doc: update claude-code usage terms

* feat: slim docker distro

* feat: slim docker distro

* push

.github/workflows/release.yml

@@ -188,6 +188,55 @@ jobs:
         path: hindsight-integrations/openclaw/*.tgz
         retention-days: 1
 
+  release-ai-sdk-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/ai-sdk
+      run: npm ci
+
+    - name: Build
+      working-directory: ./hindsight-integrations/ai-sdk
+      run: npm run build
+
+    - name: Publish to npm
+      working-directory: ./hindsight-integrations/ai-sdk
+      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/ai-sdk
+      run: npm pack
+
+    - name: Upload artifacts
+      uses: actions/upload-artifact@v4
+      with:
+        name: ai-sdk-integration
+        path: hindsight-integrations/ai-sdk/*.tgz
+        retention-days: 1
+
   release-control-plane:
     runs-on: ubuntu-latest
     environment: npm
@@ -291,6 +340,7 @@ jobs:
         retention-days: 1
 
   release-docker-images:
+    name: Release Docker (${{ matrix.image_name }}${{ matrix.tag_suffix }})
     runs-on: ubuntu-latest
     permissions:
       contents: read
@@ -300,10 +350,28 @@ jobs:
         include:
           - target: api-only
             image_name: hindsight-api
+            tag_suffix: ""
+            build_args: ""
+          - target: api-only
+            image_name: hindsight-api
+            tag_suffix: "-slim"
+            build_args: |
+              INCLUDE_LOCAL_MODELS=false
+              PRELOAD_ML_MODELS=false
           - target: cp-only
             image_name: hindsight-control-plane
+            tag_suffix: ""
+            build_args: ""
           - target: standalone
             image_name: hindsight
+            tag_suffix: ""
+            build_args: ""
+          - target: standalone
+            image_name: hindsight
+            tag_suffix: "-slim"
+            build_args: |
+              INCLUDE_LOCAL_MODELS=false
+              PRELOAD_ML_MODELS=false
 
     steps:
     - uses: actions/checkout@v4
@@ -341,6 +409,9 @@ jobs:
       uses: docker/metadata-action@v5
       with:
         images: ghcr.io/${{ github.repository_owner }}/${{ matrix.image_name }}
+        flavor: |
+          latest=auto
+          suffix=${{ matrix.tag_suffix }}
         tags: |
           type=semver,pattern={{version}},value=${{ steps.get_version.outputs.VERSION }}
           type=semver,pattern={{major}}.{{minor}},value=${{ steps.get_version.outputs.VERSION }}
@@ -366,7 +437,7 @@ jobs:
     # - name: Smoke test - verify container starts
     #   env:
     #     GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-    #   run: ./scripts/docker-smoke-test.sh "${{ matrix.image_name }}:test" "${{ matrix.target }}"
+    #   run: ./docker/test-image.sh "${{ matrix.image_name }}:test" "${{ matrix.target }}"
 
     # Build multi-platform and push to release tags
     - name: Build and push release images
@@ -375,6 +446,7 @@ jobs:
         context: .
         file: docker/standalone/Dockerfile
         target: ${{ matrix.target }}
+        build-args: ${{ matrix.build_args }}
         push: true
         platforms: linux/amd64,linux/arm64
         tags: ${{ steps.meta.outputs.tags }}
@@ -415,7 +487,7 @@ jobs:
 
   create-github-release:
     runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-openclaw-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
 
@@ -444,6 +516,12 @@ jobs:
         name: openclaw-integration
         path: ./artifacts/openclaw-integration
 
+    - name: Download AI SDK Integration
+      uses: actions/download-artifact@v4
+      with:
+        name: ai-sdk-integration
+        path: ./artifacts/ai-sdk-integration
+
     - name: Download Control Plane
       uses: actions/download-artifact@v4
       with:
@@ -487,6 +565,8 @@ jobs:
         cp artifacts/typescript-client/*.tgz release-assets/ || true
         # OpenClaw Integration
         cp artifacts/openclaw-integration/*.tgz release-assets/ || true
+        # AI SDK Integration
+        cp artifacts/ai-sdk-integration/*.tgz release-assets/ || true
         # Control Plane
         cp artifacts/control-plane/*.tgz release-assets/ || true
         # Rust CLI binaries

.github/workflows/test.yml

@@ -9,42 +9,11 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  build-python-packages:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        include:
-          - name: hindsight-all
-            path: hindsight
-          - name: hindsight-api
-            path: hindsight-api
-          - name: hindsight-client
-            path: hindsight-clients/python
-          - name: hindsight-embed
-            path: hindsight-embed
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Install uv
-      uses: astral-sh/setup-uv@v5
-      with:
-        enable-cache: true
-
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version-file: ".python-version"
-
-    - name: Build ${{ matrix.name }}
-      working-directory: ./${{ matrix.path }}
-      run: uv build
-
   build-api-python-versions:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.11', '3.12', '3.13']
+        python-version: ['3.11', '3.12', '3.13', '3.14']
 
     steps:
     - uses: actions/checkout@v4
@@ -105,6 +74,29 @@ jobs:
       working-directory: ./hindsight-integrations/openclaw
       run: npm run build
 
+  build-ai-sdk-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/ai-sdk
+      run: npm ci
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/ai-sdk
+      run: npm test
+
+    - name: Build
+      working-directory: ./hindsight-integrations/ai-sdk
+      run: npm run build
+
   build-control-plane:
     runs-on: ubuntu-latest
 
@@ -285,16 +277,35 @@ jobs:
       run: helm lint helm/hindsight
 
   build-docker-images:
+    name: Build Docker (${{ matrix.name }})
     runs-on: ubuntu-latest
     strategy:
       matrix:
         include:
           - target: api-only
             name: api
+            variant: full
+            build_args: ""
+          - target: api-only
+            name: api-slim
+            variant: slim
+            build_args: |
+              INCLUDE_LOCAL_MODELS=false
+              PRELOAD_ML_MODELS=false
           - target: cp-only
             name: control-plane
+            variant: full
+            build_args: ""
           - target: standalone
             name: standalone
+            variant: full
+            build_args: ""
+          - target: standalone
+            name: standalone-slim
+            variant: slim
+            build_args: |
+              INCLUDE_LOCAL_MODELS=false
+              PRELOAD_ML_MODELS=false
 
     steps:
     - uses: actions/checkout@v4
@@ -313,20 +324,30 @@ jobs:
     - name: Set up Docker Buildx
       uses: docker/setup-buildx-action@v3
 
-    - name: Build ${{ matrix.name }} image
+    - name: Build ${{ matrix.name }} image (${{ matrix.variant }})
       uses: docker/build-push-action@v6
       with:
         context: .
         file: docker/standalone/Dockerfile
         target: ${{ matrix.target }}
+        build-args: ${{ matrix.build_args }}
         push: false
-        load: false
+        load: ${{ matrix.variant == 'slim' }}
+        tags: hindsight-${{ matrix.name }}:test
+        cache-from: type=gha,scope=${{ matrix.name }}
+        cache-to: type=gha,mode=max,scope=${{ matrix.name }}
 
-    # TODO: Re-enable smoke test when disk space issue is resolved
-    # - name: Smoke test - verify container starts
-    #   env:
-    #     GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
-    #   run: ./scripts/docker-smoke-test.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"
+    # Only test slim variants to save disk space (they're much smaller)
+    # Slim variants require external embedding providers
+    - name: Smoke test - verify container starts
+      if: matrix.variant == 'slim'
+      env:
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        HINDSIGHT_API_EMBEDDINGS_PROVIDER: openai
+        HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        HINDSIGHT_API_RERANKER_PROVIDER: cohere
+        HINDSIGHT_API_COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
+      run: ./docker/test-image.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"
 
   test-api:
     runs-on: ubuntu-latest
@@ -790,6 +811,54 @@ jobs:
       working-directory: ./hindsight-embed
       run: ./test.sh
 
+  test-hindsight-all:
+    runs-on: ubuntu-latest
+    env:
+      HINDSIGHT_API_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      # For test_server_integration.py compatibility
+      HINDSIGHT_LLM_PROVIDER: groq
+      HINDSIGHT_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_LLM_MODEL: openai/gpt-oss-20b
+      # Prefer CPU-only PyTorch in CI
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Build hindsight-all
+      working-directory: ./hindsight
+      run: uv build
+
+    - name: Install dependencies
+      working-directory: ./hindsight
+      run: uv sync --frozen --extra test --index-strategy unsafe-best-match
+
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-all-${{ hashFiles('hindsight/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-all-
+          ${{ runner.os }}-huggingface-
+
+    - name: Run unit tests
+      working-directory: ./hindsight
+      run: uv run pytest tests/ -v
+
   test-doc-examples:
     runs-on: ubuntu-latest
     needs: test-rust-cli
@@ -1044,4 +1113,52 @@ jobs:
           git diff --stat
           exit 1
         fi
-        echo "✓ All generated files are up to date"
+        echo "✓ All generated files are up to date"
+
+  check-openapi-compatibility:
+    runs-on: ubuntu-latest
+    env:
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        fetch-depth: 0  # Fetch full git history to access base branch
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Install hindsight-dev dependencies
+      run: |
+        cd hindsight-dev && uv sync --frozen --index-strategy unsafe-best-match
+
+    - name: Check OpenAPI compatibility with base branch
+      run: |
+        # Get the base branch (usually main)
+        BASE_BRANCH="${{ github.base_ref }}"
+
+        if [ -z "$BASE_BRANCH" ]; then
+          echo "⚠️  Warning: No base branch found (not a PR?). Skipping compatibility check."
+          exit 0
+        fi
+
+        echo "Checking OpenAPI compatibility against base branch: $BASE_BRANCH"
+
+        # Extract the old OpenAPI spec from base branch
+        git show "origin/$BASE_BRANCH:hindsight-docs/static/openapi.json" > /tmp/old-openapi.json
+
+        if [ ! -s /tmp/old-openapi.json ]; then
+          echo "⚠️  Warning: Could not find OpenAPI spec in base branch. Skipping compatibility check."
+          exit 0
+        fi
+
+        # Check compatibility using our tool
+        cd hindsight-dev
+        uv run check-openapi-compatibility /tmp/old-openapi.json ../hindsight-docs/static/openapi.json

README.md

@@ -59,7 +59,7 @@ docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
 
 You can modify the LLM provider by setting `HINDSIGHT_API_LLM_PROVIDER`. Valid options are `openai`, `anthropic`, `gemini`, `groq`, `ollama`, and `lmstudio`. The documentation provides more details on [supported models](https://hindsight.vectorize.io/developer/models).
 
-API: http://localhost:8888  
+API: http://localhost:8888
 UI: http://localhost:9999
 
 Install client:

docker/README.md

@@ -0,0 +1,135 @@
+# Docker Testing
+
+Scripts for testing Hindsight Docker images locally and in CI.
+
+## Scripts
+
+### `test-image.sh`
+
+General-purpose Docker image test script. Starts a container and verifies it becomes healthy.
+
+**Usage:**
+```bash
+./docker/test-image.sh <image> [target]
+```
+
+**Arguments:**
+- `image` - Docker image to test (e.g., `hindsight:test`, `ghcr.io/vectorize-io/hindsight:latest`)
+- `target` - Optional: `cp-only` for control plane, `api-only` for API, or `standalone` (default)
+
+**Environment Variables:**
+- `GROQ_API_KEY` - Required for API/standalone images
+- `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 (for slim images)
+- `HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY` - OpenAI API key for embeddings
+- `HINDSIGHT_API_RERANKER_PROVIDER` - Reranker provider (for slim images)
+- `HINDSIGHT_API_COHERE_API_KEY` - Cohere API key for reranking
+- `SMOKE_TEST_TIMEOUT` - Timeout in seconds (default: 120)
+
+**Examples:**
+
+Test a full image (with local ML models):
+```bash
+export GROQ_API_KEY=gsk_xxx
+./docker/test-image.sh hindsight:test
+```
+
+Test a slim image (with external providers):
+```bash
+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
+export HINDSIGHT_API_COHERE_API_KEY=xxx
+./docker/test-image.sh hindsight-slim:test
+```
+
+### `test-slim-local.sh`
+
+Convenience wrapper for testing slim images locally. Automatically configures external providers.
+
+**Usage:**
+```bash
+# Set API keys
+export GROQ_API_KEY=gsk_xxx
+export OPENAI_API_KEY=sk-xxx
+export COHERE_API_KEY=xxx
+
+# Run test
+./docker/test-slim-local.sh [image]
+```
+
+**Or inline:**
+```bash
+GROQ_API_KEY=gsk_xxx \
+OPENAI_API_KEY=sk-xxx \
+COHERE_API_KEY=xxx \
+./docker/test-slim-local.sh hindsight-slim:test
+```
+
+This script:
+- ✅ Validates API keys are set
+- ✅ Configures OpenAI embeddings automatically
+- ✅ Configures Cohere reranking automatically
+- ✅ Calls `test-image.sh` with the right configuration
+
+## Building and Testing Locally
+
+### Build a slim image
+
+```bash
+docker build \
+  --build-arg INCLUDE_LOCAL_MODELS=false \
+  --build-arg PRELOAD_ML_MODELS=false \
+  --target standalone \
+  -t hindsight-slim:test \
+  -f docker/standalone/Dockerfile \
+  .
+```
+
+### Test the slim image
+
+```bash
+# With API keys
+export GROQ_API_KEY=gsk_xxx
+export OPENAI_API_KEY=sk-xxx
+export COHERE_API_KEY=xxx
+
+# Run test
+./docker/test-slim-local.sh hindsight-slim:test
+```
+
+## Expected Output
+
+**Successful test:**
+```
+Starting smoke test for: hindsight-slim:test
+  Target: standalone
+  Health endpoint: http://localhost:8888/health
+  Timeout: 120s
+
+Starting container...
+Waiting for health endpoint at http://localhost:8888/health...
+  Still waiting... (10s)
+  Still waiting... (20s)
+
+Container is healthy after 25s
+
+=== Health Response ===
+{
+    "status": "healthy",
+    "database": "connected"
+}
+
+Smoke test PASSED
+```
+
+## CI Integration
+
+These scripts are used in CI to validate Docker images on every PR:
+
+- `.github/workflows/test.yml` - Runs `test-image.sh` for slim variants with OpenAI/Cohere
+- `.github/workflows/release.yml` - Can optionally run smoke tests during release
+
+See the workflows for the exact configuration.

docker/standalone/Dockerfile

@@ -208,6 +208,10 @@ ENV HINDSIGHT_API_LOG_LEVEL=info
 ENV HINDSIGHT_ENABLE_API=true
 ENV HINDSIGHT_ENABLE_CP=false
 ENV PYTHONUNBUFFERED=1
+# Suppress verbose transformers/HuggingFace model loading warnings
+ENV TRANSFORMERS_VERBOSITY=error
+ENV HF_HUB_VERBOSITY=error
+ENV TOKENIZERS_PARALLELISM=false
 
 CMD ["/app/start-all.sh"]
 
@@ -336,6 +340,10 @@ ENV HINDSIGHT_CP_DATAPLANE_API_URL=http://localhost:8888
 ENV HINDSIGHT_ENABLE_API=true
 ENV HINDSIGHT_ENABLE_CP=true
 ENV PYTHONUNBUFFERED=1
+# Suppress verbose transformers/HuggingFace model loading warnings
+ENV TRANSFORMERS_VERBOSITY=error
+ENV HF_HUB_VERBOSITY=error
+ENV TOKENIZERS_PARALLELISM=false
 
