fix: docker startup fails with named docker volumes

2026-02-19 13:43:04 +01:00
541 changed files with 18852 additions and 33202 deletions
@@ -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=gpt-4o-mini
+HINDSIGHT_API_LLM_MODEL=o3-mini
 HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1

 # Example: Anthropic Claude configuration
@@ -31,9 +31,6 @@ 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
@@ -46,14 +46,6 @@ 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
@@ -85,18 +77,6 @@ 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
@@ -108,8 +88,6 @@ 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:
@@ -259,55 +237,6 @@ 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
@@ -339,14 +268,11 @@ 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=$(npm publish --access public --ignore-scripts 2>&1)
+        OUTPUT=$(npm publish --access public 2>&1)
        EXIT_CODE=$?
        echo "$OUTPUT"
        if [ $EXIT_CODE -ne 0 ]; then
@@ -561,7 +487,7 @@ jobs:

  create-github-release:
    runs-on: ubuntu-latest
-    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]
+    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]
    permissions:
      contents: write

@@ -596,12 +522,6 @@ 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:
@@ -640,7 +560,6 @@ 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
@@ -648,8 +567,6 @@ 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
@@ -97,29 +97,6 @@ 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

@@ -194,9 +171,9 @@ jobs:
  test-rust-cli:
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -204,12 +181,6 @@ 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

@@ -256,46 +227,25 @@ 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..120}; do
+        for i in {1..60}; do
          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
            echo "API server is ready after ${i}s"
            break
          fi
-          if [ $i -eq 120 ]; then
-            echo "API server failed to start after 120s"
+          if [ $i -eq 60 ]; then
+            echo "API server failed to start after 60s"
            cat /tmp/api-server.log
            exit 1
          fi
@@ -390,21 +340,12 @@ 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:
-        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
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        HINDSIGHT_API_EMBEDDINGS_PROVIDER: openai
+        HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        HINDSIGHT_API_RERANKER_PROVIDER: cohere
        HINDSIGHT_API_COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
      run: ./docker/test-image.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"
@@ -412,13 +353,14 @@ jobs:
  test-api:
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
      GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
      COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
      HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -426,12 +368,6 @@ 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:
@@ -478,9 +414,9 @@ jobs:
  test-python-client:
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -489,12 +425,6 @@ 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:
@@ -522,46 +452,25 @@ 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..120}; do
+        for i in {1..60}; do
          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
            echo "API server is ready after ${i}s"
            break
          fi
-          if [ $i -eq 120 ]; then
-            echo "API server failed to start after 120s"
+          if [ $i -eq 60 ]; then
+            echo "API server failed to start after 60s"
            cat /tmp/api-server.log
            exit 1
          fi
@@ -581,9 +490,9 @@ jobs:
  test-typescript-client:
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -592,12 +501,6 @@ 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:
@@ -630,46 +533,25 @@ 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..120}; do
+        for i in {1..60}; do
          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
            echo "API server is ready after ${i}s"
            break
          fi
-          if [ $i -eq 120 ]; then
-            echo "API server failed to start after 120s"
+          if [ $i -eq 60 ]; then
+            echo "API server failed to start after 60s"
            cat /tmp/api-server.log
            exit 1
          fi
@@ -689,9 +571,9 @@ jobs:
  test-rust-client:
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -700,12 +582,6 @@ 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:
@@ -737,46 +613,25 @@ 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..120}; do
+        for i in {1..60}; do
          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
            echo "API server is ready after ${i}s"
            break
          fi
-          if [ $i -eq 120 ]; then
-            echo "API server failed to start after 120s"
+          if [ $i -eq 60 ]; then
+            echo "API server failed to start after 60s"
            cat /tmp/api-server.log
            exit 1
          fi
@@ -796,9 +651,9 @@ jobs:
  test-go-client:
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)
@@ -807,12 +662,6 @@ 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:
@@ -838,46 +687,25 @@ 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..120}; do
+        for i in {1..60}; do
          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
            echo "API server is ready after ${i}s"
            break
          fi
-          if [ $i -eq 120 ]; then
-            echo "API server failed to start after 120s"
+          if [ $i -eq 60 ]; then
+            echo "API server failed to start after 60s"
            cat /tmp/api-server.log
            exit 1
          fi
@@ -901,9 +729,9 @@ jobs:
  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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      HINDSIGHT_EMBED_PACKAGE_PATH: ${{ github.workspace }}/hindsight-embed
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
@@ -912,12 +740,6 @@ 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" >> $GITHUB_ENV
-
    - name: Install uv
      uses: astral-sh/setup-uv@v5
      with:
@@ -974,22 +796,21 @@ jobs:
      run: |
        cat > .env << EOF
        HINDSIGHT_API_LLM_PROVIDER=${{ 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..120}; do
+        for i in {1..60}; do
          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
            echo "API server is ready after ${i}s"
            break
          fi
-          if [ $i -eq 120 ]; then
-            echo "API server failed to start after 120s"
+          if [ $i -eq 60 ]; then
+            echo "API server failed to start after 60s"
            cat /tmp/api-server.log
            exit 1
          fi
@@ -1009,9 +830,9 @@ jobs:
  test-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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -1019,12 +840,6 @@ 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" >> $GITHUB_ENV
-
    - name: Install uv
      uses: astral-sh/setup-uv@v5
      with:
@@ -1072,22 +887,21 @@ jobs:
      run: |
        cat > .env << EOF
        HINDSIGHT_API_LLM_PROVIDER=${{ 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..120}; do
+        for i in {1..60}; do
          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
            echo "API server is ready after ${i}s"
            break
          fi
-          if [ $i -eq 120 ]; then
-            echo "API server failed to start after 120s"
+          if [ $i -eq 60 ]; then
+            echo "API server failed to start after 60s"
            cat /tmp/api-server.log
            exit 1
          fi
@@ -1162,53 +976,18 @@ 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: vertexai
-      HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
-      HINDSIGHT_API_LLM_MODEL: google/gemini-2.5-flash-lite
+      HINDSIGHT_API_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      # Prefer CPU-only PyTorch in CI
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
    - uses: actions/checkout@v4

-    - name: 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:
@@ -1244,25 +1023,19 @@ jobs:
  test-hindsight-all:
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      # For test_server_integration.py compatibility
-      HINDSIGHT_LLM_PROVIDER: vertexai
-      HINDSIGHT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY: /tmp/gcp-credentials.json
-      HINDSIGHT_LLM_MODEL: google/gemini-2.5-flash-lite
+      HINDSIGHT_LLM_PROVIDER: groq
+      HINDSIGHT_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_LLM_MODEL: openai/gpt-oss-20b
      # Prefer CPU-only PyTorch in CI
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

    steps:
    - uses: actions/checkout@v4

-    - name: 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:
@@ -1299,9 +1072,9 @@ jobs:
    runs-on: ubuntu-latest
    needs: test-rust-cli
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      HINDSIGHT_API_URL: http://localhost:8888
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
@@ -1309,12 +1082,6 @@ 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:
@@ -1357,46 +1124,25 @@ 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..120}; do
+        for i in {1..60}; do
          if curl -sf http://localhost:8888/health > /dev/null 2>&1; then
            echo "API server is ready after ${i}s"
            break
          fi
-          if [ $i -eq 120 ]; then
-            echo "API server failed to start after 120s"
+          if [ $i -eq 60 ]; then
+            echo "API server failed to start after 60s"
            cat /tmp/api-server.log
            exit 1
          fi
@@ -1418,9 +1164,9 @@ jobs:
  test-upgrade:
    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_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu

@@ -1429,12 +1175,6 @@ 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

@@ -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., gpt-4o-mini, claude-sonnet-4-20250514)
+- `HINDSIGHT_API_LLM_MODEL`: Model name (e.g., o3-mini, claude-sonnet-4-20250514)

 Optional (uses local models by default):
 - `HINDSIGHT_API_EMBEDDINGS_PROVIDER`: local (default) or tei
 - `HINDSIGHT_API_RERANKER_PROVIDER`: local (default) or tei
 - `HINDSIGHT_API_DATABASE_URL`: External PostgreSQL (uses embedded pg0 by default)
- `HINDSIGHT_API_ENABLE_BANK_CONFIG_API`: Enable per-bank config API (default: true)
+- `HINDSIGHT_API_ENABLE_BANK_CONFIG_API`: Enable per-bank config API (default: false, disabled for security)
@@ -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 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.
+The easiest way use Hindsight with an existing agent is with the LLM Wrapper. You can add memory to your agent with 2 lines of code. That will swap your current LLM client out with the Hindsight wrapper. After that, memories will be stored and retrieved automatically as you make LLM calls.

 If you need more control over how and when your agent stores and recalls memories, there's also a simple API you can integrate with using the SDKs or directly via HTTP.

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

 - **World:** Facts about the world ("The stove gets hot")
 - **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
@@ -307,5 +307,3 @@ 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="" />
@@ -97,7 +97,7 @@ fi
 if [ "$ENABLE_CP" = "true" ]; then
    echo "🎛️  Starting Control Plane..."
    cd /app/control-plane
-    PORT="${HINDSIGHT_CP_PORT:-9999}" node server.js &
+    PORT=9999 node server.js &
    CP_PID=$!
    PIDS+=($CP_PID)
 else
@@ -110,7 +110,7 @@ echo "✅ Hindsight is running!"
 echo ""
 echo "📍 Access:"
 if [ "$ENABLE_CP" = "true" ]; then
-    echo "   Control Plane: http://localhost:${HINDSIGHT_CP_PORT:-9999}"
+    echo "   Control Plane: http://localhost:9999"
 fi
 if [ "$ENABLE_API" = "true" ]; then
    echo "   API:           http://localhost:8888"
@@ -13,9 +13,9 @@
 #   target  - Optional: 'cp-only' for control plane, otherwise assumes API image (default: api)
 #
 # Environment variables:
-#   HINDSIGHT_API_LLM_API_KEY                   - Required for API/standalone images (LLM verification)
-#   HINDSIGHT_API_LLM_PROVIDER                  - LLM provider (default: openai)
-#   HINDSIGHT_API_LLM_MODEL                     - LLM model (default: gpt-4o-mini)
+#   GROQ_API_KEY                                - Required for API/standalone images (LLM verification)
+#   HINDSIGHT_API_LLM_PROVIDER                  - LLM provider (default: groq)
+#   HINDSIGHT_API_LLM_MODEL                     - LLM model (default: llama-3.3-70b-versatile)
 #   HINDSIGHT_API_EMBEDDINGS_PROVIDER           - Embeddings provider (optional, for slim images: openai, cohere, tei)
 #   HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY     - OpenAI API key for embeddings (optional)
 #   HINDSIGHT_API_RERANKER_PROVIDER             - Reranker provider (optional, for slim images: cohere, tei)
@@ -34,7 +34,7 @@
 #   ./docker/test-image.sh hindsight-control-plane:test cp-only
 #
 #   # Test slim image with external providers
-#   export HINDSIGHT_API_LLM_API_KEY=sk_xxx
+#   export GROQ_API_KEY=gsk_xxx
 #   export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
 #   export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=sk-xxx
 #   export HINDSIGHT_API_RERANKER_PROVIDER=cohere
@@ -60,8 +60,8 @@ IMAGE="${1:-}"
 TARGET="${2:-api}"
 TIMEOUT="${SMOKE_TEST_TIMEOUT:-120}"
 CONTAINER_NAME="${SMOKE_TEST_CONTAINER_NAME:-hindsight-smoke-test}"
-LLM_PROVIDER="${HINDSIGHT_API_LLM_PROVIDER:-openai}"
-LLM_MODEL="${HINDSIGHT_API_LLM_MODEL:-gpt-4o-mini}"
+LLM_PROVIDER="${HINDSIGHT_API_LLM_PROVIDER:-groq}"
+LLM_MODEL="${HINDSIGHT_API_LLM_MODEL:-llama-3.3-70b-versatile}"

 # Validate arguments
 if [ -z "$IMAGE" ]; then
@@ -88,9 +88,9 @@ else
 fi

 # Check for required environment variables
-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${NC}"
-    echo "Set it with: export HINDSIGHT_API_LLM_API_KEY=your-api-key"
+if [ "$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"
    exit 2
 fi

@@ -123,25 +123,9 @@ 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=$LLM_PROVIDER"
-    if [ -n "${HINDSIGHT_API_LLM_API_KEY:-}" ]; then
-        DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${HINDSIGHT_API_LLM_API_KEY}"
-    fi
+    DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${GROQ_API_KEY}"
    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}"
@@ -6,17 +6,24 @@
 # 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:
-#   OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
+#   GROQ_API_KEY=gsk_xxx OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
 #

 set -euo pipefail

 # Check for required API keys
+if [ -z "${GROQ_API_KEY:-}" ]; then
+    echo "❌ Error: GROQ_API_KEY environment variable is required"
+    echo "Set it with: export GROQ_API_KEY=gsk_xxx"
+    exit 1
+fi
+
 if [ -z "${OPENAI_API_KEY:-}" ]; then
    echo "❌ Error: OPENAI_API_KEY environment variable is required"
    echo "Set it with: export OPENAI_API_KEY=sk-xxx"
@@ -34,10 +41,7 @@ IMAGE="${1:-hindsight-slim:test}"
 echo "Testing image: $IMAGE"
 echo ""

-# Set up LLM and external providers
-export HINDSIGHT_API_LLM_PROVIDER=openai
-export HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY
-export HINDSIGHT_API_LLM_MODEL=gpt-4o-mini
+# Set up external providers
 export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
 export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=$OPENAI_API_KEY
 export HINDSIGHT_API_RERANKER_PROVIDER=cohere
@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.15
-appVersion: "0.4.15"
+version: 0.4.12
+appVersion: "0.4.12"
 keywords:
  - ai
  - memory
@@ -46,4 +46,4 @@ __all__ = [
    "RemoteTEICrossEncoder",
    "LLMConfig",
 ]
-__version__ = "0.4.15"
+__version__ = "0.4.12"
@@ -1,88 +0,0 @@
-"""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")
@@ -1,36 +0,0 @@
-"""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")
@@ -1,68 +0,0 @@
-"""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")
@@ -1,34 +0,0 @@
-"""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
@@ -1,46 +0,0 @@
-"""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
@@ -1,83 +0,0 @@
-"""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")
@@ -1,35 +0,0 @@
-"""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")
@@ -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, MemoryFact, TokenUsage
+from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, 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
@@ -97,12 +97,6 @@ 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."""

