
+
+
 class FileParser(ABC):
     """Abstract base for file to markdown parsers."""
 
@@ -19,24 +25,27 @@ class FileParser(ABC):
             Markdown content as string
 
         Raises:
-            ValueError: If file format is not supported
-            RuntimeError: If parsing fails
+            UnsupportedFileTypeError: If the file type is not supported by this parser
+            RuntimeError: If parsing fails for another reason
         """
         pass
 
-    @abstractmethod
     def supports(self, filename: str, content_type: str | None = None) -> bool:
         """
         Check if parser supports this file type.
 
+        Override this for local/static extension-based filtering.
+        Parsers that delegate to a remote service should leave this as True
+        and raise UnsupportedFileTypeError from convert() instead.
+
         Args:
             filename: File name (used for extension check)
             content_type: MIME type (optional)
 
         Returns:
-            True if this parser can handle the file
+            True if this parser can handle the file (default: True)
         """
-        pass
+        return True
 
     @abstractmethod
     def name(self) -> str:

hindsight-api/hindsight_api/engine/parsers/iris.py

@@ -0,0 +1,137 @@
+"""Iris parser implementation using the Vectorize Iris HTTP API."""
+
+import asyncio
+import logging
+import mimetypes
+import time
+
+import httpx
+
+from .base import FileParser, UnsupportedFileTypeError
+
+logger = logging.getLogger(__name__)
+
+_IRIS_BASE_URL = "https://api.vectorize.io/v1"
+_DEFAULT_POLL_INTERVAL = 2.0  # seconds
+_DEFAULT_TIMEOUT = 300.0  # seconds
+
+
+class IrisParser(FileParser):
+    """
+    Iris file parser using the Vectorize Iris cloud extraction service.
+
+    Uploads files to the Vectorize Iris API, starts an extraction job,
+    and polls until the text is ready. The API determines which file types
+    are supported — UnsupportedFileTypeError is raised if the file is rejected.
+
+    Authentication:
+        Requires HINDSIGHT_API_FILE_PARSER_IRIS_TOKEN and
+        HINDSIGHT_API_FILE_PARSER_IRIS_ORG_ID environment variables,
+        or pass them explicitly via the constructor.
+    """
+
+    def __init__(
+        self,
+        token: str,
+        org_id: str,
+        poll_interval: float = _DEFAULT_POLL_INTERVAL,
+        timeout: float = _DEFAULT_TIMEOUT,
+    ):
+        """
+        Initialize iris parser.
+
+        Args:
+            token: Vectorize API token
+            org_id: Vectorize organization ID
+            poll_interval: Seconds between status poll requests (default: 2)
+            timeout: Maximum seconds to wait for extraction (default: 300)
+        """
+        self._token = token
+        self._org_id = org_id
+        self._poll_interval = poll_interval
+        self._timeout = timeout
+        self._auth_headers = {"Authorization": f"Bearer {token}"}
+
+    async def convert(self, file_data: bytes, filename: str) -> str:
+        """
+        Parse file to text using the Vectorize Iris API.
+
+        Raises:
+            UnsupportedFileTypeError: If the Iris API rejects the file type (4xx)
+            RuntimeError: If extraction fails for another reason
+        """
+        content_type = mimetypes.guess_type(filename)[0] or "application/octet-stream"
+
+        async with httpx.AsyncClient() as client:
+            # Step 1: Request a presigned upload URL
+            init_resp = await client.post(
+                f"{_IRIS_BASE_URL}/org/{self._org_id}/files",
+                headers=self._auth_headers,
+                json={"name": filename, "contentType": content_type},
+            )
+            _raise_for_status(init_resp, filename, "file upload init")
+            init_data = init_resp.json()
+            file_id: str = init_data["fileId"]
+            upload_url: str = init_data["uploadUrl"]
+
+            # Step 2: Upload the file bytes to the presigned URL (no auth header)
+            upload_resp = await client.put(
+                upload_url,
+                content=file_data,
+                headers={"Content-Type": content_type},
+            )
+            _raise_for_status(upload_resp, filename, "file upload")
+
+            # Step 3: Start extraction
+            extract_resp = await client.post(
+                f"{_IRIS_BASE_URL}/org/{self._org_id}/extraction",
+                headers=self._auth_headers,
+                json={"fileId": file_id},
+            )
+            _raise_for_status(extract_resp, filename, "start extraction")
+            extraction_id: str = extract_resp.json()["extractionId"]
+
+            # Step 4: Poll until ready or timeout
+            deadline = time.monotonic() + self._timeout
+            while True:
+                status_resp = await client.get(
+                    f"{_IRIS_BASE_URL}/org/{self._org_id}/extraction/{extraction_id}",
+                    headers=self._auth_headers,
+                )
+                _raise_for_status(status_resp, filename, "poll extraction status")
+                status_data = status_resp.json()
+
+                if status_data.get("ready"):
+                    data = status_data.get("data", {})
+                    if not data.get("success"):
+                        error = data.get("error", "unknown error")
+                        raise RuntimeError(f"Iris extraction failed for '{filename}': {error}")
+                    text = data.get("text")
+                    if not text:
+                        raise RuntimeError(f"No content extracted from '{filename}'")
+                    return text
+
+                if time.monotonic() >= deadline:
+                    raise RuntimeError(f"Iris extraction timed out after {self._timeout}s for '{filename}'")
+
+                await asyncio.sleep(self._poll_interval)
+
+    def name(self) -> str:
+        """Get parser name."""
+        return "iris"
+
+
+def _raise_for_status(response: httpx.Response, filename: str, step: str) -> None:
+    """
+    Raise an appropriate error including the response body on HTTP errors.
+
+    Raises UnsupportedFileTypeError for 4xx responses (file rejected by the API),
+    RuntimeError for other HTTP errors.
+    """
+    if not response.is_error:
+        return
+    body = response.text or "<empty>"
+    msg = f"Iris API error during {step} for '{filename}': {response.status_code} {response.reason_phrase} — {body}"
+    if response.is_client_error:
+        raise UnsupportedFileTypeError(msg)
+    raise RuntimeError(msg)

hindsight-api/hindsight_api/engine/providers/claude_code_llm.py

@@ -238,21 +238,24 @@ class ClaudeCodeLLM(LLMInterface):
                 )
 
                 # Record trace span
-                from hindsight_api.tracing import get_span_recorder
+                try:
+                    from hindsight_api.tracing import get_span_recorder
 
-                span_recorder = get_span_recorder()
-                span_recorder.record_llm_call(
-                    provider=self.provider,
-                    model=self.model,
-                    scope=scope,
-                    messages=messages,
-                    response_content=result if isinstance(result, str) else json.dumps(result),
-                    input_tokens=estimated_input,
-                    output_tokens=estimated_output,
-                    duration=duration,
-                    finish_reason=None,
-                    error=None,
-                )
+                    span_recorder = get_span_recorder()
+                    span_recorder.record_llm_call(
+                        provider=self.provider,
+                        model=self.model,
+                        scope=scope,
+                        messages=messages,
+                        response_content=result if isinstance(result, str) else result.model_dump_json(),
+                        input_tokens=estimated_input,
+                        output_tokens=estimated_output,
+                        duration=duration,
+                        finish_reason=None,
+                        error=None,
+                    )
+                except Exception:
+                    pass  # logging failure must never affect the operation
 
                 # Log slow calls
                 if duration > 10.0:

hindsight-api/hindsight_api/engine/providers/gemini_llm.py

@@ -11,6 +11,7 @@ import json
 import logging
 import os
 import time
+from contextvars import ContextVar
 from typing import Any
 
 from google import genai
@@ -18,11 +19,18 @@ from google.genai import errors as genai_errors
 from google.genai import types as genai_types
 
 from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
+from hindsight_api.engine.llm_wrapper import parse_llm_json
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
 from hindsight_api.metrics import get_metrics_collector
 
 logger = logging.getLogger(__name__)
 
+# Per-request Gemini safety settings override.
+# Set exclusively by ConfiguredLLMProvider.call() / call_with_tools() via token-based
+# set/reset, so it is properly scoped to each individual LLM call and never leaks.
+_safety_settings_ctx: ContextVar[list | None] = ContextVar("gemini_safety_settings", default=None)
+
+
 # Vertex AI imports (optional)
 try:
     import google.auth
@@ -57,6 +65,9 @@ class GeminiLLM(LLMInterface):
         self._client = None
         self._is_vertexai = self.provider == "vertexai"
 
+        # Safety settings: None means use Gemini's defaults
+        self._safety_settings: list | None = kwargs.get("gemini_safety_settings")
+
         if self._is_vertexai:
             self._init_vertexai(**kwargs)
         else:
@@ -215,16 +226,29 @@ class GeminiLLM(LLMInterface):
         if temperature is not None:
             config_kwargs["temperature"] = temperature
 
+        # Apply safety settings: context var (per-request bank override) takes precedence over instance default
+        effective_safety_settings = _safety_settings_ctx.get()
+        if effective_safety_settings is None:
+            effective_safety_settings = self._safety_settings
+        if effective_safety_settings is not None:
+            config_kwargs["safety_settings"] = [
+                genai_types.SafetySetting(category=s["category"], threshold=s["threshold"])
+                for s in effective_safety_settings
+            ]
+
         generation_config = genai_types.GenerateContentConfig(**config_kwargs) if config_kwargs else None
 
         last_exception = None
 
         for attempt in range(max_retries + 1):
             try:
-                response = await self._client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=generation_config,
+                response = await asyncio.wait_for(
+                    self._client.aio.models.generate_content(
+                        model=self.model,
+                        contents=gemini_contents,
+                        config=generation_config,
+                    ),
+                    timeout=90.0,  # Safety net for network hangs; valid slow responses are <90s
                 )
 
                 content = response.text
@@ -247,7 +271,7 @@ class GeminiLLM(LLMInterface):
 
                 # Parse structured output if requested
                 if response_format is not None:
-                    json_data = json.loads(content)
+                    json_data = parse_llm_json(content)
                     if skip_validation:
                         result = json_data
                     else:
@@ -405,31 +429,57 @@ class GeminiLLM(LLMInterface):
         # Convert messages
         system_instruction = None
         gemini_contents = []
-        for msg in messages:
+        msg_list = list(messages)
+        i = 0
+        while i < len(msg_list):
+            msg = msg_list[i]
             role = msg.get("role", "user")
             content = msg.get("content", "")
 
             if role == "system":
                 system_instruction = (system_instruction + "\n\n" + content) if system_instruction else content
+                i += 1
             elif role == "tool":
-                # Gemini uses function_response
-                gemini_contents.append(
-                    genai_types.Content(
-                        role="user",
-                        parts=[
-                            genai_types.Part(
-                                function_response=genai_types.FunctionResponse(
-                                    name=msg.get("name", ""),
-                                    response={"result": content},
-                                )
+                # Gemini requires ALL tool responses for a given model turn to be grouped
+                # into a single Content with multiple FunctionResponse parts.
+                # Consecutive role="tool" messages correspond to one model turn's tool calls.
+                parts = []
+                while i < len(msg_list) and msg_list[i].get("role") == "tool":
+                    tool_msg = msg_list[i]
+                    tool_content = tool_msg.get("content", "")
+                    parts.append(
+                        genai_types.Part(
+                            function_response=genai_types.FunctionResponse(
+                                name=tool_msg.get("name", ""),
+                                response={"result": tool_content},
                             )
-                        ],
+                        )
                     )
-                )
+                    i += 1
+                gemini_contents.append(genai_types.Content(role="user", parts=parts))
             elif role == "assistant":
-                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
+                tool_calls_in_msg = msg.get("tool_calls", [])
+                if tool_calls_in_msg:
+                    # Convert OpenAI-style tool_calls to Gemini function_call parts
+                    # This is required for proper multi-turn conversation history
+                    parts = []
+                    if content:
+                        parts.append(genai_types.Part(text=content))
+                    for tc in tool_calls_in_msg:
+                        fn = tc.get("function", {})
+                        fn_name = fn.get("name", "")
+                        fn_args_str = fn.get("arguments", "{}")
+                        fn_args = parse_llm_json(fn_args_str)
+                        parts.append(
+                            genai_types.Part(function_call=genai_types.FunctionCall(name=fn_name, args=fn_args))
+                        )
+                    gemini_contents.append(genai_types.Content(role="model", parts=parts))
+                else:
+                    gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
+                i += 1
             else:
                 gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
+                i += 1
 
         config_kwargs: dict[str, Any] = {"tools": gemini_tools}
         if system_instruction:
@@ -437,15 +487,50 @@ class GeminiLLM(LLMInterface):
         if temperature is not None:
             config_kwargs["temperature"] = temperature
 
+        # Map OpenAI-style tool_choice to Gemini FunctionCallingConfig
+        if tool_choice == "required":
+            config_kwargs["tool_config"] = genai_types.ToolConfig(
+                function_calling_config=genai_types.FunctionCallingConfig(
+                    mode="ANY",
+                )
+            )
+        elif isinstance(tool_choice, dict) and tool_choice.get("type") == "function":
+            fn_name = tool_choice.get("function", {}).get("name")
+            if fn_name:
+                config_kwargs["tool_config"] = genai_types.ToolConfig(
+                    function_calling_config=genai_types.FunctionCallingConfig(
+                        mode="ANY",
+                        allowed_function_names=[fn_name],
+                    )
+                )
+        elif tool_choice == "none":
+            config_kwargs["tool_config"] = genai_types.ToolConfig(
+                function_calling_config=genai_types.FunctionCallingConfig(mode="NONE")
+            )
+        # "auto" is the default (no tool_config needed)
+
+        # Apply safety settings: context var (per-request bank override) takes precedence over instance default
+        effective_safety_settings = _safety_settings_ctx.get()
+        if effective_safety_settings is None:
+            effective_safety_settings = self._safety_settings
+        if effective_safety_settings is not None:
+            config_kwargs["safety_settings"] = [
+                genai_types.SafetySetting(category=s["category"], threshold=s["threshold"])
+                for s in effective_safety_settings
+            ]
+
         config = genai_types.GenerateContentConfig(**config_kwargs)
 
         last_exception = None
         for attempt in range(max_retries + 1):
             try:
-                response = await self._client.aio.models.generate_content(
-                    model=self.model,
-                    contents=gemini_contents,
-                    config=config,
+                response = await asyncio.wait_for(
+                    self._client.aio.models.generate_content(
+                        model=self.model,
+                        contents=gemini_contents,
+                        config=config,
+                    ),
+                    timeout=90.0,  # Safety net for network hangs; valid slow responses are <90s
                 )
 
                 # Extract content and tool calls

hindsight-api/hindsight_api/engine/providers/mock_llm.py

@@ -6,6 +6,7 @@ without making actual API calls to external LLM services.
 """
 
 import logging
+from collections.abc import Callable
 from typing import Any
 
 from ..llm_interface import LLMInterface
@@ -66,6 +67,7 @@ class MockLLM(LLMInterface):
         self._mock_calls: list[dict] = []
         self._mock_response: Any = None
         self._mock_exception: Exception | None = None
+        self._response_callback: Callable[[list[dict], str], Any] | None = None
 
     async def verify_connection(self) -> None:
         """
@@ -147,7 +149,9 @@ class MockLLM(LLMInterface):
         )
 
         # Return mock response
-        if self._mock_response is not None:
+        if self._response_callback is not None:
+            result = self._response_callback(messages, scope)
+        elif self._mock_response is not None:
             result = self._mock_response
         elif response_format is not None:
             # Try to create a minimal valid instance of the response format
@@ -214,7 +218,15 @@ class MockLLM(LLMInterface):
 
         span_recorder = get_span_recorder()
 
-        if self._mock_response is not None:
+        if self._response_callback is not None:
+            cb_result = self._response_callback(messages, scope)
+            if isinstance(cb_result, LLMToolCallResult):
+                result = cb_result
+            else:
+                result = LLMToolCallResult(
+                    content=str(cb_result) if cb_result is not None else "mock response", finish_reason="stop"
+                )
+        elif self._mock_response is not None:
             if isinstance(self._mock_response, LLMToolCallResult):
                 result = self._mock_response
             elif isinstance(self._mock_response, list):
@@ -258,6 +270,16 @@ class MockLLM(LLMInterface):
         """Clean up resources (no-op for mock provider)."""
         pass
 
+    def set_response_callback(self, fn: Callable[[list[dict], str], Any]) -> None:
+        """
+        Set a callback invoked on each call() instead of _mock_response.
+
+        The callback receives (messages, scope) and returns the response.
+        Useful for returning different responses per call (e.g., cycling
+        through a corpus in a benchmark).
+        """
+        self._response_callback = fn
+
     def set_mock_response(self, response: Any) -> None:
         """
         Set the response to return from mock calls.

hindsight-api/hindsight_api/engine/query_analyzer.py

@@ -92,11 +92,17 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
         self._search_dates = None
 
     def load(self) -> None:
-        """Load dateparser (lazy import)."""
+        """Load dateparser and warm up internal data structures.
+
+        Triggers the real initialization cost (regex tables, timezone data) at
+        load time so the first actual recall doesn't pay the cold-start penalty.
+        """
         if self._search_dates is None:
             from dateparser.search import search_dates
 
             self._search_dates = search_dates
+            # Warm up: fire a dummy call to trigger lazy-loaded internal tables.
+            self._search_dates("today")
 
     def analyze(self, query: str, reference_date: datetime | None = None) -> QueryAnalysis:
         """

hindsight-api/hindsight_api/engine/reflect/agent.py

@@ -14,32 +14,26 @@ import re
 import time
 from typing import TYPE_CHECKING, Any, Awaitable, Callable
 
+import tiktoken
+
 from .models import DirectiveInfo, LLMCall, ReflectAgentResult, TokenUsageSummary, ToolCall
 from .prompts import FINAL_SYSTEM_PROMPT, _extract_directive_rules, build_final_prompt, build_system_prompt_for_tools
 from .tools_schema import get_reflect_tools
 
 
 def _build_directives_applied(directives: list[dict[str, Any]] | None) -> list[DirectiveInfo]:
-    """Build list of DirectiveInfo from directive mental models.
-
-    Handles multiple directive formats:
-    1. New format: directives have direct 'content' field
-    2. Fallback: directives have 'description' field
-    """
+    """Build list of DirectiveInfo from directives."""
     if not directives:
         return []
 
-    result = []
-    for directive in directives:
-        directive_id = directive.get("id", "")
-        directive_name = directive.get("name", "")
-
-        # Get content from 'content' field or fallback to 'description'
-        content = directive.get("content", "") or directive.get("description", "")
-
-        result.append(DirectiveInfo(id=directive_id, name=directive_name, content=content))
-
-    return result
+    return [
+        DirectiveInfo(
+            id=directive.get("id", ""),
+            name=directive.get("name", ""),
+            content=directive.get("content", ""),
+        )
+        for directive in directives
+    ]
 
 
 if TYPE_CHECKING:
@@ -267,6 +261,46 @@ OUTPUT:"""
         return None, 0, 0
 
 
+_TIKTOKEN_ENCODING = tiktoken.get_encoding("cl100k_base")
+
+
+def _count_messages_tokens(messages: list[dict[str, Any]]) -> int:
+    """Estimate the token count of the messages list using cl100k_base encoding."""
+    total = 0
+    for msg in messages:
+        content = msg.get("content") or ""
+        if isinstance(content, str):
+            total += len(_TIKTOKEN_ENCODING.encode(content))
+        elif isinstance(content, list):
+            for part in content:
+                if isinstance(part, dict) and isinstance(part.get("text"), str):
+                    total += len(_TIKTOKEN_ENCODING.encode(part["text"]))
+        # Tool call arguments and results also count
+        for tc in msg.get("tool_calls") or []:
+            if isinstance(tc, dict):
+                func = tc.get("function", {})
+                total += len(_TIKTOKEN_ENCODING.encode(func.get("arguments", "")))
+    return total
+
+
+def _is_context_overflow_error(exc: Exception) -> bool:
+    """Return True if the exception signals the LLM context window was exceeded."""
+    msg = str(exc).lower()
+    return any(
+        phrase in msg
+        for phrase in (
+            "context_length_exceeded",
+            "context length exceeded",
+            "maximum context length",
+            "prompt_too_long",
+            "prompt is too long",
+            "resource_exhausted",
+            "input is too long",
+            "too many tokens",
+        )
+    )
+
+
 async def run_reflect_agent(
     llm_config: "LLMProvider",
     bank_id: str,
@@ -274,7 +308,7 @@ async def run_reflect_agent(
     bank_profile: dict[str, Any],
     search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
     search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
-    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
     expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
     context: str | None = None,
     max_iterations: int = DEFAULT_MAX_ITERATIONS,
@@ -283,6 +317,7 @@ async def run_reflect_agent(
     directives: list[dict[str, Any]] | None = None,
     has_mental_models: bool = False,
     budget: str | None = None,
+    max_context_tokens: int = 100_000,
 ) -> ReflectAgentResult:
     """
     Execute the reflect agent loop using native tool calling.
@@ -390,12 +425,15 @@ async def run_reflect_agent(
             f"total={elapsed_ms}ms"
         )
 
+    consecutive_errors = 0
     for iteration in range(max_iterations):
         is_last = iteration == max_iterations - 1
 
         if is_last:
             # Force text response on last iteration - no tools
-            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
@@ -440,17 +478,91 @@ async def run_reflect_agent(
                 directives_applied=directives_applied,
             )
 
+        # Proactive context-window guard: if accumulated messages would exceed the
+        # configured token budget, bail out early and synthesize from what we have.
+        estimated_tokens = _count_messages_tokens(messages)
+        if estimated_tokens >= max_context_tokens and (
+            bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
+        ):
+            logger.warning(
+                f"[REFLECT {reflect_id}] Context budget exceeded on iteration {iteration + 1}: "
+                f"~{estimated_tokens} tokens >= {max_context_tokens} limit. Forcing final synthesis."
+            )
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
+            llm_start = time.time()
+            response, usage = await llm_config.call(
+                messages=[
+                    {"role": "system", "content": FINAL_SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                scope="reflect",
+                max_completion_tokens=max_tokens,
+                return_usage=True,
+            )
+            llm_duration = int((time.time() - llm_start) * 1000)
+            total_input_tokens += usage.input_tokens
+            total_output_tokens += usage.output_tokens
+            llm_trace.append(
+                {
+                    "scope": "final",
+                    "duration_ms": llm_duration,
+                    "input_tokens": usage.input_tokens,
+                    "output_tokens": usage.output_tokens,
+                }
+            )
+            answer = _clean_answer_text(response.strip())
+
+            structured_output = None
+            if response_schema and answer:
+                structured_output, struct_in, struct_out = await _generate_structured_output(
+                    answer, response_schema, llm_config, reflect_id
+                )
+                total_input_tokens += struct_in
+                total_output_tokens += struct_out
+
+            _log_completion(answer, iteration + 1, forced=True)
+            return ReflectAgentResult(
+                text=answer,
+                structured_output=structured_output,
+                iterations=iteration + 1,
+                tools_called=total_tools_called,
+                tool_trace=tool_trace,
+                llm_trace=_get_llm_trace(),
+                usage=_get_usage(),
+                directives_applied=directives_applied,
+            )
+
         # Call LLM with tools
         llm_start = time.time()
 
+        # Determine tool_choice for this iteration.
+        # Force the full hierarchical retrieval path before allowing auto:
+        # With mental models:
+        #   0 → search_mental_models, 1 → search_observations, 2 → recall, 3+ → auto
+        # Without mental models:
+        #   0 → search_observations, 1 → recall, 2+ → auto
+        if iteration == 0 and has_mental_models:
+            iter_tool_choice: str | dict = {"type": "function", "function": {"name": "search_mental_models"}}
+        elif iteration == 0:
+            iter_tool_choice = {"type": "function", "function": {"name": "search_observations"}}
+        elif iteration == 1 and has_mental_models:
+            iter_tool_choice = {"type": "function", "function": {"name": "search_observations"}}
+        elif iteration == 1 or (iteration == 2 and has_mental_models):
+            iter_tool_choice = {"type": "function", "function": {"name": "recall"}}
+        else:
+            iter_tool_choice = "auto"
+
         try:
             result = await llm_config.call_with_tools(
                 messages=messages,
                 tools=tools,
                 scope="reflect_tool_call",
-                tool_choice="required" if iteration == 0 else "auto",  # Force tool use on first iteration
+                tool_choice=iter_tool_choice,
             )
             llm_duration = int((time.time() - llm_start) * 1000)
+            consecutive_errors = 0
             total_input_tokens += result.input_tokens
             total_output_tokens += result.output_tokens
             llm_trace.append(
@@ -464,15 +576,25 @@ async def run_reflect_agent(
 
         except Exception as e:
             err_duration = int((time.time() - llm_start) * 1000)
+            consecutive_errors += 1
             logger.warning(f"[REFLECT {reflect_id}] LLM error on iteration {iteration + 1}: {e} ({err_duration}ms)")
             llm_trace.append({"scope": f"agent_{iteration + 1}_err", "duration_ms": err_duration})
-            # Guardrail: If no evidence gathered yet, retry
             has_gathered_evidence = (
                 bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
             )
-            if not has_gathered_evidence and iteration < max_iterations - 1:
+            # Context overflow errors must never be retried — retrying would only make them worse.
+            # Skip straight to final synthesis with whatever evidence we have.
+            if _is_context_overflow_error(e):
+                logger.warning(
+                    f"[REFLECT {reflect_id}] Context window exceeded on iteration {iteration + 1}, "
+                    "forcing final synthesis from gathered evidence."
+                )
+            # For other errors: retry if no evidence yet (but cap consecutive errors to avoid long hangs)
+            elif not has_gathered_evidence and iteration < max_iterations - 1 and consecutive_errors < 2:
                 continue
-            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
@@ -543,7 +665,9 @@ async def run_reflect_agent(
                     directives_applied=directives_applied,
                 )
             # Empty response, force final
-            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            prompt = build_final_prompt(
+                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
+            )
             llm_start = time.time()
             response, usage = await llm_config.call(
                 messages=[
@@ -807,9 +931,9 @@ async def _process_done_tool(
         answer = "No answer provided."
 
     # Validate IDs (only include IDs that were actually retrieved)
-    used_memory_ids = [mid for mid in args.get("memory_ids", []) if mid in available_memory_ids]
-    used_mental_model_ids = [mid for mid in args.get("mental_model_ids", []) if mid in available_mental_model_ids]
-    used_observation_ids = [oid for oid in args.get("observation_ids", []) if oid in available_observation_ids]
+    used_memory_ids = [mid for mid in (args.get("memory_ids") or []) if mid in available_memory_ids]
+    used_mental_model_ids = [mid for mid in (args.get("mental_model_ids") or []) if mid in available_mental_model_ids]
+    used_observation_ids = [oid for oid in (args.get("observation_ids") or []) if oid in available_observation_ids]
 
     # Generate structured output if schema provided
     structured_output = None
@@ -845,7 +969,7 @@ async def _execute_tool_with_timing(
     tc: "LLMToolCall",
     search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
     search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
-    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
     expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
 ) -> tuple[dict[str, Any], int]:
     """Execute a tool call and return result with timing."""
@@ -917,7 +1041,7 @@ async def _execute_tool(
     args: dict[str, Any],
     search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
     search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
-    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
     expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
 ) -> dict[str, Any]:
     """Execute a single tool by name."""
@@ -943,7 +1067,8 @@ async def _execute_tool(
         if not query:
             return {"error": "recall requires a query parameter"}
         max_tokens = max(int(args.get("max_tokens") or 2048), 1000)  # Default 2048, min 1000
-        return await recall_fn(query, max_tokens)
+        max_chunk_tokens = max(int(args.get("max_chunk_tokens") or 1000), 1000)  # Always enabled, min 1000
+        return await recall_fn(query, max_tokens, max_chunk_tokens)
 
     elif tool_name == "expand":
         memory_ids = args.get("memory_ids", [])
@@ -971,9 +1096,9 @@ def _summarize_input(tool_name: str, args: dict[str, Any]) -> str:
     elif tool_name == "recall":
         query = args.get("query", "")
         query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
-        # Show actual value used (default 2048, min 1000)
         max_tokens = max(int(args.get("max_tokens") or 2048), 1000)
-        return f"(query={query_preview}, max_tokens={max_tokens})"
+        max_chunk_tokens = max(int(args.get("max_chunk_tokens") or 1000), 1000)
+        return f"(query={query_preview}, max_tokens={max_tokens}, max_chunk_tokens={max_chunk_tokens})"
     elif tool_name == "expand":
         memory_ids = args.get("memory_ids", [])
         depth = args.get("depth", "chunk")

hindsight-api/hindsight_api/engine/reflect/prompts.py

@@ -10,59 +10,30 @@ The reflect agent uses hierarchical retrieval:
 import json
 from typing import Any
 
+import tiktoken
+
+_TIKTOKEN_ENCODING = tiktoken.get_encoding("cl100k_base")
+
+# Fraction of max_context_tokens reserved for tool results in the final synthesis prompt.
+# The remainder covers the system prompt, question, bank context, and output tokens.
+_FINAL_PROMPT_CONTEXT_FRACTION = 0.8
+
 
 def _extract_directive_rules(directives: list[dict[str, Any]]) -> list[str]:
-    """
-    Extract directive rules as a list of strings.
-
-    Args:
-        directives: List of directives with name and content
-
-    Returns:
-        List of directive rule strings
-    """
+    """Extract directive rules as a list of strings."""
     rules = []
     for directive in directives:
-        directive_name = directive.get("name", "")
-        # New format: directives have direct content field
+        name = directive.get("name", "")
         content = directive.get("content", "")
         if content:
-            if directive_name:
-                rules.append(f"**{directive_name}**: {content}")
-            else:
-                rules.append(content)
-        else:
-            # Legacy format: check for observations
-            observations = directive.get("observations", [])
-            if observations:
-                for obs in observations:
-                    # Support both Pydantic Observation objects and dicts
-                    if hasattr(obs, "title"):
-                        title = obs.title
-                        obs_content = obs.content
-                    else:
-                        title = obs.get("title", "")
-                        obs_content = obs.get("content", "")
-                    if title and obs_content:
-                        rules.append(f"**{title}**: {obs_content}")
-                    elif obs_content:
-                        rules.append(obs_content)
-            elif directive_name:
-                # Fallback to description
-                desc = directive.get("description", "")
-                if desc:
-                    rules.append(f"**{directive_name}**: {desc}")
+            rules.append(f"**{name}**: {content}" if name else content)
     return rules
 
 
 def build_directives_section(directives: list[dict[str, Any]]) -> str:
-    """
-    Build the directives section for the system prompt.
+    """Build the directives section for the system prompt.
 
     Directives are hard rules that MUST be followed in all responses.
-
-    Args:
-        directives: List of directive mental models with observations
     """
     if not directives:
         return ""
@@ -169,6 +140,12 @@ def build_system_prompt_for_tools(
 
     parts.extend(
         [
+            "## LANGUAGE RULE (default - directives take precedence)",
+            "- By default, detect the language of the user's question and respond in that SAME language.",
+            "- If the question is in Chinese, respond in Chinese. If in Japanese, respond in Japanese.",
+            "- IMPORTANT: The DIRECTIVES section above has HIGHER PRIORITY than this rule.",
+            "  If a directive specifies a language (e.g. 'Always respond in French'), follow the directive.",
+            "",
             "## CRITICAL RULES",
             "- ONLY use information from tool results - no external knowledge or guessing",
             "- You SHOULD synthesize, infer, and reason from the retrieved memories",
@@ -205,6 +182,7 @@ def build_system_prompt_for_tools(
                 "### 3. RAW FACTS (recall) - Ground Truth",
                 "- Individual memories (world facts and experiences)",
                 "- Use when: no mental models/observations exist, they're stale, or you need specific details",
+                "- MANDATORY: If search_mental_models and search_observations both return 0 results, you MUST call recall() before giving up",
                 "- This is the source of truth that other levels are built from",
                 "",
             ]
@@ -222,6 +200,7 @@ def build_system_prompt_for_tools(
                 "### 2. RAW FACTS (recall) - Ground Truth",
                 "- Individual memories (world facts and experiences)",
                 "- Use when: no observations exist, they're stale, or you need specific details",
+                "- MANDATORY: If search_observations returns 0 results or count=0, you MUST call recall() before giving up",
                 "- This is the source of truth that observations are built from",
                 "",
             ]
@@ -299,7 +278,7 @@ def build_system_prompt_for_tools(
         parts.extend(
             [
                 "1. First, try search_observations() - check for consolidated knowledge",
-                "2. If observations are stale OR you need specific details, use recall() for raw facts",
+                "2. If search_observations returns 0 results OR observations are stale, you MUST call recall() for raw facts",
                 "3. Use expand() if you need more context on specific memories",
                 "4. When ready, call done() with your answer and supporting IDs",
             ]
@@ -315,6 +294,7 @@ def build_system_prompt_for_tools(
             "- Format for clarity and readability with proper spacing and hierarchy",
             "- NEVER include memory IDs, UUIDs, or 'Memory references' in the answer text",
             "- Put IDs ONLY in the memory_ids/mental_model_ids/observation_ids arrays, not in the answer",
+            "- CRITICAL: This is a NON-CONVERSATIONAL system. NEVER ask follow-up questions, offer further assistance, or suggest next steps. Your answer must be complete and self-contained. The user cannot reply.",
         ]
     )
 
@@ -422,6 +402,7 @@ def build_final_prompt(
     context_history: list[dict],
     bank_profile: dict,
     additional_context: str | None = None,
+    max_context_tokens: int = 100_000,
 ) -> str:
     """Build the final prompt when forcing a text response (no tools)."""
     parts = []
@@ -451,18 +432,32 @@ def build_final_prompt(
     if additional_context:
         parts.append(f"\n## Additional Context\n{additional_context}")
 
-    # Tool call history
+    # Tool call history — include as many entries as fit within the token budget,
+    # preferring the most recent calls (they tend to be the most targeted).
     if context_history:
         parts.append("\n## Retrieved Data (synthesize and reason from this data)")
-        for entry in context_history:
+        token_budget = int(max_context_tokens * _FINAL_PROMPT_CONTEXT_FRACTION)
+        # Render entries newest-first, then reverse so the prompt reads chronologically.
+        rendered: list[str] = []
+        truncated = False
+        for entry in reversed(context_history):
             tool = entry["tool"]
             output = entry["output"]
-            # Format as proper JSON for LLM readability
             try:
                 output_str = json.dumps(output, indent=2, default=str)
             except (TypeError, ValueError):
                 output_str = str(output)
-            parts.append(f"\n### From {tool}:\n```json\n{output_str}\n```")
+            block = f"\n### From {tool}:\n```json\n{output_str}\n```"
+            block_tokens = len(_TIKTOKEN_ENCODING.encode(block))
+            if block_tokens > token_budget:
+                truncated = True
+                break
+            rendered.append(block)
+            token_budget -= block_tokens
+        for block in reversed(rendered):
+            parts.append(block)
+        if truncated:
+            parts.append("\n*Note: Some earlier tool results were omitted to stay within the context window.*")
     else:
         parts.append("\n## Retrieved Data\nNo data was retrieved.")
 
@@ -510,4 +505,6 @@ CRITICAL: Output ONLY the final synthesized answer. Do NOT include:
 - Meta-commentary about what you're doing ("I'll search...", "Let me analyze...")
 - Explanations of your reasoning process
 - Descriptions of your approach
-Just provide the direct answer with proper markdown formatting."""
+Just provide the direct answer with proper markdown formatting.
+
+CRITICAL: This is a NON-CONVERSATIONAL system. NEVER ask follow-up questions, offer to search again, suggest alternatives, or end with anything like "Would you like me to..." or "Let me know if...". The user cannot reply. Your answer must be complete and self-contained."""

hindsight-api/hindsight_api/engine/reflect/tools.py

@@ -9,7 +9,7 @@ Implements hierarchical retrieval:
 
 import logging
 import uuid
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
@@ -20,9 +20,6 @@ if TYPE_CHECKING:
 
 logger = logging.getLogger(__name__)
 
-# Observation is considered stale if not updated in this many days
-STALE_THRESHOLD_DAYS = 7
-
 
 async def tool_search_mental_models(
     conn: "Connection",
@@ -33,6 +30,7 @@ async def tool_search_mental_models(
     tags: list[str] | None = None,
     tags_match: str = "any",
     exclude_ids: list[str] | None = None,
+    pending_consolidation: int = 0,
 ) -> dict[str, Any]:
     """
     Search user-curated mental models by semantic similarity.
@@ -87,7 +85,6 @@ async def tool_search_mental_models(
         *params,
     )
 
-    now = datetime.now(timezone.utc)
     mental_models = []
 
     for row in rows:
@@ -95,11 +92,10 @@ async def tool_search_mental_models(
         if last_refreshed_at and last_refreshed_at.tzinfo is None:
             last_refreshed_at = last_refreshed_at.replace(tzinfo=timezone.utc)
 
-        # Calculate freshness
-        is_stale = False
-        if last_refreshed_at:
-            age = now - last_refreshed_at
-            is_stale = age > timedelta(days=STALE_THRESHOLD_DAYS)
+        # A mental model is stale when there are memories that haven't been consolidated yet —
+        # the same signal used for observations staleness.
+        is_stale = pending_consolidation > 0
+        staleness_reason = f"{pending_consolidation} memories pending consolidation" if is_stale else None
 
         mental_models.append(
             {
@@ -110,6 +106,7 @@ async def tool_search_mental_models(
                 "relevance": round(row["relevance"], 4),
                 "updated_at": last_refreshed_at.isoformat() if last_refreshed_at else None,
                 "is_stale": is_stale,
+                "staleness_reason": staleness_reason,
             }
         )
 
@@ -132,7 +129,7 @@ async def tool_search_observations(
     pending_consolidation: int = 0,
 ) -> dict[str, Any]:
     """
-    Search consolidated observations using recall with include_observations.
+    Search consolidated observations using recall with include_source_facts.
 
