fix: openclaw improve config setup

* fix: openclaw binds embed versioning

* fix: openclaw binds embed versioning

.env.example

@@ -2,7 +2,7 @@
 # Copy this file to .env and fill in your values
 
 # LLM Configuration (Required)
-# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio
+# Supported providers: openai, groq, ollama, gemini, anthropic, lmstudio, vertexai
 HINDSIGHT_API_LLM_PROVIDER=openai
 HINDSIGHT_API_LLM_API_KEY=your-api-key-here
 HINDSIGHT_API_LLM_MODEL=o3-mini
@@ -13,6 +13,13 @@ HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 # HINDSIGHT_API_LLM_API_KEY=your-anthropic-api-key
 # HINDSIGHT_API_LLM_MODEL=claude-sonnet-4-20250514
 
+# Example: Google Vertex AI configuration
+# HINDSIGHT_API_LLM_PROVIDER=vertexai
+# HINDSIGHT_API_LLM_MODEL=google/gemini-2.0-flash-001
+# HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID=your-gcp-project-id
+# HINDSIGHT_API_LLM_VERTEXAI_REGION=us-central1
+# HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY=/path/to/service-account-key.json  # Optional, uses ADC if not set
+
 # Example: LM Studio local configuration (Qwen 2.5 32B recommended)
 # HINDSIGHT_API_LLM_PROVIDER=lmstudio
 # HINDSIGHT_API_LLM_API_KEY=lmstudio

.github/workflows/release.yml

@@ -139,7 +139,7 @@ jobs:
         path: hindsight-clients/typescript/*.tgz
         retention-days: 1
 
-  release-moltbot-integration:
+  release-openclaw-integration:
     runs-on: ubuntu-latest
     environment: npm
 
@@ -153,15 +153,15 @@ jobs:
         registry-url: 'https://registry.npmjs.org'
 
     - name: Install dependencies
-      working-directory: ./hindsight-integrations/moltbot
+      working-directory: ./hindsight-integrations/openclaw
       run: npm ci
 
     - name: Build
-      working-directory: ./hindsight-integrations/moltbot
+      working-directory: ./hindsight-integrations/openclaw
       run: npm run build
 
     - name: Publish to npm
-      working-directory: ./hindsight-integrations/moltbot
+      working-directory: ./hindsight-integrations/openclaw
       run: |
         set +e
         OUTPUT=$(npm publish --access public 2>&1)
@@ -178,14 +178,14 @@ jobs:
         NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
 
     - name: Pack for GitHub release
-      working-directory: ./hindsight-integrations/moltbot
+      working-directory: ./hindsight-integrations/openclaw
       run: npm pack
 
     - name: Upload artifacts
       uses: actions/upload-artifact@v4
       with:
-        name: moltbot-integration
-        path: hindsight-integrations/moltbot/*.tgz
+        name: openclaw-integration
+        path: hindsight-integrations/openclaw/*.tgz
         retention-days: 1
 
   release-control-plane:
@@ -415,7 +415,7 @@ jobs:
 
   create-github-release:
     runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-moltbot-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
+    needs: [release-python-packages, release-typescript-client, release-openclaw-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
     permissions:
       contents: write
 
@@ -438,11 +438,11 @@ jobs:
         name: typescript-client
         path: ./artifacts/typescript-client
 
-    - name: Download Moltbot Integration
+    - name: Download OpenClaw Integration
       uses: actions/download-artifact@v4
       with:
-        name: moltbot-integration
-        path: ./artifacts/moltbot-integration
+        name: openclaw-integration
+        path: ./artifacts/openclaw-integration
 
     - name: Download Control Plane
       uses: actions/download-artifact@v4
@@ -485,8 +485,8 @@ jobs:
         cp artifacts/python-packages/hindsight-embed/dist/* release-assets/ || true
         # TypeScript client
         cp artifacts/typescript-client/*.tgz release-assets/ || true
-        # Moltbot Integration
-        cp artifacts/moltbot-integration/*.tgz release-assets/ || true
+        # OpenClaw Integration
+        cp artifacts/openclaw-integration/*.tgz release-assets/ || true
         # Control Plane
         cp artifacts/control-plane/*.tgz release-assets/ || true
         # Rust CLI binaries

.github/workflows/test.yml

@@ -82,7 +82,7 @@ jobs:
     - name: Build TypeScript client
       run: npm run build --workspace=hindsight-clients/typescript
 
-  build-moltbot-integration:
+  build-openclaw-integration:
     runs-on: ubuntu-latest
 
     steps:
@@ -94,15 +94,15 @@ jobs:
         node-version: '22'
 
     - name: Install dependencies
-      working-directory: ./hindsight-integrations/moltbot
+      working-directory: ./hindsight-integrations/openclaw
       run: npm ci
 
     - name: Run tests
-      working-directory: ./hindsight-integrations/moltbot
+      working-directory: ./hindsight-integrations/openclaw
       run: npm test
 
     - name: Build
-      working-directory: ./hindsight-integrations/moltbot
+      working-directory: ./hindsight-integrations/openclaw
       run: npm run build
 
   build-control-plane:

docker/standalone/Dockerfile

@@ -169,16 +169,33 @@ ENV PATH="/app/api/.venv/bin:${PATH}"
 
 # Pre-download ML models to avoid runtime download (conditional)
 # Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
+# Includes retry logic with exponential backoff for transient network failures
 ARG PRELOAD_ML_MODELS
 ARG INCLUDE_LOCAL_MODELS
+ENV HF_HUB_DOWNLOAD_TIMEOUT=600
 RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-    /app/api/.venv/bin/python -c "\
+    MAX_RETRIES=3; \
+    RETRY_DELAY=10; \
+    for i in $(seq 1 $MAX_RETRIES); do \
+      echo "Attempt $i/$MAX_RETRIES: Downloading ML models..."; \
+      /app/api/.venv/bin/python -c "\
+import os; os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '600'; \
 from sentence_transformers import SentenceTransformer, CrossEncoder; \
 print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Models cached successfully')"; \
+print('Models cached successfully')" && break; \
+      if [ $i -lt $MAX_RETRIES ]; then \
+        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
+        sleep $RETRY_DELAY; \
+        RETRY_DELAY=$((RETRY_DELAY * 2)); \
+      fi; \
+    done; \
+    if [ $i -eq $MAX_RETRIES ] && ! /app/api/.venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" 2>/dev/null; then \
+      echo "ERROR: Failed to download models after $MAX_RETRIES attempts"; \
+      exit 1; \
+    fi; \
     elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
     else echo "Skipping ML model preload"; fi
 
@@ -277,16 +294,33 @@ ENV PATH="/app/api/.venv/bin:${PATH}"
 
 # Pre-download ML models to avoid runtime download (conditional)
 # Only runs if both PRELOAD_ML_MODELS=true AND INCLUDE_LOCAL_MODELS=true
+# Includes retry logic with exponential backoff for transient network failures
 ARG PRELOAD_ML_MODELS
 ARG INCLUDE_LOCAL_MODELS
+ENV HF_HUB_DOWNLOAD_TIMEOUT=600
 RUN if [ "$PRELOAD_ML_MODELS" = "true" ] && [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-    /app/api/.venv/bin/python -c "\
+    MAX_RETRIES=3; \
+    RETRY_DELAY=10; \
+    for i in $(seq 1 $MAX_RETRIES); do \
+      echo "Attempt $i/$MAX_RETRIES: Downloading ML models..."; \
+      /app/api/.venv/bin/python -c "\
+import os; os.environ['HF_HUB_DOWNLOAD_TIMEOUT'] = '600'; \
 from sentence_transformers import SentenceTransformer, CrossEncoder; \
 print('Downloading embedding model...'); \
 SentenceTransformer('BAAI/bge-small-en-v1.5'); \
 print('Downloading cross-encoder model...'); \
 CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2'); \
-print('Models cached successfully')"; \
+print('Models cached successfully')" && break; \
+      if [ $i -lt $MAX_RETRIES ]; then \
+        echo "Attempt $i failed, retrying in ${RETRY_DELAY}s..."; \
+        sleep $RETRY_DELAY; \
+        RETRY_DELAY=$((RETRY_DELAY * 2)); \
+      fi; \
+    done; \
+    if [ $i -eq $MAX_RETRIES ] && ! /app/api/.venv/bin/python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" 2>/dev/null; then \
+      echo "ERROR: Failed to download models after $MAX_RETRIES attempts"; \
+      exit 1; \
+    fi; \
     elif [ "$INCLUDE_LOCAL_MODELS" != "true" ]; then echo "Skipping ML model preload (local-models not included)"; \
     else echo "Skipping ML model preload"; fi
 

helm/hindsight/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.2
-appVersion: "0.4.2"
+version: 0.4.5
+appVersion: "0.4.5"
 keywords:
   - ai
   - memory

hindsight-api/hindsight_api/__init__.py

@@ -46,4 +46,4 @@ __all__ = [
     "RemoteTEICrossEncoder",
     "LLMConfig",
 ]
-__version__ = "0.4.2"
+__version__ = "0.4.5"

hindsight-api/hindsight_api/api/http.py

@@ -92,8 +92,7 @@ class RecallRequest(BaseModel):
     query: str
     types: list[str] | None = Field(
         default=None,
-        description="List of fact types to recall: 'world', 'experience', 'observation'. Defaults to world and experience if not specified. "
-        "Note: 'opinion' is accepted but ignored (opinions are excluded from recall).",
+        description="List of fact types to recall: 'world', 'experience', 'observation'. Defaults to world and experience if not specified.",
     )
     budget: Budget = Budget.MID
     max_tokens: int = 4096
@@ -504,13 +503,6 @@ class ReflectRequest(BaseModel):
     )
 
 
-class OpinionItem(BaseModel):
-    """Model for an opinion with confidence score."""
-
-    text: str
-    confidence: float
-
-
 class ReflectFact(BaseModel):
     """A fact used in think response."""
 
@@ -529,7 +521,7 @@ class ReflectFact(BaseModel):
 
     id: str | None = None
     text: str
-    type: str | None = None  # fact type: world, experience, opinion
+    type: str | None = None  # fact type: world, experience, observation
     context: str | None = None
     occurred_start: str | None = None
     occurred_end: str | None = None
@@ -1412,9 +1404,10 @@ def create_app(
                 worker_id=worker_id,
                 executor=memory.execute_task,
                 poll_interval_ms=config.worker_poll_interval_ms,
-                batch_size=config.worker_batch_size,
                 max_retries=config.worker_max_retries,
                 tenant_extension=getattr(memory, "_tenant_extension", None),
+                max_slots=config.worker_max_slots,
+                consolidation_max_slots=config.worker_consolidation_max_slots,
             )
             poller_task = asyncio.create_task(poller.run())
             logging.info(f"Worker poller started (worker_id={worker_id})")
@@ -1707,9 +1700,7 @@ def _register_routes(app: FastAPI):
         description="Recall memory using semantic similarity and spreading activation.\n\n"
         "The type parameter is optional and must be one of:\n"
         "- `world`: General knowledge about people, places, events, and things that happen\n"
-        "- `experience`: Memories about experience, conversations, actions taken, and tasks performed\n"
-        "- `opinion`: The bank's formed beliefs, perspectives, and viewpoints\n\n"
-        "Set `include_entities=true` to get entity observations alongside recall results.",
+        "- `experience`: Memories about experience, conversations, actions taken, and tasks performed",
         operation_id="recall_memories",
         tags=["Memory"],
     )
@@ -1723,10 +1714,8 @@ def _register_routes(app: FastAPI):
         metrics = get_metrics_collector()
 
         try:
-            # Default to world and experience if not specified (exclude observation and opinion)
-            # Filter out 'opinion' even if requested - opinions are excluded from recall
+            # Default to world and experience if not specified (exclude observation)
             fact_types = request.types if request.types else list(VALID_RECALL_FACT_TYPES)
-            fact_types = [ft for ft in fact_types if ft != "opinion"]
 
             # Parse query_timestamp if provided
             question_date = None
@@ -1858,8 +1847,7 @@ def _register_routes(app: FastAPI):
         "2. Retrieves world facts relevant to the query\n"
         "3. Retrieves existing opinions (bank's perspectives)\n"
         "4. Uses LLM to formulate a contextual answer\n"
-        "5. Extracts and stores any new opinions formed\n"
-        "6. Returns plain text answer, the facts used, and new opinions",
+        "5. Returns plain text answer and the facts used",
         operation_id="reflect",
         tags=["Memory"],
     )

hindsight-api/hindsight_api/api/mcp.py

@@ -29,15 +29,26 @@ logger = logging.getLogger(__name__)
 # Default bank_id from environment variable
 DEFAULT_BANK_ID = os.environ.get("HINDSIGHT_MCP_BANK_ID", "default")
 
+# MCP authentication token (optional - if set, Bearer token auth is required)
+MCP_AUTH_TOKEN = os.environ.get("HINDSIGHT_API_MCP_AUTH_TOKEN")
+
 # Context variable to hold the current bank_id
 _current_bank_id: ContextVar[str | None] = ContextVar("current_bank_id", default=None)
 
+# Context variable to hold the current API key (for tenant auth propagation)
+_current_api_key: ContextVar[str | None] = ContextVar("current_api_key", default=None)
+
 
 def get_current_bank_id() -> str | None:
     """Get the current bank_id from context."""
     return _current_bank_id.get()
 
 
+def get_current_api_key() -> str | None:
+    """Get the current API key from context."""
+    return _current_api_key.get()
+
+
 def create_mcp_server(memory: MemoryEngine) -> FastMCP:
     """
     Create and configure the Hindsight MCP server.
@@ -54,6 +65,7 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
     # Configure and register tools using shared module
     config = MCPToolsConfig(
         bank_id_resolver=get_current_bank_id,
+        api_key_resolver=get_current_api_key,  # Propagate API key for tenant auth
         include_bank_id_param=True,  # HTTP MCP supports multi-bank via parameter
         tools=None,  # All tools
         retain_fire_and_forget=False,  # HTTP MCP supports sync/async modes
@@ -65,7 +77,11 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
 
 
 class MCPMiddleware:
-    """ASGI middleware that extracts bank_id from header or path and sets context.
+    """ASGI middleware that handles authentication and extracts bank_id from header or path.
+
+    Authentication:
+        If HINDSIGHT_API_MCP_AUTH_TOKEN is set, all requests must include a valid
+        Authorization header with Bearer token or direct token matching the configured value.
 
     Bank ID can be provided via:
     1. X-Bank-Id header (recommended for Claude Code)
@@ -74,7 +90,7 @@ class MCPMiddleware:
 
     For Claude Code, configure with:
         claude mcp add --transport http hindsight http://localhost:8888/mcp \\
-            --header "X-Bank-Id: my-bank"
+            --header "X-Bank-Id: my-bank" --header "Authorization: Bearer <token>"
     """
 
     def __init__(self, app, memory: MemoryEngine):
@@ -98,6 +114,22 @@ class MCPMiddleware:
             await self.mcp_app(scope, receive, send)
             return
 
+        # Extract auth token from header (for tenant auth propagation)
+        auth_header = self._get_header(scope, "Authorization")
+        auth_token: str | None = None
+        if auth_header:
+            # Support both "Bearer <token>" and direct token
+            auth_token = auth_header[7:].strip() if auth_header.startswith("Bearer ") else auth_header.strip()
+
+        # Authenticate if MCP_AUTH_TOKEN is configured
+        if MCP_AUTH_TOKEN:
+            if not auth_token:
+                await self._send_error(send, 401, "Authorization header required")
+                return
+            if auth_token != MCP_AUTH_TOKEN:
+                await self._send_error(send, 401, "Invalid authentication token")
+                return
+
         path = scope.get("path", "")
 
         # Strip any mount prefix (e.g., /mcp) that FastAPI might not have stripped
@@ -132,8 +164,10 @@ class MCPMiddleware:
             bank_id = DEFAULT_BANK_ID
             logger.debug(f"Using default bank_id: {bank_id}")
 
-        # Set bank_id context
-        token = _current_bank_id.set(bank_id)
+        # Set bank_id and api_key context
+        bank_id_token = _current_bank_id.set(bank_id)
+        # Store the auth token for tenant extension to validate
+        api_key_token = _current_api_key.set(auth_token) if auth_token else None
         try:
             new_scope = scope.copy()
             new_scope["path"] = new_path
@@ -152,7 +186,9 @@ class MCPMiddleware:
 
             await self.mcp_app(new_scope, receive, send_wrapper)
         finally:
-            _current_bank_id.reset(token)
+            _current_bank_id.reset(bank_id_token)
+            if api_key_token is not None:
+                _current_api_key.reset(api_key_token)
 
     async def _send_error(self, send, status: int, message: str):
         """Send an error response."""
@@ -176,6 +212,10 @@ def create_mcp_app(memory: MemoryEngine):
     """
     Create an ASGI app that handles MCP requests.
 