 CMD ["/app/start-all.sh"]
 

docker/test-image.sh

@@ -6,28 +6,40 @@
 # Can be run locally or in CI pipelines.
 #
 # Usage:
-#   ./scripts/docker-smoke-test.sh <image> [target]
+#   ./docker/test-image.sh <image> [target]
 #
 # Arguments:
 #   image   - Docker image to test (e.g., hindsight-api:test, ghcr.io/vectorize-io/hindsight:latest)
 #   target  - Optional: 'cp-only' for control plane, otherwise assumes API image (default: api)
 #
 # Environment variables:
-#   GROQ_API_KEY              - Required for API/standalone images (LLM verification)
-#   HINDSIGHT_API_LLM_PROVIDER - LLM provider (default: groq)
-#   HINDSIGHT_API_LLM_MODEL    - LLM model (default: llama-3.3-70b-versatile)
-#   SMOKE_TEST_TIMEOUT        - Timeout in seconds (default: 120)
-#   SMOKE_TEST_CONTAINER_NAME - Container name (default: hindsight-smoke-test)
+#   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)
+#   HINDSIGHT_API_COHERE_API_KEY                - Cohere API key for reranking (optional)
+#   SMOKE_TEST_TIMEOUT                          - Timeout in seconds (default: 120)
+#   SMOKE_TEST_CONTAINER_NAME                   - Container name (default: hindsight-smoke-test)
 #
 # Examples:
-#   # Test a locally built image
-#   ./scripts/docker-smoke-test.sh hindsight-api:test
+#   # Test a locally built full image
+#   ./docker/test-image.sh hindsight-api:test
 #
 #   # Test a released image
-#   ./scripts/docker-smoke-test.sh ghcr.io/vectorize-io/hindsight:latest
+#   ./docker/test-image.sh ghcr.io/vectorize-io/hindsight:latest
 #
 #   # Test control plane image
-#   ./scripts/docker-smoke-test.sh hindsight-control-plane:test cp-only
+#   ./docker/test-image.sh hindsight-control-plane:test cp-only
+#
+#   # Test slim image with external providers
+#   export GROQ_API_KEY=gsk_xxx
+#   export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
+#   export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=sk-xxx
+#   export HINDSIGHT_API_RERANKER_PROVIDER=cohere
+#   export HINDSIGHT_API_COHERE_API_KEY=xxx
+#   ./docker/test-image.sh hindsight-slim:test
 #
 # Exit codes:
 #   0 - Success (container healthy)
@@ -108,12 +120,32 @@ if [ "$TARGET" = "cp-only" ]; then
         -p "${HEALTH_PORT}:${HEALTH_PORT}" \
         "$IMAGE"
 else
-    docker run -d --name "$CONTAINER_NAME" \
-        -e HINDSIGHT_API_LLM_PROVIDER="$LLM_PROVIDER" \
-        -e HINDSIGHT_API_LLM_API_KEY="${GROQ_API_KEY}" \
-        -e HINDSIGHT_API_LLM_MODEL="$LLM_MODEL" \
-        -p "${HEALTH_PORT}:${HEALTH_PORT}" \
-        "$IMAGE"
+    # Build docker run command with required and optional env vars
+    DOCKER_CMD="docker run -d --name $CONTAINER_NAME"
+    DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_PROVIDER=$LLM_PROVIDER"
+    DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_API_KEY=${GROQ_API_KEY}"
+    DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_LLM_MODEL=$LLM_MODEL"
+
+    # Add optional embeddings provider config
+    if [ -n "${HINDSIGHT_API_EMBEDDINGS_PROVIDER:-}" ]; then
+        DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_EMBEDDINGS_PROVIDER=${HINDSIGHT_API_EMBEDDINGS_PROVIDER}"
+    fi
+    if [ -n "${HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY:-}" ]; then
+        DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=${HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY}"
+    fi
+
+    # Add optional reranker provider config
+    if [ -n "${HINDSIGHT_API_RERANKER_PROVIDER:-}" ]; then
+        DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_RERANKER_PROVIDER=${HINDSIGHT_API_RERANKER_PROVIDER}"
+    fi
+    if [ -n "${HINDSIGHT_API_COHERE_API_KEY:-}" ]; then
+        DOCKER_CMD="$DOCKER_CMD -e HINDSIGHT_API_COHERE_API_KEY=${HINDSIGHT_API_COHERE_API_KEY}"
+    fi
+
+    DOCKER_CMD="$DOCKER_CMD -p ${HEALTH_PORT}:${HEALTH_PORT}"
+    DOCKER_CMD="$DOCKER_CMD $IMAGE"
+
+    eval $DOCKER_CMD
 fi
 
 # Wait for health endpoint

docker/test-slim-local.sh

@@ -0,0 +1,51 @@
+#!/bin/bash
+#
+# Local Test Script for Slim Docker Images
+#
+# This script makes it easy to test slim images locally with external providers.
+# It expects API keys to be set in environment variables.
+#
+# Usage:
+#   export GROQ_API_KEY=gsk_xxx
+#   export OPENAI_API_KEY=sk-xxx
+#   export COHERE_API_KEY=xxx
+#   ./docker/test-slim-local.sh
+#
+# Or inline:
+#   GROQ_API_KEY=gsk_xxx OPENAI_API_KEY=sk_xxx COHERE_API_KEY=xxx ./docker/test-slim-local.sh
+#
+
+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"
+    exit 1
+fi
+
+if [ -z "${COHERE_API_KEY:-}" ]; then
+    echo "❌ Error: COHERE_API_KEY environment variable is required"
+    echo "Set it with: export COHERE_API_KEY=xxx"
+    exit 1
+fi
+
+# Configuration
+IMAGE="${1:-hindsight-slim:test}"
+echo "Testing image: $IMAGE"
+echo ""
+
+# Set up external providers
+export HINDSIGHT_API_EMBEDDINGS_PROVIDER=openai
+export HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY=$OPENAI_API_KEY
+export HINDSIGHT_API_RERANKER_PROVIDER=cohere
+export HINDSIGHT_API_COHERE_API_KEY=$COHERE_API_KEY
+
+# Run the test
+exec "$(dirname "$0")/test-image.sh" "$IMAGE" standalone

helm/hindsight/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.8
-appVersion: "0.4.8"
+version: 0.4.9
+appVersion: "0.4.9"
 keywords:
   - ai
   - memory

hindsight-api/hindsight_api/__init__.py

@@ -46,4 +46,4 @@ __all__ = [
     "RemoteTEICrossEncoder",
     "LLMConfig",
 ]
-__version__ = "0.4.8"
+__version__ = "0.4.9"

hindsight-api/hindsight_api/alembic/versions/w8r9s0t1u2v3_fix_mental_models_pk_isolation.py

@@ -0,0 +1,60 @@
+"""Fix mental_models primary key to be scoped per bank
+
+Revision ID: w8r9s0t1u2v3
+Revises: v7q8r9s0t1u2
+Create Date: 2026-02-05
+
+This migration fixes a critical bank isolation bug where mental_models.id was
+globally unique across all banks instead of being scoped per bank. This caused
+conflicts when different banks tried to use the same custom ID.
+
+CRITICAL FIX: Changes primary key from (id) to (bank_id, id) to ensure proper isolation.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "w8r9s0t1u2v3"
+down_revision: str | Sequence[str] | None = "v7q8r9s0t1u2"
+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:
+    """Change mental_models primary key from (id) to (bank_id, id) for proper bank isolation."""
+    schema = _get_schema_prefix()
+
+    # Drop the old primary key constraint (just id)
+    # Note: The constraint might be named differently on different DBs
+    # Try both old names (pinned_reflections_pkey from original, mental_models_pkey from rename)
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS pinned_reflections_pkey")
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS mental_models_pkey")
+
+    # Create the new composite primary key (bank_id, id)
+    # This ensures IDs are scoped per bank, not globally
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD CONSTRAINT mental_models_pkey PRIMARY KEY (bank_id, id)
+    """)
+
+
+def downgrade() -> None:
+    """Revert mental_models primary key from (bank_id, id) to (id)."""
+    schema = _get_schema_prefix()
+
+    # Drop the composite primary key
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS mental_models_pkey")
+
+    # Restore the old primary key (just id)
+    # WARNING: This downgrade will fail if there are duplicate IDs across banks
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD CONSTRAINT mental_models_pkey PRIMARY KEY (id)
+    """)

hindsight-api/hindsight_api/api/http.py

@@ -5,6 +5,7 @@ This module provides the create_app function to create and configure
 the FastAPI application with all API endpoints.
 """
 
+import asyncio
 import json
 import logging
 import uuid
@@ -35,7 +36,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from hindsight_api import MemoryEngine
 from hindsight_api.engine.db_utils import acquire_with_retry
-from hindsight_api.engine.memory_engine import Budget, fq_table
+from hindsight_api.engine.memory_engine import Budget, _get_tiktoken_encoding, fq_table
 from hindsight_api.engine.reflect.observations import Observation
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, TokenUsage
 from hindsight_api.engine.search.tags import TagsMatch
@@ -45,6 +46,8 @@ from hindsight_api.models import RequestContext
 
 logger = logging.getLogger(__name__)
 
+MAX_QUERY_TOKENS = 500  # Maximum tokens allowed in recall query
+
 
 class EntityIncludeOptions(BaseModel):
     """Options for including entity observations in recall results."""
@@ -520,7 +523,9 @@ class ReflectFact(BaseModel):
     )
 
     id: str | None = None
-    text: str
+    text: str = Field(
+        description="Fact text. When type='observation', this contains markdown-formatted consolidated knowledge"
+    )
     type: str | None = None  # fact type: world, experience, observation
     context: str | None = None
     occurred_start: str | None = None
@@ -585,7 +590,7 @@ class ReflectResponse(BaseModel):
     model_config = ConfigDict(
         json_schema_extra={
             "example": {
-                "text": "Based on my understanding, AI is a transformative technology...",
+                "text": "## AI Overview\n\nBased on my understanding, AI is a **transformative technology**:\n\n- Used extensively in healthcare\n- Discussed in recent conversations\n- Continues to evolve rapidly",
                 "based_on": {
                     "memories": [
                         {"id": "123", "text": "AI is used in healthcare", "type": "world"},
@@ -613,7 +618,9 @@ class ReflectResponse(BaseModel):
         }
     )
 
-    text: str
+    text: str = Field(
+        description="The reflect response as well-formatted markdown (headers, lists, bold/italic, code blocks, etc.)"
+    )
     based_on: ReflectBasedOn | None = Field(
         default=None,
         description="Evidence used to generate the response. Only present when include.facts is set.",
@@ -863,6 +870,7 @@ class ListDocumentsResponse(BaseModel):
                         "updated_at": "2024-01-15T10:30:00Z",
                         "text_length": 5420,
                         "memory_unit_count": 15,
+                        "tags": ["user_a", "session_123"],
                     }
                 ],
                 "total": 50,
@@ -1110,7 +1118,9 @@ class MentalModelResponse(BaseModel):
     bank_id: str
     name: str
     source_query: str
-    content: str
+    content: str = Field(
+        description="The mental model content as well-formatted markdown (auto-generated from reflect endpoint)"
+    )
     tags: list[str] = Field(default_factory=list)
     max_tokens: int = Field(default=2048)
     trigger: MentalModelTrigger = Field(default_factory=MentalModelTrigger)
@@ -1134,6 +1144,7 @@ class CreateMentalModelRequest(BaseModel):
     model_config = ConfigDict(
         json_schema_extra={
             "example": {
+                "id": "team-communication",
                 "name": "Team Communication Preferences",
                 "source_query": "How does the team prefer to communicate?",
                 "tags": ["team"],
@@ -1143,6 +1154,9 @@ class CreateMentalModelRequest(BaseModel):
         }
     )
 
+    id: str | None = Field(
+        None, description="Optional custom ID for the mental model (alphanumeric lowercase with hyphens)"
+    )
     name: str = Field(description="Human-readable name for the mental model")
     source_query: str = Field(description="The query to run to generate content")
     tags: list[str] = Field(default_factory=list, description="Tags for scoped visibility")
@@ -1153,7 +1167,8 @@ class CreateMentalModelRequest(BaseModel):
 class CreateMentalModelResponse(BaseModel):
     """Response model for mental model creation."""
 
-    operation_id: str = Field(description="Operation ID to track progress")
+    mental_model_id: str | None = Field(None, description="ID of the created mental model")
+    operation_id: str = Field(description="Operation ID to track refresh progress")
 
 
 class UpdateMentalModelRequest(BaseModel):
@@ -1718,6 +1733,15 @@ def _register_routes(app: FastAPI):
         handler_start = time.time()
         metrics = get_metrics_collector()
 
+        # Validate query length to prevent expensive operations on oversized queries
+        encoding = _get_tiktoken_encoding()
+        query_tokens = len(encoding.encode(request.query))
+        if query_tokens > MAX_QUERY_TOKENS:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Query too long: {query_tokens} tokens exceeds maximum of {MAX_QUERY_TOKENS}. Please shorten your query.",
+            )
+
         try:
             # Default to world and experience if not specified (exclude observation)
             fact_types = request.types if request.types else list(VALID_RECALL_FACT_TYPES)
@@ -1832,6 +1856,15 @@ def _register_routes(app: FastAPI):
             raise HTTPException(status_code=e.status_code, detail=e.reason)
         except (AuthenticationError, HTTPException):
             raise
+        except (asyncio.TimeoutError, TimeoutError):
+            handler_duration = time.time() - handler_start
+            logger.error(
+                f"[RECALL TIMEOUT] bank={bank_id} handler_duration={handler_duration:.3f}s - database query timed out"
+            )
+            raise HTTPException(
+                status_code=504,
+                detail="Request timed out while searching memories. Try a shorter or more specific query.",
+            )
         except Exception as e:
             import traceback
 
@@ -1889,17 +1922,17 @@ def _register_routes(app: FastAPI):
                 directives = []
                 for fact_type, facts in core_result.based_on.items():
                     if fact_type == "directives":
-                        # Directives have different structure (id, name, content)
+                        # Directives are dicts with id, name, content (not MemoryFact objects)
                         for directive in facts:
                             directives.append(
                                 ReflectDirective(
-                                    id=directive.id,
-                                    name=directive.name,
-                                    content=directive.content,
+                                    id=directive["id"],
+                                    name=directive["name"],
+                                    content=directive["content"],
                                 )
                             )
-                    elif fact_type == "mental_models":
-                        # Mental models are MemoryFact with type "mental_models"
+                    elif fact_type == "mental-models":
+                        # Mental models are MemoryFact with type "mental-models" (note: hyphen, not underscore)
                         for fact in facts:
                             mental_models.append(
                                 ReflectMentalModel(
@@ -2386,6 +2419,7 @@ def _register_routes(app: FastAPI):
                 name=body.name,
                 source_query=body.source_query,
                 content="Generating content...",
+                mental_model_id=body.id if body.id else None,
                 tags=body.tags if body.tags else None,
                 max_tokens=body.max_tokens,
                 trigger=body.trigger.model_dump() if body.trigger else None,
@@ -2397,7 +2431,7 @@ def _register_routes(app: FastAPI):
                 mental_model_id=mental_model["id"],
                 request_context=request_context,
             )
-            return CreateMentalModelResponse(operation_id=result["operation_id"])
+            return CreateMentalModelResponse(mental_model_id=mental_model["id"], operation_id=result["operation_id"])
         except ValueError as e:
             raise HTTPException(status_code=400, detail=str(e))
         except (AuthenticationError, HTTPException):

hindsight-api/hindsight_api/config.py

@@ -447,6 +447,22 @@ class HindsightConfig:
     # Reflect agent settings
     reflect_max_iterations: int
 
+    def validate(self) -> None:
+        """Validate configuration values and raise errors for invalid combinations."""
+        # RETAIN_MAX_COMPLETION_TOKENS must be greater than RETAIN_CHUNK_SIZE
+        # to ensure the LLM has enough output capacity to extract facts from chunks
+        if self.retain_max_completion_tokens <= self.retain_chunk_size:
+            raise ValueError(
+                f"Invalid configuration: HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS "
+                f"({self.retain_max_completion_tokens}) must be greater than "
+                f"HINDSIGHT_API_RETAIN_CHUNK_SIZE ({self.retain_chunk_size}). "
+                f"\n\nYou have two options to fix this:"
+                f"\n  1. Increase HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS to a value > {self.retain_chunk_size}"
+                f"\n  2. Use a model that supports at least {self.retain_max_completion_tokens} output tokens"
+                f"\n     (current model: {self.retain_llm_model or self.llm_model}, "
+                f"provider: {self.retain_llm_provider or self.llm_provider})"
+            )
+
     @classmethod
     def from_env(cls) -> "HindsightConfig":
         """Create configuration from environment variables."""
@@ -454,7 +470,7 @@ class HindsightConfig:
         llm_provider = os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER)
         llm_model = os.getenv(ENV_LLM_MODEL) or _get_default_model_for_provider(llm_provider)
 
-        return cls(
+        config = cls(
             # Database
             database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
             database_schema=os.getenv(ENV_DATABASE_SCHEMA, DEFAULT_DATABASE_SCHEMA),
@@ -631,6 +647,8 @@ class HindsightConfig:
             # Reflect agent settings
             reflect_max_iterations=int(os.getenv(ENV_REFLECT_MAX_ITERATIONS, str(DEFAULT_REFLECT_MAX_ITERATIONS))),
         )
+        config.validate()
+        return config
 
     def get_llm_base_url(self) -> str:
         """Get the LLM base URL, with provider-specific defaults."""