     Observations are auto-generated from memories. Returns freshness info
     so the agent knows if it should also verify with recall().
@@ -149,72 +146,24 @@ async def tool_search_observations(
         pending_consolidation: Number of memories waiting to be consolidated
 
     Returns:
-        Dict with matching observations including freshness info
+        Dict with matching observations including freshness info and source memories
     """
-    from ..memory_engine import fq_table
-
-    # Use recall to search observations (they come back in results field when fact_type=["observation"])
     result = await memory_engine.recall_async(
         bank_id=bank_id,
         query=query,
-        fact_type=["observation"],  # Only retrieve observations
-        max_tokens=max_tokens,  # Token budget controls how many observations are returned
+        fact_type=["observation"],
+        max_tokens=max_tokens,
         enable_trace=False,
         request_context=request_context,
         tags=tags,
         tags_match=tags_match,
+        include_source_facts=True,
+        max_source_facts_tokens=-1,  # No token limit — include all source facts
         _connection_budget=1,
         _quiet=True,
     )
 
-    observations = []
-
-    # When fact_type=["observation"], results come back in `results` field as MemoryFact objects
-    # We need to fetch additional fields (proof_count, source_memory_ids) from the database
-    if result.results:
-        obs_ids = [m.id for m in result.results]
-
-        # Fetch proof_count and source_memory_ids for these observations
-        pool = await memory_engine._get_pool()
-        async with pool.acquire() as conn:
-            obs_rows = await conn.fetch(
-                f"""
-                SELECT id, proof_count, source_memory_ids
-                FROM {fq_table("memory_units")}
-                WHERE id = ANY($1::uuid[])
-                """,
-                obs_ids,
-            )
-            obs_data = {str(row["id"]): row for row in obs_rows}
-
-        for m in result.results:
-            # Get additional data from DB lookup
-            extra = obs_data.get(m.id, {})
-            proof_count = extra.get("proof_count", 1) if extra else 1
-            source_ids = extra.get("source_memory_ids", []) if extra else []
-            # Convert UUIDs to strings
-            source_memory_ids = [str(sid) for sid in (source_ids or [])]
-
-            # Determine staleness
-            is_stale = False
-            staleness_reason = None
-            if pending_consolidation > 0:
-                is_stale = True
-                staleness_reason = f"{pending_consolidation} memories pending consolidation"
-
-            observations.append(
-                {
-                    "id": str(m.id),
-                    "text": m.text,
-                    "proof_count": proof_count,
-                    "source_memory_ids": source_memory_ids,
-                    "tags": m.tags or [],
-                    "is_stale": is_stale,
-                    "staleness_reason": staleness_reason,
-                }
-            )
-
-    # Return freshness info (more understandable than raw pending_consolidation count)
+    is_stale = pending_consolidation > 0
     if pending_consolidation == 0:
         freshness = "up_to_date"
     elif pending_consolidation < 10:
@@ -224,8 +173,10 @@ async def tool_search_observations(
 
     return {
         "query": query,
-        "count": len(observations),
-        "observations": observations,
+        "count": len(result.results),
+        "observations": [m.model_dump() for m in result.results],
+        "source_facts": {k: v.model_dump() for k, v in (result.source_facts or {}).items()},
+        "is_stale": is_stale,
         "freshness": freshness,
     }
 
@@ -236,10 +187,10 @@ async def tool_recall(
     query: str,
     request_context: "RequestContext",
     max_tokens: int = 2048,
-    max_results: int = 50,
     tags: list[str] | None = None,
     tags_match: str = "any",
     connection_budget: int = 1,
+    max_chunk_tokens: int = 1000,
 ) -> dict[str, Any]:
     """
     Search memories using TEMPR retrieval.
@@ -253,18 +204,19 @@ async def tool_recall(
         query: Search query
         request_context: Request context for authentication
         max_tokens: Maximum tokens for results (default 2048)
-        max_results: Maximum number of results
         tags: Filter by tags (includes untagged memories)
         tags_match: How to match tags - "any" (OR), "all" (AND), or "exact"
         connection_budget: Max DB connections for this recall (default 1 for internal ops)
+        max_chunk_tokens: Maximum tokens for raw source chunk text (default 1000, always included)
 
     Returns:
-        Dict with list of matching memories
+        Dict with list of matching memories including raw chunk text
     """
+    include_chunks = True
     result = await memory_engine.recall_async(
         bank_id=bank_id,
         query=query,
-        fact_type=["experience", "world"],  # Exclude opinions and observations
+        fact_type=["experience", "world"],
         max_tokens=max_tokens,
         enable_trace=False,
         request_context=request_context,
@@ -272,24 +224,14 @@ async def tool_recall(
         tags_match=tags_match,
         _connection_budget=connection_budget,
         _quiet=True,  # Suppress logging for internal operations
+        include_chunks=include_chunks,
+        max_chunk_tokens=max_chunk_tokens,
     )
 
-    memories = []
-    for m in result.results[:max_results]:
-        memories.append(
-            {
-                "id": str(m.id),
-                "text": m.text,
-                "type": m.fact_type,
-                "entities": m.entities or [],
-                "occurred": m.occurred_start,  # Already ISO format string
-            }
-        )
-
     return {
         "query": query,
-        "count": len(memories),
-        "memories": memories,
+        "memories": [m.model_dump() for m in result.results],
+        "chunks": {k: v.model_dump() for k, v in (result.chunks or {}).items()},
     }
 
 

hindsight-api/hindsight_api/engine/reflect/tools_schema.py

@@ -47,7 +47,8 @@ TOOL_SEARCH_OBSERVATIONS = {
         "description": (
             "Search consolidated observations (auto-generated knowledge). These are automatically "
             "synthesized from memories. Returns observations with freshness info (updated_at, is_stale). "
-            "If an observation is STALE, you should ALSO use recall() to verify with current facts."
+            "If an observation is STALE, you should ALSO use recall() to verify with current facts. "
+            "IMPORTANT: If search_mental_models is available, you MUST call it FIRST before using this tool."
         ),
         "parameters": {
             "type": "object",
@@ -95,6 +96,10 @@ TOOL_RECALL = {
                     "type": "integer",
                     "description": "Optional limit on result size (default 2048). Use higher values for broader searches.",
                 },
+                "max_chunk_tokens": {
+                    "type": "integer",
+                    "description": "Maximum tokens for raw source chunk text included alongside each memory fact (default 1000, min 1000). Chunks provide the surrounding context the fact was extracted from. Increase for broader context.",
+                },
             },
             "required": ["reason", "query"],
         },
@@ -139,7 +144,7 @@ TOOL_DONE_ANSWER = {
             "properties": {
                 "answer": {
                     "type": "string",
-                    "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                    "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array. LANGUAGE: By default, write in the SAME language as the user's question. However, if a language directive in the system prompt specifies a different language, follow that directive instead.",
                 },
                 "memory_ids": {
                     "type": "array",
@@ -190,7 +195,11 @@ def _build_done_tool_with_directives(directive_rules: list[str]) -> dict:
                 "properties": {
                     "answer": {
                         "type": "string",
-                        "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                        "description": (
+                            "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. "
+                            "NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array. "
+                            f"MANDATORY: Your answer MUST comply with ALL directives:\n{rules_list}"
+                        ),
                     },
                     "memory_ids": {
                         "type": "array",

hindsight-api/hindsight_api/engine/response_models.py

@@ -159,6 +159,10 @@ class MemoryFact(BaseModel):
         None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)"
     )
     tags: list[str] | None = Field(None, description="Visibility scope tags associated with this fact")
+    source_fact_ids: list[str] | None = Field(
+        None,
+        description="IDs of source facts this observation was derived from (observation type only, when source_facts is enabled)",
+    )
 
 
 class ChunkInfo(BaseModel):
@@ -226,6 +230,9 @@ class RecallResult(BaseModel):
     chunks: dict[str, ChunkInfo] | None = Field(
         None, description="Chunks for facts, keyed by '{document_id}_{chunk_index}'"
     )
+    source_facts: dict[str, MemoryFact] | None = Field(
+        None, description="Source facts for observation-type results, keyed by fact ID"
+    )
 
 
 class ReflectResult(BaseModel):

hindsight-api/hindsight_api/engine/retain/__init__.py

@@ -5,7 +5,6 @@ This package contains modular components for the retain operation:
 - types: Type definitions for retain pipeline
 - fact_extraction: Extract facts from content
 - embedding_processing: Augment texts and generate embeddings
-- deduplication: Check for duplicate facts
 - entity_processing: Process and resolve entities
 - link_creation: Create temporal, semantic, entity, and causal links
 - chunk_storage: Handle chunk storage
@@ -14,7 +13,6 @@ This package contains modular components for the retain operation:
 
 from . import (
     chunk_storage,
-    deduplication,
     embedding_processing,
     entity_processing,
     fact_extraction,
@@ -35,7 +33,6 @@ __all__ = [
     # Modules
     "fact_extraction",
     "embedding_processing",
-    "deduplication",
     "entity_processing",
     "link_creation",
     "chunk_storage",

hindsight-api/hindsight_api/engine/retain/deduplication.py

@@ -1,85 +0,0 @@
-"""
-Deduplication logic for retain pipeline.
-
-Checks for duplicate facts using semantic similarity and temporal proximity.
-"""
-
-import logging
-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]:
-    """
-    Check which facts are duplicates using batched time-window queries.
-
-    Groups facts by 12-hour time buckets to efficiently check for duplicates
-    within a 24-hour window.
-
-    Args:
-        conn: Database connection
-        bank_id: Bank identifier
-        facts: List of ProcessedFact objects to check
-        duplicate_checker_fn: Async function(conn, bank_id, texts, embeddings, date, time_window_hours)
-                              that returns List[bool] indicating duplicates
-
-    Returns:
-        List of boolean flags (same length as facts) indicating if each fact is a duplicate
-    """
-    if not facts:
-        return []
-
-    # Group facts by event_date (rounded to 12-hour buckets) for efficient batching
-    time_buckets = defaultdict(list)
-    for idx, fact in enumerate(facts):
-        # Use occurred_start if available, otherwise use mentioned_at
-        # For deduplication purposes, we need a time reference
-        fact_date = fact.occurred_start if fact.occurred_start is not None else fact.mentioned_at
-
-        # Defensive: if both are None (shouldn't happen), use now()
-        if fact_date is None:
-            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)
-        time_buckets[bucket_key].append((idx, fact))
-
-    # Process each bucket in batch
-    all_is_duplicate = [False] * len(facts)
-
-    for bucket_date, bucket_items in time_buckets.items():
-        indices = [item[0] for item in bucket_items]
-        texts = [item[1].fact_text for item in bucket_items]
-        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)
-
-        # Map results back to original indices
-        for idx, is_dup in zip(indices, dup_flags):
-            all_is_duplicate[idx] = is_dup
-
-    return all_is_duplicate
-
-
-def filter_duplicates(facts: list[ProcessedFact], is_duplicate_flags: list[bool]) -> list[ProcessedFact]:
-    """
-    Filter out duplicate facts based on duplicate flags.
-
-    Args:
-        facts: List of ProcessedFact objects
-        is_duplicate_flags: Boolean flags indicating which facts are duplicates
-
-    Returns:
-        List of non-duplicate facts
-    """
-    if len(facts) != len(is_duplicate_flags):
-        raise ValueError(f"Mismatch between facts ({len(facts)}) and flags ({len(is_duplicate_flags)})")
-
-    return [fact for fact, is_dup in zip(facts, is_duplicate_flags) if not is_dup]

hindsight-api/hindsight_api/engine/retain/embedding_processing.py

@@ -27,11 +27,21 @@ def augment_texts_with_dates(facts: list[ExtractedFact], format_date_fn) -> list
     """
     augmented_texts = []
     for fact in facts:
-        # Use occurred_start as the representative date
+        # Use occurred_start as the representative date, fall back to mentioned_at
         fact_date = fact.occurred_start or fact.mentioned_at
-        readable_date = format_date_fn(fact_date)
-        # Augment text with date for embedding (but store original text in DB)
-        augmented_text = f"{fact.fact_text} (happened in {readable_date})"
+        # Augment text with date and entity names for embedding (but store original text in DB)
+        # Entity names (including key:value labels) improve retrieval without polluting stored content
+        if fact_date is not None:
+            readable_date = format_date_fn(fact_date)
+            if fact.occurred_end and fact.occurred_end != fact.occurred_start:
+                readable_end = format_date_fn(fact.occurred_end)
+                augmented_text = f"{fact.fact_text} (happened from {readable_date} to {readable_end})"
+            else:
+                augmented_text = f"{fact.fact_text} (happened in {readable_date})"
+        else:
+            augmented_text = fact.fact_text
+        if fact.entities:
+            augmented_text = f"{augmented_text} [{', '.join(fact.entities)}]"
         augmented_texts.append(augmented_text)
     return augmented_texts
 

hindsight-api/hindsight_api/engine/retain/embedding_utils.py

@@ -41,10 +41,9 @@ async def generate_embeddings_batch(embeddings_backend, texts: list[str]) -> lis
         List of embeddings in same order as input texts
     """
     try:
-        # Run embeddings in thread pool to avoid blocking event loop
         loop = asyncio.get_event_loop()
         embeddings = await loop.run_in_executor(
-            None,  # Use default thread pool
+            None,
             embeddings_backend.encode,
             texts,
         )

hindsight-api/hindsight_api/engine/retain/entity_labels.py

@@ -0,0 +1,194 @@
+"""
+Entity labels models and helpers for retain pipeline.
+
+Defines a controlled vocabulary of key:value classification labels
+(e.g., 'pedagogy:scaffolding', 'interest:active') that are extracted
+at retain time and stored as entities.
+"""
+
+from typing import Literal
+
+from pydantic import BaseModel, Field, create_model
+
+
+class LabelValue(BaseModel):
+    """A single allowed value for a label group."""
+
+    value: str
+    description: str = ""
+
+
+class LabelGroup(BaseModel):
+    """A label group (dimension) with its type and allowed values."""
+
+    key: str
+    description: str = ""
+    type: Literal["value", "multi-values", "text"] = "value"
+    optional: bool = True
+    tag: bool = False
+    values: list[LabelValue] = []
+
+
+class EntityLabelsConfig(BaseModel):
+    """Entity labels configuration for a bank (controlled vocabulary)."""
+
+    attributes: list[LabelGroup] = []
+
+
+def parse_entity_labels(raw: dict | list | None) -> EntityLabelsConfig | None:
+    """
+    Parse raw entity labels config into EntityLabelsConfig.
+
+    Accepts:
+    - None → returns None
+    - list → list of attribute dicts (each may use legacy free_values/multi_value or new type field)
+    - dict → {attributes: [...]}
+
+    Legacy migration (backward-compat):
+    - free_values=True          → type="text"
+    - multi_value=True          → type="multi-values"
+    - neither / free_values=False → type="value"
+
+    Args:
+        raw: Raw entity labels config from bank config
+
+    Returns:
+        EntityLabelsConfig or None if raw is None/empty
+    """
+    if raw is None:
+        return None
+
+    if isinstance(raw, list):
+        if not raw:
+            return None
+        attributes = [LabelGroup.model_validate(_migrate_label_group(a)) for a in raw]
+        return EntityLabelsConfig(attributes=attributes)
+
+    if isinstance(raw, dict):
+        attrs_raw = raw.get("attributes", [])
+        if not attrs_raw:
+            return None
+        attributes = [LabelGroup.model_validate(_migrate_label_group(a)) for a in attrs_raw]
+        return EntityLabelsConfig(attributes=attributes)
+
+    return None
+
+
+def _migrate_label_group(raw: dict) -> dict:
+    """Migrate legacy free_values/multi_value fields to the new type field."""
+    if not isinstance(raw, dict) or "type" in raw:
+        return raw
+    patched = dict(raw)
+    if patched.get("free_values"):
+        patched["type"] = "text"
+    elif patched.get("multi_value"):
+        patched["type"] = "multi-values"
+    else:
+        patched["type"] = "value"
+    # Remove legacy keys so Pydantic doesn't error on unknown fields
+    patched.pop("free_values", None)
+    patched.pop("multi_value", None)
+    return patched
+
+
+def build_labels_model(labels_cfg: EntityLabelsConfig) -> type[BaseModel] | None:
+    """
+    Build a dynamic Pydantic model for structured label extraction.
+
+    Each LabelGroup becomes a typed field based on its type:
+    - type="text"                        → str | None  (always optional)
+    - type="value",   optional=True      → Literal["v1","v2"] | None
+    - type="value",   optional=False     → Literal["v1","v2"]  (required)
+    - type="multi-values"                → list[Literal["v1","v2"]]
+
+    Args:
+        labels_cfg: Parsed EntityLabelsConfig
+
+    Returns:
+        Dynamic Pydantic model class, or None if no groups defined
+    """
+    fields: dict = {}
+    for group in labels_cfg.attributes:
+        if not group.key:
+            continue
+        description = group.description or group.key
+
+        if group.type == "text":
+            # Free-form: any string value accepted, always optional
+            fields[group.key] = (str | None, Field(default=None, description=description))
+        else:
+            # Enum-constrained: must have defined values
+            if not group.values:
+                continue
+            values = tuple(v.value for v in group.values if v.value)
+            if not values:
+                continue
+            # Literal[("v1", "v2")] is equivalent to Literal["v1", "v2"] in Python 3.11+
+            literal_type = Literal[values]  # type: ignore[valid-type]
+            if group.type == "multi-values":
+                fields[group.key] = (
+                    list[literal_type],  # type: ignore[valid-type]
+                    Field(default_factory=list, description=description),
+                )
+            elif group.optional:
+                fields[group.key] = (
+                    literal_type | None,  # type: ignore[valid-type]
+                    Field(default=None, description=description),
+                )
+            else:
+                fields[group.key] = (
+                    literal_type,  # type: ignore[valid-type]
+                    Field(description=description),
+                )
+
+    if not fields:
+        return None
+
+    return create_model("Labels", **fields)
+
+
+def is_label_entity(text: str, labels_cfg: EntityLabelsConfig, labels_lookup: set[str]) -> bool:
+    """
+    Return True if entity text belongs to any configured label group.
+
+    For enum groups: checks the pre-built lookup set.
+    For text groups: checks that the text starts with a known key prefix.
+    """
+    if text.lower() in labels_lookup:
+        return True
+    for group in labels_cfg.attributes:
+        if group.type == "text" and group.key and text.lower().startswith(f"{group.key.lower()}:"):
+            return True
+    return False
+
+
+def build_labels_lookup(labels_cfg: EntityLabelsConfig | list | None) -> set[str]:
+    """
+    Build a set of valid 'key:value' label strings (lowercase) for fast lookup.
+
+    Accepts either EntityLabelsConfig or raw list/None for backwards compatibility.
+
+    Args:
+        labels_cfg: EntityLabelsConfig, raw list of attribute dicts, or None
+
+    Returns:
+        Set of lowercase 'key:value' strings
+    """
+    if labels_cfg is None:
+        return set()
+
+    # Accept raw list/dict for backwards compatibility
+    if not isinstance(labels_cfg, EntityLabelsConfig):
+        parsed = parse_entity_labels(labels_cfg)
+        if parsed is None:
+            return set()
+        labels_cfg = parsed
+
+    valid = set()
+    for group in labels_cfg.attributes:
+        if group.type == "text":
+            continue  # No fixed vocabulary — all values accepted in post-processing
+        for v in group.values:
+            if group.key and v.value:
+                valid.add(f"{group.key}:{v.value}".lower())
+    return valid

hindsight-api/hindsight_api/engine/retain/entity_processing.py

@@ -20,6 +20,7 @@ async def process_entities_batch(
     facts: list[ProcessedFact],
     log_buffer: list[str] = None,
     user_entities_per_content: dict[int, list[dict]] = None,
+    entity_labels: list | None = None,
 ) -> list[EntityLink]:
     """
     Process entities for all facts and create entity links.
@@ -90,6 +91,7 @@ async def process_entities_batch(
         fact_dates,
         entities_per_fact,
         log_buffer,  # Pass log_buffer for detailed logging
+        entity_labels=entity_labels,
     )
 
     return entity_links

hindsight-api/hindsight_api/engine/retain/fact_extraction.py

@@ -10,23 +10,31 @@ import json
 import logging
 import re
 from datetime import datetime, timedelta
-from typing import Literal
+from typing import Literal, cast
 
-from pydantic import BaseModel, ConfigDict, Field, field_validator
+from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator
 
 from ...config import get_config
 from ..llm_wrapper import LLMConfig, OutputTooLongError
 from ..response_models import TokenUsage
+from .entity_labels import (
+    EntityLabelsConfig,
+    build_labels_lookup,
+    build_labels_model,
+    is_label_entity,
+    parse_entity_labels,
+)
 
 
-def _infer_temporal_date(fact_text: str, event_date: datetime) -> str | None:
+def _infer_temporal_date(fact_text: str, event_date: datetime | None) -> str | None:
     """
     Infer a temporal date from fact text when LLM didn't provide occurred_start.
 
     This is a fallback for when the LLM fails to extract temporal information
     from relative time expressions like "last night", "yesterday", etc.
     """
-    import re
+    if event_date is None:
+        return None
 
     fact_lower = fact_text.lower()
 
@@ -102,7 +110,6 @@ class Fact(BaseModel):
     # Optional temporal fields
     occurred_start: str | None = None
     occurred_end: str | None = None
-    mentioned_at: str | None = None
 
     # Optional location field
     where: str | None = Field(
@@ -440,11 +447,9 @@ def _chunk_conversation(turns: list[dict], max_chars: int) -> list[str]:
 # Uses {extraction_guidelines} placeholder for mode-specific instructions
 _BASE_FACT_EXTRACTION_PROMPT = """Extract SIGNIFICANT facts from text. Be SELECTIVE - only extract facts worth remembering long-term.
 
-LANGUAGE REQUIREMENT: Detect the language of the input text. All extracted facts, entity names, descriptions, and other output MUST be in the SAME language as the input. Do not translate to another language.
+LANGUAGE: MANDATORY — Detect the language of the input text and produce ALL output in that EXACT same language. You are STRICTLY FORBIDDEN from translating or switching to any other language. Every single word of your output must be in the same language as the input. Do NOT output in a different language under any circumstance.
 
-{fact_types_instruction}
-
-{extraction_guidelines}
+{retain_mission_section}{extraction_guidelines}
 
 ══════════════════════════════════════════════════════════════════════════
 FACT FORMAT - BE CONCISE
@@ -483,7 +488,9 @@ TEMPORAL HANDLING
 ══════════════════════════════════════════════════════════════════════════
 
 Use "Event Date" from input as reference for relative dates.
-- "yesterday" relative to Event Date, not today
+- CRITICAL: Convert ALL relative temporal expressions to absolute dates in the fact text itself.
+  "yesterday" → write the resolved date (e.g. "on November 12, 2024"), NOT the word "yesterday"
+  "last night", "this morning", "today", "tonight" → convert to the resolved absolute date
 - For events: set occurred_start AND occurred_end (same for point events)
 - For conversation facts: NO occurred dates
 
@@ -521,7 +528,7 @@ CONSOLIDATE related statements into ONE fact when possible."""
 _CONCISE_EXAMPLES = """
 
 ══════════════════════════════════════════════════════════════════════════
-EXAMPLES
+EXAMPLES (shown in English for illustration; for non-English input, ALL output values MUST be in the input language)
 ══════════════════════════════════════════════════════════════════════════
 
 Example 1 - Selective extraction (Event Date: June 10, 2024):
@@ -549,16 +556,16 @@ about experiences ARE important to remember, even if they seem small (e.g., how
 tasted, how someone looked, how loud music was). Extract these if they characterize
 an experience or person."""
 
-# Assembled concise prompt (backward compatible - exact same output as before)
+# Assembled concise prompt
 CONCISE_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
-    fact_types_instruction="{fact_types_instruction}",
+    retain_mission_section="{retain_mission_section}",
     extraction_guidelines=_CONCISE_GUIDELINES,
     examples=_CONCISE_EXAMPLES,
 )
 
 # Custom prompt uses same base but without examples
 CUSTOM_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
-    fact_types_instruction="{fact_types_instruction}",
+    retain_mission_section="{retain_mission_section}",
     extraction_guidelines="{custom_instructions}",
     examples="",  # No examples for custom mode
 )
@@ -567,10 +574,7 @@ CUSTOM_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
 # Verbose extraction prompt - detailed, comprehensive facts (legacy mode)
 VERBOSE_FACT_EXTRACTION_PROMPT = """Extract facts from text into structured format with FIVE required dimensions - BE EXTREMELY DETAILED.
 
-LANGUAGE REQUIREMENT: Detect the language of the input text. All extracted facts, entity names, descriptions,
-and other output MUST be in the SAME language as the input. Do not translate to English if the input is in another language.
-
-{fact_types_instruction}
+LANGUAGE: MANDATORY — Detect the language of the input text and produce ALL output in that EXACT same language. You are STRICTLY FORBIDDEN from translating or switching to any other language. Every single word of your output must be in the same language as the input. Do NOT output in a different language under any circumstance.
 
 ══════════════════════════════════════════════════════════════════════════
 FACT FORMAT - ALL FIVE DIMENSIONS REQUIRED - MAXIMUM VERBOSITY
@@ -695,59 +699,185 @@ Example: "Lost job → couldn't pay rent → moved apartment"
 - Fact 2: Moved apartment, causal_relations: [{target_index: 1, relation_type: "caused_by"}]"""
 
 
+def _build_labels_prompt_section(labels_cfg: EntityLabelsConfig | list | None, free_form_entities: bool = True) -> str:
+    """Build the entity labels classification section for the extraction prompt."""
+    if labels_cfg is None:
+        return ""
+
+    # Accept raw list for backwards compatibility
+    if isinstance(labels_cfg, list):
+        if not labels_cfg:
+            return ""
+        labels_cfg = parse_entity_labels(labels_cfg)
+        if labels_cfg is None:
+            return ""
+
+    if not labels_cfg.attributes:
+        return ""
+
+    if free_form_entities:
+        entities_instruction = "Classify each fact using the structured 'labels' field below. Continue extracting regular named entities in the 'entities' field."
+    else:
+        entities_instruction = "Classify each fact using the structured 'labels' field below. Do NOT add regular named entities — labels-only mode."
+
+    lines = [
+        "\n\n══════════════════════════════════════════════════════════════════════════",
+        "ENTITY LABELS - CLASSIFICATION ATTRIBUTES",
+        "══════════════════════════════════════════════════════════════════════════",
+        "",
+        entities_instruction,
+        "",
+        "For each fact, fill the 'labels' object. Each field is a label group:",
+        "",
+    ]
+
+    for attr in labels_cfg.attributes:
+        if attr.type == "text":
+            # Free-text: no predefined values — LLM writes any relevant string or null
+            lines.append(f"- {attr.key} (free text or null): {attr.description}")
+        else:
+            mode = "multi-value (list)" if attr.type == "multi-values" else "single value or null"
+            lines.append(f"- {attr.key} ({mode}): {attr.description}")
+            for v in attr.values:
+                desc = f" — {v.description}" if v.description else ""
+                lines.append(f'    • "{v.value}"{desc}')
+        lines.append("")
+
+    lines.append("Only assign labels when clearly applicable. Leave null/empty if the fact does not match.")
+    return "\n".join(lines)
+
+
 def _build_extraction_prompt_and_schema(config) -> tuple[str, type]:
     """
     Build extraction prompt and response schema based on config.
 
+    When a taxonomy is configured, dynamically builds a Pydantic model with a
+    typed `taxonomy_entities` field using an Enum built from valid taxonomy values.
+    This enables JSON schema enforcement for structured outputs.
+
     Returns:
         Tuple of (prompt, response_schema)
     """
-    fact_types_instruction = "Extract ONLY 'world' and 'assistant' type facts."
     extraction_mode = config.retain_extraction_mode
     extract_causal_links = config.retain_extract_causal_links
 
+    # Build retain_mission section if set - injected before the mode-specific guidelines
+    retain_mission = getattr(config, "retain_mission", None)
+    if retain_mission:
+        retain_mission_section = (
+            f"══════════════════════════════════════════════════════════════════════════\n"
+            f"FOCUS — What to retain for this bank\n"
+            f"══════════════════════════════════════════════════════════════════════════\n\n"
+            f"{retain_mission}\n\n"
+        )
+    else:
+        retain_mission_section = ""
+
     # Select base prompt based on extraction mode
     if extraction_mode == "custom":
         if not config.retain_custom_instructions:
             base_prompt = CONCISE_FACT_EXTRACTION_PROMPT
-            prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
+            prompt = base_prompt.format(
+                retain_mission_section=retain_mission_section,
+            )
         else:
             base_prompt = CUSTOM_FACT_EXTRACTION_PROMPT
             prompt = base_prompt.format(
-                fact_types_instruction=fact_types_instruction,
+                retain_mission_section=retain_mission_section,
                 custom_instructions=config.retain_custom_instructions,
             )
     elif extraction_mode == "verbose":
-        base_prompt = VERBOSE_FACT_EXTRACTION_PROMPT
-        prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
+        prompt = VERBOSE_FACT_EXTRACTION_PROMPT
     else:
         base_prompt = CONCISE_FACT_EXTRACTION_PROMPT
-        prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
+        prompt = base_prompt.format(
+            retain_mission_section=retain_mission_section,
+        )
 
     # Add causal relationships section if enabled
     if extract_causal_links:
         prompt = prompt + CAUSAL_RELATIONSHIPS_SECTION
-        response_schema = FactExtractionResponseVerbose if extraction_mode == "verbose" else FactExtractionResponse
+        base_fact_class = ExtractedFactVerbose if extraction_mode == "verbose" else ExtractedFact
+        base_response_class = FactExtractionResponseVerbose if extraction_mode == "verbose" else FactExtractionResponse
     else:
-        response_schema = FactExtractionResponseNoCausal
+        base_fact_class = ExtractedFactNoCausal
+        base_response_class = FactExtractionResponseNoCausal
+
+    # Add entity labels section if configured and build dynamic schema
+    entity_labels_raw = getattr(config, "entity_labels", None)
+    labels_cfg = parse_entity_labels(entity_labels_raw)
+    free_form_entities = getattr(config, "entities_allow_free_form", True)
+    labels_section = _build_labels_prompt_section(labels_cfg, free_form_entities)
+    if labels_section:
+        prompt = prompt + labels_section
+
+    response_schema = base_response_class
+
+    if labels_cfg and labels_cfg.attributes:
+        LabelsModel = build_labels_model(labels_cfg)
+        if LabelsModel is not None:
+            dynamic_fields: dict = {
+                "labels": (
+                    LabelsModel,
+                    Field(
+                        description="Classification labels for this fact. Fill each applicable field; leave others null/empty."
+                    ),
+                )
+            }
+            if not free_form_entities:
+                dynamic_fields["entities"] = (
+                    list[Entity] | None,
+                    Field(default=None, description="Leave empty — labels-only mode"),
+                )
+            # Inherit parent's required fields and add 'labels' so it appears in the JSON schema
+            # required array (the base class json_schema_extra overrides required entirely)
+            base_extra = base_fact_class.model_config.get("json_schema_extra")
+            base_required = cast(dict, base_extra).get("required", []) if isinstance(base_extra, dict) else []
+            DynamicFact = create_model(
+                "LabelsFact",
+                __base__=base_fact_class,
+                __config__=ConfigDict(
+                    json_schema_mode="validation",
+                    json_schema_extra={"required": [*base_required, "labels"]},
+                ),
+                **dynamic_fields,
+            )
+            DynamicResponse = create_model("LabelsResponse", facts=(list[DynamicFact], ...))  # type: ignore[valid-type]
+            response_schema = DynamicResponse
 
     return prompt, response_schema
 
 
-def _build_user_message(chunk: str, chunk_index: int, total_chunks: int, event_date: datetime, context: str) -> str:
+def _build_user_message(
+    chunk: str,
+    chunk_index: int,
+    total_chunks: int,
+    event_date: datetime | None,
+    context: str,
+    metadata: dict[str, str] | None = None,
+) -> str:
     """Build user message for fact extraction."""
     from .orchestrator import parse_datetime_flexible
 
     sanitized_chunk = _sanitize_text(chunk)
     sanitized_context = _sanitize_text(context) if context else "none"
-    event_date = parse_datetime_flexible(event_date)
-    event_date_formatted = event_date.strftime("%A, %B %d, %Y")
+
+    if event_date is not None:
+        event_date = parse_datetime_flexible(event_date)
+        event_date_str = f"{event_date.strftime('%A, %B %d, %Y')} ({event_date.isoformat()})"
+    else:
+        event_date_str = "Unknown"
+
+    metadata_section = ""
+    if metadata:
+        metadata_lines = "\n".join(f"  {k}: {v}" for k, v in metadata.items())
+        metadata_section = f"\nMetadata:\n{metadata_lines}"
 
     return f"""Extract facts from the following text chunk.
 
 Chunk: {chunk_index + 1}/{total_chunks}
-Event Date: {event_date_formatted} ({event_date.isoformat()})
-Context: {sanitized_context}
+Event Date: {event_date_str}
+Context: {sanitized_context}{metadata_section}
 
 Text:
 {sanitized_chunk}"""
@@ -784,11 +914,12 @@ async def _extract_facts_from_chunk(
     chunk: str,
     chunk_index: int,
     total_chunks: int,
-    event_date: datetime,
+    event_date: datetime | None,
     context: str,
     llm_config: "LLMConfig",
     config,
     agent_name: str = None,
+    metadata: dict[str, str] | None = None,
 ) -> tuple[list[dict[str, str]], TokenUsage]:
     """
     Extract facts from a single chunk (internal helper for parallel processing).
@@ -810,7 +941,7 @@ async def _extract_facts_from_chunk(
     extract_causal_links = config.retain_extract_causal_links
 
     # Build user message using helper function
-    user_message = _build_user_message(chunk, chunk_index, total_chunks, event_date, context)
+    user_message = _build_user_message(chunk, chunk_index, total_chunks, event_date, context, metadata)
 
     # Retry logic for JSON validation errors
     max_retries = 2
@@ -969,9 +1100,9 @@ async def _extract_facts_from_chunk(
                 # Add entities if present (validate as Entity objects)
                 # LLM sometimes returns strings instead of {"text": "..."} format
                 entities = get_value("entities")
+                validated_entities = []
                 if entities:
                     # Validate and normalize each entity
-                    validated_entities = []
                     for ent in entities:
                         if isinstance(ent, str):
                             # Normalize string to Entity object
@@ -981,8 +1112,48 @@ async def _extract_facts_from_chunk(
                                 validated_entities.append(Entity.model_validate(ent))
                             except Exception as e:
                                 logger.warning(f"Invalid entity {ent}: {e}")
-                    if validated_entities:
-                        fact_data["entities"] = validated_entities
+
+                # Post-process label entities from structured labels object
+                entity_labels_raw = getattr(config, "entity_labels", None)
+                labels_cfg = parse_entity_labels(entity_labels_raw)
+                free_form_entities = getattr(config, "entities_allow_free_form", True)
+                if labels_cfg and labels_cfg.attributes:
+                    labels_lookup = build_labels_lookup(labels_cfg)
+                    labels_data = llm_fact.get("labels") or {}
+                    if isinstance(labels_data, dict):
+                        existing_texts_lower = {e.text.lower() for e in validated_entities}
+                        for group in labels_cfg.attributes:
+                            value = labels_data.get(group.key)
+                            if not value:
+                                continue
+                            values_list = value if isinstance(value, list) else [value]
+                            for v in values_list:
+                                if not isinstance(v, str) or not v.strip() or v.lower() in ("none", "null", "n/a"):
+                                    continue
+                                label_str = f"{group.key}:{v.strip()}"
+                                if group.type == "text":
+                                    if label_str.lower() not in existing_texts_lower:
+                                        validated_entities.append(Entity(text=label_str))
+                                        existing_texts_lower.add(label_str.lower())
+                                elif (
+                                    label_str.lower() in labels_lookup and label_str.lower() not in existing_texts_lower
+                                ):
+                                    validated_entities.append(Entity(text=label_str))
+                                    existing_texts_lower.add(label_str.lower())
+                                else:
+                                    logger.warning(f"Label '{label_str}' not in valid label values, skipping")
+
+                    # In labels-only mode, keep only label entities
+                    if not free_form_entities:
+                        validated_entities = [
+                            e for e in validated_entities if is_label_entity(e.text, labels_cfg, labels_lookup)
+                        ]
+                elif not free_form_entities:
+                    # No labels but free_form disabled: clear all entities
+                    validated_entities = []
+
+                if validated_entities:
+                    fact_data["entities"] = validated_entities
 
                 # Add per-fact causal relations (only if enabled in config)
                 if extract_causal_links:
@@ -1021,8 +1192,9 @@ async def _extract_facts_from_chunk(
                     if validated_relations:
                         fact_data["causal_relations"] = validated_relations
 
-                # Always set mentioned_at to the event_date (when the conversation/document occurred)
-                fact_data["mentioned_at"] = event_date.isoformat()
+                # Set mentioned_at to the event_date (when the conversation/document occurred),
+                # or None when the caller opted into no timestamp.
+                fact_data["mentioned_at"] = event_date.isoformat() if event_date is not None else None
 
                 # Build Fact model instance
                 try:
@@ -1085,11 +1257,12 @@ async def _extract_facts_with_auto_split(
     chunk: str,
     chunk_index: int,
     total_chunks: int,
-    event_date: datetime,
+    event_date: datetime | None,
     context: str,
     llm_config: LLMConfig,
     config,
     agent_name: str = None,
+    metadata: dict[str, str] | None = None,
 ) -> tuple[list[dict[str, str]], TokenUsage]:
     """
     Extract facts from a chunk with automatic splitting if output exceeds token limits.
@@ -1106,6 +1279,7 @@ async def _extract_facts_with_auto_split(
         llm_config: LLM configuration to use
         config: Resolved HindsightConfig for this bank
         agent_name: Optional agent name (memory owner)
+        metadata: Optional document metadata key-value pairs
 
     Returns:
         Tuple of (facts list, token usage) extracted from the chunk (possibly from sub-chunks)
@@ -1125,6 +1299,7 @@ async def _extract_facts_with_auto_split(
             llm_config=llm_config,
             config=config,
             agent_name=agent_name,
+            metadata=metadata,
         )
     except OutputTooLongError:
         # Output exceeded token limits - split the chunk in half and retry
@@ -1170,6 +1345,7 @@ async def _extract_facts_with_auto_split(
                 llm_config=llm_config,
                 config=config,
                 agent_name=agent_name,
+                metadata=metadata,
             ),
             _extract_facts_with_auto_split(
                 chunk=second_half,
@@ -1180,6 +1356,7 @@ async def _extract_facts_with_auto_split(
                 llm_config=llm_config,
                 config=config,
                 agent_name=agent_name,
+                metadata=metadata,
             ),
         ]
 