+    Authentication:
+        Set HINDSIGHT_API_MCP_AUTH_TOKEN to require Bearer token authentication.
+        If not set, MCP endpoint is open (for local development).
+
     Bank ID can be provided via:
     1. X-Bank-Id header: claude mcp add --transport http hindsight http://localhost:8888/mcp --header "X-Bank-Id: my-bank"
     2. URL path: /mcp/{bank_id}/

hindsight-api/hindsight_api/config.py

@@ -108,13 +108,17 @@ ENV_MCP_LOCAL_BANK_ID = "HINDSIGHT_API_MCP_LOCAL_BANK_ID"
 ENV_MCP_INSTRUCTIONS = "HINDSIGHT_API_MCP_INSTRUCTIONS"
 ENV_MENTAL_MODEL_REFRESH_CONCURRENCY = "HINDSIGHT_API_MENTAL_MODEL_REFRESH_CONCURRENCY"
 
+# Vertex AI configuration
+ENV_LLM_VERTEXAI_PROJECT_ID = "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID"
+ENV_LLM_VERTEXAI_REGION = "HINDSIGHT_API_LLM_VERTEXAI_REGION"
+ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY = "HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY"
+
 # Retain settings
 ENV_RETAIN_MAX_COMPLETION_TOKENS = "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"
 ENV_RETAIN_CHUNK_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_SIZE"
 ENV_RETAIN_EXTRACT_CAUSAL_LINKS = "HINDSIGHT_API_RETAIN_EXTRACT_CAUSAL_LINKS"
 ENV_RETAIN_EXTRACTION_MODE = "HINDSIGHT_API_RETAIN_EXTRACTION_MODE"
 ENV_RETAIN_CUSTOM_INSTRUCTIONS = "HINDSIGHT_API_RETAIN_CUSTOM_INSTRUCTIONS"
-ENV_RETAIN_OBSERVATIONS_ASYNC = "HINDSIGHT_API_RETAIN_OBSERVATIONS_ASYNC"
 
 # Observations settings (consolidated knowledge from facts)
 ENV_ENABLE_OBSERVATIONS = "HINDSIGHT_API_ENABLE_OBSERVATIONS"
@@ -139,8 +143,9 @@ ENV_WORKER_ENABLED = "HINDSIGHT_API_WORKER_ENABLED"
 ENV_WORKER_ID = "HINDSIGHT_API_WORKER_ID"
 ENV_WORKER_POLL_INTERVAL_MS = "HINDSIGHT_API_WORKER_POLL_INTERVAL_MS"
 ENV_WORKER_MAX_RETRIES = "HINDSIGHT_API_WORKER_MAX_RETRIES"
-ENV_WORKER_BATCH_SIZE = "HINDSIGHT_API_WORKER_BATCH_SIZE"
 ENV_WORKER_HTTP_PORT = "HINDSIGHT_API_WORKER_HTTP_PORT"
+ENV_WORKER_MAX_SLOTS = "HINDSIGHT_API_WORKER_MAX_SLOTS"
+ENV_WORKER_CONSOLIDATION_MAX_SLOTS = "HINDSIGHT_API_WORKER_CONSOLIDATION_MAX_SLOTS"
 
 # Reflect agent settings
 ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"
@@ -156,6 +161,11 @@ DEFAULT_LLM_INITIAL_BACKOFF = 1.0  # Initial backoff in seconds for retry expone
 DEFAULT_LLM_MAX_BACKOFF = 60.0  # Max backoff cap in seconds for retry exponential backoff
 DEFAULT_LLM_TIMEOUT = 120.0  # seconds
 
+# Vertex AI defaults
+DEFAULT_LLM_VERTEXAI_PROJECT_ID = None  # Required for Vertex AI
+DEFAULT_LLM_VERTEXAI_REGION = "us-central1"
+DEFAULT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY = None  # Optional, uses ADC if not set
+
 DEFAULT_EMBEDDINGS_PROVIDER = "local"
 DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
 DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU = False  # Force CPU mode for local embeddings (avoids MPS/XPC issues on macOS)
@@ -200,7 +210,6 @@ DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS = True  # Extract causal links between facts
 DEFAULT_RETAIN_EXTRACTION_MODE = "concise"  # Extraction mode: "concise", "verbose", or "custom"
 RETAIN_EXTRACTION_MODES = ("concise", "verbose", "custom")  # Allowed extraction modes
 DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS = None  # Custom extraction guidelines (only used when mode="custom")
-DEFAULT_RETAIN_OBSERVATIONS_ASYNC = False  # Run observation generation async (after retain completes)
 
 # Observations defaults (consolidated knowledge from facts)
 DEFAULT_ENABLE_OBSERVATIONS = True  # Observations enabled by default
@@ -221,8 +230,9 @@ DEFAULT_WORKER_ENABLED = True  # API runs worker by default (standalone mode)
 DEFAULT_WORKER_ID = None  # Will use hostname if not specified
 DEFAULT_WORKER_POLL_INTERVAL_MS = 500  # Poll database every 500ms
 DEFAULT_WORKER_MAX_RETRIES = 3  # Max retries before marking task failed
-DEFAULT_WORKER_BATCH_SIZE = 10  # Tasks to claim per poll cycle
 DEFAULT_WORKER_HTTP_PORT = 8889  # HTTP port for worker metrics/health
+DEFAULT_WORKER_MAX_SLOTS = 10  # Total concurrent tasks per worker
+DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS = 2  # Max concurrent consolidation tasks per worker
 
 # Reflect agent settings
 DEFAULT_REFLECT_MAX_ITERATIONS = 10  # Max tool call iterations before forcing response
@@ -312,6 +322,11 @@ class HindsightConfig:
     llm_max_backoff: float
     llm_timeout: float
 
+    # Vertex AI configuration
+    llm_vertexai_project_id: str | None
+    llm_vertexai_region: str
+    llm_vertexai_service_account_key: str | None
+
     # Per-operation LLM configuration (None = use default LLM config)
     retain_llm_provider: str | None
     retain_llm_api_key: str | None
@@ -382,7 +397,6 @@ class HindsightConfig:
     retain_extract_causal_links: bool
     retain_extraction_mode: str
     retain_custom_instructions: str | None
-    retain_observations_async: bool
 
     # Observations settings (consolidated knowledge from facts)
     enable_observations: bool
@@ -407,8 +421,9 @@ class HindsightConfig:
     worker_id: str | None
     worker_poll_interval_ms: int
     worker_max_retries: int
-    worker_batch_size: int
     worker_http_port: int
+    worker_max_slots: int
+    worker_consolidation_max_slots: int
 
     # Reflect agent settings
     reflect_max_iterations: int
@@ -430,6 +445,11 @@ class HindsightConfig:
             llm_initial_backoff=float(os.getenv(ENV_LLM_INITIAL_BACKOFF, str(DEFAULT_LLM_INITIAL_BACKOFF))),
             llm_max_backoff=float(os.getenv(ENV_LLM_MAX_BACKOFF, str(DEFAULT_LLM_MAX_BACKOFF))),
             llm_timeout=float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT))),
+            # Vertex AI
+            llm_vertexai_project_id=os.getenv(ENV_LLM_VERTEXAI_PROJECT_ID) or DEFAULT_LLM_VERTEXAI_PROJECT_ID,
+            llm_vertexai_region=os.getenv(ENV_LLM_VERTEXAI_REGION, DEFAULT_LLM_VERTEXAI_REGION),
+            llm_vertexai_service_account_key=os.getenv(ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY)
+            or DEFAULT_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY,
             # Per-operation LLM config (None = use default)
             retain_llm_provider=os.getenv(ENV_RETAIN_LLM_PROVIDER) or None,
             retain_llm_api_key=os.getenv(ENV_RETAIN_LLM_API_KEY) or None,
@@ -545,10 +565,6 @@ class HindsightConfig:
                 os.getenv(ENV_RETAIN_EXTRACTION_MODE, DEFAULT_RETAIN_EXTRACTION_MODE)
             ),
             retain_custom_instructions=os.getenv(ENV_RETAIN_CUSTOM_INSTRUCTIONS) or DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS,
-            retain_observations_async=os.getenv(
-                ENV_RETAIN_OBSERVATIONS_ASYNC, str(DEFAULT_RETAIN_OBSERVATIONS_ASYNC)
-            ).lower()
-            == "true",
             # Observations settings (consolidated knowledge from facts)
             enable_observations=os.getenv(ENV_ENABLE_OBSERVATIONS, str(DEFAULT_ENABLE_OBSERVATIONS)).lower() == "true",
             consolidation_batch_size=int(
@@ -569,8 +585,11 @@ class HindsightConfig:
             worker_id=os.getenv(ENV_WORKER_ID) or DEFAULT_WORKER_ID,
             worker_poll_interval_ms=int(os.getenv(ENV_WORKER_POLL_INTERVAL_MS, str(DEFAULT_WORKER_POLL_INTERVAL_MS))),
             worker_max_retries=int(os.getenv(ENV_WORKER_MAX_RETRIES, str(DEFAULT_WORKER_MAX_RETRIES))),
-            worker_batch_size=int(os.getenv(ENV_WORKER_BATCH_SIZE, str(DEFAULT_WORKER_BATCH_SIZE))),
             worker_http_port=int(os.getenv(ENV_WORKER_HTTP_PORT, str(DEFAULT_WORKER_HTTP_PORT))),
+            worker_max_slots=int(os.getenv(ENV_WORKER_MAX_SLOTS, str(DEFAULT_WORKER_MAX_SLOTS))),
+            worker_consolidation_max_slots=int(
+                os.getenv(ENV_WORKER_CONSOLIDATION_MAX_SLOTS, str(DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS))
+            ),
             # Reflect agent settings
             reflect_max_iterations=int(os.getenv(ENV_REFLECT_MAX_ITERATIONS, str(DEFAULT_REFLECT_MAX_ITERATIONS))),
         )

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

@@ -865,7 +865,14 @@ Focus on DURABLE knowledge that serves this mission, not ephemeral state.
         )
         # Parse JSON response - should be an array
         if isinstance(result, str):
-            result = json.loads(result)
+            # Strip markdown code fences (some models wrap JSON in ```json ... ```)
+            clean = result.strip()
+            if clean.startswith("```"):
+                clean = clean.split("\n", 1)[1] if "\n" in clean else clean[3:]
+                if clean.endswith("```"):
+                    clean = clean[:-3]
+                clean = clean.strip()
+            result = json.loads(clean)
         # Ensure result is a list
         if isinstance(result, list):
             return result

hindsight-api/hindsight_api/engine/cross_encoder.py

@@ -614,7 +614,7 @@ class FlashRankCrossEncoder(CrossEncoderModel):
             return
 
         try:
-            from flashrank import Ranker  # type: ignore[import-untyped]
+            from flashrank import Ranker
         except ImportError:
             raise ImportError("flashrank is required for FlashRankCrossEncoder. Install it with: pip install flashrank")
 
@@ -641,7 +641,7 @@ class FlashRankCrossEncoder(CrossEncoderModel):
 
     def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
         """Synchronous predict - processes each query group."""
-        from flashrank import RerankRequest  # type: ignore[import-untyped]
+        from flashrank import RerankRequest
 
         if not pairs:
             return []

hindsight-api/hindsight_api/engine/embeddings.py

@@ -545,7 +545,7 @@ class CohereEmbeddings(Embeddings):
                 model=self.model,
                 input_type=self.input_type,
             )
-            if response.embeddings:
+            if response.embeddings and isinstance(response.embeddings, list):
                 self._dimension = len(response.embeddings[0])
 
         logger.info(f"Embeddings: Cohere provider initialized (model: {self.model}, dim: {self._dimension})")

hindsight-api/hindsight_api/engine/interface.py

@@ -442,49 +442,6 @@ class MemoryEngineInterface(ABC):
         """
         ...
 
-    @abstractmethod
-    async def get_entity_observations(
-        self,
-        bank_id: str,
-        entity_id: str,
-        *,
-        limit: int = 10,
-        request_context: "RequestContext",
-    ) -> list[Any]:
-        """
-        Get observations for an entity.
-
-        Args:
-            bank_id: The memory bank ID.
-            entity_id: The entity ID.
-            limit: Maximum observations.
-            request_context: Request context for authentication.
-
-        Returns:
-            List of EntityObservation objects.
-        """
-        ...
-
-    @abstractmethod
-    async def regenerate_entity_observations(
-        self,
-        bank_id: str,
-        entity_id: str,
-        entity_name: str,
-        *,
-        request_context: "RequestContext",
-    ) -> None:
-        """
-        Regenerate observations for an entity.
-
-        Args:
-            bank_id: The memory bank ID.
-            entity_id: The entity ID.
-            entity_name: The entity's canonical name.
-            request_context: Request context for authentication.
-        """
-        ...
-
     # =========================================================================
     # Statistics & Operations
     # =========================================================================

hindsight-api/hindsight_api/engine/llm_wrapper.py

@@ -16,6 +16,15 @@ from google.genai import errors as genai_errors
 from google.genai import types as genai_types
 from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinishReasonError
 
+# Vertex AI imports (conditional)
+try:
+    import google.auth
+    from google.oauth2 import service_account
+
+    VERTEXAI_AVAILABLE = True
+except ImportError:
+    VERTEXAI_AVAILABLE = False
+
 from ..config import (
     DEFAULT_LLM_MAX_CONCURRENT,
     DEFAULT_LLM_TIMEOUT,
@@ -88,7 +97,7 @@ class LLMProvider:
         self.groq_service_tier = groq_service_tier or os.getenv(ENV_LLM_GROQ_SERVICE_TIER, "auto")
 
         # Validate provider
-        valid_providers = ["openai", "groq", "ollama", "gemini", "anthropic", "lmstudio", "mock"]
+        valid_providers = ["openai", "groq", "ollama", "gemini", "anthropic", "lmstudio", "vertexai", "mock"]
         if self.provider not in valid_providers:
             raise ValueError(f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}")
 
@@ -105,8 +114,51 @@ class LLMProvider:
             elif self.provider == "lmstudio":
                 self.base_url = "http://localhost:1234/v1"
 
-        # Validate API key (not needed for ollama, lmstudio, or mock)
-        if self.provider not in ("ollama", "lmstudio", "mock") and not self.api_key:
+        # Vertex AI config — stored for client creation below
+        self._vertexai_project_id: str | None = None
+        self._vertexai_region: str | None = None
+        self._vertexai_credentials: Any = None
+
+        if self.provider == "vertexai":
+            from ..config import get_config
+
+            config = get_config()
+
+            self._vertexai_project_id = config.llm_vertexai_project_id
+            if not self._vertexai_project_id:
+                raise ValueError(
+                    "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID is required for Vertex AI provider. "
+                    "Set it to your GCP project ID."
+                )
+
+            self._vertexai_region = config.llm_vertexai_region or "us-central1"
+            service_account_key = config.llm_vertexai_service_account_key
+
+            # Load explicit service account credentials if provided
+            if service_account_key:
+                if not VERTEXAI_AVAILABLE:
+                    raise ValueError(
+                        "Vertex AI service account auth requires 'google-auth' package. "
+                        "Install with: pip install google-auth"
+                    )
+                self._vertexai_credentials = service_account.Credentials.from_service_account_file(
+                    service_account_key,
+                    scopes=["https://www.googleapis.com/auth/cloud-platform"],
+                )
+                logger.info(f"Vertex AI: Using service account key: {service_account_key}")
+
+            # Strip google/ prefix from model name — native SDK uses bare names
+            # e.g. "google/gemini-2.0-flash-lite-001" -> "gemini-2.0-flash-lite-001"
+            if self.model.startswith("google/"):
+                self.model = self.model[len("google/") :]
+
+            logger.info(
+                f"Vertex AI: project={self._vertexai_project_id}, region={self._vertexai_region}, "
+                f"model={self.model}, auth={'service_account' if service_account_key else 'ADC'}"
+            )
+
+        # Validate API key (not needed for ollama, lmstudio, vertexai, or mock)
+        if self.provider not in ("ollama", "lmstudio", "vertexai", "mock") and not self.api_key:
             raise ValueError(f"API key not found for {self.provider}")
 
         # Get timeout config (set HINDSIGHT_API_LLM_TIMEOUT for local LLMs that need longer timeouts)
@@ -132,6 +184,17 @@ class LLMProvider:
             if self.timeout:
                 anthropic_kwargs["timeout"] = self.timeout
             self._anthropic_client = AsyncAnthropic(**anthropic_kwargs)
+        elif self.provider == "vertexai":
+            # Native genai SDK with Vertex AI — handles ADC automatically,
+            # or uses explicit service account credentials if provided
+            client_kwargs = {
+                "vertexai": True,
+                "project": self._vertexai_project_id,
+                "location": self._vertexai_region,
+            }
+            if self._vertexai_credentials is not None:
+                client_kwargs["credentials"] = self._vertexai_credentials
+            self._gemini_client = genai.Client(**client_kwargs)
         elif self.provider in ("ollama", "lmstudio"):
             # Use dummy key if not provided for local
             api_key = self.api_key or "local"
@@ -223,8 +286,8 @@ class LLMProvider:
                     return_usage,
                 )
 
-            # Handle Gemini provider separately
-            if self.provider == "gemini":
+            # Handle Gemini and Vertex AI providers (both use native genai SDK)
+            if self.provider in ("gemini", "vertexai"):
                 return await self._call_gemini(
                     messages,
                     response_format,
@@ -342,11 +405,13 @@ class LLMProvider:
                         schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2)}"
 
                         if call_params["messages"] and call_params["messages"][0].get("role") == "system":
-                            call_params["messages"][0]["content"] += schema_msg
+                            first_msg = call_params["messages"][0]
+                            if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
+                                first_msg["content"] += schema_msg
                         elif call_params["messages"]:
-                            call_params["messages"][0]["content"] = (
-                                schema_msg + "\n\n" + call_params["messages"][0]["content"]
-                            )
+                            first_msg = call_params["messages"][0]
+                            if isinstance(first_msg, dict) and isinstance(first_msg.get("content"), str):
+                                first_msg["content"] = schema_msg + "\n\n" + first_msg["content"]
                     if self.provider not in ("lmstudio", "ollama"):
                         # LM Studio and Ollama don't support json_object response format reliably
                         # We rely on the schema in the system message instead
@@ -586,8 +651,8 @@ class LLMProvider:
                     messages, tools, max_completion_tokens, max_retries, initial_backoff, max_backoff, start_time, scope
                 )
 
-            # Handle Gemini (convert to Gemini tool format)
-            if self.provider == "gemini":
+            # Handle Gemini and Vertex AI (convert to Gemini tool format)
+            if self.provider in ("gemini", "vertexai"):
                 return await self._call_with_tools_gemini(
                     messages, tools, max_retries, initial_backoff, max_backoff, start_time, scope
                 )
@@ -917,18 +982,20 @@ class LLMProvider:
                 tool_calls: list[LLMToolCall] = []
 
                 if response.candidates and response.candidates[0].content:
-                    for part in response.candidates[0].content.parts:
-                        if hasattr(part, "text") and part.text:
-                            content = part.text
-                        if hasattr(part, "function_call") and part.function_call:
-                            fc = part.function_call
-                            tool_calls.append(
-                                LLMToolCall(
-                                    id=f"gemini_{len(tool_calls)}",
-                                    name=fc.name,
-                                    arguments=dict(fc.args) if fc.args else {},
+                    parts = response.candidates[0].content.parts
+                    if parts:
+                        for part in parts:
+                            if hasattr(part, "text") and part.text:
+                                content = part.text
+                            if hasattr(part, "function_call") and part.function_call:
+                                fc = part.function_call
+                                tool_calls.append(
+                                    LLMToolCall(
+                                        id=f"gemini_{len(tool_calls)}",
+                                        name=fc.name,
+                                        arguments=dict(fc.args) if fc.args else {},
+                                    )
                                 )
-                            )
 
                 finish_reason = "tool_calls" if tool_calls else "stop"
 
@@ -1504,6 +1571,10 @@ class LLMProvider:
         """Clear the recorded mock calls."""
         self._mock_calls = []
 
+    async def cleanup(self) -> None:
+        """Clean up resources."""
+        pass
+
     @classmethod
     def for_memory(cls) -> "LLMProvider":
         """Create provider for memory operations from environment variables."""

hindsight-api/hindsight_api/engine/memory_engine.py

@@ -504,12 +504,11 @@ class MemoryEngine(MemoryEngineInterface):
         if request_context is None:
             raise AuthenticationError("RequestContext is required when tenant extension is configured")
 
-        # For internal/background operations (e.g., worker tasks), skip extension authentication
-        # if the schema has already been set by execute_task via the _schema field.
+        # For internal/background operations (e.g., worker tasks), skip extension authentication.
+        # The task was already authenticated at submission time, and execute_task sets _current_schema
+        # from the task's _schema field. For public schema tasks, _current_schema keeps its default "public".
         if request_context.internal:
-            current = _current_schema.get()
-            if current and current != "public":
-                return current
+            return _current_schema.get()
 
         # Let AuthenticationError propagate - HTTP layer will convert to 401
         tenant_context = await self._tenant_extension.authenticate(request_context)
@@ -789,7 +788,7 @@ class MemoryEngine(MemoryEngineInterface):
                 kwargs = {"name": self._pg0_instance_name}
                 if self._pg0_port is not None:
                     kwargs["port"] = self._pg0_port
-                pg0 = EmbeddedPostgres(**kwargs)  # type: ignore[invalid-argument-type] - dict kwargs
+                pg0 = EmbeddedPostgres(**kwargs)
                 # Check if pg0 is already running before we start it
                 was_already_running = await pg0.is_running()
                 self.db_url = await pg0.ensure_running()
@@ -889,6 +888,23 @@ class MemoryEngine(MemoryEngineInterface):
             # Use configured database schema for migrations (defaults to "public")
             run_migrations(self.db_url, schema=get_config().database_schema)
 
+            # Migrate all existing tenant schemas (if multi-tenant)
+            if self._tenant_extension is not None:
+                try:
+                    tenants = await self._tenant_extension.list_tenants()
+                    if tenants:
+                        logger.info(f"Running migrations on {len(tenants)} tenant schemas...")
+                        for tenant in tenants:
+                            schema = tenant.schema
+                            if schema and schema != "public":
+                                try:
+                                    run_migrations(self.db_url, schema=schema)
+                                except Exception as e:
+                                    logger.warning(f"Failed to migrate tenant schema {schema}: {e}")
+                        logger.info("Tenant schema migrations completed")
+                except Exception as e:
+                    logger.warning(f"Failed to run tenant schema migrations: {e}")
+
             # Ensure embedding column dimension matches the model's dimension
             # This is done after migrations and after embeddings.initialize()
             ensure_embedding_dimension(self.db_url, self.embeddings.dimension, schema=get_config().database_schema)
@@ -1175,15 +1191,15 @@ class MemoryEngine(MemoryEngineInterface):
             context: Context about when/why this memory was formed
             event_date: When the event occurred (defaults to now)
             document_id: Optional document ID for tracking (always upserts if document already exists)
-            fact_type_override: Override fact type ('world', 'experience', 'opinion')
-            confidence_score: Confidence score for opinions (0.0 to 1.0)
+            fact_type_override: Override fact type ('world', 'experience')
+            confidence_score: Confidence score (0.0 to 1.0)
             request_context: Request context for authentication.
 