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

@@ -143,6 +143,9 @@ async def run_consolidation_job(
         "skipped": 0,
     }
 
+    # Track all unique tags from consolidated memories for mental model refresh filtering
+    consolidated_tags: set[str] = set()
+
     batch_num = 0
     last_progress_timings = {}  # Track timings at last progress log
     while True:
@@ -176,6 +179,11 @@ async def run_consolidation_job(
         for memory in memories:
             mem_start = time.time()
 
+            # Track tags from this memory for mental model refresh filtering
+            memory_tags = memory.get("tags") or []
+            if memory_tags:
+                consolidated_tags.update(memory_tags)
+
             # Process the memory (uses its own connection internally)
             async with pool.acquire() as conn:
                 result = await _process_memory(
@@ -284,10 +292,12 @@ async def run_consolidation_job(
         perf.log(f"[4] Timing breakdown: {', '.join(timing_parts)}")
 
     # Trigger mental model refreshes for models with refresh_after_consolidation=true
+    # SECURITY: Only refresh mental models with matching tags (or all if no tags were consolidated)
     mental_models_refreshed = await _trigger_mental_model_refreshes(
         memory_engine=memory_engine,
         bank_id=bank_id,
         request_context=request_context,
+        consolidated_tags=list(consolidated_tags) if consolidated_tags else None,
         perf=perf,
     )
     stats["mental_models_refreshed"] = mental_models_refreshed
@@ -301,15 +311,20 @@ async def _trigger_mental_model_refreshes(
     memory_engine: "MemoryEngine",
     bank_id: str,
     request_context: "RequestContext",
+    consolidated_tags: list[str] | None = None,
     perf: ConsolidationPerfLog | None = None,
 ) -> int:
     """
     Trigger refreshes for mental models with refresh_after_consolidation=true.
 
+    SECURITY: Only triggers refresh for mental models whose tags overlap with the
+    consolidated memory tags, preventing unnecessary refreshes across security boundaries.
+
     Args:
         memory_engine: MemoryEngine instance
         bank_id: Bank identifier
         request_context: Request context for authentication
+        consolidated_tags: Tags from memories that were consolidated (None = refresh all)
         perf: Performance logging
 
     Returns:
@@ -318,22 +333,52 @@ async def _trigger_mental_model_refreshes(
     pool = memory_engine._pool
 
     # Find mental models with refresh_after_consolidation=true
+    # SECURITY: Control which mental models get refreshed based on tags
     async with pool.acquire() as conn:
-        rows = await conn.fetch(
-            f"""
-            SELECT id, name
-            FROM {fq_table("mental_models")}
-            WHERE bank_id = $1
-              AND (trigger->>'refresh_after_consolidation')::boolean = true
-            """,
-            bank_id,
-        )
+        if consolidated_tags:
+            # Tagged memories were consolidated - refresh:
+            # 1. Mental models with overlapping tags (security boundary)
+            # 2. Untagged mental models (they're "global" and available to all contexts)
+            # DO NOT refresh mental models with different tags
+            rows = await conn.fetch(
+                f"""
+                SELECT id, name, tags
+                FROM {fq_table("mental_models")}
+                WHERE bank_id = $1
+                  AND (trigger->>'refresh_after_consolidation')::boolean = true
+                  AND (
+                    (tags IS NOT NULL AND tags != '{{}}' AND tags && $2::varchar[])
+                    OR (tags IS NULL OR tags = '{{}}')
+                  )
+                """,
+                bank_id,
+                consolidated_tags,
+            )
+        else:
+            # Untagged memories were consolidated - only refresh untagged mental models
+            # SECURITY: Tagged mental models are NOT refreshed when untagged memories are consolidated
+            rows = await conn.fetch(
+                f"""
+                SELECT id, name, tags
+                FROM {fq_table("mental_models")}
+                WHERE bank_id = $1
+                  AND (trigger->>'refresh_after_consolidation')::boolean = true
+                  AND (tags IS NULL OR tags = '{{}}')
+                """,
+                bank_id,
+            )
 
     if not rows:
         return 0
 
     if perf:
-        perf.log(f"[5] Triggering refresh for {len(rows)} mental models with refresh_after_consolidation=true")
+        if consolidated_tags:
+            perf.log(
+                f"[5] Triggering refresh for {len(rows)} mental models with refresh_after_consolidation=true "
+                f"(filtered by tags: {consolidated_tags})"
+            )
+        else:
+            perf.log(f"[5] Triggering refresh for {len(rows)} mental models with refresh_after_consolidation=true")
 
     # Submit refresh tasks for each mental model
     refreshed_count = 0
@@ -385,7 +430,8 @@ async def _process_memory(
     memory_id = memory["id"]
     fact_tags = memory.get("tags") or []
 
-    # Find related observations using the full recall system (NO tag filtering)
+    # Find related observations using the full recall system
+    # SECURITY: Pass tags to ensure observations don't leak across security boundaries
     t0 = time.time()
     related_observations = await _find_related_observations(
         conn=conn,
@@ -393,6 +439,7 @@ async def _process_memory(
         bank_id=bank_id,
         query=fact_text,
         request_context=request_context,
+        tags=fact_tags,  # Pass source memory's tags for security
     )
     if perf:
         perf.record_timing("recall", time.time() - t0)
@@ -666,17 +713,20 @@ async def _find_related_observations(
     bank_id: str,
     query: str,
     request_context: "RequestContext",
+    tags: list[str] | None = None,
 ) -> list[dict[str, Any]]:
     """
     Find observations related to the given query using optimized recall.
 
-    IMPORTANT: We do NOT filter by tags here. Consolidation needs to see ALL
-    potentially related observations regardless of scope, so the LLM can
-    decide on tag routing (same scope update vs cross-scope create).
+    SECURITY: Filters by tags using all_strict matching to prevent cross-tenant/cross-user
+    information leakage. Observations are only consolidated within the same tag scope.
 
     Uses max_tokens to naturally limit observations (no artificial count limit).
     Includes source memories with dates for LLM context.
 
+    Args:
+        tags: Optional tags to filter observations (uses all_strict matching for security)
+
     Returns:
         List of related observations with their tags, source memories, and dates
     """
@@ -685,14 +735,19 @@ async def _find_related_observations(
     from ...config import get_config
 
     config = get_config()
+
+    # SECURITY: Use all_strict matching if tags provided to prevent cross-scope consolidation
+    tags_match = "all_strict" if tags else "any"
+
     recall_result = await memory_engine.recall_async(
         bank_id=bank_id,
         query=query,
         max_tokens=config.consolidation_max_tokens,  # Token budget for observations (configurable)
         fact_type=["observation"],  # Only retrieve observations
         request_context=request_context,
+        tags=tags,  # Filter by source memory's tags
+        tags_match=tags_match,  # Use strict matching for security
         _quiet=True,  # Suppress logging
-        # NO tags parameter - intentionally get ALL observations
     )
 
     # If no observations returned, return empty list

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

@@ -2,7 +2,7 @@
 
 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.
 
-You must output ONLY valid JSON with no markdown formatting, no code blocks, and no additional text.
+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.
 
 ## EXTRACT DURABLE KNOWLEDGE, NOT EPHEMERAL STATE
 Facts often describe events or actions. Extract the DURABLE KNOWLEDGE implied by the fact, not the transient state.
@@ -32,13 +32,16 @@ BAD examples:
 
 ## MERGE RULES (when comparing to existing observations):
 1. REDUNDANT: Same information worded differently → update existing
-2. CONTRADICTION: Opposite information about same topic → update with history (e.g., "used to X, now Y")
-3. UPDATE: New state replacing old state → update with history
+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, capture the CHANGE (before → after)
+- 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"""
@@ -68,10 +71,15 @@ Instructions:
    - New topic → CREATE new observation
    - Purely ephemeral → return []
 
-Output JSON array of actions:
+Output JSON array of actions (the "text" field should use markdown formatting for structure):
 [
-  {{"action": "update", "learning_id": "uuid-from-observations", "text": "updated knowledge", "reason": "..."}},
-  {{"action": "create", "text": "new durable knowledge", "reason": "..."}}
+  {{"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": "..."}}
 ]
 
-Return [] if fact contains no durable knowledge."""
+Return [] if fact contains no durable knowledge.
+
+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"""

hindsight-api/hindsight_api/engine/cross_encoder.py

@@ -9,6 +9,7 @@ Configuration via environment variables - see hindsight_api.config for all env v
 import asyncio
 import logging
 import os
+import warnings
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
 
@@ -162,11 +163,28 @@ class LocalSTCrossEncoder(CrossEncoderModel):
             except Exception as e:
                 logger.warning(f"Failed to detect GPU/MPS, falling back to CPU: {e}")
 
-        self._model = CrossEncoder(
-            self.model_name,
-            device=device,
-            model_kwargs={"low_cpu_mem_usage": False},
-        )
+        # Suppress verbose transformers warnings during model loading
+        # This suppresses the "UNEXPECTED" warnings from CrossEncoder which are harmless
+        # but look alarming to users (e.g., "embeddings.position_ids | UNEXPECTED")
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", message=".*was not found in model state dict.*")
+            warnings.filterwarnings("ignore", message=".*UNEXPECTED.*")
+
+            # Also suppress transformers library logging temporarily
+            transformers_logger = logging.getLogger("transformers")
+            original_level = transformers_logger.level
+            transformers_logger.setLevel(logging.ERROR)
+
+            try:
+                self._model = CrossEncoder(
+                    self.model_name,
+                    device=device,
+                    model_kwargs={"low_cpu_mem_usage": False},
+                )
+            finally:
+                # Restore original logging level
+                transformers_logger.setLevel(original_level)
 
         # Initialize shared executor (limited workers naturally limits concurrency)
         if LocalSTCrossEncoder._executor is None:

hindsight-api/hindsight_api/engine/embeddings.py

@@ -11,6 +11,7 @@ Configuration via environment variables - see hindsight_api.config for all env v
 
 import logging
 import os
+import warnings
 from abc import ABC, abstractmethod
 
 import httpx
@@ -157,11 +158,28 @@ class LocalSTEmbeddings(Embeddings):
             except Exception as e:
                 logger.warning(f"Failed to detect GPU/MPS, falling back to CPU: {e}")
 
-        self._model = SentenceTransformer(
-            self.model_name,
-            device=device,
-            model_kwargs={"low_cpu_mem_usage": False},
-        )
+        # Suppress verbose transformers warnings during model loading
+        # This suppresses the "UNEXPECTED" warnings from BertModel which are harmless
+        # but look alarming to users (e.g., "embeddings.position_ids | UNEXPECTED")
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", message=".*was not found in model state dict.*")
+            warnings.filterwarnings("ignore", message=".*UNEXPECTED.*")
+
+            # Also suppress transformers library logging temporarily
+            transformers_logger = logging.getLogger("transformers")
+            original_level = transformers_logger.level
+            transformers_logger.setLevel(logging.ERROR)
+
+            try:
+                self._model = SentenceTransformer(
+                    self.model_name,
+                    device=device,
+                    model_kwargs={"low_cpu_mem_usage": False},
+                )
+            finally:
+                # Restore original logging level
+                transformers_logger.setLevel(original_level)
 
         self._dimension = self._model.get_sentence_embedding_dimension()
         logger.info(f"Embeddings: local provider initialized (dim: {self._dimension})")

hindsight-api/hindsight_api/engine/memory_engine.py

@@ -625,11 +625,19 @@ class MemoryEngine(MemoryEngineInterface):
 
         source_query = mental_model["source_query"]
 
+        # SECURITY: If the mental model has tags, pass them to reflect with "all_strict" matching
+        # to ensure it can only access other mental models/memories with the SAME tags.
+        # This prevents cross-tenant/cross-user information leakage by excluding untagged content.
+        tags = mental_model.get("tags")
+        tags_match = "all_strict" if tags else "any"
+
         # Run reflect to generate new content, excluding the mental model being refreshed
         reflect_result = await self.reflect_async(
             bank_id=bank_id,
             query=source_query,
             request_context=internal_context,
+            tags=tags,
+            tags_match=tags_match,
             exclude_mental_model_ids=[mental_model_id],
         )
 
@@ -3304,7 +3312,8 @@ class MemoryEngine(MemoryEngineInterface):
                     created_at,
                     updated_at,
                     LENGTH(original_text) as text_length,
-                    retain_params
+                    retain_params,
+                    tags
                 FROM {fq_table("documents")}
                 {where_clause}
                 ORDER BY created_at DESC
@@ -3360,6 +3369,7 @@ class MemoryEngine(MemoryEngineInterface):
                         "text_length": row["text_length"] or 0,
                         "memory_unit_count": unit_count,
                         "retain_params": row["retain_params"] if row["retain_params"] else None,
+                        "tags": row["tags"] if row["tags"] else [],
                     }
                 )
 
@@ -3663,12 +3673,14 @@ class MemoryEngine(MemoryEngineInterface):
 
         # Load directives from the dedicated directives table
         # Directives are hard rules that must be followed in all responses
+        # Use isolation_mode=True to prevent tag-scoped directives from leaking into untagged operations
         directives_raw = await self.list_directives(
             bank_id=bank_id,
             tags=tags,
             tags_match=tags_match,
             active_only=True,
             request_context=request_context,
+            isolation_mode=True,
         )
         # Convert directive format to the expected format for reflect agent
         # The agent expects: name, description (optional), observations (list of {title, content})
@@ -3737,7 +3749,16 @@ class MemoryEngine(MemoryEngineInterface):
         # Extract memories from recall tool outputs - only include memories the agent actually used
         # agent_result.used_memory_ids contains validated IDs from the done action
         used_memory_ids_set = set(agent_result.used_memory_ids) if agent_result.used_memory_ids else set()
-        based_on: dict[str, list[MemoryFact]] = {"world": [], "experience": [], "opinion": [], "observation": []}
+        # based_on stores facts, mental models, and directives
+        # Note: directives list stores raw directive dicts (not MemoryFact), which will be converted to Directive objects
+        based_on: dict[str, list[MemoryFact] | list[dict[str, Any]]] = {
+            "world": [],
+            "experience": [],
+            "opinion": [],
+            "observation": [],
+            "mental-models": [],
+            "directives": [],
+        }
         seen_memory_ids: set[str] = set()
         for tc in agent_result.tool_trace:
             if tc.tool == "recall" and "memories" in tc.output:
@@ -3839,38 +3860,15 @@ class MemoryEngine(MemoryEngineInterface):
                         )
                 # List all models lookup - don't add to based_on (too verbose, just a listing)
 
