fixes

* chore: remove dead code

* chore: remove extract_opinions from test and regenerate openapi

- Remove extract_opinions parameter from test_fact_extraction_analysis
- Regenerate OpenAPI spec after removing entity observations code

* chore: update generated files and apply formatting

- Regenerate Python and TypeScript client SDKs after main merge
- Apply ruff formatting to llm_wrapper.py

* fix: accept and filter deprecated 'opinion' fact type in recall

The dead code removal eliminated support for the 'opinion' fact type,
but existing clients may still pass it. Instead of rejecting it with
a ValueError, silently filter it out before validation to maintain
backward compatibility.

.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
@@ -26,6 +33,7 @@ HINDSIGHT_API_LOG_LEVEL=info
 
 # Database (Optional - uses embedded pg0 by default)
 # HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@host:5432/db
+# HINDSIGHT_API_DATABASE_SCHEMA=public  # PostgreSQL schema name (default: public)
 
 # Embeddings Configuration (Optional - uses local by default)
 # Provider: "local" (default) or "tei" (HuggingFace Text Embeddings Inference)

.github/workflows/release.yml

@@ -139,6 +139,55 @@ jobs:
         path: hindsight-clients/typescript/*.tgz
         retention-days: 1
 
+  release-moltbot-integration:
+    runs-on: ubuntu-latest
+    environment: npm
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '22'
+        registry-url: 'https://registry.npmjs.org'
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/moltbot
+      run: npm ci
+
+    - name: Build
+      working-directory: ./hindsight-integrations/moltbot
+      run: npm run build
+
+    - name: Publish to npm
+      working-directory: ./hindsight-integrations/moltbot
+      run: |
+        set +e
+        OUTPUT=$(npm publish --access public 2>&1)
+        EXIT_CODE=$?
+        echo "$OUTPUT"
+        if [ $EXIT_CODE -ne 0 ]; then
+          if echo "$OUTPUT" | grep -q "cannot publish over"; then
+            echo "Package version already published, skipping..."
+            exit 0
+          fi
+          exit $EXIT_CODE
+        fi
+      env:
+        NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }}
+
+    - name: Pack for GitHub release
+      working-directory: ./hindsight-integrations/moltbot
+      run: npm pack
+
+    - name: Upload artifacts
+      uses: actions/upload-artifact@v4
+      with:
+        name: moltbot-integration
+        path: hindsight-integrations/moltbot/*.tgz
+        retention-days: 1
+
   release-control-plane:
     runs-on: ubuntu-latest
     environment: npm
@@ -366,7 +415,7 @@ jobs:
 
   create-github-release:
     runs-on: ubuntu-latest
-    needs: [release-python-packages, release-typescript-client, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
+    needs: [release-python-packages, release-typescript-client, release-moltbot-integration, release-control-plane, release-rust-cli, release-docker-images, release-helm-chart]
     permissions:
       contents: write
 
@@ -389,6 +438,12 @@ jobs:
         name: typescript-client
         path: ./artifacts/typescript-client
 
+    - name: Download Moltbot Integration
+      uses: actions/download-artifact@v4
+      with:
+        name: moltbot-integration
+        path: ./artifacts/moltbot-integration
+
     - name: Download Control Plane
       uses: actions/download-artifact@v4
       with:
@@ -430,6 +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
         # Control Plane
         cp artifacts/control-plane/*.tgz release-assets/ || true
         # Rust CLI binaries

.github/workflows/test.yml

@@ -82,6 +82,29 @@ jobs:
     - name: Build TypeScript client
       run: npm run build --workspace=hindsight-clients/typescript
 
+  build-moltbot-integration:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '22'
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/moltbot
+      run: npm ci
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/moltbot
+      run: npm test
+
+    - name: Build
+      working-directory: ./hindsight-integrations/moltbot
+      run: npm run build
+
   build-control-plane:
     runs-on: ubuntu-latest
 
@@ -153,8 +176,15 @@ jobs:
     - name: Build docs
       run: npm run build --workspace=hindsight-docs
 
-  build-rust-cli:
+  test-rust-cli:
     runs-on: ubuntu-latest
+    env:
+      HINDSIGHT_API_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      HINDSIGHT_API_URL: http://localhost:8888
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
 
     steps:
     - uses: actions/checkout@v4
@@ -171,6 +201,10 @@ jobs:
           hindsight-cli/target
         key: ${{ runner.os }}-cargo-${{ hashFiles('**/Cargo.lock') }}
 
+    - name: Run unit tests
+      working-directory: hindsight-cli
+      run: cargo test
+
     - name: Build CLI
       working-directory: hindsight-cli
       run: cargo build --release
@@ -182,29 +216,6 @@ jobs:
         path: hindsight-cli/target/release/hindsight
         retention-days: 1
 
-  test-rust-cli:
-    runs-on: ubuntu-latest
-    needs: build-rust-cli
-    env:
-      HINDSIGHT_API_LLM_PROVIDER: groq
-      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
-      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
-      HINDSIGHT_API_URL: http://localhost:8888
-      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Download CLI artifact
-      uses: actions/download-artifact@v4
-      with:
-        name: hindsight-cli
-        path: /tmp/cli
-
-    - name: Make CLI executable
-      run: chmod +x /tmp/cli/hindsight
-
     - name: Install uv
       uses: astral-sh/setup-uv@v5
       with:
@@ -222,7 +233,7 @@ jobs:
 
     - name: Install API dependencies
       working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
     - name: Create .env file
       run: |
@@ -251,7 +262,7 @@ jobs:
 
     - name: Run CLI smoke test
       run: |
-        HINDSIGHT_CLI=/tmp/cli/hindsight ./hindsight-cli/smoke-test.sh
+        HINDSIGHT_CLI=hindsight-cli/target/release/hindsight ./hindsight-cli/smoke-test.sh
 
     - name: Show API server logs
       if: always()
@@ -352,7 +363,7 @@ jobs:
 
     - name: Install dependencies
       working-directory: ./hindsight-api
-      run: uv sync --extra test --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --extra test --no-install-project --index-strategy unsafe-best-match
 
     - name: Cache HuggingFace models
       uses: actions/cache@v4
@@ -413,11 +424,11 @@ jobs:
 
     - name: Install client test dependencies
       working-directory: ./hindsight-clients/python
-      run: uv sync --extra test --index-strategy unsafe-best-match
+      run: uv sync --frozen --extra test --index-strategy unsafe-best-match
 
     - name: Install API dependencies
       working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
     - name: Create .env file
       run: |
@@ -490,7 +501,7 @@ jobs:
 
     - name: Install API dependencies
       working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
     - name: Install TypeScript client dependencies
       working-directory: ./hindsight-clients/typescript
@@ -578,7 +589,7 @@ jobs:
 
     - name: Install API dependencies
       working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
     - name: Create .env file
       run: |
@@ -645,11 +656,11 @@ jobs:
 
     - name: Install API dependencies
       working-directory: ./hindsight-api
-      run: uv sync --no-install-project --index-strategy unsafe-best-match
+      run: uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
     - name: Install integration test dependencies
       working-directory: ./hindsight-integration-tests
-      run: uv sync
+      run: uv sync --frozen
 
     - name: Cache HuggingFace models
       uses: actions/cache@v4
@@ -729,7 +740,7 @@ jobs:
 
     - name: Install dependencies
       working-directory: ./hindsight-integrations/litellm
-      run: uv sync --extra dev
+      run: uv sync --frozen --extra dev
 
     - name: Run tests
       working-directory: ./hindsight-integrations/litellm
@@ -760,7 +771,7 @@ jobs:
 
     - name: Install dependencies
       working-directory: ./hindsight-embed
-      run: uv sync --index-strategy unsafe-best-match
+      run: uv sync --frozen --index-strategy unsafe-best-match
 
     - name: Cache HuggingFace models
       uses: actions/cache@v4
@@ -777,7 +788,7 @@ jobs:
 
   test-doc-examples:
     runs-on: ubuntu-latest
-    needs: build-rust-cli
+    needs: test-rust-cli
     env:
       HINDSIGHT_API_LLM_PROVIDER: groq
       HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
@@ -820,11 +831,11 @@ jobs:
       working-directory: ./hindsight-api
       run: |
         uv build
-        uv sync --no-install-project --index-strategy unsafe-best-match
+        uv sync --frozen --no-install-project --index-strategy unsafe-best-match
 
     - name: Install Python client dependencies
       working-directory: ./hindsight-clients/python
-      run: uv sync --extra test --index-strategy unsafe-best-match
+      run: uv sync --frozen --extra test --index-strategy unsafe-best-match
 
     - name: Install TypeScript client
       run: |
@@ -887,6 +898,66 @@ jobs:
         echo "=== API Server Logs ==="
         cat /tmp/api-server.log || echo "No API server log found"
 
+  test-upgrade:
+    runs-on: ubuntu-latest
+    env:
+      HINDSIGHT_API_LLM_PROVIDER: groq
+      HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
+      HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      UV_INDEX: pytorch=https://download.pytorch.org/whl/cpu
+
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        fetch-depth: 0  # Full history needed for git clone of tags
+
+    - name: Fetch tags
+      run: git fetch --tags
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v5
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version-file: ".python-version"
+
+    - name: Cache HuggingFace models
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/huggingface
+        key: ${{ runner.os }}-huggingface-${{ hashFiles('hindsight-api/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-huggingface-
+
+    - name: Install hindsight-dev dependencies
+      working-directory: ./hindsight-dev
+      run: uv sync --frozen --extra test --index-strategy unsafe-best-match
+
+    - name: Install current hindsight-api
+      working-directory: ./hindsight-api
+      run: uv sync --frozen --index-strategy unsafe-best-match
+
+    - name: Pre-download models
+      working-directory: ./hindsight-api
+      run: |
+        uv run python -c "
+        from sentence_transformers import SentenceTransformer, CrossEncoder
+        print('Downloading embedding model...')
+        SentenceTransformer('BAAI/bge-small-en-v1.5')
+        print('Downloading cross-encoder model...')
+        CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+        print('Models downloaded successfully')
+        "
+
+    - name: Run upgrade tests
+      working-directory: ./hindsight-dev
+      run: uv run pytest upgrade_tests/ -v --tb=short
+
   verify-generated-files:
     runs-on: ubuntu-latest
     env:
@@ -928,9 +999,9 @@ jobs:
 