         Returns:
             List of created unit IDs
         """
         # Build content dict
-        content_dict: RetainContentDict = {"content": content, "context": context}  # type: ignore[typeddict-item] - building incrementally
+        content_dict: RetainContentDict = {"content": content, "context": context}
         if event_date:
             content_dict["event_date"] = event_date
         if document_id:
@@ -1231,8 +1247,8 @@ class MemoryEngine(MemoryEngineInterface):
                 - "document_id" (optional): Document ID for this specific content item
             document_id: **DEPRECATED** - Use "document_id" key in each content dict instead.
                         Applies the same document_id to ALL content items that don't specify their own.
-            fact_type_override: Override fact type for all facts ('world', 'experience', 'opinion')
-            confidence_score: Confidence score for opinions (0.0 to 1.0)
+            fact_type_override: Override fact type for all facts ('world', 'experience')
+            confidence_score: Confidence score (0.0 to 1.0)
             return_usage: If True, returns tuple of (unit_ids, TokenUsage). Default False for backward compatibility.
 
         Returns:
@@ -1554,16 +1570,16 @@ class MemoryEngine(MemoryEngineInterface):
         if fact_type is None:
             fact_type = list(VALID_RECALL_FACT_TYPES)
 
-        # Validate fact types early
+        # Filter out 'opinion' early (deprecated, silently ignore)
+        fact_type = [ft for ft in fact_type if ft != "opinion"]
+
+        # Validate fact types
         invalid_types = set(fact_type) - VALID_RECALL_FACT_TYPES
         if invalid_types:
             raise ValueError(
                 f"Invalid fact type(s): {', '.join(sorted(invalid_types))}. "
                 f"Must be one of: {', '.join(sorted(VALID_RECALL_FACT_TYPES))}"
             )
-
-        # Filter out 'opinion' - opinions are no longer returned from recall
-        fact_type = [ft for ft in fact_type if ft != "opinion"]
         if not fact_type:
             # All requested types were opinions - return empty result
             return RecallResultModel(results=[], entities={}, chunks={})
@@ -2219,44 +2235,15 @@ class MemoryEngine(MemoryEngineInterface):
                     )
                 top_results_dicts.append(result_dict)
 
-            # Get entities for each fact if include_entities is requested
-            fact_entity_map = {}  # unit_id -> list of (entity_id, entity_name)
-            if include_entities and top_scored:
-                unit_ids = [uuid.UUID(sr.id) for sr in top_scored]
-                if unit_ids:
-                    async with acquire_with_retry(pool) as entity_conn:
-                        entity_rows = await entity_conn.fetch(
-                            f"""
-                            SELECT ue.unit_id, e.id as entity_id, e.canonical_name
-                            FROM {fq_table("unit_entities")} ue
-                            JOIN {fq_table("entities")} e ON ue.entity_id = e.id
-                            WHERE ue.unit_id = ANY($1::uuid[])
-                            """,
-                            unit_ids,
-                        )
-                        for row in entity_rows:
-                            unit_id = str(row["unit_id"])
-                            if unit_id not in fact_entity_map:
-                                fact_entity_map[unit_id] = []
-                            fact_entity_map[unit_id].append(
-                                {"entity_id": str(row["entity_id"]), "canonical_name": row["canonical_name"]}
-                            )
-
             # Convert results to MemoryFact objects
             memory_facts = []
             for result_dict in top_results_dicts:
-                result_id = str(result_dict.get("id"))
-                # Get entity names for this fact
-                entity_names = None
-                if include_entities and result_id in fact_entity_map:
-                    entity_names = [e["canonical_name"] for e in fact_entity_map[result_id]]
-
                 memory_facts.append(
                     MemoryFact(
-                        id=result_id,
+                        id=str(result_dict.get("id")),
                         text=result_dict.get("text"),
                         fact_type=result_dict.get("fact_type", "world"),
-                        entities=entity_names,
+                        entities=None,  # Entity observations removed
                         context=result_dict.get("context"),
                         occurred_start=result_dict.get("occurred_start"),
                         occurred_end=result_dict.get("occurred_end"),
@@ -2267,38 +2254,12 @@ class MemoryEngine(MemoryEngineInterface):
                     )
                 )
 
-            # Fetch entity observations if requested
+            # Entity observations removed - always set to None
             entities_dict = None
-            total_entity_tokens = 0
-            total_chunk_tokens = 0
-            if include_entities and fact_entity_map:
-                # Collect unique entities in order of fact relevance (preserving order from top_scored)
-                # Use a list to maintain order, but track seen entities to avoid duplicates
-                entities_ordered = []  # list of (entity_id, entity_name) tuples
-                seen_entity_ids = set()
-
-                # Iterate through facts in relevance order
-                for sr in top_scored:
-                    unit_id = sr.id
-                    if unit_id in fact_entity_map:
-                        for entity in fact_entity_map[unit_id]:
-                            entity_id = entity["entity_id"]
-                            entity_name = entity["canonical_name"]
-                            if entity_id not in seen_entity_ids:
-                                entities_ordered.append((entity_id, entity_name))
-                                seen_entity_ids.add(entity_id)
-
-                # Return entities with empty observations (summaries now live in mental models)
-                entities_dict = {}
-                for entity_id, entity_name in entities_ordered:
-                    entities_dict[entity_name] = EntityState(
-                        entity_id=entity_id,
-                        canonical_name=entity_name,
-                        observations=[],  # Mental models provide this now
-                    )
 
             # Fetch chunks if requested
             chunks_dict = None
+            total_chunk_tokens = 0
             if include_chunks and top_scored:
                 from .response_models import ChunkInfo
 
@@ -2367,7 +2328,6 @@ class MemoryEngine(MemoryEngineInterface):
             # Log final recall stats
             total_time = time.time() - recall_start
             num_chunks = len(chunks_dict) if chunks_dict else 0
-            num_entities = len(entities_dict) if entities_dict else 0
             # Include wait times in log if significant
             wait_parts = []
             if semaphore_wait > 0.01:
@@ -2376,7 +2336,7 @@ class MemoryEngine(MemoryEngineInterface):
                 wait_parts.append(f"conn={max_conn_wait:.3f}s")
             wait_info = f" | waits: {', '.join(wait_parts)}" if wait_parts else ""
             log_buffer.append(
-                f"[RECALL {recall_id}] Complete: {len(top_scored)} facts ({total_tokens} tok), {num_chunks} chunks ({total_chunk_tokens} tok), {num_entities} entities ({total_entity_tokens} tok) | {fact_type_summary} | {total_time:.3f}s{wait_info}"
+                f"[RECALL {recall_id}] Complete: {len(top_scored)} facts ({total_tokens} tok), {num_chunks} chunks ({total_chunk_tokens} tok) | {fact_type_summary} | {total_time:.3f}s{wait_info}"
             )
             if not quiet:
                 logger.info("\n" + "\n".join(log_buffer))
@@ -3550,7 +3510,6 @@ class MemoryEngine(MemoryEngineInterface):
             ReflectResult containing:
                 - text: Plain text answer
                 - based_on: Empty dict (agent retrieves facts dynamically)
-                - new_opinions: Empty list
                 - structured_output: None (not yet supported for agentic reflect)
         """
         # Use cached LLM config
@@ -3875,7 +3834,6 @@ class MemoryEngine(MemoryEngineInterface):
         result = ReflectResult(
             text=agent_result.text,
             based_on=based_on,
-            new_opinions=[],  # Learnings stored as mental models
             structured_output=agent_result.structured_output,
             usage=usage,
             tool_trace=tool_trace_result,
@@ -3904,32 +3862,6 @@ class MemoryEngine(MemoryEngineInterface):
 
         return result
 
-    async def get_entity_observations(
-        self,
-        bank_id: str,
-        entity_id: str,
-        *,
-        limit: int = 10,
-        request_context: "RequestContext",
-    ) -> list[Any]:
-        """
-        Get observations for an entity.
-
-        NOTE: Entity observations/summaries have been moved to mental models.
-        This method returns an empty list. Use mental models for entity summaries.
-
-        Args:
-            bank_id: bank IDentifier
-            entity_id: Entity UUID to get observations for
-            limit: Ignored (kept for backwards compatibility)
-            request_context: Request context for authentication.
-
-        Returns:
-            Empty list (observations now in mental models)
-        """
-        await self._authenticate_tenant(request_context)
-        return []
-
     async def list_entities(
         self,
         bank_id: str,
@@ -4116,36 +4048,6 @@ class MemoryEngine(MemoryEngineInterface):
         await self._authenticate_tenant(request_context)
         return EntityState(entity_id=entity_id, canonical_name=entity_name, observations=[])
 
-    async def regenerate_entity_observations(
-        self,
-        bank_id: str,
-        entity_id: str,
-        entity_name: str,
-        *,
-        version: str | None = None,
-        conn=None,
-        request_context: "RequestContext",
-    ) -> list[str]:
-        """
-        Regenerate observations for an entity.
-
-        NOTE: Entity observations/summaries have been moved to mental models.
-        This method is now a no-op and returns an empty list.
-
-        Args:
-            bank_id: bank IDentifier
-            entity_id: Entity UUID
-            entity_name: Canonical name of the entity
-            version: Entity's last_seen timestamp when task was created (for deduplication)
-            conn: Optional database connection (ignored)
-            request_context: Request context for authentication.
-
-        Returns:
-            Empty list (observations now in mental models)
-        """
-        await self._authenticate_tenant(request_context)
-        return []
-
     # =========================================================================
     # Statistics & Operations (for HTTP API layer)
     # =========================================================================
@@ -4256,9 +4158,6 @@ class MemoryEngine(MemoryEngineInterface):
         if not entity_row:
             return None
 
-        # Get observations for the entity
-        observations = await self.get_entity_observations(bank_id, entity_id, limit=20, request_context=request_context)
-
         return {
             "id": str(entity_row["id"]),
             "canonical_name": entity_row["canonical_name"],
@@ -4266,7 +4165,7 @@ class MemoryEngine(MemoryEngineInterface):
             "first_seen": entity_row["first_seen"].isoformat() if entity_row["first_seen"] else None,
             "last_seen": entity_row["last_seen"].isoformat() if entity_row["last_seen"] else None,
             "metadata": entity_row["metadata"] or {},
-            "observations": observations,
+            "observations": [],
         }
 
     def _parse_observations(self, observations_raw: list):

hindsight-api/hindsight_api/engine/response_models.py

@@ -263,7 +263,6 @@ class ReflectResult(BaseModel):
                         }
                     ],
                 },
-                "new_opinions": ["Machine learning has great potential in healthcare"],
                 "structured_output": {"summary": "ML in healthcare", "confidence": 0.9},
                 "usage": {"input_tokens": 1500, "output_tokens": 500, "total_tokens": 2000},
             }
@@ -272,9 +271,8 @@ class ReflectResult(BaseModel):
 
     text: str = Field(description="The formulated answer text")
     based_on: dict[str, Any] = Field(
-        description="Facts used to formulate the answer, organized by type (world, experience, opinion, mental_models, directives)"
+        description="Facts used to formulate the answer, organized by type (world, experience, mental_models, directives)"
     )
-    new_opinions: list[str] = Field(default_factory=list, description="List of newly formed opinions during reflection")
     structured_output: dict[str, Any] | None = Field(
         default=None,
         description="Structured output parsed according to the provided response schema. Only present when response_schema was provided.",
@@ -297,24 +295,6 @@ class ReflectResult(BaseModel):
     )
 
 
-class Opinion(BaseModel):
-    """
-    An opinion with confidence score.
-
-    Opinions represent the bank's formed perspectives on topics,
-    with a confidence level indicating strength of belief.
-    """
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {"text": "Machine learning has great potential in healthcare", "confidence": 0.85}
-        }
-    )
-
-    text: str = Field(description="The opinion text")
-    confidence: float = Field(description="Confidence score between 0.0 and 1.0")
-
-
 class EntityObservation(BaseModel):
     """
     An observation about an entity.

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

@@ -693,7 +693,6 @@ async def _extract_facts_from_chunk(
     context: str,
     llm_config: "LLMConfig",
     agent_name: str = None,
-    extract_opinions: bool = False,
 ) -> tuple[list[dict[str, str]], TokenUsage]:
     """
     Extract facts from a single chunk (internal helper for parallel processing).
@@ -707,17 +706,9 @@ async def _extract_facts_from_chunk(
 
     logger = logging.getLogger(__name__)
 
-    memory_bank_context = f"\n- Your name: {agent_name}" if agent_name and extract_opinions else ""
-
-    # Determine which fact types to extract based on the flag
+    # Determine which fact types to extract
     # Note: We use "assistant" in the prompt but convert to "bank" for storage
-    if extract_opinions:
-        # Opinion extraction uses a separate prompt (not this one)
-        fact_types_instruction = "Extract ONLY 'opinion' type facts (formed opinions, beliefs, and perspectives). DO NOT extract 'world' or 'assistant' facts."
-    else:
-        fact_types_instruction = (
-            "Extract ONLY 'world' and 'assistant' type facts. DO NOT extract opinions - those are extracted separately."
-        )
+    fact_types_instruction = "Extract ONLY 'world' and 'assistant' type facts."
 
     # Check config for extraction mode and causal link extraction
     config = get_config()
@@ -768,9 +759,12 @@ async def _extract_facts_from_chunk(
 
     # Build user message with metadata and chunk content in a clear format
     # Format event_date with day of week for better temporal reasoning
+    # Handle both datetime objects and ISO string formats (from deserialized async tasks)
+    from .orchestrator import parse_datetime_flexible
+
+    event_date = parse_datetime_flexible(event_date)
     event_date_formatted = event_date.strftime("%A, %B %d, %Y")  # e.g., "Monday, June 10, 2024"
     user_message = f"""Extract facts from the following text chunk.
-{memory_bank_context}
 
 Chunk: {chunk_index + 1}/{total_chunks}
 Event Date: {event_date_formatted} ({event_date.isoformat()})
@@ -1029,7 +1023,6 @@ async def _extract_facts_with_auto_split(
     context: str,
     llm_config: LLMConfig,
     agent_name: str = None,
-    extract_opinions: bool = False,
 ) -> tuple[list[dict[str, str]], TokenUsage]:
     """
     Extract facts from a chunk with automatic splitting if output exceeds token limits.
@@ -1045,7 +1038,6 @@ async def _extract_facts_with_auto_split(
         context: Context about the conversation/document
         llm_config: LLM configuration to use
         agent_name: Optional agent name (memory owner)
-        extract_opinions: If True, extract ONLY opinions. If False, extract world and agent facts (no opinions)
 
     Returns:
         Tuple of (facts list, token usage) extracted from the chunk (possibly from sub-chunks)
@@ -1064,7 +1056,6 @@ async def _extract_facts_with_auto_split(
             context=context,
             llm_config=llm_config,
             agent_name=agent_name,
-            extract_opinions=extract_opinions,
         )
     except OutputTooLongError:
         # Output exceeded token limits - split the chunk in half and retry
@@ -1109,7 +1100,6 @@ async def _extract_facts_with_auto_split(
                 context=context,
                 llm_config=llm_config,
                 agent_name=agent_name,
-                extract_opinions=extract_opinions,
             ),
             _extract_facts_with_auto_split(
                 chunk=second_half,
@@ -1119,7 +1109,6 @@ async def _extract_facts_with_auto_split(
                 context=context,
                 llm_config=llm_config,
                 agent_name=agent_name,
-                extract_opinions=extract_opinions,
             ),
         ]
 
@@ -1143,7 +1132,6 @@ async def extract_facts_from_text(
     llm_config: LLMConfig,
     agent_name: str,
     context: str = "",
-    extract_opinions: bool = False,
 ) -> tuple[list[Fact], list[tuple[str, int]], TokenUsage]:
     """
     Extract semantic facts from conversational or narrative text using LLM.
@@ -1160,7 +1148,6 @@ async def extract_facts_from_text(
         context: Context about the conversation/document
         llm_config: LLM configuration to use
         agent_name: Agent name (memory owner)
-        extract_opinions: If True, extract ONLY opinions. If False, extract world and bank facts (no opinions)
 
     Returns:
         Tuple of (facts, chunks, usage) where:
@@ -1188,7 +1175,6 @@ async def extract_facts_from_text(
             context=context,
             llm_config=llm_config,
             agent_name=agent_name,
-            extract_opinions=extract_opinions,
         )
         for i, chunk in enumerate(chunks)
     ]
@@ -1220,7 +1206,7 @@ SECONDS_PER_FACT = 10
 
 
 async def extract_facts_from_contents(
-    contents: list[RetainContent], llm_config, agent_name: str, extract_opinions: bool = False
+    contents: list[RetainContent], llm_config, agent_name: str
 ) -> tuple[list[ExtractedFactType], list[ChunkMetadata], TokenUsage]:
     """
     Extract facts from multiple content items in parallel.
@@ -1235,7 +1221,6 @@ async def extract_facts_from_contents(
         contents: List of RetainContent objects to process
         llm_config: LLM configuration for fact extraction
         agent_name: Name of the agent (for agent-related fact detection)
-        extract_opinions: If True, extract only opinions; otherwise world/bank facts
 
     Returns:
         Tuple of (extracted_facts, chunks_metadata, usage)
@@ -1254,7 +1239,6 @@ async def extract_facts_from_contents(
             context=item.context,
             llm_config=llm_config,
             agent_name=agent_name,
-            extract_opinions=extract_opinions,
         )
         fact_extraction_tasks.append(task)
 