-        # Add directives to based_on["mental-models"] (they are mental models with subtype='directive')
-        for directive in directives:
-            # Extract summary from observations
-            summary_parts: list[str] = []
-            for obs in directive.get("observations", []):
-                # Support both Pydantic Observation objects and dicts
-                if hasattr(obs, "content"):
-                    content = obs.content
-                    title = obs.title
-                else:
-                    content = obs.get("content", "")
-                    title = obs.get("title", "")
-                if title and content:
-                    summary_parts.append(f"{title}: {content}")
-                elif content:
-                    summary_parts.append(content)
-
-            # Fallback to description if no observations
-            if not summary_parts and directive.get("description"):
-                summary_parts.append(directive["description"])
-
-            directive_name = directive.get("name", "")
-            directive_summary = "; ".join(summary_parts) if summary_parts else ""
-            based_on["mental-models"].append(
-                MemoryFact(
-                    id=directive.get("id", ""),
-                    text=f"{directive_name}: {directive_summary}",
-                    fact_type="mental-models",
-                    context="directive (directive)",
-                    occurred_start=None,
-                    occurred_end=None,
-                )
+        # Add directives to based_on["directives"]
+        # Store raw directive dicts (with id, name, content) for http.py to convert to ReflectDirective
+        for directive_raw in directives_raw:
+            based_on["directives"].append(
+                {
+                    "id": directive_raw["id"],
+                    "name": directive_raw["name"],
+                    "content": directive_raw["content"],
+                }
             )
 
         # Build directives_applied from agent result
@@ -4719,11 +4717,19 @@ class MemoryEngine(MemoryEngineInterface):
         if not mental_model:
             return None
 
+        # SECURITY: If the mental model has tags, pass them to reflect with "all_strict" matching
+        # to ensure it can only access other mental models/memories with the SAME tags.
+        # This prevents cross-tenant/cross-user information leakage by excluding untagged content.
+        tags = mental_model.get("tags")
+        tags_match = "all_strict" if tags else "any"
+
         # Run reflect with the source query, excluding the mental model being refreshed
         reflect_result = await self.reflect_async(
             bank_id=bank_id,
             query=mental_model["source_query"],
             request_context=request_context,
+            tags=tags,
+            tags_match=tags_match,
             exclude_mental_model_ids=[mental_model_id],
         )
 
@@ -4736,6 +4742,7 @@ class MemoryEngine(MemoryEngineInterface):
                         "id": str(fact.id),
                         "text": fact.text,
                         "type": fact_type,
+                        "context": fact.context,  # Include context to distinguish directives from mental models in UI
                     }
                     for fact in facts
                 ]
@@ -4924,6 +4931,7 @@ class MemoryEngine(MemoryEngineInterface):
         limit: int = 100,
         offset: int = 0,
         request_context: "RequestContext",
+        isolation_mode: bool = False,
     ) -> list[dict[str, Any]]:
         """List directives for a bank.
 
@@ -4935,6 +4943,9 @@ class MemoryEngine(MemoryEngineInterface):
             limit: Maximum number of results
             offset: Offset for pagination
             request_context: Request context for authentication
+            isolation_mode: When True and tags=None, only return directives with no tags.
+                This prevents tag-scoped directives from leaking into untagged operations.
+                Default False (normal API behavior - returns all directives when tags=None)
 
         Returns:
             List of directive dicts
@@ -4944,6 +4955,8 @@ class MemoryEngine(MemoryEngineInterface):
 
         async with acquire_with_retry(pool) as conn:
             # Build filters
+            from .search.tags import build_tags_where_clause
+
             filters = ["bank_id = $1"]
             params: list[Any] = [bank_id]
             param_idx = 2
@@ -4951,15 +4964,23 @@ class MemoryEngine(MemoryEngineInterface):
             if active_only:
                 filters.append("is_active = TRUE")
 
+            # Apply tags filter:
+            # - If tags provided: use standard filtering (with strict modes support)
+            # - If tags=None and isolation_mode=True: only include directives with NO tags
+            #   (prevents tag-scoped directives from leaking into untagged reflect/refresh)
+            # - If tags=None and isolation_mode=False: no filtering (normal API behavior)
             if tags:
-                if tags_match == "all":
-                    filters.append(f"tags @> ${param_idx}::varchar[]")
-                elif tags_match == "exact":
-                    filters.append(f"tags = ${param_idx}::varchar[]")
-                else:  # any
-                    filters.append(f"tags && ${param_idx}::varchar[]")
-                params.append(tags)
-                param_idx += 1
+                tags_clause, tags_params, param_idx = build_tags_where_clause(
+                    tags=tags, param_offset=param_idx, table_alias="", match=tags_match
+                )
+                if tags_clause:
+                    # Remove leading "AND " from clause since we're building filters list
+                    filters.append(tags_clause.replace("AND ", "", 1))
+                    params.extend(tags_params)
+            elif isolation_mode:
+                # Isolation mode: only include directives with empty/null tags
+                # This ensures tag-scoped directives don't apply to untagged operations
+                filters.append("(tags IS NULL OR tags = '{}')")
 
             params.extend([limit, offset])
 

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

@@ -291,61 +291,202 @@ class ClaudeCodeLLM(LLMInterface):
         tool_choice: str | dict[str, Any] = "auto",
     ) -> LLMToolCallResult:
         """
-        Make an LLM API call with tool/function calling support.
+        Make an LLM API call with tool/function calling support using Claude Agent SDK.
 
-        Note: This is a simplified implementation. Full tool support would require
-        integrating with Claude Agent SDK's tool system.
+        This implementation uses ClaudeSDKClient (not query()) because custom tools via
+        SDK MCP servers are only supported with the client. Tools are converted from OpenAI
+        format to SDK MCP tools, and tool names are formatted as mcp__hindsight_tools__{name}.
 
         Args:
             messages: List of message dicts. Can include tool results with role='tool'.
             tools: List of tool definitions in OpenAI format.
-            max_completion_tokens: Maximum tokens in response.
-            temperature: Sampling temperature.
+            max_completion_tokens: Maximum tokens in response (not used by Claude Agent SDK).
+            temperature: Sampling temperature (not used by Claude Agent SDK).
             scope: Scope identifier for tracking.
             max_retries: Maximum retry attempts.
             initial_backoff: Initial backoff time in seconds.
             max_backoff: Maximum backoff time in seconds.
-            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
+            tool_choice: How to choose tools (not used by Claude Agent SDK).
 
         Returns:
             LLMToolCallResult with content and/or tool_calls.
         """
-        # For now, use regular call without tools
-        # Full implementation would require mapping OpenAI tool format to Claude Agent SDK tools
-        logger.warning(
-            "Claude Code provider does not fully support tool calling yet. Falling back to regular text completion."
+        from claude_agent_sdk import (
+            AssistantMessage,
+            ClaudeAgentOptions,
+            ClaudeSDKClient,
+            SdkMcpTool,
+            TextBlock,
+            ToolUseBlock,
+            create_sdk_mcp_server,
         )
 
-        result = await self.call(
-            messages=messages,
-            response_format=None,
-            max_completion_tokens=max_completion_tokens,
-            temperature=temperature,
-            scope=scope,
-            max_retries=max_retries,
-            initial_backoff=initial_backoff,
-            max_backoff=max_backoff,
-            return_usage=True,
+        start_time = time.time()
+
+        # Convert OpenAI tool format to Claude Agent SDK SdkMcpTool format
+        sdk_tools: list[SdkMcpTool] = []
+        tool_names: list[str] = []
+
+        for tool in tools:
+            func = tool.get("function", {})
+            tool_name = func.get("name", "")
+            tool_description = func.get("description", "")
+            parameters = func.get("parameters", {})
+
+            # Create a handler with proper closure to avoid transport issues
+            def make_handler(name: str):
+                async def handler(args: dict[str, Any]) -> dict[str, Any]:
+                    # Return immediately with success - tool execution happens externally
+                    return {
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": f"[Tool {name} called successfully]",
+                            }
+                        ]
+                    }
+
+                return handler
+
+            sdk_tools.append(
+                SdkMcpTool(
+                    name=tool_name,
+                    description=tool_description,
+                    input_schema=parameters,
+                    handler=make_handler(tool_name),
+                )
+            )
+            tool_names.append(tool_name)
+
+        # Create an MCP server with the tools
+        mcp_server = create_sdk_mcp_server(
+            name="hindsight_tools",
+            version="1.0.0",
+            tools=sdk_tools if sdk_tools else None,
         )
 
-        if isinstance(result, tuple):
-            text, usage = result
-            return LLMToolCallResult(
-                content=text,
-                tool_calls=[],
-                finish_reason="stop",
-                input_tokens=usage.input_tokens,
-                output_tokens=usage.output_tokens,
-            )
-        else:
-            # Fallback if return_usage didn't work as expected
-            return LLMToolCallResult(
-                content=str(result),
-                tool_calls=[],
-                finish_reason="stop",
-                input_tokens=0,
-                output_tokens=0,
-            )
+        # Build system prompt and user content from messages
+        system_prompt = ""
+        user_content = ""
+
+        for msg in messages:
+            role = msg.get("role", "user")
+            content = msg.get("content", "")
+
+            if role == "system":
+                system_prompt += ("\n\n" + content) if system_prompt else content
+            elif role == "user":
+                user_content += ("\n\n" + content) if user_content else content
+            elif role == "assistant":
+                # Include previous assistant messages as context
+                user_content += f"\n\n[Previous assistant response: {content}]"
+            elif role == "tool":
+                # Tool results are already in tool_results_map, append to user context
+                tool_call_id = msg.get("tool_call_id", "")
+                user_content += f"\n\n[Tool result for {tool_call_id}: {content}]"
+
+        # Format tool names for SDK MCP servers: mcp__{server_name}__{tool_name}
+        # This is required by the Claude Agent SDK for MCP server tools
+        allowed_tool_names = [f"mcp__hindsight_tools__{name}" for name in tool_names]
+
+        # Configure SDK options with MCP server
+        options = ClaudeAgentOptions(
+            system_prompt=system_prompt if system_prompt else None,
+            max_turns=1,  # Single-turn for API-style interactions
+            mcp_servers={"hindsight_tools": mcp_server} if sdk_tools else {},
+            allowed_tools=allowed_tool_names if allowed_tool_names else [],
+        )
+
+        # Call Claude Agent SDK with retry logic
+        last_exception = None
+        for attempt in range(max_retries + 1):
+            try:
+                full_text = ""
+                tool_calls: list[LLMToolCall] = []
+
+                # Use ClaudeSDKClient for tool calling support
+                # Note: query() does NOT support custom tools, only ClaudeSDKClient does
+                async with ClaudeSDKClient(options=options) as client:
+                    # Send the query
+                    await client.query(user_content)
+
+                    # Receive response
+                    async for message in client.receive_response():
+                        if isinstance(message, AssistantMessage):
+                            for block in message.content:
+                                if isinstance(block, TextBlock):
+                                    full_text += block.text
+                                elif isinstance(block, ToolUseBlock):
+                                    # SDK returns tool names with MCP prefix (mcp__hindsight_tools__{name})
+                                    # Strip the prefix to return original tool name expected by caller
+                                    tool_name = block.name
+                                    if tool_name.startswith("mcp__hindsight_tools__"):
+                                        tool_name = tool_name.replace("mcp__hindsight_tools__", "", 1)
+
+                                    tool_calls.append(
+                                        LLMToolCall(
+                                            id=block.id,
+                                            name=tool_name,
+                                            arguments=block.input,
+                                        )
+                                    )
+
+                # Record metrics
+                duration = time.time() - start_time
+                metrics = get_metrics_collector()
+
+                # Estimate token usage (Claude Agent SDK doesn't report exact counts)
+                estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
+                estimated_output = len(full_text) // 4
+
+                metrics.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    duration=duration,
+                    input_tokens=estimated_input,
+                    output_tokens=estimated_output,
+                    success=True,
+                )
+
+                # Log slow calls
+                if duration > 10.0:
+                    logger.info(
+                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, time={duration:.3f}s"
+                    )
+
+                return LLMToolCallResult(
+                    content=full_text if full_text else None,
+                    tool_calls=tool_calls,
+                    finish_reason="tool_calls" if tool_calls else "stop",
+                    input_tokens=estimated_input,
+                    output_tokens=estimated_output,
+                )
+
+            except Exception as e:
+                last_exception = e
+
+                # Check for authentication errors
+                error_str = str(e).lower()
+                if "auth" in error_str or "login" in error_str or "credential" in error_str:
+                    logger.error(f"Claude Code authentication error: {e}")
+                    raise RuntimeError(
+                        f"Claude Code authentication failed: {e}\n\n"
+                        "Run 'claude auth login' to authenticate with Claude Pro/Max."
+                    ) from e
+
+                if attempt < max_retries:
+                    backoff = min(initial_backoff * (2**attempt), max_backoff)
+                    logger.warning(f"Claude Code tool call error (attempt {attempt + 1}/{max_retries + 1}): {e}")
+                    await asyncio.sleep(backoff)
+                    continue
+                else:
+                    logger.error(f"Claude Code tool call error after {max_retries + 1} attempts: {e}")
+                    raise
+
+        if last_exception:
+            raise last_exception
+        raise RuntimeError("Claude Code tool call failed after all retries")
 
     async def cleanup(self) -> None:
         """Clean up resources (no HTTP client to close for Claude Agent SDK)."""

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

@@ -177,6 +177,9 @@ class CodexLLM(LLMInterface):
             schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
             system_instruction += schema_msg
 
+        # gpt-5.2-codex only supports "detailed" reasoning summary
+        reasoning_summary = "detailed" if "5.2" in self.model else self.reasoning_summary
+
         # Build Codex request payload
         payload = {
             "model": self.model,
@@ -192,7 +195,7 @@ class CodexLLM(LLMInterface):
             "tools": [],
             "tool_choice": "auto",
             "parallel_tool_calls": True,
-            "reasoning": {"summary": self.reasoning_summary},
+            "reasoning": {"summary": reasoning_summary},
             "store": False,  # Codex uses stateless mode
             "stream": True,  # SSE streaming
             "include": ["reasoning.encrypted_content"],
@@ -283,13 +286,20 @@ class CodexLLM(LLMInterface):
                         "Run 'codex auth login' to re-authenticate."
                     ) from e
 
+                # Log the actual error message from the API
+                error_detail = e.response.text[:500] if hasattr(e.response, "text") else str(e)
+
                 if attempt < max_retries:
                     backoff = min(initial_backoff * (2**attempt), max_backoff)
-                    logger.warning(f"Codex HTTP error {status_code} (attempt {attempt + 1}/{max_retries + 1})")
+                    logger.warning(
+                        f"Codex HTTP error {status_code} (attempt {attempt + 1}/{max_retries + 1}): {error_detail}"
+                    )
                     await asyncio.sleep(backoff)
                     continue
                 else:
-                    logger.error(f"Codex HTTP error after {max_retries + 1} attempts: {e}")
+                    logger.error(
+                        f"Codex HTTP error after {max_retries + 1} attempts: Status {status_code}, Detail: {error_detail}"
+                    )
                     raise
 
             except httpx.RequestError as e:
@@ -379,8 +389,22 @@ class CodexLLM(LLMInterface):
         """
         Make API call with tool calling support.
 