@@ -113,10 +107,6 @@ 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):
@@ -199,9 +189,6 @@ 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):
@@ -353,9 +340,6 @@ 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):
@@ -383,15 +367,7 @@ class MemoryItem(BaseModel):
    )

    content: str
-    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)."
-        ),
-    )
+    timestamp: datetime | None = None
    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.")
@@ -403,16 +379,6 @@ 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
@@ -422,14 +388,12 @@ 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', or the special value 'unset' to store without a timestamp."
+                    f"Invalid timestamp/event_date format: '{v}'. Expected ISO format like '2024-01-15T10:30:00' or '2024-01-15T10:30:00Z'"
                ) from e
        raise ValueError(f"timestamp must be a string or datetime, got {type(v).__name__}")

@@ -449,6 +413,7 @@ class RetainRequest(BaseModel):
                    },
                ],
                "async": False,
+                "document_tags": ["user_a", "user_b"],
            }
        }
    )
@@ -461,8 +426,7 @@ class RetainRequest(BaseModel):
    )
    document_tags: list[str] | None = Field(
        default=None,
-        description="Deprecated. Use item-level tags instead.",
-        deprecated=True,
+        description="Tags applied to all items in this request. These are merged with any item-level tags.",
    )


@@ -899,103 +863,18 @@ class CreateBankRequest(BaseModel):
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
-                "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": "Alice",
+                "disposition": {"skepticism": 3, "literalism": 3, "empathy": 3},
+                "mission": "I am a PM helping my engineering team stay organized",
            }
        }
    )

-    # 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
+    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")


 class BankConfigUpdate(BaseModel):
@@ -1255,14 +1134,6 @@ 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."""

@@ -1830,12 +1701,6 @@ 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


@@ -1948,19 +1813,12 @@ 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, q=q, tags=tags, tags_match=tags_match, request_context=request_context
-            )
+            data = await app.state.memory.get_graph_data(bank_id, type, limit=limit, 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:
@@ -2009,8 +1867,6 @@ 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:
@@ -2042,8 +1898,6 @@ 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:
@@ -2105,10 +1959,6 @@ 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(
@@ -2127,16 +1977,14 @@ 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)
-            def _fact_to_result(fact: "MemoryFact") -> RecallResult:
-                return RecallResult(
+            recall_results = [
+                RecallResult(
                    id=fact.id,
                    text=fact.text,
                    type=fact.fact_type,
@@ -2148,10 +1996,9 @@ 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,
                )
-
-            recall_results = [_fact_to_result(fact) for fact in core_result.results]
+                for fact in core_result.results
+            ]

            # Convert chunks from engine to HTTP API format
            chunks_response = None
@@ -2179,19 +2026,11 @@ 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
@@ -2385,34 +2224,141 @@ def _register_routes(app: FastAPI):
    ):
        """Get statistics about memory nodes and links for a memory bank."""
        try:
-            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)
+            # 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,
+                )
+
        except (AuthenticationError, HTTPException):
            raise
        except Exception as e:
@@ -2447,8 +2393,6 @@ 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:
@@ -2488,8 +2432,6 @@ 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:
@@ -2551,8 +2493,6 @@ 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:
@@ -2711,8 +2651,6 @@ 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:
@@ -2744,8 +2682,6 @@ 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:
@@ -2788,8 +2724,6 @@ 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:
@@ -2822,8 +2756,6 @@ 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:
@@ -2860,8 +2792,6 @@ 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:
@@ -2900,8 +2830,6 @@ 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:
@@ -2933,8 +2861,6 @@ 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:
@@ -2954,13 +2880,7 @@ def _register_routes(app: FastAPI):
    )
    async def api_list_documents(
        bank_id: str,
-        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'"
-        ),
+        q: str | None = None,
        limit: int = 100,
        offset: int = 0,
        request_context: RequestContext = Depends(get_request_context),
@@ -2970,25 +2890,15 @@ def _register_routes(app: FastAPI):

        Args:
            bank_id: Memory Bank ID (from path)
-            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)
+            q: Search query (searches document ID and metadata)
            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,
-                tags=tags,
-                tags_match=tags_match,
-                limit=limit,
-                offset=offset,
-                request_context=request_context,
+                bank_id=bank_id, search_query=q, 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:
@@ -3021,8 +2931,6 @@ 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:
@@ -3076,8 +2984,6 @@ 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:
@@ -3107,8 +3013,6 @@ 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:
@@ -3153,8 +3057,6 @@ 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:
@@ -3191,8 +3093,6 @@ 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:
@@ -3224,8 +3124,6 @@ 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:
@@ -3258,8 +3156,6 @@ 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:
@@ -3276,7 +3172,6 @@ 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)."""
@@ -3296,8 +3191,6 @@ 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:
@@ -3314,7 +3207,6 @@ 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)
@@ -3341,8 +3233,6 @@ 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:
@@ -3371,8 +3261,6 @@ 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:
@@ -3398,18 +3286,21 @@ 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 if provided (stored in DB for display only, deprecated)
-            if request.name is not None:
+            # 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:
                await app.state.memory.update_bank(
                    bank_id,
                    name=request.name,
+                    mission=mission_value,
                    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)
+            # 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
+                )

            # Get final profile
            final_profile = await app.state.memory.get_bank_profile(bank_id, request_context=request_context)
@@ -3426,8 +3317,6 @@ 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:
@@ -3453,18 +3342,21 @@ def _register_routes(app: FastAPI):
            # Ensure bank exists
            await app.state.memory.get_bank_profile(bank_id, request_context=request_context)

-            # Update name if provided (stored in DB for display only, deprecated)
-            if request.name is not None:
+            # 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:
                await app.state.memory.update_bank(
                    bank_id,
                    name=request.name,
+                    mission=mission_value,
                    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)
+            # 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
+                )

            # Get final profile
            final_profile = await app.state.memory.get_bank_profile(bank_id, request_context=request_context)
@@ -3481,8 +3373,6 @@ 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:
@@ -3512,8 +3402,6 @@ 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:
@@ -3540,8 +3428,6 @@ 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:
@@ -3551,42 +3437,6 @@ def _register_routes(app: FastAPI):
            logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/observations: {error_detail}")
            raise HTTPException(status_code=500, detail=str(e))

-    @app.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,
@@ -3601,19 +3451,9 @@ 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 re-enable.",
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to 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)

@@ -3621,8 +3461,6 @@ 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:
@@ -3648,19 +3486,9 @@ 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 re-enable.",
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to 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)

@@ -3669,8 +3497,6 @@ 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))
@@ -3697,19 +3523,9 @@ 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 re-enable.",
+                detail="Bank configuration API is disabled. Set HINDSIGHT_API_ENABLE_BANK_CONFIG_API=true to 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)

@@ -3718,8 +3534,6 @@ 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:
@@ -3745,8 +3559,6 @@ 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:
@@ -3792,9 +3604,7 @@ def _register_routes(app: FastAPI):
            contents = []
            for item in request.items:
                content_dict = {"content": item.content}
-                if item.timestamp == "unset":
-                    content_dict["event_date"] = None
-                elif item.timestamp:
+                if item.timestamp:
                    content_dict["event_date"] = item.timestamp
                if item.context:
                    content_dict["context"] = item.context
@@ -3806,8 +3616,6 @@ 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_:
@@ -4036,8 +3844,6 @@ 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:
@@ -8,48 +8,12 @@ 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 = {
@@ -118,11 +82,16 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
    """
    mcp = FastMCP("hindsight-mcp-server")

-    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(
-        {
+    # Configure and register tools using shared module
+    config = MCPToolsConfig(
+        bank_id_resolver=get_current_bank_id,
+        api_key_resolver=get_current_api_key,  # Propagate API key for tenant auth
+        tenant_id_resolver=get_current_tenant_id,  # Propagate tenant_id for usage metering
+        api_key_id_resolver=get_current_api_key_id,  # Propagate api_key_id for usage metering
+        include_bank_id_param=multi_bank,
+        tools=None
+        if multi_bank
+        else {
            "retain",
            "recall",
            "reflect",
@@ -132,40 +101,8 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
            "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",
-            "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,
+        },  # Scoped tools for single-bank mode (excludes bank management: list_banks, create_bank)
+        retain_fire_and_forget=False,  # HTTP MCP supports sync/async modes
    )

    register_mcp_tools(mcp, memory, config)
@@ -331,7 +268,7 @@ 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), extra_headers=e.headers)
+                await self._send_error(send, 401, str(e))
                return

        # Set schema from tenant context so downstream DB queries use the correct schema
@@ -413,17 +350,14 @@ class MCPMiddleware:
            if schema_token is not None:
                _current_schema.reset(schema_token)

-    async def _send_error(self, send, status: int, message: str, extra_headers: dict[str, str] | None = None):
+    async def _send_error(self, send, status: int, message: str):
        """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": headers,
+                "headers": [(b"content-type", b"application/json")],
            }
        )
        await send(
@@ -218,10 +218,6 @@ 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"

@@ -232,12 +228,13 @@ 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
@@ -252,18 +249,13 @@ 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"

@@ -290,9 +282,7 @@ 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"
@@ -318,13 +308,6 @@ 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"
@@ -333,18 +316,18 @@ DEFAULT_LLM_PROVIDER = "openai"

 # Provider-specific default models
 PROVIDER_DEFAULT_MODELS = {
-    "openai": "gpt-4o-mini",
+    "openai": "o3-mini",
    "anthropic": "claude-haiku-4-5-20251001",
    "gemini": "gemini-2.5-flash",
    "groq": "openai/gpt-oss-120b",
    "ollama": "gemma3:12b",
    "lmstudio": "local-model",
-    "vertexai": "google/gemini-2.5-flash-lite",
+    "vertexai": "gemini-2.0-flash-001",
    "openai-codex": "gpt-5.2-codex",
    "claude-code": "claude-sonnet-4-5-20250929",
    "mock": "mock-model",
 }
-DEFAULT_LLM_MODEL = "gpt-4o-mini"  # Fallback if provider not in table
+DEFAULT_LLM_MODEL = "o3-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
@@ -356,9 +339,6 @@ 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)
@@ -382,8 +362,6 @@ 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"

@@ -406,12 +384,12 @@ DEFAULT_LOG_LEVEL = "info"
 DEFAULT_LOG_FORMAT = "text"  # Options: "text", "json"
 DEFAULT_WORKERS = 1
 DEFAULT_MCP_ENABLED = True
-DEFAULT_MCP_ENABLED_TOOLS: list[str] | None = None  # None = all tools enabled
-DEFAULT_ENABLE_BANK_CONFIG_API = True
+DEFAULT_ENABLE_BANK_CONFIG_API = False  # Disabled by default for security
 DEFAULT_GRAPH_RETRIEVER = "link_expansion"  # Options: "link_expansion", "mpfp", "bfs"
 DEFAULT_MPFP_TOP_K_NEIGHBORS = 20  # Fan-out limit per node in MPFP graph traversal
 DEFAULT_RECALL_MAX_CONCURRENT = 32  # Max concurrent recall operations per worker
 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
@@ -420,10 +398,8 @@ 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

@@ -438,9 +414,7 @@ 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_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
+DEFAULT_CONSOLIDATION_MAX_TOKENS = 1024  # Max tokens for recall when finding related observations

 # Database migrations
 DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
@@ -462,12 +436,6 @@ 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
@@ -573,9 +541,6 @@ 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
@@ -643,8 +608,6 @@ 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
@@ -653,7 +616,6 @@ 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
@@ -668,12 +630,10 @@ 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)
@@ -698,24 +658,7 @@ 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
@@ -741,7 +684,6 @@ class HindsightConfig:

    # Reflect agent settings
    reflect_max_iterations: int
-    reflect_max_context_tokens: int

    # OpenTelemetry tracing configuration
    otel_traces_enabled: bool
@@ -782,27 +724,12 @@ class HindsightConfig:
    # 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
@@ -923,8 +850,6 @@ 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,
@@ -1057,9 +982,6 @@ 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)),
@@ -1067,9 +989,6 @@ 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
@@ -1097,10 +1016,8 @@ 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(
@@ -1138,15 +1055,9 @@ 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
@@ -1166,20 +1077,6 @@ 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"),
@@ -16,7 +16,6 @@ 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

@@ -150,8 +149,8 @@ class ConfigResolver:
        try:
            async with self.pool.acquire() as conn:
                row = await conn.fetchrow(
-                    f"""
-                    SELECT config FROM {fq_table("banks")} WHERE bank_id = $1
+                    """
+                    SELECT config FROM banks WHERE bank_id = $1
                    """,
                    bank_id,
                )
@@ -242,8 +241,8 @@ class ConfigResolver:
        # Merge with existing config (JSONB || operator)
        async with self.pool.acquire() as conn:
            await conn.execute(
-                f"""
-                UPDATE {fq_table("banks")}
+                """
+                UPDATE banks
                SET config = config || $1::jsonb,
                    updated_at = now()
                WHERE bank_id = $2
@@ -263,9 +262,9 @@ class ConfigResolver:
        """
        async with self.pool.acquire() as conn:
            await conn.execute(
-                f"""
-                UPDATE {fq_table("banks")}
-                SET config = '{{}}'::jsonb,
+                """
+                UPDATE banks
+                SET config = '{}'::jsonb,
                    updated_at = now()
                WHERE bank_id = $1
                """,
@@ -1,66 +1,85 @@
 """Prompts for the consolidation engine."""

-# 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."
+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.

-# 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."""
+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.

-# Data section — format placeholders {facts_text} and {observations_text} are substituted at call time
-_BATCH_DATA_SECTION = """
-NEW FACTS:
-{facts_text}
+## EXTRACT DURABLE KNOWLEDGE, NOT EPHEMERAL STATE
+Facts often describe events or actions. Extract the DURABLE KNOWLEDGE implied by the fact, not the transient state.

-EXISTING OBSERVATIONS (JSON array, pooled from recalls across all facts above):
+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):
 {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

-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)"""
+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 []

-# Output format — JSON braces escaped as {{ }} so .format() leaves them literal
-_BATCH_OUTPUT_FORMAT = """
-Output a JSON object with three arrays.
+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": "..."}}
+]

-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"}}]}}
+Return [] if fact contains no durable knowledge.