@@ -1366,6 +1350,8 @@ def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainC
 
     Modifies facts in place.
     """
+    from .orchestrator import parse_datetime_flexible
+
     # Group facts by content_index
     current_content_idx = 0
     content_fact_start = 0
@@ -1380,10 +1366,10 @@ def _add_temporal_offsets(facts: list[ExtractedFactType], contents: list[RetainC
         fact_position = i - content_fact_start
         offset = timedelta(seconds=fact_position * SECONDS_PER_FACT)
 
-        # Apply offset to all temporal fields
+        # Apply offset to all temporal fields (handle both datetime objects and ISO strings)
         if fact.occurred_start:
-            fact.occurred_start = fact.occurred_start + offset
+            fact.occurred_start = parse_datetime_flexible(fact.occurred_start) + offset
         if fact.occurred_end:
-            fact.occurred_end = fact.occurred_end + offset
+            fact.occurred_end = parse_datetime_flexible(fact.occurred_end) + offset
         if fact.mentioned_at:
-            fact.mentioned_at = fact.mentioned_at + offset
+            fact.mentioned_at = parse_datetime_flexible(fact.mentioned_at) + offset

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

@@ -8,6 +8,7 @@ import logging
 import time
 import uuid
 from datetime import UTC, datetime
+from typing import Any
 
 from ..db_utils import acquire_with_retry
 from . import bank_utils
@@ -18,6 +19,39 @@ def utcnow():
     return datetime.now(UTC)
 
 
+def parse_datetime_flexible(value: Any) -> datetime:
+    """
+    Parse a datetime value that could be either a datetime object or an ISO string.
+
+    This handles datetime values from both direct Python calls and deserialized JSON
+    (where datetime objects are serialized as ISO strings).
+
+    Args:
+        value: Either a datetime object or an ISO format string
+
+    Returns:
+        datetime object (timezone-aware)
+
+    Raises:
+        TypeError: If value is neither datetime nor string
+        ValueError: If string is not a valid ISO datetime
+    """
+    if isinstance(value, datetime):
+        # Ensure timezone-aware
+        if value.tzinfo is None:
+            return value.replace(tzinfo=UTC)
+        return value
+    elif isinstance(value, str):
+        # Parse ISO format string (handles both 'Z' and '+00:00' timezone formats)
+        dt = datetime.fromisoformat(value.replace("Z", "+00:00"))
+        # Ensure timezone-aware
+        if dt.tzinfo is None:
+            return dt.replace(tzinfo=UTC)
+        return dt
+    else:
+        raise TypeError(f"Expected datetime or string, got {type(value).__name__}")
+
+
 from ..response_models import TokenUsage
 from . import (
     chunk_storage,
@@ -89,10 +123,18 @@ async def retain_batch(
         # Merge item-level tags with document-level tags
         item_tags = item.get("tags", []) or []
         merged_tags = list(set(item_tags + (document_tags or [])))
+
+        # Handle event_date: parse flexibly (handles both datetime objects and ISO strings)
+        event_date_value = item.get("event_date")
+        if event_date_value:
+            event_date_value = parse_datetime_flexible(event_date_value)
+        else:
+            event_date_value = utcnow()
+
         content = RetainContent(
             content=item["content"],
             context=item.get("context", ""),
-            event_date=item.get("event_date") or utcnow(),
+            event_date=event_date_value,
             metadata=item.get("metadata", {}),
             entities=item.get("entities", []),
             tags=merged_tags,
@@ -101,11 +143,8 @@ async def retain_batch(
 
     # Step 1: Extract facts from all contents
     step_start = time.time()
-    extract_opinions = fact_type_override == "opinion"
 
-    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(
-        contents, llm_config, agent_name, extract_opinions
-    )
+    extracted_facts, chunks, usage = await fact_extraction.extract_facts_from_contents(contents, llm_config, agent_name)
     log_buffer.append(
         f"[1] Extract facts: {len(extracted_facts)} facts, {len(chunks)} chunks from {len(contents)} contents in {time.time() - step_start:.3f}s"
     )

hindsight-api/hindsight_api/engine/task_backend.py

@@ -182,7 +182,16 @@ class BrokerTaskBackend(TaskBackend):
         operation_id = task_dict.get("operation_id")
         task_type = task_dict.get("type", "unknown")
         bank_id = task_dict.get("bank_id")
-        payload_json = json.dumps(task_dict)
+
+        # Custom encoder to handle datetime objects
+        from datetime import datetime
+
+        def datetime_encoder(obj):
+            if isinstance(obj, datetime):
+                return obj.isoformat()
+            raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable")
+
+        payload_json = json.dumps(task_dict, default=datetime_encoder)
 
         schema = self._schema_getter() if self._schema_getter else self._schema
         table = fq_table("async_operations", schema)

hindsight-api/hindsight_api/engine/utils.py

@@ -19,7 +19,6 @@ async def extract_facts(
     context: str = "",
     llm_config: "LLMConfig" = None,
     agent_name: str = None,
-    extract_opinions: bool = False,
 ) -> tuple[list["Fact"], list[tuple[str, int]]]:
     """
     Extract semantic facts from text using LLM.
@@ -36,7 +35,6 @@ async def extract_facts(
         context: Context about the conversation/document
         llm_config: LLM configuration to use
         agent_name: Optional agent name to help identify agent-related facts
-        extract_opinions: If True, extract ONLY opinions. If False, extract world and agent facts (no opinions)
 
     Returns:
         Tuple of (facts, chunks) where:
@@ -55,7 +53,6 @@ async def extract_facts(
         context=context,
         llm_config=llm_config,
         agent_name=agent_name,
-        extract_opinions=extract_opinions,
     )
 
     if not facts:

hindsight-api/hindsight_api/main.py

@@ -180,6 +180,9 @@ def main():
             llm_initial_backoff=config.llm_initial_backoff,
             llm_max_backoff=config.llm_max_backoff,
             llm_timeout=config.llm_timeout,
+            llm_vertexai_project_id=config.llm_vertexai_project_id,
+            llm_vertexai_region=config.llm_vertexai_region,
+            llm_vertexai_service_account_key=config.llm_vertexai_service_account_key,
             retain_llm_provider=config.retain_llm_provider,
             retain_llm_api_key=config.retain_llm_api_key,
             retain_llm_model=config.retain_llm_model,
@@ -236,7 +239,6 @@ def main():
             retain_extract_causal_links=config.retain_extract_causal_links,
             retain_extraction_mode=config.retain_extraction_mode,
             retain_custom_instructions=config.retain_custom_instructions,
-            retain_observations_async=config.retain_observations_async,
             enable_observations=config.enable_observations,
             consolidation_batch_size=config.consolidation_batch_size,
             consolidation_max_tokens=config.consolidation_max_tokens,
@@ -251,8 +253,9 @@ def main():
             worker_id=config.worker_id,
             worker_poll_interval_ms=config.worker_poll_interval_ms,
             worker_max_retries=config.worker_max_retries,
-            worker_batch_size=config.worker_batch_size,
             worker_http_port=config.worker_http_port,
+            worker_max_slots=config.worker_max_slots,
+            worker_consolidation_max_slots=config.worker_consolidation_max_slots,
             reflect_max_iterations=config.reflect_max_iterations,
             mental_model_refresh_concurrency=config.mental_model_refresh_concurrency,
         )
@@ -380,7 +383,7 @@ def main():
 
         threading.Thread(target=run_idle_checker, daemon=True).start()
 
-    uvicorn.run(**uvicorn_config)  # type: ignore[invalid-argument-type] - dict kwargs
+    uvicorn.run(**uvicorn_config)
 
 
 if __name__ == "__main__":

hindsight-api/hindsight_api/mcp_tools.py

@@ -32,6 +32,9 @@ class MCPToolsConfig:
     # How to resolve bank_id for operations
     bank_id_resolver: Callable[[], str | None]
 
+    # How to resolve API key for tenant auth (optional)
+    api_key_resolver: Callable[[], str | None] | None = None
+
     # Whether to include bank_id as a parameter on tools (for multi-bank support)
     include_bank_id_param: bool = False
 
@@ -46,6 +49,16 @@ class MCPToolsConfig:
     retain_fire_and_forget: bool = False  # If True, use asyncio.create_task pattern
 
 
+def _get_request_context(config: MCPToolsConfig) -> RequestContext:
+    """Create RequestContext with API key from resolver if available.
+
+    This enables tenant auth to work with MCP tools by propagating
+    the Bearer token from the MCP middleware to the memory engine.
+    """
+    api_key = config.api_key_resolver() if config.api_key_resolver else None
+    return RequestContext(api_key=api_key)
+
+
 def parse_timestamp(timestamp: str) -> datetime | None:
     """Parse an ISO format timestamp string.
 
@@ -155,12 +168,14 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                 if error:
                     return {"status": "error", "message": error}
 
+                request_context = _get_request_context(config)
+
                 async def _retain():
                     try:
                         await memory.retain_batch_async(
                             bank_id=target_bank,
                             contents=[content_dict],
-                            request_context=RequestContext(),
+                            request_context=request_context,
                         )
                     except Exception as e:
                         logger.error(f"Error storing memory: {e}", exc_info=True)
@@ -196,16 +211,17 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                         return f"Error: {error}"
 
                     contents = [content_dict]
+                    request_context = _get_request_context(config)
                     if async_processing:
                         result = await memory.submit_async_retain(
-                            bank_id=target_bank, contents=contents, request_context=RequestContext()
+                            bank_id=target_bank, contents=contents, request_context=request_context
                         )
                         return f"Memory queued for background processing (operation_id: {result.get('operation_id', 'N/A')})"
                     else:
                         await memory.retain_batch_async(
                             bank_id=target_bank,
                             contents=contents,
-                            request_context=RequestContext(),
+                            request_context=request_context,
                         )
                         return f"Memory stored successfully in bank '{target_bank}'"
                 except Exception as e:
@@ -237,12 +253,14 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
             if error:
                 return {"status": "error", "message": error}
 
+            request_context = _get_request_context(config)
+
             async def _retain():
                 try:
                     await memory.retain_batch_async(
                         bank_id=target_bank,
                         contents=[content_dict],
-                        request_context=RequestContext(),
+                        request_context=request_context,
                     )
                 except Exception as e:
                     logger.error(f"Error storing memory: {e}", exc_info=True)
@@ -280,7 +298,7 @@ def _register_recall(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                     fact_type=list(VALID_RECALL_FACT_TYPES),
                     budget=Budget.HIGH,
                     max_tokens=max_tokens,
-                    request_context=RequestContext(),
+                    request_context=_get_request_context(config),
                 )
 
                 return recall_result.model_dump_json(indent=2)
@@ -311,7 +329,7 @@ def _register_recall(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                     fact_type=list(VALID_RECALL_FACT_TYPES),
                     budget=Budget.HIGH,
                     max_tokens=max_tokens,
-                    request_context=RequestContext(),
+                    request_context=_get_request_context(config),
                 )
 
                 return recall_result.model_dump()
@@ -370,7 +388,7 @@ def _register_reflect(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig
                     query=query,
                     budget=budget_enum,
                     context=context,
-                    request_context=RequestContext(),
+                    request_context=_get_request_context(config),
                 )
 
                 return reflect_result.model_dump_json(indent=2)
@@ -423,7 +441,7 @@ def _register_reflect(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig
                     query=query,
                     budget=budget_enum,
                     context=context,
-                    request_context=RequestContext(),
+                    request_context=_get_request_context(config),
                 )
 
                 return reflect_result.model_dump()
@@ -447,7 +465,7 @@ def _register_list_banks(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCon
             JSON list of banks with their IDs, names, dispositions, and missions.
         """
         try:
-            banks = await memory.list_banks(request_context=RequestContext())
+            banks = await memory.list_banks(request_context=_get_request_context(config))
             return json.dumps({"banks": banks}, indent=2)
         except Exception as e:
             logger.error(f"Error listing banks: {e}", exc_info=True)
@@ -471,8 +489,9 @@ def _register_create_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCo
             mission: Optional mission describing who the agent is and what they're trying to accomplish
         """
         try:
+            request_context = _get_request_context(config)
             # get_bank_profile auto-creates bank if it doesn't exist
-            profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())
+            profile = await memory.get_bank_profile(bank_id, request_context=request_context)
 
             # Update name/mission if provided
             if name is not None or mission is not None:
@@ -480,10 +499,10 @@ def _register_create_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCo
                     bank_id,
                     name=name,
                     mission=mission,
-                    request_context=RequestContext(),
+                    request_context=request_context,
                 )
                 # Fetch updated profile
-                profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())
+                profile = await memory.get_bank_profile(bank_id, request_context=request_context)
 
             # Serialize disposition if it's a Pydantic model
             if "disposition" in profile and hasattr(profile["disposition"], "model_dump"):

hindsight-api/hindsight_api/metrics.py

@@ -189,7 +189,7 @@ class MetricsCollectorBase:
         Args:
             provider: LLM provider name (openai, anthropic, gemini, groq, ollama, lmstudio)
             model: Model name
-            scope: Scope identifier (e.g., "memory", "reflect", "entity_observation")
+            scope: Scope identifier (e.g., "memory", "reflect", "consolidation")
             duration: Call duration in seconds
             input_tokens: Number of input/prompt tokens
             output_tokens: Number of output/completion tokens
@@ -321,7 +321,7 @@ class MetricsCollector(MetricsCollectorBase):
                 pass
 
         Args:
-            operation: Operation name (retain, recall, reflect, entity_observation)
+            operation: Operation name (retain, recall, reflect, consolidation)
             bank_id: Memory bank ID
             source: Source of the operation (api, reflect, internal)
             budget: Optional budget level (low, mid, high)
@@ -371,7 +371,7 @@ class MetricsCollector(MetricsCollectorBase):
         Args:
             provider: LLM provider name (openai, anthropic, gemini, groq, ollama, lmstudio)
             model: Model name
-            scope: Scope identifier (e.g., "memory", "reflect", "entity_observation")
+            scope: Scope identifier (e.g., "memory", "reflect", "consolidation")
             duration: Call duration in seconds
             input_tokens: Number of input/prompt tokens
             output_tokens: Number of output/completion tokens

hindsight-api/hindsight_api/pg0.py

@@ -40,7 +40,7 @@ class EmbeddedPostgres:
             # Only set port if explicitly specified
             if self.port is not None:
                 kwargs["port"] = self.port
-            self._pg0 = Pg0(**kwargs)  # type: ignore[invalid-argument-type] - dict kwargs
+            self._pg0 = Pg0(**kwargs)
         return self._pg0
 
     async def start(self, max_retries: int = 5, retry_delay: float = 4.0) -> str:

hindsight-api/hindsight_api/worker/main.py

@@ -124,12 +124,6 @@ def main():
         default=config.worker_poll_interval_ms,
         help=f"Poll interval in milliseconds (default: {config.worker_poll_interval_ms}, env: HINDSIGHT_API_WORKER_POLL_INTERVAL_MS)",
     )
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=config.worker_batch_size,
-        help=f"Tasks to claim per poll (default: {config.worker_batch_size}, env: HINDSIGHT_API_WORKER_BATCH_SIZE)",
-    )
     parser.add_argument(
         "--max-retries",
         type=int,
@@ -168,8 +162,9 @@ def main():
 
     print(f"Starting Hindsight Worker: {args.worker_id}")
     print(f"  Poll interval: {args.poll_interval}ms")
-    print(f"  Batch size: {args.batch_size}")
     print(f"  Max retries: {args.max_retries}")
+    print(f"  Max slots: {config.worker_max_slots}")
+    print(f"  Consolidation max slots: {config.worker_consolidation_max_slots}")
     print(f"  HTTP server: {args.http_host}:{args.http_port}")
     print()
 
@@ -213,9 +208,10 @@ def main():
             worker_id=args.worker_id,
             executor=memory.execute_task,
             poll_interval_ms=args.poll_interval,
-            batch_size=args.batch_size,
             max_retries=args.max_retries,
             tenant_extension=tenant_extension,
+            max_slots=config.worker_max_slots,
+            consolidation_max_slots=config.worker_consolidation_max_slots,
         )
 
         # Create the HTTP app for metrics/health

hindsight-api/hindsight_api/worker/poller.py

@@ -57,10 +57,11 @@ class WorkerPoller:
         worker_id: str,
         executor: Callable[[dict[str, Any]], Awaitable[None]],
         poll_interval_ms: int = 500,
-        batch_size: int = 10,
         max_retries: int = 3,
         schema: str | None = None,
         tenant_extension: "TenantExtension | None" = None,
+        max_slots: int = 10,
+        consolidation_max_slots: int = 2,
     ):
         """
         Initialize the worker poller.
@@ -70,28 +71,32 @@ class WorkerPoller:
             worker_id: Unique identifier for this worker
             executor: Async function to execute tasks (typically MemoryEngine.execute_task)
             poll_interval_ms: Interval between polls when no tasks found (milliseconds)
-            batch_size: Maximum number of tasks to claim per poll cycle
             max_retries: Maximum retry attempts before marking task as failed
             schema: Database schema for single-tenant support (ignored if tenant_extension is set)
             tenant_extension: Extension for dynamic multi-tenant discovery. If set, list_tenants()
                             is called on each poll cycle to discover schemas dynamically.
+            max_slots: Maximum concurrent tasks per worker
+            consolidation_max_slots: Maximum concurrent consolidation tasks per worker
         """
         self._pool = pool
         self._worker_id = worker_id
         self._executor = executor
         self._poll_interval_ms = poll_interval_ms
-        self._batch_size = batch_size
         self._max_retries = max_retries
         self._schema = schema
         self._tenant_extension = tenant_extension
+        self._max_slots = max_slots
+        self._consolidation_max_slots = consolidation_max_slots
         self._shutdown = asyncio.Event()
         self._current_tasks: set[asyncio.Task] = set()
         self._in_flight_count = 0
         self._in_flight_lock = asyncio.Lock()
         self._last_progress_log = 0.0
         self._tasks_completed_since_log = 0
-        # Track active tasks locally: operation_id -> (op_type, bank_id, schema)
-        self._active_tasks: dict[str, tuple[str, str, str | None]] = {}
+        # Track active tasks locally: operation_id -> (op_type, bank_id, schema, asyncio.Task)
+        self._active_tasks: dict[str, tuple[str, str, str | None, asyncio.Task]] = {}
+        # Track in-flight tasks by operation type
+        self._in_flight_by_type: dict[str, int] = {}
 
     async def _get_schemas(self) -> list[str | None]:
         """Get list of schemas to poll. Returns [None] for public schema."""
@@ -102,59 +107,114 @@ class WorkerPoller:
         # Single schema mode
         return [self._schema]
 
+    async def _get_available_slots(self) -> tuple[int, int]:
+        """
+        Calculate available slots for claiming tasks.
+
+        Returns:
+            (total_available, consolidation_available) tuple
+        """
+        async with self._in_flight_lock:
+            total_in_flight = self._in_flight_count
+            consolidation_in_flight = self._in_flight_by_type.get("consolidation", 0)
+
+        total_available = max(0, self._max_slots - total_in_flight)
+        consolidation_available = max(0, self._consolidation_max_slots - consolidation_in_flight)
+
+        return total_available, consolidation_available
+
+    async def wait_for_active_tasks(self, timeout: float = 10.0) -> bool:
+        """
+        Wait for all active background tasks to complete (test helper).
+
+        This is a test-only utility that allows tests to synchronize with
+        fire-and-forget background tasks without using sleep().
+
+        Args:
+            timeout: Maximum time to wait in seconds
+
+        Returns:
+            True if all tasks completed, False if timeout was reached
+        """
+        start_time = asyncio.get_event_loop().time()
+        while True:
+            async with self._in_flight_lock:
+                if self._in_flight_count == 0:
+                    return True
+
+            elapsed = asyncio.get_event_loop().time() - start_time
+            if elapsed >= timeout:
+                return False
+
+            # Short sleep to avoid busy-waiting
+            await asyncio.sleep(0.01)
+
     async def claim_batch(self) -> list[ClaimedTask]:
         """
-        Claim up to batch_size pending tasks atomically across all tenant schemas.
+        Claim pending tasks atomically across all tenant schemas,
+        respecting slot limits (total and consolidation).
 