     - name: Install Python dependencies
       run: |
-        cd hindsight-dev && uv sync --index-strategy unsafe-best-match
-        cd ../hindsight-api && uv sync --index-strategy unsafe-best-match
-        cd ../hindsight-embed && uv sync --index-strategy unsafe-best-match
+        cd hindsight-dev && uv sync --frozen --index-strategy unsafe-best-match
+        cd ../hindsight-api && uv sync --frozen --index-strategy unsafe-best-match
+        cd ../hindsight-embed && uv sync --frozen --index-strategy unsafe-best-match
 
     - name: Run generate-openapi
       run: ./scripts/generate-openapi.sh

.gitignore

@@ -29,6 +29,7 @@ nltk_data/
 
 # Monitoring stack (Prometheus/Grafana binaries and data)
 .monitoring/
+.pgbouncer/
 
 # Large benchmark datasets (will be downloaded automatically)
 **/longmemeval_s_cleaned.json
@@ -44,9 +45,12 @@ hindsight-docs/static/llms-full.txt
 
 hindsight-dev/benchmarks/locomo/results/
 hindsight-dev/benchmarks/longmemeval/results/
+hindsight-dev/benchmarks/consolidation/results/
+benchmarks/results/
 hindsight-cli/target
 hindsight-clients/rust/target
 .claude
 whats-next.md
 TASK.md
-CHANGELOG.md
+# Changelog is now tracked in hindsight-docs/src/pages/changelog.md
+# CHANGELOG.md

AGENTS.md

@@ -1,153 +1,3 @@
 # AGENTS.md
 
-This document captures architectural decisions and coding conventions for the Hindsight project.
-
-## Documentation
-
-- **Main documentation**: [hindsight-docs/docs/developer/](./hindsight-docs/docs/developer/)
-- **Use case patterns**: [hindsight-docs/docs/cookbook/](./hindsight-docs/docs/cookbook/)
-- **API reference**: Auto-generated from OpenAPI spec
-
-## Project Structure
-
-```
-hindsight/              # Python package for embedded usage
-hindsight-api/          # FastAPI server (core memory engine)
-hindsight-cli/          # Rust CLI client
-hindsight-embed/        # Embedded CLI (no server needed)
-hindsight-control-plane/ # Next.js admin UI
-hindsight-docs/         # Docusaurus documentation site
-hindsight-dev/          # Development tools and benchmarks
-hindsight-integrations/ # Framework integrations (LangChain, etc.)
-hindsight-clients/      # Generated API clients (Python, TypeScript, Rust)
-```
-
-## Core Concepts
-
-### Memory Banks
-- Each bank is an isolated memory store (like a "brain" for one user/agent)
-- Banks contain: memory units (facts), entities, documents, entity links
-- Banks have a **disposition** (personality traits) and **background** (context)
-- Bank isolation is strict - no cross-bank data leakage
-
-### Memory Types
-- **World facts**: General knowledge ("The sky is blue")
-- **Experience facts**: Personal experiences ("I visited Paris in 2023")
-- **Opinion facts**: Beliefs with confidence scores ("Paris is beautiful" - 0.9 confidence)
-
-### Operations
-- **Retain**: Store new memories (extracts facts, entities, relationships)
-- **Recall**: Retrieve memories (semantic, BM25, graph, temporal search)
-- **Reflect**: Deep analysis to form new insights/opinions
-
-## API Design Decisions
-
-### Single Bank Per Request
-- All API endpoints (`recall`, `reflect`, `retain`) operate on a single bank
-- Multi-bank queries are the **client/agent's responsibility** to orchestrate
-- This keeps the API simple and the isolation model clear
-
-### Disposition Traits (3-trait system)
-- **Skepticism** (1-5): How skeptical vs trusting when forming opinions
-- **Literalism** (1-5): How literally to interpret information
-- **Empathy** (1-5): How much to consider emotional context
-- These influence the `reflect` operation, not `recall`
-- Background info also only affects `reflect` (opinion formation)
-
-## Multi-Bank Architecture Patterns
-
-See [hindsight-docs/docs/cookbook/](./hindsight-docs/docs/cookbook/) for detailed guides:
-
-- **Per-User Memory**: One bank per user, simplest pattern
-- **Support Agent + Shared Knowledge**: User bank + shared docs bank, client orchestrates
-
-## Developer Guide
-
-### Running the API Server
-
-```bash
-# From project root
-./scripts/dev/start-api.sh
-
-# With options
-./scripts/dev/start-api.sh --reload --port 8888 --log-level debug
-```
-
-### Running Tests
-
-```bash
-# API tests
-cd hindsight-api
-uv run pytest tests/
-
-# Specific test
-uv run pytest tests/test_http_api_integration.py -v
-```
-
-### Generating OpenAPI Spec
-
-After changing API endpoints, regenerate the OpenAPI spec and docs:
-
-```bash
-./scripts/generate-openapi.sh
-```
-
-This will:
-1. Generate `openapi.json` at project root
-2. Copy to `hindsight-docs/openapi.json`
-3. Regenerate API reference documentation
-
-### Generating API Clients
-
-After updating the OpenAPI spec, regenerate all clients:
-
-```bash
-./scripts/generate-clients.sh
-```
-
-This generates:
-- **Rust client**: `hindsight-clients/rust/` (via progenitor in build.rs)
-- **Python client**: `hindsight-clients/python/` (via openapi-generator Docker)
-- **TypeScript client**: `hindsight-clients/typescript/` (via @hey-api/openapi-ts)
-
-Note: The maintained wrapper `hindsight_client.py` and `README.md` are preserved during regeneration.
-
-### Running the Documentation Site
-
-```bash
-./scripts/dev/start-docs.sh
-```
-
-### Running the Control Plane
-
-```bash
-./scripts/dev/start-control-plane.sh
-```
-
-## Code Style
-
-### Python (hindsight-api)
-- Use `uv` for package management
-- Async throughout (asyncpg, async FastAPI endpoints)
-- Pydantic models for request/response validation
-- No py files at project root - maintain clean directory structure
-
-### TypeScript (control-plane, clients)
-- Next.js with App Router for control plane
-- Tailwind CSS with shadcn/ui components
-
-### Rust (CLI)
-- Async with tokio
-- reqwest for HTTP client
-- progenitor for API client generation
-
-## Database
-
-- PostgreSQL with pgvector extension
-- Schema managed via Alembic migrations in `hindsight-api/alembic/`, db migrations happen during api startup, no manual commands
-- Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
-
-# Branding
-## Colors
-- Primary: gradient from #0074d9 to #009296
-
+See [CLAUDE.md](./CLAUDE.md) for project documentation and coding conventions.

CLAUDE.md

@@ -7,8 +7,7 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 Hindsight is an agent memory system that provides long-term memory for AI agents using biomimetic data structures. Memories are organized as:
 - **World facts**: General knowledge ("The sky is blue")
 - **Experience facts**: Personal experiences ("I visited Paris in 2023")
-- **Opinion facts**: Beliefs with confidence scores ("Paris is beautiful" - 0.9 confidence)
-- **Observations**: Complex mental models derived from reflection
+- **Mental models**: Consolidated knowledge synthesized from facts ("User prefers functional programming patterns")
 
 ## Development Commands
 
@@ -101,13 +100,59 @@ cd hindsight-control-plane && npm run dev
 Main operations:
 - **Retain**: Store memories, extracts facts/entities/relationships
 - **Recall**: Retrieve memories via 4 parallel strategies (semantic, BM25, graph, temporal) + reranking
-- **Reflect**: Deep analysis forming new opinions/observations (disposition-aware)
+- **Reflect**: Disposition-aware reasoning using memories and mental models.
 
 ### Database
 PostgreSQL with pgvector. Schema managed via Alembic migrations in `hindsight-api/hindsight_api/alembic/`. Migrations run automatically on API startup.
 
 Key tables: `banks`, `memory_units`, `documents`, `entities`, `entity_links`
 
+### Adding Database Migrations
+
+1. **Create a new migration file** in `hindsight-api/hindsight_api/alembic/versions/`:
+   - File name format: `<revision_id>_<description>.py` (e.g., `f1a2b3c4d5e6_add_new_index.py`)
+   - Use a unique hex revision ID (12 chars)
+   - Set `down_revision` to the previous migration's revision ID
+
+2. **Migration template**:
+   ```python
+   """Description of the migration
+
+   Revision ID: f1a2b3c4d5e6
+   Revises: <previous_revision_id>
+   Create Date: YYYY-MM-DD
+   """
+   from collections.abc import Sequence
+   from alembic import context, op
+
+   revision: str = "f1a2b3c4d5e6"
+   down_revision: str | Sequence[str] | None = "<previous_revision_id>"
+   branch_labels: str | Sequence[str] | None = None
+   depends_on: str | Sequence[str] | None = None
+
+   def _get_schema_prefix() -> str:
+       """Get schema prefix for table names (required for multi-tenant support)."""
+       schema = context.config.get_main_option("target_schema")
+       return f'"{schema}".' if schema else ""
+
+   def upgrade() -> None:
+       schema = _get_schema_prefix()
+       op.execute(f"CREATE INDEX ... ON {schema}table_name(...)")
+
+   def downgrade() -> None:
+       schema = _get_schema_prefix()
+       op.execute(f"DROP INDEX IF EXISTS {schema}index_name")
+   ```
+
+3. **Run migrations locally**:
+   ```bash
+   # Set database URL and run migrations
+   uv run hindsight-admin run-db-migration
+
+   # Run on a specific tenant schema
+   uv run hindsight-admin run-db-migration --schema tenant_xyz
+   ```
+
 ## Key Conventions
 
 ### Code Quality
@@ -128,12 +173,63 @@ This runs the same checks as the pre-commit hook (Ruff for Python, ESLint/Pretti
 - Multi-bank queries are client responsibility to orchestrate
 - Disposition traits only affect reflect, not recall
 