-        Note: This is a basic implementation. Full tool calling support for Codex
-        may require additional SSE event parsing.
+        Parses Codex SSE stream to extract tool calls from response.output_item.done events.
+        Tools are converted from OpenAI format to Codex format (flat structure at top level).
+
+        Args:
+            messages: List of message dicts. Can include tool results with role='tool'.
+            tools: List of tool definitions in OpenAI format.
+            max_completion_tokens: Maximum tokens in response.
+            temperature: Sampling temperature.
+            scope: Scope identifier for tracking.
+            max_retries: Maximum retry attempts.
+            initial_backoff: Initial backoff time in seconds.
+            max_backoff: Maximum backoff time in seconds.
+            tool_choice: How to choose tools - "auto", "none", "required", or specific function.
+
+        Returns:
+            LLMToolCallResult with content and/or tool_calls.
         """
         start_time = time.time()
 
@@ -413,20 +437,22 @@ class CodexLLM(LLMInterface):
                 )
 
         # Convert tools to Codex format
+        # Codex expects tools with type and name/description/parameters at top level
         codex_tools = []
         for tool in tools:
             func = tool.get("function", {})
             codex_tools.append(
                 {
                     "type": "function",
-                    "function": {
-                        "name": func.get("name", ""),
-                        "description": func.get("description", ""),
-                        "parameters": func.get("parameters", {}),
-                    },
+                    "name": func.get("name", ""),
+                    "description": func.get("description", ""),
+                    "parameters": func.get("parameters", {}),
                 }
             )
 
+        # gpt-5.2-codex only supports "detailed" reasoning summary
+        reasoning_summary = "detailed" if "5.2" in self.model else self.reasoning_summary
+
         payload = {
             "model": self.model,
             "instructions": system_instruction,
@@ -434,7 +460,7 @@ class CodexLLM(LLMInterface):
             "tools": codex_tools,
             "tool_choice": tool_choice,
             "parallel_tool_calls": True,
-            "reasoning": {"summary": self.reasoning_summary},
+            "reasoning": {"summary": reasoning_summary},
             "store": False,
             "stream": True,
             "include": ["reasoning.encrypted_content"],
@@ -451,8 +477,16 @@ class CodexLLM(LLMInterface):
 
         url = f"{self.base_url}/codex/responses"
 
+        # Debug logging for troubleshooting
+        logger.debug(f"Codex tool call request: url={url}, model={payload['model']}, tools={len(codex_tools)}")
+
         try:
             response = await self._client.post(url, json=payload, headers=headers, timeout=120.0)
+
+            # Log response details on error
+            if response.status_code != 200:
+                logger.error(f"Codex API error {response.status_code}: {response.text[:500]}")
+
             response.raise_for_status()
 
             # Parse SSE for tool calls and content
@@ -512,13 +546,30 @@ class CodexLLM(LLMInterface):
                     if event_type == "response.text.delta" and "delta" in data:
                         content += data["delta"]
 
-                    # Extract tool calls
-                    elif event_type == "response.function_call_arguments.delta":
-                        # Handle tool call events (implementation depends on actual Codex SSE format)
-                        pass
+                    # Extract completed tool calls from response.output_item.done
+                    elif event_type == "response.output_item.done":
+                        item = data.get("item", {})
+                        if item.get("type") == "function_call" and item.get("status") == "completed":
+                            tool_name = item.get("name", "")
+                            arguments_str = item.get("arguments", "{}")
+                            call_id = item.get("call_id", "")
 
-                except json.JSONDecodeError:
-                    pass
+                            try:
+                                arguments = json.loads(arguments_str)
+                            except json.JSONDecodeError:
+                                logger.warning(f"Failed to parse tool arguments: {arguments_str}")
+                                arguments = {}
+
+                            tool_calls.append(
+                                LLMToolCall(
+                                    id=call_id,
+                                    name=tool_name,
+                                    arguments=arguments,
+                                )
+                            )
+
+                except json.JSONDecodeError as e:
+                    logger.warning(f"Failed to parse SSE data: {e}, data_str: {data_str[:200]}")
 
         return content if content else None, tool_calls
 

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

@@ -65,6 +65,7 @@ class MockLLM(LLMInterface):
         # Storage for test verification
         self._mock_calls: list[dict] = []
         self._mock_response: Any = None
+        self._mock_exception: Exception | None = None
 
     async def verify_connection(self) -> None:
         """
@@ -124,6 +125,10 @@ class MockLLM(LLMInterface):
         self._mock_calls.append(call_record)
         logger.debug(f"Mock LLM call recorded: scope={scope}, model={self.model}")
 
+        # Raise mock exception if configured
+        if self._mock_exception is not None:
+            raise self._mock_exception
+
         # Return mock response
         if self._mock_response is not None:
             result = self._mock_response
@@ -183,6 +188,10 @@ class MockLLM(LLMInterface):
         }
         self._mock_calls.append(call_record)
 
+        # Raise mock exception if configured
+        if self._mock_exception is not None:
+            raise self._mock_exception
+
         if self._mock_response is not None:
             if isinstance(self._mock_response, LLMToolCallResult):
                 return self._mock_response
@@ -215,6 +224,16 @@ class MockLLM(LLMInterface):
         """
         self._mock_response = response
 
+    def set_mock_exception(self, exception: Exception) -> None:
+        """
+        Set an exception to raise from mock calls.
+
+        Args:
+            exception: The exception to raise on the next call.
+                      After raising, the exception is cleared.
+        """
+        self._mock_exception = exception
+
     def get_mock_calls(self) -> list[dict]:
         """
         Get the list of recorded mock calls.
@@ -230,5 +249,6 @@ class MockLLM(LLMInterface):
         return self._mock_calls
 
     def clear_mock_calls(self) -> None:
-        """Clear the recorded mock calls."""
+        """Clear the recorded mock calls and any set exception."""
         self._mock_calls = []
+        self._mock_exception = None

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

@@ -871,21 +871,21 @@ async def _execute_tool(
         query = args.get("query")
         if not query:
             return {"error": "search_mental_models requires a query parameter"}
-        max_results = args.get("max_results") or 5
+        max_results = int(args.get("max_results") or 5)
         return await search_mental_models_fn(query, max_results)
 
     elif tool_name == "search_observations":
         query = args.get("query")
         if not query:
             return {"error": "search_observations requires a query parameter"}
-        max_tokens = max(args.get("max_tokens") or 5000, 1000)  # Default 5000, min 1000
+        max_tokens = max(int(args.get("max_tokens") or 5000), 1000)  # Default 5000, min 1000
         return await search_observations_fn(query, max_tokens)
 
     elif tool_name == "recall":
         query = args.get("query")
         if not query:
             return {"error": "recall requires a query parameter"}
-        max_tokens = max(args.get("max_tokens") or 2048, 1000)  # Default 2048, min 1000
+        max_tokens = max(int(args.get("max_tokens") or 2048), 1000)  # Default 2048, min 1000
         return await recall_fn(query, max_tokens)
 
     elif tool_name == "expand":
@@ -904,18 +904,18 @@ def _summarize_input(tool_name: str, args: dict[str, Any]) -> str:
     if tool_name == "search_mental_models":
         query = args.get("query", "")
         query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
-        max_results = args.get("max_results") or 5
+        max_results = int(args.get("max_results") or 5)
         return f"(query={query_preview}, max_results={max_results})"
     elif tool_name == "search_observations":
         query = args.get("query", "")
         query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
-        max_tokens = max(args.get("max_tokens") or 5000, 1000)
+        max_tokens = max(int(args.get("max_tokens") or 5000), 1000)
         return f"(query={query_preview}, max_tokens={max_tokens})"
     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(args.get("max_tokens") or 2048, 1000)
+        max_tokens = max(int(args.get("max_tokens") or 2048), 1000)
         return f"(query={query_preview}, max_tokens={max_tokens})"
     elif tool_name == "expand":
         memory_ids = args.get("memory_ids", [])

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

@@ -31,7 +31,7 @@ class ReflectAction(BaseModel):
         default=None, description="Observation sections for done action (when output_mode=observations)"
     )
     # Plain text answer fields (for output_mode=answer)
-    answer: str | None = Field(default=None, description="Plain text answer for done action (no markdown)")
+    answer: str | None = Field(default=None, description="Well-formatted markdown answer for done action")
     answer_memory_ids: list[str] | None = Field(
         default=None, description="Memory IDs supporting the answer", alias="memory_ids"
     )

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

@@ -148,7 +148,15 @@ def build_system_prompt_for_tools(
 
     parts = []
 
-    # Inject directives at the VERY START for maximum prominence
+    # Anti-hallucination rule at the very top
+    parts.extend(
+        [
+            "CRITICAL: You MUST ONLY use information from retrieved tool results. NEVER make up names, people, events, or entities.",
+            "",
+        ]
+    )
+
+    # Inject directives after anti-hallucination rule
     if directives:
         parts.append(build_directives_section(directives))
 
@@ -162,7 +170,7 @@ def build_system_prompt_for_tools(
     parts.extend(
         [
             "## CRITICAL RULES",
-            "- You must NEVER fabricate information that has no basis in retrieved data",
+            "- ONLY use information from tool results - no external knowledge or guessing",
             "- You SHOULD synthesize, infer, and reason from the retrieved memories",
             "- You MUST search before saying you don't have information",
             "",
@@ -300,9 +308,11 @@ def build_system_prompt_for_tools(
     parts.extend(
         [
             "",
-            "## Output Format: Plain Text Answer",
-            "Call done() with a plain text 'answer' field.",
-            "- Do NOT use markdown formatting",
+            "## Output Format: Well-Formatted Markdown Answer",
+            "Call done() with a well-formatted markdown 'answer' field.",
+            "- USE markdown formatting for structure (headers, lists, bold, italic, code blocks, tables, etc.)",
+            "- CRITICAL: Add blank lines before and after block elements (tables, code blocks, lists)",
+            "- 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",
         ]
@@ -463,21 +473,41 @@ def build_final_prompt(
     parts.append(
         "\n## Instructions\n"
         "Provide a thoughtful answer by synthesizing and reasoning from the retrieved data above. "
-        "You can make reasonable inferences from the memories, but don't completely fabricate information."
+        "You can make reasonable inferences from the memories, but don't completely fabricate information. "
         "If the exact answer isn't stated, use what IS stated to give the best possible answer. "
-        "Only say 'I don't have information' if the retrieved data is truly unrelated to the question."
+        "Only say 'I don't have information' if the retrieved data is truly unrelated to the question.\n\n"
+        "IMPORTANT: Output ONLY the final answer. Do NOT include meta-commentary like "
+        '"I\'ll search..." or "Let me analyze...". Do NOT explain your reasoning process. '
+        "Just provide the direct synthesized answer."
     )
 
     return "\n".join(parts)
 
 
-FINAL_SYSTEM_PROMPT = """You are a thoughtful assistant that synthesizes answers from retrieved memories.
+FINAL_SYSTEM_PROMPT = """CRITICAL: You MUST ONLY use information from retrieved tool results. NEVER make up names, people, events, or entities.
+
+You are a thoughtful assistant that synthesizes answers from retrieved memories.
 
 Your approach:
 - Reason over the retrieved memories to answer the question
 - Make reasonable inferences when the exact answer isn't explicitly stated
 - Connect related memories to form a complete picture
 - Be helpful - if you have related information, use it to give the best possible answer
+- ONLY use information from tool results - no external knowledge or guessing
 
 Only say "I don't have information" if the retrieved data is truly unrelated to the question.
-Do NOT fabricate information that has no basis in the retrieved data."""
+
+FORMATTING: Use proper markdown formatting in your answer:
+- Headers (##, ###) for sections
+- Lists (bullet or numbered) for enumerations
+- Bold/italic for emphasis
+- Tables with proper syntax (ensure blank line before and after)
+- Code blocks where appropriate
+- CRITICAL: Always add blank lines before and after block elements (tables, code blocks, lists)
+- Proper spacing between sections
+
+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."""

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

@@ -54,19 +54,18 @@ async def tool_search_mental_models(
         Dict with matching mental models including content and freshness info
     """
     from ..memory_engine import fq_table
+    from ..search.tags import build_tags_where_clause
 
     # Build filters dynamically
     filters = ""
     params: list[Any] = [bank_id, str(query_embedding), max_results]
     next_param = 4
 
+    # Use the centralized tag filtering logic
     if tags:
-        if tags_match == "all":
-            filters += f" AND tags @> ${next_param}::varchar[]"
-        else:
-            filters += f" AND (tags && ${next_param}::varchar[] OR tags IS NULL OR tags = '{{}}')"
-        params.append(tags)
-        next_param += 1
+        tag_clause, tag_params, next_param = build_tags_where_clause(tags, param_offset=next_param, match=tags_match)
+        filters += f" {tag_clause}"
+        params.extend(tag_params)
 
     if exclude_ids:
         filters += f" AND id != ALL(${next_param}::text[])"

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

@@ -139,7 +139,7 @@ TOOL_DONE_ANSWER = {
             "properties": {
                 "answer": {
                     "type": "string",
-                    "description": "Your response as plain text. Do NOT use markdown formatting. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                    "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
                 },
                 "memory_ids": {
                     "type": "array",
@@ -190,7 +190,7 @@ def _build_done_tool_with_directives(directive_rules: list[str]) -> dict:
                 "properties": {
                     "answer": {
                         "type": "string",
-                        "description": "Your response as plain text. Do NOT use markdown formatting. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                        "description": "Your response as well-formatted markdown. Use headers, lists, bold/italic, and code blocks for clarity. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
                     },
                     "memory_ids": {
                         "type": "array",

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

@@ -542,7 +542,12 @@ Output: ONLY 2 facts (skip coffee preference - too trivial):
 QUALITY OVER QUANTITY
 ══════════════════════════════════════════════════════════════════════════
 
-Ask: "Would this be useful to recall in 6 months?" If no, skip it."""
+Ask: "Would this be useful to recall in 6 months?" If no, skip it.
+
+IMPORTANT: Sensory/emotional details and observations that provide meaningful context
+about experiences ARE important to remember, even if they seem small (e.g., how food
+tasted, how someone looked, how loud music was). Extract these if they characterize
+an experience or person."""
 