         Uses FOR UPDATE SKIP LOCKED to ensure no conflicts with other workers.
 
-        For consolidation tasks specifically, skips pending tasks if there's already
-        a processing consolidation for the same bank (to avoid duplicate work).
-
-        If tenant_extension is configured, dynamically discovers schemas on each call.
-
         Returns:
             List of ClaimedTask objects containing operation_id, task_dict, and schema
         """
+        # Calculate available slots
+        total_available, consolidation_available = await self._get_available_slots()
+
+        if total_available <= 0:
+            return []
+
         schemas = await self._get_schemas()
         all_tasks: list[ClaimedTask] = []
-        remaining_batch = self._batch_size
+        remaining_total = total_available
+        remaining_consolidation = consolidation_available
 
         for schema in schemas:
-            if remaining_batch <= 0:
+            if remaining_total <= 0:
                 break
 
-            tasks = await self._claim_batch_for_schema(schema, remaining_batch)
+            tasks = await self._claim_batch_for_schema(schema, remaining_total, remaining_consolidation)
+
+            # Update remaining slots based on what was claimed
+            for task in tasks:
+                op_type = task.task_dict.get("operation_type", "unknown")
+                if op_type == "consolidation":
+                    remaining_consolidation -= 1
+
             all_tasks.extend(tasks)
-            remaining_batch -= len(tasks)
+            remaining_total -= len(tasks)
 
         return all_tasks
 
-    async def _claim_batch_for_schema(self, schema: str | None, limit: int) -> list[ClaimedTask]:
-        """Claim tasks from a specific schema."""
+    async def _claim_batch_for_schema(
+        self, schema: str | None, limit: int, consolidation_limit: int
+    ) -> list[ClaimedTask]:
+        """Claim tasks from a specific schema respecting slot limits."""
+        try:
+            return await self._claim_batch_for_schema_inner(schema, limit, consolidation_limit)
+        except Exception as e:
+            logger.warning(f"Worker {self._worker_id} failed to claim tasks for schema {schema or 'public'}: {e}")
+            return []
+
+    async def _claim_batch_for_schema_inner(
+        self, schema: str | None, limit: int, consolidation_limit: int
+    ) -> list[ClaimedTask]:
+        """Inner implementation for claiming tasks from a specific schema with slot limits."""
         table = fq_table("async_operations", schema)
 
         async with self._pool.acquire() as conn:
             async with conn.transaction():
-                # Select and lock pending tasks
-                # For consolidation: skip if same bank already has one processing
-                rows = await conn.fetch(
+                # Strategy: Claim non-consolidation tasks first, then consolidation up to limit
+
+                # 1. Claim non-consolidation tasks (up to limit)
+                non_consolidation_rows = await conn.fetch(
                     f"""
                     SELECT operation_id, task_payload
-                    FROM {table} AS pending
-                    WHERE status = 'pending' AND task_payload IS NOT NULL
-                    AND (
-                        -- Non-consolidation tasks: always claimable
-                        operation_type != 'consolidation'
-                        OR
-                        -- Consolidation: only if no other consolidation processing for same bank
-                        NOT EXISTS (
-                            SELECT 1 FROM {table} AS processing
-                            WHERE processing.bank_id = pending.bank_id
-                            AND processing.operation_type = 'consolidation'
-                            AND processing.status = 'processing'
-                        )
-                    )
+                    FROM {table}
+                    WHERE status = 'pending'
+                      AND task_payload IS NOT NULL
+                      AND operation_type != 'consolidation'
                     ORDER BY created_at
                     LIMIT $1
                     FOR UPDATE SKIP LOCKED
@@ -162,11 +222,39 @@ class WorkerPoller:
                     limit,
                 )
 
-                if not rows:
+                claimed_count = len(non_consolidation_rows)
+                remaining_limit = limit - claimed_count
+
+                # 2. Claim consolidation tasks (up to consolidation_limit and remaining_limit)
+                consolidation_rows = []
+                if consolidation_limit > 0 and remaining_limit > 0:
+                    consolidation_rows = await conn.fetch(
+                        f"""
+                        SELECT operation_id, task_payload
+                        FROM {table} AS pending
+                        WHERE status = 'pending'
+                          AND task_payload IS NOT NULL
+                          AND operation_type = 'consolidation'
+                          AND NOT EXISTS (
+                              SELECT 1 FROM {table} AS processing
+                              WHERE processing.bank_id = pending.bank_id
+                                AND processing.operation_type = 'consolidation'
+                                AND processing.status = 'processing'
+                          )
+                        ORDER BY created_at
+                        LIMIT $1
+                        FOR UPDATE SKIP LOCKED
+                        """,
+                        min(consolidation_limit, remaining_limit),
+                    )
+
+                all_rows = non_consolidation_rows + consolidation_rows
+
+                if not all_rows:
                     return []
 
                 # Claim the tasks by updating status and worker_id
-                operation_ids = [row["operation_id"] for row in rows]
+                operation_ids = [row["operation_id"] for row in all_rows]
                 await conn.execute(
                     f"""
                     UPDATE {table}
@@ -184,7 +272,7 @@ class WorkerPoller:
                         task_dict=json.loads(row["task_payload"]),
                         schema=schema,
                     )
-                    for row in rows
+                    for row in all_rows
                 ]
 
     async def _mark_completed(self, operation_id: str, schema: str | None):
@@ -250,18 +338,43 @@ class WorkerPoller:
             logger.warning(f"Task {operation_id} failed, will retry (attempt {retry_count + 1}/{self._max_retries})")
 
     async def execute_task(self, task: ClaimedTask):
-        """Execute a single task and update its status."""
+        """Execute a single task as a background job (fire-and-forget)."""
         task_type = task.task_dict.get("type", "unknown")
+        operation_type = task.task_dict.get("operation_type", "unknown")
         bank_id = task.task_dict.get("bank_id", "unknown")
 
+        # Create background task
+        bg_task = asyncio.create_task(self._execute_task_inner(task))
+
         # Track this task as active
         async with self._in_flight_lock:
-            self._active_tasks[task.operation_id] = (task_type, bank_id, task.schema)
+            self._active_tasks[task.operation_id] = (task_type, bank_id, task.schema, bg_task)
+            self._in_flight_count += 1
+            self._in_flight_by_type[operation_type] = self._in_flight_by_type.get(operation_type, 0) + 1
+
+        # Add cleanup callback
+        bg_task.add_done_callback(lambda _: asyncio.create_task(self._cleanup_task(task.operation_id, operation_type)))
+
+    async def _cleanup_task(self, operation_id: str, operation_type: str):
+        """Remove task from tracking after completion."""
+        async with self._in_flight_lock:
+            if operation_id in self._active_tasks:
+                self._active_tasks.pop(operation_id, None)
+                self._in_flight_count -= 1
+                count = self._in_flight_by_type.get(operation_type, 0)
+                if count > 0:
+                    self._in_flight_by_type[operation_type] = count - 1
+                    if self._in_flight_by_type[operation_type] == 0:
+                        del self._in_flight_by_type[operation_type]
+
+    async def _execute_task_inner(self, task: ClaimedTask):
+        """Inner task execution with error handling."""
+        task_type = task.task_dict.get("type", "unknown")
+        bank_id = task.task_dict.get("bank_id", "unknown")
 
         try:
             schema_info = f", schema={task.schema}" if task.schema else ""
             logger.debug(f"Executing task {task.operation_id} (type={task_type}, bank={bank_id}{schema_info})")
-            # Pass schema to executor so it can set the correct context
             if task.schema:
                 task.task_dict["_schema"] = task.schema
             await self._executor(task.task_dict)
@@ -271,10 +384,6 @@ class WorkerPoller:
             error_msg = f"{type(e).__name__}: {e}\n{traceback.format_exc()}"
             logger.error(f"Task {task.operation_id} failed: {e}")
             await self._retry_or_fail(task.operation_id, error_msg, task.schema)
-        finally:
-            # Remove from active tasks
-            async with self._in_flight_lock:
-                self._active_tasks.pop(task.operation_id, None)
 
     async def recover_own_tasks(self) -> int:
         """
@@ -293,20 +402,23 @@ class WorkerPoller:
         total_count = 0
 
         for schema in schemas:
-            table = fq_table("async_operations", schema)
+            try:
+                table = fq_table("async_operations", schema)
 
-            result = await self._pool.execute(
-                f"""
-                UPDATE {table}
-                SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
-                WHERE status = 'processing' AND worker_id = $1
-                """,
-                self._worker_id,
-            )
+                result = await self._pool.execute(
+                    f"""
+                    UPDATE {table}
+                    SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
+                    WHERE status = 'processing' AND worker_id = $1
+                    """,
+                    self._worker_id,
+                )
 
-            # Parse "UPDATE N" to get count
-            count = int(result.split()[-1]) if result else 0
-            total_count += count
+                # Parse "UPDATE N" to get count
+                count = int(result.split()[-1]) if result else 0
+                total_count += count
+            except Exception as e:
+                logger.warning(f"Worker {self._worker_id} failed to recover tasks for schema {schema or 'public'}: {e}")
 
         if total_count > 0:
             logger.info(f"Worker {self._worker_id} recovered {total_count} stale tasks from previous run")
@@ -314,59 +426,59 @@ class WorkerPoller:
 
     async def run(self):
         """
-        Main polling loop.
+        Main polling loop with fire-and-forget task execution.
 
-        Continuously polls for pending tasks, claims them, and executes them
-        until shutdown is signaled.
-
-        If tenant_extension is configured, dynamically discovers schemas on each poll.
+        Continuously polls for pending tasks, spawns them as background tasks,
+        and immediately continues polling (up to slot limits).
         """
-        # Recover any tasks from a previous crash before starting
         await self.recover_own_tasks()
 
-        logger.info(f"Worker {self._worker_id} starting polling loop")
+        logger.info(
+            f"Worker {self._worker_id} starting polling loop "
+            f"(max_slots={self._max_slots}, consolidation_max_slots={self._consolidation_max_slots})"
+        )
 
         while not self._shutdown.is_set():
             try:
-                # Claim a batch of tasks (across all tenant schemas if configured)
+                # Claim a batch of tasks (respecting slot limits)
                 tasks = await self.claim_batch()
 
                 if tasks:
                     # Log batch info
                     task_types: dict[str, int] = {}
                     schemas_seen: set[str | None] = set()
+                    consolidation_count = 0
                     for task in tasks:
                         t = task.task_dict.get("type", "unknown")
+                        op_type = task.task_dict.get("operation_type", "unknown")
                         task_types[t] = task_types.get(t, 0) + 1
                         schemas_seen.add(task.schema)
+                        if op_type == "consolidation":
+                            consolidation_count += 1
+
                     types_str = ", ".join(f"{k}:{v}" for k, v in task_types.items())
                     schemas_str = ", ".join(s or "public" for s in schemas_seen)
                     logger.info(
-                        f"Worker {self._worker_id} claimed {len(tasks)} tasks: {types_str} (schemas: {schemas_str})"
+                        f"Worker {self._worker_id} claimed {len(tasks)} tasks "
+                        f"({consolidation_count} consolidation): {types_str} (schemas: {schemas_str})"
                     )
 
-                    # Track in-flight tasks
-                    async with self._in_flight_lock:
-                        self._in_flight_count += len(tasks)
+                    # Spawn tasks as background jobs (fire-and-forget)
+                    for task in tasks:
+                        await self.execute_task(task)
 
-                    # Execute tasks concurrently
-                    try:
-                        await asyncio.gather(
-                            *[self.execute_task(task) for task in tasks],
-                            return_exceptions=True,
-                        )
-                    finally:
-                        async with self._in_flight_lock:
-                            self._in_flight_count -= len(tasks)
-                else:
-                    # No tasks found, wait before polling again
-                    try:
-                        await asyncio.wait_for(
-                            self._shutdown.wait(),
-                            timeout=self._poll_interval_ms / 1000,
-                        )
-                    except asyncio.TimeoutError:
-                        pass  # Normal timeout, continue polling
+                    # Continue immediately to claim more tasks (if slots available)
+                    continue
+
+                # No tasks claimed (either no pending tasks or slots full)
+                # Wait before polling again
+                try:
+                    await asyncio.wait_for(
+                        self._shutdown.wait(),
+                        timeout=self._poll_interval_ms / 1000,
+                    )
+                except asyncio.TimeoutError:
+                    pass  # Normal timeout, continue polling
 
                 # Log progress stats periodically
                 await self._log_progress_if_due()
@@ -397,15 +509,27 @@ class WorkerPoller:
         while asyncio.get_event_loop().time() - start_time < timeout:
             async with self._in_flight_lock:
                 in_flight = self._in_flight_count
+                active_task_objects = [task_info[3] for task_info in self._active_tasks.values()]
 
             if in_flight == 0:
                 logger.info(f"Worker {self._worker_id} graceful shutdown complete")
                 return
 
             logger.info(f"Worker {self._worker_id} waiting for {in_flight} in-flight tasks")
-            await asyncio.sleep(0.5)
 
-        logger.warning(f"Worker {self._worker_id} shutdown timeout after {timeout}s")
+            # Wait for at least one task to complete
+            if active_task_objects:
+                done, _ = await asyncio.wait(active_task_objects, timeout=0.5, return_when=asyncio.FIRST_COMPLETED)
+            else:
+                await asyncio.sleep(0.5)
+
+        logger.warning(f"Worker {self._worker_id} shutdown timeout after {timeout}s, cancelling remaining tasks")
+
+        # Cancel remaining tasks
+        async with self._in_flight_lock:
+            for operation_id, (_, _, _, bg_task) in list(self._active_tasks.items()):
+                if not bg_task.done():
+                    bg_task.cancel()
 
     async def _log_progress_if_due(self):
         """Log progress stats every PROGRESS_LOG_INTERVAL seconds."""
@@ -416,14 +540,19 @@ class WorkerPoller:
         self._last_progress_log = now
 
         try:
-            # Get local active tasks (this worker only)
+            # Get local active tasks
             async with self._in_flight_lock:
                 in_flight = self._in_flight_count
-                active_tasks = dict(self._active_tasks)  # Copy to avoid holding lock
+                in_flight_by_type = dict(self._in_flight_by_type)
+                active_tasks = dict(self._active_tasks)
 
-            # Build local processing breakdown grouped by (op_type, bank_id)
+            consolidation_count = in_flight_by_type.get("consolidation", 0)
+            available_slots = self._max_slots - in_flight
+            available_consolidation_slots = self._consolidation_max_slots - consolidation_count
+
+            # Build local processing breakdown
             task_groups: dict[tuple[str, str], int] = {}
-            for op_type, bank_id, _ in active_tasks.values():
+            for op_type, bank_id, _, _ in active_tasks.values():
                 key = (op_type, bank_id)
                 task_groups[key] = task_groups.get(key, 0) + 1
 
@@ -432,7 +561,7 @@ class WorkerPoller:
             if len(processing_info) > 10:
                 processing_str += f" +{len(processing_info) - 10} more"
 
-            # Get global stats from DB across all schemas
+            # Get global stats from DB
             schemas = await self._get_schemas()
             global_pending = 0
             all_worker_counts: dict[str, int] = {}
@@ -444,7 +573,6 @@ class WorkerPoller:
                     row = await conn.fetchrow(f"SELECT COUNT(*) as count FROM {table} WHERE status = 'pending'")
                     global_pending += row["count"] if row else 0
 
-                    # Get processing breakdown by worker
                     worker_rows = await conn.fetch(
                         f"""
                         SELECT worker_id, COUNT(*) as count
@@ -457,7 +585,6 @@ class WorkerPoller:
                         wid = wr["worker_id"] or "unknown"
                         all_worker_counts[wid] = all_worker_counts.get(wid, 0) + wr["count"]
 
-            # Format other workers' processing counts
             other_workers = []
             for wid, cnt in all_worker_counts.items():
                 if wid != self._worker_id:
@@ -466,7 +593,9 @@ class WorkerPoller:
 
             schemas_str = ", ".join(s or "public" for s in schemas)
             logger.info(
-                f"[WORKER_STATS] worker={self._worker_id} in_flight={in_flight} | "
+                f"[WORKER_STATS] worker={self._worker_id} "
+                f"slots={in_flight}/{self._max_slots} (consolidation={consolidation_count}/{self._consolidation_max_slots}) | "
+                f"available={available_slots} (consolidation={available_consolidation_slots}) | "
                 f"global: pending={global_pending} (schemas: {schemas_str}) | "
                 f"others: {others_str} | "
                 f"my_active: {processing_str}"

hindsight-api/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "hindsight-api"
-version = "0.4.2"
+version = "0.4.5"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -34,6 +34,7 @@ dependencies = [
     "opentelemetry-exporter-prometheus>=0.41b0",
     "dateparser>=1.2.2",
     "google-genai>=1.0.0",
+    "google-auth>=2.0.0",
     "anthropic>=0.40.0",
     "typer>=0.9.0",
     "cohere>=5.0.0",
@@ -141,6 +142,11 @@ known-third-party = ["alembic"]
 quote-style = "double"
 indent-style = "space"
 
+[tool.uv]
+# Allow uv to search all configured indexes for packages, not just the first one
+# This prevents dependency resolution failures when using pytorch index + PyPI
+index-strategy = "unsafe-best-match"
+
 [tool.ty]
 # Type checking configuration
 # ty is an extremely fast Python type checker from Astral (same team as ruff/uv)

hindsight-api/tests/test_consolidation.py

@@ -346,11 +346,11 @@ class TestConsolidationIntegration:
         or when one directly updates another (e.g., location change).
 