+### Control Plane API Routes
+
+When adding or modifying parameters in the dataplane API (hindsight-api), you must also update the control plane routes that proxy to it:
+
+1. **API Routes** (`hindsight-control-plane/src/app/api/`):
+   - `recall/route.ts` - proxies to `/v1/default/banks/{bank_id}/memories/recall`
+   - `reflect/route.ts` - proxies to `/v1/default/banks/{bank_id}/reflect`
+   - `memories/retain/route.ts` - proxies to `/v1/default/banks/{bank_id}/memories/retain`
+   - Other routes follow the same pattern
+
+2. **Client types** (`hindsight-control-plane/src/lib/api.ts`):
+   - Update the TypeScript type definitions for `recall()`, `reflect()`, `retain()` etc.
+
+3. **Checklist when adding new API parameters**:
+   - Add parameter extraction in the route handler (destructure from `body`)
+   - Pass the parameter to the SDK call
+   - Update the client type definition in `lib/api.ts`
+   - Update any UI components that need to use the new parameter
+
 ### Python Style
 - Python 3.11+, type hints required
 - Async throughout (asyncpg, async FastAPI)
 - Pydantic models for request/response
 - Ruff for linting (line-length 120)
 - No Python files at project root - maintain clean directory structure
+- **Never use multi-item tuple return values** - prefer dataclass or Pydantic model for structured returns
+
+### Type Safety with Pydantic Models
+**NEVER use raw `dict` types for structured data.** Always use Pydantic models:
+- Use Pydantic `BaseModel` for all data structures passed between functions
+- Add `@field_validator` for type coercion (e.g., ensuring datetimes are timezone-aware)
+- Avoid `dict.get()` patterns - use typed model attributes instead
+- Parse external data (JSON, API responses) into Pydantic models at the boundary
+- This catches type errors at parse time, not deep in business logic
+
+```python
+# BAD - error-prone dict access
+def process(data: dict) -> str:
+    return data.get("name", "")  # No validation, silent failures
+
+# GOOD - typed and validated
+class UserData(BaseModel):
+    name: str
+    created_at: datetime
+
+    @field_validator("created_at", mode="before")
+    @classmethod
+    def ensure_tz_aware(cls, v):
+        if isinstance(v, str):
+            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
+        if v.tzinfo is None:
+            return v.replace(tzinfo=timezone.utc)
+        return v
+
+def process(data: UserData) -> str:
+    return data.name  # Type-safe, validated at construction
+```
 
 ### TypeScript Style
 - Next.js App Router for control plane

README.md

@@ -1,11 +1,11 @@
 <div align="center">
 
-![Hindsight Banner](./hindsight-docs/static/img/banner.svg)
+![Hindsight Banner](./hindsight-docs/static/img/hindsight-github-banner.png)
 
 [Documentation](https://hindsight.vectorize.io) • [Paper](https://arxiv.org/abs/2512.12818) • [Cookbook](https://hindsight.vectorize.io/cookbook) • [Hindsight Cloud](https://vectorize.io/hindsight/cloud)
 
 [![CI](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml/badge.svg)](https://github.com/vectorize-io/hindsight/actions/workflows/release.yml)
-[![Slack Community](https://img.shields.io/badge/Slack-Join%20Community-4A154B?logo=slack)](https://join.slack.com/t/hindsight-space/shared_invite/zt-3klo21kua-VUCC_zHP5rIcXFB1_5yw6A)
+[![Slack Community](https://img.shields.io/badge/Slack-Join%20Community-4A154B?logo=slack)](https://join.slack.com/t/hindsight-space/shared_invite/zt-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 ![PyPI - Downloads](https://img.shields.io/pypi/dm/hindsight-api?label=PyPI)
 ![NPM Downloads](https://img.shields.io/npm/dm/%40vectorize-io%2Fhindsight-client?logoColor=orange&label=NPM&color=blue&link=https%3A%2F%2Fwww.npmjs.com%2Fpackage%2F%40vectorize-io%2Fhindsight-client)
@@ -17,55 +17,31 @@
 
 ## What is Hindsight?
 
-Hindsight™ is an agent memory system built to create smarter agents that learn over time. It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.
+Hindsight™ is an agent memory system built to create smarter agents that learn over time. Most agent memory systems focus on recalling conversation history. Hindsight is focused on making agents that learn, not just remember.
 
-Hindsight addresses common challenges that have frustrated AI engineers building agents to automate tasks and assist users with conversational interfaces. Many of these challenges stem directly from a lack of memory.
 
-- **Inconsistency:** Agents complete tasks successfully one time, then fail when asked to complete the same task again. Memory gives the agent a mechanism to remember what worked and what didn't and to use that information to reduce errors and improve consistency.
-- **Hallucinations:** Long term memory can be seeded with external knowledge to ground agent behavior in reliable sources to augment training data.
-- **Cognitive Overload:** As workflows get complex, retrievals, tool calls, user messages and agent responses can grow to fill the context window leading to context rot. Short term memory optimization allows agents to reduce tokens and focus context by removing irrelevant details.
+<video src="https://github.com/user-attachments/assets/923b798d-3581-4897-bb62-9cfa5a931682" controls></video>
 
-## How is Hindsight Different From Other Memory Systems?
-
-![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
-
-Most agent memory implementation rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:
-
-- **World:** Facts about the world ("The stove gets hot")
-- **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
-- **Opinion:** Beliefs with confidence scores ("I shouldn't touch the stove again" - .99 confidence)
-- **Observation:** Complex mental models derived by reflecting on facts and experiences ("Curling irons, ovens, and fire are also hot. I shouldn't touch those either.")
-
-Memories in Hindsight are stored in banks (i.e. memory banks). When memories are added to Hindsight, they are pushed into either the world facts or experiences memory pathway. They are then represented as a combination of entities, relationships, and time series with sparse/dense vector representations to aid in later recall.
-
-Hindsight provides three simple methods to interact with the system:
-
-- **Retain:** Provide information to Hindsight that you want it to remember
-- **Recall:** Retrieve memories from Hindsight
-- **Reflect:** Reflect on memories and experiences to generate new observations and insights from existing memories.
-
-### Agent Memory That Learns
-
-A key goal of Hindsight is to build agent memory that enables agents to learn and improve over time. This is the role of the `reflect` operation which provides the agent to form broader opinions and observations over time.
-
-For example, imagine a product support agent that is helping a user troubleshoot a problem. It uses a `search-documentation` tool it found on an MCP server. Later in the conversation, the agent discovers that the documentation returned from the tool wasn't for the product the user was asking about. The agent now has an experience in its memory bank. And just like humans, we want that agent to learn from its experience.
-
-As the agent gains more experiences, `reflect` allows the agent to form observations about what worked, what didn't, and what to do differently the next time it encounters a similar task.
-
----
+It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.
 
 ## Memory Performance & Accuracy
 
-Hindsight has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational
-AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of December 2025 is shown here:
+Hindsight is the most accurate agent memory system ever tested according to benchmark performance. It has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of January 2026 is shown here:
 
 ![Overview](./hindsight-docs/static/img/hindsight-bench.jpg)
 
-The benchmark performance data for Hindsight and GPT-4o (full context) have been reproduced by research collaborators at the Virginia Tech [Sanghani Center for Artificial Intelligence and Data Analytics](https://sanghani.cs.vt.edu/) and The Washington Post. Other scores are self-reported by software vendors.
+The benchmark performance data for Hindsight has been independently reproduced by research collaborators at the Virginia Tech [Sanghani Center for Artificial Intelligence and Data Analytics](https://sanghani.cs.vt.edu/) and The Washington Post. Other scores are self-reported by software vendors.
 
-A thorough examination of the techniques implemented in Hindsight and detailed breakdowns of benchmark performance are [available on arXiv](https://arxiv.org/abs/2512.12818). This research is currently being prepared for conference submission and the wider peer review process.
+Hindsight is being used in production at Fortune 500 enterprises and by a growing number of AI startups. 
+
+## Adding Hindsight to Your AI Agents
+
+The easiest way use Hindsight with an existing agent is with the LLM Wrapper. You can add memory to your agent with 2 lines of code. That will swap your current LLM client out with the Hindsight wrapper. After that, memories will be stored and retrieved automatically as you make LLM calls.
+
+If you need more control over how and when your agent stores and recalls memories, there's also a simple API you can integrate with using the SDKs or directly via HTTP.
+
+![Hindsight Banner](./hindsight-docs/static/img/migration-code.png)
 
-The benchmark results from this research can be inspected in our [visual benchmark explorer](https://hindsight-benchmarks.vercel.app). As additional improvements are made to Hindsight, new benchmark data will be available for review using this same tool.
 
 ## Quick Start
 
@@ -148,8 +124,45 @@ await client.recall('my-bank', 'What does Alice like?');
 
 ---
 
+## Use Cases
+
+
+Hindsight is built to support conversational AI agents as well as agents that are intended to perform tasks autonomously. The ideal use case for Hindsight are agents that require a blend of these features such as AI employees that need to handle open-ended tasks, change behavior based on user feedback, and learn to perform complex tasks to automate work at a level that approximates a human work. Hindsight can be used with simple AI workflows like those built with n8n and other similar tools, but may be overkill for such applications.
+
+### Per-User Memories and Chat History
+
+One of the simpler use cases you can use Hindsight for is to personalize AI chatbots and other conversational agents by storing and recalling memories associated with individual users.
+
+The requirements for this use case usually look something like this:
+
+![Per-User Memories](./hindsight-docs/static/img/per-user-memory-requirements.png)
+
+<video src="https://github.com/user-attachments/assets/4805e8e1-e7d1-47c6-a4f8-2344a5ec8906" controls></video>
+
+Satisfying these requirements in Hindsight is straightforward. When new user inputs and tool calls are ingested into Hindsight using the retain operation, custom metadata can be used to enrich the new memories. Metadata provides a convenient way to isolate memories that need to be restricted to a given user. Once these are fed into the retain operation, any raw memories and mental models that get created can be filtered when retrieving relevant memories. 
+
+![Per-User Memories](./hindsight-docs/static/img/per-user-memory-howto.png)
+
+---
+
 ## Architecture & Operations
 
+![Overview](./hindsight-docs/static/img/hindsight-overview.webp)
+
+Most agent memory implementation rely on basic vector search or sometimes use a knowledge graph. Hindsight uses biomimetic data structures to organize agent memories in a way that is more like how human memory works:
+
+- **World:** Facts about the world ("The stove gets hot")
+- **Experiences:** Agent's own experiences ("I touched the stove and it really hurt")
+- **Mental Models:** Learned understanding of the agent's world formed by reflecting on raw memories and experiences.
+
+Memories in Hindsight are stored in banks (i.e. memory banks). When memories are added to Hindsight, they are pushed into either the world facts or experiences memory pathway. They are then represented as a combination of entities, relationships, and time series with sparse/dense vector representations to aid in later recall.
+
+Hindsight provides three simple methods to interact with the system:
+
+- **Retain:** Provide information to Hindsight that you want it to remember
+- **Recall:** Retrieve memories from Hindsight
+- **Reflect:** Reflect on memories and experiences to generate new observations and insights from existing memories.
+
 ### Retain
 
 The `retain` operation is used to push new memories into Hindsight. It tells Hindsight to _retain_ the information you pass in as an input.
@@ -208,7 +221,7 @@ The final output is trimmed as needed to fit within the token limit.
 