 # Assembled concise prompt (backward compatible - exact same output as before)
 CONCISE_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
@@ -641,6 +646,7 @@ For EVENTS (fact_kind="event") - MUST SET BOTH occurred_start AND occurred_end:
 - Convert relative dates → absolute using Event Date as reference
 - If Event Date is "Saturday, March 15, 2020", then "yesterday" = Friday, March 14, 2020
 - Dates mentioned in text (e.g., "in March 2020") should use THAT year, not current year
+- CRITICAL: If the content mentions an absolute date (e.g., "March 15, 2024", "2024-03-15"), you MUST extract it and set occurred_start in ISO format
 - Always include the day name (Monday, Tuesday, etc.) in the 'when' field
 - Set occurred_start AND occurred_end to WHEN IT HAPPENED (not when mentioned)
 - For single-day/point events: set occurred_end = occurred_start (same timestamp)
@@ -1005,6 +1011,29 @@ Text:
 
         except BadRequestError as e:
             last_error = e
+            error_str = str(e).lower()
+
+            # Check if error is related to max_tokens/completion_tokens not being supported
+            if any(
+                keyword in error_str
+                for keyword in [
+                    "max_tokens",
+                    "max_completion_tokens",
+                    "maximum context",
+                    "token limit",
+                    "context length",
+                ]
+            ):
+                # Provide helpful error message with configuration suggestions
+                raise ValueError(
+                    f"Model does not support the required output token limit.\n\n"
+                    f"The model '{llm_config.model}' (provider: {llm_config.provider}) failed with: {e}\n\n"
+                    f"You have two options to fix this:\n"
+                    f"  1. Use a different model that supports at least {config.retain_max_completion_tokens} output tokens\n"
+                    f"  2. Decrease HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS to a value your model supports\n"
+                    f"     (current value: {config.retain_max_completion_tokens}, must be > RETAIN_CHUNK_SIZE={config.retain_chunk_size})"
+                ) from e
+
             if "json_validate_failed" in str(e):
                 logger.warning(
                     f"          [1.3.{chunk_index + 1}] Attempt {attempt + 1}/{max_retries} failed with JSON validation error: {e}"
@@ -1347,28 +1376,21 @@ def _convert_causal_relations(relations_from_llm, fact_start_idx: int) -> list[C
 
 def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainContent]) -> None:
     """
-    Add time offsets to preserve fact ordering within each content.
+    Add time offsets to preserve fact ordering across all contents.
 
-    This allows retrieval to distinguish between facts that happened earlier vs later
-    in the same conversation, even when the base event_date is the same.
+    This allows retrieval to distinguish between facts from different documents/conversations
+    even when they have the same base event_date, and also between facts within the same
+    conversation.
+
+    Uses absolute position across all facts to ensure unique timestamps.
 
     Modifies facts in place.
     """
     from .orchestrator import parse_datetime_flexible
 
-    # Group facts by content_index
-    current_content_idx = 0
-    content_fact_start = 0
-
     for i, fact in enumerate(facts):
-        if fact.content_index != current_content_idx:
-            # Moved to next content
-            current_content_idx = fact.content_index
-            content_fact_start = i
-
-        # Calculate position within this content
-        fact_position = i - content_fact_start
-        offset = timedelta(seconds=fact_position * SECONDS_PER_FACT)
+        # Use absolute position across all facts to ensure uniqueness across different contents
+        offset = timedelta(seconds=i * SECONDS_PER_FACT)
 
         # Apply offset to all temporal fields (handle both datetime objects and ISO strings)
         if fact.occurred_start:

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

@@ -158,6 +158,13 @@ async def retain_batch(
                 # Handle document tracking even with no facts
                 if document_id:
                     combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
+                    # Collect tags from all content items and merge with document_tags
+                    all_tags = set(document_tags or [])
+                    for item in contents_dicts:
+                        item_tags = item.get("tags", []) or []
+                        all_tags.update(item_tags)
+                    merged_tags = list(all_tags)
+
                     retain_params = {}
                     if contents_dicts:
                         first_item = contents_dicts[0]
@@ -172,7 +179,7 @@ async def retain_batch(
                         if first_item.get("metadata"):
                             retain_params["metadata"] = first_item["metadata"]
                     await fact_storage.handle_document_tracking(
-                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params, document_tags
+                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
                     )
                 else:
                     # Check for per-item document_ids
@@ -186,6 +193,13 @@ async def retain_batch(
 
                     for doc_id, doc_contents in contents_by_doc.items():
                         combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
+                        # Collect tags from all content items for this document and merge with document_tags
+                        all_tags = set(document_tags or [])
+                        for _, item in doc_contents:
+                            item_tags = item.get("tags", []) or []
+                            all_tags.update(item_tags)
+                        merged_tags = list(all_tags)
+
                         retain_params = {}
                         if doc_contents:
                             first_item = doc_contents[0][1]
@@ -200,7 +214,7 @@ async def retain_batch(
                             if first_item.get("metadata"):
                                 retain_params["metadata"] = first_item["metadata"]
                         await fact_storage.handle_document_tracking(
-                            conn, bank_id, doc_id, combined_content, is_first_batch, retain_params, document_tags
+                            conn, bank_id, doc_id, combined_content, is_first_batch, retain_params, merged_tags
                         )
 
         total_time = time.time() - start_time
@@ -252,6 +266,13 @@ async def retain_batch(
                 # Legacy: single document_id parameter
                 combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
                 retain_params = {}
+                # Collect tags from all content items and merge with document_tags
+                all_tags = set(document_tags or [])
+                for item in contents_dicts:
+                    item_tags = item.get("tags", []) or []
+                    all_tags.update(item_tags)
+                merged_tags = list(all_tags)
+
                 if contents_dicts:
                     first_item = contents_dicts[0]
                     if first_item.get("context"):
@@ -266,7 +287,7 @@ async def retain_batch(
                         retain_params["metadata"] = first_item["metadata"]
 
                 await fact_storage.handle_document_tracking(
-                    conn, bank_id, document_id, combined_content, is_first_batch, retain_params, document_tags
+                    conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
                 )
                 document_ids_added.append(document_id)
                 doc_id_mapping[None] = document_id  # For backwards compatibility
@@ -294,6 +315,13 @@ async def retain_batch(
                             # Combine content for this document
                             combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
 
+                            # Collect tags from all content items for this document and merge with document_tags
+                            all_tags = set(document_tags or [])
+                            for _, item in doc_contents:
+                                item_tags = item.get("tags", []) or []
+                                all_tags.update(item_tags)
+                            merged_tags = list(all_tags)
+
                             # Extract retain params from first content item
                             retain_params = {}
                             if doc_contents:
@@ -316,7 +344,7 @@ async def retain_batch(
                                 combined_content,
                                 is_first_batch,
                                 retain_params,
-                                document_tags,
+                                merged_tags,
                             )
                             document_ids_added.append(actual_doc_id)
 

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

@@ -188,7 +188,7 @@ def get_system_message(disposition: DispositionTraits) -> str:
         " ".join(instructions) if instructions else "Balance your disposition traits when interpreting information."
     )
 
-    return f"You are a person with your own thoughts, experiences, opinions, and disposition. When asked a question, respond naturally like a human would - share what you genuinely think based on what you know and have experienced. {disposition_instruction} Be direct, express your views confidently, and use 'I think', 'I believe', 'in my view', etc. Respond in plain text without markdown formatting. IMPORTANT: Detect the language of the question and respond in the SAME language. Do not translate to English if the question is in another language."
+    return f"You are a person with your own thoughts, experiences, opinions, and disposition. When asked a question, respond naturally like a human would - share what you genuinely think based on what you know and have experienced. {disposition_instruction} Be direct, express your views confidently, and use 'I think', 'I believe', 'in my view', etc. Respond in plain text without markdown formatting. CRITICAL: ONLY use the facts and information provided in the prompt - do not make up names, events, or information that weren't mentioned. If you don't have enough information to answer, say so. IMPORTANT: Detect the language of the question and respond in the SAME language. Do not translate to English if the question is in another language."
 
 
 async def reflect(

hindsight-api/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "hindsight-api"
-version = "0.4.8"
+version = "0.4.9"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"

hindsight-api/tests/conftest.py

@@ -220,3 +220,34 @@ async def memory(pg0_db_url, embeddings, cross_encoder, query_analyzer):
             await mem.close()
     except Exception:
         pass
+
+
+@pytest_asyncio.fixture(scope="function")
+async def memory_no_llm_verify(pg0_db_url, embeddings, cross_encoder, query_analyzer):
+    """
+    Provide a MemoryEngine instance that skips LLM connection verification.
+
+    This fixture is useful for tests that override the LLM configuration
+    after initialization (e.g., to test specific providers).
+    """
+    mem = MemoryEngine(
+        db_url=pg0_db_url,
+        memory_llm_provider="mock",  # Use mock provider as placeholder
+        memory_llm_api_key="",
+        memory_llm_model="mock",
+        embeddings=embeddings,
+        cross_encoder=cross_encoder,
+        query_analyzer=query_analyzer,
+        pool_min_size=1,
+        pool_max_size=5,
+        run_migrations=False,
+        task_backend=SyncTaskBackend(),
+        skip_llm_verification=True,  # Skip verification - will be overridden by test
+    )
+    await mem.initialize()
+    yield mem
+    try:
+        if mem._pool and not mem._pool._closing:
+            await mem.close()
+    except Exception:
+        pass

hindsight-api/tests/test_llm_provider.py

@@ -1,5 +1,10 @@
 """
-Test LLM provider with different models using actual memory operations.
+Test LLM provider with different models using actual Hindsight memory operations.
+
+Tests validate that providers work correctly with:
+1. Retain (memory ingestion with fact extraction)
+2. Reflect (memory retrieval with tool calling)
+3. Mental models (consolidated knowledge generation)
 """
 import os
 from datetime import datetime
@@ -33,6 +38,12 @@ MODEL_MATRIX = [
     # Ollama models (local)
     ("ollama", "gemma3:12b"),
     ("ollama", "gemma3:1b"),
+    # Claude Code (uses Claude Agent SDK with Claude models)
+    ("claude-code", "claude-sonnet-4-20250514"),
+    # OpenAI Codex (uses MCP with Codex-specific models)
+    ("openai-codex", "gpt-5.2-codex"),
+    # Mock provider (for testing)
+    ("mock", "mock"),
 ]
 
 
@@ -48,6 +59,171 @@ def get_api_key_for_provider(provider: str) -> str | None:
     return os.getenv(env_var) if env_var else None
 
 
+def should_skip_provider(provider: str, model: str = "") -> tuple[bool, str]:
+    """Check if provider should be skipped and return reason."""
+    # Never skip mock provider
+    if provider == "mock":
+        return False, ""
+
+    # Skip claude-code and openai-codex in CI (require local auth)
+    if os.getenv("CI") and provider in ("claude-code", "openai-codex"):
+        return True, f"{provider} not available in CI (requires local authentication)"
+
+    # Skip Ollama in CI (no models available)
+    if provider == "ollama" and os.getenv("CI"):
+        return True, "Ollama not available in CI"
+
+    # Skip Ollama gemma models (don't support tool calling)
+    if provider == "ollama" and "gemma" in model.lower():
+        return True, f"Ollama {model} does not support tool calling"
+
+    # Other providers need an API key
+    if provider not in ("ollama", "claude-code", "openai-codex", "mock"):
+        api_key = get_api_key_for_provider(provider)
+        if not api_key:
+            return True, f"No API key available (set {provider.upper()}_API_KEY)"
+
+    return False, ""
+
+
+@pytest.mark.parametrize("provider,model", MODEL_MATRIX)
+@pytest.mark.asyncio
+@pytest.mark.timeout(300)  # Increase timeout for slow models like groq gpt-oss-120b
+async def test_llm_provider_api_methods(provider: str, model: str):
+    """
+    Test all LLM API methods used by Hindsight at runtime.
+    This validates that the provider correctly implements the LLMInterface.
+
+    Tests:
+    1. verify_connection() - Connection verification
+    2. call() with plain text - Basic LLM call
+    3. call() with response_format - Structured output (used in fact extraction)
+    4. call_with_tools() - Tool calling (used in reflect agent)
+    """
+    # Skip mock provider - it's a test stub, not a real LLM implementation
+    if provider == "mock":
+        pytest.skip("Mock provider is a test stub, not a real LLM")
+
+    should_skip, reason = should_skip_provider(provider, model)
+    if should_skip:
+        pytest.skip(f"Skipping {provider}/{model}: {reason}")
+
+    api_key = get_api_key_for_provider(provider)
+
+    llm = LLMProvider(
+        provider=provider,
+        api_key=api_key or "",
+        base_url="",
+        model=model,
+    )
+
+    print(f"\n{provider}/{model} - API methods test:")
+
+    # Test 1: verify_connection()
+    try:
+        await llm.verify_connection()
+        print("  ✓ verify_connection()")
+    except Exception as e:
+        pytest.fail(f"{provider}/{model} verify_connection() failed: {e}")
+
+    # Test 2: call() with plain text
+    try:
+        response = await llm.call(
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": "What is 2+2? Answer in one word."},
+            ],
+            max_completion_tokens=50,
+        )
+        assert response is not None, "call() returned None"
+        assert len(response) > 0, "call() returned empty string"
+        print(f"  ✓ call() plain text: {response[:50]}")
+    except Exception as e:
+        pytest.fail(f"{provider}/{model} call() plain text failed: {e}")
+
+    # Test 3: call() with response_format (structured output)
+    # Skip for models that don't support structured output
+    skip_structured_output = (provider == "groq" and "gpt-oss-120b" in model.lower())
+    if skip_structured_output:
+        print(f"  ⊘ call() structured output: skipped (model doesn't support response_format)")
+    else:
+        try:
+            from pydantic import BaseModel
+
+            class TestResponse(BaseModel):
+                answer: str
+                confidence: str
+
+            response = await llm.call(
+                messages=[
+                    {"role": "system", "content": "You are a math assistant."},
+                    {"role": "user", "content": "What is the capital of France?"},
+                ],
+                response_format=TestResponse,
+                max_completion_tokens=100,
+            )
+            assert isinstance(response, TestResponse), f"Expected TestResponse, got {type(response)}"
+            assert hasattr(response, "answer"), "Structured output missing 'answer' field"
+            assert hasattr(response, "confidence"), "Structured output missing 'confidence' field"
+            print(f"  ✓ call() structured output: answer={response.answer}, confidence={response.confidence}")
+        except Exception as e:
+            pytest.fail(f"{provider}/{model} call() structured output failed: {e}")
+
+    # Test 4: call_with_tools() (tool calling)
+    try:
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get the weather for a location",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {"type": "string", "description": "City name"},
+                            "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                        },
+                        "required": ["location"],
+                    },
+                },
+            }
+        ]
+
+        result = await llm.call_with_tools(
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant with access to tools."},
+                {"role": "user", "content": "What's the weather like in Paris?"},
+            ],
+            tools=tools,
+            max_completion_tokens=500,  # Increased from 200 to give models enough space for tool calls
+        )
+
+        assert result is not None, "call_with_tools() returned None"
+        assert hasattr(result, "tool_calls"), "Result missing 'tool_calls' attribute"
+
+        # Nano models may hit token limits before making tool calls - that's acceptable
+        is_nano_model = "nano" in model.lower()
+        if is_nano_model and len(result.tool_calls) == 0:
+            # Check if it hit length limit (expected for nano models)
+            if hasattr(result, "finish_reason") and result.finish_reason == "length":
+                print(f"  ✓ call_with_tools(): nano model hit token limit (expected)")
+            else:
+                pytest.fail(f"Nano model made 0 tool calls but didn't hit length limit (finish_reason={getattr(result, 'finish_reason', 'unknown')})")
+        else:
+            assert len(result.tool_calls) > 0, f"Expected at least 1 tool call, got {len(result.tool_calls)}"
+
+            # Verify tool call structure
+            tool_call = result.tool_calls[0]
+            assert hasattr(tool_call, "name"), "Tool call missing 'name'"
+            assert hasattr(tool_call, "arguments"), "Tool call missing 'arguments'"
+            assert tool_call.name == "get_weather", f"Expected 'get_weather', got '{tool_call.name}'"
+            assert "location" in tool_call.arguments, "Tool call arguments missing 'location'"
+
+            print(f"  ✓ call_with_tools(): {tool_call.name}({tool_call.arguments})")
+    except Exception as e:
+        pytest.fail(f"{provider}/{model} call_with_tools() failed: {e}")
+
+
 @pytest.mark.parametrize("provider,model", MODEL_MATRIX)
 @pytest.mark.asyncio
 async def test_llm_provider_memory_operations(provider: str, model: str):
@@ -55,16 +231,16 @@ async def test_llm_provider_memory_operations(provider: str, model: str):
     Test LLM provider with actual memory operations: fact extraction and reflect.
     All models must pass this test.
     """
+    # Skip mock provider - it's a test stub, not designed for real operations
+    if provider == "mock":
+        pytest.skip("Mock provider is a test stub, not designed for real operations")
+
+    should_skip, reason = should_skip_provider(provider, model)
+    if should_skip:
+        pytest.skip(f"Skipping {provider}/{model}: {reason}")
+
     api_key = get_api_key_for_provider(provider)
 