         Given:
-        - "Nicolò lives in Italy"
-        - "Nicolò moved to the US recently" (updates the living location)
+        - "Alex lives in Italy"
+        - "Alex moved to the US recently" (updates the living location)
 
         The second fact should UPDATE the first, not create a separate observation.
-        But unrelated facts like "Nicolò works at Vectorize" should stay separate.
+        But unrelated facts like "Alex works at Vectorize" should stay separate.
         """
         bank_id = f"test-consolidation-merge-{uuid.uuid4().hex[:8]}"
 
@@ -360,14 +360,14 @@ class TestConsolidationIntegration:
         # Retain a memory about living location
         await memory.retain_async(
             bank_id=bank_id,
-            content="Nicolò lives in Italy.",
+            content="Alex lives in Italy.",
             request_context=request_context,
         )
 
         # Retain an unrelated memory (different topic - should NOT merge)
         await memory.retain_async(
             bank_id=bank_id,
-            content="Nicolò works at Vectorize as an engineer.",
+            content="Alex works at Vectorize as an engineer.",
             request_context=request_context,
         )
 
@@ -384,7 +384,7 @@ class TestConsolidationIntegration:
         # Add a memory that UPDATES the living location (should merge with first)
         await memory.retain_async(
             bank_id=bank_id,
-            content="Nicolò recently moved to the United States.",
+            content="Alex recently moved to the United States.",
             request_context=request_context,
         )
 
@@ -485,9 +485,9 @@ class TestConsolidationIntegration:
         they should be merged into ONE observation that captures the change.
 
         Example:
-        - "Nicolò loves pizza"
-        - "Nicolò hates pizza"
-        → Should become: "Nicolò used to love pizza but now hates it" (or similar)
+        - "Alex loves pizza"
+        - "Alex hates pizza"
+        → Should become: "Alex used to love pizza but now hates it" (or similar)
         """
         bank_id = f"test-consolidation-contradict-{uuid.uuid4().hex[:8]}"
 
@@ -497,7 +497,7 @@ class TestConsolidationIntegration:
         # Add initial fact
         await memory.retain_async(
             bank_id=bank_id,
-            content="Nicolò loves pizza.",
+            content="Alex loves pizza.",
             request_context=request_context,
         )
 
@@ -515,7 +515,7 @@ class TestConsolidationIntegration:
         # Add contradicting fact (same person, same topic, opposite sentiment)
         await memory.retain_async(
             bank_id=bank_id,
-            content="Nicolò hates pizza.",
+            content="Alex hates pizza.",
             request_context=request_context,
         )
 

hindsight-api/tests/test_fact_extraction_analysis.py

@@ -58,7 +58,6 @@ async def test_fact_extraction_basic_analysis(llm_config):
         llm_config=llm_config,
         agent_name="test-agent",
         context="Friday Standup meeting",
-        extract_opinions=False,
     )
 
     duration = time.time() - start_time

hindsight-api/tests/test_http_api_integration.py

@@ -1098,3 +1098,134 @@ async def test_version_endpoint_returns_correct_version(api_client):
     assert isinstance(features["worker"], bool)
 
     print(f"Version endpoint returned: api_version={result['api_version']}, features={features}")
+
+
+@pytest.mark.asyncio
+async def test_retain_with_timestamp_async(api_client, test_bank_id):
+    """Test that async retain accepts timestamp field and serializes correctly."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {
+                    "content": "Test memory with timestamp",
+                    "context": "test",
+                    "timestamp": "2026-01-30T11:45:00Z"
+                }
+            ],
+            "async": True
+        }
+    )
+    
+    assert response.status_code == 200, f"Expected 200, got {response.status_code}: {response.text}"
+    data = response.json()
+    assert data["success"] is True
+    assert data["async"] is True
+    assert "operation_id" in data
+    
+
+@pytest.mark.asyncio
+async def test_retain_with_timestamp_sync(api_client, test_bank_id):
+    """Test that sync retain accepts timestamp field."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {
+                    "content": "Test memory with timestamp sync",
+                    "context": "test", 
+                    "timestamp": "2026-01-30T11:45:00Z"
+                }
+            ],
+            "async": False
+        }
+    )
+    
+    assert response.status_code == 200, f"Expected 200, got {response.status_code}: {response.text}"
+    data = response.json()
+    assert data["success"] is True
+    assert data["async"] is False
+    
+
+@pytest.mark.asyncio
+async def test_retain_with_multiple_timestamps(api_client, test_bank_id):
+    """Test that multiple items with different timestamp formats work."""
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {
+                    "content": "Event 1",
+                    "timestamp": "2026-01-30T11:45:00Z"  # With Z
+                },
+                {
+                    "content": "Event 2", 
+                    "timestamp": "2026-01-30T12:00:00+00:00"  # With timezone
+                },
+                {
+                    "content": "Event 3"  # No timestamp
+                }
+            ],
+            "async": True
+        }
+    )
+    
+    assert response.status_code == 200, f"Expected 200, got {response.status_code}: {response.text}"
+    data = response.json()
+    assert data["success"] is True
+    assert data["items_count"] == 3
+
+
+@pytest.mark.asyncio
+async def test_retain_with_timestamp_async_complete_processing(api_client, test_bank_id):
+    """Test that async retain with timestamp completes full processing including fact extraction."""
+    # Submit async retain with timestamp
+    response = await api_client.post(
+        f"/v1/default/banks/{test_bank_id}/memories",
+        json={
+            "items": [
+                {
+                    "content": "The quarterly meeting was held on January 30th 2026",
+                    "context": "meetings",
+                    "timestamp": "2026-01-30T11:45:00Z"
+                }
+            ],
+            "async": True
+        }
+    )
+    
+    assert response.status_code == 200, f"Expected 200, got {response.status_code}: {response.text}"
+    data = response.json()
+    assert data["success"] is True
+    assert data["async"] is True
+    operation_id = data["operation_id"]
+    
+    # Wait for async processing to complete (poll operation status)
+    max_wait_seconds = 30
+    poll_interval = 0.5
+    elapsed = 0
+    operation_completed = False
+    
+    while elapsed < max_wait_seconds:
+        response = await api_client.get(f"/v1/default/banks/{test_bank_id}/operations/{operation_id}")
+        if response.status_code == 200:
+            op_status = response.json()
+            if op_status.get("status") == "completed":
+                operation_completed = True
+                break
+            elif op_status.get("status") == "failed":
+                raise AssertionError(f"Operation failed: {op_status.get('error_message')}")
+        
+        await asyncio.sleep(poll_interval)
+        elapsed += poll_interval
+    
+    assert operation_completed, f"Async operation did not complete within {max_wait_seconds} seconds"
+    
+    # Verify memories were actually stored
+    response = await api_client.get(
+        f"/v1/default/banks/{test_bank_id}/memories/list",
+        params={"limit": 10}
+    )
+    assert response.status_code == 200
+    items = response.json()["items"]
+    assert len(items) > 0, "Should have stored memories after async processing"

hindsight-api/tests/test_mcp_routing.py

@@ -97,3 +97,47 @@ def test_path_parsing_logic():
     bank_id, remaining = parse_path("/my-bank/some/path")
     assert bank_id == "my-bank"
     assert remaining == "/some/path"
+
+
+@pytest.mark.asyncio
+async def test_api_key_context_variable():
+    """Test that API key context variable works correctly."""
+    from hindsight_api.api.mcp import get_current_api_key, _current_api_key
+
+    # Initially None
+    assert get_current_api_key() is None
+
+    # Set and verify
+    token = _current_api_key.set("test-api-key-123")
+    try:
+        assert get_current_api_key() == "test-api-key-123"
+    finally:
+        _current_api_key.reset(token)
+
+    # Back to None after reset
+    assert get_current_api_key() is None
+
+
+@pytest.mark.asyncio
+async def test_mcp_tools_propagate_api_key(mock_memory):
+    """Test that MCP tools propagate API key to RequestContext."""
+    from hindsight_api.api.mcp import create_mcp_server, _current_bank_id, _current_api_key
+
+    mcp_server = create_mcp_server(mock_memory)
+    tools = mcp_server._tool_manager._tools
+
+    # Set both bank_id and api_key context
+    bank_token = _current_bank_id.set("test-bank")
+    api_key_token = _current_api_key.set("test-bearer-token")
+    try:
+        retain_tool = tools["retain"]
+        result = await retain_tool.fn(content="test content", context="test_context", async_processing=False)
+        assert "successfully" in result.lower()
+
+        # Verify the memory was called with request_context containing api_key
+        mock_memory.retain_batch_async.assert_called_once()
+        call_kwargs = mock_memory.retain_batch_async.call_args.kwargs
+        assert call_kwargs["request_context"].api_key == "test-bearer-token"
+    finally:
+        _current_bank_id.reset(bank_token)
+        _current_api_key.reset(api_key_token)

hindsight-api/tests/test_metrics.py

@@ -358,7 +358,7 @@ class TestLLMMetrics:
         collector.record_llm_call(
             provider="gemini",
             model="gemini-pro",
-            scope="entity_observation",
+            scope="memory",
             duration=2.0,
             success=True,
         )
@@ -369,11 +369,11 @@ class TestLLMMetrics:
         assert call_args[0][0] == 1
         assert call_args[0][1]["provider"] == "gemini"
         assert call_args[0][1]["model"] == "gemini-pro"
-        assert call_args[0][1]["scope"] == "entity_observation"
+        assert call_args[0][1]["scope"] == "memory"
 
     def test_record_llm_call_different_scopes(self, collector):
         """Test recording LLM calls with different scopes."""
-        scopes = ["memory", "reflect", "entity_observation", "answer"]
+        scopes = ["memory", "reflect", "consolidation", "answer"]
 
         for scope in scopes:
             collector.llm_duration.record.reset_mock()

hindsight-api/tests/test_multilingual.py

@@ -469,7 +469,6 @@ async def test_mixed_language_entities(memory, request_context):
             budget=Budget.MID,
             max_tokens=1000,
             fact_type=["world"],
-            include_entities=True,
             request_context=request_context,
         )
 

hindsight-api/tests/test_observations.py

@@ -91,156 +91,13 @@ async def test_entity_extraction_on_retain(memory, request_context):
             await conn.execute("DELETE FROM entities WHERE bank_id = $1", bank_id)
 
 
-@pytest.mark.asyncio
-async def test_regenerate_entity_observations(memory, request_context):
-    """
-    Test explicit regeneration of summary for an entity.
-    """
-    bank_id = f"test_regen_obs_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Store facts about an entity
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Sarah is a product manager who loves user research and data analysis.",
-            context="work info",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        await memory.wait_for_background_tasks()
-
-        # Find the Sarah entity
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            entity_row = await conn.fetchrow(
-                """
-                SELECT id, canonical_name
-                FROM entities
-                WHERE bank_id = $1 AND LOWER(canonical_name) LIKE '%sarah%'
-                LIMIT 1
-                """,
-                bank_id
-            )
-
-        if entity_row:
-            entity_id = str(entity_row['id'])
-            entity_name = entity_row['canonical_name']
-
-            # Manually regenerate summary (via observations API for backwards compat)
-            created_ids = await memory.regenerate_entity_observations(
-                bank_id=bank_id,
-                entity_id=entity_id,
-                entity_name=entity_name,
-                request_context=request_context,
-            )
-
-            print(f"\n=== Regenerated Summary ===")
-            print(f"Created {len(created_ids)} summary for {entity_name}")
-
-            # Get entity state
-            state = await memory.get_entity_state(
-                bank_id, entity_id, entity_name, request_context=request_context
-            )
-            for obs in state.observations:
-                print(f"  - {obs.text}")
-
-            # Verify summary was created
-            if len(created_ids) > 0:
-                assert len(state.observations) == 1, "Should have exactly 1 observation (the summary)"
-                print(f"Summary regenerated successfully")
-            else:
-                print(f"Note: No summary was regenerated")
-
-        else:
-            print(f"Note: No 'Sarah' entity was extracted")
-
-    finally:
-        # Cleanup
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            await conn.execute("DELETE FROM memory_units WHERE bank_id = $1", bank_id)
-            await conn.execute("DELETE FROM entities WHERE bank_id = $1", bank_id)
-
-
-@pytest.mark.asyncio
-async def test_entity_state_retrieval(memory, request_context):
-    """
-    Test retrieving entity state with facts.
-    """
-    bank_id = f"test_entity_state_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Store facts
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice works at Google as a senior software engineer.",
-            context="work info",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice loves hiking and outdoor photography.",
-            context="hobbies",
-            event_date=datetime(2024, 1, 16, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        # Find the Alice entity
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            entity_row = await conn.fetchrow(
-                """
-                SELECT id, canonical_name
-                FROM entities
-                WHERE bank_id = $1 AND LOWER(canonical_name) LIKE '%alice%'
-                LIMIT 1
-                """,
-                bank_id
-            )
-
-        assert entity_row is not None, "Alice entity should have been extracted"
-
-        entity_id = str(entity_row['id'])
-        entity_name = entity_row['canonical_name']
-
-        # Check fact count
-        async with pool.acquire() as conn:
-            fact_count = await conn.fetchval(
-                "SELECT COUNT(*) FROM unit_entities WHERE entity_id = $1",
-                entity_row['id']
-            )
-
-        print(f"\n=== Entity State Test ===")
-        print(f"Entity: {entity_name} (id: {entity_id})")
-        print(f"Linked facts: {fact_count}")
-
-        # Get entity state
-        state = await memory.get_entity_state(
-            bank_id, entity_id, entity_name, request_context=request_context
-        )
-
-        assert state.entity_id == entity_id
-        assert state.canonical_name == entity_name
-        print(f"Entity state retrieved successfully")
-
-    finally:
-        # Cleanup
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            await conn.execute("DELETE FROM memory_units WHERE bank_id = $1", bank_id)
-            await conn.execute("DELETE FROM entities WHERE bank_id = $1", bank_id)
-
-
 @pytest.mark.asyncio
 async def test_search_with_include_entities(memory, request_context):
     """
-    Test that search with include_entities=True returns entity information.
+    Test that recall accepts include_entities parameter for backwards compatibility.
 
-    This test verifies that:
-    1. Entities are extracted after retain
-    2. Entity info is returned in recall results with include_entities=True
+    Note: Entity observations have been deprecated. This test verifies the parameter
+    is still accepted without errors.
     """
     bank_id = f"test_search_ent_{datetime.now(timezone.utc).timestamp()}"
 
@@ -249,10 +106,6 @@ async def test_search_with_include_entities(memory, request_context):
         contents = [
             "Alice is a data scientist who works on recommendation systems at Netflix.",
             "Alice presented her research at the ML conference last month.",
-            "Alice is an expert in deep learning and neural networks.",
-            "Alice graduated from Stanford with a PhD in Computer Science.",
-            "Alice leads a team of 5 data scientists at Netflix.",
-            "Alice published a paper on collaborative filtering algorithms.",
         ]
 
         for i, content in enumerate(contents):
@@ -267,7 +120,7 @@ async def test_search_with_include_entities(memory, request_context):
         # Wait for background tasks
         await memory.wait_for_background_tasks()
 
-        # Search with include_entities=True
+        # Search with include_entities=True (should be accepted for backwards compatibility)
         result = await memory.recall_async(
             bank_id=bank_id,
             query="What does Alice do?",
@@ -279,98 +132,9 @@ async def test_search_with_include_entities(memory, request_context):
             request_context=request_context,
         )
 
-        print(f"\n=== Search Results ===")
-        print(f"Found {len(result.results)} facts")
-        for fact in result.results:
-            print(f"  - {fact.text}")
-            if fact.entities:
-                print(f"    Entities: {', '.join(fact.entities)}")
-
-        # Verify results
+        # Verify recall works
         assert len(result.results) > 0, "Should find some facts"
-
-        # Check if entities are included in facts
-        facts_with_entities = [f for f in result.results if f.entities]
-        assert len(facts_with_entities) > 0, "Some facts should have entity information"
-        print(f"{len(facts_with_entities)} facts have entity information")
-
-        # Check if entity info is returned
-        if result.entities:
-            print(f"Entity info included for {len(result.entities)} entities")
-
-            # Verify Alice entity is in results
-            alice_found = False
-            for name, state in result.entities.items():
-                assert state.canonical_name == name, "Entity canonical_name should match key"
-                assert state.entity_id, "Entity should have an ID"
-                if "alice" in name.lower():
-                    alice_found = True
-                    print(f"Alice entity found: {name}")
-
-            assert alice_found, "Alice entity should be in recall results"
-
-    finally:
-        # Cleanup
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            await conn.execute("DELETE FROM memory_units WHERE bank_id = $1", bank_id)
-            await conn.execute("DELETE FROM entities WHERE bank_id = $1", bank_id)
-
-
-@pytest.mark.asyncio
-async def test_get_entity_state(memory, request_context):
-    """
-    Test getting the full state of an entity.
-    """
-    bank_id = f"test_entity_state_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-        # Store facts
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Bob is a frontend developer who specializes in React and TypeScript.",
-            context="work info",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        await memory.wait_for_background_tasks()
-
-        # Find entity
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            entity_row = await conn.fetchrow(
-                """
-                SELECT id, canonical_name
-                FROM entities
-                WHERE bank_id = $1 AND LOWER(canonical_name) LIKE '%bob%'
-                LIMIT 1
-                """,
-                bank_id
-            )
-
-        if entity_row:
-            entity_id = str(entity_row['id'])
-            entity_name = entity_row['canonical_name']
-
-            # Get entity state
-            state = await memory.get_entity_state(
-                bank_id=bank_id,
-                entity_id=entity_id,
-                entity_name=entity_name,
-                limit=10,
-                request_context=request_context,
-            )
-
-            print(f"\n=== Entity State for {entity_name} ===")
-            print(f"Entity ID: {state.entity_id}")
-            print(f"Canonical Name: {state.canonical_name}")
-            print(f"Observations: {len(state.observations)}")
-            for obs in state.observations:
-                print(f"  - {obs.text}")
-
-            assert state.entity_id == entity_id, "Entity ID should match"
-            assert state.canonical_name == entity_name, "Canonical name should match"
+        print(f"Found {len(result.results)} facts")
 
     finally:
         # Cleanup

hindsight-api/tests/test_retain.py

@@ -16,7 +16,6 @@ async def test_retain_with_chunks(memory, request_context):
     Test that retain function:
     1. Stores facts with associated chunks
     2. Recall returns chunk_id for each fact
-    3. Recall with include_entities=True also works (for compatibility)
     """
     bank_id = f"test_chunks_{datetime.now(timezone.utc).timestamp()}"
     document_id = "test_doc_123"
@@ -56,7 +55,6 @@ async def test_retain_with_chunks(memory, request_context):
             budget=Budget.LOW,
             max_tokens=500,
             fact_type=["world"],  # Search for world facts
-            include_entities=False,  # Disable entities for simpler test
             include_chunks=True,  # Enable chunks
             max_chunk_tokens=8192,
             request_context=request_context,
@@ -146,7 +144,6 @@ async def test_chunks_and_entities_follow_fact_order(memory, request_context):
             budget=Budget.MID,
             max_tokens=1000,
             fact_type=["world"],
-            include_entities=True,
             include_chunks=True,
             max_chunk_tokens=8192,
             request_context=request_context,

hindsight-api/tests/test_think.py

@@ -1,5 +1,5 @@
 """
-Test think function for opinion generation and consistency.
+Test reflect (think) function.
 """
 import pytest
 from datetime import datetime, timezone
@@ -7,131 +7,6 @@ from hindsight_api.engine.memory_engine import Budget
 from hindsight_api import RequestContext
 