 ### Reflect
 
-The reflect operation is used to perform a more thorough analysis of existing memories. This allows the agent to form new connections between memories which are then persisted as opinions and/or observations. When building agents, the reflect operation is a key capability to enable the agent to learn from its experiences. 
+The reflect operation is used to perform a more thorough analysis of existing memories. This allows the agent to form new connections between memories and build a more thorough understanding of its world.
 
 For example, the `reflect` operation can be used to support use cases such as:
 
@@ -242,7 +255,7 @@ client.reflect(bank_id="my-bank", query="What should I know about Alice?")
 - [CLI](https://hindsight.vectorize.io/sdks/cli)
 
 **Community:**
-- [Slack](https://join.slack.com/t/hindsight-space/shared_invite/zt-3klo21kua-VUCC_zHP5rIcXFB1_5yw6A)
+- [Slack](https://join.slack.com/t/hindsight-space/shared_invite/zt-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg)
 - [GitHub Issues](https://github.com/vectorize-io/hindsight/issues)
 
 ---

helm/hindsight/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.2.1
-appVersion: "0.2.1"
+version: 0.4.2
+appVersion: "0.4.2"
 keywords:
   - ai
   - memory

helm/hindsight/templates/_helpers.tpl

@@ -80,6 +80,22 @@ Control plane selector labels
 app.kubernetes.io/component: control-plane
 {{- end }}
 
+{{/*
+Worker labels
+*/}}
+{{- define "hindsight.worker.labels" -}}
+{{ include "hindsight.labels" . }}
+app.kubernetes.io/component: worker
+{{- end }}
+
+{{/*
+Worker selector labels
+*/}}
+{{- define "hindsight.worker.selectorLabels" -}}
+{{ include "hindsight.selectorLabels" . }}
+app.kubernetes.io/component: worker
+{{- end }}
+
 {{/*
 Create the name of the service account to use
 */}}

helm/hindsight/templates/api-deployment.yaml

@@ -55,6 +55,11 @@ spec:
         {{- end }}
         - name: HINDSIGHT_API_DATABASE_URL
           value: {{ include "hindsight.databaseUrl" . | quote }}
+        {{- /* Disable internal worker when dedicated workers are enabled */}}
+        {{- if .Values.worker.enabled }}
+        - name: HINDSIGHT_API_WORKER_ENABLED
+          value: "false"
+        {{- end }}
         {{- range $key, $value := .Values.api.env }}
         - name: {{ $key }}
           value: {{ $value | quote }}

helm/hindsight/templates/worker-service.yaml

@@ -0,0 +1,25 @@
+{{- if .Values.worker.enabled }}
+apiVersion: v1
+kind: Service
+metadata:
+  name: {{ include "hindsight.fullname" . }}-worker
+  labels:
+    {{- include "hindsight.worker.labels" . | nindent 4 }}
+  {{- if .Values.podAnnotations }}
+  annotations:
+    {{- /* Common Prometheus annotations for metrics scraping */}}
+    prometheus.io/scrape: "true"
+    prometheus.io/port: {{ .Values.worker.service.port | quote }}
+    prometheus.io/path: "/metrics"
+  {{- end }}
+spec:
+  # Headless service for StatefulSet (enables stable DNS names like worker-0.worker.namespace)
+  clusterIP: None
+  ports:
+  - port: {{ .Values.worker.service.port }}
+    targetPort: {{ .Values.worker.service.targetPort }}
+    protocol: TCP
+    name: http
+  selector:
+    {{- include "hindsight.worker.selectorLabels" . | nindent 4 }}
+{{- end }}

helm/hindsight/templates/worker-statefulset.yaml

@@ -0,0 +1,110 @@
+{{- if .Values.worker.enabled }}
+apiVersion: apps/v1
+kind: StatefulSet
+metadata:
+  name: {{ include "hindsight.fullname" . }}-worker
+  labels:
+    {{- include "hindsight.worker.labels" . | nindent 4 }}
+spec:
+  serviceName: {{ include "hindsight.fullname" . }}-worker
+  replicas: {{ .Values.worker.replicaCount }}
+  selector:
+    matchLabels:
+      {{- include "hindsight.worker.selectorLabels" . | nindent 6 }}
+  template:
+    metadata:
+      annotations:
+        {{- if not .Values.existingSecret }}
+        checksum/secret: {{ include (print $.Template.BasePath "/secret.yaml") . | sha256sum }}
+        {{- end }}
+        {{- with .Values.podAnnotations }}
+        {{- toYaml . | nindent 8 }}
+        {{- end }}
+      labels:
+        {{- include "hindsight.worker.selectorLabels" . | nindent 8 }}
+    spec:
+      {{- if .Values.serviceAccount.create }}
+      serviceAccountName: {{ include "hindsight.serviceAccountName" . }}
+      {{- end }}
+      securityContext:
+        {{- toYaml .Values.podSecurityContext | nindent 8 }}
+      containers:
+      - name: worker
+        securityContext:
+          {{- toYaml .Values.securityContext | nindent 10 }}
+        image: "{{ .Values.worker.image.repository }}:{{ .Values.worker.image.tag | default .Values.version }}"
+        imagePullPolicy: {{ .Values.worker.image.pullPolicy }}
+        command: ["hindsight-worker"]
+        ports:
+        - name: http
+          containerPort: {{ .Values.worker.service.targetPort }}
+          protocol: TCP
+        {{- if .Values.existingSecret }}
+        envFrom:
+        - secretRef:
+            name: {{ .Values.existingSecret }}
+        {{- end }}
+        env:
+        {{- /* POSTGRES_PASSWORD must be defined before DATABASE_URL for $(VAR) interpolation */}}
+        {{- if not .Values.postgresql.enabled }}
+        - name: POSTGRES_PASSWORD
+          valueFrom:
+            secretKeyRef:
+              name: {{ include "hindsight.secretName" . }}
+              key: postgres-password
+        {{- end }}
+        - name: HINDSIGHT_API_DATABASE_URL
+          value: {{ include "hindsight.databaseUrl" . | quote }}
+        {{- /* Worker ID uses pod name (StatefulSet provides stable names like worker-0, worker-1) */}}
+        - name: HINDSIGHT_API_WORKER_ID
+          valueFrom:
+            fieldRef:
+              fieldPath: metadata.name
+        {{- /* Inherit LLM config from api.env */}}
+        {{- range $key, $value := .Values.api.env }}
+        - name: {{ $key }}
+          value: {{ $value | quote }}
+        {{- end }}
+        {{- /* Worker-specific env vars */}}
+        {{- range $key, $value := .Values.worker.env }}
+        - name: {{ $key }}
+          value: {{ $value | quote }}
+        {{- end }}
+        {{- /* Only use secrets when not using existingSecret */}}
+        {{- if not .Values.existingSecret }}
+        {{- /* Inherit secrets from api.secrets */}}
+        {{- range $key, $value := .Values.api.secrets }}
+        - name: {{ $key }}
+          valueFrom:
+            secretKeyRef:
+              name: {{ include "hindsight.secretName" $ }}
+              key: {{ $key }}
+        {{- end }}
+        {{- /* Worker-specific secrets (can override api.secrets) */}}
+        {{- range $key, $value := .Values.worker.secrets }}
+        - name: {{ $key }}
+          valueFrom:
+            secretKeyRef:
+              name: {{ include "hindsight.secretName" $ }}
+              key: {{ $key }}
+        {{- end }}
+        {{- end }}
+        livenessProbe:
+          {{- toYaml .Values.worker.livenessProbe | nindent 10 }}
+        readinessProbe:
+          {{- toYaml .Values.worker.readinessProbe | nindent 10 }}
+        resources:
+          {{- toYaml .Values.worker.resources | nindent 10 }}
+      {{- with .Values.nodeSelector }}
+      nodeSelector:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+      {{- with .Values.affinity }}
+      affinity:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+      {{- with .Values.tolerations }}
+      tolerations:
+        {{- toYaml . | nindent 8 }}
+      {{- end }}
+{{- end }}

helm/hindsight/values.yaml

@@ -67,6 +67,63 @@ api:
     # HINDSIGHT_API_LLM_API_KEY: "your-api-key"
     # HINDSIGHT_API_LLM_BASE_URL: "https://api.groq.com/openai/v1"
 