-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
-    )
+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"""
@@ -29,7 +29,6 @@ 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,
@@ -43,7 +42,6 @@ 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__)
@@ -558,104 +556,6 @@ 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.
@@ -1110,19 +1010,9 @@ 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', 'zeroentropy', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'litellm', 'litellm-sdk', 'rrf'"
        )
@@ -20,7 +20,6 @@ RETRYABLE_EXCEPTIONS = (
    asyncpg.exceptions.InterfaceError,
    asyncpg.exceptions.ConnectionDoesNotExistError,
    asyncpg.exceptions.TooManyConnectionsError,
-    asyncpg.exceptions.DeadlockDetectedError,
    OSError,
    ConnectionError,
    asyncio.TimeoutError,
@@ -794,7 +794,6 @@ 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
@@ -841,7 +840,6 @@ 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
@@ -5,10 +5,6 @@ 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

@@ -16,43 +12,6 @@ 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
@@ -63,95 +22,14 @@ class EntityResolver:
    Resolves entities to canonical IDs with disambiguation.
    """

-    def __init__(self, pool: asyncpg.Pool, entity_lookup: str = "full"):
+    def __init__(self, pool: asyncpg.Pool):
        """
        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,
@@ -160,7 +38,6 @@ 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).
@@ -181,34 +58,15 @@ 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, taxonomy_lookup
-                )
+                return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)
        else:
-            return await self._resolve_entities_batch_impl(
-                conn, bank_id, entities_data, context, unit_event_date, taxonomy_lookup
-            )
+            return await self._resolve_entities_batch_impl(conn, bank_id, entities_data, context, unit_event_date)

    async def _resolve_entities_batch_impl(
-        self,
-        conn,
-        bank_id: str,
-        entities_data: list[dict],
-        context: str,
-        unit_event_date,
-        taxonomy_lookup: set[str] | None = None,
+        self, conn, bank_id: str, entities_data: list[dict], context: str, unit_event_date
    ) -> 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"""
@@ -272,103 +130,10 @@ 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: list[_EntityStat] = []
-        entities_to_create: list[_EntityToCreate] = []
+        entities_to_update = []  # (entity_id, event_date)
+        entities_to_create = []  # (idx, entity_data, event_date)

        for idx, entity_data in enumerate(entities_data):
            entity_text = entity_data["text"]
@@ -380,7 +145,7 @@ class EntityResolver:

            if not candidates:
                # Will create new entity
-                entities_to_create.append(_EntityToCreate(idx=idx, name=entity_text, event_date=entity_event_date))
+                entities_to_create.append((idx, entity_data, entity_event_date))
                continue

            # Score candidates
@@ -424,83 +189,73 @@ class EntityResolver:

            if best_score > threshold:
                entity_ids[idx] = best_candidate
-                entities_to_update.append(_EntityStat(entity_id=best_candidate, event_date=entity_event_date))
+                entities_to_update.append((best_candidate, entity_event_date))
            else:
-                entities_to_create.append(
-                    _EntityToCreate(idx=idx, name=entity_data["text"], event_date=entity_event_date)
-                )
+                entities_to_create.append((idx, entity_data, entity_event_date))

-        # 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 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,
+            )

-        # 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.
+        # Batch create new entities using COPY + INSERT for maximum speed
+        # This handles duplicates via ON CONFLICT and returns all IDs
        if entities_to_create:
-            # Group by lowercase name — deduplicate within the batch.
-            @dataclass
-            class _NameGroup:
-                name: str
-                event_date: datetime | None
-                indices: list[int] = field(default_factory=list)
+            # 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)

-            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)
+            # 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

-            # 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]
+            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)

-            # INSERT ... ON CONFLICT DO NOTHING — no row lock on already-existing entities.
-            inserted_rows = await conn.fetch(
+            # Batch INSERT ... ON CONFLICT with RETURNING
+            # Uses the batch count for mention_count instead of always 1
+            rows = await conn.fetch(
                f"""
                INSERT INTO {fq_table("entities")} (bank_id, canonical_name, first_seen, last_seen, mention_count)
-                SELECT $1, name, COALESCE(event_date, now()), COALESCE(event_date, now()), 1
-                FROM unnest($2::text[], $3::timestamptz[]) AS t(name, event_date)
+                SELECT $1, name, event_date, event_date, cnt
+                FROM unnest($2::text[], $3::timestamptz[], $4::int[]) AS t(name, event_date, cnt)
                ON CONFLICT (bank_id, LOWER(canonical_name))
-                DO NOTHING
-                RETURNING id, LOWER(canonical_name) AS name_lower
+                DO UPDATE SET
+                    mention_count = {fq_table("entities")}.mention_count + EXCLUDED.mention_count,
+                    last_seen = EXCLUDED.last_seen
+                RETURNING id
                """,
                bank_id,
                entity_names,
                entity_dates,
+                entity_counts,
            )
-            id_by_name: dict[str, str] = {row["name_lower"]: row["id"] for row in inserted_rows}

-            # 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)
+            # 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

        return entity_ids

@@ -653,7 +408,7 @@ 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, COALESCE($3, now()), COALESCE($4, now()), 1)
+            VALUES ($1, $2, $3, $4, 1)
            ON CONFLICT (bank_id, LOWER(canonical_name))
            DO UPDATE SET
                mention_count = {fq_table("entities")}.mention_count + 1,
@@ -786,14 +541,19 @@ class EntityResolver:
                        entity_id_1, entity_id_2 = entity_id_2, entity_id_1
                    cooccurrence_pairs.add((entity_id_1, entity_id_2))

-        # 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).
+        # Batch update co-occurrences
        if 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
+            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],
            )

    async def get_units_by_entity(self, entity_id: str, limit: int = 100) -> list[str]:
@@ -12,7 +12,6 @@ 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


@@ -338,8 +337,6 @@ 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",
@@ -349,9 +346,7 @@ class MemoryEngineInterface(ABC):

        Args:
            bank_id: The memory bank ID.
-            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).
+            search_query: Search query.
            limit: Maximum results.
            offset: Pagination offset.
            request_context: Request context for authentication.
@@ -60,59 +60,6 @@ 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,
@@ -124,7 +71,6 @@ 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.
@@ -193,7 +139,6 @@ 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":
@@ -236,7 +181,6 @@ 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.
@@ -249,7 +193,6 @@ 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
@@ -259,8 +202,6 @@ 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 = [
@@ -329,18 +270,6 @@ 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,
@@ -353,7 +282,6 @@ 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
@@ -522,14 +450,6 @@ 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
@@ -622,23 +542,6 @@ 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
@@ -649,9 +552,8 @@ 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), 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"):
+        # 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"):
            raise ValueError(
                "HINDSIGHT_API_LLM_API_KEY environment variable is required (unless using openai-codex or claude-code)"
            )
@@ -667,9 +569,8 @@ 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), 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"):
+        # 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"):
            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)"
@@ -686,9 +587,8 @@ 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), 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"):
+        # 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"):
            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)"
@@ -700,58 +600,5 @@ 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
@@ -238,24 +238,21 @@ class ClaudeCodeLLM(LLMInterface):
                )

                # Record trace span
-                try:
-                    from hindsight_api.tracing import get_span_recorder
+                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 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
+                span_recorder = get_span_recorder()
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=result if isinstance(result, str) else json.dumps(result),
+                    input_tokens=estimated_input,
+                    output_tokens=estimated_output,
+                    duration=duration,
+                    finish_reason=None,
+                    error=None,
+                )

                # Log slow calls
                if duration > 10.0:
@@ -11,7 +11,6 @@ import json
 import logging
 import os
 import time
-from contextvars import ContextVar
 from typing import Any

 from google import genai
@@ -19,18 +18,11 @@ 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
@@ -65,9 +57,6 @@ 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:
@@ -226,29 +215,16 @@ 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 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
+                response = await self._client.aio.models.generate_content(
+                    model=self.model,
+                    contents=gemini_contents,
+                    config=generation_config,
                )

                content = response.text
@@ -271,7 +247,7 @@ class GeminiLLM(LLMInterface):

                # Parse structured output if requested
                if response_format is not None:
-                    json_data = parse_llm_json(content)
+                    json_data = json.loads(content)
                    if skip_validation:
                        result = json_data
                    else:
@@ -429,57 +405,31 @@ class GeminiLLM(LLMInterface):
        # Convert messages
        system_instruction = None
        gemini_contents = []
-        msg_list = list(messages)
-        i = 0
-        while i < len(msg_list):
-            msg = msg_list[i]
+        for msg in messages:
            role = msg.get("role", "user")
            content = msg.get("content", "")

            if role == "system":
                system_instruction = (system_instruction + "\n\n" + content) if system_instruction else content
-                i += 1
            elif role == "tool":
-                # 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},
+                # 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},
+                                )
                            )
-                        )
+                        ],
                    )
-                    i += 1
-                gemini_contents.append(genai_types.Content(role="user", parts=parts))
+                )
            elif role == "assistant":
-                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
+                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
            else:
                gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
-                i += 1

        config_kwargs: dict[str, Any] = {"tools": gemini_tools}
        if system_instruction:
@@ -487,50 +437,15 @@ 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 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
+                response = await self._client.aio.models.generate_content(
+                    model=self.model,
+                    contents=gemini_contents,
+                    config=config,
                )

                # Extract content and tool calls
@@ -6,7 +6,6 @@ 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
@@ -67,7 +66,6 @@ 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:
        """
@@ -149,9 +147,7 @@ class MockLLM(LLMInterface):
        )

        # Return mock response
-        if self._response_callback is not None:
-            result = self._response_callback(messages, scope)
-        elif self._mock_response is not None:
+        if self._mock_response is not None:
            result = self._mock_response
        elif response_format is not None:
            # Try to create a minimal valid instance of the response format
@@ -218,15 +214,7 @@ class MockLLM(LLMInterface):

        span_recorder = get_span_recorder()

-        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 self._mock_response is not None:
            if isinstance(self._mock_response, LLMToolCallResult):
                result = self._mock_response
            elif isinstance(self._mock_response, list):
@@ -270,16 +258,6 @@ 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.
@@ -92,17 +92,11 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
        self._search_dates = None

    def load(self) -> None:
-        """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.
-        """
+        """Load dateparser (lazy import)."""
        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:
        """
@@ -14,26 +14,32 @@ 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 directives."""
+    """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
+    """
    if not directives:
        return []

-    return [
-        DirectiveInfo(
-            id=directive.get("id", ""),
-            name=directive.get("name", ""),
-            content=directive.get("content", ""),
-        )
-        for directive in directives
-    ]
+    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


 if TYPE_CHECKING:
@@ -261,46 +267,6 @@ 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,
@@ -308,7 +274,7 @@ 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, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, 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,
@@ -317,7 +283,6 @@ 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.
@@ -425,15 +390,12 @@ 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, max_context_tokens=max_context_tokens
-            )
+            prompt = build_final_prompt(query, context_history, bank_profile, context)
            llm_start = time.time()
            response, usage = await llm_config.call(
                messages=[
@@ -478,91 +440,17 @@ 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=iter_tool_choice,
+                tool_choice="required" if iteration == 0 else "auto",  # Force tool use on first iteration
            )
            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(
@@ -576,25 +464,15 @@ 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)
            )
-            # 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:
+            if not has_gathered_evidence and iteration < max_iterations - 1:
                continue
-            prompt = build_final_prompt(
-                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
-            )
+            prompt = build_final_prompt(query, context_history, bank_profile, context)
            llm_start = time.time()
            response, usage = await llm_config.call(
                messages=[
@@ -665,9 +543,7 @@ async def run_reflect_agent(
                    directives_applied=directives_applied,
                )
            # Empty response, force final
-            prompt = build_final_prompt(
-                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
-            )
+            prompt = build_final_prompt(query, context_history, bank_profile, context)
            llm_start = time.time()
            response, usage = await llm_config.call(
                messages=[
@@ -931,9 +807,9 @@ 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") 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]
+    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]

    # Generate structured output if schema provided
    structured_output = None
@@ -969,7 +845,7 @@ 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, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, 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."""
@@ -1041,7 +917,7 @@ 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, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
    expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
 ) -> dict[str, Any]:
    """Execute a single tool by name."""
@@ -1067,8 +943,7 @@ 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
-        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)
+        return await recall_fn(query, max_tokens)

    elif tool_name == "expand":
        memory_ids = args.get("memory_ids", [])
@@ -1096,9 +971,9 @@ 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)
-        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})"
+        return f"(query={query_preview}, max_tokens={max_tokens})"
    elif tool_name == "expand":
        memory_ids = args.get("memory_ids", [])
        depth = args.get("depth", "chunk")
@@ -10,30 +10,59 @@ 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."""
+    """
+    Extract directive rules as a list of strings.
+
+    Args:
+        directives: List of directives with name and content
+
+    Returns:
+        List of directive rule strings
+    """
    rules = []
    for directive in directives:
-        name = directive.get("name", "")
+        directive_name = directive.get("name", "")
+        # New format: directives have direct content field
        content = directive.get("content", "")
        if content:
-            rules.append(f"**{name}**: {content}" if name else 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}")
    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 ""
@@ -140,12 +169,6 @@ 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",
@@ -182,7 +205,6 @@ 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",
                "",
            ]
@@ -200,7 +222,6 @@ 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",
                "",
            ]
@@ -278,7 +299,7 @@ def build_system_prompt_for_tools(
        parts.extend(
            [
                "1. First, try search_observations() - check for consolidated knowledge",
-                "2. If search_observations returns 0 results OR observations are stale, you MUST call recall() for raw facts",
+                "2. If observations are stale OR you need specific details, use 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",
            ]
@@ -294,7 +315,6 @@ 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.",
        ]
    )

@@ -402,7 +422,6 @@ 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 = []
@@ -432,32 +451,18 @@ def build_final_prompt(
    if additional_context:
        parts.append(f"\n## Additional Context\n{additional_context}")

-    # 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).
+    # Tool call history
    if context_history:
        parts.append("\n## Retrieved Data (synthesize and reason from this data)")
-        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):
+        for entry in 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)
-            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.*")
+            parts.append(f"\n### From {tool}:\n```json\n{output_str}\n```")
    else:
        parts.append("\n## Retrieved Data\nNo data was retrieved.")