 
-@pytest.mark.asyncio
-async def test_think_opinion_consistency(memory, request_context):
-    """
-    Test that think function:
-    1. Generates an opinion
-    2. Stores the opinion in the database
-    3. Returns consistent response on subsequent calls with the same query
-    """
-    bank_id = f"test_think_{datetime.now(timezone.utc).timestamp()}"
-
-    try:
-
-        # Store some initial facts to give context for opinion formation
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Alice is a software engineer who has worked on 5 major projects. She always delivers on time and writes clean, well-documented code.",
-            context="performance review",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Bob recently joined the team. He missed his first deadline and his code had many bugs.",
-            context="performance review",
-            event_date=datetime(2024, 2, 1, tzinfo=timezone.utc),
-            request_context=request_context,
-        )
-
-        # First think call - should generate opinions
-        query = "Who is a more reliable engineer?"
-        result1 = await memory.reflect_async(
-            bank_id=bank_id,
-            query=query,
-            budget=Budget.LOW,
-            request_context=request_context,
-        )
-
-        print(f"\n=== First Think Call ===")
-        print(f"Answer: {result1.text}")
-
-        # Verify we got an answer
-        assert result1.text, "First think call should return an answer"
-        assert result1.based_on, "Should return based_on facts"
-
-        # Wait for background opinion processing tasks to complete
-        await memory.wait_for_background_tasks()
-
-        # Search for stored opinions to verify they were actually saved
-        pool = await memory._get_pool()
-        async with pool.acquire() as conn:
-            stored_opinions = await conn.fetch(
-                """
-                SELECT id, text, confidence_score, fact_type
-                FROM memory_units
-                WHERE bank_id = $1 AND fact_type = 'opinion'
-                ORDER BY created_at DESC
-                """,
-                bank_id
-            )
-
-        print(f"\n=== Stored Opinions in Database ===")
-        print(f"Total opinions stored: {len(stored_opinions)}")
-        for op in stored_opinions:
-            print(f"  - {op['text']} (confidence: {op['confidence_score']:.2f})")
-
-        # Verify opinions were actually written to database
-        # NOTE: Opinion extraction may not always detect opinions depending on the LLM response format
-        if len(stored_opinions) > 0:
-            assert all(op['fact_type'] == 'opinion' for op in stored_opinions), "All stored items should have fact_type='opinion'"
-            print(f"✓ Opinions were successfully stored in database")
-        else:
-            print(f"⚠ Note: No opinions were extracted/stored (this can happen if the LLM response format doesn't trigger opinion extraction)")
-
-        # Second think call - should use the stored opinions
-        result2 = await memory.reflect_async(
-            bank_id=bank_id,
-            query=query,
-            budget=Budget.LOW,
-            request_context=request_context,
-        )
-
-        print(f"\n=== Second Think Call ===")
-        print(f"Answer: {result2.text}")
-        print(f"Existing opinions used: {len(result2.based_on.get('opinion', []))}")
-        for opinion in result2.based_on.get('opinion', []):
-            print(f"  - {opinion.text}")
-
-        # Verify second call also got an answer
-        assert result2.text, "Second think call should return an answer"
-
-        # Verify second call used the stored opinions (if any were stored)
-        if len(stored_opinions) > 0:
-            assert len(result2.based_on.get('opinion', [])) > 0, "Second call should retrieve stored opinions"
-
-        # The responses should be consistent (both should mention the same person as more reliable)
-        # We'll do a basic check that they're not contradictory
-        text1_lower = result1.text.lower()
-        text2_lower = result2.text.lower()
-
-        print(f"\n=== Consistency Check ===")
-
-        # Check if Alice is mentioned as more reliable in first response
-        if 'alice' in text1_lower and ('reliable' in text1_lower or 'better' in text1_lower):
-            print("First response favors Alice")
-            # Second response should also favor Alice (consistency)
-            assert 'alice' in text2_lower, "Second response should also mention Alice"
-            print("Second response also mentions Alice - CONSISTENT ✓")
-
-        # Check if Bob is mentioned
-        if 'bob' in text1_lower:
-            print("First response mentions Bob")
-            if 'bob' in text2_lower:
-                print("Second response also mentions Bob - CONSISTENT ✓")
-
-        print(f"\n✅ Test passed - opinions were formed, stored, and used consistently")
-
-    finally:
-        # Clean up agent data
-        try:
-            await memory.delete_bank(bank_id, request_context=request_context)
-        except Exception as e:
-            print(f"Warning: Error during cleanup: {e}")
-
-
 @pytest.mark.asyncio
 async def test_think_without_prior_context(memory, request_context):
     """

hindsight-api/tests/test_vertexai_provider.py

@@ -0,0 +1,244 @@
+"""
+Test Vertex AI provider integration using native genai SDK.
+"""
+
+import os
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+# Skip all tests if google-auth not available
+pytest.importorskip("google.auth")
+
+
+def test_llm_wrapper_vertexai_missing_dependency():
+    """Test error when google-auth is not available and service account key is set."""
+    from hindsight_api.engine import llm_wrapper
+
+    # VERTEXAI_AVAILABLE only matters when a service account key is provided
+    original_available = llm_wrapper.VERTEXAI_AVAILABLE
+    try:
+        llm_wrapper.VERTEXAI_AVAILABLE = False
+
+        with patch.dict(
+            os.environ,
+            {
+                "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID": "test-project",
+                "HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY": "/path/to/key.json",
+            },
+            clear=False,
+        ):
+            from hindsight_api.config import clear_config_cache
+
+            clear_config_cache()
+
+            with pytest.raises(ValueError, match="google-auth"):
+                from hindsight_api.engine.llm_wrapper import LLMProvider
+
+                LLMProvider(
+                    provider="vertexai",
+                    api_key="",
+                    base_url="",
+                    model="google/gemini-2.0-flash-001",
+                )
+
+            clear_config_cache()
+    finally:
+        llm_wrapper.VERTEXAI_AVAILABLE = original_available
+
+
+def test_llm_wrapper_vertexai_missing_project_id():
+    """Test error when project ID is not configured."""
+    with patch.dict(os.environ, {"HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID": ""}, clear=False):
+        from hindsight_api.config import clear_config_cache
+
+        clear_config_cache()
+
+        with pytest.raises(ValueError, match="HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID"):
+            from hindsight_api.engine.llm_wrapper import LLMProvider
+
+            LLMProvider(
+                provider="vertexai",
+                api_key="",
+                base_url="",
+                model="google/gemini-2.0-flash-001",
+            )
+
+        clear_config_cache()
+
+
+def test_llm_wrapper_vertexai_adc_auth():
+    """Test Vertex AI with ADC authentication creates native genai client."""
+    from hindsight_api.engine.llm_wrapper import LLMProvider
+
+    with patch.dict(
+        os.environ,
+        {"HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID": "test-project"},
+        clear=False,
+    ):
+        from hindsight_api.config import clear_config_cache
+
+        clear_config_cache()
+
+        # genai.Client handles ADC internally — just verify it creates the client
+        with patch("google.genai.Client") as mock_client_cls:
+            mock_client_cls.return_value = MagicMock()
+
+            provider = LLMProvider(
+                provider="vertexai",
+                api_key="",
+                base_url="",
+                model="google/gemini-2.0-flash-001",
+            )
+
+            assert provider.provider == "vertexai"
+            assert provider.model == "gemini-2.0-flash-001"  # google/ prefix stripped
+            assert provider._gemini_client is not None
+
+            # Verify genai.Client was called with vertexai=True
+            mock_client_cls.assert_called_once_with(
+                vertexai=True,
+                project="test-project",
+                location="us-central1",
+            )
+
+        clear_config_cache()
+
+
+def test_llm_wrapper_vertexai_sa_auth():
+    """Test Vertex AI with service account authentication passes credentials to genai client."""
+    from hindsight_api.engine.llm_wrapper import LLMProvider
+
+    mock_credentials = MagicMock()
+
+    with patch.dict(
+        os.environ,
+        {
+            "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID": "test-project",
+            "HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY": "/path/to/key.json",
+        },
+        clear=False,
+    ):
+        from hindsight_api.config import clear_config_cache
+
+        clear_config_cache()
+
+        with patch(
+            "google.oauth2.service_account.Credentials.from_service_account_file",
+            return_value=mock_credentials,
+        ):
+            with patch("google.genai.Client") as mock_client_cls:
+                mock_client_cls.return_value = MagicMock()
+
+                provider = LLMProvider(
+                    provider="vertexai",
+                    api_key="",
+                    base_url="",
+                    model="google/gemini-2.0-flash-001",
+                )
+
+                assert provider.provider == "vertexai"
+                assert provider._gemini_client is not None
+
+                # Verify credentials were passed to genai.Client
+                mock_client_cls.assert_called_once_with(
+                    vertexai=True,
+                    project="test-project",
+                    location="us-central1",
+                    credentials=mock_credentials,
+                )
+
+        clear_config_cache()
+
+
+def test_llm_wrapper_vertexai_strips_google_prefix():
+    """Test that google/ prefix is stripped from model name for native SDK."""
+    from hindsight_api.engine.llm_wrapper import LLMProvider
+
+    with patch.dict(
+        os.environ,
+        {"HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID": "test-project"},
+        clear=False,
+    ):
+        from hindsight_api.config import clear_config_cache
+
+        clear_config_cache()
+
+        with patch("google.genai.Client") as mock_client_cls:
+            mock_client_cls.return_value = MagicMock()
+
+            provider = LLMProvider(
+                provider="vertexai",
+                api_key="",
+                base_url="",
+                model="google/gemini-2.0-flash-lite-001",
+            )
+
+            assert provider.model == "gemini-2.0-flash-lite-001"
+
+        clear_config_cache()
+
+
+def test_llm_wrapper_vertexai_no_prefix_model():
+    """Test that model without google/ prefix is unchanged."""
+    from hindsight_api.engine.llm_wrapper import LLMProvider
+
+    with patch.dict(
+        os.environ,
+        {"HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID": "test-project"},
+        clear=False,
+    ):
+        from hindsight_api.config import clear_config_cache
+
+        clear_config_cache()
+
+        with patch("google.genai.Client") as mock_client_cls:
+            mock_client_cls.return_value = MagicMock()
+
+            provider = LLMProvider(
+                provider="vertexai",
+                api_key="",
+                base_url="",
+                model="gemini-2.0-flash-001",
+            )
+
+            assert provider.model == "gemini-2.0-flash-001"
+
+        clear_config_cache()
+
+
+@pytest.mark.asyncio
+@pytest.mark.skipif(
+    not os.getenv("HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID"),
+    reason="Vertex AI integration tests require HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID",
+)
+async def test_vertexai_integration_actual_api():
+    """
+    Integration test with actual Vertex AI API.
+
+    Requires:
+    - HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID
+    - ADC or HINDSIGHT_API_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY
+    """
+    from hindsight_api.engine.llm_wrapper import LLMProvider
+
+    provider = LLMProvider(
+        provider="vertexai",
+        api_key="",
+        base_url="",
+        model="google/gemini-2.0-flash-001",
+    )
+
+    try:
+        # Simple test call
+        response = await provider.call(
+            messages=[{"role": "user", "content": "Say 'ok' and nothing else"}],
+            max_completion_tokens=10,
+        )
+
+        assert response is not None
+        assert isinstance(response, str)
+        assert len(response) > 0
+
+    finally:
+        await provider.cleanup()

hindsight-api/tests/test_worker.py

@@ -156,7 +156,6 @@ class TestWorkerPoller:
             pool=pool,
             worker_id="test-worker-1",
             executor=mock_executor,
-            batch_size=10,
         )
 
         claimed = await poller.claim_batch()
@@ -177,8 +176,8 @@ class TestWorkerPoller:
             assert row["worker_id"] == "test-worker-1"
 
     @pytest.mark.asyncio
-    async def test_claim_batch_respects_batch_size(self, pool, clean_operations):
-        """Test that claim_batch respects the batch_size limit."""
+    async def test_claim_batch_respects_max_slots(self, pool, clean_operations):
+        """Test that claim_batch respects the max_slots limit."""
         from hindsight_api.worker import WorkerPoller
 
         # Create 10 pending tasks
@@ -196,12 +195,11 @@ class TestWorkerPoller:
                 payload,
             )
 
-        # Claim with batch_size=3
         poller = WorkerPoller(
             pool=pool,
             worker_id="test-worker-1",
             executor=lambda x: None,
-            batch_size=3,
+            max_slots=3,  # Limit to 3 concurrent tasks
         )
 
         claimed = await poller.claim_batch()
@@ -238,11 +236,14 @@ class TestWorkerPoller:
             executor=mock_executor,
         )
 
-        # Execute the task
+        # Execute the task (fire-and-forget)
         task_dict = json.loads(payload)
         claimed_task = ClaimedTask(operation_id=str(op_id), task_dict=task_dict, schema=None)
         await poller.execute_task(claimed_task)
 
+        # Wait for background task to complete
+        completed = await poller.wait_for_active_tasks(timeout=5.0)
+        assert completed, "Task did not complete within timeout"
         assert len(executed) == 1
 
         # Verify task is marked as completed
@@ -283,11 +284,15 @@ class TestWorkerPoller:
             max_retries=3,
         )
 
-        # Execute (should fail and retry)
+        # Execute (should fail and retry) - fire-and-forget
         task_dict = json.loads(payload)
         claimed_task = ClaimedTask(operation_id=str(op_id), task_dict=task_dict, schema=None)
         await poller.execute_task(claimed_task)
 
+        # Wait for background task to complete
+        completed = await poller.wait_for_active_tasks(timeout=5.0)
+        assert completed, "Task did not complete within timeout"
+
         # Verify task is back to pending with incremented retry_count
         row = await pool.fetchrow(
             "SELECT status, retry_count, worker_id FROM async_operations WHERE operation_id = $1",
@@ -327,11 +332,15 @@ class TestWorkerPoller:
             max_retries=3,
         )
 
-        # Execute (should fail permanently)
+        # Execute (should fail permanently) - fire-and-forget
         task_dict = json.loads(payload)
         claimed_task = ClaimedTask(operation_id=str(op_id), task_dict=task_dict, schema=None)
         await poller.execute_task(claimed_task)
 
+        # Wait for background task to complete
+        completed = await poller.wait_for_active_tasks(timeout=5.0)
+        assert completed, "Task did not complete within timeout"
+
         # Verify task is marked as failed
         row = await pool.fetchrow(
             "SELECT status, error_message FROM async_operations WHERE operation_id = $1",
@@ -388,7 +397,6 @@ class TestWorkerPoller:
             pool=pool,
             worker_id="test-worker-1",
             executor=lambda x: None,
-            batch_size=10,
         )
 
         claimed = await poller.claim_batch()
@@ -440,7 +448,6 @@ class TestWorkerPoller:
             pool=pool,
             worker_id="test-worker-1",
             executor=lambda x: None,
-            batch_size=10,
         )
 
         claimed = await poller.claim_batch()
@@ -607,7 +614,6 @@ class TestConcurrentWorkers:
                 pool=pool,
                 worker_id=worker_id,
                 executor=lambda x: None,
-                batch_size=5,  # Each worker tries to claim 5
             )
             claimed = await poller.claim_batch()
             workers_claimed[worker_id] = [task.operation_id for task in claimed]
@@ -680,7 +686,6 @@ class TestConcurrentWorkers:
             pool=pool,
             worker_id="new-worker",
             executor=lambda x: None,
-            batch_size=10,
         )
 
         claimed = await poller.claim_batch()
@@ -879,7 +884,6 @@ class TestDynamicTenantDiscovery:
             pool=pool,
             worker_id="test-worker-1",
             executor=lambda x: None,
-            batch_size=10,
             tenant_extension=mock_extension,
         )
 
@@ -946,7 +950,6 @@ class TestDynamicTenantDiscovery:
             pool=pool,
             worker_id="test-worker-1",
             executor=lambda x: None,
-            batch_size=10,
             tenant_extension=dynamic_extension,
         )
 
@@ -1008,7 +1011,6 @@ class TestDynamicTenantDiscovery:
             pool=pool,
             worker_id="test-worker-1",
             executor=lambda x: None,
-            batch_size=10,
         )
 
         claimed = await poller.claim_batch()
@@ -1017,3 +1019,198 @@ class TestDynamicTenantDiscovery:
         # All tasks should have schema=None (public)
         for task in claimed:
             assert task.schema is None
+
+
+async def test_worker_fire_and_forget_nonblocking(pool, clean_operations):
+    """
+    Test that worker continues polling while tasks run (fire-and-forget pattern).
+
+    This test verifies the FIX: With the old blocking behavior, the worker would
+    wait for all tasks in a batch to complete before claiming more. This test
+    would FAIL with the old code because tasks 3-4 wouldn't be claimed until
+    tasks 1-2 complete. With fire-and-forget, tasks 3-4 are claimed immediately.
+    """
+    from hindsight_api.worker.poller import WorkerPoller
+
+    task_started = {}  # operation_id -> Event (set when task starts)
+    task_canfinish = {}  # operation_id -> Event (wait before finishing)
+
+    async def blocking_executor(task_dict: dict):
+        op_id = task_dict["operation_id"]
+        # Signal that this task has started
+        started = asyncio.Event()
+        task_started[op_id] = started
+        started.set()
+
+        # Block until we're told to finish
+        finish = asyncio.Event()
+        task_canfinish[op_id] = finish
+        await finish.wait()
+
+    poller = WorkerPoller(
+        pool=pool,
+        worker_id="test-worker",
+        executor=blocking_executor,
+        poll_interval_ms=50,  # Fast polling
+        max_slots=10,
+        consolidation_max_slots=2,
+    )
+
+    bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
+
+    # Submit initial 2 tasks
+    task_ids = []
+    for i in range(2):
+        op_id = uuid.uuid4()
+        task_ids.append(str(op_id))
+        payload = json.dumps({"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id})
+        await pool.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload)
+            VALUES ($1, $2, 'retain', 'pending', $3::jsonb)
+            """,
+            op_id,
+            bank_id,
+            payload,
+        )
+
+    poll_task = asyncio.create_task(poller.run())
+
+    try:
+        # Wait for first 2 tasks to start executing (but not finish)
+        for i in range(100):  # Try for up to 1 second
+            if len(task_started) >= 2:
+                break
+            await asyncio.sleep(0.01)
+        assert len(task_started) == 2, f"Expected 2 tasks started, got {len(task_started)}"
+
+        # Verify tasks are in_flight
+        async with poller._in_flight_lock:
+            assert poller._in_flight_count == 2
+
+        # NOW submit 2 more tasks WHILE the first 2 are still running
+        for i in range(2):
+            op_id = uuid.uuid4()
+            task_ids.append(str(op_id))
+            payload = json.dumps({"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id})
+            await pool.execute(
+                """
+                INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload)
+                VALUES ($1, $2, 'retain', 'pending', $3::jsonb)
+                """,
+                op_id,
+                bank_id,
+                payload,
+            )
+
+        # KEY ASSERTION: Worker should claim tasks 3-4 WITHOUT waiting for 1-2 to finish
+        # This would FAIL with the old blocking behavior
+        for i in range(100):  # Try for up to 1 second
+            if len(task_started) >= 4:
+                break
+            await asyncio.sleep(0.01)
+
+        assert len(task_started) == 4, (
+            f"Fire-and-forget FAILED: Expected 4 tasks started, got {len(task_started)}. "
+            "This means the worker blocked waiting for the first batch to complete."
+        )
+
+        # Verify all 4 tasks are in-flight
+        async with poller._in_flight_lock:
+            assert poller._in_flight_count == 4
+
+        # Clean up: allow all tasks to finish
+        for event in task_canfinish.values():
+            event.set()
+
+    finally:
+        # Ensure cleanup
+        for event in task_canfinish.values():
+            event.set()
+        await poller.shutdown_graceful(timeout=2.0)
+        try:
+            await asyncio.wait_for(poll_task, timeout=1.0)
+        except asyncio.CancelledError:
+            pass
+
+
+async def test_worker_slot_limits_enforced(pool, clean_operations):
+    """Test that worker respects max_slots and won't exceed the limit."""
+    from hindsight_api.worker.poller import WorkerPoller
+
+    tasks_started = set()
+    task_events = {}
+
+    async def controlled_executor(task_dict: dict):
+        op_id = task_dict["operation_id"]
+        tasks_started.add(op_id)
+        event = asyncio.Event()
+        task_events[op_id] = event
+        await event.wait()
+
+    poller = WorkerPoller(
+        pool=pool,
+        worker_id="test-worker",
+        executor=controlled_executor,
+        poll_interval_ms=50,
+        max_slots=3,  # Only allow 3 concurrent tasks
+        consolidation_max_slots=1,
+    )
+
+    # Submit 10 tasks
+    bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
+    for i in range(10):
+        op_id = uuid.uuid4()
+        payload = json.dumps({"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id})
+        await pool.execute(
+            """
+            INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload)
+            VALUES ($1, $2, 'retain', 'pending', $3::jsonb)
+            """,
+            op_id,
+            bank_id,
+            payload,
+        )
+
+    poll_task = asyncio.create_task(poller.run())
+
+    try:
+        # Wait for slots to fill
+        for i in range(100):
+            if len(tasks_started) >= 3:
+                break
+            await asyncio.sleep(0.01)
+
+        # Should have claimed exactly 3 tasks (slot limit)
+        assert len(tasks_started) == 3
+
+        # Wait to ensure no additional tasks are claimed
+        for i in range(30):
+            await asyncio.sleep(0.01)
+        assert len(tasks_started) == 3, "Worker exceeded slot limit!"
+
+        # Release tasks one by one and verify remaining are claimed
+        completed = 0
+        while completed < 10 and len(tasks_started) < 10:
+            # Release the next batch
+            events_to_release = list(task_events.values())[completed:completed+3]
+            for event in events_to_release:
+                event.set()
+            completed += len(events_to_release)
+
+            # Wait for new tasks to be claimed
+            for i in range(100):
+                if len(tasks_started) >= min(completed + 3, 10):
+                    break
+                await asyncio.sleep(0.01)
+
+        assert len(tasks_started) == 10
+
+    finally:
+        for event in task_events.values():
+            event.set()
+        await poller.shutdown_graceful(timeout=2.0)
+        try:
+            await asyncio.wait_for(poll_task, timeout=1.0)
+        except asyncio.CancelledError:
+            pass