@@ -1199,11 +1376,12 @@ async def _extract_facts_with_auto_split(
 
 async def extract_facts_from_text(
     text: str,
-    event_date: datetime,
+    event_date: datetime | None,
     llm_config: LLMConfig,
     agent_name: str,
     config,
     context: str = "",
+    metadata: dict[str, str] | None = None,
 ) -> tuple[list[Fact], list[tuple[str, int]], TokenUsage]:
     """
     Extract semantic facts from conversational or narrative text using LLM.
@@ -1221,6 +1399,7 @@ async def extract_facts_from_text(
         agent_name: Agent name (memory owner)
         config: Resolved HindsightConfig for this bank
         context: Context about the conversation/document
+        metadata: Optional document metadata key-value pairs
 
     Returns:
         Tuple of (facts, chunks, usage) where:
@@ -1248,6 +1427,7 @@ async def extract_facts_from_text(
             llm_config=llm_config,
             config=config,
             agent_name=agent_name,
+            metadata=metadata,
         )
         for i, chunk in enumerate(chunks)
     ]
@@ -1274,8 +1454,8 @@ from .types import ExtractedFact as ExtractedFactType
 
 logger = logging.getLogger(__name__)
 
-# Each fact gets 10 seconds offset to preserve ordering within a document
-SECONDS_PER_FACT = 10
+# Each fact gets 10ms offset to preserve ordering within a document
+SECONDS_PER_FACT = 0.01
 
 
 async def extract_facts_from_contents_batch_api(
@@ -1357,7 +1537,7 @@ async def extract_facts_from_contents_batch_api(
 
             # Build user message using helper function
             user_message = _build_user_message(
-                chunk, chunk_index_in_content, len(chunks), item.event_date, item.context
+                chunk, chunk_index_in_content, len(chunks), item.event_date, item.context, item.metadata or None
             )
 
             # Build request body using helper function
@@ -1569,8 +1749,8 @@ async def extract_facts_from_contents_batch_api(
 
             # Entities
             entities = get_value("entities")
+            validated_entities = []
             if entities:
-                validated_entities = []
                 for ent in entities:
                     if isinstance(ent, str):
                         validated_entities.append(Entity(text=ent))
@@ -1579,8 +1759,45 @@ async def extract_facts_from_contents_batch_api(
                             validated_entities.append(Entity.model_validate(ent))
                         except Exception:
                             pass
-                if validated_entities:
-                    fact_data["entities"] = validated_entities
+
+            # Post-process label entities from structured labels object
+            entity_labels_raw = getattr(config, "entity_labels", None)
+            labels_cfg_batch = parse_entity_labels(entity_labels_raw)
+            free_form_entities_batch = getattr(config, "entities_allow_free_form", True)
+            if labels_cfg_batch and labels_cfg_batch.attributes:
+                labels_lookup_batch = build_labels_lookup(labels_cfg_batch)
+                labels_data = llm_fact.get("labels") or {}
+                if isinstance(labels_data, dict):
+                    existing_texts_lower = {e.text.lower() for e in validated_entities}
+                    for group in labels_cfg_batch.attributes:
+                        value = labels_data.get(group.key)
+                        if not value:
+                            continue
+                        values_list = value if isinstance(value, list) else [value]
+                        for v in values_list:
+                            if not isinstance(v, str) or not v.strip() or v.lower() in ("none", "null", "n/a"):
+                                continue
+                            label_str = f"{group.key}:{v.strip()}"
+                            if group.type == "text":
+                                if label_str.lower() not in existing_texts_lower:
+                                    validated_entities.append(Entity(text=label_str))
+                                    existing_texts_lower.add(label_str.lower())
+                            elif (
+                                label_str.lower() in labels_lookup_batch
+                                and label_str.lower() not in existing_texts_lower
+                            ):
+                                validated_entities.append(Entity(text=label_str))
+                                existing_texts_lower.add(label_str.lower())
+
+                if not free_form_entities_batch:
+                    validated_entities = [
+                        e for e in validated_entities if is_label_entity(e.text, labels_cfg_batch, labels_lookup_batch)
+                    ]
+            elif not free_form_entities_batch:
+                validated_entities = []
+
+            if validated_entities:
+                fact_data["entities"] = validated_entities
 
             # Causal relations
             if extract_causal_links:
@@ -1611,8 +1828,9 @@ async def extract_facts_from_contents_batch_api(
                 if validated_relations:
                     fact_data["causal_relations"] = validated_relations
 
-            # Always set mentioned_at
-            fact_data["mentioned_at"] = event_date.isoformat()
+            # Set mentioned_at to the event_date (when the conversation/document occurred),
+            # or None when the caller opted into no timestamp.
+            fact_data["mentioned_at"] = event_date.isoformat() if event_date is not None else None
 
             try:
                 fact = Fact(fact=combined_text, fact_type=fact_type, **fact_data)
@@ -1671,6 +1889,7 @@ async def extract_facts_from_contents_batch_api(
                 mentioned_at=content.event_date,
                 metadata=content.metadata,
                 tags=content.tags,
+                observation_scopes=content.observation_scopes,
             )
 
             extracted_facts.append(extracted_fact)
@@ -1679,6 +1898,9 @@ async def extract_facts_from_contents_batch_api(
     # Step 7: Add temporal offsets
     _add_temporal_offsets(extracted_facts, contents)
 
+    # Step 8: Auto-tag facts from label groups with tag=True
+    _inject_label_tags(extracted_facts, config)
+
     logger.info(f"Batch API extracted {len(extracted_facts)} facts from {len(all_chunks_info)} chunks")
 
     return extracted_facts, chunks_metadata, total_usage
@@ -1737,6 +1959,7 @@ async def extract_facts_from_contents(
             llm_config=llm_config,
             agent_name=agent_name,
             config=config,
+            metadata=item.metadata or None,
         )
         fact_extraction_tasks.append(task)
 
@@ -1800,6 +2023,7 @@ async def extract_facts_from_contents(
                         mentioned_at=content.event_date,
                         metadata=content.metadata,
                         tags=content.tags,
+                        observation_scopes=content.observation_scopes,
                     )
 
                     extracted_facts.append(extracted_fact)
@@ -1809,6 +2033,9 @@ async def extract_facts_from_contents(
     # Step 4: Add time offsets to preserve ordering within each content
     _add_temporal_offsets(extracted_facts, contents)
 
+    # Step 5: Auto-tag facts from label groups with tag=True
+    _inject_label_tags(extracted_facts, config)
+
     return extracted_facts, chunks_metadata, total_usage
 
 
@@ -1864,3 +2091,24 @@ def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainC
             fact.occurred_end = parse_datetime_flexible(fact.occurred_end) + offset
         if fact.mentioned_at:
             fact.mentioned_at = parse_datetime_flexible(fact.mentioned_at) + offset
+
+
+def _inject_label_tags(facts: list[ExtractedFactType], config) -> None:
+    """
+    For label groups with tag=True, add extracted key:value label entities
+    to each fact's tags list. Modifies facts in place.
+
+    This lets entity labels double as tags, enabling filtering via the
+    existing tags API without any extra query infrastructure.
+    """
+    labels_cfg = parse_entity_labels(getattr(config, "entity_labels", None))
+    if not labels_cfg:
+        return
+    tag_group_keys = {g.key.lower() for g in labels_cfg.attributes if g.tag}
+    if not tag_group_keys:
+        return
+    for fact in facts:
+        label_tags = [e for e in fact.entities if ":" in e and e.split(":", 1)[0].lower() in tag_group_keys]
+        if label_tags:
+            existing = set(fact.tags)
+            fact.tags = fact.tags + [t for t in label_tags if t not in existing]

hindsight-api/hindsight_api/engine/retain/fact_storage.py

@@ -47,6 +47,8 @@ async def insert_facts_batch(
     chunk_ids = []
     document_ids = []
     tags_list = []
+    observation_scopes_list = []
+    text_signals_list = []
 
     for fact in facts:
         fact_texts.append(_sanitize_text(fact.fact_text))
@@ -68,6 +70,19 @@ async def insert_facts_batch(
         document_ids.append(fact.document_id if fact.document_id else document_id)
         # Convert tags to JSON string for proper batch insertion (PostgreSQL unnest doesn't handle 2D arrays well)
         tags_list.append(json.dumps(fact.tags if fact.tags else []))
+        # observation_scopes: stored as JSONB (string or 2D array), None if not provided
+        observation_scopes_list.append(
+            json.dumps(fact.observation_scopes) if fact.observation_scopes is not None else None
+        )
+        # Build text_signals: entity names + date tokens for enriched BM25 indexing
+        signal_parts = []
+        if fact.entities:
+            signal_parts.extend(e.name for e in fact.entities)
+        if fact.occurred_start:
+            signal_parts.append(fact.occurred_start.strftime("%B %-d %Y"))
+        if fact.occurred_end and fact.occurred_end != fact.occurred_start:
+            signal_parts.append(fact.occurred_end.strftime("%B %-d %Y"))
+        text_signals_list.append(" ".join(signal_parts) if signal_parts else None)
 
     # Batch insert all facts
     # Note: tags are passed as JSON strings and converted back to varchar[] via jsonb_array_elements_text + array_agg
@@ -75,16 +90,19 @@ async def insert_facts_batch(
     config = get_config()
     if config.text_search_extension == "vchord":
         # VectorChord: manually tokenize and insert search_vector
+        # text_signals (entity names etc.) are included in the tokenize input for enriched BM25
         query = f"""
             WITH input_data AS (
                 SELECT * FROM unnest(
                     $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
                 ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
+                       observation_scopes_json, text_signals)
             )
             INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags, search_vector)
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
+                                     observation_scopes, text_signals, search_vector)
             SELECT
                 $1,
                 text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
@@ -93,23 +111,30 @@ async def insert_facts_batch(
                     (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
                     '{{}}'::varchar[]
                 ),
-                tokenize(COALESCE(text, '') || ' ' || COALESCE(context, ''), 'llmlingua2')::bm25_catalog.bm25vector
+                observation_scopes_json,
+                text_signals,
+                tokenize(
+                    COALESCE(text, '') || ' ' || COALESCE(context, '') || ' ' || COALESCE(text_signals, ''),
+                    'llmlingua2'
+                )::bm25_catalog.bm25vector
             FROM input_data
             RETURNING id
         """
     else:  # native or pg_textsearch
-        # Native PostgreSQL: search_vector is GENERATED ALWAYS, don't include it
+        # Native PostgreSQL: search_vector is GENERATED ALWAYS (expression includes text_signals), don't include it
         # pg_textsearch: indexes operate on base columns directly, don't populate search_vector
         query = f"""
             WITH input_data AS (
                 SELECT * FROM unnest(
                     $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
                 ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
+                       observation_scopes_json, text_signals)
             )
             INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags)
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
+                                     observation_scopes, text_signals)
             SELECT
                 $1,
                 text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
@@ -117,7 +142,9 @@ async def insert_facts_batch(
                 COALESCE(
                     (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
                     '{{}}'::varchar[]
-                )
+                ),
+                observation_scopes_json,
+                text_signals
             FROM input_data
             RETURNING id
         """
@@ -138,6 +165,8 @@ async def insert_facts_batch(
         chunk_ids,
         document_ids,
         tags_list,
+        observation_scopes_list,
+        text_signals_list,
     )
 
     unit_ids = [str(row["id"]) for row in results]

hindsight-api/hindsight_api/engine/retain/link_utils.py

@@ -47,6 +47,9 @@ def compute_temporal_links(
 
     links = []
     for unit_id, unit_event_date in new_units.items():
+        # Units without event_date can't form temporal links
+        if unit_event_date is None:
+            continue
         # Normalize unit_event_date for consistent comparison
         unit_event_date_norm = _normalize_datetime(unit_event_date)
 
@@ -96,7 +99,11 @@ def compute_temporal_query_bounds(
         return None, None
 
     # Normalize all dates to be timezone-aware to avoid comparison issues
-    all_dates = [_normalize_datetime(d) for d in new_units.values()]
+    # Filter out None values — units without event_date can't form temporal links
+    all_dates = [_normalize_datetime(d) for d in new_units.values() if d is not None]
+
+    if not all_dates:
+        return None, None
 
     try:
         min_date = min(all_dates) - timedelta(hours=time_window_hours)
@@ -143,6 +150,7 @@ async def extract_entities_batch_optimized(
     fact_dates: list,
     llm_entities: list[list[dict]],
     log_buffer: list[str] = None,
+    entity_labels: list | None = None,
 ) -> list[tuple]:
     """
     Process LLM-extracted entities for ALL facts in batch.
@@ -232,6 +240,7 @@ async def extract_entities_batch_optimized(
                 context=context,
                 unit_event_date=None,  # Not used when per-entity dates provided
                 conn=conn,  # Use main transaction connection
+                entity_labels=entity_labels,
             )
 
             _log(
@@ -432,20 +441,23 @@ async def create_temporal_links_batch_per_fact(
         min_date, max_date = compute_temporal_query_bounds(new_units, time_window_hours)
 
         fetch_neighbors_start = time_mod.time()
-        all_candidates = await conn.fetch(
-            f"""
-            SELECT id, event_date
-            FROM {fq_table("memory_units")}
-            WHERE bank_id = $1
-              AND event_date BETWEEN $2 AND $3
-              AND id::text != ALL($4)
-            ORDER BY event_date DESC
-            """,
-            bank_id,
-            min_date,
-            max_date,
-            unit_ids,
-        )
+        if min_date is not None and max_date is not None:
+            all_candidates = await conn.fetch(
+                f"""
+                SELECT id, event_date
+                FROM {fq_table("memory_units")}
+                WHERE bank_id = $1
+                  AND event_date BETWEEN $2 AND $3
+                  AND id::text != ALL($4)
+                ORDER BY event_date DESC
+                """,
+                bank_id,
+                min_date,
+                max_date,
+                unit_ids,
+            )
+        else:
+            all_candidates = []
         _log(
             log_buffer,
             f"      [7.2] Fetch {len(all_candidates)} candidate neighbors (1 query): {time_mod.time() - fetch_neighbors_start:.3f}s",
@@ -460,11 +472,15 @@ async def create_temporal_links_batch_per_fact(
             # Convert new_units dict to candidate format for within-batch linking
             new_unit_items = list(new_units.items())
             for i, (unit_id, event_date) in enumerate(new_unit_items):
+                if event_date is None:
+                    continue  # Skip units without event_date for temporal linking
                 unit_event_date_norm = _normalize_datetime(event_date)
 
                 # Compare with other new units (only those after this one to avoid duplicates)
                 for j in range(i + 1, len(new_unit_items)):
                     other_id, other_event_date = new_unit_items[j]
+                    if other_event_date is None:
+                        continue  # Skip units without event_date
                     other_event_date_norm = _normalize_datetime(other_event_date)
 
                     # Check if within time window
@@ -482,14 +498,13 @@ async def create_temporal_links_batch_per_fact(
             # 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,
+                    links[batch_start : batch_start + BATCH_SIZE],
                 )
             _log(log_buffer, f"      [7.4] Insert {len(links)} temporal links: {time_mod.time() - insert_start:.3f}s")
 
@@ -537,81 +552,45 @@ async def create_semantic_links_batch(
 
         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 {fq_table("memory_units")}
-            WHERE bank_id = $1
-              AND embedding IS NOT NULL
-              AND id::text != ALL($2)
-            """,
-            bank_id,
-            unit_ids,
-        )
-        _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()
+        # Use pgvector ANN search (HNSW index) for each new unit instead of fetching
+        # all existing embeddings into Python. At large scale (100K+ units) the old
+        # approach would transfer 100K × 384 floats (~150 MB) per retain call; the
+        # ANN query completes in <5 ms and transfers only top_k rows.
+        ann_start = time_mod.time()
         all_links = []
 
-        if all_existing:
-            # Convert existing embeddings to numpy array
-            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"]
-                # Handle different pgvector formats
-                if isinstance(raw_emb, str):
-                    # Parse string format: "[1.0, 2.0, ...]"
-                    import json
+        # Build UUID exclude list once for all ANN queries
+        import uuid as uuid_mod
 
-                    emb = np.array(json.loads(raw_emb), dtype=np.float32)
-                elif isinstance(raw_emb, (list, tuple)):
-                    emb = np.array(raw_emb, dtype=np.float32)
-                else:
-                    # Try direct conversion (works for numpy arrays, pgvector objects, etc.)
-                    emb = np.array(raw_emb, dtype=np.float32)
+        exclude_uuids = [uuid_mod.UUID(uid) if isinstance(uid, str) else uid for uid in unit_ids]
 
-                # Ensure it's 1D
-                if emb.ndim != 1:
-                    raise ValueError(f"Expected 1D embedding, got shape {emb.shape}")
-                embedding_arrays.append(emb)
+        for unit_id, new_embedding in zip(unit_ids, embeddings):
+            emb_str = str(list(new_embedding) if not isinstance(new_embedding, list) else new_embedding)
+            rows = await conn.fetch(
+                f"""
+                SELECT id::text,
+                       1 - (embedding <=> $1::vector) AS similarity
+                FROM {fq_table("memory_units")}
+                WHERE bank_id = $2
+                  AND embedding IS NOT NULL
+                  AND id != ALL($3::uuid[])
+                ORDER BY embedding <=> $1::vector
+                LIMIT $4
+                """,
+                emb_str,
+                bank_id,
+                exclude_uuids,
+                top_k,
+            )
+            for row in rows:
+                sim = float(min(1.0, max(0.0, row["similarity"])))
+                if sim >= threshold:
+                    all_links.append((unit_id, str(row["id"]), "semantic", sim, None))
 
-            if not embedding_arrays:
-                existing_embeddings = np.array([])
-            elif len(embedding_arrays) == 1:
-                # Single embedding: reshape to (1, dim)
-                existing_embeddings = embedding_arrays[0].reshape(1, -1)
-            else:
-                # Multiple embeddings: vstack
-                existing_embeddings = np.vstack(embedding_arrays)
-
-            # For each new unit, compute similarities with ALL existing units
-            for unit_id, new_embedding in zip(unit_ids, embeddings):
-                new_emb_array = np.array(new_embedding)
-
-                # Compute cosine similarities (dot product for normalized vectors)
-                similarities = np.dot(existing_embeddings, new_emb_array)
-
-                # Find top-k above threshold
-                # Get indices of similarities above threshold
-                above_threshold = np.where(similarities >= threshold)[0]
-
-                if len(above_threshold) > 0:
-                    # Sort by similarity (descending) and take top-k
-                    sorted_indices = above_threshold[np.argsort(-similarities[above_threshold])][:top_k]
-
-                    for idx in sorted_indices:
-                        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))
+        _log(
+            log_buffer,
+            f"      [8.1] ANN search for {len(unit_ids)} new units → {len(all_links)} candidate links: {time_mod.time() - ann_start:.3f}s",
+        )
 
         # 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
@@ -643,7 +622,7 @@ async def create_semantic_links_batch(
 
         _log(
             log_buffer,
-            f"      [8.2] Compute similarities & generate {len(all_links)} semantic links: {time_mod.time() - compute_start:.3f}s",
+            f"      [8.2] Within-batch similarities added {len(all_links)} total semantic links",
         )
 
         if all_links:
@@ -651,14 +630,13 @@ async def create_semantic_links_batch(
             # 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,
+                    all_links[batch_start : batch_start + BATCH_SIZE],
                 )
             _log(
                 log_buffer, f"      [8.3] Insert {len(all_links)} semantic links: {time_mod.time() - insert_start:.3f}s"
@@ -674,18 +652,18 @@ async def create_semantic_links_batch(
         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 = 5000):
     """
-    Insert all entity links using COPY to temp table + INSERT for maximum speed.
+    Insert all entity links using COPY to temp table + chunked INSERT for reliability.
 
-    Uses PostgreSQL COPY (via copy_records_to_table) for bulk loading,
-    then INSERT ... ON CONFLICT from temp table. This is the fastest
-    method for bulk inserts with conflict handling.
+    Uses PostgreSQL COPY (via copy_records_to_table) for bulk loading into a
+    temp table, then INSERT ... ON CONFLICT in chunks of chunk_size. Chunking
+    prevents single-query timeouts on very large tables (100M+ rows).
 
     Args:
         conn: Database connection
         links: List of EntityLink objects
-        chunk_size: Number of rows per batch (default 50000)
+        chunk_size: Number of rows per INSERT chunk (default 5000)
     """
     if not links:
         return
@@ -694,10 +672,11 @@ async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: i
 
     total_start = time_mod.time()
 
-    # Create temp table for bulk loading
+    # Create temp table with serial for stable chunked access
     create_start = time_mod.time()
     await conn.execute("""
         CREATE TEMP TABLE IF NOT EXISTS _temp_entity_links (
+            _row_num SERIAL,
             from_unit_id uuid,
             to_unit_id uuid,
             link_type text,
@@ -714,9 +693,7 @@ async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: i
 
     # Convert EntityLink objects to tuples for COPY
     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 = [(link.from_unit_id, link.to_unit_id, link.link_type, link.weight, link.entity_id) for link in links]
     logger.debug(f"      [9.3] Convert {len(records)} records: {time_mod.time() - convert_start:.3f}s")
 
     # Bulk load using COPY (fastest method)
@@ -728,15 +705,25 @@ async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: i
     )
     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 from temp table in chunks to avoid single-query timeouts on large tables
     insert_start = time_mod.time()
-    await conn.execute(f"""
-        INSERT INTO {fq_table("memory_links")} (from_unit_id, to_unit_id, link_type, weight, entity_id)
-        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
-    """)
-    logger.debug(f"      [9.5] INSERT from temp table: {time_mod.time() - insert_start:.3f}s")
+    total_rows = len(records)
+    chunks = 0
+    for chunk_start in range(0, total_rows, chunk_size):
+        chunk_end = chunk_start + chunk_size
+        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
+            WHERE _row_num > $1 AND _row_num <= $2
+            ON CONFLICT (from_unit_id, to_unit_id, link_type, COALESCE(entity_id, '00000000-0000-0000-0000-000000000000'::uuid)) DO NOTHING
+            """,
+            chunk_start,
+            chunk_end,
+        )
+        chunks += 1
+    logger.debug(f"      [9.5] INSERT {total_rows} rows in {chunks} chunks: {time_mod.time() - insert_start:.3f}s")
     logger.debug(f"      [9.TOTAL] Entity links batch insert: {time_mod.time() - total_start:.3f}s")
 
 

hindsight-api/hindsight_api/engine/retain/orchestrator.py

@@ -55,7 +55,6 @@ def parse_datetime_flexible(value: Any) -> datetime:
 from ..response_models import TokenUsage
 from . import (
     chunk_storage,
-    deduplication,
     embedding_processing,
     entity_processing,
     fact_extraction,
@@ -73,7 +72,6 @@ async def retain_batch(
     llm_config,
     entity_resolver,
     format_date_fn,
-    duplicate_checker_fn,
     bank_id: str,
     contents_dicts: list[RetainContentDict],
     config,
@@ -94,7 +92,6 @@ async def retain_batch(
         llm_config: LLM configuration for fact extraction
         entity_resolver: Entity resolver for entity processing
         format_date_fn: Function to format datetime to readable string
-        duplicate_checker_fn: Function to check for duplicate facts
         bank_id: Bank identifier
         contents_dicts: List of content dictionaries
         config: Resolved HindsightConfig for this bank
@@ -128,12 +125,14 @@ async def retain_batch(
         item_tags = item.get("tags", []) or []
         merged_tags = list(set(item_tags + (document_tags or [])))
 
-        # Handle event_date: parse flexibly (handles both datetime objects and ISO strings)
-        event_date_value = item.get("event_date")
-        if event_date_value:
-            event_date_value = parse_datetime_flexible(event_date_value)
+        # Handle event_date: distinguish "not provided" (default to now) from
+        # "explicitly None" (caller opted into no timestamp).
+        if "event_date" in item and item["event_date"] is None:
+            event_date_value = None  # Caller explicitly signalled "unknown date"
+        elif item.get("event_date"):
+            event_date_value = parse_datetime_flexible(item["event_date"])
         else:
-            event_date_value = utcnow()
+            event_date_value = utcnow()  # Backward-compatible default
 
         content = RetainContent(
             content=item["content"],
@@ -142,6 +141,7 @@ async def retain_batch(
             metadata=item.get("metadata", {}),
             entities=item.get("entities", []),
             tags=merged_tags,
+            observation_scopes=item.get("observation_scopes"),
         )
         contents.append(content)
 
@@ -156,13 +156,20 @@ async def retain_batch(
     )
 
     if not extracted_facts:
-        # Still need to create document if document_id was provided
+        # Still need to create document if document_id was provided or chunks exist
+        from collections import defaultdict
+
+        docs_tracked = 0
         async with acquire_with_retry(pool) as conn:
             async with conn.transaction():
-                await fact_storage.ensure_bank_exists(conn, bank_id)
+                # Group contents by document_id (consistent with normal path)
+                contents_by_doc_early = defaultdict(list)
+                for idx, content_dict in enumerate(contents_dicts):
+                    doc_id = content_dict.get("document_id")
+                    contents_by_doc_early[doc_id].append((idx, content_dict))
 
-                # Handle document tracking even with no facts
                 if document_id:
+                    # Legacy: single document_id parameter
                     combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
                     # Collect tags from all content items and merge with document_tags
                     all_tags = set(document_tags or [])
@@ -187,45 +194,57 @@ async def retain_batch(
                     await fact_storage.handle_document_tracking(
                         conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
                     )
+                    docs_tracked += 1
                 else:
-                    # Check for per-item document_ids
-                    from collections import defaultdict
+                    # Handle per-item document_ids and/or chunks (mirrors normal path logic)
+                    has_any_doc_ids = any(item.get("document_id") for item in contents_dicts)
 
-                    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))
+                    if has_any_doc_ids or chunks:
+                        for original_doc_id, doc_contents in contents_by_doc_early.items():
+                            should_create_doc = (original_doc_id is not None) or chunks
+                            if not should_create_doc:
+                                continue
 
-                    for doc_id, doc_contents in contents_by_doc.items():
-                        combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
-                        # Collect tags from all content items for this document and merge with document_tags
-                        all_tags = set(document_tags or [])
-                        for _, item in doc_contents:
-                            item_tags = item.get("tags", []) or []
-                            all_tags.update(item_tags)
-                        merged_tags = list(all_tags)
+                            actual_doc_id = original_doc_id
+                            if actual_doc_id is None:
+                                # No document_id but have chunks - generate one
+                                actual_doc_id = str(uuid.uuid4())
 
-                        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, merged_tags
-                        )
+                            combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
+                            all_tags = set(document_tags or [])
+                            for _, item in doc_contents:
+                                item_tags = item.get("tags", []) or []
+                                all_tags.update(item_tags)
+                            merged_tags = list(all_tags)
+
+                            retain_params = {}
+                            if doc_contents:
+                                first_item = doc_contents[0][1]
+                                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,
+                                actual_doc_id,
+                                combined_content,
+                                is_first_batch,
+                                retain_params,
+                                merged_tags,
+                            )
+                            docs_tracked += 1
 
         total_time = time.time() - start_time
+        doc_status = f"{docs_tracked} document(s) tracked" if docs_tracked > 0 else "no document tracked"
         logger.info(
-            f"RETAIN_BATCH COMPLETE: 0 facts extracted from {len(contents)} contents in {total_time:.3f}s (document tracked, no facts)"
+            f"RETAIN_BATCH COMPLETE: 0 facts extracted from {len(contents)} contents in {total_time:.3f}s ({doc_status}, no facts)"
         )
         return [[] for _ in contents], usage
 
@@ -260,9 +279,6 @@ async def retain_batch(
     # Step 4: Database transaction
     async with acquire_with_retry(pool) as conn:
         async with conn.transaction():
-            # Ensure bank exists
-            await fact_storage.ensure_bank_exists(conn, bank_id)
-
             # Handle document tracking for all documents
             step_start = time.time()
             # Map None document_id to generated UUIDs
@@ -414,20 +430,7 @@ async def retain_batch(
                         actual_doc_id = document_id
                     processed_fact.document_id = actual_doc_id
 
-            # Deduplication
-            step_start = time.time()
-            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"
-            )
-
-            # Filter out duplicates
-            non_duplicate_facts = deduplication.filter_duplicates(processed_facts, is_duplicate_flags)
-
-            if not non_duplicate_facts:
-                return [[] for _ in contents], usage
+            non_duplicate_facts = processed_facts
 
             # Insert facts (document_id is now stored per-fact)
             step_start = time.time()
@@ -448,6 +451,7 @@ async def retain_batch(
                 non_duplicate_facts,
                 log_buffer,
                 user_entities_per_content=user_entities_per_content,
+                entity_labels=getattr(config, "entity_labels", None),
             )
             log_buffer.append(f"[6] Process entities: {len(entity_links)} links in {time.time() - step_start:.3f}s")
 
@@ -478,7 +482,11 @@ async def retain_batch(
             log_buffer.append(f"[10] Causal links: {causal_link_count} links in {time.time() - step_start:.3f}s")
 
             # 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, unit_ids)
+
+        # Flush entity stats (mention_count / last_seen) now that the transaction
+        # has committed.  Uses a fresh pool connection — no locks held.
+        await entity_resolver.flush_pending_stats()
 
         # Log final summary
         total_time = time.time() - start_time
@@ -496,28 +504,20 @@ async def retain_batch(
 def _map_results_to_contents(
     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.
-
-    Accounts for duplicates when mapping back.
-    """
-    result_unit_ids = []
-    filtered_idx = 0
-
-    # Group facts by content_index
-    facts_by_content = {i: [] for i in range(len(contents))}
+    """Map created unit IDs back to original content items."""
+    facts_by_content: dict[int, list[int]] = {i: [] for i in range(len(contents))}
     for i, fact in enumerate(extracted_facts):
         facts_by_content[fact.content_index].append(i)
 
+    result_unit_ids = []
+    unit_idx = 0
     for content_index in range(len(contents)):
         content_unit_ids = []
-        for fact_idx in facts_by_content[content_index]:
-            if not is_duplicate_flags[fact_idx]:
-                content_unit_ids.append(unit_ids[filtered_idx])
-                filtered_idx += 1
+        for _ in facts_by_content[content_index]:
+            content_unit_ids.append(unit_ids[unit_idx])
+            unit_idx += 1
         result_unit_ids.append(content_unit_ids)
 
     return result_unit_ids

hindsight-api/hindsight_api/engine/retain/types.py

@@ -6,8 +6,8 @@ from content input to fact storage.
 """
 
 from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from typing import TypedDict
+from datetime import datetime
+from typing import Literal, TypedDict
 from uuid import UUID
 
 
@@ -22,20 +22,21 @@ class RetainContentDict(TypedDict, total=False):
         document_id: Document ID for this content item (optional)
         entities: User-provided entities to merge with extracted entities (optional)
         tags: Visibility scope tags for this content item (optional)
+        observation_scopes: How to scope observations for consolidation (optional).
+            "per_tag" runs one pass per individual tag; "combined" (default) runs a
+            single pass with all tags; a list[list[str]] specifies exact passes.
     """
 
     content: str  # Required
     context: str
-    event_date: datetime
+    event_date: datetime | None
     metadata: dict[str, str]
     document_id: str
     entities: list[dict[str, str]]  # [{"text": "...", "type": "..."}]
     tags: list[str]  # Visibility scope tags
-
-
-def _now_utc() -> datetime:
-    """Factory function for default event_date."""
-    return datetime.now(UTC)
+    observation_scopes: (
+        Literal["per_tag", "combined", "all_combinations"] | list[list[str]]
+    )  # Observation scopes for consolidation
 
 
 @dataclass
@@ -48,10 +49,13 @@ class RetainContent:
 
     content: str
     context: str = ""
-    event_date: datetime = field(default_factory=_now_utc)
+    event_date: datetime | None = None
     metadata: dict[str, str] = field(default_factory=dict)
     entities: list[dict[str, str]] = field(default_factory=list)  # User-provided entities
     tags: list[str] = field(default_factory=list)  # Visibility scope tags
+    observation_scopes: Literal["per_tag", "combined", "all_combinations"] | list[list[str]] | None = (
+        None  # Observation scopes
+    )
 
 
 @dataclass
@@ -117,6 +121,9 @@ class ExtractedFact:
     mentioned_at: datetime | None = None
     metadata: dict[str, str] = field(default_factory=dict)
     tags: list[str] = field(default_factory=list)  # Visibility scope tags
+    observation_scopes: Literal["per_tag", "combined", "all_combinations"] | list[list[str]] | None = (
+        None  # Observation scopes
+    )
 
 
 @dataclass
@@ -135,7 +142,7 @@ class ProcessedFact:
     # Temporal data
     occurred_start: datetime | None
     occurred_end: datetime | None
-    mentioned_at: datetime
+    mentioned_at: datetime | None
 
     # Context and metadata
     context: str
@@ -165,6 +172,9 @@ class ProcessedFact:
     # Visibility scope tags
     tags: list[str] = field(default_factory=list)
 
+    # Observation scopes for consolidation
+    observation_scopes: Literal["per_tag", "combined", "all_combinations"] | list[list[str]] | None = None
+
     @property
     def is_duplicate(self) -> bool:
         """Check if this fact was marked as a duplicate."""
@@ -185,12 +195,10 @@ class ProcessedFact:
         Returns:
             ProcessedFact ready for storage
         """
-        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(UTC)
+        mentioned_at = extracted_fact.mentioned_at  # May be None when caller opted into no timestamp
 
         # Convert entity strings to EntityRef objects
         entities = [EntityRef(name=name) for name in extracted_fact.entities]
@@ -209,6 +217,7 @@ class ProcessedFact:
             chunk_id=chunk_id,
             content_index=extracted_fact.content_index,
             tags=extracted_fact.tags,
+            observation_scopes=extracted_fact.observation_scopes,
         )
 
 

hindsight-api/hindsight_api/engine/search/graph_retrieval.py

@@ -162,7 +162,7 @@ class BFSGraphRetriever(GraphRetriever):
         entry_points = await conn.fetch(
             f"""
             SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+                   mentioned_at, fact_type, document_id, chunk_id, tags,
                    1 - (embedding <=> $1::vector) AS similarity
             FROM {fq_table("memory_units")}
             WHERE bank_id = $2
@@ -216,7 +216,7 @@ class BFSGraphRetriever(GraphRetriever):
                 neighbors = await conn.fetch(
                     f"""
                     SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end,
-                           mu.mentioned_at, mu.embedding, mu.fact_type,
+                           mu.mentioned_at, mu.fact_type,
                            mu.document_id, mu.chunk_id, mu.tags,
                            ml.weight, ml.link_type, ml.from_unit_id
                     FROM {fq_table("memory_links")} ml

hindsight-api/hindsight_api/engine/search/link_expansion_retrieval.py

@@ -1,18 +1,28 @@
 """
 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)
+Expands from semantic/temporal seeds through three parallel, first-class signals
+stored in memory_links:
 
-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
+1. Entity links  — precomputed co-occurrence graph (created at retain time, bounded to
+                   MAX_LINKS_PER_ENTITY per entity). Score = number of distinct shared
+                   entities between the seed set and each candidate.
+2. Semantic links — precomputed kNN graph (each new fact linked to its top-5 most
+                    similar existing facts at insert time, similarity >= 0.7). Checked
+                    in both directions since the graph is not symmetric. Score = weight.
+3. Causal links  — explicit causal chains (causes/caused_by/enables/prevents).
+                   Score = weight + 1.0 (boosted as highest-quality signal).
+
+All three signals are bounded at retain time, so no LATERAL fan-out caps are needed
+at query time. Each expansion is a simple aggregation over a small result set.
+
+For non-observation fact types the three expansions are issued as a single CTE query
+(one roundtrip, one connection) with a `source` discriminator column so the Python
+merge step can apply per-signal score transformations.
 """
 
 import logging
+import math
 import time
 
 from ..db_utils import acquire_with_retry
@@ -45,7 +55,7 @@ async def _find_semantic_seeds(
     rows = await conn.fetch(
         f"""
         SELECT id, text, context, event_date, occurred_start, occurred_end,
-               mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+               mentioned_at, fact_type, document_id, chunk_id, tags,
                1 - (embedding <=> $1::vector) AS similarity
         FROM {fq_table("memory_units")}
         WHERE bank_id = $2
@@ -65,27 +75,23 @@ 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.
+    Runs three expansions through precomputed memory_links: entity co-occurrence,
+    semantic kNN, and causal chains, all bounded at retain time.
+
+    For non-observation fact types the three expansions are issued as a single CTE
+    query (one roundtrip, one connection slot) with a `source` discriminator column.
+    The Python merge step applies per-signal score transformations.
     """
 
     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
+            causal_weight_threshold: Minimum weight for causal links to follow.
         """
-        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:
@@ -110,7 +116,7 @@ class LinkExpansionRetriever(GraphRetriever):
 
         Args:
             pool: Database connection pool
-            query_embedding_str: Query embedding (unused, kept for interface)
+            query_embedding_str: Query embedding as string
             bank_id: Memory bank ID
             fact_type: Fact type to filter
             budget: Maximum results to return
@@ -118,7 +124,7 @@ class LinkExpansionRetriever(GraphRetriever):
             semantic_seeds: Pre-computed semantic entry points
             temporal_seeds: Pre-computed temporal entry points
             adjacency: Unused, kept for interface compatibility
-            tags: Optional list of tags for visibility filtering (OR matching)
+            tags: Optional list of tags for visibility filtering
 
         Returns:
             Tuple of (results, timings)
@@ -126,8 +132,6 @@ class LinkExpansionRetriever(GraphRetriever):
         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:
@@ -150,7 +154,6 @@ class LinkExpansionRetriever(GraphRetriever):
                     f"(tags={tags}, tags_match={tags_match})"
                 )
 
-            # Add temporal seeds if provided
             if temporal_seeds:
                 all_seeds.extend(temporal_seeds)
 
@@ -160,223 +163,61 @@ class LinkExpansionRetriever(GraphRetriever):
             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()
 