@@ -505,6 +510,4 @@ 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.
-
-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."""
+Just provide the direct answer with proper markdown formatting."""
@@ -9,7 +9,7 @@ Implements hierarchical retrieval:

 import logging
 import uuid
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 from typing import TYPE_CHECKING, Any

 if TYPE_CHECKING:
@@ -20,6 +20,9 @@ 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",
@@ -30,7 +33,6 @@ 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.
@@ -85,6 +87,7 @@ async def tool_search_mental_models(
        *params,
    )

+    now = datetime.now(timezone.utc)
    mental_models = []

    for row in rows:
@@ -92,10 +95,11 @@ 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)

-        # 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
+        # Calculate freshness
+        is_stale = False
+        if last_refreshed_at:
+            age = now - last_refreshed_at
+            is_stale = age > timedelta(days=STALE_THRESHOLD_DAYS)

        mental_models.append(
            {
@@ -106,7 +110,6 @@ 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,
            }
        )

@@ -129,7 +132,7 @@ async def tool_search_observations(
    pending_consolidation: int = 0,
 ) -> dict[str, Any]:
    """
-    Search consolidated observations using recall with include_source_facts.
+    Search consolidated observations using recall with include_observations.

    Observations are auto-generated from memories. Returns freshness info
    so the agent knows if it should also verify with recall().
@@ -146,24 +149,72 @@ async def tool_search_observations(
        pending_consolidation: Number of memories waiting to be consolidated

    Returns:
-        Dict with matching observations including freshness info and source memories
+        Dict with matching observations including freshness info
    """
+    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"],
-        max_tokens=max_tokens,
+        fact_type=["observation"],  # Only retrieve observations
+        max_tokens=max_tokens,  # Token budget controls how many observations are returned
        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,
    )

-    is_stale = pending_consolidation > 0
+    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)
    if pending_consolidation == 0:
        freshness = "up_to_date"
    elif pending_consolidation < 10:
@@ -173,10 +224,8 @@ async def tool_search_observations(

    return {
        "query": query,
-        "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,
+        "count": len(observations),
+        "observations": observations,
        "freshness": freshness,
    }

@@ -187,10 +236,10 @@ 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.
@@ -204,19 +253,18 @@ 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 including raw chunk text
+        Dict with list of matching memories
    """
-    include_chunks = True
    result = await memory_engine.recall_async(
        bank_id=bank_id,
        query=query,
-        fact_type=["experience", "world"],
+        fact_type=["experience", "world"],  # Exclude opinions and observations
        max_tokens=max_tokens,
        enable_trace=False,
        request_context=request_context,
@@ -224,14 +272,24 @@ 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,
-        "memories": [m.model_dump() for m in result.results],
-        "chunks": {k: v.model_dump() for k, v in (result.chunks or {}).items()},
+        "count": len(memories),
+        "memories": memories,
    }


@@ -47,8 +47,7 @@ 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. "
-            "IMPORTANT: If search_mental_models is available, you MUST call it FIRST before using this tool."
+            "If an observation is STALE, you should ALSO use recall() to verify with current facts."
        ),
        "parameters": {
            "type": "object",
@@ -96,10 +95,6 @@ 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"],
        },
@@ -144,7 +139,7 @@ TOOL_DONE_ANSWER = {
            "properties": {
                "answer": {
                    "type": "string",
-                    "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array. 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.",
+                    "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.",
                },
                "memory_ids": {
                    "type": "array",
@@ -195,11 +190,7 @@ def _build_done_tool_with_directives(directive_rules: list[str]) -> dict:
                "properties": {
                    "answer": {
                        "type": "string",
-                        "description": (
-                            "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. "
-                            "NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array. "
-                            f"MANDATORY: Your answer MUST comply with ALL directives:\n{rules_list}"
-                        ),
+                        "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.",
                    },
                    "memory_ids": {
                        "type": "array",
@@ -159,10 +159,6 @@ 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):
@@ -230,9 +226,6 @@ 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):
@@ -5,6 +5,7 @@ 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
@@ -13,6 +14,7 @@ This package contains modular components for the retain operation:

 from . import (
    chunk_storage,
+    deduplication,
    embedding_processing,
    entity_processing,
    fact_extraction,
@@ -33,6 +35,7 @@ __all__ = [
    # Modules
    "fact_extraction",
    "embedding_processing",
+    "deduplication",
    "entity_processing",
    "link_creation",
    "chunk_storage",
@@ -0,0 +1,85 @@
+"""
+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]
@@ -27,21 +27,11 @@ 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, fall back to mentioned_at
+        # Use occurred_start as the representative date
        fact_date = fact.occurred_start or fact.mentioned_at
-        # 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)}]"
+        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})"
        augmented_texts.append(augmented_text)
    return augmented_texts

@@ -41,9 +41,10 @@ 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,
+            None,  # Use default thread pool
            embeddings_backend.encode,
            texts,
        )
@@ -1,194 +0,0 @@
-"""
-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
@@ -20,7 +20,6 @@ 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.
@@ -91,7 +90,6 @@ 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
@@ -10,31 +10,23 @@ import json
 import logging
 import re
 from datetime import datetime, timedelta
-from typing import Literal, cast
+from typing import Literal

-from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator
+from pydantic import BaseModel, ConfigDict, Field, 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 | None) -> str | None:
+def _infer_temporal_date(fact_text: str, event_date: datetime) -> str | None:
    """
    Infer a temporal date from fact text when LLM didn't provide occurred_start.

    This is a fallback for when the LLM fails to extract temporal information
    from relative time expressions like "last night", "yesterday", etc.
    """
-    if event_date is None:
-        return None
+    import re

    fact_lower = fact_text.lower()

@@ -110,6 +102,7 @@ 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(
@@ -447,9 +440,11 @@ 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: 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.
+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.

-{retain_mission_section}{extraction_guidelines}
+{fact_types_instruction}
+
+{extraction_guidelines}

 ══════════════════════════════════════════════════════════════════════════
 FACT FORMAT - BE CONCISE
@@ -488,9 +483,7 @@ TEMPORAL HANDLING
 ══════════════════════════════════════════════════════════════════════════

 Use "Event Date" from input as reference for relative dates.
- 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
+- "yesterday" relative to Event Date, not today
 - For events: set occurred_start AND occurred_end (same for point events)
 - For conversation facts: NO occurred dates

@@ -528,7 +521,7 @@ CONSOLIDATE related statements into ONE fact when possible."""
 _CONCISE_EXAMPLES = """

 ══════════════════════════════════════════════════════════════════════════
-EXAMPLES (shown in English for illustration; for non-English input, ALL output values MUST be in the input language)
+EXAMPLES
 ══════════════════════════════════════════════════════════════════════════

 Example 1 - Selective extraction (Event Date: June 10, 2024):
@@ -556,16 +549,16 @@ 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
+# Assembled concise prompt (backward compatible - exact same output as before)
 CONCISE_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
-    retain_mission_section="{retain_mission_section}",
+    fact_types_instruction="{fact_types_instruction}",
    extraction_guidelines=_CONCISE_GUIDELINES,
    examples=_CONCISE_EXAMPLES,
 )

 # Custom prompt uses same base but without examples
 CUSTOM_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
-    retain_mission_section="{retain_mission_section}",
+    fact_types_instruction="{fact_types_instruction}",
    extraction_guidelines="{custom_instructions}",
    examples="",  # No examples for custom mode
 )
@@ -574,7 +567,10 @@ 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: 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.
+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}

 ══════════════════════════════════════════════════════════════════════════
 FACT FORMAT - ALL FIVE DIMENSIONS REQUIRED - MAXIMUM VERBOSITY
@@ -699,185 +695,59 @@ 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(
-                retain_mission_section=retain_mission_section,
-            )
+            prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
        else:
            base_prompt = CUSTOM_FACT_EXTRACTION_PROMPT
            prompt = base_prompt.format(
-                retain_mission_section=retain_mission_section,
+                fact_types_instruction=fact_types_instruction,
                custom_instructions=config.retain_custom_instructions,
            )
    elif extraction_mode == "verbose":
-        prompt = VERBOSE_FACT_EXTRACTION_PROMPT
+        base_prompt = VERBOSE_FACT_EXTRACTION_PROMPT
+        prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
    else:
        base_prompt = CONCISE_FACT_EXTRACTION_PROMPT
-        prompt = base_prompt.format(
-            retain_mission_section=retain_mission_section,
-        )
+        prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)

    # Add causal relationships section if enabled
    if extract_causal_links:
        prompt = prompt + CAUSAL_RELATIONSHIPS_SECTION
-        base_fact_class = ExtractedFactVerbose if extraction_mode == "verbose" else ExtractedFact
-        base_response_class = FactExtractionResponseVerbose if extraction_mode == "verbose" else FactExtractionResponse
+        response_schema = FactExtractionResponseVerbose if extraction_mode == "verbose" else FactExtractionResponse
    else:
-        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
+        response_schema = FactExtractionResponseNoCausal

    return prompt, response_schema


-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:
+def _build_user_message(chunk: str, chunk_index: int, total_chunks: int, event_date: datetime, context: str) -> 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"
-
-    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}"
+    event_date = parse_datetime_flexible(event_date)
+    event_date_formatted = event_date.strftime("%A, %B %d, %Y")

    return f"""Extract facts from the following text chunk.

 Chunk: {chunk_index + 1}/{total_chunks}
-Event Date: {event_date_str}
-Context: {sanitized_context}{metadata_section}
+Event Date: {event_date_formatted} ({event_date.isoformat()})
+Context: {sanitized_context}

 Text:
 {sanitized_chunk}"""
@@ -914,12 +784,11 @@ async def _extract_facts_from_chunk(
    chunk: str,
    chunk_index: int,
    total_chunks: int,
-    event_date: datetime | None,
+    event_date: datetime,
    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).
@@ -941,7 +810,7 @@ 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, metadata)
+    user_message = _build_user_message(chunk, chunk_index, total_chunks, event_date, context)

    # Retry logic for JSON validation errors
    max_retries = 2
@@ -1100,9 +969,9 @@ 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
@@ -1112,48 +981,8 @@ async def _extract_facts_from_chunk(
                                validated_entities.append(Entity.model_validate(ent))
                            except Exception as e:
                                logger.warning(f"Invalid entity {ent}: {e}")
-
-                # 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
+                    if validated_entities:
+                        fact_data["entities"] = validated_entities

                # Add per-fact causal relations (only if enabled in config)
                if extract_causal_links:
@@ -1192,9 +1021,8 @@ async def _extract_facts_from_chunk(
                    if validated_relations:
                        fact_data["causal_relations"] = validated_relations

-                # 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
+                # Always set mentioned_at to the event_date (when the conversation/document occurred)
+                fact_data["mentioned_at"] = event_date.isoformat()

                # Build Fact model instance
                try:
@@ -1257,12 +1085,11 @@ async def _extract_facts_with_auto_split(
    chunk: str,
    chunk_index: int,
    total_chunks: int,
-    event_date: datetime | None,
+    event_date: datetime,
    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.
@@ -1279,7 +1106,6 @@ 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)
@@ -1299,7 +1125,6 @@ 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
@@ -1345,7 +1170,6 @@ 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,
@@ -1356,7 +1180,6 @@ async def _extract_facts_with_auto_split(
                llm_config=llm_config,
                config=config,
                agent_name=agent_name,
-                metadata=metadata,
            ),
        ]

@@ -1376,12 +1199,11 @@ async def _extract_facts_with_auto_split(

 async def extract_facts_from_text(
    text: str,
-    event_date: datetime | None,
+    event_date: datetime,
    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.
@@ -1399,7 +1221,6 @@ 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:
@@ -1427,7 +1248,6 @@ async def extract_facts_from_text(
            llm_config=llm_config,
            config=config,
            agent_name=agent_name,
-            metadata=metadata,
        )
        for i, chunk in enumerate(chunks)
    ]
@@ -1537,7 +1357,7 @@ 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, item.metadata or None
+                chunk, chunk_index_in_content, len(chunks), item.event_date, item.context
            )

            # Build request body using helper function
@@ -1749,8 +1569,8 @@ 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))
@@ -1759,45 +1579,8 @@ async def extract_facts_from_contents_batch_api(
                            validated_entities.append(Entity.model_validate(ent))
                        except Exception:
                            pass
-
-            # 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
+                if validated_entities:
+                    fact_data["entities"] = validated_entities

            # Causal relations
            if extract_causal_links:
@@ -1828,9 +1611,8 @@ async def extract_facts_from_contents_batch_api(
                if validated_relations:
                    fact_data["causal_relations"] = validated_relations

-            # 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
+            # Always set mentioned_at
+            fact_data["mentioned_at"] = event_date.isoformat()

            try:
                fact = Fact(fact=combined_text, fact_type=fact_type, **fact_data)
@@ -1889,7 +1671,6 @@ 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)
@@ -1898,9 +1679,6 @@ 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
@@ -1959,7 +1737,6 @@ 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)

@@ -2023,7 +1800,6 @@ 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)
@@ -2033,9 +1809,6 @@ 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


@@ -2091,24 +1864,3 @@ 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]
@@ -47,8 +47,6 @@ 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))
@@ -70,19 +68,6 @@ 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
@@ -90,19 +75,16 @@ 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[], $15::jsonb[], $16::text[]
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
-                       observation_scopes_json, text_signals)
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
            )
            INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
-                                     observation_scopes, text_signals, search_vector)
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags, search_vector)
            SELECT
                $1,
                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
@@ -111,30 +93,23 @@ async def insert_facts_batch(
                    (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
                    '{{}}'::varchar[]
                ),
-                observation_scopes_json,
-                text_signals,
-                tokenize(
-                    COALESCE(text, '') || ' ' || COALESCE(context, '') || ' ' || COALESCE(text_signals, ''),
-                    'llmlingua2'
-                )::bm25_catalog.bm25vector
+                tokenize(COALESCE(text, '') || ' ' || COALESCE(context, ''), 'llmlingua2')::bm25_catalog.bm25vector
            FROM input_data
            RETURNING id
        """
    else:  # native or pg_textsearch
-        # Native PostgreSQL: search_vector is GENERATED ALWAYS (expression includes text_signals), don't include it
+        # Native PostgreSQL: search_vector is GENERATED ALWAYS, don't include it
        # pg_textsearch: indexes operate on base columns directly, don't populate search_vector
        query = f"""
            WITH input_data AS (
                SELECT * FROM unnest(
                    $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[], $15::jsonb[], $16::text[]
+                    $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json,
-                       observation_scopes_json, text_signals)
+                       context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
            )
            INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags,
-                                     observation_scopes, text_signals)
+                                     context, fact_type, confidence_score, metadata, chunk_id, document_id, tags)
            SELECT
                $1,
                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
@@ -142,9 +117,7 @@ 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
        """
@@ -165,8 +138,6 @@ 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]
@@ -47,9 +47,6 @@ 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)