hindsight-cli/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "hindsight-cli"
-version = "0.4.2"
+version = "0.4.5"
 edition = "2021"
 authors = ["Hindsight Team"]
 description = "A beautiful CLI for Hindsight - semantic memory system"

hindsight-cli/src/commands/bank.rs

@@ -500,6 +500,8 @@ pub fn delete(
 pub fn consolidate(
     client: &ApiClient,
     bank_id: &str,
+    wait: bool,
+    poll_interval: u64,
     verbose: bool,
     output_format: OutputFormat,
 ) -> Result<()> {
@@ -517,17 +519,82 @@ pub fn consolidate(
 
     match response {
         Ok(result) => {
+            let operation_id = result.operation_id.clone();
+
             if output_format == OutputFormat::Pretty {
                 ui::print_success("Consolidation triggered");
-                println!("  {} {}", ui::dim("Operation ID:"), result.operation_id);
+                println!("  {} {}", ui::dim("Operation ID:"), operation_id);
                 if result.deduplicated {
                     println!("  {} {}", ui::dim("Note:"), "Reusing existing pending consolidation task");
                 }
-                println!();
-                println!("{}", ui::dim("Use 'hindsight operation get' to check the operation status."));
             } else {
                 output::print_output(&result, output_format)?;
             }
+
+            if !wait {
+                if output_format == OutputFormat::Pretty {
+                    println!();
+                    println!("{}", ui::dim("Use --wait to poll for completion, or 'hindsight operation get' to check status."));
+                }
+                return Ok(());
+            }
+
+            // Poll for completion
+            if output_format == OutputFormat::Pretty {
+                println!();
+                println!("{}", ui::dim(&format!("Polling every {}s for completion...", poll_interval)));
+            }
+
+            let start = std::time::Instant::now();
+            loop {
+                std::thread::sleep(std::time::Duration::from_secs(poll_interval));
+                let elapsed = start.elapsed().as_secs();
+
+                let ops_result = client.list_operations(bank_id, verbose);
+                match ops_result {
+                    Ok(ops) => {
+                        // Find the operation by ID
+                        let op = ops.operations.iter().find(|o| o.id == operation_id);
+
+                        match op.map(|o| o.status.as_str()) {
+                            Some("completed") => {
+                                if output_format == OutputFormat::Pretty {
+                                    ui::print_success(&format!("Consolidation completed ({}s)", elapsed));
+                                }
+                                break;
+                            }
+                            Some("failed") => {
+                                let error_msg = op
+                                    .and_then(|o| o.error_message.as_ref())
+                                    .map(|s| s.as_str())
+                                    .unwrap_or("Unknown error");
+                                if output_format == OutputFormat::Pretty {
+                                    ui::print_error(&format!("Consolidation failed: {}", error_msg));
+                                }
+                                std::process::exit(1);
+                            }
+                            Some(status) => {
+                                if output_format == OutputFormat::Pretty {
+                                    println!("  ⏳ {} ({}s elapsed)", status, elapsed);
+                                }
+                            }
+                            None => {
+                                if output_format == OutputFormat::Pretty {
+                                    ui::print_warning(&format!("Operation {} not found in list", operation_id));
+                                }
+                                break;
+                            }
+                        }
+                    }
+                    Err(e) => {
+                        if output_format == OutputFormat::Pretty {
+                            ui::print_error(&format!("Failed to check operation status: {}", e));
+                        }
+                        return Err(e);
+                    }
+                }
+            }
+
             Ok(())
         }
         Err(e) => Err(e),

hindsight-cli/src/commands/document.rs

@@ -1,4 +1,6 @@
 use anyhow::Result;
+use chrono::{Duration as ChronoDuration, NaiveDate, Utc};
+use std::collections::BTreeMap;
 use crate::api::ApiClient;
 use crate::output::{self, OutputFormat};
 use crate::ui;
@@ -7,11 +9,17 @@ pub fn list(
     client: &ApiClient,
     agent_id: &str,
     query: Option<String>,
+    date: Option<String>,
     limit: i32,
     offset: i32,
     verbose: bool,
     output_format: OutputFormat,
 ) -> Result<()> {
+    // If date filter is provided, use the date-aware listing
+    if date.is_some() {
+        return list_with_date(client, agent_id, date.as_deref(), verbose, output_format);
+    }
+
     let spinner = if output_format == OutputFormat::Pretty {
         Some(ui::create_spinner("Fetching documents..."))
     } else {
@@ -50,6 +58,139 @@ pub fn list(
     }
 }
 
+/// List documents with date filtering
+fn list_with_date(
+    client: &ApiClient,
+    bank_id: &str,
+    date_filter: Option<&str>,
+    verbose: bool,
+    output_format: OutputFormat,
+) -> Result<()> {
+    let spinner = if output_format == OutputFormat::Pretty {
+        Some(ui::create_spinner("Fetching all documents..."))
+    } else {
+        None
+    };
+
+    // Fetch all documents with pagination
+    let all_docs = fetch_all_documents(client, bank_id, verbose)?;
+
+    if let Some(mut sp) = spinner {
+        sp.finish();
+    }
+
+    // Parse the date filter
+    let target_date = parse_date_filter(date_filter)?;
+
+    // Filter and group documents by date
+    let mut by_date: BTreeMap<String, Vec<serde_json::Value>> = BTreeMap::new();
+    let mut filtered_count = 0;
+
+    for doc in all_docs {
+        let created_at = doc.get("created_at")
+            .and_then(|v| v.as_str())
+            .unwrap_or("");
+
+        // Parse the date part (YYYY-MM-DD) from created_at
+        let doc_date = created_at.split('T').next().unwrap_or("");
+
+        // Apply date filter if specified
+        if let Some(ref target) = target_date {
+            let target_str = target.format("%Y-%m-%d").to_string();
+            if doc_date != target_str {
+                continue;
+            }
+        }
+
+        filtered_count += 1;
+        by_date.entry(doc_date.to_string()).or_default().push(doc);
+    }
+
+    // Output
+    if output_format == OutputFormat::Pretty {
+        let filter_desc = match date_filter {
+            None | Some("yesterday") => "yesterday".to_string(),
+            Some("today") => "today".to_string(),
+            Some("all") => "all dates".to_string(),
+            Some(d) => d.to_string(),
+        };
+
+        ui::print_info(&format!(
+            "Documents for bank '{}' (filter: {}, showing: {})",
+            bank_id, filter_desc, filtered_count
+        ));
+        println!();
+
+        // Show documents grouped by date (reverse order - newest first)
+        for (date_str, docs) in by_date.iter().rev() {
+            println!("  {} ({} documents)", date_str, docs.len());
+            for doc in docs {
+                let id = doc.get("id").and_then(|v| v.as_str()).unwrap_or("unknown");
+                let mem_count = doc.get("memory_unit_count").and_then(|v| v.as_i64()).unwrap_or(0);
+                println!("    - {} ({} memories)", id, mem_count);
+            }
+            println!();
+        }
+    } else {
+        // JSON/YAML output - convert to a list structure
+        let output: Vec<serde_json::Value> = by_date.values().flatten().cloned().collect();
+        output::print_output(&output, output_format)?;
+    }
+
+    Ok(())
+}
+
+/// Fetch all documents with pagination
+fn fetch_all_documents(
+    client: &ApiClient,
+    bank_id: &str,
+    verbose: bool,
+) -> Result<Vec<serde_json::Value>> {
+    let mut all_docs = Vec::new();
+    let mut offset = 0;
+    let limit = 500;
+
+    loop {
+        let response = client.list_documents(bank_id, None, Some(limit), Some(offset), verbose)?;
+
+        if response.items.is_empty() {
+            break;
+        }
+
+        // Convert Map<String, Value> to Value for each item
+        for item in response.items {
+            all_docs.push(serde_json::Value::Object(item));
+        }
+
+        offset += limit;
+
+        // Check if we've fetched everything
+        if all_docs.len() >= response.total as usize {
+            break;
+        }
+    }
+
+    Ok(all_docs)
+}
+
+/// Parse date filter string into a NaiveDate
+fn parse_date_filter(filter: Option<&str>) -> Result<Option<NaiveDate>> {
+    match filter {
+        None | Some("yesterday") => {
+            // Default to yesterday
+            Ok(Some(Utc::now().date_naive() - ChronoDuration::days(1)))
+        }
+        Some("today") => Ok(Some(Utc::now().date_naive())),
+        Some("all") => Ok(None), // No filtering
+        Some(date_str) => {
+            // Try to parse as YYYY-MM-DD
+            NaiveDate::parse_from_str(date_str, "%Y-%m-%d")
+                .map(Some)
+                .map_err(|e| anyhow::anyhow!("Invalid date format '{}': {}. Use YYYY-MM-DD, 'yesterday', 'today', or 'all'", date_str, e))
+        }
+    }
+}
+
 pub fn get(
     client: &ApiClient,
     agent_id: &str,

hindsight-cli/src/main.rs

@@ -260,6 +260,14 @@ enum BankCommands {
     Consolidate {
         /// Bank ID
         bank_id: String,
+
+        /// Wait for consolidation to complete (poll for status)
+        #[arg(long)]
+        wait: bool,
+
+        /// Poll interval in seconds (only used with --wait)
+        #[arg(long, default_value = "10")]
+        poll_interval: u64,
     },
 
     /// Clear all observations for a bank
@@ -441,6 +449,10 @@ enum DocumentCommands {
         #[arg(short = 'q', long)]
         query: Option<String>,
 
+        /// Filter by date (yesterday, today, YYYY-MM-DD, or all)
+        #[arg(short = 'd', long)]
+        date: Option<String>,
+
         /// Maximum number of results
         #[arg(short = 'l', long, default_value = "100")]
         limit: i32,
@@ -754,8 +766,8 @@ fn run() -> Result<()> {
             BankCommands::Delete { bank_id, yes } => {
                 commands::bank::delete(&client, &bank_id, yes, verbose, output_format)
             }
-            BankCommands::Consolidate { bank_id } => {
-                commands::bank::consolidate(&client, &bank_id, verbose, output_format)
+            BankCommands::Consolidate { bank_id, wait, poll_interval } => {
+                commands::bank::consolidate(&client, &bank_id, wait, poll_interval, verbose, output_format)
             }
             BankCommands::ClearObservations { bank_id, yes } => {
                 commands::bank::clear_observations(&client, &bank_id, yes, verbose, output_format)
@@ -792,8 +804,8 @@ fn run() -> Result<()> {
 
         // Document commands
         Commands::Document(doc_cmd) => match doc_cmd {
-            DocumentCommands::List { bank_id, query, limit, offset } => {
-                commands::document::list(&client, &bank_id, query, limit, offset, verbose, output_format)
+            DocumentCommands::List { bank_id, query, date, limit, offset } => {
+                commands::document::list(&client, &bank_id, query, date, limit, offset, verbose, output_format)
             }
             DocumentCommands::Get { bank_id, document_id } => {
                 commands::document::get(&client, &bank_id, &document_id, verbose, output_format)

hindsight-clients/python/hindsight_client_api/__init__.py

@@ -7,14 +7,14 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.
 """  # noqa: E501
 
 
-__version__ = "0.4.2"
+__version__ = "0.4.5"
 
 # import apis into sdk package
 from hindsight_client_api.api.banks_api import BanksApi

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

@@ -5,7 +5,7 @@
 
     HTTP API for Hindsight
 
-    The version of the OpenAPI document: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.
@@ -1644,7 +1644,7 @@ class MemoryApi:
     ) -> RecallResponse:
         """Recall memory
 
-        Recall memory using semantic similarity and spreading activation.  The type parameter is optional and must be one of: - `world`: General knowledge about people, places, events, and things that happen - `experience`: Memories about experience, conversations, actions taken, and tasks performed - `opinion`: The bank's formed beliefs, perspectives, and viewpoints  Set `include_entities=true` to get entity observations alongside recall results.
+        Recall memory using semantic similarity and spreading activation.  The type parameter is optional and must be one of: - `world`: General knowledge about people, places, events, and things that happen - `experience`: Memories about experience, conversations, actions taken, and tasks performed
 
         :param bank_id: (required)
         :type bank_id: str
@@ -1720,7 +1720,7 @@ class MemoryApi:
     ) -> ApiResponse[RecallResponse]:
         """Recall memory
 
-        Recall memory using semantic similarity and spreading activation.  The type parameter is optional and must be one of: - `world`: General knowledge about people, places, events, and things that happen - `experience`: Memories about experience, conversations, actions taken, and tasks performed - `opinion`: The bank's formed beliefs, perspectives, and viewpoints  Set `include_entities=true` to get entity observations alongside recall results.
+        Recall memory using semantic similarity and spreading activation.  The type parameter is optional and must be one of: - `world`: General knowledge about people, places, events, and things that happen - `experience`: Memories about experience, conversations, actions taken, and tasks performed
 
         :param bank_id: (required)
         :type bank_id: str
@@ -1796,7 +1796,7 @@ class MemoryApi:
     ) -> RESTResponseType:
         """Recall memory
 
-        Recall memory using semantic similarity and spreading activation.  The type parameter is optional and must be one of: - `world`: General knowledge about people, places, events, and things that happen - `experience`: Memories about experience, conversations, actions taken, and tasks performed - `opinion`: The bank's formed beliefs, perspectives, and viewpoints  Set `include_entities=true` to get entity observations alongside recall results.
+        Recall memory using semantic similarity and spreading activation.  The type parameter is optional and must be one of: - `world`: General knowledge about people, places, events, and things that happen - `experience`: Memories about experience, conversations, actions taken, and tasks performed
 
         :param bank_id: (required)
         :type bank_id: str
@@ -1950,7 +1950,7 @@ class MemoryApi:
     ) -> ReflectResponse:
         """Reflect and generate answer
 
-        Reflect and formulate an answer using bank identity, world facts, and opinions.  This endpoint: 1. Retrieves experience (conversations and events) 2. Retrieves world facts relevant to the query 3. Retrieves existing opinions (bank's perspectives) 4. Uses LLM to formulate a contextual answer 5. Extracts and stores any new opinions formed 6. Returns plain text answer, the facts used, and new opinions
+        Reflect and formulate an answer using bank identity, world facts, and opinions.  This endpoint: 1. Retrieves experience (conversations and events) 2. Retrieves world facts relevant to the query 3. Retrieves existing opinions (bank's perspectives) 4. Uses LLM to formulate a contextual answer 5. Returns plain text answer and the facts used
 
         :param bank_id: (required)
         :type bank_id: str
@@ -2026,7 +2026,7 @@ class MemoryApi:
     ) -> ApiResponse[ReflectResponse]:
         """Reflect and generate answer
 
-        Reflect and formulate an answer using bank identity, world facts, and opinions.  This endpoint: 1. Retrieves experience (conversations and events) 2. Retrieves world facts relevant to the query 3. Retrieves existing opinions (bank's perspectives) 4. Uses LLM to formulate a contextual answer 5. Extracts and stores any new opinions formed 6. Returns plain text answer, the facts used, and new opinions
+        Reflect and formulate an answer using bank identity, world facts, and opinions.  This endpoint: 1. Retrieves experience (conversations and events) 2. Retrieves world facts relevant to the query 3. Retrieves existing opinions (bank's perspectives) 4. Uses LLM to formulate a contextual answer 5. Returns plain text answer and the facts used
 
         :param bank_id: (required)
         :type bank_id: str
@@ -2102,7 +2102,7 @@ class MemoryApi:
     ) -> RESTResponseType:
         """Reflect and generate answer
 
-        Reflect and formulate an answer using bank identity, world facts, and opinions.  This endpoint: 1. Retrieves experience (conversations and events) 2. Retrieves world facts relevant to the query 3. Retrieves existing opinions (bank's perspectives) 4. Uses LLM to formulate a contextual answer 5. Extracts and stores any new opinions formed 6. Returns plain text answer, the facts used, and new opinions
+        Reflect and formulate an answer using bank identity, world facts, and opinions.  This endpoint: 1. Retrieves experience (conversations and events) 2. Retrieves world facts relevant to the query 3. Retrieves existing opinions (bank's perspectives) 4. Uses LLM to formulate a contextual answer 5. Returns plain text answer and the facts used
 
         :param bank_id: (required)
         :type bank_id: str

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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1\n"\
+               "Version of the API: 0.4.3\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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.

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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     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: 0.4.1
+    The version of the OpenAPI document: 0.4.3
     Generated by OpenAPI Generator (https://openapi-generator.tech)
 
     Do not edit the class manually.