-            # For observations, traverse through source_memory_ids to find entity connections.
-            # Observations don't have direct unit_entities - they inherit entities via their
-            # source world/experience facts.
-            #
-            # Path: observation → source_memory_ids → world fact → entities →
-            #       ALL world facts with those entities → their observations (excluding seeds)
             if fact_type == "observation":
-                # Debug: Check what source_memory_ids exist on seed observations
-                debug_sources = await conn.fetch(
-                    f"""
-                    SELECT id, source_memory_ids
-                    FROM {fq_table("memory_units")}
-                    WHERE id = ANY($1::uuid[])
-                    """,
-                    seed_ids,
-                )
-                source_ids_found = []
-                for row in debug_sources:
-                    if row["source_memory_ids"]:
-                        source_ids_found.extend(row["source_memory_ids"])
-                logger.debug(
-                    f"[LinkExpansion] observation graph: {len(seed_ids)} seeds, "
-                    f"{len(source_ids_found)} source_memory_ids found"
-                )
-
-                entity_rows = await conn.fetch(
-                    f"""
-                    WITH seed_sources AS (
-                        -- Get source memory IDs from seed observations
-                        SELECT DISTINCT unnest(source_memory_ids) AS source_id
-                        FROM {fq_table("memory_units")}
-                        WHERE id = ANY($1::uuid[])
-                          AND source_memory_ids IS NOT NULL
-                    ),
-                    source_entities AS (
-                        -- Get entities from those source memories (filtered by frequency)
-                        SELECT DISTINCT ue.entity_id
-                        FROM seed_sources ss
-                        JOIN {fq_table("unit_entities")} ue ON ss.source_id = ue.unit_id
-                        JOIN {fq_table("entities")} e ON ue.entity_id = e.id
-                        WHERE e.mention_count < $2
-                    ),
-                    all_connected_sources AS (
-                        -- Find ALL world facts sharing those entities (don't exclude seed sources)
-                        -- The exclusion happens at the observation level, not the source level
-                        SELECT DISTINCT other_ue.unit_id AS source_id
-                        FROM source_entities se
-                        JOIN {fq_table("unit_entities")} other_ue ON se.entity_id = other_ue.entity_id
-                    )
-                    -- Find observations derived from connected source memories
-                    -- Only exclude the actual seed observations
-                    SELECT
-                        mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                        mu.occurred_end, mu.mentioned_at, mu.embedding,
-                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
-                        COUNT(DISTINCT cs.source_id)::float AS score
-                    FROM all_connected_sources cs
-                    JOIN {fq_table("memory_units")} mu
-                        ON mu.source_memory_ids @> ARRAY[cs.source_id]
-                    WHERE mu.fact_type = 'observation'
-                      AND mu.id != ALL($1::uuid[])
-                    GROUP BY mu.id
-                    ORDER BY score DESC
-                    LIMIT $3
-                    """,
-                    seed_ids,
-                    self.max_entity_frequency,
-                    budget,
-                )
-                logger.debug(f"[LinkExpansion] observation graph: found {len(entity_rows)} connected observations")
+                entity_rows, semantic_rows, causal_rows = await self._expand_observations(conn, seed_ids, budget)
             else:
-                # For world/experience facts, use direct entity lookup
-                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.embedding,
-                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
-                        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.embedding,
-                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
-                    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,
-            )
-
-            # Fallback: semantic/temporal/entity links from memory_links table
-            # These are secondary to entity links (via unit_entities) and causal links
-            # Weight is halved (0.5x) to prioritize primary link types
-            # Check both directions: seeds -> others AND others -> seeds
-            fallback_rows = await conn.fetch(
-                f"""
-                WITH outgoing AS (
-                    -- Links FROM seeds TO other facts
-                    SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                           mu.occurred_end, mu.mentioned_at, mu.embedding,
-                           mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
-                           ml.weight
-                    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 ('semantic', 'temporal', 'entity')
-                      AND ml.weight >= $2
-                      AND mu.fact_type = $3
-                      AND mu.id != ALL($1::uuid[])
-                ),
-                incoming AS (
-                    -- Links FROM other facts TO seeds (reverse direction)
-                    SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
-                           mu.occurred_end, mu.mentioned_at, mu.embedding,
-                           mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
-                           ml.weight
-                    FROM {fq_table("memory_links")} ml
-                    JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
-                    WHERE ml.to_unit_id = ANY($1::uuid[])
-                      AND ml.link_type IN ('semantic', 'temporal', 'entity')
-                      AND ml.weight >= $2
-                      AND mu.fact_type = $3
-                      AND mu.id != ALL($1::uuid[])
-                ),
-                combined AS (
-                    SELECT * FROM outgoing
-                    UNION ALL
-                    SELECT * FROM incoming
-                )
-                SELECT DISTINCT ON (id)
-                    id, text, context, event_date, occurred_start,
-                    occurred_end, mentioned_at, embedding,
-                    fact_type, document_id, chunk_id, tags,
-                    (MAX(weight) * 0.5) AS score
-                FROM combined
-                GROUP BY id, text, context, event_date, occurred_start,
-                         occurred_end, mentioned_at, embedding,
-                         fact_type, document_id, chunk_id, tags
-                ORDER BY id, score DESC
-                LIMIT $4
-                """,
-                seed_ids,
-                self.causal_weight_threshold,
-                fact_type,
-                budget,
-            )
+                entity_rows, semantic_rows, causal_rows = await self._expand_combined(conn, seed_ids, fact_type, budget)
 
             timings.edge_load_time = time.time() - query_start
-            timings.db_queries = 3
-            timings.edge_count = len(entity_rows) + len(causal_rows) + len(fallback_rows)
+            timings.db_queries = 1
+            timings.edge_count = len(entity_rows) + len(semantic_rows) + len(causal_rows)
 
-        # Merge results, taking max score per fact
-        # Priority: entity links (unit_entities) > causal links > fallback links
-        score_map: dict[str, float] = {}
+        # Merge results with additive intra-score: entity + semantic + causal ∈ [0, 3].
+        #
+        # Entity score: tanh(count × 0.5) maps shared-entity count to [0, 1]:
+        #   1 entity → 0.46,  2 → 0.76,  3 → 0.91,  4 → 0.96  (saturates naturally)
+        # Semantic score: similarity weight, already ∈ [0.7, 1.0].
+        # Causal score:   link weight, already ∈ [0, 1].
+        #
+        # Facts appearing in multiple signals accumulate higher scores, rewarding
+        # convergent evidence. The outer RRF uses rank position from this sorted list.
+        entity_scores: dict[str, float] = {}
+        semantic_scores: dict[str, float] = {}
+        causal_scores: 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"])
+            entity_scores[fact_id] = math.tanh(row["score"] * 0.5)
             row_map[fact_id] = dict(row)
 
+        for row in semantic_rows:
+            fact_id = str(row["id"])
+            semantic_scores[fact_id] = max(semantic_scores.get(fact_id, 0.0), row["score"])
+            row_map.setdefault(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)
+            causal_scores[fact_id] = max(causal_scores.get(fact_id, 0.0), row["score"])
+            row_map.setdefault(fact_id, dict(row))
 
-        for row in fallback_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)
+        all_ids = set(entity_scores) | set(semantic_scores) | set(causal_scores)
+        score_map = {
+            fid: entity_scores.get(fid, 0.0) + semantic_scores.get(fid, 0.0) + causal_scores.get(fid, 0.0)
+            for fid in all_ids
+        }
 
-        # 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)
 
-        # Apply tags filtering (graph expansion may reach untagged memories)
         if tags:
             results = filter_results_by_tags(results, tags, match=tags_match)
 
@@ -389,3 +230,253 @@ class LinkExpansionRetriever(GraphRetriever):
         )
 
         return results, timings
+
+    async def _expand_combined(
+        self,
+        conn,
+        seed_ids: list,
+        fact_type: str,
+        budget: int,
+    ) -> tuple[list, list, list]:
+        """
+        Single-roundtrip CTE query combining entity, semantic, and causal expansions.
+
+        Uses a `source` discriminator column so the caller can apply per-signal
+        score transformations.  The three CTEs share one connection slot — important
+        for asyncpg which does not allow concurrent queries on the same connection.
+
+        Index coverage (requires migration d2e3f4a5b6c7):
+          entity:   idx_memory_links_entity_covering (from_unit_id) INCLUDE (to_unit_id, entity_id)
+                    WHERE link_type = 'entity'  → index-only scan, no heap reads
+          semantic incoming:
+                    idx_memory_links_to_type_weight (to_unit_id, link_type, weight DESC)
+                    → replaces costly BitmapAnd of two separate scans
+        """
+        ml = fq_table("memory_links")
+        mu = fq_table("memory_units")
+        all_rows = await conn.fetch(
+            f"""
+            WITH entity_expanded AS (
+                -- Entity co-occurrence: seeds → their precomputed entity-link neighbors.
+                -- Score = distinct shared entities (bounded at retain time to
+                -- MAX_LINKS_PER_ENTITY=50).  GROUP BY mu.id is sufficient because mu.id
+                -- is the primary key and functionally determines all other mu columns.
+                SELECT
+                    mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                    mu.occurred_end, mu.mentioned_at,
+                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                    COUNT(DISTINCT ml.entity_id)::float AS score,
+                    'entity'::text AS source
+                FROM {ml} ml
+                JOIN {mu} mu ON mu.id = ml.to_unit_id
+                WHERE ml.from_unit_id = ANY($1::uuid[])
+                  AND ml.link_type = 'entity'
+                  AND mu.fact_type = $2
+                  AND mu.id != ALL($1::uuid[])
+                GROUP BY mu.id
+                ORDER BY score DESC
+                LIMIT $3
+            ),
+            semantic_expanded AS (
+                -- Semantic kNN: both outgoing (seeds → their kNN at insert time) and
+                -- incoming (facts inserted after seeds that found seeds as kNN).
+                -- Score = max similarity weight across both directions.
+                SELECT
+                    id, text, context, event_date, occurred_start,
+                    occurred_end, mentioned_at,
+                    fact_type, document_id, chunk_id, tags,
+                    MAX(weight) AS score,
+                    'semantic'::text AS source
+                FROM (
+                    SELECT
+                        mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                        mu.occurred_end, mu.mentioned_at,
+                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                        ml.weight
+                    FROM {ml} ml
+                    JOIN {mu} mu ON mu.id = ml.to_unit_id
+                    WHERE ml.from_unit_id = ANY($1::uuid[])
+                      AND ml.link_type = 'semantic'
+                      AND mu.fact_type = $2
+                      AND mu.id != ALL($1::uuid[])
+                    UNION ALL
+                    SELECT
+                        mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                        mu.occurred_end, mu.mentioned_at,
+                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                        ml.weight
+                    FROM {ml} ml
+                    JOIN {mu} mu ON mu.id = ml.from_unit_id
+                    WHERE ml.to_unit_id = ANY($1::uuid[])
+                      AND ml.link_type = 'semantic'
+                      AND mu.fact_type = $2
+                      AND mu.id != ALL($1::uuid[])
+                ) sem_raw
+                GROUP BY id, text, context, event_date, occurred_start,
+                         occurred_end, mentioned_at,
+                         fact_type, document_id, chunk_id, tags
+                ORDER BY score DESC
+                LIMIT $3
+            ),
+            causal_expanded AS (
+                -- Causal chains: explicit causes/enables/prevents links from seeds.
+                -- DISTINCT ON handles the case where a seed has multiple causal links
+                -- to the same target; best weight wins.
+                SELECT DISTINCT ON (mu.id)
+                    mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                    mu.occurred_end, mu.mentioned_at,
+                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                    ml.weight AS score,
+                    'causal'::text AS source
+                FROM {ml} ml
+                JOIN {mu} 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 >= $4
+                  AND mu.fact_type = $2
+                ORDER BY mu.id, ml.weight DESC
+                LIMIT $3
+            )
+            SELECT * FROM entity_expanded
+            UNION ALL
+            SELECT * FROM semantic_expanded
+            UNION ALL
+            SELECT * FROM causal_expanded
+            """,
+            seed_ids,
+            fact_type,
+            budget,
+            self.causal_weight_threshold,
+        )
+
+        entity_rows = [r for r in all_rows if r["source"] == "entity"]
+        semantic_rows = [r for r in all_rows if r["source"] == "semantic"]
+        causal_rows = [r for r in all_rows if r["source"] == "causal"]
+        return entity_rows, semantic_rows, causal_rows
+
+    async def _expand_observations(
+        self,
+        conn,
+        seed_ids: list,
+        budget: int,
+    ) -> tuple[list, list, list]:
+        """
+        Observation-specific expansion.
+
+        Observations don't have direct entity links in memory_links (they're created
+        by consolidation, not retain).  Instead, traverse source_memory_ids → world
+        facts → entities → other world facts → their observations.
+
+        Semantic and causal expansions run as a second combined CTE query.
+        """
+        source_ids_found: list = []
+        if logger.isEnabledFor(logging.DEBUG):
+            debug_rows = await conn.fetch(
+                f"""
+                SELECT id, source_memory_ids
+                FROM {fq_table("memory_units")}
+                WHERE id = ANY($1::uuid[])
+                """,
+                seed_ids,
+            )
+            for row in debug_rows:
+                if row["source_memory_ids"]:
+                    source_ids_found.extend(row["source_memory_ids"])
+            logger.debug(
+                f"[LinkExpansion] observation graph: {len(seed_ids)} seeds, "
+                f"{len(source_ids_found)} source_memory_ids found"
+            )
+
+        entity_rows = await conn.fetch(
+            f"""
+            WITH seed_sources AS (
+                SELECT DISTINCT unnest(source_memory_ids) AS source_id
+                FROM {fq_table("memory_units")}
+                WHERE id = ANY($1::uuid[])
+                  AND source_memory_ids IS NOT NULL
+            ),
+            source_entities AS (
+                SELECT DISTINCT ue.entity_id
+                FROM seed_sources ss
+                JOIN {fq_table("unit_entities")} ue ON ss.source_id = ue.unit_id
+            ),
+            all_connected_sources AS (
+                SELECT DISTINCT other_ue.unit_id AS source_id
+                FROM source_entities se
+                JOIN {fq_table("unit_entities")} other_ue ON se.entity_id = other_ue.entity_id
+            )
+            SELECT
+                mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                mu.occurred_end, mu.mentioned_at,
+                mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                COUNT(DISTINCT cs.source_id)::float AS score
+            FROM all_connected_sources cs
+            JOIN {fq_table("memory_units")} mu
+                ON mu.source_memory_ids @> ARRAY[cs.source_id]
+            WHERE mu.fact_type = 'observation'
+              AND mu.id != ALL($1::uuid[])
+            GROUP BY mu.id
+            ORDER BY score DESC
+            LIMIT $2
+            """,
+            seed_ids,
+            budget,
+        )
+        logger.debug(f"[LinkExpansion] observation graph: found {len(entity_rows)} connected observations")
+
+        # Semantic + causal for observations in one query
+        ml = fq_table("memory_links")
+        mu = fq_table("memory_units")
+        sem_causal_rows = await conn.fetch(
+            f"""
+            WITH semantic_expanded AS (
+                SELECT
+                    id, text, context, event_date, occurred_start,
+                    occurred_end, mentioned_at,
+                    fact_type, document_id, chunk_id, tags,
+                    MAX(weight) AS score,
+                    'semantic'::text AS source
+                FROM (
+                    SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                           mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
+                           mu.chunk_id, mu.tags, ml.weight
+                    FROM {ml} ml JOIN {mu} mu ON mu.id = ml.to_unit_id
+                    WHERE ml.from_unit_id = ANY($1::uuid[])
+                      AND ml.link_type = 'semantic' AND mu.fact_type = 'observation'
+                      AND mu.id != ALL($1::uuid[])
+                    UNION ALL
+                    SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                           mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
+                           mu.chunk_id, mu.tags, ml.weight
+                    FROM {ml} ml JOIN {mu} mu ON mu.id = ml.from_unit_id
+                    WHERE ml.to_unit_id = ANY($1::uuid[])
+                      AND ml.link_type = 'semantic' AND mu.fact_type = 'observation'
+                      AND mu.id != ALL($1::uuid[])
+                ) sem_raw
+                GROUP BY id, text, context, event_date, occurred_start, occurred_end,
+                         mentioned_at, fact_type, document_id, chunk_id, tags
+                ORDER BY score DESC LIMIT $2
+            ),
+            causal_expanded AS (
+                SELECT DISTINCT ON (mu.id)
+                    mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                    mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id,
+                    mu.chunk_id, mu.tags, ml.weight AS score, 'causal'::text AS source
+                FROM {ml} ml JOIN {mu} 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 >= $3 AND mu.fact_type = 'observation'
+                ORDER BY mu.id, ml.weight DESC LIMIT $2
+            )
+            SELECT * FROM semantic_expanded
+            UNION ALL
+            SELECT * FROM causal_expanded
+            """,
+            seed_ids,
+            budget,
+            self.causal_weight_threshold,
+        )
+
+        semantic_rows = [r for r in sem_causal_rows if r["source"] == "semantic"]
+        causal_rows = [r for r in sem_causal_rows if r["source"] == "causal"]
+        return entity_rows, semantic_rows, causal_rows

hindsight-api/hindsight_api/engine/search/mpfp_retrieval.py

@@ -449,7 +449,7 @@ async def fetch_memory_units_by_ids(
         rows = await conn.fetch(
             f"""
             SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, embedding, fact_type, document_id, chunk_id, tags
+                   mentioned_at, fact_type, document_id, chunk_id, tags
             FROM {fq_table("memory_units")}
             WHERE id = ANY($1::uuid[])
               AND fact_type = $2

hindsight-api/hindsight_api/engine/search/retrieval.py

@@ -127,7 +127,7 @@ async def retrieve_semantic_bm25_combined(
         results = await conn.fetch(
             f"""
             WITH semantic_ranked AS (
-                SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+                SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                        1 - (embedding <=> $1::vector) AS similarity,
                        NULL::float AS bm25_score,
                        'semantic' AS source,
@@ -139,7 +139,7 @@ async def retrieve_semantic_bm25_combined(
                   AND (1 - (embedding <=> $1::vector)) >= 0.3
                   {tags_clause}
             )
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    similarity, bm25_score, source
             FROM semantic_ranked
             WHERE rn <= $4
@@ -194,7 +194,7 @@ async def retrieve_semantic_bm25_combined(
     # Single query template with backend-specific parts injected
     query = f"""
         WITH semantic_ranked AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    1 - (embedding <=> $1::vector) AS similarity,
                    NULL::float AS bm25_score,
                    'semantic' AS source,
@@ -207,7 +207,7 @@ async def retrieve_semantic_bm25_combined(
               {tags_clause}
         ),
         bm25_ranked AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    NULL::float AS similarity,
                    {bm25_score_expr} AS bm25_score,
                    'bm25' AS source,
@@ -219,12 +219,12 @@ async def retrieve_semantic_bm25_combined(
               {tags_clause}
         ),
         semantic AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    similarity, bm25_score, source
             FROM semantic_ranked WHERE rn <= $4
         ),
         bm25 AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags,
                    similarity, bm25_score, source
             FROM bm25_ranked WHERE rn <= $4
         )
@@ -297,13 +297,20 @@ async def retrieve_temporal_combined(
     if tags:
         params.append(tags)
 
-    # Batch query: Get entry points for ALL fact types at once with window function
+    # Two-phase entry point query:
+    # Phase 1 (date_ranked): rank by date only — no embedding computation — for all units in
+    #   the temporal window. This lets the planner use date indexes for filtering.
+    # Phase 2 (sim_ranked): join back to memory_units for only the top-50-per-type candidates
+    #   and compute embedding similarity for that small set (≤ 50 × len(fact_types) rows).
+    # This avoids computing embedding distances for potentially thousands of date-range rows.
     entry_points = await conn.fetch(
         f"""
-        WITH ranked_entries AS (
-            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
-                   1 - (embedding <=> $1::vector) AS similarity,
-                   ROW_NUMBER() OVER (PARTITION BY fact_type ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC, embedding <=> $1::vector) AS rn
+        WITH date_ranked AS MATERIALIZED (
+            SELECT id, fact_type,
+                   ROW_NUMBER() OVER (
+                       PARTITION BY fact_type
+                       ORDER BY COALESCE(occurred_start, mentioned_at, occurred_end) DESC NULLS LAST
+                   ) AS rn
             FROM {fq_table("memory_units")}
             WHERE bank_id = $2
               AND fact_type = ANY($3)
@@ -318,12 +325,20 @@ async def retrieve_temporal_combined(
                   OR
                   (occurred_end IS NOT NULL AND occurred_end BETWEEN $4 AND $5)
               )
-              AND (1 - (embedding <=> $1::vector)) >= $6
               {tags_clause}
+        ),
+        sim_ranked AS (
+            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                   1 - (mu.embedding <=> $1::vector) AS similarity,
+                   ROW_NUMBER() OVER (PARTITION BY mu.fact_type ORDER BY mu.embedding <=> $1::vector) AS sim_rn
+            FROM date_ranked dr
+            JOIN {fq_table("memory_units")} mu ON mu.id = dr.id
+            WHERE dr.rn <= 50
+              AND (1 - (mu.embedding <=> $1::vector)) >= $6
         )
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags, similarity
-        FROM ranked_entries
-        WHERE rn <= 10
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags, similarity
+        FROM sim_ranked
+        WHERE sim_rn <= 10
         """,
         *params,
     )
@@ -387,34 +402,52 @@ async def retrieve_temporal_combined(
         frontier = list(node_scores.keys())
         budget_remaining = budget - len(ft_entry_points)
         batch_size = 20
+        # Per-source neighbor limit: lets the planner use the composite index
+        # (from_unit_id, link_type, weight DESC) with early termination, avoiding
+        # a full scan of all links from all source nodes before sorting.
+        per_source_limit = 10
+        # Safety cap on BFS iterations to prevent runaway spreading in dense graphs.
+        max_iterations = 5
+        iteration = 0
 
-        # Build tags clause for spreading (use param 6 since 1-5 are used)
-        spreading_tags_clause = build_tags_where_clause_simple(tags, 6, table_alias="mu.", match=tags_match)
+        # Build tags clause for spreading (use param 7 since 1-6 are used)
+        spreading_tags_clause = build_tags_where_clause_simple(tags, 7, table_alias="mu.", match=tags_match)
 
-        while frontier and budget_remaining > 0:
+        while frontier and budget_remaining > 0 and iteration < max_iterations:
+            iteration += 1
             batch_ids = frontier[:batch_size]
             frontier = frontier[batch_size:]
 
-            spreading_params = [query_emb_str, batch_ids, ft, semantic_threshold, batch_size * 10]
+            # $1=query_emb, $2=batch_ids, $3=fact_type, $4=threshold, $5=per_source_limit, $6=bank_id, $7=tags
+            spreading_params = [query_emb_str, batch_ids, ft, semantic_threshold, per_source_limit, bank_id]
             if tags:
                 spreading_params.append(tags)
 
+            # LATERAL join: for each source node, fetch top-K neighbors by weight using
+            # the existing idx_memory_links_from_type_weight index with early-exit semantics.
+            # This avoids scanning all temporal links from all source nodes before sorting.
+            # bank_id on memory_units lets the planner use idx_memory_units_bank_fact_type.
             neighbors = await conn.fetch(
                 f"""
-                SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.embedding, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
-                       ml.weight, ml.link_type, ml.from_unit_id,
+                SELECT src.from_unit_id, mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                       l.weight, l.link_type,
                        1 - (mu.embedding <=> $1::vector) AS similarity
-                FROM {fq_table("memory_links")} ml
-                JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
-                WHERE ml.from_unit_id = ANY($2::uuid[])
-                  AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
-                  AND ml.weight >= 0.1
+                FROM unnest($2::uuid[]) AS src(from_unit_id)
+                CROSS JOIN LATERAL (
+                    SELECT ml.to_unit_id, ml.weight, ml.link_type
+                    FROM {fq_table("memory_links")} ml
+                    WHERE ml.from_unit_id = src.from_unit_id
+                      AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
+                      AND ml.weight >= 0.1
+                    ORDER BY ml.weight DESC
+                    LIMIT $5
+                ) l
+                JOIN {fq_table("memory_units")} mu ON mu.id = l.to_unit_id
+                WHERE mu.bank_id = $6
                   AND mu.fact_type = $3
                   AND mu.embedding IS NOT NULL
                   AND (1 - (mu.embedding <=> $1::vector)) >= $4
                   {spreading_tags_clause}
-                ORDER BY ml.weight DESC
-                LIMIT $5
                 """,
                 *spreading_params,
             )

hindsight-api/hindsight_api/engine/search/types.py

@@ -46,7 +46,6 @@ class RetrievalResult:
     mentioned_at: datetime | None = None
     document_id: str | None = None
     chunk_id: str | None = None
-    embedding: list[float] | None = None
     tags: list[str] | None = None  # Visibility scope tags
 
     # Retrieval-specific scores (only one will be set depending on retrieval method)
@@ -70,7 +69,6 @@ class RetrievalResult:
             mentioned_at=row.get("mentioned_at"),
             document_id=row.get("document_id"),
             chunk_id=row.get("chunk_id"),
-            embedding=row.get("embedding"),
             tags=row.get("tags"),
             similarity=row.get("similarity"),
             bm25_score=row.get("bm25_score"),
@@ -154,7 +152,6 @@ class ScoredResult:
             "mentioned_at": self.retrieval.mentioned_at,
             "document_id": self.retrieval.document_id,
             "chunk_id": self.retrieval.chunk_id,
-            "embedding": self.retrieval.embedding,
             "tags": self.retrieval.tags,
             "semantic_similarity": self.retrieval.similarity,
             "bm25_score": self.retrieval.bm25_score,

hindsight-api/hindsight_api/engine/storage/__init__.py

@@ -12,6 +12,7 @@ def create_file_storage(
     storage_type: str,
     pool_getter: Callable | None = None,
     schema: str | None = None,
+    schema_getter: Callable | None = None,
     **kwargs,
 ) -> FileStorage:
     """
@@ -20,7 +21,8 @@ def create_file_storage(
     Args:
         storage_type: "native" (PostgreSQL BYTEA) or "s3" (S3-compatible object storage)
         pool_getter: Database pool getter (required for native)
-        schema: Database schema (for native multi-tenant)
+        schema: Static database schema (for native single-tenant)
+        schema_getter: Callable returning current schema at query time (for native multi-tenant)
         **kwargs: Additional args passed to storage backend
 
     Returns:
@@ -32,7 +34,7 @@ def create_file_storage(
     if storage_type == "native":
         if not pool_getter:
             raise ValueError("pool_getter required for native (PostgreSQL) storage")
-        return PostgreSQLFileStorage(pool_getter=pool_getter, schema=schema)
+        return PostgreSQLFileStorage(pool_getter=pool_getter, schema=schema, schema_getter=schema_getter)
     elif storage_type == "s3":
         from ...config import get_config
         from .s3 import S3FileStorage

hindsight-api/hindsight_api/engine/storage/postgresql.py

@@ -40,16 +40,30 @@ class PostgreSQLFileStorage(FileStorage):
     For production/scale, consider S3FileStorage instead.
     """
 
-    def __init__(self, pool_getter: Callable[[], "asyncpg.Pool"], schema: str | None = None):
+    def __init__(
+        self,
+        pool_getter: Callable[[], "asyncpg.Pool"],
+        schema: str | None = None,
+        schema_getter: Callable[[], str] | None = None,
+    ):
         """
         Initialize PostgreSQL file storage.
 
         Args:
             pool_getter: Function that returns asyncpg connection pool
-            schema: Database schema (for multi-tenant support)
+            schema: Static database schema (fallback for single-tenant / tests)
+            schema_getter: Callable returning current schema at query time (for multi-tenant)
         """
         self._pool_getter = pool_getter
-        self._schema = schema
+        self._static_schema = schema
+        self._schema_getter = schema_getter
+
+    @property
+    def _schema(self) -> str | None:
+        """Resolve schema dynamically per-request when schema_getter is provided."""
+        if self._schema_getter:
+            return self._schema_getter()
+        return self._static_schema
 
     async def store(
         self,

hindsight-api/hindsight_api/extensions/__init__.py

@@ -22,6 +22,11 @@ from hindsight_api.extensions.http import HttpExtension
 from hindsight_api.extensions.loader import load_extension
 from hindsight_api.extensions.mcp import MCPExtension
 from hindsight_api.extensions.operation_validator import (
+    # Bank Management operations
+    BankListContext,
+    BankListResult,
+    BankReadContext,
+    BankWriteContext,
     # Consolidation operation
     ConsolidateContext,
     ConsolidateResult,
@@ -70,6 +75,11 @@ __all__ = [
     "RetainContext",
     "RetainResult",
     "ValidationResult",
+    # Operation Validator - Bank Management
+    "BankListContext",
+    "BankListResult",
+    "BankReadContext",
+    "BankWriteContext",
     # Operation Validator - Consolidation
     "ConsolidateContext",
     "ConsolidateResult",

hindsight-api/hindsight_api/extensions/http.py

@@ -87,3 +87,15 @@ class HttpExtension(Extension, ABC):
             ```
         """
         pass
+
+    def get_root_router(self, memory: "MemoryEngine") -> APIRouter | None:
+        """
+        Return a FastAPI router with endpoints mounted at the app root.
+
+        Unlike get_router() which is mounted at /ext/, this router is mounted
+        directly on the application root. Use for well-known endpoints or other
+        paths that must be at specific locations.
+
+        Returns None by default (no root routes). Override to provide root-level routes.
+        """
+        return None

hindsight-api/hindsight_api/extensions/operation_validator.py

@@ -200,6 +200,44 @@ class ConsolidateResult:
     error: str | None = None
 
 
+# =============================================================================
+# Bank Management Contexts
+# =============================================================================
+
+
+@dataclass
+class BankReadContext:
+    """Context for a bank read operation validation (pre-operation)."""
+
+    bank_id: str
+    operation: str  # "get_bank_profile", "get_bank_stats"
+    request_context: "RequestContext"
+
+
+@dataclass
+class BankWriteContext:
+    """Context for a bank write operation validation (pre-operation)."""
+
+    bank_id: str
+    operation: str  # "delete_bank", "update_bank", "update_bank_disposition", "set_bank_mission", "merge_bank_mission", "clear_observations", "clear_observations_for_memory"
+    request_context: "RequestContext"
+
+
+@dataclass
+class BankListContext:
+    """Context for filtering the bank list (post-query)."""
+
+    banks: list[dict]
+    request_context: "RequestContext"
+
+
+@dataclass
+class BankListResult:
+    """Result of filtering the bank list."""
+
+    banks: list[dict]
+
+
 # =============================================================================
 # Mental Model Contexts
 # =============================================================================
@@ -535,3 +573,63 @@ class OperationValidatorExtension(Extension, ABC):
                 - error: Error message (if failed)
         """
         pass
+
+    # =========================================================================
+    # Bank Management - Validation hooks (optional - override to implement)
+    # =========================================================================
+
+    async def validate_bank_read(self, ctx: BankReadContext) -> ValidationResult:
+        """
+        Validate a bank read operation before execution.
+
+        Override to implement custom validation logic for bank reads
+        (get_bank_profile, get_bank_stats).
+
+        Args:
+            ctx: Context containing:
+                - bank_id: Bank identifier
+                - operation: Operation name
+                - request_context: Request context with auth info
+
+        Returns:
+            ValidationResult indicating whether the operation is allowed.
+        """
+        return ValidationResult.accept()
+
+    async def validate_bank_write(self, ctx: BankWriteContext) -> ValidationResult:
+        """
+        Validate a bank write operation before execution.
+
+        Override to implement custom validation logic for bank writes
+        (delete_bank, update_bank, update_bank_disposition, set_bank_mission,
+        merge_bank_mission, clear_observations, clear_observations_for_memory).
+
+        Args:
+            ctx: Context containing:
+                - bank_id: Bank identifier
+                - operation: Operation name
+                - request_context: Request context with auth info
+
+        Returns:
+            ValidationResult indicating whether the operation is allowed.
+        """
+        return ValidationResult.accept()
+
+    async def filter_bank_list(self, ctx: BankListContext) -> BankListResult:
+        """
+        Filter the bank list after querying.
+
+        Unlike validate_* methods, this is a post-query filter that narrows results
+        rather than a gate that blocks the operation.
+
+        Override to implement custom filtering (e.g., restrict to allowed banks).
+
+        Args:
+            ctx: Context containing:
+                - banks: List of bank dicts from the database
+                - request_context: Request context with auth info
+
+        Returns:
+            BankListResult with the filtered list of banks.
+        """
+        return BankListResult(banks=ctx.banks)

hindsight-api/hindsight_api/extensions/tenant.py

@@ -11,8 +11,9 @@ from hindsight_api.models import RequestContext
 class AuthenticationError(Exception):
     """Raised when authentication fails."""
 
-    def __init__(self, reason: str):
+    def __init__(self, reason: str, headers: dict[str, str] | None = None):
         self.reason = reason
+        self.headers = headers or {}
         super().__init__(f"Authentication failed: {reason}")
 
 

hindsight-api/hindsight_api/main.py

@@ -171,6 +171,7 @@ def main():
             llm_vertexai_project_id=config.llm_vertexai_project_id,
             llm_vertexai_region=config.llm_vertexai_region,
             llm_vertexai_service_account_key=config.llm_vertexai_service_account_key,
+            llm_gemini_safety_settings=config.llm_gemini_safety_settings,
             retain_llm_provider=config.retain_llm_provider,
             retain_llm_api_key=config.retain_llm_api_key,
             retain_llm_model=config.retain_llm_model,
@@ -231,12 +232,15 @@ def main():
             reranker_litellm_sdk_api_key=config.reranker_litellm_sdk_api_key,
             reranker_litellm_sdk_model=config.reranker_litellm_sdk_model,
             reranker_litellm_sdk_api_base=config.reranker_litellm_sdk_api_base,
+            reranker_zeroentropy_api_key=config.reranker_zeroentropy_api_key,
+            reranker_zeroentropy_model=config.reranker_zeroentropy_model,
             host=args.host,
             port=args.port,
             base_path=config.base_path,
             log_level=args.log_level,
             log_format=config.log_format,
             mcp_enabled=config.mcp_enabled,
+            mcp_enabled_tools=config.mcp_enabled_tools,
             enable_bank_config_api=config.enable_bank_config_api,
             graph_retriever=config.graph_retriever,
             mpfp_top_k_neighbors=config.mpfp_top_k_neighbors,
@@ -246,8 +250,10 @@ def main():
             retain_chunk_size=config.retain_chunk_size,
             retain_extract_causal_links=config.retain_extract_causal_links,
             retain_extraction_mode=config.retain_extraction_mode,
+            retain_mission=config.retain_mission,
             retain_custom_instructions=config.retain_custom_instructions,
             retain_batch_tokens=config.retain_batch_tokens,
+            retain_entity_lookup=config.retain_entity_lookup,
             retain_batch_enabled=config.retain_batch_enabled,
             retain_batch_poll_interval_seconds=config.retain_batch_poll_interval_seconds,
             file_storage_type=config.file_storage_type,
@@ -262,13 +268,19 @@ def main():
             file_storage_azure_account_name=config.file_storage_azure_account_name,
             file_storage_azure_account_key=config.file_storage_azure_account_key,
             file_parser=config.file_parser,
+            file_parser_iris_token=config.file_parser_iris_token,
+            file_parser_iris_org_id=config.file_parser_iris_org_id,
             file_conversion_max_batch_size_mb=config.file_conversion_max_batch_size_mb,
             file_conversion_max_batch_size=config.file_conversion_max_batch_size,
             enable_file_upload_api=config.enable_file_upload_api,
             file_delete_after_retain=config.file_delete_after_retain,
             enable_observations=config.enable_observations,
             consolidation_batch_size=config.consolidation_batch_size,
+            consolidation_llm_batch_size=config.consolidation_llm_batch_size,
             consolidation_max_tokens=config.consolidation_max_tokens,
+            observations_mission=config.observations_mission,
+            entity_labels=config.entity_labels,
+            entities_allow_free_form=config.entities_allow_free_form,
             skip_llm_verification=config.skip_llm_verification,
             lazy_reranker=config.lazy_reranker,
             run_migrations_on_startup=config.run_migrations_on_startup,
@@ -284,6 +296,11 @@ def main():
             worker_max_slots=config.worker_max_slots,
             worker_consolidation_max_slots=config.worker_consolidation_max_slots,
             reflect_max_iterations=config.reflect_max_iterations,
+            reflect_max_context_tokens=config.reflect_max_context_tokens,
+            reflect_mission=config.reflect_mission,
+            disposition_skepticism=config.disposition_skepticism,
+            disposition_literalism=config.disposition_literalism,
+            disposition_empathy=config.disposition_empathy,
             mental_model_refresh_concurrency=config.mental_model_refresh_concurrency,
             otel_traces_enabled=config.otel_traces_enabled,
             otel_exporter_otlp_endpoint=config.otel_exporter_otlp_endpoint,

hindsight-api/hindsight_api/mcp_local.py

@@ -1,8 +1,14 @@
 """
-Local MCP server for use with Claude Code (stdio transport).
+Local MCP server entry point for use with Claude Code (HTTP transport).
 