-    # Skip Ollama tests in CI (no models available)
-    if provider == "ollama" and os.getenv("CI"):
-        pytest.skip(f"Skipping {provider}/{model}: Ollama not available in CI")
-
-    # Other providers need an API key
-    if provider != "ollama" and not api_key:
-        pytest.skip(f"Skipping {provider}/{model}: no API key available")
-
     llm = LLMProvider(
         provider=provider,
         api_key=api_key or "",
@@ -122,3 +298,115 @@ async def test_llm_provider_memory_operations(provider: str, model: str):
 
     assert response is not None, f"{provider}/{model} reflect returned None"
     assert len(response) > 10, f"{provider}/{model} reflect response too short"
+
+
+@pytest.mark.parametrize("provider,model", [
+    ("claude-code", "claude-sonnet-4-20250514"),
+    ("openai-codex", "gpt-5.2-codex"),
+])
+@pytest.mark.asyncio
+async def test_llm_provider_consolidation(memory_no_llm_verify, request_context, provider: str, model: str):
+    """
+    Test LLM provider with consolidation (automatic mental model generation from observations).
+    This validates that the provider can generate synthesized knowledge from raw memories.
+
+    This test is limited to claude-code and codex since they're the critical providers
+    that needed tool calling fixes for reflect and consolidation operations.
+    """
+    should_skip, reason = should_skip_provider(provider, model)
+    if should_skip:
+        pytest.skip(f"Skipping {provider}/{model}: {reason}")
+
+    # Use provider-specific LLM for this test
+    api_key = get_api_key_for_provider(provider)
+    memory_no_llm_verify._consolidation_llm = LLMProvider(
+        provider=provider,
+        api_key=api_key or "",
+        base_url="",
+        model=model,
+    )
+    # Also need retain LLM for ingesting data
+    memory_no_llm_verify._retain_llm = memory_no_llm_verify._consolidation_llm
+
+    test_bank_id = f"llm_test_consolidation_{provider}_{model}_{datetime.now().timestamp()}"
+
+    # Enable observations for this bank
+    from hindsight_api.config import get_config
+    config = get_config()
+    original_value = config.enable_observations
+    config.enable_observations = True
+
+    try:
+        # Retain memories to consolidate
+        test_content = """
+        Bob prefers functional programming with Rust and Haskell.
+        He emphasizes immutability and pure functions in code reviews.
+        Bob advocates for type safety and compile-time guarantees.
+        He avoids mutable state and prefers declarative code patterns.
+        """
+
+        await memory_no_llm_verify.retain_async(
+            bank_id=test_bank_id,
+            content=test_content,
+            context="Team coding preferences",
+            event_date=datetime(2024, 12, 1),
+            request_context=request_context,
+        )
+
+        print(f"\n{provider}/{model} - Consolidation test:")
+
+        # Run consolidation to generate observations (mental models)
+        from hindsight_api.engine.consolidation.consolidator import run_consolidation_job
+
+        result = await run_consolidation_job(
+            memory_engine=memory_no_llm_verify,
+            bank_id=test_bank_id,
+            request_context=request_context,
+        )
+
+        print(f"  Processed: {result.get('memories_processed', 0)} memories")
+        print(f"  Created: {result.get('observations_created', 0)} observations")
+        print(f"  Updated: {result.get('observations_updated', 0)} observations")
+
+        # Verify consolidation ran successfully
+        assert result["status"] in ["success", "no_new_memories"], f"{provider}/{model} consolidation failed"
+
+        # If observations were created, verify they contain relevant content
+        if result.get("observations_created", 0) > 0:
+            observations = await memory_no_llm_verify.list_mental_models_consolidated(
+                bank_id=test_bank_id,
+                request_context=request_context,
+            )
+
+            assert len(observations) > 0, f"{provider}/{model} consolidation created 0 observations"
+
+            # Check first observation contains relevant information
+            obs_content = observations[0].get("content", "").lower()
+            relevant_terms = ["bob", "functional", "rust", "immutab", "type"]
+            matches = [term for term in relevant_terms if term in obs_content]
+
+            print(f"  Observation preview: {observations[0].get('content', '')[:200]}...")
+            print(f"  Found {len(matches)} relevant terms: {matches}")
+
+            assert len(matches) >= 2, (
+                f"{provider}/{model} consolidated observation doesn't contain relevant info. "
+                f"Expected at least 2 of {relevant_terms}, found {len(matches)}: {matches}"
+            )
+
+    finally:
+        # Restore original config
+        config.enable_observations = original_value
+
+
+# NOTE: The tests above validate the critical Hindsight operations:
+#
+# test_llm_provider_memory_operations (ALL providers):
+#   - Fact extraction (retain): tests structured output generation
+#   - Reflect: tests memory retrieval and reasoning (uses tool calling for claude-code/codex)
+#
+# test_llm_provider_consolidation (claude-code and codex only):
+#   - Consolidation: tests automatic mental model generation from observations
+#   - Requires MemoryEngine fixture with working LLM (from .env or env vars)
+#   - Run your local LLM server OR set HINDSIGHT_API_LLM_PROVIDER/API_KEY/MODEL env vars
+#
+# For full end-to-end integration tests using the HTTP API, see tests/test_http_api_integration.py

hindsight-api/tests/test_mental_models.py

@@ -312,6 +312,49 @@ class TestDirectiveTags:
         # Cleanup
         await memory.delete_bank(bank_id, request_context=request_context)
 
+    async def test_list_all_directives_without_filter(self, memory: MemoryEngine, request_context):
+        """Test that listing directives without tags returns ALL directives (both tagged and untagged)."""
+        bank_id = f"test-directive-list-all-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Create untagged directive
+        await memory.create_directive(
+            bank_id=bank_id,
+            name="Untagged Directive",
+            content="This has no tags",
+            request_context=request_context,
+        )
+
+        # Create tagged directive
+        await memory.create_directive(
+            bank_id=bank_id,
+            name="Tagged Directive",
+            content="This has tags",
+            tags=["project-x"],
+            request_context=request_context,
+        )
+
+        # List ALL directives (no tag filter, isolation_mode defaults to False)
+        all_directives = await memory.list_directives(
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+
+        # Should return BOTH tagged and untagged directives
+        assert len(all_directives) == 2
+        directive_names = {d["name"] for d in all_directives}
+        assert "Untagged Directive" in directive_names
+        assert "Tagged Directive" in directive_names
+
+        # Verify the tagged directive has its tags
+        tagged = next(d for d in all_directives if d["name"] == "Tagged Directive")
+        assert tagged["tags"] == ["project-x"]
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
 
 class TestReflect:
     """Test reflect endpoint."""
@@ -399,6 +442,161 @@ class TestDirectivesInReflect:
         # Cleanup
         await memory.delete_bank(bank_id, request_context=request_context)
 
+    async def test_tagged_directive_not_applied_without_tags(self, memory: MemoryEngine, request_context):
+        """Test that directives with tags are NOT applied to untagged reflect operations."""
+        bank_id = f"test-directive-isolation-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Add some untagged content
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[
+                {"content": "The sky is blue."},
+                {"content": "Water is wet."},
+            ],
+            request_context=request_context,
+        )
+
+        # Add some tagged content for the project-x context
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[
+                {"content": "The sky is blue according to project X standards.", "tags": ["project-x"]},
+                {"content": "Project X color guidelines specify sky is blue.", "tags": ["project-x"]},
+            ],
+            request_context=request_context,
+        )
+        await memory.wait_for_background_tasks()
+
+        # Create an untagged directive (should be applied)
+        await memory.create_directive(
+            bank_id=bank_id,
+            name="General Policy",
+            content="Always be polite and start responses with 'Hello!'",
+            request_context=request_context,
+        )
+
+        # Create a tagged directive (should NOT be applied to untagged reflect)
+        await memory.create_directive(
+            bank_id=bank_id,
+            name="Tagged Policy",
+            content="ALWAYS respond in ALL CAPS and end with 'PROJECT-X ONLY'",
+            tags=["project-x"],
+            request_context=request_context,
+        )
+
+        # Run reflect without tags - should only apply the untagged directive
+        result = await memory.reflect_async(
+            bank_id=bank_id,
+            query="What color is the sky?",
+            request_context=request_context,
+        )
+
+        response_lower = result.text.lower()
+
+        # 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?",
+            tags=["project-x"],
+            tags_match="all_strict",
+            request_context=request_context,
+        )
+
+        response_tagged_lower = result_tagged.text.lower()
+
+        # With strict matching and tags, should apply the tagged directive
+        assert "project-x only" in response_tagged_lower, f"Tagged directive should be applied with tags: {result_tagged.text}"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    async def test_reflect_based_on_structure(self, memory: MemoryEngine, request_context):
+        """Test that reflect returns correct based_on structure with directives and memories separated."""
+        bank_id = f"test-reflect-based-on-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Add some memories
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[
+                {"content": "Alice works at Google as a software engineer."},
+                {"content": "Bob is a product manager at Microsoft."},
+                {"content": "The team meets every Monday at 9am."},
+            ],
+            request_context=request_context,
+        )
+        await memory.wait_for_background_tasks()
+
+        # Create a directive
+        directive = await memory.create_directive(
+            bank_id=bank_id,
+            name="Professional Tone",
+            content="Always maintain a professional and formal tone in responses.",
+            request_context=request_context,
+        )
+        directive_id = directive["id"]
+
+        # Run reflect which returns the core result
+        result = await memory.reflect_async(
+            bank_id=bank_id,
+            query="Who works at Google?",
+            request_context=request_context,
+        )
+
+        # Verify based_on structure exists
+        assert result.based_on is not None
+
+        # Verify directives key exists and contains our directive
+        assert "directives" in result.based_on
+        directives_list = result.based_on.get("directives", [])
+
+        # Verify directives are dicts with id, name, content (not MemoryFact objects)
+        assert len(directives_list) > 0, "Should have at least one directive"
+        directive_found = False
+        for d in directives_list:
+            assert isinstance(d, dict), f"Directive should be dict, got {type(d)}"
+            assert "id" in d, "Directive dict should have 'id'"
+            assert "name" in d, "Directive dict should have 'name'"
+            assert "content" in d, "Directive dict should have 'content'"
+            # Check if this is our directive
+            if d["id"] == directive_id:
+                directive_found = True
+                assert d["name"] == "Professional Tone"
+                assert "professional" in d["content"].lower()
+
+        assert directive_found, f"Our directive {directive_id} should be in based_on.directives"
+
+        # Verify memories (world/experience) are separate from directives
+        has_memories = "world" in result.based_on or "experience" in result.based_on
+        assert has_memories, "Should have world or experience memories"
+
+        # Verify that if mental-models key exists, it's separate from directives
+        if "mental-models" in result.based_on:
+            mental_models = result.based_on.get("mental-models", [])
+            # Verify mental models are MemoryFact objects, not dicts like directives
+            for mm in mental_models:
+                assert hasattr(mm, "fact_type"), "Mental model should be MemoryFact with fact_type"
+                assert mm.fact_type == "mental-models"
+                assert hasattr(mm, "context")
+                assert "mental model" in mm.context.lower()
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
 
 class TestDirectivesPromptInjection:
     """Test that directives are properly injected into the system prompt."""
@@ -451,3 +649,198 @@ class TestDirectivesPromptInjection:
         directives_pos = prompt.find("## DIRECTIVES")
         critical_rules_pos = prompt.find("## CRITICAL RULES")
         assert directives_pos < critical_rules_pos
+
+
+class TestMentalModelRefreshTagSecurity:
+    """Test that mental model refresh respects tag-based security boundaries."""
+
+    async def test_refresh_with_tags_only_accesses_same_tagged_models(
+        self, memory: MemoryEngine, request_context
+    ):
+        """Test that refreshing a mental model with tags can only access other models with the same tags.
+
+        This is a security test to ensure that mental models with tags (e.g., user:alice)
+        cannot access mental models from other scopes (e.g., user:bob or no tags) during refresh.
+        """
+        bank_id = f"test-refresh-tags-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Add some facts with different tags
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[
+                {"content": "Alice works on the frontend React project. Alice's favorite color is blue.", "tags": ["user:alice"]},
+                {"content": "Alice prefers working in the morning. Alice drinks coffee every day.", "tags": ["user:alice"]},
+                {"content": "Bob works on the backend API services. Bob's favorite language is Python.", "tags": ["user:bob"]},
+                {"content": "Bob prefers working at night. Bob drinks tea every day.", "tags": ["user:bob"]},
+                {"content": "The company has 100 employees and is growing fast.", "tags": []},  # No tags
+            ],
+            request_context=request_context,
+        )
+
+        # Wait for background processing
+        await memory.wait_for_background_tasks()
+
+        # Create mental model for user:alice with sensitive data
+        mm_alice = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Alice's Work Profile",
+            source_query="What does Alice work on?",
+            content="Alice is a frontend engineer specializing in React",
+            tags=["user:alice"],
+            request_context=request_context,
+        )
+
+        # Create mental model for user:bob with sensitive data
+        mm_bob = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Bob's Work Profile",
+            source_query="What does Bob work on?",
+            content="Bob is a backend engineer specializing in Python",
+            tags=["user:bob"],
+            request_context=request_context,
+        )
+
+        # Create mental model with no tags (should not be accessible from tagged models)
+        mm_untagged = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Company Info",
+            source_query="What is the company info?",
+            content="The company has 100 employees",
+            request_context=request_context,
+        )
+
+        # Create a mental model for user:alice that will be refreshed
+        mm_alice_refresh = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Alice's Summary",
+            source_query="What are all the facts about work and preferences?",  # Broad query that should match all facts
+            content="Initial content",
+            tags=["user:alice"],
+            request_context=request_context,
+        )
+
+        # Refresh Alice's mental model
+        refreshed = await memory.refresh_mental_model(
+            bank_id=bank_id,
+            mental_model_id=mm_alice_refresh["id"],
+            request_context=request_context,
+        )
+
+        # SECURITY CHECK: The refreshed content should ONLY include information from
+        # memories/models tagged with user:alice, NOT from user:bob or untagged
+        refreshed_content = refreshed["content"].lower()
+
+        # Should include Alice's content (either from facts or mental models)
+        assert "alice" in refreshed_content, \
+            "Refreshed model should access memories/models with matching tags (user:alice)"
+
+        # MUST NOT include Bob's content (security violation)
+        assert "bob" not in refreshed_content and "python" not in refreshed_content and "tea" not in refreshed_content, \
+            f"SECURITY VIOLATION: Refreshed model accessed memories/models with different tags (user:bob). Content: {refreshed_content}"
+
+        # MUST NOT include untagged content (security violation)
+        assert "100 employees" not in refreshed_content and "growing fast" not in refreshed_content, \
+            f"SECURITY VIOLATION: Refreshed model accessed untagged memories/models. Content: {refreshed_content}"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    async def test_consolidation_only_refreshes_matching_tagged_models(
+        self, memory: MemoryEngine, request_context
+    ):
+        """Test that consolidation only triggers refresh for mental models with matching tags.
+
+        This is a security test to ensure that when tagged memories are consolidated,
+        only mental models with overlapping tags get refreshed, not all mental models.
+        """
+        bank_id = f"test-consolidation-refresh-{uuid.uuid4().hex[:8]}"
+
+        # Ensure bank exists
+        await memory.get_bank_profile(bank_id, request_context=request_context)
+
+        # Create mental models with different tags, all with refresh_after_consolidation=true
+        mm_alice = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Alice's Model",
+            source_query="What about Alice?",
+            content="Initial Alice content",
+            tags=["user:alice"],
+            trigger={"refresh_after_consolidation": True},
+            request_context=request_context,
+        )
+
+        mm_bob = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Bob's Model",
+            source_query="What about Bob?",
+            content="Initial Bob content",
+            tags=["user:bob"],
+            trigger={"refresh_after_consolidation": True},
+            request_context=request_context,
+        )
+
+        mm_untagged = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Untagged Model",
+            source_query="What about general stuff?",
+            content="Initial untagged content",
+            trigger={"refresh_after_consolidation": True},
+            request_context=request_context,
+        )
+
+        # Record initial last_refreshed_at timestamps
+        alice_initial = mm_alice["last_refreshed_at"]
+        bob_initial = mm_bob["last_refreshed_at"]
+        untagged_initial = mm_untagged["last_refreshed_at"]
+
+        # Add memories with user:alice tags
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[
+                {"content": "Alice likes React", "tags": ["user:alice"]},
+                {"content": "Alice drinks coffee", "tags": ["user:alice"]},
+            ],
+            request_context=request_context,
+        )
+
+        # Trigger consolidation manually (this should only refresh Alice's mental model)
+        from hindsight_api.engine.consolidation.consolidator import run_consolidation_job
+
+        result = await run_consolidation_job(
+            memory_engine=memory,
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+
+        # Wait for background refresh tasks to complete
+        await memory.wait_for_background_tasks()
+
+        # Check that mental models were refreshed appropriately
+        mm_alice_after = await memory.get_mental_model(
+            bank_id, mm_alice["id"], request_context=request_context
+        )
+        mm_bob_after = await memory.get_mental_model(
+            bank_id, mm_bob["id"], request_context=request_context
+        )
+        mm_untagged_after = await memory.get_mental_model(
+            bank_id, mm_untagged["id"], request_context=request_context
+        )
+
+        # SECURITY CHECK: Only Alice's mental model and untagged model should be refreshed
+        # Alice's model should be refreshed (tags match)
+        assert mm_alice_after["last_refreshed_at"] != alice_initial or mm_alice_after["content"] != mm_alice["content"], \
+            "Alice's mental model should be refreshed when user:alice memories are consolidated"
+
+        # Bob's model should NOT be refreshed (tags don't match)
+        assert mm_bob_after["last_refreshed_at"] == bob_initial, \
+            "SECURITY VIOLATION: Bob's mental model was refreshed even though user:bob memories were not consolidated"
+
+        # Untagged model should be refreshed (untagged models are always refreshed)
+        assert mm_untagged_after["last_refreshed_at"] != untagged_initial or mm_untagged_after["content"] != mm_untagged["content"], \
+            "Untagged mental model should be refreshed after any consolidation"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)