+# Worker settings (distributed task processing)
+# When enabled, dedicated worker pods process tasks and the API's internal worker is disabled
+worker:
+  enabled: false
+  replicaCount: 2
+  image:
+    repository: ghcr.io/vectorize-io/hindsight-api
+    pullPolicy: IfNotPresent
+    # tag defaults to .Values.version if not specified
+
+  service:
+    # Service for metrics scraping (headless for StatefulSet)
+    port: 8889
+    targetPort: 8889
+
+  # Resource limits and requests
+  resources:
+    limits:
+      cpu: 2000m
+      memory: 4Gi
+    requests:
+      cpu: 500m
+      memory: 1Gi
+
+  # Liveness and readiness probes
+  livenessProbe:
+    httpGet:
+      path: /health
+      port: 8889
+    initialDelaySeconds: 30
+    periodSeconds: 10
+    timeoutSeconds: 5
+    failureThreshold: 3
+
+  readinessProbe:
+    httpGet:
+      path: /health
+      port: 8889
+    initialDelaySeconds: 10
+    periodSeconds: 5
+    timeoutSeconds: 3
+    failureThreshold: 3
+
+  # Worker-specific environment variables
+  env:
+    # Poll interval in milliseconds (how often to check for new tasks)
+    HINDSIGHT_API_WORKER_POLL_INTERVAL_MS: "500"
+    # Number of tasks to claim per poll cycle
+    HINDSIGHT_API_WORKER_BATCH_SIZE: "10"
+    # Max retries before marking a task as failed
+    HINDSIGHT_API_WORKER_MAX_RETRIES: "3"
+    # HTTP port for metrics/health (matches service.targetPort)
+    HINDSIGHT_API_WORKER_HTTP_PORT: "8889"
+
+  # Secret environment variables (inherited from api.secrets if not specified)
+  secrets: {}
+
 # Image settings for control plane
 controlPlane:
   enabled: true

hindsight-api/hindsight_api/__init__.py

@@ -46,4 +46,4 @@ __all__ = [
     "RemoteTEICrossEncoder",
     "LLMConfig",
 ]
-__version__ = "0.1.0"
+__version__ = "0.4.2"

hindsight-api/hindsight_api/admin/cli.py

@@ -244,6 +244,65 @@ def run_db_migration(
     typer.echo("Database migrations completed successfully")
 
 
+async def _decommission_worker(db_url: str, worker_id: str, schema: str = "public") -> int:
+    """Release all tasks owned by a worker, setting them back to pending status."""
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
+    if is_pg0:
+        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
+    resolved_url = await resolve_database_url(db_url)
+
+    conn = await asyncpg.connect(resolved_url)
+    try:
+        table = _fq_table("async_operations", schema)
+        result = await conn.fetch(
+            f"""
+            UPDATE {table}
+            SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
+            WHERE worker_id = $1 AND status = 'processing'
+            RETURNING operation_id
+            """,
+            worker_id,
+        )
+        return len(result)
+    finally:
+        await conn.close()
+
+
+@app.command(name="decommission-worker")
+def decommission_worker(
+    worker_id: str = typer.Argument(..., help="Worker ID to decommission"),
+    schema: str = typer.Option("public", "--schema", "-s", help="Database schema"),
+    yes: bool = typer.Option(False, "--yes", "-y", help="Skip confirmation prompt"),
+):
+    """Release all tasks owned by a worker (sets status back to pending).
+
+    Use this command when a worker has crashed or been removed without graceful shutdown.
+    All tasks that were being processed by the worker will be released back to the queue
+    so other workers can pick them up.
+    """
+    config = HindsightConfig.from_env()
+
+    if not config.database_url:
+        typer.echo("Error: Database URL not configured.", err=True)
+        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
+        raise typer.Exit(1)
+
+    if not yes:
+        typer.confirm(
+            f"This will release all tasks owned by worker '{worker_id}' back to pending. Continue?",
+            abort=True,
+        )
+
+    typer.echo(f"Decommissioning worker '{worker_id}' (schema: {schema})...")
+
+    count = asyncio.run(_decommission_worker(config.database_url, worker_id, schema))
+
+    if count > 0:
+        typer.echo(f"Released {count} task(s) from worker '{worker_id}'")
+    else:
+        typer.echo(f"No tasks found for worker '{worker_id}'")
+
+
 def main():
     app()
 

hindsight-api/hindsight_api/alembic/versions/f1a2b3c4d5e6_add_memory_links_composite_index.py

@@ -0,0 +1,44 @@
+"""add_memory_links_from_type_weight_index
+
+Revision ID: f1a2b3c4d5e6
+Revises: e0a1b2c3d4e5
+Create Date: 2025-01-12
+
+Add composite index on memory_links (from_unit_id, link_type, weight DESC)
+to optimize MPFP graph traversal queries that need top-k edges per type.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "f1a2b3c4d5e6"
+down_revision: str | Sequence[str] | None = "e0a1b2c3d4e5"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add composite index for efficient MPFP edge loading."""
+    schema = _get_schema_prefix()
+    # Create composite index for efficient top-k per (from_node, link_type) queries
+    # This enables LATERAL joins to use index-only scans with early termination
+    # Note: Not using CONCURRENTLY here as it requires running outside a transaction
+    # For production with large tables, consider running this manually with CONCURRENTLY
+    op.execute(
+        f"CREATE INDEX IF NOT EXISTS idx_memory_links_from_type_weight "
+        f"ON {schema}memory_links(from_unit_id, link_type, weight DESC)"
+    )
+
+
+def downgrade() -> None:
+    """Remove the composite index."""
+    schema = _get_schema_prefix()
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_links_from_type_weight")

hindsight-api/hindsight_api/alembic/versions/g2a3b4c5d6e7_add_tags_column.py

@@ -0,0 +1,48 @@
+"""add_tags_column
+
+Revision ID: g2a3b4c5d6e7
+Revises: f1a2b3c4d5e6
+Create Date: 2025-01-13
+
+Add tags column to memory_units and documents tables for visibility scoping.
+Tags enable filtering memories by scope (e.g., user IDs, session IDs) during recall/reflect.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "g2a3b4c5d6e7"
+down_revision: str | Sequence[str] | None = "f1a2b3c4d5e6"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (e.g., 'tenant_x.' or '' for public)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add tags column to memory_units and documents tables."""
+    schema = _get_schema_prefix()
+
+    # Add tags column to memory_units table
+    op.execute(f"ALTER TABLE {schema}memory_units ADD COLUMN IF NOT EXISTS tags VARCHAR[] NOT NULL DEFAULT '{{}}'")
+
+    # Create GIN index for efficient array containment queries (tags && ARRAY['x'])
+    op.execute(f"CREATE INDEX IF NOT EXISTS idx_memory_units_tags ON {schema}memory_units USING GIN (tags)")
+
+    # Add tags column to documents table for document-level tags
+    op.execute(f"ALTER TABLE {schema}documents ADD COLUMN IF NOT EXISTS tags VARCHAR[] NOT NULL DEFAULT '{{}}'")
+
+
+def downgrade() -> None:
+    """Remove tags columns and index."""
+    schema = _get_schema_prefix()
+
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_tags")
+    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS tags")
+    op.execute(f"ALTER TABLE {schema}documents DROP COLUMN IF EXISTS tags")

hindsight-api/hindsight_api/alembic/versions/h3c4d5e6f7g8_mental_models_v4.py

@@ -0,0 +1,112 @@
+"""mental_models_v4
+
+Revision ID: h3c4d5e6f7g8
+Revises: g2a3b4c5d6e7
+Create Date: 2026-01-08 00:00:00.000000
+
+This migration implements the v4 mental models system:
+1. Deletes existing observation memory_units (observations now in mental models)
+2. Adds mission column to banks (replacing background)
+3. Creates mental_models table with final schema
+
+Mental models can reference entities when an entity is "promoted" to a mental model.
+Summary content is stored as JSONB observations with per-observation fact attribution.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "h3c4d5e6f7g8"
+down_revision: str | Sequence[str] | None = "g2a3b4c5d6e7"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Apply mental models v4 changes."""
+    schema = _get_schema_prefix()
+
+    # Step 1: Delete observation memory_units (cascades to unit_entities links)
+    # Observations are now handled through mental models, not memory_units
+    op.execute(f"DELETE FROM {schema}memory_units WHERE fact_type = 'observation'")
+
+    # Step 2: Drop observation-specific index (if it exists)
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_observation_date")
+
+    # Step 3: Add mission column to banks (replacing background)
+    op.execute(f"ALTER TABLE {schema}banks ADD COLUMN IF NOT EXISTS mission TEXT")
+
+    # Migrate: copy background to mission if background column exists
+    # Use DO block to check column existence first (idempotent for re-runs)
+    schema_name = context.config.get_main_option("target_schema") or "public"
+    op.execute(f"""
+        DO $$
+        BEGIN
+            IF EXISTS (
+                SELECT 1 FROM information_schema.columns
+                WHERE table_schema = '{schema_name}' AND table_name = 'banks' AND column_name = 'background'
+            ) THEN
+                UPDATE {schema}banks
+                SET mission = background
+                WHERE mission IS NULL;
+            END IF;
+        END $$;
+    """)
+
+    # Remove background column (replaced by mission)
+    op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS background")
+
+    # Step 4: Create mental_models table with final v4 schema (if not exists)
+    op.execute(f"""
+        CREATE TABLE IF NOT EXISTS {schema}mental_models (
+            id VARCHAR(64) NOT NULL,
+            bank_id VARCHAR(64) NOT NULL,
+            subtype VARCHAR(32) NOT NULL,
+            name VARCHAR(256) NOT NULL,
+            description TEXT NOT NULL,
+            entity_id UUID,
+            observations JSONB DEFAULT '{{"observations": []}}'::jsonb,
+            links VARCHAR[],
+            tags VARCHAR[] DEFAULT '{{}}',
+            last_updated TIMESTAMP WITH TIME ZONE,
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            PRIMARY KEY (id, bank_id),
+            FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE,
+            FOREIGN KEY (entity_id) REFERENCES {schema}entities(id) ON DELETE SET NULL,
+            CONSTRAINT ck_mental_models_subtype CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned'))
+        )
+    """)
+
+    # Step 5: Create indexes for efficient queries (if not exist)
+    op.execute(f"CREATE INDEX IF NOT EXISTS idx_mental_models_bank_id ON {schema}mental_models(bank_id)")
+    op.execute(f"CREATE INDEX IF NOT EXISTS idx_mental_models_subtype ON {schema}mental_models(bank_id, subtype)")
+    op.execute(f"CREATE INDEX IF NOT EXISTS idx_mental_models_entity_id ON {schema}mental_models(entity_id)")
+    # GIN index for efficient tags array filtering
+    op.execute(f"CREATE INDEX IF NOT EXISTS idx_mental_models_tags ON {schema}mental_models USING GIN(tags)")
+
+
+def downgrade() -> None:
+    """Revert mental models v4 changes."""
+    schema = _get_schema_prefix()
+
+    # Drop mental_models table (cascades to indexes)
+    op.execute(f"DROP TABLE IF EXISTS {schema}mental_models CASCADE")
+
+    # Add back background column to banks
+    op.execute(f"ALTER TABLE {schema}banks ADD COLUMN IF NOT EXISTS background TEXT")
+
+    # Migrate mission back to background
+    op.execute(f"UPDATE {schema}banks SET background = mission WHERE background IS NULL")
+
+    # Remove mission column
+    op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS mission")
+
+    # Note: Cannot restore deleted observations - they are lost on downgrade

hindsight-api/hindsight_api/alembic/versions/i4d5e6f7g8h9_delete_opinions.py

@@ -0,0 +1,41 @@
+"""delete_opinions
+
+Revision ID: i4d5e6f7g8h9
+Revises: h3c4d5e6f7g8
+Create Date: 2026-01-15 00:00:00.000000
+
+This migration removes opinion facts from memory_units.
+Opinions are no longer a separate fact type - they are now represented
+through mental model observations with confidence scores.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "i4d5e6f7g8h9"
+down_revision: str | Sequence[str] | None = "h3c4d5e6f7g8"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Delete opinion memory_units."""
+    schema = _get_schema_prefix()
+
+    # Delete opinion memory_units (cascades to unit_entities links)
+    # Opinions are now handled through mental model observations
+    op.execute(f"DELETE FROM {schema}memory_units WHERE fact_type = 'opinion'")
+
+
+def downgrade() -> None:
+    """Cannot restore deleted opinions."""
+    # Note: Cannot restore deleted opinions - they are lost on downgrade
+    pass

hindsight-api/hindsight_api/alembic/versions/j5e6f7g8h9i0_mental_model_versions.py