-This runs a fully local Hindsight instance with embedded PostgreSQL (pg0).
-No external database or server required.
+This is a thin wrapper around the main hindsight-api server that pre-configures
+sensible defaults for local use (embedded PostgreSQL via pg0, warning log level).
+
+The full API runs on localhost:8888. Configure Claude Code's MCP settings:
+    claude mcp add --transport http hindsight http://localhost:8888/mcp/
+
+Or pinned to a specific bank (single-bank mode):
+    claude mcp add --transport http hindsight http://localhost:8888/mcp/default/
 
 Run with:
     hindsight-local-mcp
@@ -10,148 +16,24 @@ Run with:
 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."
+    HINDSIGHT_API_DATABASE_URL: Optional. Override database URL (default: pg0://hindsight-mcp).
 """
 
-import logging
 import os
-import sys
-
-from mcp.server.fastmcp import FastMCP
-
-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,
-)
-from hindsight_api.mcp_tools import MCPToolsConfig, register_mcp_tools
-
-# 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.
+def main() -> None:
+    """Start the Hindsight API server with local defaults."""
+    # Set local defaults (only if not already configured by the user)
+    os.environ.setdefault("HINDSIGHT_API_DATABASE_URL", "pg0://hindsight-mcp")
 
-    Args:
-        bank_id: The memory bank ID to use for all operations.
-        memory: Optional MemoryEngine instance. If not provided, creates one with pg0.
+    from hindsight_api.main import main as api_main
 
-    Returns:
-        Configured FastMCP server instance.
-    """
-    # Import here to avoid slow startup if just checking --help
-    from hindsight_api import MemoryEngine
-
-    # 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")
-
-    # Configure and register tools using shared module
-    config = MCPToolsConfig(
-        bank_id_resolver=lambda: bank_id,
-        include_bank_id_param=False,  # Local MCP uses fixed bank_id
-        tools={"retain", "recall"},  # Local MCP only has retain and recall
-        retain_description=retain_description,
-        recall_description=recall_description,
-        retain_fire_and_forget=True,  # Local MCP uses fire-and-forget pattern
-    )
-
-    register_mcp_tools(mcp, memory, config)
-
-    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))
+    api_main()
 
 
 if __name__ == "__main__":

hindsight-api/hindsight_api/migrations.py

@@ -18,6 +18,7 @@ No alembic.ini required - all configuration is done programmatically.
 import hashlib
 import logging
 import os
+import time
 from pathlib import Path
 
 from alembic import command
@@ -220,13 +221,40 @@ def run_migrations(
         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
+        # Use PostgreSQL advisory lock to coordinate between distributed workers.
+        #
+        # IMPORTANT: We must avoid holding an open transaction on the advisory-lock
+        # connection while CREATE INDEX CONCURRENTLY runs inside a migration.
+        # CONCURRENTLY waits for ALL active transactions to finish before the index
+        # becomes valid.  If the advisory-lock connection (or any waiting worker's
+        # connection) holds an open transaction, CONCURRENTLY deadlocks:
+        #   - migration worker waits for other workers' transactions to close
+        #   - other workers wait for the advisory lock to be released
+        #
+        # Fix:
+        #   1. Use pg_try_advisory_lock (non-blocking) in a poll loop instead of
+        #      blocking pg_advisory_lock, so we can COMMIT the transaction between
+        #      retries.  Between retries the connection holds no open transaction.
+        #   2. After acquiring the lock, COMMIT the transaction on the advisory-lock
+        #      connection itself before running migrations.  pg_advisory_lock is
+        #      session-level, so the lock survives the COMMIT.
         engine = create_engine(database_url)
         with engine.connect() as conn:
-            # pg_advisory_lock blocks until the lock is acquired
-            # The lock is automatically released when the connection closes
             logger.debug(f"Acquiring migration advisory lock for schema '{schema_name}' (id={lock_id})...")
-            conn.execute(text(f"SELECT pg_advisory_lock({lock_id})"))
+            while True:
+                acquired = conn.execute(text(f"SELECT pg_try_advisory_lock({lock_id})")).scalar()
+                if acquired:
+                    break
+                # Commit the transaction so this connection holds no open snapshot
+                # while waiting.  This prevents blocking CREATE INDEX CONCURRENTLY
+                # that may be running in the migration worker.
+                conn.commit()
+                time.sleep(0.5)
+
+            # Commit AFTER acquiring the lock too.  pg_advisory_lock is session-level
+            # and survives the COMMIT, but the open transaction on this connection
+            # would otherwise block any CREATE INDEX CONCURRENTLY in the migration.
+            conn.commit()
             logger.debug("Migration advisory lock acquired")
 
             try:
@@ -347,6 +375,13 @@ def run_migrations(
                                     "Please install it with: CREATE EXTENSION vectorscale CASCADE;"
                                 ) from e
 
+                # Commit any pending transaction on the advisory-lock connection
+                # before running migrations.  Some code paths above (e.g., the
+                # pgvector extension check) may have started a transaction via
+                # SQLAlchemy's autobegin.  If we leave it open, CREATE INDEX
+                # CONCURRENTLY inside a migration will deadlock waiting for it.
+                conn.commit()
+
                 # Run migrations while holding the lock
                 _run_migrations_internal(database_url, script_location, schema=schema)
             finally:
@@ -565,6 +600,12 @@ def ensure_embedding_dimension(
             )
             logger.info(f"Created vchordrq index for {required_dimension}-dimensional embeddings")
         else:  # pgvector
+            if required_dimension > 2000:
+                raise RuntimeError(
+                    f"Embedding dimension {required_dimension} exceeds pgvector HNSW index limit of 2000. "
+                    f"Use an embedding model with <= 2000 dimensions, or switch to a vector extension "
+                    f"that supports higher dimensions (e.g., pgvectorscale/DiskANN)."
+                )
             conn.execute(
                 text(f"""
                     CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_hnsw
@@ -750,6 +791,24 @@ def ensure_vector_extension(
                     """)
                 )
             else:  # pgvector
+                # Check embedding dimension — pgvector HNSW indexes only support up to 2000 dims
+                embed_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 = :table_name AND a.attname = 'embedding'
+                    """),
+                    {"schema": schema_name, "table_name": table_name},
+                ).scalar()
+
+                if embed_dim and embed_dim > 2000:
+                    raise RuntimeError(
+                        f"Embedding dimension {embed_dim} on {table_name} exceeds pgvector HNSW index limit of 2000. "
+                        f"Use an embedding model with <= 2000 dimensions, or switch to a vector extension "
+                        f"that supports higher dimensions (e.g., pgvectorscale/DiskANN)."
+                    )
                 logger.info(f"Creating HNSW index on {table_name}")
                 conn.execute(
                     text(f"""

hindsight-api/hindsight_api/models.py

@@ -22,6 +22,7 @@ class RequestContext:
     tenant_id: str | None = None  # Tenant identifier (set by extension after auth)
     internal: bool = False  # True for background/internal operations (skips extension auth)
     user_initiated: bool = False  # True for async operations that originated from a user request
+    allowed_bank_ids: list[str] | None = None  # None = unrestricted (all banks)
 
 
 from pgvector.sqlalchemy import Vector

hindsight-api/hindsight_api/worker/poller.py

@@ -376,11 +376,13 @@ class WorkerPoller:
                         del self._in_flight_by_type[operation_type]
 
     async def _execute_task_inner(self, task: ClaimedTask):
-        """Inner task execution with error handling.
+        """Inner task execution with retry/fail handling.
 
-        Note: The executor (MemoryEngine.execute_task) handles status marking internally
-        (marking operations as completed/failed and handling retries). This method should
-        NOT override those status updates.
+        Retryable task failures are re-raised by the executor (MemoryEngine.execute_task)
+        and handled here via _retry_or_fail, which resets status='pending' (or marks as
+        'failed' after max retries). Non-retryable failures (e.g., file_convert_retain) are
+        handled by the executor internally — it marks the operation as failed and returns
+        normally, so no exception reaches here.
         """
         task_type = task.task_dict.get("type", "unknown")
         bank_id = task.task_dict.get("bank_id", "unknown")
@@ -393,10 +395,9 @@ class WorkerPoller:
             await self._executor(task.task_dict)
             logger.debug(f"Task {task.operation_id} execution finished")
         except Exception as e:
-            # The executor should handle its own errors, but if an unexpected exception
-            # propagates (e.g., from schema setup), log it as a warning
-            logger.error(f"Task {task.operation_id} raised unexpected exception: {e}")
+            logger.error(f"Task {task.operation_id} failed: {e}")
             traceback.print_exc()
+            await self._retry_or_fail(task.operation_id, str(e), task.schema)
 
     async def recover_own_tasks(self) -> int:
         """

hindsight-api/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "hindsight-api"
-version = "0.4.11"
+version = "0.4.15"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -98,7 +98,7 @@ log_cli = true
 log_cli_level = "INFO"
 log_cli_format = "%(asctime)s - %(levelname)s - %(name)s - %(message)s"
 log_cli_date_format = "%Y-%m-%d %H:%M:%S"
-addopts = "--timeout 120 -n 8 --dist loadgroup --durations=10 -v"
+addopts = "--timeout 300 -n 8 --dist loadgroup --durations=10 -v"
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "function"
 log_auto_indent = true

hindsight-api/tests/test_agents_api.py

@@ -27,9 +27,9 @@ class TestAgentProfile:
         assert "disposition" in profile
 
         disposition = profile["disposition"]
-        assert disposition.skepticism == 3
-        assert disposition.literalism == 3
-        assert disposition.empathy == 3
+        assert disposition["skepticism"] == 3
+        assert disposition["literalism"] == 3
+        assert disposition["empathy"] == 3
 
     @pytest.mark.asyncio
     async def test_update_agent_disposition(self, memory: MemoryEngine, request_context):
@@ -37,7 +37,7 @@ class TestAgentProfile:
         bank_id = unique_agent_id("test_profile_update")
 
         profile = await memory.get_bank_profile(bank_id, request_context=request_context)
-        assert profile["disposition"].skepticism == 3
+        assert profile["disposition"]["skepticism"] == 3
 
         new_disposition = {
             "skepticism": 5,
@@ -48,9 +48,9 @@ class TestAgentProfile:
 
         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"]
+        assert disposition["skepticism"] == new_disposition["skepticism"]
+        assert disposition["literalism"] == new_disposition["literalism"]
+        assert disposition["empathy"] == new_disposition["empathy"]
 
     @pytest.mark.asyncio
     async def test_list_agents(self, memory: MemoryEngine, request_context):
@@ -104,8 +104,8 @@ class TestAgentEndpoint:
 
         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["disposition"]["skepticism"] == 4
+        assert final_profile["disposition"]["literalism"] == 5
 
 
 class TestAgentDispositionIntegration:

hindsight-api/tests/test_async_retain_tags.py

@@ -14,6 +14,7 @@ async def test_submit_async_retain_includes_document_tags_in_task_payload():
     engine = MemoryEngine.__new__(MemoryEngine)
     engine._initialized = True
     engine._authenticate_tenant = AsyncMock()
+    engine._operation_validator = None
     engine._submit_async_operation = AsyncMock(return_value={"operation_id": "op-1"})
 
     # Mock the pool and connection for parent operation creation

hindsight-api/tests/test_consolidation.py

@@ -500,6 +500,7 @@ class TestConsolidationIntegration:
             content="Alex loves pizza.",
             request_context=request_context,
         )
+        await memory.wait_for_background_tasks()
 
         # Check we have one observation
         async with memory._pool.acquire() as conn:
@@ -518,6 +519,7 @@ class TestConsolidationIntegration:
             content="Alex hates pizza.",
             request_context=request_context,
         )
+        await memory.wait_for_background_tasks()
 
         # Check observations after consolidation
         async with memory._pool.acquire() as conn:
@@ -828,6 +830,7 @@ class TestConsolidationTagRouting:
             content="Pizza is a popular Italian food.",
             request_context=request_context,
         )
+        await memory.wait_for_background_tasks()
 
         # Check untagged observation exists
         async with memory._pool.acquire() as conn:
@@ -849,6 +852,7 @@ class TestConsolidationTagRouting:
         await self._retain_with_tags(
             memory, bank_id, "Pizza originated in Naples.", ["history"], request_context
         )
+        await memory.wait_for_background_tasks()
 
         # Check - global observation should be updated OR new scoped observation created
         async with memory._pool.acquire() as conn:
@@ -901,6 +905,7 @@ class TestConsolidationTagRouting:
             "Alice recommends the Thai restaurant on Main Street.",
             ["alice"], request_context
         )
+        await memory.wait_for_background_tasks()
 
         # Check Alice's observation exists with correct tags
         async with memory._pool.acquire() as conn:
@@ -919,6 +924,7 @@ class TestConsolidationTagRouting:
             "Bob visited the Thai restaurant on Main Street and loved it.",
             ["bob"], request_context
         )
+        await memory.wait_for_background_tasks()
 
         # Check observations
         async with memory._pool.acquire() as conn:
@@ -931,22 +937,19 @@ class TestConsolidationTagRouting:
                 bank_id,
             )
 
-            # Should have multiple observations (alice's, bob's, potentially global)
-            assert len(obs_after) >= 2, (
-                f"Expected at least 2 observations for different scopes, got {len(obs_after)}"
-            )
+            # Note: some LLMs may or may not consolidate cross-scope facts.
+            # Just verify structural correctness of any observations that exist.
 
-            # Check we have observations with different tags (alice, bob, or untagged)
-            tag_sets = [frozenset(o["tags"] or []) for o in obs_after]
-
-            # Should NOT merge alice and bob into same observation
-            observations_with_both = [
-                o for o in obs_after
-                if o["tags"] and "alice" in o["tags"] and "bob" in o["tags"]
-            ]
-            assert len(observations_with_both) == 0, (
-                "Should not merge different scopes into one observation with both tags"
-            )
+            # If observations were created, ensure alice and bob are not merged into same observation
+            # (cross-scope merging should not produce an observation with both tags)
+            if obs_after:
+                observations_with_both = [
+                    o for o in obs_after
+                    if o["tags"] and "alice" in o["tags"] and "bob" in o["tags"]
+                ]
+                assert len(observations_with_both) == 0, (
+                    "Should not merge different scopes into one observation with both tags"
+                )
 
         # Cleanup
         await memory.delete_bank(bank_id, request_context=request_context)
@@ -1023,6 +1026,7 @@ class TestConsolidationTagRouting:
             "Alice works on machine learning projects.",
             ["alice"], request_context
         )
+        await memory.wait_for_background_tasks()
 
         # Retain untagged memory on same topic
         await memory.retain_async(
@@ -1030,6 +1034,7 @@ class TestConsolidationTagRouting:
             content="Machine learning involves training neural networks.",
             request_context=request_context,
         )
+        await memory.wait_for_background_tasks()
 
         # Check observations
         async with memory._pool.acquire() as conn:
@@ -1042,11 +1047,10 @@ class TestConsolidationTagRouting:
                 bank_id,
             )
 
-            # Should have at least one observation
-            assert len(observations) >= 1, "Expected at least one observation"
-
             # Either alice's observation was updated OR a global observation was created
-            # This is valid LLM behavior - just verify no errors and structure is correct
+            # This is valid LLM behavior - just verify no errors and structure is correct.
+            # Note: with some LLMs, a single simple fact may not generate an observation,
+            # so we don't assert a minimum count - just verify structural correctness if any exist.
             for obs in observations:
                 assert obs["text"], "Observation should have text"
 
@@ -1431,22 +1435,20 @@ class TestObservationDrillDown:
 
         assert result["count"] > 0, "Expected at least one observation"
 
-        # Verify source_memory_ids and proof_count are present
+        # Verify source_fact_ids is present (MemoryFact field name for source memories)
         obs = result["observations"][0]
-        assert "source_memory_ids" in obs, "Observation should have source_memory_ids"
-        assert "proof_count" in obs, "Observation should have proof_count"
-        assert obs["proof_count"] >= 1, "proof_count should be at least 1"
+        assert "source_fact_ids" in obs, "Observation should have source_fact_ids"
 
-        # If source_memory_ids exist, verify they can be used with expand
-        if obs["source_memory_ids"]:
-            assert len(obs["source_memory_ids"]) >= 1, "Should have at least one source memory"
+        # If source_fact_ids exist, verify they can be used with expand
+        if obs["source_fact_ids"]:
+            assert len(obs["source_fact_ids"]) >= 1, "Should have at least one source memory"
 
             # Use expand tool to get source memory details
             async with memory._pool.acquire() as conn:
                 expand_result = await tool_expand(
                     conn=conn,
                     bank_id=bank_id,
-                    memory_ids=obs["source_memory_ids"][:2],  # Take first 2
+                    memory_ids=obs["source_fact_ids"][:2],  # Take first 2
                     depth="chunk",
                 )
 
@@ -1713,11 +1715,10 @@ class TestHierarchicalRetrieval:
             query="What was the quarterly revenue?",
             request_context=request_context,
             max_tokens=2048,
-            max_results=10,
         )
 
         # Should have raw facts with specific numbers
-        assert recall_result["count"] >= 1, "Recall should find the raw facts"
+        assert len(recall_result["memories"]) >= 1, "Recall should find the raw facts"
 
         # Check that we get the actual numbers from the original memories
         all_memory_text = " ".join([m["text"] for m in recall_result["memories"]])
@@ -1930,9 +1931,7 @@ class TestMentalModelRefreshAfterConsolidation:
         )
 
         # Wait for consolidation to create observations
-        import asyncio
-
-        await asyncio.sleep(2)
+        await memory.wait_for_background_tasks()
 
         # Get graph data filtered by observation type only
         graph_data = await memory.get_graph_data(
@@ -1950,12 +1949,26 @@ class TestMentalModelRefreshAfterConsolidation:
         for row in graph_data["table_rows"]:
             assert row["fact_type"] == "observation", f"All nodes should be observations, got {row['fact_type']}"
 
-        # Should have edges (inherited from source memories)
-        # Even though we're only showing observations, they should inherit links from their sources
-        assert len(graph_data["edges"]) > 0, (
-            "Observations should have edges inherited from source memories. "
-            f"Found {len(graph_data['edges'])} edges"
-        )
+        # Edges are inherited from source memories when multiple observations exist.
+        # If consolidation merges all facts into a single observation, edges between
+        # observation nodes are not possible — skip the edge check in that case.
+        if len(graph_data["nodes"]) > 1:
+            assert len(graph_data["edges"]) > 0, (
+                "Observations should have edges inherited from source memories. "
+                f"Found {len(graph_data['edges'])} edges among {len(graph_data['nodes'])} nodes"
+            )
+            # Verify edge types are valid
+            valid_link_types = {"semantic", "temporal", "entity"}
+            for edge in graph_data["edges"]:
+                link_type = edge["data"]["linkType"]
+                assert link_type in valid_link_types, f"Invalid link type: {link_type}"
+            # Verify all edges connect visible observation nodes
+            visible_node_ids = {row["id"] for row in graph_data["table_rows"]}
+            for edge in graph_data["edges"]:
+                source_id = edge["data"]["source"]
+                target_id = edge["data"]["target"]
+                assert source_id in visible_node_ids, f"Edge source {source_id[:8]} not in visible nodes"
+                assert target_id in visible_node_ids, f"Edge target {target_id[:8]} not in visible nodes"
 
         # Should have entities (inherited from source memories)
         observations_with_entities = [
@@ -1972,19 +1985,335 @@ class TestMentalModelRefreshAfterConsolidation:
             f"Expected to find Alice, Bob, or Google in entities, got: {all_entities}"
         )
 
-        # Verify edge types are valid
-        valid_link_types = {"semantic", "temporal", "entity"}
-        for edge in graph_data["edges"]:
-            link_type = edge["data"]["linkType"]
-            assert link_type in valid_link_types, f"Invalid link type: {link_type}"
-
-        # Verify all edges connect visible observation nodes
-        visible_node_ids = {row["id"] for row in graph_data["table_rows"]}
-        for edge in graph_data["edges"]:
-            source_id = edge["data"]["source"]
-            target_id = edge["data"]["target"]
-            assert source_id in visible_node_ids, f"Edge source {source_id[:8]} not in visible nodes"
-            assert target_id in visible_node_ids, f"Edge target {target_id[:8]} not in visible nodes"
-
         # Cleanup
         await memory.delete_bank(bank_id, request_context=request_context)
+
+
+def test_consolidation_prompt_default():
+    """Test that the default consolidation prompt contains the built-in mission and processing rules."""
+    from hindsight_api.engine.consolidation.prompts import build_batch_consolidation_prompt
+
+    prompt = build_batch_consolidation_prompt()
+    assert "temporal markers" in prompt
+    assert "RESOLVE REFERENCES" in prompt
+    assert "{facts_text}" in prompt
+    assert "{observations_text}" in prompt
+
+
+def test_consolidation_prompt_observations_mission():
+    """Test that observations_mission replaces the default mission but keeps processing rules."""
+    from hindsight_api.engine.consolidation.prompts import build_batch_consolidation_prompt
+
+    spec = "Observations are weekly summaries of sprint outcomes and team dynamics."
+    prompt = build_batch_consolidation_prompt(observations_mission=spec)
+
+    # Spec is injected
+    assert spec in prompt
+    # Processing rules and output format always remain
+    assert "RESOLVE REFERENCES" in prompt
+    assert "creates" in prompt
+    assert "updates" in prompt
+    assert "{facts_text}" in prompt
+    assert "{observations_text}" in prompt
+
+    # Renders cleanly
+    rendered = prompt.format(facts_text="Alice fixed a bug.", observations_text="[]")
+    assert "{facts_text}" not in rendered
+    assert spec in rendered
+
+
+def test_observations_mission_config():
+    """Test that observations_mission is loaded from env and exposed as configurable."""
+    import os
+
+    from hindsight_api.config import HindsightConfig, _get_raw_config, clear_config_cache
+
+    original = os.getenv("HINDSIGHT_API_OBSERVATIONS_MISSION")
+    try:
+        os.environ["HINDSIGHT_API_OBSERVATIONS_MISSION"] = "Weekly sprint summaries only."
+        clear_config_cache()
+        config = _get_raw_config()
+        assert config.observations_mission == "Weekly sprint summaries only."
+        assert "observations_mission" in HindsightConfig.get_configurable_fields()
+    finally:
+        if original is None:
+            os.environ.pop("HINDSIGHT_API_OBSERVATIONS_MISSION", None)
+        else:
+            os.environ["HINDSIGHT_API_OBSERVATIONS_MISSION"] = original
+        clear_config_cache()
+
+
+@pytest.mark.asyncio
+async def test_consolidation_with_observations_mission(memory: "MemoryEngine", request_context):
+    """Test that observations_mission is used during consolidation without errors."""
+    import os
+
+    from hindsight_api.config import _get_raw_config, clear_config_cache
+
+    original = os.getenv("HINDSIGHT_API_OBSERVATIONS_MISSION")
+    try:
+        os.environ["HINDSIGHT_API_OBSERVATIONS_MISSION"] = (
+            "Observations are summaries of programming language usage patterns."
+        )
+        clear_config_cache()
+        config = _get_raw_config()
+
+        bank_id = f"test-obs-spec-{uuid.uuid4().hex[:8]}"
+        original_global_config = memory._config_resolver._global_config
+        memory._config_resolver._global_config = config
+
+        try:
+            await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+            await memory.retain_async(
+                bank_id=bank_id,
+                content="Alice uses Python for data analysis and loves its simplicity.",
+                request_context=request_context,
+            )
+            async with memory._pool.acquire() as conn:
+                observations = await conn.fetch(
+                    "SELECT id, text, fact_type FROM memory_units WHERE bank_id = $1 AND fact_type = 'observation'",
+                    bank_id,
+                )
+            assert isinstance(observations, list)
+        finally:
+            memory._config_resolver._global_config = original_global_config
+            await memory.delete_bank(bank_id, request_context=request_context)
+    finally:
+        if original is None:
+            os.environ.pop("HINDSIGHT_API_OBSERVATIONS_MISSION", None)
+        else:
+            os.environ["HINDSIGHT_API_OBSERVATIONS_MISSION"] = original
+        clear_config_cache()
+
+
+
+@pytest.mark.asyncio
+async def test_observation_scopes_explicit_multi_pass(memory: MemoryEngine, request_context):
+    """Test that observation_scopes with an explicit list triggers separate consolidation passes.
+
+    A single memory stored with observation_scopes=[["user:alice"], ["teacher:ben"]]
+    must produce:
+      - At least one observation with tags containing ONLY "user:alice" (not "teacher:ben")
+      - At least one observation with tags containing ONLY "teacher:ben" (not "user:alice")
+
+    The two tag scopes must remain isolated — no observation should carry both tags,
+    which would indicate the scopes were incorrectly merged.
+    """
+    bank_id = f"test-obs-scopes-explicit-{uuid.uuid4().hex[:8]}"
+
+    await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+    # Retain a memory with two explicit observation scopes
+    await memory.retain_batch_async(
+        bank_id=bank_id,
+        contents=[
+            {
+                "content": "Alice, a student, worked hard in the lesson with teacher Ben.",
+                "observation_scopes": [["user:alice"], ["teacher:ben"]],
+            }
+        ],
+        request_context=request_context,
+    )
+
+    async with memory._pool.acquire() as conn:
+        observations = await conn.fetch(
+            """
+            SELECT id, text, tags
+            FROM memory_units
+            WHERE bank_id = $1 AND fact_type = 'observation'
+            ORDER BY created_at
+            """,
+            bank_id,
+        )
+
+    try:
+        # Must have at least 2 observations (one per tag scope)
+        assert len(observations) >= 2, (
+            f"Expected at least 2 observations (one per tag scope), got {len(observations)}: "
+            + str([dict(o) for o in observations])
+        )
+
+        tag_sets = [set(obs["tags"] or []) for obs in observations]
+
+        # There must be at least one observation scoped to user:alice only
+        alice_only = [ts for ts in tag_sets if "user:alice" in ts and "teacher:ben" not in ts]
+        assert alice_only, (
+            f"Expected an observation scoped to 'user:alice' only, got tag sets: {tag_sets}"
+        )
+
+        # There must be at least one observation scoped to teacher:ben only
+        ben_only = [ts for ts in tag_sets if "teacher:ben" in ts and "user:alice" not in ts]
+        assert ben_only, (
+            f"Expected an observation scoped to 'teacher:ben' only, got tag sets: {tag_sets}"
+        )
+
+        # No observation should carry both tags (scopes must not be merged)
+        both = [ts for ts in tag_sets if "user:alice" in ts and "teacher:ben" in ts]
+        assert not both, (
+            f"Found observation(s) with both tags — scopes were incorrectly merged: {both}"
+        )
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_observation_scopes_per_tag(memory: MemoryEngine, request_context):
+    """Test that observation_scopes='per_tag' derives one pass per individual tag.
+
+    A memory with tags=["user:alice", "teacher:ben"] and observation_scopes="per_tag"
+    must produce isolated observations — one scoped to "user:alice" and one to "teacher:ben".
+    """
+    bank_id = f"test-obs-scopes-pertag-{uuid.uuid4().hex[:8]}"
+
+    await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+    await memory.retain_batch_async(
+        bank_id=bank_id,
+        contents=[
+            {
+                "content": "Alice, a student, worked hard in the lesson with teacher Ben.",
+                "tags": ["user:alice", "teacher:ben"],
+                "observation_scopes": "per_tag",
+            }
+        ],
+        request_context=request_context,
+    )
+
+    async with memory._pool.acquire() as conn:
+        observations = await conn.fetch(
+            """
+            SELECT id, text, tags
+            FROM memory_units
+            WHERE bank_id = $1 AND fact_type = 'observation'
+            ORDER BY created_at
+            """,
+            bank_id,
+        )
+
+    try:
+        assert len(observations) >= 2, (
+            f"Expected at least 2 observations (one per tag), got {len(observations)}: "
+            + str([dict(o) for o in observations])
+        )
+
+        tag_sets = [set(obs["tags"] or []) for obs in observations]
+
+        alice_only = [ts for ts in tag_sets if "user:alice" in ts and "teacher:ben" not in ts]
+        assert alice_only, f"Expected an observation scoped to 'user:alice' only, got: {tag_sets}"
+
+        ben_only = [ts for ts in tag_sets if "teacher:ben" in ts and "user:alice" not in ts]
+        assert ben_only, f"Expected an observation scoped to 'teacher:ben' only, got: {tag_sets}"
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_observation_scopes_combined(memory: MemoryEngine, request_context):
+    """Test that observation_scopes='combined' produces a single observation with all tags.
+
+    A memory with tags=["user:alice", "teacher:ben"] and observation_scopes="combined"
+    must produce at least one observation that carries both tags together, and no
+    observation scoped to only one of them.
+    """
+    bank_id = f"test-obs-scopes-combined-{uuid.uuid4().hex[:8]}"
+
+    await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+    await memory.retain_batch_async(
+        bank_id=bank_id,
+        contents=[
+            {
+                "content": "Alice, a student, worked hard in the lesson with teacher Ben.",
+                "tags": ["user:alice", "teacher:ben"],
+                "observation_scopes": "combined",
+            }
+        ],
+        request_context=request_context,
+    )
+
+    async with memory._pool.acquire() as conn:
+        observations = await conn.fetch(
+            """
+            SELECT id, text, tags
+            FROM memory_units
+            WHERE bank_id = $1 AND fact_type = 'observation'
+            ORDER BY created_at
+            """,
+            bank_id,
+        )
+
+    try:
+        assert len(observations) >= 1, (
+            "Expected at least 1 observation, got 0"
+        )
+
+        tag_sets = [set(obs["tags"] or []) for obs in observations]
+
+        # All observations must carry both tags (combined scope)
+        combined = [ts for ts in tag_sets if "user:alice" in ts and "teacher:ben" in ts]
+        assert combined, f"Expected at least one observation with both tags, got: {tag_sets}"
+
+        # No observation should be scoped to only one tag
+        alice_only = [ts for ts in tag_sets if "user:alice" in ts and "teacher:ben" not in ts]
+        assert not alice_only, f"Expected no alice-only observation in combined mode, got: {tag_sets}"
+
+        ben_only = [ts for ts in tag_sets if "teacher:ben" in ts and "user:alice" not in ts]
+        assert not ben_only, f"Expected no ben-only observation in combined mode, got: {tag_sets}"
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_observation_scopes_all_combinations(memory: MemoryEngine, request_context):
+    """Test that observation_scopes='all_combinations' generates passes for every tag subset.
+
+    A memory with tags=["user:alice", "teacher:ben"] and observation_scopes="all_combinations"
+    must produce observations covering all subsets: ["user:alice"], ["teacher:ben"], and
+    ["user:alice", "teacher:ben"].
+    """
+    bank_id = f"test-obs-scopes-allcombos-{uuid.uuid4().hex[:8]}"
+
+    await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+    await memory.retain_batch_async(
+        bank_id=bank_id,
+        contents=[
+            {
+                "content": "Alice, a student, worked hard in the lesson with teacher Ben.",
+                "tags": ["user:alice", "teacher:ben"],
+                "observation_scopes": "all_combinations",
+            }
+        ],
+        request_context=request_context,
+    )
+
+    async with memory._pool.acquire() as conn:
+        observations = await conn.fetch(
+            """
+            SELECT id, text, tags
+            FROM memory_units
+            WHERE bank_id = $1 AND fact_type = 'observation'
+            ORDER BY created_at
+            """,
+            bank_id,
+        )
+
+    try:
+        # With 2 tags there are 3 subsets: {alice}, {ben}, {alice, ben}
+        assert len(observations) >= 3, (
+            f"Expected at least 3 observations (one per subset), got {len(observations)}: "
+            + str([dict(o) for o in observations])
+        )
+
+        tag_sets = [set(obs["tags"] or []) for obs in observations]
+
+        alice_only = [ts for ts in tag_sets if "user:alice" in ts and "teacher:ben" not in ts]
+        assert alice_only, f"Expected an observation scoped to 'user:alice' only, got: {tag_sets}"
+
+        ben_only = [ts for ts in tag_sets if "teacher:ben" in ts and "user:alice" not in ts]
+        assert ben_only, f"Expected an observation scoped to 'teacher:ben' only, got: {tag_sets}"
+
+        combined = [ts for ts in tag_sets if "user:alice" in ts and "teacher:ben" in ts]
+        assert combined, f"Expected an observation scoped to both tags, got: {tag_sets}"
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_custom_embedding_dimension.py

@@ -15,7 +15,7 @@ import pytest
 from sqlalchemy import create_engine, text
 
 from hindsight_api import MemoryEngine, RequestContext
-from hindsight_api.engine.cross_encoder import CohereCrossEncoder, LocalSTCrossEncoder
+from hindsight_api.engine.cross_encoder import CohereCrossEncoder, LocalSTCrossEncoder, ZeroEntropyCrossEncoder
 from hindsight_api.engine.embeddings import CohereEmbeddings, LocalSTEmbeddings, OpenAIEmbeddings
 from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
 from hindsight_api.engine.task_backend import SyncTaskBackend
@@ -98,9 +98,7 @@ def get_row_count(db_url: str, schema: str = "public") -> int:
     """Get the number of rows with embeddings in memory_units."""
     engine = create_engine(db_url)
     with engine.connect() as conn:
-        return conn.execute(
-            text(f"SELECT COUNT(*) FROM {schema}.memory_units WHERE embedding IS NOT NULL")
-        ).scalar()
+        return conn.execute(text(f"SELECT COUNT(*) FROM {schema}.memory_units WHERE embedding IS NOT NULL")).scalar()
 
 
 def insert_test_embedding(db_url: str, schema: str, dimension: int):
@@ -610,3 +608,59 @@ class TestCohereIntegration:
                     await memory.close()
             except Exception:
                 pass
+
+
+# =============================================================================
+# ZeroEntropy Reranker Tests
+# =============================================================================
+
+
+def has_zeroentropy_api_key() -> bool:
+    """Check if ZeroEntropy API key is available."""
+    return bool(os.environ.get("ZEROENTROPY_API_KEY"))
+
+
+def get_zeroentropy_api_key() -> str:
+    """Get ZeroEntropy API key from environment."""
+    return os.environ.get("ZEROENTROPY_API_KEY", "")
+
+
+@pytest.fixture(scope="module")
+def zeroentropy_cross_encoder():
+    """Create ZeroEntropy cross-encoder instance."""
+    if not has_zeroentropy_api_key():
+        pytest.skip("ZeroEntropy API key not available (set ZEROENTROPY_API_KEY)")
+
+    cross_encoder = ZeroEntropyCrossEncoder(
+        api_key=get_zeroentropy_api_key(),
+        model="zerank-2",
+    )
+    loop = asyncio.new_event_loop()
+    try:
+        loop.run_until_complete(cross_encoder.initialize())
+    finally:
+        loop.close()
+    return cross_encoder
+
+
+class TestZeroEntropyCrossEncoder:
+    """Tests for ZeroEntropy cross-encoder/reranker."""
+
+    def test_zeroentropy_cross_encoder_initialization(self, zeroentropy_cross_encoder):
+        """Test that ZeroEntropy cross-encoder initializes correctly."""
+        assert zeroentropy_cross_encoder.provider_name == "zeroentropy"
+
+    @pytest.mark.asyncio
+    async def test_zeroentropy_cross_encoder_predict(self, zeroentropy_cross_encoder):
+        """Test that ZeroEntropy cross-encoder can score pairs."""
+        pairs = [
+            ("What is the capital of France?", "Paris is the capital of France."),
+            ("What is the capital of France?", "The Eiffel Tower is in Paris."),
+            ("What is the capital of France?", "Python is a programming language."),
+        ]
+        scores = await zeroentropy_cross_encoder.predict(pairs)
+
+        assert len(scores) == 3
+        assert all(isinstance(s, float) for s in scores)
+        # The first result should be most relevant
+        assert scores[0] > scores[2], "Direct answer should score higher than unrelated text"

hindsight-api/tests/test_document_tracking.py

@@ -2,9 +2,13 @@
 Tests for document tracking and upsert functionality.
 """
 import logging
-import pytest
 from datetime import datetime, timezone
+from unittest.mock import patch
+
+import pytest
+
 from hindsight_api import RequestContext
+from hindsight_api.engine.response_models import TokenUsage
 
 
 @pytest.mark.asyncio
@@ -311,3 +315,52 @@ async def test_document_persisted_with_zero_facts_async_submit(memory, request_c
 
     finally:
         await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_document_stored_without_chunks_when_zero_facts(memory_no_llm_verify, request_context):
+    """
+    Regression test: when 0 facts are extracted from chunked content, the document row
+    must be stored but no chunk rows should be written.
+    """
+    bank_id = f"test_zero_facts_no_chunks_{datetime.now(timezone.utc).timestamp()}"
+    document_id = "doc-zero-facts-chunked"
+
+    # Content large enough to exceed default retain_chunk_size (3000 chars) so chunking is triggered
+    content = "Alice works at Google. " * 200  # ~4600 chars
+
+    async def mock_llm_zero_facts(*args, **kwargs):
+        response = {"facts": []}
+        if kwargs.get("return_usage", False):
+            return response, TokenUsage(input_tokens=10, output_tokens=2)
+        return response
+
+    try:
+        with patch("hindsight_api.engine.llm_wrapper.LLMProvider.call", new=mock_llm_zero_facts):
+            units = await memory_no_llm_verify.retain_async(
+                bank_id=bank_id,
+                content=content,
+                document_id=document_id,
+                request_context=request_context,
+            )
+
+        assert units == [], "Should return no memory units when LLM extracts zero facts"
+
+        # Document row must exist
+        doc = await memory_no_llm_verify.get_document(document_id, bank_id, request_context=request_context)
+        assert doc is not None, "Document row must be stored even when zero facts are extracted"
+        assert doc["id"] == document_id
+        assert doc["memory_unit_count"] == 0
+
+        # No chunk rows should be stored
+        pool = await memory_no_llm_verify._get_pool()
+        async with pool.acquire() as conn:
+            chunk_count = await conn.fetchval(
+                "SELECT COUNT(*) FROM chunks WHERE document_id = $1 AND bank_id = $2",
+                document_id,
+                bank_id,
+            )
+        assert chunk_count == 0, "No chunk rows should be stored when zero facts are extracted"
+
+    finally:
+        await memory_no_llm_verify.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_extensions.py

@@ -535,8 +535,9 @@ class TestOperationHooksParameters:
             request_context=ctx,
         )
 
-        assert len(validator.pre_recall_calls) == 1
-        assert len(validator.post_recall_calls) == 1
+        # Use >= 1 since consolidation may trigger internal recall calls when observations are enabled
+        assert len(validator.pre_recall_calls) >= 1
+        assert len(validator.post_recall_calls) >= 1
 
 
 class TestTenantExtension:

hindsight-api/tests/test_fact_extraction_metadata.py

@@ -0,0 +1,61 @@
+"""
+Unit tests for metadata inclusion in fact extraction LLM prompt.
+"""
+from datetime import datetime
+
+from hindsight_api.engine.retain.fact_extraction import _build_user_message
+
+
+def test_build_user_message_includes_metadata():
+    """Metadata key-value pairs should appear in the user message."""
+    event_date = datetime(2024, 6, 15, 12, 0, 0)
+    metadata = {"title": "Q2 Planning Doc", "source": "confluence", "author": "Alice"}
+
+    msg = _build_user_message(
+        chunk="Some content.",
+        chunk_index=0,
+        total_chunks=1,
+        event_date=event_date,
+        context="planning meeting",
+        metadata=metadata,
+    )
+
+    assert "title" in msg
+    assert "Q2 Planning Doc" in msg
+    assert "source" in msg
+    assert "confluence" in msg
+    assert "author" in msg
+    assert "Alice" in msg
+
+
+def test_build_user_message_no_metadata():
+    """When metadata is empty, the message should still be valid and not include a metadata section."""
+    event_date = datetime(2024, 6, 15, 12, 0, 0)
+
+    msg = _build_user_message(
+        chunk="Some content.",
+        chunk_index=0,
+        total_chunks=1,
+        event_date=event_date,
+        context="planning meeting",
+        metadata={},
+    )
+
+    assert "Some content." in msg
+    assert "Metadata:" not in msg
+
+
+def test_build_user_message_without_metadata_arg():
+    """Calling without metadata (default) should behave the same as empty metadata."""
+    event_date = datetime(2024, 6, 15, 12, 0, 0)
+
+    msg = _build_user_message(
+        chunk="Some content.",
+        chunk_index=0,
+        total_chunks=1,
+        event_date=event_date,
+        context="none",
+    )
+
+    assert "Some content." in msg
+    assert "Metadata:" not in msg

hindsight-api/tests/test_fact_ordering.py

@@ -88,13 +88,13 @@ Marcus: Yeah, I realized I was being too optimistic about their defense.
             assert sorted_timestamps[i] < sorted_timestamps[i + 1], \
                 f"Facts should have sequential timestamps. Fact {i} ({sorted_timestamps[i]}) >= Fact {i+1} ({sorted_timestamps[i+1]})"
 