@@ -99,11 +96,7 @@ def compute_temporal_query_bounds(
        return None, None

    # Normalize all dates to be timezone-aware to avoid comparison issues
-    # 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
+    all_dates = [_normalize_datetime(d) for d in new_units.values()]

    try:
        min_date = min(all_dates) - timedelta(hours=time_window_hours)
@@ -150,7 +143,6 @@ 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.
@@ -240,7 +232,6 @@ 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(
@@ -441,23 +432,20 @@ 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()
-        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 = []
+        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,
+        )
        _log(
            log_buffer,
            f"      [7.2] Fetch {len(all_candidates)} candidate neighbors (1 query): {time_mod.time() - fetch_neighbors_start:.3f}s",
@@ -472,15 +460,11 @@ 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
@@ -498,13 +482,14 @@ 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
                    """,
-                    links[batch_start : batch_start + BATCH_SIZE],
+                    batch,
                )
            _log(log_buffer, f"      [7.4] Insert {len(links)} temporal links: {time_mod.time() - insert_start:.3f}s")

@@ -552,46 +537,82 @@ async def create_semantic_links_batch(

        import numpy as np

-        # 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 = []
-
-        # Build UUID exclude list once for all ANN queries
-        import uuid as uuid_mod
-
-        exclude_uuids = [uuid_mod.UUID(uid) if isinstance(uid, str) else uid for uid in unit_ids]
-
-        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))
-
+        # 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] ANN search for {len(unit_ids)} new units → {len(all_links)} candidate links: {time_mod.time() - ann_start:.3f}s",
+            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()
+        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
+
+                    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)
+
+                # Ensure it's 1D
+                if emb.ndim != 1:
+                    raise ValueError(f"Expected 1D embedding, got shape {emb.shape}")
+                embedding_arrays.append(emb)
+
+            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))
+
        # 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
        if len(unit_ids) > 1:
@@ -622,7 +643,7 @@ async def create_semantic_links_batch(

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

        if all_links:
@@ -630,13 +651,14 @@ 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
                    """,
-                    all_links[batch_start : batch_start + BATCH_SIZE],
+                    batch,
                )
            _log(
                log_buffer, f"      [8.3] Insert {len(all_links)} semantic links: {time_mod.time() - insert_start:.3f}s"
@@ -652,18 +674,18 @@ async def create_semantic_links_batch(
        raise


-async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: int = 5000):
+async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: int = 50000):
    """
-    Insert all entity links using COPY to temp table + chunked INSERT for reliability.
+    Insert all entity links using COPY to temp table + INSERT for maximum speed.

-    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).
+    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.

    Args:
        conn: Database connection
        links: List of EntityLink objects
-        chunk_size: Number of rows per INSERT chunk (default 5000)
+        chunk_size: Number of rows per batch (default 50000)
    """
    if not links:
        return
@@ -672,11 +694,10 @@ async def insert_entity_links_batch(conn, links: list[EntityLink], chunk_size: i

    total_start = time_mod.time()

-    # Create temp table with serial for stable chunked access
+    # Create temp table for bulk loading
    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,
@@ -693,7 +714,9 @@ 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 = [(link.from_unit_id, link.to_unit_id, link.link_type, link.weight, link.entity_id) for link in links]
+    records = []
+    for link in links:
+        records.append((link.from_unit_id, link.to_unit_id, link.link_type, link.weight, link.entity_id))
    logger.debug(f"      [9.3] Convert {len(records)} records: {time_mod.time() - convert_start:.3f}s")

    # Bulk load using COPY (fastest method)
@@ -705,25 +728,15 @@ 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 in chunks to avoid single-query timeouts on large tables
+    # Insert from temp table with ON CONFLICT (single query for all rows)
    insert_start = time_mod.time()
-    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")
+    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")
    logger.debug(f"      [9.TOTAL] Entity links batch insert: {time_mod.time() - total_start:.3f}s")


@@ -55,6 +55,7 @@ def parse_datetime_flexible(value: Any) -> datetime:
 from ..response_models import TokenUsage
 from . import (
    chunk_storage,
+    deduplication,
    embedding_processing,
    entity_processing,
    fact_extraction,
@@ -72,6 +73,7 @@ async def retain_batch(
    llm_config,
    entity_resolver,
    format_date_fn,
+    duplicate_checker_fn,
    bank_id: str,
    contents_dicts: list[RetainContentDict],
    config,
@@ -92,6 +94,7 @@ 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
@@ -125,14 +128,12 @@ async def retain_batch(
        item_tags = item.get("tags", []) or []
        merged_tags = list(set(item_tags + (document_tags or [])))

-        # 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"])
+        # Handle event_date: parse flexibly (handles both datetime objects and ISO strings)
+        event_date_value = item.get("event_date")
+        if event_date_value:
+            event_date_value = parse_datetime_flexible(event_date_value)
        else:
-            event_date_value = utcnow()  # Backward-compatible default
+            event_date_value = utcnow()

        content = RetainContent(
            content=item["content"],
@@ -141,7 +142,6 @@ async def retain_batch(
            metadata=item.get("metadata", {}),
            entities=item.get("entities", []),
            tags=merged_tags,
-            observation_scopes=item.get("observation_scopes"),
        )
        contents.append(content)

@@ -162,6 +162,8 @@ async def retain_batch(
        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):
@@ -279,6 +281,9 @@ 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
@@ -430,7 +435,20 @@ async def retain_batch(
                        actual_doc_id = document_id
                    processed_fact.document_id = actual_doc_id

-            non_duplicate_facts = processed_facts
+            # 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

            # Insert facts (document_id is now stored per-fact)
            step_start = time.time()
@@ -451,7 +469,6 @@ 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")

@@ -482,11 +499,7 @@ 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, 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()
+            result_unit_ids = _map_results_to_contents(contents, extracted_facts, is_duplicate_flags, unit_ids)

        # Log final summary
        total_time = time.time() - start_time
@@ -504,20 +517,28 @@ 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."""
-    facts_by_content: dict[int, list[int]] = {i: [] for i in range(len(contents))}
+    """
+    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))}
    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 _ in facts_by_content[content_index]:
-            content_unit_ids.append(unit_ids[unit_idx])
-            unit_idx += 1
+        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
        result_unit_ids.append(content_unit_ids)

    return result_unit_ids
@@ -6,8 +6,8 @@ from content input to fact storage.
 """

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


@@ -22,21 +22,20 @@ 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 | None
+    event_date: datetime
    metadata: dict[str, str]
    document_id: str
    entities: list[dict[str, str]]  # [{"text": "...", "type": "..."}]
    tags: list[str]  # Visibility scope tags
-    observation_scopes: (
-        Literal["per_tag", "combined", "all_combinations"] | list[list[str]]
-    )  # Observation scopes for consolidation
+
+
+def _now_utc() -> datetime:
+    """Factory function for default event_date."""
+    return datetime.now(UTC)


@dataclass
@@ -49,13 +48,10 @@ class RetainContent:

    content: str
    context: str = ""
-    event_date: datetime | None = None
+    event_date: datetime = field(default_factory=_now_utc)
    metadata: dict[str, str] = field(default_factory=dict)
    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
@@ -121,9 +117,6 @@ 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
@@ -142,7 +135,7 @@ class ProcessedFact:
    # Temporal data
    occurred_start: datetime | None
    occurred_end: datetime | None
-    mentioned_at: datetime | None
+    mentioned_at: datetime

    # Context and metadata
    context: str
@@ -172,9 +165,6 @@ 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."""
@@ -195,10 +185,12 @@ 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  # May be None when caller opted into no timestamp
+        mentioned_at = extracted_fact.mentioned_at or datetime.now(UTC)

        # Convert entity strings to EntityRef objects
        entities = [EntityRef(name=name) for name in extracted_fact.entities]
@@ -217,7 +209,6 @@ class ProcessedFact:
            chunk_id=chunk_id,
            content_index=extracted_fact.content_index,
            tags=extracted_fact.tags,
-            observation_scopes=extracted_fact.observation_scopes,
        )


@@ -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, fact_type, document_id, chunk_id, tags,
+                   mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
                   1 - (embedding <=> $1::vector) AS similarity
            FROM {fq_table("memory_units")}
            WHERE bank_id = $2
@@ -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.fact_type,
+                           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
                    FROM {fq_table("memory_links")} ml
@@ -1,28 +1,18 @@
 """
 Link Expansion graph retrieval.

-Expands from semantic/temporal seeds through three parallel, first-class signals
-stored in memory_links:
+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)

-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.
+Characteristics:
+- 2-3 DB queries (seed finding + parallel entity/causal expansion)
+- Sublinear: only touches connected facts via indexes
+- No iteration, no propagation, no normalization
+- Target: <100ms
 """

 import logging
-import math
 import time

 from ..db_utils import acquire_with_retry
@@ -55,7 +45,7 @@ async def _find_semantic_seeds(
    rows = await conn.fetch(
        f"""
        SELECT id, text, context, event_date, occurred_start, occurred_end,
-               mentioned_at, fact_type, document_id, chunk_id, tags,
+               mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
               1 - (embedding <=> $1::vector) AS similarity
        FROM {fq_table("memory_units")}
        WHERE bank_id = $2
@@ -75,23 +65,27 @@ class LinkExpansionRetriever(GraphRetriever):
    """
    Graph retrieval via direct link expansion from seeds.

-    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.
+    Expands through entity co-occurrence and causal links in a single query.
+    Fast and simple alternative to MPFP.
    """

    def __init__(
        self,
+        max_entity_frequency: int = 500,
        causal_weight_threshold: float = 0.3,
+        causal_limit_per_seed: int = 10,
    ):
        """
+        Initialize link expansion retriever.
+
        Args:
-            causal_weight_threshold: Minimum weight for causal links to follow.
+            max_entity_frequency: Skip entities appearing in more than this many facts
+            causal_weight_threshold: Minimum weight for causal links
+            causal_limit_per_seed: Max causal links to follow per seed
        """
+        self.max_entity_frequency = max_entity_frequency
        self.causal_weight_threshold = causal_weight_threshold
+        self.causal_limit_per_seed = causal_limit_per_seed

    @property
    def name(self) -> str:
@@ -116,7 +110,7 @@ class LinkExpansionRetriever(GraphRetriever):

        Args:
            pool: Database connection pool
-            query_embedding_str: Query embedding as string
+            query_embedding_str: Query embedding (unused, kept for interface)
            bank_id: Memory bank ID
            fact_type: Fact type to filter
            budget: Maximum results to return
@@ -124,7 +118,7 @@ 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
+            tags: Optional list of tags for visibility filtering (OR matching)

        Returns:
            Tuple of (results, timings)
@@ -132,6 +126,8 @@ 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:
@@ -154,6 +150,7 @@ class LinkExpansionRetriever(GraphRetriever):
                    f"(tags={tags}, tags_match={tags_match})"
                )

+            # Add temporal seeds if provided
            if temporal_seeds:
                all_seeds.extend(temporal_seeds)

@@ -163,61 +160,223 @@ 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":
-                entity_rows, semantic_rows, causal_rows = await self._expand_observations(conn, seed_ids, budget)
+                # 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")
            else:
-                entity_rows, semantic_rows, causal_rows = await self._expand_combined(conn, seed_ids, fact_type, budget)
+                # 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,
+            )

            timings.edge_load_time = time.time() - query_start
-            timings.db_queries = 1
-            timings.edge_count = len(entity_rows) + len(semantic_rows) + len(causal_rows)
+            timings.db_queries = 3
+            timings.edge_count = len(entity_rows) + len(causal_rows) + len(fallback_rows)

-        # 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] = {}
+        # Merge results, taking max score per fact
+        # Priority: entity links (unit_entities) > causal links > fallback links
+        score_map: dict[str, float] = {}
        row_map: dict[str, dict] = {}

        for row in entity_rows:
            fact_id = str(row["id"])
-            entity_scores[fact_id] = math.tanh(row["score"] * 0.5)
+            score_map[fact_id] = max(score_map.get(fact_id, 0), row["score"])
            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"])
-            causal_scores[fact_id] = max(causal_scores.get(fact_id, 0.0), row["score"])
-            row_map.setdefault(fact_id, dict(row))
+            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
-        }
+        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)

+        # 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)

@@ -230,253 +389,3 @@ 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
@@ -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, fact_type, document_id, chunk_id, tags
+                   mentioned_at, embedding, fact_type, document_id, chunk_id, tags
            FROM {fq_table("memory_units")}
            WHERE id = ANY($1::uuid[])
              AND fact_type = $2
@@ -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, fact_type, document_id, chunk_id, tags,
+                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,
                       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, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, 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, fact_type, document_id, chunk_id, tags,
+            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,
                   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, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
                   NULL::float AS similarity,
                   {bm25_score_expr} AS bm25_score,
                   '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, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, 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, fact_type, document_id, chunk_id, tags,
+            SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
                   similarity, bm25_score, source
            FROM bm25_ranked WHERE rn <= $4
        )
@@ -297,20 +297,13 @@ async def retrieve_temporal_combined(
    if tags:
        params.append(tags)

-    # 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.
+    # Batch query: Get entry points for ALL fact types at once with window function
    entry_points = await conn.fetch(
        f"""
-        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
+        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
            FROM {fq_table("memory_units")}
            WHERE bank_id = $2
              AND fact_type = ANY($3)
@@ -325,20 +318,12 @@ 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, fact_type, document_id, chunk_id, tags, similarity
-        FROM sim_ranked
-        WHERE sim_rn <= 10
+        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
        """,
        *params,
    )
@@ -402,52 +387,34 @@ 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 7 since 1-6 are used)
-        spreading_tags_clause = build_tags_where_clause_simple(tags, 7, table_alias="mu.", match=tags_match)
+        # 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)

-        while frontier and budget_remaining > 0 and iteration < max_iterations:
-            iteration += 1
+        while frontier and budget_remaining > 0:
            batch_ids = frontier[:batch_size]
            frontier = frontier[batch_size:]

-            # $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]
+            spreading_params = [query_emb_str, batch_ids, ft, semantic_threshold, batch_size * 10]
            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 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,
+                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,
                       1 - (mu.embedding <=> $1::vector) AS similarity
-                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
+                FROM {fq_table("memory_links")} ml
+                JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
+                WHERE ml.from_unit_id = ANY($2::uuid[])
+                  AND ml.link_type IN ('temporal', 'causes', 'caused_by', 'enables', 'prevents')
+                  AND ml.weight >= 0.1
                  AND mu.fact_type = $3
                  AND mu.embedding IS NOT NULL
                  AND (1 - (mu.embedding <=> $1::vector)) >= $4
                  {spreading_tags_clause}
+                ORDER BY ml.weight DESC
+                LIMIT $5
                """,
                *spreading_params,
            )
@@ -46,6 +46,7 @@ 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)
@@ -69,6 +70,7 @@ 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"),
@@ -152,6 +154,7 @@ 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,
@@ -12,7 +12,6 @@ def create_file_storage(
    storage_type: str,
    pool_getter: Callable | None = None,
    schema: str | None = None,
-    schema_getter: Callable | None = None,
    **kwargs,
 ) -> FileStorage:
    """
@@ -21,8 +20,7 @@ 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: Static database schema (for native single-tenant)
-        schema_getter: Callable returning current schema at query time (for native multi-tenant)
+        schema: Database schema (for native multi-tenant)
        **kwargs: Additional args passed to storage backend

    Returns:
@@ -34,7 +32,7 @@ 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, schema_getter=schema_getter)
+        return PostgreSQLFileStorage(pool_getter=pool_getter, schema=schema)
    elif storage_type == "s3":
        from ...config import get_config
        from .s3 import S3FileStorage
@@ -40,30 +40,16 @@ class PostgreSQLFileStorage(FileStorage):
    For production/scale, consider S3FileStorage instead.
    """

-    def __init__(
-        self,
-        pool_getter: Callable[[], "asyncpg.Pool"],
-        schema: str | None = None,
-        schema_getter: Callable[[], str] | None = None,
-    ):
+    def __init__(self, pool_getter: Callable[[], "asyncpg.Pool"], schema: str | None = None):
        """
        Initialize PostgreSQL file storage.