@@ -0,0 +1,95 @@
+"""mental_model_versions
+
+Revision ID: j5e6f7g8h9i0
+Revises: i4d5e6f7g8h9
+Create Date: 2026-01-16 00:00:00.000000
+
+This migration adds versioning support for mental models:
+1. Creates mental_model_versions table to store observation snapshots
+2. Adds version column to mental_models for tracking current version
+
+This enables changelog/diff functionality for mental model observations.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "j5e6f7g8h9i0"
+down_revision: str | Sequence[str] | None = "i4d5e6f7g8h9"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Create mental_model_versions table and add version tracking."""
+    schema = _get_schema_prefix()
+
+    # Create mental_model_versions table for storing observation snapshots
+    op.execute(f"""
+        CREATE TABLE {schema}mental_model_versions (
+            id SERIAL PRIMARY KEY,
+            mental_model_id VARCHAR(64) NOT NULL,
+            bank_id VARCHAR(64) NOT NULL,
+            version INT NOT NULL,
+            observations JSONB NOT NULL DEFAULT '{{"observations": []}}'::jsonb,
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            FOREIGN KEY (mental_model_id, bank_id)
+                REFERENCES {schema}mental_models(id, bank_id) ON DELETE CASCADE,
+            UNIQUE (mental_model_id, bank_id, version)
+        )
+    """)
+
+    # Index for efficient version queries (get latest, list versions)
+    op.execute(f"""
+        CREATE INDEX idx_mental_model_versions_lookup
+        ON {schema}mental_model_versions(mental_model_id, bank_id, version DESC)
+    """)
+
+    # Add version column to mental_models to track current version
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD COLUMN IF NOT EXISTS version INT NOT NULL DEFAULT 0
+    """)
+
+    # Migrate existing mental models: create version 1 for any that have observations
+    op.execute(f"""
+        INSERT INTO {schema}mental_model_versions (mental_model_id, bank_id, version, observations, created_at)
+        SELECT id, bank_id, 1, observations, COALESCE(last_updated, created_at)
+        FROM {schema}mental_models
+        WHERE observations IS NOT NULL
+          AND observations != '{{"observations": []}}'::jsonb
+          AND (observations->'observations') IS NOT NULL
+          AND jsonb_array_length(observations->'observations') > 0
+    """)
+
+    # Update version to 1 for migrated mental models
+    op.execute(f"""
+        UPDATE {schema}mental_models
+        SET version = 1
+        WHERE observations IS NOT NULL
+          AND observations != '{{"observations": []}}'::jsonb
+          AND (observations->'observations') IS NOT NULL
+          AND jsonb_array_length(observations->'observations') > 0
+    """)
+
+
+def downgrade() -> None:
+    """Remove mental_model_versions table and version column."""
+    schema = _get_schema_prefix()
+
+    # Drop index
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_mental_model_versions_lookup")
+
+    # Drop versions table
+    op.execute(f"DROP TABLE IF EXISTS {schema}mental_model_versions")
+
+    # Remove version column from mental_models
+    op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS version")

hindsight-api/hindsight_api/alembic/versions/k6f7g8h9i0j1_add_directive_subtype.py

@@ -0,0 +1,58 @@
+"""add_directive_subtype
+
+Revision ID: k6f7g8h9i0j1
+Revises: j5e6f7g8h9i0
+Create Date: 2026-01-16 00:00:00.000000
+
+This migration adds 'directive' to the mental_models subtype constraint.
+Directives are hard rules with user-provided observations that the reflect agent must follow.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "k6f7g8h9i0j1"
+down_revision: str | Sequence[str] | None = "j5e6f7g8h9i0"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add 'directive' to mental_models subtype constraint."""
+    schema = _get_schema_prefix()
+
+    # Drop existing constraint
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
+
+    # Create new constraint with 'directive' added
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD CONSTRAINT ck_mental_models_subtype
+        CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned', 'directive'))
+    """)
+
+
+def downgrade() -> None:
+    """Remove 'directive' from mental_models subtype constraint."""
+    schema = _get_schema_prefix()
+
+    # First delete any directives (cannot downgrade if they exist)
+    op.execute(f"DELETE FROM {schema}mental_models WHERE subtype = 'directive'")
+
+    # Drop constraint with directive
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
+
+    # Recreate original constraint without directive
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD CONSTRAINT ck_mental_models_subtype
+        CHECK (subtype IN ('structural', 'emergent', 'pinned', 'learned'))
+    """)

hindsight-api/hindsight_api/alembic/versions/l7g8h9i0j1k2_add_worker_columns.py

@@ -0,0 +1,109 @@
+"""add_worker_columns
+
+Revision ID: l7g8h9i0j1k2
+Revises: k6f7g8h9i0j1
+Create Date: 2026-01-19 00:00:00.000000
+
+This migration adds columns to async_operations for distributed worker support:
+- worker_id: ID of the worker that claimed the task
+- claimed_at: When the task was claimed
+- retry_count: Number of retry attempts
+- task_payload: The serialized task dictionary
+"""
+
+from collections.abc import Sequence
+
+import sqlalchemy as sa
+from alembic import context, op
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = "l7g8h9i0j1k2"
+down_revision: str | Sequence[str] | None = "k6f7g8h9i0j1"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add worker columns to async_operations."""
+    schema = _get_schema_prefix()
+
+    # Add worker_id column (ID of worker that claimed the task)
+    op.add_column(
+        "async_operations",
+        sa.Column("worker_id", sa.Text(), nullable=True),
+        schema=context.config.get_main_option("target_schema") or None,
+    )
+
+    # Add claimed_at column (when task was claimed by worker)
+    op.add_column(
+        "async_operations",
+        sa.Column("claimed_at", postgresql.TIMESTAMP(timezone=True), nullable=True),
+        schema=context.config.get_main_option("target_schema") or None,
+    )
+
+    # Add retry_count column (number of retry attempts)
+    op.add_column(
+        "async_operations",
+        sa.Column("retry_count", sa.Integer(), server_default="0", nullable=False),
+        schema=context.config.get_main_option("target_schema") or None,
+    )
+
+    # Add task_payload column (serialized task dictionary)
+    op.add_column(
+        "async_operations",
+        sa.Column(
+            "task_payload",
+            postgresql.JSONB(astext_type=sa.Text()),
+            nullable=True,
+        ),
+        schema=context.config.get_main_option("target_schema") or None,
+    )
+
+    # Add index for efficient worker polling (pending tasks ordered by creation time)
+    op.execute(
+        f"CREATE INDEX idx_async_operations_pending_claim ON {schema}async_operations (status, created_at) "
+        f"WHERE status = 'pending' AND task_payload IS NOT NULL"
+    )
+
+    # Add index for finding tasks by worker_id (for decommissioning)
+    op.execute(
+        f"CREATE INDEX idx_async_operations_worker_id ON {schema}async_operations (worker_id) WHERE worker_id IS NOT NULL"
+    )
+
+
+def downgrade() -> None:
+    """Remove worker columns from async_operations."""
+    schema = _get_schema_prefix()
+
+    # Drop indexes
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_async_operations_pending_claim")
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_async_operations_worker_id")
+
+    # Drop columns
+    op.drop_column(
+        "async_operations",
+        "task_payload",
+        schema=context.config.get_main_option("target_schema") or None,
+    )
+    op.drop_column(
+        "async_operations",
+        "retry_count",
+        schema=context.config.get_main_option("target_schema") or None,
+    )
+    op.drop_column(
+        "async_operations",
+        "claimed_at",
+        schema=context.config.get_main_option("target_schema") or None,
+    )
+    op.drop_column(
+        "async_operations",
+        "worker_id",
+        schema=context.config.get_main_option("target_schema") or None,
+    )

hindsight-api/hindsight_api/alembic/versions/m8h9i0j1k2l3_mental_model_id_to_text.py

@@ -0,0 +1,41 @@
+"""mental_model_id_to_text
+
+Revision ID: m8h9i0j1k2l3
+Revises: l7g8h9i0j1k2
+Create Date: 2026-01-19 00:00:00.000000
+
+This migration changes the mental_models.id column from VARCHAR(64) to TEXT
+to support longer model IDs (e.g., entity names that exceed 64 characters).
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "m8h9i0j1k2l3"
+down_revision: str | Sequence[str] | None = "l7g8h9i0j1k2"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Change mental_models.id from VARCHAR(64) to TEXT."""
+    schema = _get_schema_prefix()
+
+    # Alter the id column type from VARCHAR(64) to TEXT
+    op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN id TYPE TEXT")
+
+
+def downgrade() -> None:
+    """Revert mental_models.id from TEXT to VARCHAR(64)."""
+    schema = _get_schema_prefix()
+
+    # Note: This may fail if any id values exceed 64 characters
+    op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN id TYPE VARCHAR(64)")

hindsight-api/hindsight_api/alembic/versions/n9i0j1k2l3m4_learnings_and_pinned_reflections.py