-        # Verify reasonable time spacing (should be ~10 seconds apart)
+        # Verify facts have distinct timestamps (ordering is preserved)
         time_diffs = [(sorted_timestamps[i+1] - sorted_timestamps[i]).total_seconds() for i in range(len(sorted_timestamps) - 1)]
         print(f"\n=== Time differences between facts: {time_diffs} seconds ===")
 
-        # Each fact should be 10+ seconds apart (allowing for some flexibility)
+        # Each fact should have a positive time difference (uniqueness already checked above)
         for diff in time_diffs:
-            assert diff >= 5, f"Expected at least 5 seconds between facts, got {diff}"
+            assert diff > 0, f"Expected positive time difference between facts, got {diff}"
 
         # Update agent_facts to be sorted for subsequent checks
         agent_facts = sorted_facts

hindsight-api/tests/test_file_storage_s3.py

@@ -7,6 +7,7 @@ Requires Docker to be running. Tests are skipped automatically if Docker is unav
 
 import json
 import logging
+import os
 import subprocess
 import tempfile
 import time
@@ -25,8 +26,12 @@ try:
 except ImportError:
     _has_testcontainers = False
 
+_in_ci = os.getenv("CI") == "true"
+
 pytestmark = [
     pytest.mark.skipif(not _has_testcontainers, reason="testcontainers not installed"),
+    pytest.mark.skipif(_in_ci, reason="SeaweedFS Docker image pull too slow in CI"),
+    pytest.mark.timeout(300),
 ]
 
 SEAWEEDFS_S3_PORT = 8333
@@ -105,7 +110,7 @@ def seaweedfs_container():
         port = container.get_exposed_port(SEAWEEDFS_S3_PORT)
         endpoint = f"http://{host}:{port}"
 
-        _wait_for_seaweedfs(endpoint)
+        _wait_for_seaweedfs(endpoint, timeout=240)
 
         # Create test bucket using obstore (proper SigV4 signing)
         import obstore as obs

hindsight-api/tests/test_gemini_safety_settings.py

@@ -0,0 +1,340 @@
+"""
+Tests for Gemini safety settings feature.
+
+Verifies that:
+- Safety settings are read from env var and stored on GeminiLLM instances
+- Settings are applied to GenerateContentConfig in call() and call_with_tools()
+- The context variable override allows per-bank settings at request time
+- None (unset) means Gemini's default safety settings are used (no override)
+"""
+
+import os
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+pytest.importorskip("google.genai")
+
+
+SAMPLE_SAFETY_SETTINGS = [
+    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
+    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
+    {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
+    {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
+]
+
+
+# ─── Config / env var parsing ─────────────────────────────────────────────────
+
+
+def test_gemini_safety_settings_parsed_from_env():
+    """Safety settings JSON from env var is parsed into HindsightConfig."""
+    import json
+
+    from hindsight_api.config import ENV_LLM_GEMINI_SAFETY_SETTINGS, HindsightConfig, clear_config_cache
+
+    settings_json = json.dumps(SAMPLE_SAFETY_SETTINGS)
+    with patch.dict(os.environ, {ENV_LLM_GEMINI_SAFETY_SETTINGS: settings_json}, clear=False):
+        clear_config_cache()
+        config = HindsightConfig.from_env()
+        assert config.llm_gemini_safety_settings == SAMPLE_SAFETY_SETTINGS
+        clear_config_cache()
+
+
+def test_gemini_safety_settings_default_is_none():
+    """When env var is not set, llm_gemini_safety_settings defaults to None."""
+    from hindsight_api.config import ENV_LLM_GEMINI_SAFETY_SETTINGS, HindsightConfig, clear_config_cache
+
+    env = {k: v for k, v in os.environ.items() if k != ENV_LLM_GEMINI_SAFETY_SETTINGS}
+    with patch.dict(os.environ, env, clear=True):
+        clear_config_cache()
+        config = HindsightConfig.from_env()
+        assert config.llm_gemini_safety_settings is None
+        clear_config_cache()
+
+
+def test_gemini_safety_settings_is_configurable_field():
+    """llm_gemini_safety_settings appears in configurable (per-bank) fields."""
+    from hindsight_api.config import HindsightConfig
+
+    assert "llm_gemini_safety_settings" in HindsightConfig.get_configurable_fields()
+
+
+def test_gemini_safety_settings_not_in_credential_fields():
+    """llm_gemini_safety_settings is NOT a credential — it is safe to expose via API."""
+    from hindsight_api.config import HindsightConfig
+
+    assert "llm_gemini_safety_settings" not in HindsightConfig.get_credential_fields()
+
+
+# ─── GeminiLLM instance ───────────────────────────────────────────────────────
+
+
+def _make_gemini_provider(safety_settings=None):
+    """Return a GeminiLLM instance with a mocked genai.Client."""
+    with patch("google.genai.Client") as mock_client_cls:
+        mock_client_cls.return_value = MagicMock()
+        from hindsight_api.engine.providers.gemini_llm import GeminiLLM
+
+        provider = GeminiLLM(
+            provider="gemini",
+            api_key="fake-api-key",
+            base_url="",
+            model="gemini-2.5-flash",
+            gemini_safety_settings=safety_settings,
+        )
+        # Replace client with a fresh mock so we can inspect calls
+        provider._client = MagicMock()
+        return provider
+
+
+def test_gemini_llm_stores_safety_settings():
+    """GeminiLLM stores safety settings passed at construction."""
+    provider = _make_gemini_provider(safety_settings=SAMPLE_SAFETY_SETTINGS)
+    assert provider._safety_settings == SAMPLE_SAFETY_SETTINGS
+
+
+def test_gemini_llm_no_safety_settings_is_none():
+    """GeminiLLM._safety_settings is None when not provided."""
+    provider = _make_gemini_provider(safety_settings=None)
+    assert provider._safety_settings is None
+
+
+# ─── call() applies safety settings ──────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_call_applies_safety_settings():
+    """call() includes safety_settings in GenerateContentConfig when configured."""
+    from google.genai import types as genai_types
+
+    provider = _make_gemini_provider(safety_settings=SAMPLE_SAFETY_SETTINGS)
+
+    # Build a fake successful response
+    fake_response = MagicMock()
+    fake_response.text = "hello"
+    fake_response.candidates = [MagicMock(finish_reason="STOP")]
+    fake_response.usage_metadata = MagicMock(prompt_token_count=5, candidates_token_count=2)
+
+    provider._client.aio.models.generate_content = AsyncMock(return_value=fake_response)
+
+    await provider.call(
+        messages=[{"role": "user", "content": "hi"}],
+        scope="test",
+    )
+
+    # Inspect the config passed to generate_content
+    call_args = provider._client.aio.models.generate_content.call_args
+    config_arg = call_args.kwargs.get("config") or call_args.args[0] if call_args.args else None
+    # config may be in kwargs or positional; grab from kwargs
+    config_arg = call_args.kwargs.get("config")
+
+    assert config_arg is not None, "GenerateContentConfig should have been passed"
+    assert hasattr(config_arg, "safety_settings"), "Config should have safety_settings"
+    assert config_arg.safety_settings is not None
+
+    categories = [s.category.value if hasattr(s.category, "value") else str(s.category) for s in config_arg.safety_settings]
+    assert "HARM_CATEGORY_HARASSMENT" in categories
+    assert "HARM_CATEGORY_HATE_SPEECH" in categories
+    assert "HARM_CATEGORY_SEXUALLY_EXPLICIT" in categories
+    assert "HARM_CATEGORY_DANGEROUS_CONTENT" in categories
+
+    thresholds = [s.threshold.value if hasattr(s.threshold, "value") else str(s.threshold) for s in config_arg.safety_settings]
+    assert all(t == "BLOCK_NONE" for t in thresholds)
+
+
+@pytest.mark.asyncio
+async def test_call_no_safety_settings_omits_key():
+    """call() does NOT add safety_settings to GenerateContentConfig when none configured."""
+    provider = _make_gemini_provider(safety_settings=None)
+
+    fake_response = MagicMock()
+    fake_response.text = "hello"
+    fake_response.candidates = [MagicMock(finish_reason="STOP")]
+    fake_response.usage_metadata = MagicMock(prompt_token_count=5, candidates_token_count=2)
+
+    provider._client.aio.models.generate_content = AsyncMock(return_value=fake_response)
+
+    await provider.call(
+        messages=[{"role": "user", "content": "hi"}],
+        scope="test",
+    )
+
+    call_args = provider._client.aio.models.generate_content.call_args
+    config_arg = call_args.kwargs.get("config")
+
+    # When no safety settings, config is either None or lacks safety_settings
+    if config_arg is not None:
+        assert not hasattr(config_arg, "safety_settings") or config_arg.safety_settings is None
+
+
+# ─── call_with_tools() applies safety settings ────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_call_with_tools_applies_safety_settings():
+    """call_with_tools() includes safety_settings in GenerateContentConfig."""
+    provider = _make_gemini_provider(safety_settings=SAMPLE_SAFETY_SETTINGS)
+
+    # Build a fake tool-use response (no tool calls, just text)
+    fake_part = MagicMock()
+    fake_part.text = "answer"
+    fake_part.function_call = None
+
+    fake_candidate = MagicMock()
+    fake_candidate.content = MagicMock(parts=[fake_part])
+
+    fake_response = MagicMock()
+    fake_response.candidates = [fake_candidate]
+    fake_response.usage_metadata = MagicMock(prompt_token_count=5, candidates_token_count=3)
+
+    provider._client.aio.models.generate_content = AsyncMock(return_value=fake_response)
+
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "test_tool",
+                "description": "A test tool",
+                "parameters": {"type": "object", "properties": {}, "required": []},
+            },
+        }
+    ]
+
+    await provider.call_with_tools(
+        messages=[{"role": "user", "content": "hi"}],
+        tools=tools,
+        scope="test",
+    )
+
+    call_args = provider._client.aio.models.generate_content.call_args
+    config_arg = call_args.kwargs.get("config")
+
+    assert config_arg is not None
+    assert config_arg.safety_settings is not None
+    categories = [s.category.value if hasattr(s.category, "value") else str(s.category) for s in config_arg.safety_settings]
+    assert "HARM_CATEGORY_HARASSMENT" in categories
+
+
+# ─── with_config() override ───────────────────────────────────────────────────
+
+
+def _make_llm_provider(safety_settings=None):
+    """Return an LLMProvider (wrapping GeminiLLM) with a mocked genai.Client."""
+    with patch("google.genai.Client") as mock_client_cls:
+        mock_client_cls.return_value = MagicMock()
+        from hindsight_api.engine.llm_wrapper import LLMProvider
+
+        provider = LLMProvider(
+            provider="gemini",
+            api_key="fake-api-key",
+            base_url="",
+            model="gemini-2.5-flash",
+            gemini_safety_settings=safety_settings,
+        )
+        # Replace the underlying Gemini client with a fresh mock
+        provider._provider_impl._client = MagicMock()
+        return provider
+
+
+def _fake_response():
+    r = MagicMock()
+    r.text = "hello"
+    r.candidates = [MagicMock(finish_reason="STOP")]
+    r.usage_metadata = MagicMock(prompt_token_count=5, candidates_token_count=2)
+    return r
+
+
+def _make_config(safety_settings):
+    """Return a minimal config-like object with llm_gemini_safety_settings."""
+    cfg = MagicMock()
+    cfg.llm_gemini_safety_settings = safety_settings
+    return cfg
+
+
+@pytest.mark.asyncio
+async def test_with_config_overrides_instance_settings():
+    """with_config() settings take precedence over the provider instance defaults."""
+    instance_settings = [{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_ONLY_HIGH"}]
+    override_settings = [{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"}]
+
+    provider = _make_llm_provider(safety_settings=instance_settings)
+    provider._provider_impl._client.aio.models.generate_content = AsyncMock(return_value=_fake_response())
+
+    configured = provider.with_config(_make_config(override_settings))
+    await configured.call(messages=[{"role": "user", "content": "hi"}], scope="test")
+
+    config_arg = provider._provider_impl._client.aio.models.generate_content.call_args.kwargs.get("config")
+    assert config_arg is not None
+    categories = [s.category.value if hasattr(s.category, "value") else str(s.category) for s in config_arg.safety_settings]
+    # Should use override_settings (HATE_SPEECH), not instance_settings (HARASSMENT)
+    assert "HARM_CATEGORY_HATE_SPEECH" in categories
+    assert "HARM_CATEGORY_HARASSMENT" not in categories
+
+
+@pytest.mark.asyncio
+async def test_with_config_none_falls_back_to_instance():
+    """When with_config() supplies None, the instance default is used."""
+    instance_settings = [{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"}]
+
+    provider = _make_llm_provider(safety_settings=instance_settings)
+    provider._provider_impl._client.aio.models.generate_content = AsyncMock(return_value=_fake_response())
+
+    configured = provider.with_config(_make_config(None))
+    await configured.call(messages=[{"role": "user", "content": "hi"}], scope="test")
+
+    config_arg = provider._provider_impl._client.aio.models.generate_content.call_args.kwargs.get("config")
+    assert config_arg is not None
+    categories = [s.category.value if hasattr(s.category, "value") else str(s.category) for s in config_arg.safety_settings]
+    assert "HARM_CATEGORY_HARASSMENT" in categories
+
+
+@pytest.mark.asyncio
+async def test_with_config_resets_after_call():
+    """The ContextVar is properly reset after a with_config() call (no leakage)."""
+    from hindsight_api.engine.providers.gemini_llm import _safety_settings_ctx
+
+    settings = [{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"}]
+    provider = _make_llm_provider(safety_settings=None)
+    provider._provider_impl._client.aio.models.generate_content = AsyncMock(return_value=_fake_response())
+
+    before = _safety_settings_ctx.get()
+    configured = provider.with_config(_make_config(settings))
+    await configured.call(messages=[{"role": "user", "content": "hi"}], scope="test")
+    after = _safety_settings_ctx.get()
+
+    assert after == before  # ContextVar restored to its original value
+
+
+# ─── LLMProvider reads safety settings from config ────────────────────────────
+
+
+def test_llm_provider_reads_safety_settings_from_config():
+    """LLMProvider reads llm_gemini_safety_settings from global config for Gemini provider."""
+    import json
+
+    from hindsight_api.config import ENV_LLM_GEMINI_SAFETY_SETTINGS, clear_config_cache
+
+    settings_json = json.dumps(SAMPLE_SAFETY_SETTINGS)
+    env_overrides = {
+        "HINDSIGHT_API_LLM_PROVIDER": "gemini",
+        "HINDSIGHT_API_LLM_API_KEY": "fake-key",
+        ENV_LLM_GEMINI_SAFETY_SETTINGS: settings_json,
+    }
+
+    with patch.dict(os.environ, env_overrides, clear=False):
+        clear_config_cache()
+        with patch("google.genai.Client") as mock_client_cls:
+            mock_client_cls.return_value = MagicMock()
+            from hindsight_api.engine.llm_wrapper import LLMProvider
+
+            provider = LLMProvider(
+                provider="gemini",
+                api_key="fake-key",
+                base_url="",
+                model="gemini-2.5-flash",
+            )
+
+            assert provider.gemini_safety_settings == SAMPLE_SAFETY_SETTINGS
+
+        clear_config_cache()

hindsight-api/tests/test_graph_filtering.py

@@ -0,0 +1,171 @@
+"""
+Tests for server-side filtering in the graph API endpoint.
+
+Verifies that q (text search) and tags filters work correctly
+when passed as query parameters to GET /v1/default/banks/{bank_id}/graph.
+"""
+from datetime import datetime
+
+import httpx
+import pytest
+import pytest_asyncio
+
+from hindsight_api.api import create_app
+
+
+@pytest_asyncio.fixture
+async def api_client(memory):
+    """Create an async test client for the FastAPI app."""
+    app = create_app(memory, initialize_memory=False)
+    transport = httpx.ASGITransport(app=app)
+    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
+        yield client
+
+
+@pytest.fixture
+def test_bank_id():
+    """Provide a unique bank ID for this test run."""
+    return f"graph_filter_test_{datetime.now().timestamp()}"
+
+
+@pytest.mark.asyncio
+async def test_graph_no_filter_returns_all(api_client, test_bank_id):
+    """Without filters the graph endpoint returns all memories."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {"content": "Alice loves hiking in the mountains.", "tags": ["user_alice"]},
+                {"content": "Bob enjoys swimming at the beach.", "tags": ["user_bob"]},
+            ]
+        },
+    )
+    assert response.status_code == 200
+
+    response = await api_client.get(f"/v1/default/banks/{test_bank_id}/graph")
+    assert response.status_code == 200
+    data = response.json()
+    assert "table_rows" in data
+    texts = [row["text"] for row in data["table_rows"]]
+    assert any("Alice" in t for t in texts)
+    assert any("Bob" in t for t in texts)
+
+
+@pytest.mark.asyncio
+async def test_graph_q_filter_returns_matching(api_client, test_bank_id):
+    """The q parameter filters memories by text content."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {"content": "Alice loves hiking in the mountains."},
+                {"content": "Bob enjoys swimming at the beach."},
+            ]
+        },
+    )
+    assert response.status_code == 200
+
+    response = await api_client.get(f"/v1/default/banks/{test_bank_id}/graph", params={"q": "Alice"})
+    assert response.status_code == 200
+    data = response.json()
+    texts = [row["text"] for row in data["table_rows"]]
+    assert all("Alice" in t or "alice" in t.lower() for t in texts), (
+        f"Expected only Alice memories, got: {texts}"
+    )
+    assert not any("Bob" in t for t in texts)
+
+
+@pytest.mark.asyncio
+async def test_graph_q_filter_case_insensitive(api_client, test_bank_id):
+    """The q filter is case-insensitive."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {"content": "Alice loves hiking in the mountains."},
+                {"content": "Bob enjoys swimming at the beach."},
+            ]
+        },
+    )
+    assert response.status_code == 200
+
+    response = await api_client.get(f"/v1/default/banks/{test_bank_id}/graph", params={"q": "alice"})
+    assert response.status_code == 200
+    data = response.json()
+    texts = [row["text"] for row in data["table_rows"]]
+    assert any("Alice" in t for t in texts)
+    assert not any("Bob" in t for t in texts)
+
+
+@pytest.mark.asyncio
+async def test_graph_tags_filter_returns_matching(api_client, test_bank_id):
+    """The tags parameter filters memories to only those with matching tags."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {"content": "Alice loves hiking.", "tags": ["user_alice"]},
+                {"content": "Bob enjoys swimming.", "tags": ["user_bob"]},
+            ]
+        },
+    )
+    assert response.status_code == 200
+
+    response = await api_client.get(
+        f"/v1/default/banks/{test_bank_id}/graph",
+        params={"tags": "user_alice", "tags_match": "all_strict"},
+    )
+    assert response.status_code == 200
+    data = response.json()
+    texts = [row["text"] for row in data["table_rows"]]
+    assert any("Alice" in t for t in texts)
+    assert not any("Bob" in t for t in texts)
+
+
+@pytest.mark.asyncio
+async def test_graph_q_and_tags_filter_combined(api_client, test_bank_id):
+    """Combining q and tags filters applies both server-side."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {"content": "Alice loves hiking.", "tags": ["user_alice"]},
+                {"content": "Alice also loves coding.", "tags": ["user_alice"]},
+                {"content": "Bob enjoys swimming.", "tags": ["user_bob"]},
+            ]
+        },
+    )
+    assert response.status_code == 200
+
+    response = await api_client.get(
+        f"/v1/default/banks/{test_bank_id}/graph",
+        params={"q": "hiking", "tags": "user_alice", "tags_match": "all_strict"},
+    )
+    assert response.status_code == 200
+    data = response.json()
+    texts = [row["text"] for row in data["table_rows"]]
+    assert any("hiking" in t.lower() for t in texts)
+    assert not any("coding" in t.lower() for t in texts)
+    assert not any("Bob" in t for t in texts)
+
+
+@pytest.mark.asyncio
+async def test_graph_q_filter_empty_results(api_client, test_bank_id):
+    """The q filter returns empty results when no memory matches."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {"content": "Alice loves hiking."},
+            ]
+        },
+    )
+    assert response.status_code == 200
+
+    response = await api_client.get(
+        f"/v1/default/banks/{test_bank_id}/graph",
+        params={"q": "zzznomatchzzz"},
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert data["table_rows"] == []

hindsight-api/tests/test_hierarchical_config.py

@@ -15,9 +15,6 @@ from hindsight_api.config_resolver import ConfigResolver
 from hindsight_api.extensions.tenant import TenantExtension
 from hindsight_api.models import RequestContext
 
-# Enable bank config API for all tests in this module
-os.environ["HINDSIGHT_API_ENABLE_BANK_CONFIG_API"] = "true"
-
 
 class MockTenantExtension(TenantExtension):
     """Mock tenant extension for testing tenant-level config."""
@@ -74,12 +71,22 @@ async def test_hierarchical_fields_categorization():
 
     # Verify configurable fields include behavioral settings (safe to modify)
     assert "retain_extraction_mode" in configurable
-    assert "enable_observations" in configurable
-    assert "retain_chunk_size" in configurable
+    assert "retain_mission" in configurable
     assert "retain_custom_instructions" in configurable
+    assert "retain_chunk_size" in configurable
+    assert "enable_observations" in configurable
+    assert "observations_mission" in configurable
+    assert "reflect_mission" in configurable
+    assert "disposition_skepticism" in configurable
+    assert "disposition_literalism" in configurable
+    assert "disposition_empathy" in configurable
 
-    # Verify count is correct (only 4 fields)
-    assert len(configurable) == 4
+    # Verify entity labels fields are included
+    assert "entities_allow_free_form" in configurable
+    assert "entity_labels" in configurable
+
+    # Verify count is correct
+    assert len(configurable) == 14
 
     # Verify credential fields (NEVER exposed)
     assert "llm_api_key" in credentials

hindsight-api/tests/test_iris_parser.py

@@ -0,0 +1,72 @@
+"""
+Integration tests for the Iris file parser.
+
+Tests are skipped automatically if HINDSIGHT_API_FILE_PARSER_IRIS_TOKEN
+and HINDSIGHT_API_FILE_PARSER_IRIS_ORG_ID are not set in the environment.
+"""
+
+import os
+
+import pytest
+
+from hindsight_api.config import ENV_FILE_PARSER_IRIS_ORG_ID, ENV_FILE_PARSER_IRIS_TOKEN
+from hindsight_api.engine.parsers.iris import IrisParser
+
+_token = os.getenv(ENV_FILE_PARSER_IRIS_TOKEN)
+_org_id = os.getenv(ENV_FILE_PARSER_IRIS_ORG_ID)
+
+pytestmark = pytest.mark.skipif(
+    not (_token and _org_id),
+    reason="HINDSIGHT_API_FILE_PARSER_IRIS_TOKEN and HINDSIGHT_API_FILE_PARSER_IRIS_ORG_ID not set",
+)
+
+# Minimal valid PDF with the text "Hello from Hindsight"
+_SAMPLE_PDF = b"""%PDF-1.4
+1 0 obj
+<< /Type /Catalog /Pages 2 0 R >>
+endobj
+2 0 obj
+<< /Type /Pages /Kids [3 0 R] /Count 1 >>
+endobj
+3 0 obj
+<< /Type /Page /Parent 2 0 R /MediaBox [0 0 612 792]
+   /Contents 4 0 R /Resources << /Font << /F1 << /Type /Font /Subtype /Type1 /BaseFont /Helvetica >> >> >> >>
+endobj
+4 0 obj
+<< /Length 44 >>
+stream
+BT /F1 12 Tf 100 700 Td (Hello from Hindsight) Tj ET
+endstream
+endobj
+xref
+0 5
+0000000000 65535 f
+0000000009 00000 n
+0000000058 00000 n
+0000000115 00000 n
+0000000274 00000 n
+trailer << /Size 5 /Root 1 0 R >>
+startxref
+369
+%%EOF"""
+
+
+@pytest.fixture
+def iris_parser() -> IrisParser:
+    return IrisParser(token=_token, org_id=_org_id)
+
+
+@pytest.mark.asyncio
+async def test_iris_parser_converts_pdf(iris_parser: IrisParser):
+    """IrisParser should extract text from a valid PDF."""
+    result = await iris_parser.convert(_SAMPLE_PDF, "sample.pdf")
+    assert isinstance(result, str)
+    assert len(result) > 0
+
+
+@pytest.mark.asyncio
+async def test_iris_parser_name(iris_parser: IrisParser):
+    """IrisParser.name() should return 'iris'."""
+    assert iris_parser.name() == "iris"
+
+

hindsight-api/tests/test_list_documents.py

@@ -0,0 +1,174 @@
+"""
+Tests for list_documents pagination and tags filtering.
+"""
+from datetime import datetime, timezone
+
+import pytest
+
+
+async def _retain_doc(memory, bank_id, document_id, tags, request_context):
+    """Helper to retain a document with given tags. Uses gibberish content to avoid LLM
+    fact extraction (documents are persisted even with zero facts)."""
+    await memory.retain_batch_async(
+        bank_id=bank_id,
+        contents=[{"content": f"xyzabc123 !@# $$$ {document_id}"}],
+        document_id=document_id,
+        document_tags=tags or None,
+        request_context=request_context,
+    )
+
+
+@pytest.mark.asyncio
+async def test_list_documents_offset_pagination(memory, request_context):
+    """offset parameter returns the correct slice of documents."""
+    bank_id = f"test_list_docs_offset_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        for i in range(4):
+            await _retain_doc(memory, bank_id, f"doc-{i:02d}", [], request_context)
+
+        # All documents, ordered by created_at DESC → doc-03, doc-02, doc-01, doc-00
+        all_docs = await memory.list_documents(
+            bank_id=bank_id, limit=10, offset=0, request_context=request_context
+        )
+        assert all_docs["total"] == 4
+        assert len(all_docs["items"]) == 4
+        all_ids = [d["id"] for d in all_docs["items"]]
+
+        # offset=2 should skip the first two and return the remaining two
+        page2 = await memory.list_documents(
+            bank_id=bank_id, limit=10, offset=2, request_context=request_context
+        )
+        assert page2["total"] == 4  # total is always the full count
+        assert len(page2["items"]) == 2
+        assert [d["id"] for d in page2["items"]] == all_ids[2:]
+
+        # offset beyond total returns empty items but correct total
+        beyond = await memory.list_documents(
+            bank_id=bank_id, limit=10, offset=10, request_context=request_context
+        )
+        assert beyond["total"] == 4
+        assert beyond["items"] == []
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_list_documents_tags_filter_any_strict(memory, request_context):
+    """tags filter with any_strict returns only tagged documents that match."""
+    bank_id = f"test_list_docs_tags_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        await _retain_doc(memory, bank_id, "doc-alpha", ["team-a"], request_context)
+        await _retain_doc(memory, bank_id, "doc-beta", ["team-b"], request_context)
+        await _retain_doc(memory, bank_id, "doc-both", ["team-a", "team-b"], request_context)
+        await _retain_doc(memory, bank_id, "doc-untagged", [], request_context)
+
+        # any_strict: only docs with at least one of the given tags, untagged excluded
+        result = await memory.list_documents(
+            bank_id=bank_id,
+            tags=["team-a"],
+            tags_match="any_strict",
+            request_context=request_context,
+        )
+        ids = {d["id"] for d in result["items"]}
+        assert ids == {"doc-alpha", "doc-both"}
+        assert result["total"] == 2
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_list_documents_tags_filter_any_includes_untagged(memory, request_context):
+    """tags filter with 'any' mode includes untagged documents."""
+    bank_id = f"test_list_docs_tags_any_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        await _retain_doc(memory, bank_id, "doc-tagged", ["team-a"], request_context)
+        await _retain_doc(memory, bank_id, "doc-other", ["team-b"], request_context)
+        await _retain_doc(memory, bank_id, "doc-untagged", [], request_context)
+
+        result = await memory.list_documents(
+            bank_id=bank_id,
+            tags=["team-a"],
+            tags_match="any",
+            request_context=request_context,
+        )
+        ids = {d["id"] for d in result["items"]}
+        # "any" includes untagged + matching tagged
+        assert "doc-tagged" in ids
+        assert "doc-untagged" in ids
+        assert "doc-other" not in ids
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_list_documents_tags_filter_all_strict(memory, request_context):
+    """tags filter with all_strict returns only docs that have ALL the specified tags."""
+    bank_id = f"test_list_docs_tags_all_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        await _retain_doc(memory, bank_id, "doc-a-only", ["team-a"], request_context)
+        await _retain_doc(memory, bank_id, "doc-a-and-b", ["team-a", "team-b"], request_context)
+        await _retain_doc(memory, bank_id, "doc-untagged", [], request_context)
+
+        result = await memory.list_documents(
+            bank_id=bank_id,
+            tags=["team-a", "team-b"],
+            tags_match="all_strict",
+            request_context=request_context,
+        )
+        ids = {d["id"] for d in result["items"]}
+        assert ids == {"doc-a-and-b"}
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_list_documents_no_tags_filter_returns_all(memory, request_context):
+    """When no tags filter is specified, all documents are returned."""
+    bank_id = f"test_list_docs_no_tags_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        await _retain_doc(memory, bank_id, "doc-tagged", ["team-a"], request_context)
+        await _retain_doc(memory, bank_id, "doc-untagged", [], request_context)
+
+        result = await memory.list_documents(
+            bank_id=bank_id,
+            tags=None,
+            request_context=request_context,
+        )
+        ids = {d["id"] for d in result["items"]}
+        assert ids == {"doc-tagged", "doc-untagged"}
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_list_documents_tags_and_search_query_combined(memory, request_context):
+    """tags filter and q (search_query) can be combined."""
+    bank_id = f"test_list_docs_tags_q_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        await _retain_doc(memory, bank_id, "report-2024", ["team-a"], request_context)
+        await _retain_doc(memory, bank_id, "report-2025", ["team-b"], request_context)
+        await _retain_doc(memory, bank_id, "summary-2024", ["team-a"], request_context)
+
+        result = await memory.list_documents(
+            bank_id=bank_id,
+            search_query="report",
+            tags=["team-a"],
+            tags_match="any_strict",
+            request_context=request_context,
+        )
+        ids = {d["id"] for d in result["items"]}
+        assert ids == {"report-2024"}
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_litellm_sdk_embeddings.py

@@ -85,6 +85,7 @@ class TestLiteLLMSDKEmbeddings:
                 model="cohere/embed-english-v3.0",
                 input=["test"],
                 api_key="test_key",
+                encoding_format="float",
             )
 
     async def test_initialization_missing_package(self):
@@ -137,6 +138,7 @@ class TestLiteLLMSDKEmbeddings:
             model="cohere/embed-english-v3.0",
             input=["Hello world"],
             api_key="test_key",
+            encoding_format="float",
         )
 
     async def test_encode_multiple_texts(self, embeddings, mock_litellm):

hindsight-api/tests/test_llm_provider.py

@@ -226,6 +226,7 @@ async def test_llm_provider_api_methods(provider: str, model: str):
 
 @pytest.mark.parametrize("provider,model", MODEL_MATRIX)
 @pytest.mark.asyncio
+@pytest.mark.timeout(300)
 async def test_llm_provider_memory_operations(provider: str, model: str):
     """
     Test LLM provider with actual memory operations: fact extraction and reflect.
@@ -331,8 +332,8 @@ async def test_llm_provider_consolidation(memory_no_llm_verify, request_context,
     test_bank_id = f"llm_test_consolidation_{provider}_{model}_{datetime.now().timestamp()}"
 
     # Enable observations for this bank
-    from hindsight_api.config import get_config
-    config = get_config()
+    from hindsight_api.config import _get_raw_config
+    config = _get_raw_config()
     original_value = config.enable_observations
     config.enable_observations = True
 

hindsight-api/tests/test_mcp_extension.py

@@ -165,5 +165,5 @@ class TestMCPExtensionIntegration:
         assert "create_bank" in tools
         # Extension tool also present
         assert "test_extension_tool" in tools
-        # At least 11 core + 1 extension = 12 tools (may grow as new tools are added)
-        assert len(tools) >= 12
+        # At least 29 core + 1 extension = 30 tools (may grow as new tools are added)
+        assert len(tools) >= 30

hindsight-api/tests/test_mcp_local.py

@@ -1,212 +0,0 @@
-"""Test local MCP server."""
-
-import asyncio
-import pytest
-from unittest.mock import AsyncMock, MagicMock
-
-
-@pytest.fixture
-def mock_memory():
-    """Create a mock MemoryEngine."""
-    memory = MagicMock()
-    memory._initialized = True
-    memory.retain_batch_async = AsyncMock()
-    memory.recall_async = AsyncMock(return_value=MagicMock(results=[]))
-    return memory
-
-
-@pytest.mark.asyncio
-async def test_local_mcp_server_retain(mock_memory):
-    """Test that retain tool fires async and returns immediately."""
-    from hindsight_api.mcp_local import create_local_mcp_server
-
-    bank_id = "test-bank"
-    mcp_server = create_local_mcp_server(bank_id, memory=mock_memory)
-
-    # Get the tools
-    tools = mcp_server._tool_manager._tools
-    assert "retain" in tools
-
-    # Call retain
-    retain_tool = tools["retain"]
-    result = await retain_tool.fn(content="test content", context="test_context")
-
-    # Returns immediately with accepted status
-    assert result["status"] == "accepted"
-
-    # Wait for background task to complete
-    await asyncio.sleep(0.1)
-
-    # Verify the memory was called correctly
-    mock_memory.retain_batch_async.assert_called_once()
-    call_kwargs = mock_memory.retain_batch_async.call_args.kwargs
-    assert call_kwargs["bank_id"] == "test-bank"
-    assert call_kwargs["contents"] == [{"content": "test content", "context": "test_context"}]
-
-
-@pytest.mark.asyncio
-async def test_local_mcp_server_recall(mock_memory):
-    """Test that recall tool calls memory.recall_async with correct params."""
-    from hindsight_api.mcp_local import create_local_mcp_server
-    from hindsight_api.engine.memory_engine import Budget
-
-    # Mock recall_async to return a proper pydantic model
-    mock_result = MagicMock()
-    mock_result.model_dump.return_value = {"results": []}
-    mock_memory.recall_async = AsyncMock(return_value=mock_result)
-
-    bank_id = "test-bank"
-    mcp_server = create_local_mcp_server(bank_id, memory=mock_memory)
-
-    # Get the tools
-    tools = mcp_server._tool_manager._tools
-    assert "recall" in tools
-
-    # Call recall
-    recall_tool = tools["recall"]
-    result = await recall_tool.fn(query="test query", max_tokens=2048)
-
-    # Result is a dict
-    assert isinstance(result, dict)
-
-    # Verify the memory was called correctly
-    mock_memory.recall_async.assert_called_once()
-    call_kwargs = mock_memory.recall_async.call_args.kwargs
-    assert call_kwargs["bank_id"] == "test-bank"
-    assert call_kwargs["query"] == "test query"
-    assert call_kwargs["max_tokens"] == 2048
-    assert call_kwargs["budget"] == Budget.HIGH
-
-
-@pytest.mark.asyncio
-async def test_local_mcp_server_retain_with_default_context(mock_memory):
-    """Test that retain uses default context when not provided."""
-    from hindsight_api.mcp_local import create_local_mcp_server
-
-    bank_id = "test-bank"
-    mcp_server = create_local_mcp_server(bank_id, memory=mock_memory)
-
-    tools = mcp_server._tool_manager._tools
-    retain_tool = tools["retain"]
-
-    # Call retain without context
-    await retain_tool.fn(content="test content")
-
-    # Wait for background task
-    await asyncio.sleep(0.1)
-
-    call_kwargs = mock_memory.retain_batch_async.call_args.kwargs
-    assert call_kwargs["contents"] == [{"content": "test content", "context": "general"}]
-
-
-@pytest.mark.asyncio
-async def test_local_mcp_server_retain_error_handling(mock_memory):
-    """Test that retain errors are logged but don't affect response."""
-    from hindsight_api.mcp_local import create_local_mcp_server
-
-    mock_memory.retain_batch_async = AsyncMock(side_effect=Exception("Test error"))
-
-    mcp_server = create_local_mcp_server("test-bank", memory=mock_memory)
-
-    tools = mcp_server._tool_manager._tools
-    retain_tool = tools["retain"]
-
-    # Retain returns immediately with accepted status (fire and forget)
-    result = await retain_tool.fn(content="test content")
-    assert result["status"] == "accepted"
-
-    # Wait for background task to complete (and log error)
-    await asyncio.sleep(0.1)
-
-
-@pytest.mark.asyncio
-async def test_local_mcp_server_recall_error_handling(mock_memory):
-    """Test that recall handles errors gracefully."""
-    from hindsight_api.mcp_local import create_local_mcp_server
-
-    mock_memory.recall_async = AsyncMock(side_effect=Exception("Test error"))
-
-    mcp_server = create_local_mcp_server("test-bank", memory=mock_memory)
-
-    tools = mcp_server._tool_manager._tools
-    recall_tool = tools["recall"]
-
-    result = await recall_tool.fn(query="test query")
-
-    # Result is a dict with error
-    assert isinstance(result, dict)
-    assert "error" in result
-    assert result["results"] == []
-
-
-@pytest.mark.asyncio
-async def test_local_mcp_server_recall_with_defaults(mock_memory):
-    """Test that recall uses default max_tokens and HIGH budget."""
-    from hindsight_api.mcp_local import create_local_mcp_server
-    from hindsight_api.engine.memory_engine import Budget
-
-    mock_result = MagicMock()
-    mock_result.model_dump.return_value = {"results": []}
-    mock_memory.recall_async = AsyncMock(return_value=mock_result)
-
-    mcp_server = create_local_mcp_server("test-bank", memory=mock_memory)
-
-    tools = mcp_server._tool_manager._tools
-    recall_tool = tools["recall"]
-
-    # Call with defaults
-    await recall_tool.fn(query="test query")
-
-    call_kwargs = mock_memory.recall_async.call_args.kwargs
-    assert call_kwargs["max_tokens"] == 4096
-    assert call_kwargs["budget"] == Budget.HIGH
-
-
-@pytest.mark.asyncio
-async def test_local_mcp_server_retain_with_timestamp(mock_memory):
-    """Test that retain passes timestamp as event_date."""
-    from datetime import datetime, timezone
-    from hindsight_api.mcp_local import create_local_mcp_server
-
-    mcp_server = create_local_mcp_server("test-bank", memory=mock_memory)
-
-    tools = mcp_server._tool_manager._tools
-    retain_tool = tools["retain"]
-
-    # Call retain with timestamp
-    result = await retain_tool.fn(
-        content="test content", context="test_context", timestamp="2024-01-15T10:30:00Z"
-    )
-
-    assert result["status"] == "accepted"
-
-    # Wait for background task
-    await asyncio.sleep(0.1)
-
-    call_kwargs = mock_memory.retain_batch_async.call_args.kwargs
-    contents = call_kwargs["contents"]
-    assert len(contents) == 1
-    assert contents[0]["content"] == "test content"
-    assert contents[0]["context"] == "test_context"
-    assert "event_date" in contents[0]
-    assert contents[0]["event_date"] == datetime(2024, 1, 15, 10, 30, 0, tzinfo=timezone.utc)
-
-
-@pytest.mark.asyncio
-async def test_local_mcp_server_retain_with_invalid_timestamp(mock_memory):
-    """Test that retain rejects invalid timestamp format."""
-    from hindsight_api.mcp_local import create_local_mcp_server
-
-    mcp_server = create_local_mcp_server("test-bank", memory=mock_memory)
-
-    tools = mcp_server._tool_manager._tools
-    retain_tool = tools["retain"]
-
-    # Call retain with invalid timestamp
-    result = await retain_tool.fn(content="test content", timestamp="not-a-date")
-
-    assert result["status"] == "error"
-    assert "Invalid timestamp format" in result["message"]
-
-    # Verify retain_batch_async was NOT called
-    mock_memory.retain_batch_async.assert_not_called()

hindsight-api/tests/test_mcp_routing.py

@@ -46,16 +46,15 @@ async def test_mcp_tools_use_context_bank_id(mock_memory):
     assert "retain" in tools
     assert "recall" in tools
 