        Args:
            pool_getter: Function that returns asyncpg connection pool
-            schema: Static database schema (fallback for single-tenant / tests)
-            schema_getter: Callable returning current schema at query time (for multi-tenant)
+            schema: Database schema (for multi-tenant support)
        """
        self._pool_getter = pool_getter
-        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
+        self._schema = schema

    async def store(
        self,
@@ -22,11 +22,6 @@ 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,
@@ -75,11 +70,6 @@ __all__ = [
    "RetainContext",
    "RetainResult",
    "ValidationResult",
-    # Operation Validator - Bank Management
-    "BankListContext",
-    "BankListResult",
-    "BankReadContext",
-    "BankWriteContext",
    # Operation Validator - Consolidation
    "ConsolidateContext",
    "ConsolidateResult",
@@ -87,15 +87,3 @@ 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
@@ -200,44 +200,6 @@ 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
 # =============================================================================
@@ -573,63 +535,3 @@ 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)
@@ -11,9 +11,8 @@ from hindsight_api.models import RequestContext
 class AuthenticationError(Exception):
    """Raised when authentication fails."""

-    def __init__(self, reason: str, headers: dict[str, str] | None = None):
+    def __init__(self, reason: str):
        self.reason = reason
-        self.headers = headers or {}
        super().__init__(f"Authentication failed: {reason}")


@@ -171,7 +171,6 @@ 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,
@@ -232,15 +231,12 @@ 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,
@@ -250,10 +246,8 @@ 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,
@@ -276,11 +270,7 @@ def main():
            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,
@@ -296,11 +286,6 @@ 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,
@@ -1,14 +1,8 @@
 """
-Local MCP server entry point for use with Claude Code (HTTP transport).
+Local MCP server for use with Claude Code (stdio transport).

-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/
+This runs a fully local Hindsight instance with embedded PostgreSQL (pg0).
+No external database or server required.

 Run with:
    hindsight-local-mcp
@@ -16,24 +10,148 @@ 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_DATABASE_URL: Optional. Override database URL (default: pg0://hindsight-mcp).
+    HINDSIGHT_API_MCP_LOCAL_BANK_ID: Optional. Memory bank ID (default: "mcp").
+    HINDSIGHT_API_LOG_LEVEL: Optional. Log level (default: "warning").
+    HINDSIGHT_API_MCP_INSTRUCTIONS: Optional. Additional instructions appended to both retain and recall tools.
+
+Example custom instructions (these are ADDED to the default behavior):
+    To also store assistant actions:
+        HINDSIGHT_API_MCP_INSTRUCTIONS="Also store every action you take, including tool calls, code written, and decisions made."
+
+    To also store conversation summaries:
+        HINDSIGHT_API_MCP_INSTRUCTIONS="Also store summaries of important conversations and their outcomes."
 """

+import logging
 import os
+import sys
+
+from mcp.server.fastmcp import FastMCP
+
+from 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 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")
+def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
+    """
+    Create a stdio MCP server with retain/recall tools.

-    from hindsight_api.main import main as api_main
+    Args:
+        bank_id: The memory bank ID to use for all operations.
+        memory: Optional MemoryEngine instance. If not provided, creates one with pg0.

-    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))


 if __name__ == "__main__":
@@ -18,7 +18,6 @@ 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
@@ -221,40 +220,13 @@ 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.
-        #
-        # 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.
+        # Use PostgreSQL advisory lock to coordinate between distributed workers
        engine = create_engine(database_url)
        with engine.connect() as conn:
+            # pg_advisory_lock blocks until the lock is acquired
+            # The lock is automatically released when the connection closes
            logger.debug(f"Acquiring migration advisory lock for schema '{schema_name}' (id={lock_id})...")
-            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()
+            conn.execute(text(f"SELECT pg_advisory_lock({lock_id})"))
            logger.debug("Migration advisory lock acquired")

            try:
@@ -375,13 +347,6 @@ 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:
@@ -600,12 +565,6 @@ 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
@@ -791,24 +750,6 @@ 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"""
@@ -22,7 +22,6 @@ 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
@@ -376,13 +376,11 @@ class WorkerPoller:
                        del self._in_flight_by_type[operation_type]

    async def _execute_task_inner(self, task: ClaimedTask):
-        """Inner task execution with retry/fail handling.
+        """Inner task execution with error handling.

-        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.
+        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.
        """
        task_type = task.task_dict.get("type", "unknown")
        bank_id = task.task_dict.get("bank_id", "unknown")
@@ -395,9 +393,10 @@ class WorkerPoller:
            await self._executor(task.task_dict)
            logger.debug(f"Task {task.operation_id} execution finished")
        except Exception as e:
-            logger.error(f"Task {task.operation_id} failed: {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}")
            traceback.print_exc()
-            await self._retry_or_fail(task.operation_id, str(e), task.schema)

    async def recover_own_tasks(self) -> int:
        """
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "hindsight-api"
-version = "0.4.15"
+version = "0.4.12"
 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 300 -n 8 --dist loadgroup --durations=10 -v"
+addopts = "--timeout 120 -n 8 --dist loadgroup --durations=10 -v"
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "function"
 log_auto_indent = true
@@ -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:
@@ -14,7 +14,6 @@ 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
@@ -500,7 +500,6 @@ 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:
@@ -519,7 +518,6 @@ 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:
@@ -830,7 +828,6 @@ 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:
@@ -852,7 +849,6 @@ 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:
@@ -905,7 +901,6 @@ 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:
@@ -924,7 +919,6 @@ 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:
@@ -937,19 +931,22 @@ class TestConsolidationTagRouting:
                bank_id,
            )

-            # Note: some LLMs may or may not consolidate cross-scope facts.
-            # Just verify structural correctness of any observations that exist.
+            # 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)}"
+            )

-            # 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"
-                )
+            # 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"
+            )

        # Cleanup
        await memory.delete_bank(bank_id, request_context=request_context)
@@ -1026,7 +1023,6 @@ 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(
@@ -1034,7 +1030,6 @@ 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:
@@ -1047,10 +1042,11 @@ 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.
-            # 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.
+            # This is valid LLM behavior - just verify no errors and structure is correct
            for obs in observations:
                assert obs["text"], "Observation should have text"

@@ -1435,20 +1431,22 @@ class TestObservationDrillDown:

        assert result["count"] > 0, "Expected at least one observation"

-        # Verify source_fact_ids is present (MemoryFact field name for source memories)
+        # Verify source_memory_ids and proof_count are present
        obs = result["observations"][0]
-        assert "source_fact_ids" in obs, "Observation should have source_fact_ids"
+        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"

-        # 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"
+        # 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"

            # 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_fact_ids"][:2],  # Take first 2
+                    memory_ids=obs["source_memory_ids"][:2],  # Take first 2
                    depth="chunk",
                )

@@ -1715,10 +1713,11 @@ 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 len(recall_result["memories"]) >= 1, "Recall should find the raw facts"
+        assert recall_result["count"] >= 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"]])
@@ -1931,7 +1930,9 @@ class TestMentalModelRefreshAfterConsolidation:
        )

        # Wait for consolidation to create observations
-        await memory.wait_for_background_tasks()
+        import asyncio
+
+        await asyncio.sleep(2)

        # Get graph data filtered by observation type only
        graph_data = await memory.get_graph_data(
@@ -1949,26 +1950,12 @@ class TestMentalModelRefreshAfterConsolidation:
        for row in graph_data["table_rows"]:
            assert row["fact_type"] == "observation", f"All nodes should be observations, got {row['fact_type']}"

-        # 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 edges (inherited from source memories)
+        # Even though we're only showing observations, they should inherit links from their sources
+        assert len(graph_data["edges"]) > 0, (
+            "Observations should have edges inherited from source memories. "
+            f"Found {len(graph_data['edges'])} edges"
+        )

        # Should have entities (inherited from source memories)
        observations_with_entities = [
@@ -1985,335 +1972,19 @@ 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)
@@ -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, ZeroEntropyCrossEncoder
+from hindsight_api.engine.cross_encoder import CohereCrossEncoder, LocalSTCrossEncoder
 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
@@ -98,7 +98,9 @@ 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):
@@ -608,59 +610,3 @@ 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"
@@ -535,9 +535,8 @@ class TestOperationHooksParameters:
            request_context=ctx,
        )

-        # 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
+        assert len(validator.pre_recall_calls) == 1
+        assert len(validator.post_recall_calls) == 1


 class TestTenantExtension:
@@ -1,61 +0,0 @@
-"""
-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
@@ -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 facts have distinct timestamps (ordering is preserved)
+        # Verify reasonable time spacing (should be ~10 seconds apart)
        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 have a positive time difference (uniqueness already checked above)
+        # Each fact should be 10+ seconds apart (allowing for some flexibility)
        for diff in time_diffs:
-            assert diff > 0, f"Expected positive time difference between facts, got {diff}"
+            assert diff >= 5, f"Expected at least 5 seconds between facts, got {diff}"

        # Update agent_facts to be sorted for subsequent checks
        agent_facts = sorted_facts
@@ -7,7 +7,6 @@ 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
@@ -26,12 +25,8 @@ 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
@@ -110,7 +105,7 @@ def seaweedfs_container():
        port = container.get_exposed_port(SEAWEEDFS_S3_PORT)
        endpoint = f"http://{host}:{port}"

-        _wait_for_seaweedfs(endpoint, timeout=240)
+        _wait_for_seaweedfs(endpoint)

        # Create test bucket using obstore (proper SigV4 signing)
        import obstore as obs
@@ -1,340 +0,0 @@
-"""
-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()
@@ -1,171 +0,0 @@
-"""
-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"] == []
@@ -15,6 +15,9 @@ 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."""
@@ -71,22 +74,12 @@ async def test_hierarchical_fields_categorization():

    # Verify configurable fields include behavioral settings (safe to modify)
    assert "retain_extraction_mode" 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
+    assert "retain_chunk_size" in configurable
+    assert "retain_custom_instructions" in configurable

-    # 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 count is correct (only 4 fields)
+    assert len(configurable) == 4

    # Verify credential fields (NEVER exposed)
    assert "llm_api_key" in credentials
@@ -1,174 +0,0 @@
-"""
-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)
@@ -85,7 +85,6 @@ class TestLiteLLMSDKEmbeddings:
                model="cohere/embed-english-v3.0",
                input=["test"],
                api_key="test_key",
-                encoding_format="float",
            )

    async def test_initialization_missing_package(self):
@@ -138,7 +137,6 @@ 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):
@@ -226,7 +226,6 @@ 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.
@@ -332,8 +331,8 @@ 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_raw_config
-    config = _get_raw_config()
+    from hindsight_api.config import get_config
+    config = get_config()
    original_value = config.enable_observations
    config.enable_observations = True

@@ -165,5 +165,5 @@ class TestMCPExtensionIntegration:
        assert "create_bank" in tools
        # Extension tool also present
        assert "test_extension_tool" in tools
-        # At least 29 core + 1 extension = 30 tools (may grow as new tools are added)
-        assert len(tools) >= 30
+        # At least 11 core + 1 extension = 12 tools (may grow as new tools are added)
+        assert len(tools) >= 12
@@ -0,0 +1,212 @@
+"""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()
@@ -46,15 +46,16 @@ 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")
-        assert result["status"] == "accepted"
+        result = await retain_tool.fn(content="test content", context="test_context", async_processing=False)
+        assert "successfully" in result.lower()

        # Verify the memory was called with the context bank_id
-        mock_memory.submit_async_retain.assert_called_once()
-        call_kwargs = mock_memory.submit_async_retain.call_args.kwargs
+        mock_memory.retain_batch_async.assert_called_once()
+        call_kwargs = mock_memory.retain_batch_async.call_args.kwargs
        assert call_kwargs["bank_id"] == "context-bank-id"
    finally:
        _current_bank_id.reset(token)
@@ -132,12 +133,12 @@ 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")
-        assert result["status"] == "accepted"
+        result = await retain_tool.fn(content="test content", context="test_context", async_processing=False)
+        assert "successfully" in result.lower()

        # Verify the memory was called with request_context containing api_key
-        mock_memory.submit_async_retain.assert_called_once()
-        call_kwargs = mock_memory.submit_async_retain.call_args.kwargs
+        mock_memory.retain_batch_async.assert_called_once()
+        call_kwargs = mock_memory.retain_batch_async.call_args.kwargs
        assert call_kwargs["request_context"].api_key == "test-bearer-token"
    finally:
        _current_bank_id.reset(bank_token)
@@ -199,11 +200,11 @@ 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")
+        await retain_tool.fn(content="test content", context="test_context", async_processing=False)