@@ -0,0 +1,134 @@
+"""learnings_and_pinned_reflections
+
+Revision ID: n9i0j1k2l3m4
+Revises: m8h9i0j1k2l3
+Create Date: 2026-01-21 00:00:00.000000
+
+This migration:
+1. Creates the 'learnings' table for automatic bottom-up consolidation
+2. Creates the 'pinned_reflections' table for user-curated living documents
+3. Adds consolidation tracking columns to the 'banks' table
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "n9i0j1k2l3m4"
+down_revision: str | Sequence[str] | None = "m8h9i0j1k2l3"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Create learnings and pinned_reflections tables."""
+    schema = _get_schema_prefix()
+
+    # 1. Create learnings table
+    op.execute(f"""
+        CREATE TABLE {schema}learnings (
+            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+            bank_id VARCHAR(64) NOT NULL,
+            text TEXT NOT NULL,
+            proof_count INT NOT NULL DEFAULT 1,
+            history JSONB DEFAULT '[]'::jsonb,
+            mission_context VARCHAR(64),
+            pre_mission_change BOOLEAN DEFAULT FALSE,
+            embedding vector(384),
+            tags VARCHAR[] DEFAULT ARRAY[]::VARCHAR[],
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now()
+        )
+    """)
+
+    # Add foreign key constraint
+    op.execute(f"""
+        ALTER TABLE {schema}learnings
+        ADD CONSTRAINT fk_learnings_bank_id
+        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
+    """)
+
+    # Indexes for learnings
+    op.execute(f"CREATE INDEX idx_learnings_bank_id ON {schema}learnings(bank_id)")
+    op.execute(f"""
+        CREATE INDEX idx_learnings_embedding ON {schema}learnings
+        USING hnsw (embedding vector_cosine_ops)
+    """)
+    op.execute(f"CREATE INDEX idx_learnings_tags ON {schema}learnings USING GIN(tags)")
+
+    # Full-text search for learnings
+    op.execute(f"""
+        ALTER TABLE {schema}learnings ADD COLUMN search_vector tsvector
+        GENERATED ALWAYS AS (to_tsvector('english', text)) STORED
+    """)
+    op.execute(f"CREATE INDEX idx_learnings_text_search ON {schema}learnings USING gin(search_vector)")
+
+    # 2. Create pinned_reflections table
+    op.execute(f"""
+        CREATE TABLE {schema}pinned_reflections (
+            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+            bank_id VARCHAR(64) NOT NULL,
+            name VARCHAR(256) NOT NULL,
+            source_query TEXT NOT NULL,
+            content TEXT NOT NULL,
+            embedding vector(384),
+            tags VARCHAR[] DEFAULT ARRAY[]::VARCHAR[],
+            last_refreshed_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now()
+        )
+    """)
+
+    # Add foreign key constraint
+    op.execute(f"""
+        ALTER TABLE {schema}pinned_reflections
+        ADD CONSTRAINT fk_pinned_reflections_bank_id
+        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
+    """)
+
+    # Indexes for pinned_reflections
+    op.execute(f"CREATE INDEX idx_pinned_reflections_bank_id ON {schema}pinned_reflections(bank_id)")
+    op.execute(f"""
+        CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+        USING hnsw (embedding vector_cosine_ops)
+    """)
+    op.execute(f"CREATE INDEX idx_pinned_reflections_tags ON {schema}pinned_reflections USING GIN(tags)")
+
+    # Full-text search for pinned_reflections
+    op.execute(f"""
+        ALTER TABLE {schema}pinned_reflections ADD COLUMN search_vector tsvector
+        GENERATED ALWAYS AS (to_tsvector('english', COALESCE(name, '') || ' ' || content)) STORED
+    """)
+    op.execute(f"""
+        CREATE INDEX idx_pinned_reflections_text_search ON {schema}pinned_reflections
+        USING gin(search_vector)
+    """)
+
+    # 3. Add consolidation tracking columns to banks table
+    op.execute(f"""
+        ALTER TABLE {schema}banks
+        ADD COLUMN IF NOT EXISTS last_consolidated_at TIMESTAMP WITH TIME ZONE
+    """)
+    op.execute(f"""
+        ALTER TABLE {schema}banks
+        ADD COLUMN IF NOT EXISTS mission_changed_at TIMESTAMP WITH TIME ZONE
+    """)
+
+
+def downgrade() -> None:
+    """Drop learnings and pinned_reflections tables."""
+    schema = _get_schema_prefix()
+
+    # Drop tables
+    op.execute(f"DROP TABLE IF EXISTS {schema}learnings CASCADE")
+    op.execute(f"DROP TABLE IF EXISTS {schema}pinned_reflections CASCADE")
+
+    # Remove columns from banks
+    op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS last_consolidated_at")
+    op.execute(f"ALTER TABLE {schema}banks DROP COLUMN IF EXISTS mission_changed_at")

hindsight-api/hindsight_api/alembic/versions/o0j1k2l3m4n5_migrate_mental_models_data.py

@@ -0,0 +1,113 @@
+"""migrate_mental_models_data
+
+Revision ID: o0j1k2l3m4n5
+Revises: n9i0j1k2l3m4
+Create Date: 2026-01-21 00:00:00.000000
+
+This migration:
+1. Migrates existing 'pinned' mental models to the new 'pinned_reflections' table
+2. Migrates existing 'learned' mental models to the new 'learnings' table
+3. Deletes non-directive mental models (structural, emergent, pinned, learned)
+4. Drops the mental_model_versions table (no longer used)
+5. Adds a CHECK constraint that only 'directive' subtype is allowed
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "o0j1k2l3m4n5"
+down_revision: str | Sequence[str] | None = "n9i0j1k2l3m4"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Migrate data and clean up old mental models."""
+    schema = _get_schema_prefix()
+
+    # 1. Migrate 'pinned' mental models to pinned_reflections
+    # For pinned models, the first observation's content becomes the pinned reflection content
+    op.execute(f"""
+        INSERT INTO {schema}pinned_reflections (bank_id, name, source_query, content, tags, created_at)
+        SELECT
+            bank_id,
+            name,
+            description AS source_query,
+            COALESCE(
+                observations->'observations'->0->>'content',
+                description,
+                ''
+            ) AS content,
+            tags,
+            created_at
+        FROM {schema}mental_models
+        WHERE subtype = 'pinned'
+        ON CONFLICT DO NOTHING
+    """)
+
+    # 2. Migrate 'learned' mental models to learnings
+    # Each observation in a learned model becomes a separate learning
+    op.execute(f"""
+        INSERT INTO {schema}learnings (bank_id, text, proof_count, tags, created_at)
+        SELECT
+            mm.bank_id,
+            obs->>'content' AS text,
+            GREATEST(1, COALESCE(jsonb_array_length(obs->'evidence'), 1)) AS proof_count,
+            mm.tags,
+            mm.created_at
+        FROM {schema}mental_models mm,
+        LATERAL jsonb_array_elements(mm.observations->'observations') AS obs
+        WHERE mm.subtype = 'learned'
+          AND obs->>'content' IS NOT NULL
+          AND obs->>'content' != ''
+        ON CONFLICT DO NOTHING
+    """)
+
+    # 3. Delete all non-directive mental models (they've been migrated or are obsolete)
+    op.execute(f"""
+        DELETE FROM {schema}mental_models
+        WHERE subtype != 'directive'
+    """)
+
+    # 4. Drop the mental_model_versions table (no longer used)
+    op.execute(f"DROP TABLE IF EXISTS {schema}mental_model_versions CASCADE")
+
+    # 5. Drop old constraints and add new one that only allows 'directive'
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD CONSTRAINT ck_mental_models_subtype CHECK (subtype = 'directive')
+    """)
+
+
+def downgrade() -> None:
+    """Reverse the migration (data migration is one-way, so this just removes constraints)."""
+    schema = _get_schema_prefix()
+
+    # Remove the directive-only constraint
+    op.execute(f"ALTER TABLE {schema}mental_models DROP CONSTRAINT IF EXISTS ck_mental_models_subtype")
+
+    # Re-create mental_model_versions table
+    op.execute(f"""
+        CREATE TABLE IF NOT EXISTS {schema}mental_model_versions (
+            id SERIAL PRIMARY KEY,
+            bank_id VARCHAR(64) NOT NULL,
+            model_id VARCHAR(128) NOT NULL,
+            version INT NOT NULL,
+            observations JSONB NOT NULL,
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now()
+        )
+    """)
+    op.execute(
+        f"CREATE INDEX IF NOT EXISTS idx_mm_versions_lookup ON {schema}mental_model_versions(bank_id, model_id, version DESC)"
+    )
+
+    # Note: Data migration cannot be reversed - pinned_reflections and learnings data remains

hindsight-api/hindsight_api/alembic/versions/p1k2l3m4n5o6_new_knowledge_architecture.py