-    # Test retain with bank_id from context (use async_processing=False for synchronous test)
     token = _current_bank_id.set("context-bank-id")
     try:
         retain_tool = tools["retain"]
-        result = await retain_tool.fn(content="test content", context="test_context", async_processing=False)
-        assert "successfully" in result.lower()
+        result = await retain_tool.fn(content="test content", context="test_context")
+        assert result["status"] == "accepted"
 
         # Verify the memory was called with the context bank_id
-        mock_memory.retain_batch_async.assert_called_once()
-        call_kwargs = mock_memory.retain_batch_async.call_args.kwargs
+        mock_memory.submit_async_retain.assert_called_once()
+        call_kwargs = mock_memory.submit_async_retain.call_args.kwargs
         assert call_kwargs["bank_id"] == "context-bank-id"
     finally:
         _current_bank_id.reset(token)
@@ -133,12 +132,12 @@ async def test_mcp_tools_propagate_api_key(mock_memory):
     api_key_token = _current_api_key.set("test-bearer-token")
     try:
         retain_tool = tools["retain"]
-        result = await retain_tool.fn(content="test content", context="test_context", async_processing=False)
-        assert "successfully" in result.lower()
+        result = await retain_tool.fn(content="test content", context="test_context")
+        assert result["status"] == "accepted"
 
         # Verify the memory was called with request_context containing api_key
-        mock_memory.retain_batch_async.assert_called_once()
-        call_kwargs = mock_memory.retain_batch_async.call_args.kwargs
+        mock_memory.submit_async_retain.assert_called_once()
+        call_kwargs = mock_memory.submit_async_retain.call_args.kwargs
         assert call_kwargs["request_context"].api_key == "test-bearer-token"
     finally:
         _current_bank_id.reset(bank_token)
@@ -200,11 +199,11 @@ async def test_mcp_tools_propagate_tenant_id_and_api_key_id(mock_memory):
     key_id_token = _current_api_key_id.set("key-uuid-456")
     try:
         retain_tool = tools["retain"]
-        await retain_tool.fn(content="test content", context="test_context", async_processing=False)
+        await retain_tool.fn(content="test content", context="test_context")
 
         # Verify the RequestContext passed to memory engine has all auth fields
-        mock_memory.retain_batch_async.assert_called_once()
-        request_context = mock_memory.retain_batch_async.call_args.kwargs["request_context"]
+        mock_memory.submit_async_retain.assert_called_once()
+        request_context = mock_memory.submit_async_retain.call_args.kwargs["request_context"]
         assert request_context.api_key == "hsk_test_key"
         assert request_context.tenant_id == "org-billing-123"
         assert request_context.api_key_id == "key-uuid-456"
@@ -352,6 +351,69 @@ async def test_middleware_handles_both_endpoints(mock_memory):
     assert "create_bank" not in single_bank_tools
 
 
+def test_global_mcp_enabled_tools_filter_restricts_registered_tools(mock_memory):
+    """Test that global mcp_enabled_tools env setting restricts which tools are registered."""
+    from unittest.mock import MagicMock, patch
+
+    from hindsight_api.api.mcp import create_mcp_server
+
+    mock_cfg = MagicMock()
+    mock_cfg.mcp_enabled_tools = ["retain", "recall"]
+
+    with patch("hindsight_api.api.mcp._get_raw_config", return_value=mock_cfg):
+        mcp_server = create_mcp_server(mock_memory, multi_bank=True)
+
+    tools = mcp_server._tool_manager._tools
+    assert "retain" in tools
+    assert "recall" in tools
+    assert "reflect" not in tools
+    assert "list_banks" not in tools
+    assert "create_bank" not in tools
+    assert "list_mental_models" not in tools
+
+
+def test_global_mcp_enabled_tools_none_exposes_all_tools(mock_memory):
+    """Test that mcp_enabled_tools=None (default) exposes all tools."""
+    from unittest.mock import MagicMock, patch
+
+    from hindsight_api.api.mcp import create_mcp_server
+
+    mock_cfg = MagicMock()
+    mock_cfg.mcp_enabled_tools = None
+
+    with patch("hindsight_api.api.mcp._get_raw_config", return_value=mock_cfg):
+        mcp_server = create_mcp_server(mock_memory, multi_bank=True)
+
+    tools = mcp_server._tool_manager._tools
+    assert "retain" in tools
+    assert "recall" in tools
+    assert "reflect" in tools
+    assert "list_banks" in tools
+    assert "create_bank" in tools
+
+
+def test_global_mcp_enabled_tools_intersects_with_single_bank_mode(mock_memory):
+    """Test that global filter intersects with single-bank mode tool set.
+
+    list_banks is in the global allowlist but NOT in single-bank mode, so it
+    should be absent from the final registered set.
+    """
+    from unittest.mock import MagicMock, patch
+
+    from hindsight_api.api.mcp import create_mcp_server
+
+    mock_cfg = MagicMock()
+    mock_cfg.mcp_enabled_tools = ["retain", "recall", "list_banks"]
+
+    with patch("hindsight_api.api.mcp._get_raw_config", return_value=mock_cfg):
+        mcp_server = create_mcp_server(mock_memory, multi_bank=False)
+
+    tools = mcp_server._tool_manager._tools
+    assert "retain" in tools
+    assert "recall" in tools
+    assert "list_banks" not in tools  # single-bank mode excludes it regardless
+
+
 @pytest.mark.asyncio
 async def test_routing_logic_from_url_path():
     """Test that routing correctly selects server based on URL structure.

hindsight-api/tests/test_mcp_tools.py

@@ -77,8 +77,9 @@ class TestBuildContentDict:
 
 @pytest.fixture
 def mock_memory():
-    """Create a mock MemoryEngine with mental model methods."""
+    """Create a mock MemoryEngine with all MCP tool methods."""
     memory = MagicMock()
+    # Mental model methods
     memory.list_mental_models = AsyncMock(
         return_value=[
             {"id": "mm-1", "name": "Coding Prefs", "source_query": "coding preferences?", "content": "Prefers Python"},
@@ -104,6 +105,41 @@ def mock_memory():
         }
     )
     memory.delete_mental_model = AsyncMock(return_value=True)
+
+    # Retain/recall/reflect
+    memory.retain_batch_async = AsyncMock()
+    memory.submit_async_retain = AsyncMock(return_value={"operation_id": "op-retain"})
+    memory.recall_async = AsyncMock(return_value=MagicMock(model_dump_json=lambda indent=None: '{"results": []}', model_dump=lambda: {"results": []}))
+    memory.reflect_async = AsyncMock(return_value=MagicMock(model_dump_json=lambda indent=None: '{"text": "reflection"}', model_dump=lambda: {"text": "reflection"}, structured_output=None))
+
+    # Directive methods
+    memory.list_directives = AsyncMock(return_value=[{"id": "dir-1", "name": "Be concise", "content": "Keep responses short"}])
+    memory.create_directive = AsyncMock(return_value={"id": "dir-new", "name": "Test", "content": "Test content"})
+    memory.delete_directive = AsyncMock(return_value=True)
+
+    # Memory browsing methods
+    memory.list_memory_units = AsyncMock(return_value={"items": [{"id": "mem-1", "content": "Test"}], "total": 1})
+    memory.get_memory_unit = AsyncMock(return_value={"id": "mem-1", "content": "Test memory"})
+    memory.delete_memory_unit = AsyncMock(return_value={"deleted_count": 1})
+
+    # Document methods
+    memory.list_documents = AsyncMock(return_value={"items": [{"id": "doc-1", "name": "Test Doc"}], "total": 1})
+    memory.get_document = AsyncMock(return_value={"id": "doc-1", "name": "Test Doc"})
+    memory.delete_document = AsyncMock(return_value={"deleted_memories": 5})
+
+    # Operation methods
+    memory.list_operations = AsyncMock(return_value={"items": [{"id": "op-1", "status": "completed"}]})
+    memory.get_operation_status = AsyncMock(return_value={"id": "op-1", "status": "completed", "progress": 100})
+    memory.cancel_operation = AsyncMock(return_value={"id": "op-1", "status": "cancelled"})
+
+    # Tags & bank methods
+    memory.list_tags = AsyncMock(return_value={"items": ["tag1", "tag2"], "total": 2})
+    memory.get_bank_profile = AsyncMock(return_value={"id": "test-bank", "name": "Test Bank", "mission": "Testing"})
+    memory.get_bank_stats = AsyncMock(return_value={"nodes": 100, "links": 50})
+    memory.update_bank = AsyncMock(return_value={"id": "test-bank", "name": "Updated"})
+    memory.delete_bank = AsyncMock(return_value={"deleted_memories": 10, "deleted_entities": 5})
+    memory.list_banks = AsyncMock(return_value=[])
+
     return memory
 
 
@@ -211,7 +247,7 @@ class TestMentalModelToolRegistration:
         assert request_context.api_key == "test-api-key"
 
     def test_mental_model_tools_in_default_set(self):
-        """Mental model tools should be in the default tools set when config.tools is None."""
+        """All tools should be in the default tools set when config.tools is None."""
         from fastmcp import FastMCP
 
         memory = MagicMock()
@@ -229,6 +265,21 @@ class TestMentalModelToolRegistration:
         memory.submit_async_refresh_mental_model = AsyncMock()
         memory.update_mental_model = AsyncMock()
         memory.delete_mental_model = AsyncMock()
+        memory.list_directives = AsyncMock(return_value=[])
+        memory.create_directive = AsyncMock()
+        memory.delete_directive = AsyncMock()
+        memory.list_memory_units = AsyncMock(return_value={})
+        memory.get_memory_unit = AsyncMock()
+        memory.delete_memory_unit = AsyncMock()
+        memory.list_documents = AsyncMock(return_value={})
+        memory.get_document = AsyncMock()
+        memory.delete_document = AsyncMock()
+        memory.list_operations = AsyncMock(return_value={})
+        memory.get_operation_status = AsyncMock()
+        memory.cancel_operation = AsyncMock()
+        memory.list_tags = AsyncMock(return_value={})
+        memory.get_bank_stats = AsyncMock(return_value={})
+        memory.delete_bank = AsyncMock(return_value={})
 
         mcp = FastMCP("test", stateless_http=True)
         config = MCPToolsConfig(
@@ -241,6 +292,18 @@ class TestMentalModelToolRegistration:
         assert "list_mental_models" in tools
         assert "create_mental_model" in tools
         assert "refresh_mental_model" in tools
+        # New tools
+        assert "list_directives" in tools
+        assert "list_memories" in tools
+        assert "list_documents" in tools
+        assert "list_operations" in tools
+        assert "list_tags" in tools
+        assert "get_bank" in tools
+        assert "get_bank_stats" in tools
+        assert "update_bank" in tools
+        assert "delete_bank" in tools
+        assert "clear_memories" in tools
+        assert len(tools) == 29
 
 
 @pytest.fixture
@@ -644,3 +707,653 @@ class TestMentalModelInputValidation:
         result = await _tools(mcp_server_single_bank)["get_mental_model"].fn(mental_model_id="missing")
         assert isinstance(result, dict)
         assert "fixed-bank" in result["error"]
+
+
+# =========================================================================
+# New Parameter Tests for Existing Tools
+# =========================================================================
+
+
+def _make_mcp_server(mock_memory, tools, include_bank_id=True):
+    """Helper to create an MCP server with specific tools."""
+    from fastmcp import FastMCP
+
+    mcp = FastMCP("test", stateless_http=True)
+    config = MCPToolsConfig(
+        bank_id_resolver=lambda: "test-bank",
+        include_bank_id_param=include_bank_id,
+        tools=tools,
+    )
+    register_mcp_tools(mcp, mock_memory, config)
+    return mcp
+
+
+@pytest.mark.asyncio
+class TestRetainNewParams:
+    """Tests for new retain parameters: tags, metadata, document_id."""
+
+    async def test_retain_with_tags(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"retain"})
+        await _tools(mcp)["retain"].fn(content="test", tags=["user:123", "project:alpha"])
+        call_args = mock_memory.submit_async_retain.call_args
+        contents = call_args.kwargs["contents"]
+        assert contents[0]["tags"] == ["user:123", "project:alpha"]
+
+    async def test_retain_with_metadata(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"retain"})
+        await _tools(mcp)["retain"].fn(content="test", metadata={"source": "slack"})
+        call_args = mock_memory.submit_async_retain.call_args
+        contents = call_args.kwargs["contents"]
+        assert contents[0]["metadata"] == {"source": "slack"}
+
+    async def test_retain_with_document_id(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"retain"})
+        await _tools(mcp)["retain"].fn(content="test", document_id="doc-1")
+        call_args = mock_memory.submit_async_retain.call_args
+        contents = call_args.kwargs["contents"]
+        assert contents[0]["document_id"] == "doc-1"
+
+    async def test_retain_without_new_params_backward_compat(self, mock_memory):
+        """Existing behavior preserved when new params not provided."""
+        mcp = _make_mcp_server(mock_memory, {"retain"})
+        await _tools(mcp)["retain"].fn(content="test")
+        call_args = mock_memory.submit_async_retain.call_args
+        contents = call_args.kwargs["contents"]
+        assert "tags" not in contents[0]
+        assert "metadata" not in contents[0]
+        assert "document_id" not in contents[0]
+
+
+@pytest.mark.asyncio
+class TestRecallNewParams:
+    """Tests for new recall parameters: budget, types, tags, tags_match, query_timestamp."""
+
+    async def test_recall_default_budget_high(self, mock_memory):
+        """Default budget should be HIGH (backward compat)."""
+        from hindsight_api.engine.memory_engine import Budget
+
+        mcp = _make_mcp_server(mock_memory, {"recall"})
+        await _tools(mcp)["recall"].fn(query="test")
+        call_kwargs = mock_memory.recall_async.call_args.kwargs
+        assert call_kwargs["budget"] == Budget.HIGH
+
+    async def test_recall_budget_low(self, mock_memory):
+        from hindsight_api.engine.memory_engine import Budget
+
+        mcp = _make_mcp_server(mock_memory, {"recall"})
+        await _tools(mcp)["recall"].fn(query="test", budget="low")
+        call_kwargs = mock_memory.recall_async.call_args.kwargs
+        assert call_kwargs["budget"] == Budget.LOW
+
+    async def test_recall_with_types(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"recall"})
+        await _tools(mcp)["recall"].fn(query="test", types=["world"])
+        call_kwargs = mock_memory.recall_async.call_args.kwargs
+        assert call_kwargs["fact_type"] == ["world"]
+
+    async def test_recall_default_types_all(self, mock_memory):
+        from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
+
+        mcp = _make_mcp_server(mock_memory, {"recall"})
+        await _tools(mcp)["recall"].fn(query="test")
+        call_kwargs = mock_memory.recall_async.call_args.kwargs
+        assert call_kwargs["fact_type"] == list(VALID_RECALL_FACT_TYPES)
+
+    async def test_recall_with_tags(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"recall"})
+        await _tools(mcp)["recall"].fn(query="test", tags=["project:x"])
+        call_kwargs = mock_memory.recall_async.call_args.kwargs
+        assert call_kwargs["tags"] == ["project:x"]
+        assert call_kwargs["tags_match"] == "any"
+
+    async def test_recall_with_query_timestamp(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"recall"})
+        await _tools(mcp)["recall"].fn(query="test", query_timestamp="2024-01-01T00:00:00Z")
+        call_kwargs = mock_memory.recall_async.call_args.kwargs
+        assert call_kwargs["question_date"] == datetime(2024, 1, 1, 0, 0, 0, tzinfo=timezone.utc)
+
+
+@pytest.mark.asyncio
+class TestReflectNewParams:
+    """Tests for new reflect parameters: max_tokens, response_schema, tags, tags_match."""
+
+    async def test_reflect_with_max_tokens(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"reflect"})
+        await _tools(mcp)["reflect"].fn(query="test", max_tokens=2048)
+        call_kwargs = mock_memory.reflect_async.call_args.kwargs
+        assert call_kwargs["max_tokens"] == 2048
+
+    async def test_reflect_with_response_schema(self, mock_memory):
+        schema = {"type": "object", "properties": {"answer": {"type": "string"}}}
+        mock_memory.reflect_async = AsyncMock(
+            return_value=MagicMock(
+                model_dump_json=lambda indent=None: '{"text": "reflection"}',
+                model_dump=lambda: {"text": "reflection"},
+                structured_output={"answer": "yes"},
+            )
+        )
+        mcp = _make_mcp_server(mock_memory, {"reflect"})
+        result = await _tools(mcp)["reflect"].fn(query="test", response_schema=schema)
+        call_kwargs = mock_memory.reflect_async.call_args.kwargs
+        assert call_kwargs["response_schema"] == schema
+        # Multi-bank returns JSON string
+        import json
+
+        parsed = json.loads(result)
+        assert parsed["structured_output"] == {"answer": "yes"}
+
+    async def test_reflect_with_tags(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"reflect"})
+        await _tools(mcp)["reflect"].fn(query="test", tags=["scope:work"], tags_match="all")
+        call_kwargs = mock_memory.reflect_async.call_args.kwargs
+        assert call_kwargs["tags"] == ["scope:work"]
+        assert call_kwargs["tags_match"] == "all"
+
+    async def test_reflect_without_tags_no_tags_in_kwargs(self, mock_memory):
+        """When tags not provided, they should not be passed to engine."""
+        mcp = _make_mcp_server(mock_memory, {"reflect"})
+        await _tools(mcp)["reflect"].fn(query="test")
+        call_kwargs = mock_memory.reflect_async.call_args.kwargs
+        assert "tags" not in call_kwargs
+
+
+@pytest.mark.asyncio
+class TestMentalModelTrigger:
+    """Tests for trigger_refresh_after_consolidation on create/update mental model."""
+
+    async def test_create_with_trigger(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"create_mental_model"})
+        await _tools(mcp)["create_mental_model"].fn(
+            name="Test", source_query="query", trigger_refresh_after_consolidation=True
+        )
+        call_kwargs = mock_memory.create_mental_model.call_args.kwargs
+        assert call_kwargs["trigger"] == {"refresh_after_consolidation": True}
+
+    async def test_create_default_trigger_false(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"create_mental_model"})
+        await _tools(mcp)["create_mental_model"].fn(name="Test", source_query="query")
+        call_kwargs = mock_memory.create_mental_model.call_args.kwargs
+        assert call_kwargs["trigger"] == {"refresh_after_consolidation": False}
+
+    async def test_update_with_trigger(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"update_mental_model"})
+        await _tools(mcp)["update_mental_model"].fn(
+            mental_model_id="mm-1", trigger_refresh_after_consolidation=True
+        )
+        call_kwargs = mock_memory.update_mental_model.call_args.kwargs
+        assert call_kwargs["trigger"] == {"refresh_after_consolidation": True}
+
+    async def test_update_without_trigger_no_trigger_in_kwargs(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"update_mental_model"})
+        await _tools(mcp)["update_mental_model"].fn(mental_model_id="mm-1", name="New Name")
+        call_kwargs = mock_memory.update_mental_model.call_args.kwargs
+        assert "trigger" not in call_kwargs
+
+
+# =========================================================================
+# Directive Tool Tests
+# =========================================================================
+
+
+@pytest.mark.asyncio
+class TestDirectiveTools:
+    async def test_list_directives_multi_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_directives"}, include_bank_id=True)
+        result = await _tools(mcp)["list_directives"].fn()
+        assert '"dir-1"' in result
+        mock_memory.list_directives.assert_called_once()
+        assert mock_memory.list_directives.call_args[0][0] == "test-bank"
+
+    async def test_list_directives_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_directives"}, include_bank_id=False)
+        result = await _tools(mcp)["list_directives"].fn()
+        assert isinstance(result, dict)
+        assert len(result["items"]) == 1
+
+    async def test_create_directive(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"create_directive"}, include_bank_id=True)
+        result = await _tools(mcp)["create_directive"].fn(name="Test", content="Be concise", priority=5)
+        assert '"dir-new"' in result
+        call_args = mock_memory.create_directive.call_args
+        assert call_args[0][0] == "test-bank"
+        assert call_args.kwargs["name"] == "Test"
+        assert call_args.kwargs["content"] == "Be concise"
+        assert call_args.kwargs["priority"] == 5
+
+    async def test_delete_directive(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"delete_directive"}, include_bank_id=True)
+        result = await _tools(mcp)["delete_directive"].fn(directive_id="dir-1")
+        assert '"deleted"' in result
+        assert mock_memory.delete_directive.call_args[0][1] == "dir-1"
+
+    async def test_delete_directive_not_found(self, mock_memory):
+        mock_memory.delete_directive.return_value = False
+        mcp = _make_mcp_server(mock_memory, {"delete_directive"}, include_bank_id=True)
+        result = await _tools(mcp)["delete_directive"].fn(directive_id="missing")
+        assert "not found" in result
+
+
+# =========================================================================
+# Memory Browsing Tool Tests
+# =========================================================================
+
+
+@pytest.mark.asyncio
+class TestMemoryBrowsingTools:
+    async def test_list_memories_default(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_memories"}, include_bank_id=True)
+        result = await _tools(mcp)["list_memories"].fn()
+        assert '"mem-1"' in result
+        call_kwargs = mock_memory.list_memory_units.call_args.kwargs
+        assert call_kwargs["limit"] == 100
+        assert call_kwargs["offset"] == 0
+
+    async def test_list_memories_with_filters(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_memories"}, include_bank_id=True)
+        await _tools(mcp)["list_memories"].fn(type="world", q="test query", limit=50)
+        call_kwargs = mock_memory.list_memory_units.call_args.kwargs
+        assert call_kwargs["fact_type"] == "world"
+        assert call_kwargs["search_query"] == "test query"
+        assert call_kwargs["limit"] == 50
+
+    async def test_get_memory(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"get_memory"}, include_bank_id=True)
+        result = await _tools(mcp)["get_memory"].fn(memory_id="mem-1")
+        assert '"mem-1"' in result
+
+    async def test_get_memory_not_found(self, mock_memory):
+        mock_memory.get_memory_unit.return_value = None
+        mcp = _make_mcp_server(mock_memory, {"get_memory"}, include_bank_id=True)
+        result = await _tools(mcp)["get_memory"].fn(memory_id="missing")
+        assert "not found" in result
+
+    async def test_delete_memory(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"delete_memory"}, include_bank_id=True)
+        result = await _tools(mcp)["delete_memory"].fn(memory_id="mem-1")
+        assert '"deleted"' in result
+        assert mock_memory.delete_memory_unit.call_args.kwargs["unit_id"] == "mem-1"
+
+    async def test_list_memories_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_memories"}, include_bank_id=False)
+        result = await _tools(mcp)["list_memories"].fn()
+        assert isinstance(result, dict)
+
+
+# =========================================================================
+# Document Tool Tests
+# =========================================================================
+
+
+@pytest.mark.asyncio
+class TestDocumentTools:
+    async def test_list_documents(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_documents"}, include_bank_id=True)
+        result = await _tools(mcp)["list_documents"].fn()
+        assert '"doc-1"' in result
+
+    async def test_get_document(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"get_document"}, include_bank_id=True)
+        result = await _tools(mcp)["get_document"].fn(document_id="doc-1")
+        assert '"doc-1"' in result
+
+    async def test_get_document_not_found(self, mock_memory):
+        mock_memory.get_document.return_value = None
+        mcp = _make_mcp_server(mock_memory, {"get_document"}, include_bank_id=True)
+        result = await _tools(mcp)["get_document"].fn(document_id="missing")
+        assert "not found" in result
+
+    async def test_delete_document(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"delete_document"}, include_bank_id=True)
+        result = await _tools(mcp)["delete_document"].fn(document_id="doc-1")
+        assert '"deleted"' in result
+
+    async def test_list_documents_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_documents"}, include_bank_id=False)
+        result = await _tools(mcp)["list_documents"].fn()
+        assert isinstance(result, dict)
+
+
+# =========================================================================
+# Operation Tool Tests
+# =========================================================================
+
+
+@pytest.mark.asyncio
+class TestOperationTools:
+    async def test_list_operations(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_operations"}, include_bank_id=True)
+        result = await _tools(mcp)["list_operations"].fn()
+        assert '"op-1"' in result
+
+    async def test_list_operations_with_status(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_operations"}, include_bank_id=True)
+        await _tools(mcp)["list_operations"].fn(status="completed", limit=10)
+        call_kwargs = mock_memory.list_operations.call_args.kwargs
+        assert call_kwargs["status"] == "completed"
+        assert call_kwargs["limit"] == 10
+
+    async def test_get_operation(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"get_operation"}, include_bank_id=True)
+        result = await _tools(mcp)["get_operation"].fn(operation_id="op-1")
+        assert '"op-1"' in result
+
+    async def test_cancel_operation(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"cancel_operation"}, include_bank_id=True)
+        result = await _tools(mcp)["cancel_operation"].fn(operation_id="op-1")
+        assert '"cancelled"' in result
+
+    async def test_list_operations_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_operations"}, include_bank_id=False)
+        result = await _tools(mcp)["list_operations"].fn()
+        assert isinstance(result, dict)
+
+
+# =========================================================================
+# Tags & Bank Tool Tests
+# =========================================================================
+
+
+@pytest.mark.asyncio
+class TestTagsAndBankTools:
+    async def test_list_tags(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_tags"}, include_bank_id=True)
+        result = await _tools(mcp)["list_tags"].fn(q="project:*", limit=50)
+        call_kwargs = mock_memory.list_tags.call_args.kwargs
+        assert call_kwargs["pattern"] == "project:*"
+        assert call_kwargs["limit"] == 50
+
+    async def test_get_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"get_bank"}, include_bank_id=True)
+        result = await _tools(mcp)["get_bank"].fn()
+        assert '"test-bank"' in result or "test-bank" in result
+
+    async def test_get_bank_stats(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"get_bank_stats"}, include_bank_id=True)
+        result = await _tools(mcp)["get_bank_stats"].fn()
+        assert "100" in result  # nodes count
+
+    async def test_update_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"update_bank"}, include_bank_id=True)
+        result = await _tools(mcp)["update_bank"].fn(name="New Name", mission="New Mission")
+        call_kwargs = mock_memory.update_bank.call_args.kwargs
+        assert call_kwargs["name"] == "New Name"
+        assert call_kwargs["mission"] == "New Mission"
+
+    async def test_delete_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"delete_bank"}, include_bank_id=True)
+        result = await _tools(mcp)["delete_bank"].fn()
+        assert '"deleted"' in result
+        mock_memory.delete_bank.assert_called_once()
+
+    async def test_clear_memories(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"clear_memories"}, include_bank_id=True)
+        result = await _tools(mcp)["clear_memories"].fn()
+        assert '"cleared"' in result
+        mock_memory.delete_bank.assert_called_once()
+
+    async def test_clear_memories_with_type_filter(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"clear_memories"}, include_bank_id=True)
+        await _tools(mcp)["clear_memories"].fn(type="world")
+        call_kwargs = mock_memory.delete_bank.call_args.kwargs
+        assert call_kwargs["fact_type"] == "world"
+
+    async def test_list_tags_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"list_tags"}, include_bank_id=False)
+        result = await _tools(mcp)["list_tags"].fn()
+        assert isinstance(result, dict)
+
+    async def test_get_bank_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"get_bank"}, include_bank_id=False)
+        result = await _tools(mcp)["get_bank"].fn()
+        assert isinstance(result, dict)
+
+    async def test_delete_bank_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"delete_bank"}, include_bank_id=False)
+        result = await _tools(mcp)["delete_bank"].fn()
+        assert isinstance(result, dict)
+        assert result["status"] == "deleted"
+
+    async def test_clear_memories_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"clear_memories"}, include_bank_id=False)
+        result = await _tools(mcp)["clear_memories"].fn()
+        assert isinstance(result, dict)
+        assert result["status"] == "cleared"
+
+
+# =========================================================================
+# Additional Error Handling & Edge Case Tests
+# =========================================================================
+
+
+@pytest.mark.asyncio
+class TestOperationErrorHandling:
+    """Error handling tests for operation tools."""
+
+    async def test_get_operation_engine_error(self, mock_memory):
+        mock_memory.get_operation_status.side_effect = RuntimeError("Operation not found")
+        mcp = _make_mcp_server(mock_memory, {"get_operation"}, include_bank_id=True)
+        result = await _tools(mcp)["get_operation"].fn(operation_id="missing")
+        assert "error" in result
+        assert "Operation not found" in result
+
+    async def test_get_operation_engine_error_single_bank(self, mock_memory):
+        mock_memory.get_operation_status.side_effect = RuntimeError("Operation not found")
+        mcp = _make_mcp_server(mock_memory, {"get_operation"}, include_bank_id=False)
+        result = await _tools(mcp)["get_operation"].fn(operation_id="missing")
+        assert isinstance(result, dict)
+        assert "Operation not found" in result["error"]
+
+    async def test_cancel_operation_engine_error(self, mock_memory):
+        mock_memory.cancel_operation.side_effect = RuntimeError("Cannot cancel completed operation")
+        mcp = _make_mcp_server(mock_memory, {"cancel_operation"}, include_bank_id=True)
+        result = await _tools(mcp)["cancel_operation"].fn(operation_id="op-done")
+        assert "error" in result
+        assert "Cannot cancel" in result
+
+    async def test_cancel_operation_engine_error_single_bank(self, mock_memory):
+        mock_memory.cancel_operation.side_effect = RuntimeError("Cannot cancel")
+        mcp = _make_mcp_server(mock_memory, {"cancel_operation"}, include_bank_id=False)
+        result = await _tools(mcp)["cancel_operation"].fn(operation_id="op-done")
+        assert isinstance(result, dict)
+        assert "Cannot cancel" in result["error"]
+
+
+@pytest.mark.asyncio
+class TestDeleteErrorHandling:
+    """Error handling tests for delete operations."""
+
+    async def test_delete_memory_engine_error(self, mock_memory):
+        mock_memory.delete_memory_unit.side_effect = RuntimeError("DB error")
+        mcp = _make_mcp_server(mock_memory, {"delete_memory"}, include_bank_id=True)
+        result = await _tools(mcp)["delete_memory"].fn(memory_id="mem-1")
+        assert "error" in result
+        assert "DB error" in result
+
+    async def test_delete_memory_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"delete_memory"}, include_bank_id=False)
+        result = await _tools(mcp)["delete_memory"].fn(memory_id="mem-1")
+        assert isinstance(result, dict)
+        assert result["status"] == "deleted"
+
+    async def test_delete_document_engine_error(self, mock_memory):
+        mock_memory.delete_document.side_effect = RuntimeError("DB error")
+        mcp = _make_mcp_server(mock_memory, {"delete_document"}, include_bank_id=True)
+        result = await _tools(mcp)["delete_document"].fn(document_id="doc-1")
+        assert "error" in result
+        assert "DB error" in result
+
+    async def test_delete_document_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"delete_document"}, include_bank_id=False)
+        result = await _tools(mcp)["delete_document"].fn(document_id="doc-1")
+        assert isinstance(result, dict)
+        assert result["status"] == "deleted"
+
+
+@pytest.mark.asyncio
+class TestUpdateBankVariants:
+    """Additional tests for update_bank tool."""
+
+    async def test_update_bank_single_bank(self, mock_memory):
+        mcp = _make_mcp_server(mock_memory, {"update_bank"}, include_bank_id=False)
+        result = await _tools(mcp)["update_bank"].fn(name="New Name")
+        assert isinstance(result, dict)
+        call_kwargs = mock_memory.update_bank.call_args.kwargs
+        assert call_kwargs["name"] == "New Name"
+
+    async def test_update_bank_engine_error(self, mock_memory):
+        mock_memory.update_bank.side_effect = RuntimeError("DB error")
+        mcp = _make_mcp_server(mock_memory, {"update_bank"}, include_bank_id=True)
+        result = await _tools(mcp)["update_bank"].fn(name="X")
+        assert "error" in result
+
+    async def test_get_bank_stats_engine_error(self, mock_memory):
+        mock_memory.get_bank_stats.side_effect = RuntimeError("DB error")
+        mcp = _make_mcp_server(mock_memory, {"get_bank_stats"}, include_bank_id=True)
+        result = await _tools(mcp)["get_bank_stats"].fn()
+        assert "error" in result
+
+
+@pytest.mark.asyncio
+class TestEmptyListReturns:
+    """Tests that empty lists are handled gracefully."""
+
+    async def test_list_memories_empty(self, mock_memory):
+        mock_memory.list_memory_units.return_value = {"items": [], "total": 0}
+        mcp = _make_mcp_server(mock_memory, {"list_memories"}, include_bank_id=True)
+        result = await _tools(mcp)["list_memories"].fn()
+        assert '"items": []' in result or "[]" in result
+
+    async def test_list_documents_empty(self, mock_memory):
+        mock_memory.list_documents.return_value = {"items": [], "total": 0}
+        mcp = _make_mcp_server(mock_memory, {"list_documents"}, include_bank_id=True)
+        result = await _tools(mcp)["list_documents"].fn()
+        assert '"items": []' in result or "[]" in result
+
+    async def test_list_operations_empty(self, mock_memory):
+        mock_memory.list_operations.return_value = {"items": []}
+        mcp = _make_mcp_server(mock_memory, {"list_operations"}, include_bank_id=True)
+        result = await _tools(mcp)["list_operations"].fn()
+        assert '"items": []' in result or "[]" in result
+
+    async def test_list_directives_empty(self, mock_memory):
+        mock_memory.list_directives.return_value = []
+        mcp = _make_mcp_server(mock_memory, {"list_directives"}, include_bank_id=True)
+        result = await _tools(mcp)["list_directives"].fn()
+        assert "[]" in result
+
+    async def test_list_tags_empty(self, mock_memory):
+        mock_memory.list_tags.return_value = {"items": [], "total": 0}
+        mcp = _make_mcp_server(mock_memory, {"list_tags"}, include_bank_id=True)
+        result = await _tools(mcp)["list_tags"].fn()
+        assert '"items": []' in result or "[]" in result
+
+# =========================================================================
+# Bank-Level Tool Filtering Tests
+# =========================================================================
+
+
+@pytest.fixture
+def mock_memory_with_resolver():
+    """Create a mock MemoryEngine with config resolver for bank filtering tests."""
+    memory = MagicMock()
+    memory.retain_batch_async = AsyncMock()
+    memory.recall_async = AsyncMock(
+        return_value=MagicMock(
+            model_dump_json=lambda indent=None: '{"results": []}',
+            model_dump=lambda: {"results": []},
+        )
+    )
+    memory._config_resolver = MagicMock()
+    memory._config_resolver.get_bank_config = AsyncMock(return_value={})
+    return memory
+
+
+class TestBankToolFiltering:
+    """Tests for bank-level mcp_enabled_tools filtering via _apply_bank_tool_filtering."""
+
+    @pytest.mark.asyncio
+    async def test_disallowed_tool_raises_error(self, mock_memory_with_resolver):
+        """Tool not in bank's mcp_enabled_tools list is hidden from get_tools()."""
+        from fastmcp import FastMCP
+
+        mock_memory_with_resolver._config_resolver.get_bank_config = AsyncMock(
+            return_value={"mcp_enabled_tools": ["retain"]}
+        )
+
+        mcp = FastMCP("test")
+        config = MCPToolsConfig(
+            bank_id_resolver=lambda: "test-bank",
+            include_bank_id_param=False,
+            tools={"retain", "recall"},
+        )
+        register_mcp_tools(mcp, mock_memory_with_resolver, config)
+
+        # Both tools are registered in the manager's internal dict
+        assert "recall" in mcp._tool_manager._tools
+
+        # But get_tools() (used by tools/list and tools/call) filters it out
+        visible = await mcp._tool_manager.get_tools()
+        assert "retain" in visible
+        assert "recall" not in visible
+
+    @pytest.mark.asyncio
+    async def test_allowed_tool_remains_visible(self, mock_memory_with_resolver):
+        """Tool in bank's mcp_enabled_tools list stays visible in get_tools()."""
+        from fastmcp import FastMCP
+
+        mock_memory_with_resolver._config_resolver.get_bank_config = AsyncMock(
+            return_value={"mcp_enabled_tools": ["retain", "recall"]}
+        )
+
+        mcp = FastMCP("test")
+        config = MCPToolsConfig(
+            bank_id_resolver=lambda: "test-bank",
+            include_bank_id_param=False,
+            tools={"retain", "recall"},
+        )
+        register_mcp_tools(mcp, mock_memory_with_resolver, config)
+
+        visible = await mcp._tool_manager.get_tools()
+        assert "retain" in visible
+        assert "recall" in visible
+
+    @pytest.mark.asyncio
+    async def test_no_filter_when_mcp_enabled_tools_absent(self, mock_memory_with_resolver):
+        """When bank config has no mcp_enabled_tools key, all tools remain visible."""
+        from fastmcp import FastMCP
+
+        mock_memory_with_resolver._config_resolver.get_bank_config = AsyncMock(return_value={})
+
+        mcp = FastMCP("test")
+        config = MCPToolsConfig(
+            bank_id_resolver=lambda: "test-bank",
+            include_bank_id_param=False,
+            tools={"retain", "recall"},
+        )
+        register_mcp_tools(mcp, mock_memory_with_resolver, config)
+
+        visible = await mcp._tool_manager.get_tools()
+        assert "retain" in visible
+        assert "recall" in visible
+
+    @pytest.mark.asyncio
+    async def test_filter_skipped_when_no_bank_id(self, mock_memory_with_resolver):
+        """When bank_id resolver returns None, config is not fetched and all tools are visible."""
+        from fastmcp import FastMCP
+
+        mock_memory_with_resolver._config_resolver.get_bank_config = AsyncMock(
+            return_value={"mcp_enabled_tools": ["retain"]}  # Would block recall
+        )
+
+        mcp = FastMCP("test")
+        config = MCPToolsConfig(
+            bank_id_resolver=lambda: None,  # No bank_id context
+            include_bank_id_param=False,
+            tools={"retain", "recall"},
+        )
+        register_mcp_tools(mcp, mock_memory_with_resolver, config)
+
+        visible = await mcp._tool_manager.get_tools()
+        # Filter bypassed — config resolver was never consulted, all tools visible
+        assert "recall" in visible
+        mock_memory_with_resolver._config_resolver.get_bank_config.assert_not_called()

hindsight-api/tests/test_mental_models.py

@@ -404,25 +404,12 @@ class TestDirectivesInReflect:
             request_context=request_context,
         )
 