        # Verify the RequestContext passed to memory engine has all auth fields
-        mock_memory.submit_async_retain.assert_called_once()
-        request_context = mock_memory.submit_async_retain.call_args.kwargs["request_context"]
+        mock_memory.retain_batch_async.assert_called_once()
+        request_context = mock_memory.retain_batch_async.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"
@@ -351,69 +352,6 @@ 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.
@@ -77,9 +77,8 @@ class TestBuildContentDict:

@pytest.fixture
 def mock_memory():
-    """Create a mock MemoryEngine with all MCP tool methods."""
+    """Create a mock MemoryEngine with mental model 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"},
@@ -105,41 +104,6 @@ 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


@@ -247,7 +211,7 @@ class TestMentalModelToolRegistration:
        assert request_context.api_key == "test-api-key"

    def test_mental_model_tools_in_default_set(self):
-        """All tools should be in the default tools set when config.tools is None."""
+        """Mental model tools should be in the default tools set when config.tools is None."""
        from fastmcp import FastMCP

        memory = MagicMock()
@@ -265,21 +229,6 @@ 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(
@@ -292,18 +241,6 @@ 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
@@ -707,653 +644,3 @@ 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()
@@ -404,12 +404,25 @@ 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",
@@ -417,27 +430,11 @@ class TestDirectivesInReflect:
            "ingénieure",
            "développeur",
            "développeuse",
-            "ingénierie",
-            "française",
        ]
+        response_lower = result.text.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
-
+        # 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)
        assert (
            french_word_count >= 2
        ), f"Expected French response, but got: {result.text[:200]}"
@@ -477,7 +474,7 @@ class TestDirectivesInReflect:
        await memory.create_directive(
            bank_id=bank_id,
            name="General Policy",
-            content="You MUST include the exact phrase 'MEMO-VERIFIED' somewhere in your response.",
+            content="Always be polite and start responses with 'Hello!'",
            request_context=request_context,
        )

@@ -485,7 +482,7 @@ class TestDirectivesInReflect:
        await memory.create_directive(
            bank_id=bank_id,
            name="Tagged Policy",
-            content="You MUST include the exact phrase 'PROJECT-X-CLASSIFIED' somewhere in your response.",
+            content="ALWAYS respond in ALL CAPS and end with 'PROJECT-X ONLY'",
            tags=["project-x"],
            request_context=request_context,
        )
@@ -497,16 +494,18 @@ class TestDirectivesInReflect:
            request_context=request_context,
        )

-        # 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}"
-        )
+        response_lower = result.text.lower()

-        # Now run reflect WITH the tag - should load BOTH directives
+        # 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
        result_tagged = await memory.reflect_async(
            bank_id=bank_id,
            query="What color is the sky?",
@@ -515,14 +514,10 @@ class TestDirectivesInReflect:
            request_context=request_context,
        )

-        # 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}"
-        )
+        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}"

        # Cleanup
        await memory.delete_bank(bank_id, request_context=request_context)
@@ -15,10 +15,6 @@ 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
@@ -28,87 +24,70 @@ 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.
    """
-    max_retries = 3
-    last_error = None
+    bank_id = f"test_chinese_retain_{datetime.now(timezone.utc).timestamp()}"

-    for attempt in range(max_retries):
-        bank_id = f"test_chinese_retain_{datetime.now(timezone.utc).timestamp()}_{attempt}"
+    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。

-        try:
-            # Chinese content about a person and their activities
-            chinese_content = """
-            张伟是一位资深软件工程师，在腾讯工作了五年。他专门研究分布式系统，
-            并领导了公司微服务架构的开发。他以编写干净、文档完善的代码而闻名。
+        李明上个月加入团队担任初级开发人员。他正在学习React和Node.js。
+        李明很有热情，在代码审查中提出很好的问题。他最近完成了他的第一个功能，
+        这是一个用户认证流程。

-            李明上个月加入团队担任初级开发人员。他正在学习React和Node.js。
-            李明很有热情，在代码审查中提出很好的问题。他最近完成了他的第一个功能，
-            这是一个用户认证流程。
+        团队使用Kubernetes进行容器编排，并部署到阿里云。他们遵循敏捷方法论，
+        采用两周冲刺周期。合并前必须进行代码审查。
+        """

-            团队使用Kubernetes进行容器编排，并部署到阿里云。他们遵循敏捷方法论，
-            采用两周冲刺周期。合并前必须进行代码审查。
-            """
+        # Retain the Chinese content
+        unit_ids = await memory.retain_async(
+            bank_id=bank_id,
+            content=chinese_content,
+            context="团队概述",  # Chinese context
+            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            request_context=request_context,
+        )

-            # Retain the Chinese content
-            unit_ids = await memory.retain_async(
-                bank_id=bank_id,
-                content=chinese_content,
-                context="团队概述",  # Chinese context
-                event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-                request_context=request_context,
-            )
+        logger.info(f"Retained {len(unit_ids)} facts from Chinese content")
+        assert len(unit_ids) > 0, "Should have extracted and stored facts from Chinese content"

-            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"
+        # 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,
+        )

-            # Recall the facts with a Chinese query
-            result = await memory.recall_async(
-                bank_id=bank_id,
-                query="告诉我关于张伟的信息",  # "Tell me about Zhang Wei"
-                budget=Budget.MID,
-                max_tokens=1000,
-                fact_type=["world"],
-                request_context=request_context,
-            )
+        logger.info(f"Recalled {len(result.results)} facts")
+        assert len(result.results) > 0, "Should recall facts about Zhang Wei"

-            logger.info(f"Recalled {len(result.results)} facts")
-            assert len(result.results) > 0, "Should recall facts about Zhang Wei"
+        # Verify that the facts contain Chinese characters
+        # At least one fact should mention 张伟 (Zhang Wei) or related Chinese content
+        chinese_facts_found = 0
+        for fact in result.results:
+            logger.info(f"Fact: {fact.text[:100]}...")
+            # Check for common Chinese characters or the name
+            if any(
+                char in fact.text
+                for char in ["张", "伟", "腾讯", "软件", "工程师", "分布式", "系统", "代码"]
+            ):
+                chinese_facts_found += 1

-            # Verify that the facts contain Chinese characters
-            # At least one fact should mention 张伟 (Zhang Wei) or related Chinese content
-            chinese_facts_found = 0
-            for fact in result.results:
-                logger.info(f"Fact: {fact.text[:100]}...")
-                # Check for common Chinese characters or the name
-                if any(
-                    char in fact.text
-                    for char in ["张", "伟", "腾讯", "软件", "工程师", "分布式", "系统", "代码"]
-                ):
-                    chinese_facts_found += 1
+        logger.info(f"Found {chinese_facts_found} facts with Chinese content")
+        assert chinese_facts_found > 0, (
+            f"Expected facts to contain Chinese characters, but none found. "
+            f"Facts: {[f.text for f in result.results]}"
+        )

-            logger.info(f"Found {chinese_facts_found} facts with Chinese content")
-            assert chinese_facts_found > 0, (
-                f"Expected facts to contain Chinese characters, but none found. "
-                f"Facts: {[f.text for f in result.results]}"
-            )
+        logger.info("Chinese retain test passed - facts preserved in Chinese")

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


@pytest.mark.asyncio
@@ -154,7 +133,7 @@ async def test_reflect_chinese_content(memory, request_context):
                result = await memory.reflect_async(
                    bank_id=bank_id,
                    query=query,
-                    budget=Budget.MID,
+                    budget=Budget.LOW,
                    request_context=request_context,
                )

@@ -1,457 +0,0 @@
-"""
-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)
@@ -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 == "gpt-4o-mini", f"Expected gpt-4o-mini, got {config.llm_model}"
+        assert config.llm_model == "o3-mini", f"Expected o3-mini, got {config.llm_model}"

        # Retain should use Anthropic default
        assert (
@@ -7,17 +7,14 @@ 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
@@ -415,193 +412,3 @@ 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)
@@ -591,125 +591,6 @@ async def test_mentioned_at_from_context_string(memory, request_context):
        await memory.delete_bank(bank_id, request_context=request_context)


-# ============================================================
-# No Timestamp Tests
-# ============================================================
-
-
-@pytest.mark.asyncio
-async def test_retain_no_timestamp(memory, request_context):
-    """
-    Test retaining content with explicit "no timestamp" sentinel.
-
-    When event_date=None is passed explicitly in the dict (i.e. caller opted into
-    no timestamp), mentioned_at should be NULL in the DB rather than defaulting to now().
-    """
-    bank_id = f"test_no_timestamp_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Use retain_batch_async with explicit event_date=None key to signal "no timestamp"
-        unit_ids_list = await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[
-                {
-                    "content": "The capital of France is Paris. The Eiffel Tower is located in Paris.",
-                    "context": "general knowledge",
-                    "event_date": None,  # Explicit sentinel: no timestamp
-                }
-            ],
-            request_context=request_context,
-        )
-
-        assert len(unit_ids_list) > 0, "Should create at least one batch result"
-        unit_ids = unit_ids_list[0]
-        assert len(unit_ids) > 0, "Should have extracted and stored facts"
-
-        # Recall the facts
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="Where is the Eiffel Tower?",
-            budget=Budget.LOW,
-            max_tokens=500,
-            fact_type=["world"],
-            request_context=request_context,
-        )
-
-        assert len(result.results) > 0, "Should recall the stored fact"
-
-        # All temporal fields should be None for temporally agnostic content
-        for fact in result.results:
-            assert fact.mentioned_at is None, (
-                f"mentioned_at should be None for no-timestamp content, got {fact.mentioned_at}"
-            )
-
-        print(f"\n✓ Test passed: mentioned_at is None for {len(result.results)} fact(s)")
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_retain_omit_timestamp_defaults_to_now(memory, request_context):
-    """
-    Backward-compatibility regression test: omitting event_date still stores a real datetime.
-
-    When event_date is absent from the content dict (key not present), the orchestrator
-    should default to utcnow() — preserving existing behavior.
-    """
-    bank_id = f"test_default_timestamp_{datetime.now(timezone.utc).timestamp()}"
-    before = datetime.now(timezone.utc)
-
-    try:
-        # Omit event_date entirely — should default to now()
-        unit_ids_list = await memory.retain_batch_async(
-            bank_id=bank_id,
-            contents=[
-                {
-                    "content": "Alice is a software engineer who loves Python.",
-                    "context": "profile",
-                    # event_date intentionally omitted
-                }
-            ],
-            request_context=request_context,
-        )
-
-        after = datetime.now(timezone.utc)
-
-        assert len(unit_ids_list) > 0
-        unit_ids = unit_ids_list[0]
-        assert len(unit_ids) > 0, "Should have extracted and stored facts"
-
-        # Recall and verify mentioned_at is a real datetime close to now
-        result = await memory.recall_async(
-            bank_id=bank_id,
-            query="Who is Alice?",
-            budget=Budget.LOW,
-            max_tokens=500,
-            fact_type=["world"],
-            request_context=request_context,
-        )
-
-        assert len(result.results) > 0, "Should recall the fact"
-        fact = result.results[0]
-
-        assert fact.mentioned_at is not None, "mentioned_at should be set when event_date is omitted"
-
-        if isinstance(fact.mentioned_at, str):
-            mentioned_dt = datetime.fromisoformat(fact.mentioned_at.replace("Z", "+00:00"))
-        else:
-            mentioned_dt = fact.mentioned_at
-
-        # Should be within 60s of when we ran the test
-        assert before <= mentioned_dt <= after + timedelta(seconds=60), (
-            f"mentioned_at {mentioned_dt} should be close to now ({before} – {after})"
-        )
-
-        print(f"\n✓ Test passed: mentioned_at={mentioned_dt} is a real datetime (backward compat)")
-
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
 # ============================================================
 # Context Tracking Tests
 # ============================================================
@@ -2375,64 +2256,3 @@ async def test_retain_batch_with_per_item_tags_on_document(memory, request_conte
    finally:
        await memory.delete_bank(bank_id, request_context=request_context)
        print(f"\n=== Cleaned up bank: {bank_id} ===")
-
-
-def test_retain_mission_injected_into_prompt():
-    """Test that retain_mission is injected as a FOCUS section into any extraction mode."""
-    from unittest.mock import MagicMock
-    from hindsight_api.engine.retain.fact_extraction import _build_extraction_prompt_and_schema
-
-    spec = "Focus on technical decisions and architecture choices only."
-
-    # Test with concise mode
-    config = MagicMock()
-    config.retain_extraction_mode = "concise"
-    config.retain_mission = spec
-    config.retain_custom_instructions = None
-    config.retain_extract_causal_links = False
-
-    prompt, _ = _build_extraction_prompt_and_schema(config)
-    assert spec in prompt
-    assert "FOCUS" in prompt
-
-    # retain_mission is present regardless of extraction mode (verbose has its own template, no spec injection)
-    config.retain_extraction_mode = "verbose"
-    prompt_verbose, _ = _build_extraction_prompt_and_schema(config)
-    # verbose uses its own template - spec not injected there
-    assert spec not in prompt_verbose
-
-
-def test_retain_mission_absent_when_not_set():
-    """Test that no FOCUS section appears when retain_mission is not set."""
-    from unittest.mock import MagicMock
-    from hindsight_api.engine.retain.fact_extraction import _build_extraction_prompt_and_schema
-
-    config = MagicMock()
-    config.retain_extraction_mode = "concise"
-    config.retain_mission = None
-    config.retain_custom_instructions = None
-    config.retain_extract_causal_links = False
-
-    prompt, _ = _build_extraction_prompt_and_schema(config)
-    assert "FOCUS" not in prompt
-    assert "retain_mission_section" not in prompt
-
-
-def test_retain_mission_config_loaded_from_env():
-    """Test that retain_mission is loaded from env and is a configurable field."""
-    import os
-    from hindsight_api.config import HindsightConfig, _get_raw_config, clear_config_cache
-
-    original = os.getenv("HINDSIGHT_API_RETAIN_MISSION")
-    try:
-        os.environ["HINDSIGHT_API_RETAIN_MISSION"] = "Only technical decisions."
-        clear_config_cache()
-        config = _get_raw_config()
-        assert config.retain_mission == "Only technical decisions."
-        assert "retain_mission" in HindsightConfig.get_configurable_fields()
-    finally:
-        if original is None:
-            os.environ.pop("HINDSIGHT_API_RETAIN_MISSION", None)
-        else:
-            os.environ["HINDSIGHT_API_RETAIN_MISSION"] = original
-        clear_config_cache()
@@ -233,35 +233,6 @@ class TestFilterResultsByTags:
        assert len(filtered) == 1
        assert filtered[0].tags == ["a", "b", "c"]  # Has a, b, AND c