hindsight-api/tests/test_reflections.py

@@ -175,6 +175,45 @@ class TestMentalModelsCRUD:
         # Cleanup
         await memory.delete_bank(bank_id, request_context=request_context)
 
+    @pytest.mark.asyncio
+    async def test_create_mental_model_with_custom_id(self, memory: MemoryEngine, request_context):
+        """Test creating a mental model with a custom ID."""
+        bank_id = f"test-mental-model-custom-id-{uuid.uuid4().hex[:8]}"
+
+        # Create the bank first
+        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+        # Create a mental model with a custom ID
+        custom_id = "team-communication-preferences"
+        mental_model = await memory.create_mental_model(
+            bank_id=bank_id,
+            mental_model_id=custom_id,
+            name="Team Communication Preferences",
+            source_query="How does the team prefer to communicate?",
+            content="The team prefers async communication via Slack",
+            tags=["team", "communication"],
+            request_context=request_context,
+        )
+
+        # Verify the custom ID was used
+        assert mental_model["id"] == custom_id
+        assert mental_model["name"] == "Team Communication Preferences"
+        assert mental_model["tags"] == ["team", "communication"]
+
+        # Verify we can retrieve it with the custom ID
+        fetched = await memory.get_mental_model(
+            bank_id=bank_id,
+            mental_model_id=custom_id,
+            request_context=request_context,
+        )
+
+        assert fetched is not None
+        assert fetched["id"] == custom_id
+        assert fetched["name"] == "Team Communication Preferences"
+
+        # Cleanup
+        await memory.delete_bank(bank_id, request_context=request_context)
+
 
 class TestObservationsAPI:
     """Test observations API endpoints.

hindsight-api/tests/test_retain.py

@@ -2193,3 +2193,65 @@ If the text contains both Italian and English content, extract ONLY the Italian
 
         # Clear cache again to restore original config
         clear_config_cache()
+
+
+@pytest.mark.asyncio
+async def test_retain_batch_with_per_item_tags_on_document(memory, request_context):
+    """
+    Test that per-item tags are correctly stored on documents.
+
+    This test verifies the fix for a bug where per-item tags in content dictionaries
+    were not being merged and passed to document tracking, causing tags to be lost
+    even though they were correctly sent through the API.
+
+    Without the fix, this test would fail because:
+    - Tags are correctly passed in the content dict
+    - Tags are correctly stored on memory_units (facts)
+    - BUT tags were NOT stored on the document record itself
+    """
+    bank_id = f"test_doc_tags_{datetime.now(timezone.utc).timestamp()}"
+    document_id = "app-state-testuser"
+
+    try:
+        # Retain content with per-item tags (simulating the TasteAI use case)
+        contents = [
+            {
+                "content": '{"username":"testuser","meals":[],"preferences":{"nickname":"testuser"}}',
+                "document_id": document_id,
+                "tags": ["user:testuser", "app-type:taste-ai"],
+            }
+        ]
+
+        result = await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=contents,
+            request_context=request_context,
+        )
+
+        assert len(result) > 0, "Should have retained content"
+        print(f"\n=== Retained content with tags ===")
+
+        # Retrieve the document
+        doc = await memory.get_document(
+            document_id=document_id,
+            bank_id=bank_id,
+            request_context=request_context,
+        )
+
+        assert doc is not None, "Document should exist"
+        assert "tags" in doc, "Document should have tags field"
+
+        # This is the critical assertion - tags should be stored on the document
+        doc_tags = doc["tags"] or []
+        print(f"Document tags: {doc_tags}")
+
+        assert "user:testuser" in doc_tags, \
+            f"Document should have 'user:testuser' tag, but got: {doc_tags}"
+        assert "app-type:taste-ai" in doc_tags, \
+            f"Document should have 'app-type:taste-ai' tag, but got: {doc_tags}"
+
+        print("✓ Per-item tags correctly stored on document")
+
+    finally:
+        await memory.delete_bank(bank_id, request_context=request_context)
+        print(f"\n=== Cleaned up bank: {bank_id} ===")

hindsight-cli/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "hindsight-cli"
-version = "0.4.8"
+version = "0.4.9"
 edition = "2021"
 authors = ["Hindsight Team"]
 description = "A beautiful CLI for Hindsight - semantic memory system"

hindsight-cli/src/commands/mental_model.rs

@@ -98,6 +98,7 @@ pub fn create(
     bank_id: &str,
     name: &str,
     source_query: &str,
+    id: Option<&str>,
     verbose: bool,
     output_format: OutputFormat,
 ) -> Result<()> {
@@ -108,6 +109,7 @@ pub fn create(
     };
 
     let request = types::CreateMentalModelRequest {
+        id: id.map(|s| s.to_string()),
         name: name.to_string(),
         source_query: source_query.to_string(),
         max_tokens: 2048,

hindsight-cli/src/main.rs

@@ -596,6 +596,10 @@ enum MentalModelCommands {
 
         /// Source query to generate the mental model from
         source_query: String,
+
+        /// Optional custom ID for the mental model (alphanumeric lowercase with hyphens)
+        #[arg(long)]
+        id: Option<String>,
     },
 
     /// Update a mental model
@@ -863,8 +867,8 @@ fn run() -> Result<()> {
             MentalModelCommands::Get { bank_id, mental_model_id } => {
                 commands::mental_model::get(&client, &bank_id, &mental_model_id, verbose, output_format)
             }
-            MentalModelCommands::Create { bank_id, name, source_query } => {
-                commands::mental_model::create(&client, &bank_id, &name, &source_query, verbose, output_format)
+            MentalModelCommands::Create { bank_id, name, source_query, id } => {
+                commands::mental_model::create(&client, &bank_id, &name, &source_query, id.as_deref(), verbose, output_format)
             }
             MentalModelCommands::Update { bank_id, mental_model_id, name } => {
                 commands::mental_model::update(&client, &bank_id, &mental_model_id, name, verbose, output_format)

hindsight-clients/python/hindsight_client_api/__init__.py

@@ -7,7 +7,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/banks_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/directives_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/documents_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/entities_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/memory_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/mental_models_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/monitoring_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api/operations_api.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/api_client.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/configuration.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.
@@ -489,7 +489,7 @@ class Configuration:
         return "Python SDK Debug Report:\n"\
                "OS: {env}\n"\
                "Python Version: {pyversion}\n"\
-               "Version of the API: 0.4.8\n"\
+               "Version of the API: 0.4.9\n"\
                "SDK Package Version: 0.0.7".\
                format(env=sys.platform, pyversion=sys.version)
 

hindsight-clients/python/hindsight_client_api/exceptions.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/__init__.py

@@ -6,7 +6,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/add_background_request.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/async_operation_submit_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/background_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/bank_list_item.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/bank_list_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/bank_profile_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/bank_stats_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/budget.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/cancel_operation_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/chunk_data.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/chunk_include_options.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/chunk_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/consolidation_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/create_bank_request.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/create_directive_request.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/create_mental_model_request.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.
@@ -28,12 +28,13 @@ class CreateMentalModelRequest(BaseModel):
     """
     Request model for creating a mental model.
     """ # noqa: E501
+    id: Optional[StrictStr] = None
     name: StrictStr = Field(description="Human-readable name for the mental model")
     source_query: StrictStr = Field(description="The query to run to generate content")
     tags: Optional[List[StrictStr]] = Field(default=None, description="Tags for scoped visibility")
     max_tokens: Optional[Annotated[int, Field(le=8192, strict=True, ge=256)]] = Field(default=2048, description="Maximum tokens for generated content")
     trigger: Optional[MentalModelTrigger] = Field(default=None, description="Trigger settings")
-    __properties: ClassVar[List[str]] = ["name", "source_query", "tags", "max_tokens", "trigger"]
+    __properties: ClassVar[List[str]] = ["id", "name", "source_query", "tags", "max_tokens", "trigger"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -77,6 +78,11 @@ class CreateMentalModelRequest(BaseModel):
         # override the default output from pydantic by calling `to_dict()` of trigger
         if self.trigger:
             _dict['trigger'] = self.trigger.to_dict()
+        # set to None if id (nullable) is None
+        # and model_fields_set contains the field
+        if self.id is None and "id" in self.model_fields_set:
+            _dict['id'] = None
+
         return _dict
 
     @classmethod
@@ -89,6 +95,7 @@ class CreateMentalModelRequest(BaseModel):
             return cls.model_validate(obj)
 
         _obj = cls.model_validate({
+            "id": obj.get("id"),
             "name": obj.get("name"),
             "source_query": obj.get("source_query"),
             "tags": obj.get("tags"),

hindsight-clients/python/hindsight_client_api/models/create_mental_model_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.
@@ -18,7 +18,7 @@ import re  # noqa: F401
 import json
 
 from pydantic import BaseModel, ConfigDict, Field, StrictStr
-from typing import Any, ClassVar, Dict, List
+from typing import Any, ClassVar, Dict, List, Optional
 from typing import Optional, Set
 from typing_extensions import Self
 
@@ -26,8 +26,9 @@ class CreateMentalModelResponse(BaseModel):
     """
     Response model for mental model creation.
     """ # noqa: E501
-    operation_id: StrictStr = Field(description="Operation ID to track progress")
-    __properties: ClassVar[List[str]] = ["operation_id"]
+    mental_model_id: Optional[StrictStr] = None
+    operation_id: StrictStr = Field(description="Operation ID to track refresh progress")
+    __properties: ClassVar[List[str]] = ["mental_model_id", "operation_id"]
 
     model_config = ConfigDict(
         populate_by_name=True,
@@ -68,6 +69,11 @@ class CreateMentalModelResponse(BaseModel):
             exclude=excluded_fields,
             exclude_none=True,
         )
+        # set to None if mental_model_id (nullable) is None
+        # and model_fields_set contains the field
+        if self.mental_model_id is None and "mental_model_id" in self.model_fields_set:
+            _dict['mental_model_id'] = None
+
         return _dict
 
     @classmethod
@@ -80,6 +86,7 @@ class CreateMentalModelResponse(BaseModel):
             return cls.model_validate(obj)
 
         _obj = cls.model_validate({
+            "mental_model_id": obj.get("mental_model_id"),
             "operation_id": obj.get("operation_id")
         })
         return _obj

hindsight-clients/python/hindsight_client_api/models/delete_document_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/delete_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/directive_list_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/directive_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/disposition_traits.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/document_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/entity_detail_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/entity_include_options.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/entity_input.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/entity_list_item.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/entity_list_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/entity_observation_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/entity_state_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/features_info.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/graph_data_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/http_validation_error.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/include_options.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/list_documents_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/list_memory_units_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/list_tags_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/memory_item.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/mental_model_list_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/mental_model_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.
@@ -17,7 +17,7 @@ import pprint
 import re  # noqa: F401
 import json
 
-from pydantic import BaseModel, ConfigDict, StrictInt, StrictStr
+from pydantic import BaseModel, ConfigDict, Field, StrictInt, StrictStr
 from typing import Any, ClassVar, Dict, List, Optional
 from hindsight_client_api.models.mental_model_trigger import MentalModelTrigger
 from typing import Optional, Set
@@ -31,7 +31,7 @@ class MentalModelResponse(BaseModel):
     bank_id: StrictStr
     name: StrictStr
     source_query: StrictStr
-    content: StrictStr
+    content: StrictStr = Field(description="The mental model content as well-formatted markdown (auto-generated from reflect endpoint)")
     tags: Optional[List[StrictStr]] = None
     max_tokens: Optional[StrictInt] = 2048
     trigger: Optional[MentalModelTrigger] = None

hindsight-clients/python/hindsight_client_api/models/mental_model_trigger.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/operation_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/operation_status_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/operations_list_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/recall_request.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/recall_response.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/recall_result.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/reflect_based_on.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/reflect_directive.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

hindsight-clients/python/hindsight_client_api/models/reflect_fact.py

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.8
+    The version of the OpenAPI document: 0.4.9
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.
@@ -17,7 +17,7 @@ import pprint
 import re  # noqa: F401
 import json
 
-from pydantic import BaseModel, ConfigDict, StrictStr
+from pydantic import BaseModel, ConfigDict, Field, StrictStr
 from typing import Any, ClassVar, Dict, List, Optional
 from typing import Optional, Set
 from typing_extensions import Self
@@ -27,7 +27,7 @@ class ReflectFact(BaseModel):
     A fact used in think response.
     """ # noqa: E501
     id: Optional[StrictStr] = None
-    text: StrictStr
+    text: StrictStr = Field(description="Fact text. When type='observation', this contains markdown-formatted consolidated knowledge")
     type: Optional[StrictStr] = None
     context: Optional[StrictStr] = None
     occurred_start: Optional[StrictStr] = None