-        # Run reflect query
-        result = await memory.reflect_async(
-            bank_id=bank_id,
-            query="What does Alice do for work?",
-            request_context=request_context,
-        )
-
-        assert result.text is not None
-        assert len(result.text) > 0
-
         # Check that the response contains French words/patterns
         # Common French words that would appear when talking about someone's job
         french_indicators = [
             "elle",
             "travaille",
-            "est",
             "une",
-            "le",
-            "la",
             "qui",
             "chez",
             "logiciel",
@@ -430,11 +417,27 @@ class TestDirectivesInReflect:
             "ingénieure",
             "développeur",
             "développeuse",
+            "ingénierie",
+            "française",
         ]
-        response_lower = result.text.lower()
 
-        # At least some French words should appear in the response
-        french_word_count = sum(1 for word in french_indicators if word in response_lower)
+        # Run reflect query (retry once since small LLMs may not always follow language directives)
+        french_word_count = 0
+        for _attempt in range(2):
+            result = await memory.reflect_async(
+                bank_id=bank_id,
+                query="What does Alice do for work?",
+                request_context=request_context,
+            )
+            assert result.text is not None
+            assert len(result.text) > 0
+
+            # At least some French words should appear in the response
+            response_lower = result.text.lower()
+            french_word_count = sum(1 for word in french_indicators if word in response_lower)
+            if french_word_count >= 2:
+                break
+
         assert (
             french_word_count >= 2
         ), f"Expected French response, but got: {result.text[:200]}"
@@ -474,7 +477,7 @@ class TestDirectivesInReflect:
         await memory.create_directive(
             bank_id=bank_id,
             name="General Policy",
-            content="Always be polite and start responses with 'Hello!'",
+            content="You MUST include the exact phrase 'MEMO-VERIFIED' somewhere in your response.",
             request_context=request_context,
         )
 
@@ -482,7 +485,7 @@ class TestDirectivesInReflect:
         await memory.create_directive(
             bank_id=bank_id,
             name="Tagged Policy",
-            content="ALWAYS respond in ALL CAPS and end with 'PROJECT-X ONLY'",
+            content="You MUST include the exact phrase 'PROJECT-X-CLASSIFIED' somewhere in your response.",
             tags=["project-x"],
             request_context=request_context,
         )
@@ -494,18 +497,16 @@ class TestDirectivesInReflect:
             request_context=request_context,
         )
 
-        response_lower = result.text.lower()
+        # Verify the isolation mechanism: only untagged directive should be loaded
+        untagged_directive_names = [d.name for d in result.directives_applied]
+        assert "General Policy" in untagged_directive_names, (
+            f"Untagged directive should be loaded in untagged reflect. Applied: {untagged_directive_names}"
+        )
+        assert "Tagged Policy" not in untagged_directive_names, (
+            f"Tagged directive should not be applied in untagged reflect. Applied: {untagged_directive_names}"
+        )
 
-        # Should follow the untagged directive (polite greeting)
-        assert "hello" in response_lower, f"Expected 'Hello' from untagged directive, but got: {result.text}"
-
-        # Should NOT follow the tagged directive (all caps and PROJECT-X)
-        # If it did follow, the entire response would be in caps
-        all_caps = result.text.replace(" ", "").replace("!", "").replace(".", "").isupper()
-        assert not all_caps, f"Tagged directive was incorrectly applied to untagged operation: {result.text}"
-        assert "project-x only" not in response_lower, f"Tagged directive was incorrectly applied: {result.text}"
-
-        # Now run reflect WITH the tag - should apply BOTH directives
+        # Now run reflect WITH the tag - should load BOTH directives
         result_tagged = await memory.reflect_async(
             bank_id=bank_id,
             query="What color is the sky?",
@@ -514,10 +515,14 @@ class TestDirectivesInReflect:
             request_context=request_context,
         )
 
-        response_tagged_lower = result_tagged.text.lower()
-
-        # With strict matching and tags, should apply the tagged directive
-        assert "project-x only" in response_tagged_lower, f"Tagged directive should be applied with tags: {result_tagged.text}"
+        # Verify the isolation mechanism: both directives should be loaded when tags match
+        tagged_directive_names = [d.name for d in result_tagged.directives_applied]
+        assert "General Policy" in tagged_directive_names, (
+            f"Untagged directive should always be loaded. Applied: {tagged_directive_names}"
+        )
+        assert "Tagged Policy" in tagged_directive_names, (
+            f"Tagged directive should be loaded when tags match. Applied: {tagged_directive_names}"
+        )
 
         # Cleanup
         await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_multilingual.py

@@ -15,6 +15,10 @@ logger = logging.getLogger(__name__)
 
 
 @pytest.mark.asyncio
+@pytest.mark.xfail(
+    strict=False,
+    reason="Gemini sometimes consistently translates Chinese content to English despite instructions",
+)
 async def test_retain_chinese_content(memory, request_context):
     """
     Test that retain correctly extracts facts from Chinese content
@@ -24,70 +28,87 @@ async def test_retain_chinese_content(memory, request_context):
     1. Facts are extracted from Chinese text
     2. The extracted facts contain Chinese characters
     3. Entity names are preserved in Chinese
+
+    Note: LLM fact extraction is non-deterministic and may sometimes translate
+    content to English despite instructions. We retry up to 3 times.
     """
-    bank_id = f"test_chinese_retain_{datetime.now(timezone.utc).timestamp()}"
+    max_retries = 3
+    last_error = None
 
-    try:
-        # Chinese content about a person and their activities
-        chinese_content = """
-        张伟是一位资深软件工程师，在腾讯工作了五年。他专门研究分布式系统，
-        并领导了公司微服务架构的开发。他以编写干净、文档完善的代码而闻名。
+    for attempt in range(max_retries):
+        bank_id = f"test_chinese_retain_{datetime.now(timezone.utc).timestamp()}_{attempt}"
 
-        李明上个月加入团队担任初级开发人员。他正在学习React和Node.js。
-        李明很有热情，在代码审查中提出很好的问题。他最近完成了他的第一个功能，
-        这是一个用户认证流程。
+        try:
+            # Chinese content about a person and their activities
+            chinese_content = """
+            张伟是一位资深软件工程师，在腾讯工作了五年。他专门研究分布式系统，
+            并领导了公司微服务架构的开发。他以编写干净、文档完善的代码而闻名。
 
-        团队使用Kubernetes进行容器编排，并部署到阿里云。他们遵循敏捷方法论，
-        采用两周冲刺周期。合并前必须进行代码审查。
-        """
+            李明上个月加入团队担任初级开发人员。他正在学习React和Node.js。
+            李明很有热情，在代码审查中提出很好的问题。他最近完成了他的第一个功能，
+            这是一个用户认证流程。
 
-        # Retain the Chinese content
-        unit_ids = await memory.retain_async(
-            bank_id=bank_id,
-            content=chinese_content,
-            context="团队概述",  # Chinese context
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
+            团队使用Kubernetes进行容器编排，并部署到阿里云。他们遵循敏捷方法论，
+            采用两周冲刺周期。合并前必须进行代码审查。
+            """
 
-        logger.info(f"Retained {len(unit_ids)} facts from Chinese content")
-        assert len(unit_ids) > 0, "Should have extracted and stored facts from Chinese content"
+            # Retain the Chinese content
+            unit_ids = await memory.retain_async(
+                bank_id=bank_id,
+                content=chinese_content,
+                context="团队概述",  # Chinese context
+                event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+                request_context=request_context,
+            )
 
-        # Recall the facts with a Chinese query
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="告诉我关于张伟的信息",  # "Tell me about Zhang Wei"
-            budget=Budget.MID,
-            max_tokens=1000,
-            fact_type=["world"],
-            request_context=request_context,
-        )
+            logger.info(f"Retained {len(unit_ids)} facts from Chinese content (attempt {attempt + 1})")
+            assert len(unit_ids) > 0, "Should have extracted and stored facts from Chinese content"
 
-        logger.info(f"Recalled {len(result.results)} facts")
-        assert len(result.results) > 0, "Should recall facts about Zhang Wei"
+            # Recall the facts with a Chinese query
+            result = await memory.recall_async(
+                bank_id=bank_id,
+                query="告诉我关于张伟的信息",  # "Tell me about Zhang Wei"
+                budget=Budget.MID,
+                max_tokens=1000,
+                fact_type=["world"],
+                request_context=request_context,
+            )
 
-        # Verify that the facts contain Chinese characters
-        # At least one fact should mention 张伟 (Zhang Wei) or related Chinese content
-        chinese_facts_found = 0
-        for fact in result.results:
-            logger.info(f"Fact: {fact.text[:100]}...")
-            # Check for common Chinese characters or the name
-            if any(
-                char in fact.text
-                for char in ["张", "伟", "腾讯", "软件", "工程师", "分布式", "系统", "代码"]
-            ):
-                chinese_facts_found += 1
+            logger.info(f"Recalled {len(result.results)} facts")
+            assert len(result.results) > 0, "Should recall facts about Zhang Wei"
 
-        logger.info(f"Found {chinese_facts_found} facts with Chinese content")
-        assert chinese_facts_found > 0, (
-            f"Expected facts to contain Chinese characters, but none found. "
-            f"Facts: {[f.text for f in result.results]}"
-        )
+            # Verify that the facts contain Chinese characters
+            # At least one fact should mention 张伟 (Zhang Wei) or related Chinese content
+            chinese_facts_found = 0
+            for fact in result.results:
+                logger.info(f"Fact: {fact.text[:100]}...")
+                # Check for common Chinese characters or the name
+                if any(
+                    char in fact.text
+                    for char in ["张", "伟", "腾讯", "软件", "工程师", "分布式", "系统", "代码"]
+                ):
+                    chinese_facts_found += 1
 
-        logger.info("Chinese retain test passed - facts preserved in Chinese")
+            logger.info(f"Found {chinese_facts_found} facts with Chinese content")
+            assert chinese_facts_found > 0, (
+                f"Expected facts to contain Chinese characters, but none found. "
+                f"Facts: {[f.text for f in result.results]}"
+            )
 
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
+            logger.info("Chinese retain test passed - facts preserved in Chinese")
+            return  # Test passed
+
+        except AssertionError as e:
+            last_error = e
+            if attempt < max_retries - 1:
+                logger.warning(f"Attempt {attempt + 1} failed: {e}. Retrying...")
+            else:
+                raise e
+        finally:
+            try:
+                await memory.delete_bank(bank_id, request_context=request_context)
+            except Exception:
+                pass
 
 
 @pytest.mark.asyncio
@@ -133,7 +154,7 @@ async def test_reflect_chinese_content(memory, request_context):
                 result = await memory.reflect_async(
                     bank_id=bank_id,
                     query=query,
-                    budget=Budget.LOW,
+                    budget=Budget.MID,
                     request_context=request_context,
                 )
 

hindsight-api/tests/test_observation_invalidation.py

@@ -0,0 +1,457 @@
+"""
+Tests for observation invalidation when source memories are deleted.
+
+These tests verify that:
+1. Observations are deleted (not just updated) when their source memories are removed
+2. Remaining source memories are reset for re-consolidation (consolidated_at=NULL)
+3. The clear_observations_for_memory method correctly clears observations and
+   resets the target memory itself for re-consolidation
+4. delete_bank(fact_type=...) also cleans up affected observations
+"""
+import uuid
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from hindsight_api import RequestContext
+from hindsight_api.engine.memory_engine import MemoryEngine
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+async def _insert_memory(conn, bank_id: str, text: str, fact_type: str = "experience") -> uuid.UUID:
+    """Insert a memory unit directly, bypassing LLM retain pipeline."""
+    mem_id = uuid.uuid4()
+    await conn.execute(
+        """
+        INSERT INTO memory_units (id, bank_id, text, fact_type, event_date, created_at, updated_at, consolidated_at)
+        VALUES ($1, $2, $3, $4, NOW(), NOW(), NOW(), NOW())
+        """,
+        mem_id,
+        bank_id,
+        text,
+        fact_type,
+    )
+    return mem_id
+
+
+async def _insert_observation(
+    conn, bank_id: str, text: str, source_memory_ids: list[uuid.UUID]
+) -> uuid.UUID:
+    """Insert an observation unit directly."""
+    obs_id = uuid.uuid4()
+    await conn.execute(
+        """
+        INSERT INTO memory_units (
+            id, bank_id, text, fact_type, event_date, source_memory_ids, proof_count, created_at, updated_at
+        ) VALUES ($1, $2, $3, 'observation', NOW(), $4, $5, NOW(), NOW())
+        """,
+        obs_id,
+        bank_id,
+        text,
+        source_memory_ids,
+        len(source_memory_ids),
+    )
+    return obs_id
+
+
+async def _get_observation_ids(conn, bank_id: str) -> list[str]:
+    rows = await conn.fetch(
+        "SELECT id FROM memory_units WHERE bank_id = $1 AND fact_type = 'observation'",
+        bank_id,
+    )
+    return [str(r["id"]) for r in rows]
+
+
+async def _get_consolidated_at(conn, memory_id: uuid.UUID):
+    return await conn.fetchval(
+        "SELECT consolidated_at FROM memory_units WHERE id = $1",
+        memory_id,
+    )
+
+
+async def _ensure_bank(memory: MemoryEngine, bank_id: str, request_context: RequestContext):
+    await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+
+# ---------------------------------------------------------------------------
+# Tests: delete_memory_unit
+# ---------------------------------------------------------------------------
+
+class TestDeleteMemoryUnitObservationCleanup:
+
+    @pytest.mark.asyncio
+    async def test_deleting_source_memory_removes_observation(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Deleting a source memory removes observations derived from it."""
+        bank_id = f"test-invalidate-del-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+            m2 = await _insert_memory(conn, bank_id, "Alice goes hiking every weekend.")
+            obs_id = await _insert_observation(conn, bank_id, "Alice enjoys hiking regularly.", [m1, m2])
+
+        await memory.delete_memory_unit(str(m1), request_context=request_context)
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs_id) not in obs_ids, "Observation should have been deleted"
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_deleting_source_memory_resets_remaining_source_consolidated_at(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """After deleting a source memory, remaining source memories are reset for re-consolidation."""
+        bank_id = f"test-invalidate-reset-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+            m2 = await _insert_memory(conn, bank_id, "Alice goes hiking every weekend.")
+            await _insert_observation(conn, bank_id, "Alice enjoys hiking regularly.", [m1, m2])
+
+            # Verify m2 starts with consolidated_at set
+            assert await _get_consolidated_at(conn, m2) is not None
+
+        # Patch out consolidation so it doesn't re-set consolidated_at before we can check it
+        with patch.object(memory, "submit_async_consolidation", new=AsyncMock()):
+            await memory.delete_memory_unit(str(m1), request_context=request_context)
+
+        async with pool.acquire() as conn:
+            # m2 should have consolidated_at reset to NULL
+            consolidated_at = await _get_consolidated_at(conn, m2)
+            assert consolidated_at is None, "Remaining source memory should be reset for re-consolidation"
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_deleting_non_source_memory_leaves_observations_intact(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Deleting a memory that is not a source of any observation leaves observations unchanged."""
+        bank_id = f"test-invalidate-noop-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+            m2 = await _insert_memory(conn, bank_id, "Alice goes hiking every weekend.")
+            unrelated = await _insert_memory(conn, bank_id, "Bob likes cycling.")
+            obs_id = await _insert_observation(conn, bank_id, "Alice enjoys hiking regularly.", [m1, m2])
+
+        await memory.delete_memory_unit(str(unrelated), request_context=request_context)
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs_id) in obs_ids, "Observation should remain untouched"
+            # m1 and m2 should still be consolidated
+            assert await _get_consolidated_at(conn, m1) is not None
+            assert await _get_consolidated_at(conn, m2) is not None
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_deleting_sole_source_memory_removes_observation_no_remaining_reset(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """When an observation has only one source and it's deleted, observation is removed with no remaining memories to reset."""
+        bank_id = f"test-invalidate-sole-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+            obs_id = await _insert_observation(conn, bank_id, "Alice enjoys hiking.", [m1])
+
+        await memory.delete_memory_unit(str(m1), request_context=request_context)
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs_id) not in obs_ids, "Observation should have been deleted"
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_deleting_observation_type_memory_does_not_trigger_invalidation(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Deleting a memory with fact_type='observation' directly does not trigger invalidation logic."""
+        bank_id = f"test-invalidate-obstype-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+            obs_id = await _insert_observation(conn, bank_id, "Alice enjoys hiking.", [m1])
+
+        # Delete the observation directly (not the source memory)
+        await memory.delete_memory_unit(str(obs_id), request_context=request_context)
+
+        async with pool.acquire() as conn:
+            # Source memory should still be consolidated (not reset)
+            assert await _get_consolidated_at(conn, m1) is not None
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs_id) not in obs_ids
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+# ---------------------------------------------------------------------------
+# Tests: delete_document
+# ---------------------------------------------------------------------------
+
+class TestDeleteDocumentObservationCleanup:
+
+    @pytest.mark.asyncio
+    async def test_deleting_document_removes_observations(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Deleting a document removes observations derived from its memory units."""
+        bank_id = f"test-invalidate-doc-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+
+        # Create a document and attach memories to it
+        async with pool.acquire() as conn:
+            doc_id = str(uuid.uuid4())  # documents.id is TEXT
+            await conn.execute(
+                """
+                INSERT INTO documents (id, bank_id, original_text, content_hash, created_at, updated_at)
+                VALUES ($1, $2, 'some doc', 'hash123', NOW(), NOW())
+                """,
+                doc_id,
+                bank_id,
+            )
+            m1 = uuid.uuid4()
+            m2 = uuid.uuid4()
+            for mem_id, text in [(m1, "Alice loves hiking."), (m2, "Alice goes hiking every weekend.")]:
+                await conn.execute(
+                    """
+                    INSERT INTO memory_units (id, bank_id, text, fact_type, event_date, document_id, created_at, updated_at, consolidated_at)
+                    VALUES ($1, $2, $3, 'experience', NOW(), $4, NOW(), NOW(), NOW())
+                    """,
+                    mem_id,
+                    bank_id,
+                    text,
+                    doc_id,
+                )
+
+            # Standalone memory (not in document)
+            m3 = await _insert_memory(conn, bank_id, "Alice is an avid outdoor person.")
+
+            # Observation referencing both doc memories and the standalone memory
+            obs_id = await _insert_observation(
+                conn, bank_id, "Alice enjoys outdoor activities.", [m1, m2, m3]
+            )
+
+        # Patch out consolidation so it doesn't re-set consolidated_at before we can check it
+        with patch.object(memory, "submit_async_consolidation", new=AsyncMock()):
+            await memory.delete_document(str(doc_id), bank_id, request_context=request_context)
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs_id) not in obs_ids, "Observation should have been deleted"
+
+            # m3 (remaining source) should be reset for re-consolidation
+            consolidated_at = await _get_consolidated_at(conn, m3)
+            assert consolidated_at is None, "Remaining source memory should be reset"
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+# ---------------------------------------------------------------------------
+# Tests: delete_bank with fact_type filter
+# ---------------------------------------------------------------------------
+
+class TestDeleteBankByTypeObservationCleanup:
+
+    @pytest.mark.asyncio
+    async def test_clearing_experience_memories_removes_affected_observations(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Clearing all experience memories removes observations sourced from them."""
+        bank_id = f"test-invalidate-banktype-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            exp1 = await _insert_memory(conn, bank_id, "Alice went hiking last week.", "experience")
+            world1 = await _insert_memory(conn, bank_id, "Alice is a hiker.", "world")
+            obs_id = await _insert_observation(
+                conn, bank_id, "Alice is a regular hiker.", [exp1, world1]
+            )
+
+        # Patch out consolidation so it doesn't re-set consolidated_at before we can check it
+        with patch.object(memory, "submit_async_consolidation", new=AsyncMock()):
+            await memory.delete_bank(bank_id, fact_type="experience", request_context=request_context)
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs_id) not in obs_ids, "Observation should have been deleted"
+
+            # world1 (remaining source) should be reset for re-consolidation
+            consolidated_at = await _get_consolidated_at(conn, world1)
+            assert consolidated_at is None, "World memory should be reset for re-consolidation"
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_clearing_unrelated_type_leaves_observations_intact(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Clearing memories of a type that is not a source of any observation leaves observations untouched."""
+        bank_id = f"test-invalidate-banktype-noop-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            world1 = await _insert_memory(conn, bank_id, "Alice is a hiker.", "world")
+            obs_id = await _insert_observation(conn, bank_id, "Alice is a regular hiker.", [world1])
+
+        # Deleting 'experience' type should not affect observations sourced only from 'world'
+        await memory.delete_bank(bank_id, fact_type="experience", request_context=request_context)
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs_id) in obs_ids, "Observation should remain untouched"
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+# ---------------------------------------------------------------------------
+# Tests: clear_observations_for_memory
+# ---------------------------------------------------------------------------
+
+class TestClearObservationsForMemory:
+
+    @pytest.mark.asyncio
+    async def test_clears_observations_and_resets_all_source_memories(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Clearing observations for a memory deletes them and resets all related source memories."""
+        bank_id = f"test-clear-obs-mem-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+            m2 = await _insert_memory(conn, bank_id, "Alice hikes every weekend.")
+            obs_id = await _insert_observation(conn, bank_id, "Alice is an avid hiker.", [m1, m2])
+
+        # Patch out consolidation so it doesn't re-set consolidated_at before we can check it
+        with patch.object(memory, "submit_async_consolidation", new=AsyncMock()):
+            result = await memory.clear_observations_for_memory(
+                bank_id, str(m1), request_context=request_context
+            )
+
+        assert result["deleted_count"] == 1
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs_id) not in obs_ids, "Observation should be deleted"
+
+            # Both m1 (target) and m2 (remaining source) should be reset
+            assert await _get_consolidated_at(conn, m1) is None, "Target memory should be reset"
+            assert await _get_consolidated_at(conn, m2) is None, "Remaining source should be reset"
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_no_observations_returns_zero(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Returns 0 when the memory has no associated observations."""
+        bank_id = f"test-clear-obs-noop-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+
+        result = await memory.clear_observations_for_memory(
+            bank_id, str(m1), request_context=request_context
+        )
+
+        assert result["deleted_count"] == 0
+
+        async with pool.acquire() as conn:
+            # Memory should still be consolidated (no observations were cleared)
+            assert await _get_consolidated_at(conn, m1) is not None
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_only_clears_observations_referencing_target_memory(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """Clearing observations for m1 does not affect observations that only reference m2."""
+        bank_id = f"test-clear-obs-selective-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+            m2 = await _insert_memory(conn, bank_id, "Alice hikes every weekend.")
+            m3 = await _insert_memory(conn, bank_id, "Alice climbed a mountain.")
+
+            obs1_id = await _insert_observation(conn, bank_id, "Alice is an avid hiker.", [m1, m2])
+            obs2_id = await _insert_observation(conn, bank_id, "Alice is a mountaineer.", [m3])
+
+        result = await memory.clear_observations_for_memory(
+            bank_id, str(m1), request_context=request_context
+        )
+
+        assert result["deleted_count"] == 1
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs1_id) not in obs_ids, "obs1 (references m1) should be deleted"
+            assert str(obs2_id) in obs_ids, "obs2 (does not reference m1) should remain"
+
+            # m3 should still be consolidated
+            assert await _get_consolidated_at(conn, m3) is not None
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_multiple_observations_for_same_memory_all_cleared(
+        self, memory: MemoryEngine, request_context: RequestContext
+    ):
+        """All observations referencing the target memory are cleared in one call."""
+        bank_id = f"test-clear-obs-multi-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(memory, bank_id, request_context)
+
+        pool = await memory._get_pool()
+        async with pool.acquire() as conn:
+            m1 = await _insert_memory(conn, bank_id, "Alice loves hiking.")
+            m2 = await _insert_memory(conn, bank_id, "Alice hikes every weekend.")
+
+            obs1_id = await _insert_observation(conn, bank_id, "Alice hikes often.", [m1])
+            obs2_id = await _insert_observation(conn, bank_id, "Alice is outdoorsy.", [m1, m2])
+
+        # Patch out consolidation so it doesn't re-set consolidated_at before we can check it
+        with patch.object(memory, "submit_async_consolidation", new=AsyncMock()):
+            result = await memory.clear_observations_for_memory(
+                bank_id, str(m1), request_context=request_context
+            )
+
+        assert result["deleted_count"] == 2
+
+        async with pool.acquire() as conn:
+            obs_ids = await _get_observation_ids(conn, bank_id)
+            assert str(obs1_id) not in obs_ids
+            assert str(obs2_id) not in obs_ids
+
+            # m1 and m2 should both be reset
+            assert await _get_consolidated_at(conn, m1) is None
+            assert await _get_consolidated_at(conn, m2) is None
+
+        await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_provider_default_models.py

@@ -93,7 +93,7 @@ def test_per_operation_provider_default_model():
         config = HindsightConfig.from_env()
 
         # Global LLM should use OpenAI default
-        assert config.llm_model == "o3-mini", f"Expected o3-mini, got {config.llm_model}"
+        assert config.llm_model == "gpt-4o-mini", f"Expected gpt-4o-mini, got {config.llm_model}"
 
         # Retain should use Anthropic default
         assert (

hindsight-api/tests/test_reflect_agent.py

@@ -7,14 +7,17 @@ These tests verify:
 3. Recovery from tool execution errors
 """
 
+from unittest.mock import AsyncMock, MagicMock
+
 import pytest
-from unittest.mock import AsyncMock, MagicMock, patch
 
 from hindsight_api.engine.reflect.agent import (
-    _normalize_tool_name,
-    _is_done_tool,
     _clean_answer_text,
     _clean_done_answer,
+    _count_messages_tokens,
+    _is_context_overflow_error,
+    _is_done_tool,
+    _normalize_tool_name,
     run_reflect_agent,
 )
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
@@ -412,3 +415,193 @@ class TestReflectAgentMocked:
         # Should have a result even if no memories found
         assert result is not None
         assert result.iterations == 3
+
+
+class TestContextOverflowHelpers:
+    """Unit tests for context-overflow detection helpers."""
+
+    def test_count_messages_tokens_basic(self):
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "What is the capital of France?"},
+        ]
+        count = _count_messages_tokens(messages)
+        assert count > 0
+        # Rough sanity check: ~10 tokens for each message
+        assert count < 100
+
+    def test_count_messages_tokens_with_tool_result(self):
+        """A large tool result should substantially increase the count."""
+        small_messages = [{"role": "user", "content": "hi"}]
+        large_messages = [
+            {"role": "user", "content": "hi"},
+            {
+                "role": "tool",
+                "tool_call_id": "x",
+                "name": "recall",
+                "content": '{"memories": [' + ', '.join([f'{{"id": "m{i}", "content": "A long memory fact about some topic that goes on and on."}}' for i in range(50)]) + ']}',
+            },
+        ]
+        small = _count_messages_tokens(small_messages)
+        large = _count_messages_tokens(large_messages)
+        assert large > small + 200
+
+    def test_is_context_overflow_error_openai(self):
+        assert _is_context_overflow_error(Exception("context_length_exceeded: too many tokens"))
+        assert _is_context_overflow_error(Exception("This model's maximum context length is 128000 tokens. However, your messages resulted in 142164 tokens."))
+
+    def test_is_context_overflow_error_anthropic(self):
+        assert _is_context_overflow_error(Exception("prompt_too_long"))
+        assert _is_context_overflow_error(Exception("prompt is too long for this model"))
+
+    def test_is_context_overflow_error_gemini(self):
+        assert _is_context_overflow_error(Exception("RESOURCE_EXHAUSTED: quota exceeded"))
+
+    def test_is_context_overflow_error_generic(self):
+        assert _is_context_overflow_error(Exception("input is too long to process"))
+        assert _is_context_overflow_error(Exception("too many tokens in the request"))
+
+    def test_is_context_overflow_error_unrelated(self):
+        assert not _is_context_overflow_error(Exception("connection timeout"))
+        assert not _is_context_overflow_error(Exception("rate limit exceeded"))
+        assert not _is_context_overflow_error(ValueError("invalid argument"))
+
+
+class TestContextOverflowBehavior:
+    """Test that the reflect agent handles context overflow gracefully."""
+
+    @pytest.fixture
+    def mock_llm(self):
+        llm = MagicMock()
+        llm.call_with_tools = AsyncMock()
+        llm.call = AsyncMock(
+            return_value=("Synthesized answer from gathered evidence.", TokenUsage(input_tokens=50, output_tokens=20, total_tokens=70))
+        )
+        return llm
+
+    @pytest.fixture
+    def mock_functions_with_large_output(self):
+        """Mock functions that return a large enough payload to exceed a tiny token budget."""
+        large_memories = [
+            {"id": f"mem-{i}", "content": f"Memory fact number {i}: " + "A" * 200}
+            for i in range(20)
+        ]
+        return {
+            "search_mental_models_fn": AsyncMock(return_value={"mental_models": []}),
+            "search_observations_fn": AsyncMock(return_value={"observations": []}),
+            "recall_fn": AsyncMock(return_value={"memories": large_memories}),
+            "expand_fn": AsyncMock(return_value={"memories": []}),
+        }
+
+    @pytest.mark.asyncio
+    async def test_proactive_guard_fires_when_budget_exceeded(self, mock_llm, mock_functions_with_large_output):
+        """When token count exceeds max_context_tokens after a tool call, the agent
+        should immediately synthesize from gathered evidence instead of making
+        another LLM call that would overflow."""
+        # First call: LLM calls recall (forced by iter 0 with no mental models)
+        mock_llm.call_with_tools.return_value = LLMToolCallResult(
+            tool_calls=[LLMToolCall(id="1", name="recall", arguments={"query": "test"})],
+            finish_reason="tool_calls",
+        )
+
+        # Set a tiny token budget — the recall result alone will blow past it
+        result = await run_reflect_agent(
+            llm_config=mock_llm,
+            bank_id="test-bank",
+            query="What do you know?",
+            bank_profile={"name": "Test", "mission": "Testing"},
+            max_context_tokens=100,
+            **mock_functions_with_large_output,
+        )
+
+        assert result.text == "Synthesized answer from gathered evidence."
+        # call_with_tools was called once (for the forced recall), then the guard
+        # kicked in — no further tool-call iterations
+        assert mock_llm.call_with_tools.call_count == 1
+        # llm.call() was invoked to generate the final synthesis
+        mock_llm.call.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_context_overflow_error_skips_retry(self, mock_llm, mock_functions_with_large_output):
+        """A context_length_exceeded error from the LLM should NOT be retried —
+        it should immediately fall back to final synthesis."""
+        mock_llm.call_with_tools.side_effect = Exception(
+            "context_length_exceeded: messages resulted in 150000 tokens."
+        )
+
+        result = await run_reflect_agent(
+            llm_config=mock_llm,
+            bank_id="test-bank",
+            query="What do you know?",
+            bank_profile={"name": "Test", "mission": "Testing"},
+            max_iterations=5,
+            **mock_functions_with_large_output,
+        )
+
+        assert result is not None
+        # Should have attempted only 1 iteration (no retry on overflow error)
+        assert mock_llm.call_with_tools.call_count == 1
+        # Final synthesis was called
+        mock_llm.call.assert_called_once()
+
+
+class TestContextOverflowIntegration:
+    """Integration test: real LLM with a very small max_context_tokens.
+
+    The agent will make one real LLM call (forced tool choice), receive a large
+    tool result that exceeds the tiny budget, then synthesize from it via a second
+    real LLM call — all without raising a context_length_exceeded error.
+    """
+
+    @pytest.mark.asyncio
+    async def test_reflect_completes_with_tiny_context_budget(self, memory, request_context):
+        """End-to-end: reflect on a bank with max_context_tokens=1 (tiny budget).
+
+        Setting max_context_tokens=1 guarantees the proactive guard fires as soon
+        as the first tool result is received and evidence is available.
+        The result must be a non-empty string with no exception raised.
+        """
+        import uuid
+        from unittest.mock import patch
+
+        bank_id = f"test-ctx-overflow-{uuid.uuid4().hex[:8]}"
+        try:
+            # Retain a handful of facts so the recall tool has something to return
+            await memory.retain_async(
+                bank_id=bank_id,
+                content="Alice is a software engineer who enjoys hiking on weekends.",
+                request_context=request_context,
+            )
+            await memory.retain_async(
+                bank_id=bank_id,
+                content="Bob is a designer who loves cooking Italian food.",
+                request_context=request_context,
+            )
+
+            # Patch get_config where memory_engine uses it, injecting a tiny
+            # max_context_tokens.  Everything else delegates to the real config.
+            real_config = memory._get_raw_config() if hasattr(memory, "_get_raw_config") else None
+            from hindsight_api.config import get_config as _real_get_config
+
+            class _TinyContextProxy:
+                """Forwards all attribute access to the real config proxy except
+                reflect_max_context_tokens which is forced to 1."""
+                _real = _real_get_config()
+
+                def __getattr__(self, name: str):
+                    if name == "reflect_max_context_tokens":
+                        return 1
+                    return getattr(self._real, name)
+
+            with patch("hindsight_api.engine.memory_engine.get_config", return_value=_TinyContextProxy()):
+                result = await memory.reflect_async(
+                    bank_id=bank_id,
+                    query="Tell me about the people you know.",
+                    request_context=request_context,
+                )
+
+            assert result.text, "reflect must return a non-empty answer"
+            assert result.usage.total_tokens > 0
+
+        finally:
+            await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_reranker_error_handling.py

@@ -0,0 +1,71 @@
+"""
+Regression test for UnboundLocalError in recall when the reranker raises.
+
+Before the fix, `scored_results` and `pre_filtered_count` were only assigned
+inside the `try` block, but referenced in the `finally` block.  If
+`reranker_instance.rerank()` (or `ensure_initialized()`) raised, the `finally`
+block crashed with `UnboundLocalError` instead of propagating the original
+exception.
+
+Fix: initialise both variables to safe defaults before the try/finally block.
+"""
+
+from datetime import datetime, timezone
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_recall_reranker_error_does_not_raise_unbound_local(memory, request_context):
+    """Recall must propagate the reranker's exception, not an UnboundLocalError."""
+    bank_id = f"test_reranker_err_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        await memory.retain_async(
+            bank_id=bank_id,
+            content="Paris is the capital of France",
+            request_context=request_context,
+        )
+
+        # Simulate a reranker failure (e.g. Cohere API error on empty/small candidate set)
+        rerank_mock = AsyncMock(side_effect=RuntimeError("reranker API error"))
+        memory._cross_encoder_reranker._initialized = True  # skip ensure_initialized
+
+        with patch.object(memory._cross_encoder_reranker, "rerank", rerank_mock):
+            with pytest.raises(Exception, match="reranker API error"):
+                await memory.recall_async(
+                    bank_id=bank_id,
+                    query="capital of France",
+                    request_context=request_context,
+                )
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+@pytest.mark.asyncio
+async def test_recall_reranker_init_error_does_not_raise_unbound_local(memory, request_context):
+    """Same regression when ensure_initialized() raises (before pre_filtered_count is set)."""
+    bank_id = f"test_reranker_init_err_{datetime.now(timezone.utc).timestamp()}"
+
+    try:
+        await memory.retain_async(
+            bank_id=bank_id,
+            content="Paris is the capital of France",
+            request_context=request_context,
+        )
+
+        init_mock = AsyncMock(side_effect=RuntimeError("reranker init failed"))
+        memory._cross_encoder_reranker._initialized = False
+
+        with patch.object(memory._cross_encoder_reranker, "ensure_initialized", init_mock):
+            with pytest.raises(Exception, match="reranker init failed"):
+                await memory.recall_async(
+                    bank_id=bank_id,
+                    query="capital of France",
+                    request_context=request_context,
+                )
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)