-    def test_all_strict_superset_observation_matches_incoming_memory_tags(self):
-        """
-        Consolidation scenario: an incoming memory with tags ['user:bob', 'session:id1']
-        uses all_strict matching to find existing observations.
-
-        An observation tagged ['user:bob', 'session:id1', 'place:online'] IS matched
-        because it contains all of the incoming memory's tags (superset).
-        This is NOT exact matching — an observation with extra tags is still a valid match.
-        """
-        # Incoming memory tags (e.g. from a new retain call)
-        incoming_tags = ["user:bob", "session:id1"]
-
-        # Candidate observations with different tag sets
-        exact_match = MockResult(["user:bob", "session:id1"])
-        superset_match = MockResult(["session:id1", "user:bob", "place:online"])
-        different_user = MockResult(["user:alice", "session:id1"])
-        missing_session = MockResult(["user:bob"])
-
-        results = [exact_match, superset_match, different_user, missing_session]
-        filtered = filter_results_by_tags(results, incoming_tags, match="all_strict")
-
-        # Both exact_match and superset_match have all incoming tags → both match
-        assert len(filtered) == 2
-        assert exact_match in filtered
-        assert superset_match in filtered
-        # different_user and missing_session are excluded because they lack at least one tag
-        assert different_user not in filtered
-        assert missing_session not in filtered
-

 # ============================================================================
 # Integration Tests for tags in retain/recall/reflect
@@ -919,40 +890,3 @@ async def test_list_tags_ordered_by_count(api_client):
    # common (3) should come before medium (2) which should come before rare (1)
    assert tags.index("common") < tags.index("medium")
    assert tags.index("medium") < tags.index("rare")
-
-
-@pytest.mark.asyncio
-async def test_list_memories_includes_tags(api_client, test_bank_id):
-    """Test that list memories endpoint returns tags for each memory unit.
-
-    Regression test: tags were previously omitted from the SELECT query in
-    list_memory_units, causing the memory dialog in the UI to show no tags
-    even when memories had been stored with tags.
-    """
-    tags = ["user_alice", "session_xyz", "project_alpha", "team_eng", "env_prod", "region_us"]
-
-    response = await api_client.post(
-        f"/v1/default/banks/{test_bank_id}/memories",
-        json={
-            "items": [
-                {
-                    "content": "Alice is a senior engineer on the platform team.",
-                    "tags": tags,
-                }
-            ]
-        },
-    )
-    assert response.status_code == 200
-
-    # List memories and verify all tags are returned
-    response = await api_client.get(f"/v1/default/banks/{test_bank_id}/memories/list")
-    assert response.status_code == 200
-    result = response.json()
-
-    assert result["total"] > 0
-    memory_item = next((item for item in result["items"] if "Alice" in item["text"]), None)
-    assert memory_item is not None, "Should find the stored memory"
-    assert "tags" in memory_item, "Memory item must include a 'tags' field"
-    assert set(memory_item["tags"]) == set(tags), (
-        f"All {len(tags)} tags should be returned, got: {memory_item['tags']}"
-    )
@@ -266,10 +266,9 @@ def test_llm_span_recorder_provider_mapping(mock_time):
 # ==================== Parent Span Tests ====================


-@patch("hindsight_api.tracing._tracing_enabled", False)
 def test_create_operation_span_disabled():
    """Test that create_operation_span returns no-op when tracing is disabled."""
-    # Tracing should be disabled by default (explicitly patched for test isolation)
+    # Tracing should be disabled by default
    assert not is_tracing_enabled()

    # Should return a no-op context manager
@@ -73,10 +73,7 @@ def test_llm_wrapper_vertexai_adc_auth():

    with patch.dict(
        os.environ,
-        {
-            "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID": "test-project",
-            "HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY": "",  # Clear SA key to test ADC path
-        },
+        {"HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID": "test-project"},
        clear=False,
    ):
        from hindsight_api.config import clear_config_cache
@@ -99,10 +96,11 @@ def test_llm_wrapper_vertexai_adc_auth():
            assert provider._gemini_client is not None

            # Verify genai.Client was called with vertexai=True
-            call_kwargs = mock_client_cls.call_args.kwargs
-            assert call_kwargs["vertexai"] is True
-            assert call_kwargs["project"] == "test-project"
-            assert call_kwargs["location"] == "us-central1"
+            mock_client_cls.assert_called_once_with(
+                vertexai=True,
+                project="test-project",
+                location="us-central1",
+            )

        clear_config_cache()

@@ -143,11 +141,12 @@ def test_llm_wrapper_vertexai_sa_auth():
                assert provider._gemini_client is not None

                # Verify credentials were passed to genai.Client
-                call_kwargs = mock_client_cls.call_args.kwargs
-                assert call_kwargs["vertexai"] is True
-                assert call_kwargs["project"] == "test-project"
-                assert call_kwargs["location"] == "us-central1"
-                assert call_kwargs["credentials"] is mock_credentials
+                mock_client_cls.assert_called_once_with(
+                    vertexai=True,
+                    project="test-project",
+                    location="us-central1",
+                    credentials=mock_credentials,
+                )

        clear_config_cache()

@@ -48,11 +48,11 @@ async def pool(pg0_db_url):
@pytest_asyncio.fixture
 async def clean_operations(pool):
    """Clean up async_operations table before and after tests."""
-    # Clean before test - covers both 'test-worker-' and 'test_worker_recovery' patterns
-    await pool.execute("DELETE FROM async_operations WHERE bank_id LIKE 'test-worker-%' OR bank_id LIKE 'test_worker_%'")
+    # Clean before test
+    await pool.execute("DELETE FROM async_operations WHERE bank_id LIKE 'test-worker-%'")
    yield
    # Clean after test
-    await pool.execute("DELETE FROM async_operations WHERE bank_id LIKE 'test-worker-%' OR bank_id LIKE 'test_worker_%'")
+    await pool.execute("DELETE FROM async_operations WHERE bank_id LIKE 'test-worker-%'")


 class TestBrokerTaskBackend:
@@ -268,26 +268,24 @@ class TestWorkerPoller:
        assert row["completed_at"] is not None

    @pytest.mark.asyncio
-    async def test_executor_exception_triggers_retry(self, pool, clean_operations):
-        """Test that exceptions from the executor trigger _retry_or_fail (not a crash).
+    async def test_executor_exception_does_not_crash_poller(self, pool, clean_operations):
+        """Test that unexpected exceptions from executor are caught and don't crash the poller.

-        When the executor re-raises an exception (as MemoryEngine.execute_task does for
-        retryable task failures), the poller calls _retry_or_fail, which resets the task
-        back to 'pending' and increments retry_count so it can be reclaimed.
-
-        This is the fix for the consolidation deadlock: previously submit_task was called
-        with only a task_payload update, leaving status='processing' forever.
+        If the executor raises an unexpected exception (which MemoryEngine.execute_task should NOT do,
+        but could happen from schema setup or other infrastructure issues), the poller should catch it
+        gracefully. Status remains 'processing' since neither executor nor poller handled it.
        """
        from hindsight_api.worker import WorkerPoller
        from hindsight_api.worker.poller import ClaimedTask

+        # Create a pending task
        bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
        op_id = uuid.uuid4()
-        payload = json.dumps({"type": "consolidation", "operation_id": str(op_id), "bank_id": bank_id})
+        payload = json.dumps({"type": "test_task", "operation_id": str(op_id), "bank_id": bank_id})
        await pool.execute(
            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload, worker_id, claimed_at)
-            VALUES ($1, $2, 'consolidation', 'processing', $3::jsonb, 'test-worker-1', now())
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload, worker_id)
+            VALUES ($1, $2, 'test', 'processing', $3::jsonb, 'test-worker-1')
            """,
            op_id,
            bank_id,
@@ -295,100 +293,43 @@ class TestWorkerPoller:
        )

        async def failing_executor(task_dict):
-            raise ValueError("TimeoutError during recall")
+            raise ValueError("Unexpected infrastructure failure")

        poller = WorkerPoller(
            pool=pool,
            worker_id="test-worker-1",
            executor=failing_executor,
-            max_retries=3,
        )

+        # Execute - should catch exception without crashing
        task_dict = json.loads(payload)
        claimed_task = ClaimedTask(operation_id=str(op_id), task_dict=task_dict, schema=None)
        await poller.execute_task(claimed_task)

+        # Wait for background task to complete
        completed = await poller.wait_for_active_tasks(timeout=5.0)
        assert completed, "Task did not complete within timeout"

-        # Task must be reset to 'pending' with worker_id/claimed_at cleared — not left as
-        # 'processing', which would cause a permanent deadlock via the NOT EXISTS guard.
+        # Status stays 'processing' since the poller no longer manages status
        row = await pool.fetchrow(
-            "SELECT status, worker_id, claimed_at, retry_count FROM async_operations WHERE operation_id = $1",
+            "SELECT status FROM async_operations WHERE operation_id = $1",
            op_id,
        )
-        assert row["status"] == "pending", (
-            f"REGRESSION: Task status is '{row['status']}' instead of 'pending'. "
-            "A task stuck in 'processing' after a retry causes a consolidation deadlock."
-        )
-        assert row["worker_id"] is None, "worker_id must be cleared on retry"
-        assert row["claimed_at"] is None, "claimed_at must be cleared on retry"
-        assert row["retry_count"] == 1
-
-    @pytest.mark.asyncio
-    async def test_executor_exception_marks_failed_after_max_retries(self, pool, clean_operations):
-        """Test that a task is permanently marked 'failed' once retry_count hits max_retries.
-
-        After max_retries exhaustion the task must NOT be reset to 'pending' — it should
-        be marked 'failed' with an error message so it stops consuming retry budget.
-        """
-        from hindsight_api.worker import WorkerPoller
-        from hindsight_api.worker.poller import ClaimedTask
-
-        max_retries = 3
-        bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
-        op_id = uuid.uuid4()
-        payload = json.dumps({"type": "consolidation", "operation_id": str(op_id), "bank_id": bank_id})
-        # Insert with retry_count already at the limit
-        await pool.execute(
-            """
-            INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload, worker_id, claimed_at, retry_count)
-            VALUES ($1, $2, 'consolidation', 'processing', $3::jsonb, 'test-worker-1', now(), $4)
-            """,
-            op_id,
-            bank_id,
-            payload,
-            max_retries,
-        )
-
-        async def failing_executor(task_dict):
-            raise ValueError("Still failing after all retries")
-
-        poller = WorkerPoller(
-            pool=pool,
-            worker_id="test-worker-1",
-            executor=failing_executor,
-            max_retries=max_retries,
-        )
-
-        task_dict = json.loads(payload)
-        claimed_task = ClaimedTask(operation_id=str(op_id), task_dict=task_dict, schema=None)
-        await poller.execute_task(claimed_task)
-
-        completed = await poller.wait_for_active_tasks(timeout=5.0)
-        assert completed, "Task did not complete within timeout"
-
-        row = await pool.fetchrow(
-            "SELECT status, error_message, retry_count FROM async_operations WHERE operation_id = $1",
-            op_id,
-        )
-        assert row["status"] == "failed", (
-            f"Expected 'failed' after max retries, got '{row['status']}'"
-        )
-        assert row["error_message"] is not None
-        assert "Max retries" in row["error_message"]
-        assert row["retry_count"] == max_retries  # not incremented further
+        assert row["status"] == "processing"

    @pytest.mark.asyncio
    async def test_executor_failed_status_not_overridden(self, pool, clean_operations):
        """REGRESSION TEST: Verify poller does NOT overwrite executor's 'failed' status to 'completed'.

-        This test covers the non-retryable failure path (e.g., file_convert_retain):
-        1. Executor catches an internal error, marks the operation as 'failed' in the DB
-        2. Executor returns normally (does NOT re-raise) — so no exception reaches the poller
+        This test catches the bug where the poller always called _mark_completed() after executor
+        returned, overwriting the 'failed' status that the executor had already set.
+
+        Scenario:
+        1. Executor catches an internal error and marks the operation as 'failed' in the DB
+        2. Executor returns normally (does NOT re-raise) - this is how MemoryEngine.execute_task works
        3. The poller must NOT overwrite the 'failed' status to 'completed'

-        Retryable failures re-raise instead (see test_executor_exception_triggers_retry).
+        With the old buggy code, this test would FAIL (status would be 'completed').
        """
        from hindsight_api.worker import WorkerPoller
        from hindsight_api.worker.poller import ClaimedTask
@@ -1,6 +1,6 @@
 [package]
 name = "hindsight-cli"
-version = "0.4.15"
+version = "0.4.12"
 edition = "2021"
 authors = ["Hindsight Team"]
 description = "A beautiful CLI for Hindsight - semantic memory system"
@@ -127,7 +127,6 @@ impl ApiClient {
                mission: None,
                background: None,
                disposition: None,
-                ..Default::default()
            };
            let response = self.client.create_or_update_bank(agent_id, None, &request).await?;
            Ok(response.into_inner())
@@ -300,8 +299,6 @@ impl ApiClient {
                offset.map(|o| o as i64),
                q,
                None,
-                None,
-                None,
            ).await?;
            Ok(response.into_inner())
        })
@@ -439,7 +436,6 @@ impl ApiClient {
                mission: Some(mission.to_string()),
                background: None,
                disposition: None,
-                ..Default::default()
            };
            let response = self.client.update_bank(bank_id, None, &request).await?;
            Ok(response.into_inner())
@@ -454,7 +450,7 @@ impl ApiClient {
        _verbose: bool,
    ) -> Result<types::GraphDataResponse> {
        self.runtime.block_on(async {
-            let response = self.client.get_graph(bank_id, limit, type_filter, None, None, None, None).await?;
+            let response = self.client.get_graph(bank_id, limit, type_filter, None).await?;
            Ok(response.into_inner())
        })
    }
--- a/Show More
+++ b/Show More