@@ -0,0 +1,194 @@
+"""new_knowledge_architecture
+
+Revision ID: p1k2l3m4n5o6
+Revises: o0j1k2l3m4n5
+Create Date: 2026-01-21 00:00:00.000000
+
+This migration implements the new knowledge architecture:
+1. Drops the 'learnings' table (mental models are now in memory_units)
+2. Renames 'pinned_reflections' to 'reflections'
+3. Drops the 'mental_models' table completely
+4. Creates 'directives' table for hard rules
+5. Adds mental model support columns to 'memory_units' (proof_count, source_memory_ids, history)
+
+The new architecture:
+- Directives: Hard rules in their own table
+- Mental Models: Stored in memory_units with fact_type='mental_model'
+- Reflections: User-curated documents (renamed from pinned_reflections)
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "p1k2l3m4n5o6"
+down_revision: str | Sequence[str] | None = "o0j1k2l3m4n5"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Implement new knowledge architecture."""
+    schema = _get_schema_prefix()
+
+    # 1. Drop the learnings table (mental models will be in memory_units)
+    op.execute(f"DROP TABLE IF EXISTS {schema}learnings CASCADE")
+
+    # 2. Rename pinned_reflections to reflections
+    op.execute(f"ALTER TABLE IF EXISTS {schema}pinned_reflections RENAME TO reflections")
+
+    # Rename indexes for reflections
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_pinned_reflections_bank_id RENAME TO idx_reflections_bank_id")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_pinned_reflections_embedding RENAME TO idx_reflections_embedding")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_pinned_reflections_tags RENAME TO idx_reflections_tags")
+    op.execute(
+        f"ALTER INDEX IF EXISTS {schema}idx_pinned_reflections_text_search RENAME TO idx_reflections_text_search"
+    )
+
+    # Rename foreign key constraint
+    op.execute(f"""
+        ALTER TABLE {schema}reflections
+        DROP CONSTRAINT IF EXISTS fk_pinned_reflections_bank_id
+    """)
+    op.execute(f"""
+        ALTER TABLE {schema}reflections
+        ADD CONSTRAINT fk_reflections_bank_id
+        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
+    """)
+
+    # 3. Drop the mental_models table completely
+    op.execute(f"DROP TABLE IF EXISTS {schema}mental_models CASCADE")
+
+    # 4. Create directives table
+    op.execute(f"""
+        CREATE TABLE {schema}directives (
+            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+            bank_id VARCHAR(64) NOT NULL,
+            name VARCHAR(256) NOT NULL,
+            content TEXT NOT NULL,
+            priority INT NOT NULL DEFAULT 0,
+            is_active BOOLEAN NOT NULL DEFAULT TRUE,
+            tags VARCHAR[] DEFAULT ARRAY[]::VARCHAR[],
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now()
+        )
+    """)
+
+    # Add foreign key and indexes for directives
+    op.execute(f"""
+        ALTER TABLE {schema}directives
+        ADD CONSTRAINT fk_directives_bank_id
+        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
+    """)
+    op.execute(f"CREATE INDEX idx_directives_bank_id ON {schema}directives(bank_id)")
+    op.execute(f"CREATE INDEX idx_directives_bank_active ON {schema}directives(bank_id, is_active)")
+    op.execute(f"CREATE INDEX idx_directives_tags ON {schema}directives USING GIN(tags)")
+
+    # 5. Add mental model support columns to memory_units
+    # proof_count: Number of memories that support this mental model
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD COLUMN IF NOT EXISTS proof_count INT DEFAULT 1
+    """)
+
+    # source_memory_ids: Array of memory IDs that consolidated into this mental model
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD COLUMN IF NOT EXISTS source_memory_ids UUID[] DEFAULT ARRAY[]::UUID[]
+    """)
+
+    # history: JSONB array tracking changes to mental models
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD COLUMN IF NOT EXISTS history JSONB DEFAULT '[]'::jsonb
+    """)
+
+    # Add index for finding mental models
+    op.execute(f"""
+        CREATE INDEX IF NOT EXISTS idx_memory_units_mental_models
+        ON {schema}memory_units(bank_id, fact_type)
+        WHERE fact_type = 'mental_model'
+    """)
+
+    # 6. Update fact_type check constraint to include 'mental_model'
+    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD CONSTRAINT memory_units_fact_type_check
+        CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation', 'mental_model'))
+    """)
+
+
+def downgrade() -> None:
+    """Reverse the migration."""
+    schema = _get_schema_prefix()
+
+    # Restore original fact_type check constraint (without 'mental_model')
+    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD CONSTRAINT memory_units_fact_type_check
+        CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation'))
+    """)
+
+    # Drop mental model columns from memory_units
+    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS proof_count")
+    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS source_memory_ids")
+    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS history")
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_mental_models")
+
+    # Drop directives table
+    op.execute(f"DROP TABLE IF EXISTS {schema}directives CASCADE")
+
+    # Rename reflections back to pinned_reflections
+    op.execute(f"ALTER TABLE IF EXISTS {schema}reflections RENAME TO pinned_reflections")
+
+    # Restore indexes
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_bank_id RENAME TO idx_pinned_reflections_bank_id")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_embedding RENAME TO idx_pinned_reflections_embedding")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_tags RENAME TO idx_pinned_reflections_tags")
+    op.execute(
+        f"ALTER INDEX IF EXISTS {schema}idx_reflections_text_search RENAME TO idx_pinned_reflections_text_search"
+    )
+
+    # Restore foreign key
+    op.execute(f"""
+        ALTER TABLE {schema}pinned_reflections
+        DROP CONSTRAINT IF EXISTS fk_reflections_bank_id
+    """)
+    op.execute(f"""
+        ALTER TABLE {schema}pinned_reflections
+        ADD CONSTRAINT fk_pinned_reflections_bank_id
+        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
+    """)
+
+    # Re-create learnings table
+    op.execute(f"""
+        CREATE TABLE IF NOT EXISTS {schema}learnings (
+            id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+            bank_id VARCHAR(64) NOT NULL,
+            text TEXT NOT NULL,
+            proof_count INT NOT NULL DEFAULT 1,
+            history JSONB DEFAULT '[]'::jsonb,
+            mission_context VARCHAR(64),
+            pre_mission_change BOOLEAN DEFAULT FALSE,
+            embedding vector(384),
+            tags VARCHAR[] DEFAULT ARRAY[]::VARCHAR[],
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now()
+        )
+    """)
+    op.execute(f"""
+        ALTER TABLE {schema}learnings
+        ADD CONSTRAINT fk_learnings_bank_id
+        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
+    """)
+
+    # Note: mental_models table recreation is complex and would need separate handling

hindsight-api/hindsight_api/alembic/versions/q2l3m4n5o6p7_fix_mental_model_fact_type.py

@@ -0,0 +1,50 @@
+"""fix_mental_model_fact_type
+
+Revision ID: q2l3m4n5o6p7
+Revises: p1k2l3m4n5o6
+Create Date: 2026-01-21 13:30:00.000000
+
+Fix the fact_type check constraint to include 'mental_model'.
+This is a fix for p1k2l3m4n5o6 which should have included this change.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+# revision identifiers, used by Alembic.
+revision: str = "q2l3m4n5o6p7"
+down_revision: str | Sequence[str] | None = "p1k2l3m4n5o6"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add 'mental_model' to the fact_type check constraint."""
+    schema = _get_schema_prefix()
+
+    # Drop the old constraint and add the new one with mental_model included
+    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD CONSTRAINT memory_units_fact_type_check
+        CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation', 'mental_model'))
+    """)
+
+
+def downgrade() -> None:
+    """Remove 'mental_model' from the fact_type check constraint."""
+    schema = _get_schema_prefix()
+
+    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD CONSTRAINT memory_units_fact_type_check
+        CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation'))
+    """)

hindsight-api/hindsight_api/alembic/versions/r3m4n5o6p7q8_add_reflect_response_to_reflections.py

@@ -0,0 +1,47 @@
+"""Add reflect_response JSONB column to reflections
+
+Revision ID: r3m4n5o6p7q8
+Revises: q2l3m4n5o6p7
+Create Date: 2026-01-21
+
+This migration adds a reflect_response JSONB column to store the full
+reflect API response payload, including based_on facts and trace data.
+
+Note: Table was renamed from pinned_reflections to reflections in p1k2l3m4n5o6.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "r3m4n5o6p7q8"
+down_revision: str | Sequence[str] | None = "q2l3m4n5o6p7"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add reflect_response JSONB column to reflections."""
+    schema = _get_schema_prefix()
+
+    # Add reflect_response column to store the full reflect API response
+    op.execute(f"""
+        ALTER TABLE {schema}reflections
+        ADD COLUMN IF NOT EXISTS reflect_response JSONB
+    """)
+
+
+def downgrade() -> None:
+    """Remove reflect_response column from reflections."""
+    schema = _get_schema_prefix()
+
+    op.execute(f"""
+        ALTER TABLE {schema}reflections
+        DROP COLUMN IF EXISTS reflect_response
+    """)

hindsight-api/hindsight_api/alembic/versions/s4n5o6p7q8r9_add_consolidated_at_to_memory_units.py

@@ -0,0 +1,53 @@
+"""Add consolidated_at column to memory_units for incremental consolidation tracking.
+
+This allows consolidation to track progress at the memory level rather than
+using a bank-level watermark. If consolidation crashes, already-processed
+memories won't be reprocessed.
+
+Revision ID: s4n5o6p7q8r9
+Revises: r3m4n5o6p7q8
+Create Date: 2025-01-22
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "s4n5o6p7q8r9"
+down_revision: str | Sequence[str] | None = "r3m4n5o6p7q8"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    schema = _get_schema_prefix()
+
+    # Add consolidated_at column to memory_units
+    op.execute(
+        f"""
+        ALTER TABLE {schema}memory_units
+        ADD COLUMN IF NOT EXISTS consolidated_at TIMESTAMPTZ DEFAULT NULL
+        """
+    )
+
+    # Create index for efficient querying of unconsolidated memories
+    op.execute(
+        f"""
+        CREATE INDEX IF NOT EXISTS idx_memory_units_unconsolidated
+        ON {schema}memory_units (bank_id, created_at)
+        WHERE consolidated_at IS NULL AND fact_type IN ('experience', 'world')
+        """
+    )
+
+
+def downgrade() -> None:
+    schema = _get_schema_prefix()
+
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_unconsolidated")
+    op.execute(f"ALTER TABLE {schema}memory_units DROP COLUMN IF EXISTS consolidated_at")

hindsight-api/hindsight_api/alembic/versions/t5o6p7q8r9s0_rename_mental_models_to_observations.py

@@ -0,0 +1,134 @@
+"""Rename mental_model fact_type to observation and reflections table to mental_models
+
+Revision ID: t5o6p7q8r9s0
+Revises: s4n5o6p7q8r9
+Create Date: 2026-01-26
+
+This migration implements the terminology rename:
+1. mental_model (fact_type in memory_units) -> observation
+2. reflections table -> mental_models table
+
+The new terminology:
+- Observations: Consolidated knowledge synthesized from facts (was mental_model)
+- Mental Models: Stored reflect responses (was reflections)
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "t5o6p7q8r9s0"
+down_revision: str | Sequence[str] | None = "s4n5o6p7q8r9"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Rename mental_model -> observation and reflections -> mental_models."""
+    schema = _get_schema_prefix()
+
+    # 1. Update fact_type values: mental_model -> observation
+    op.execute(f"""
+        UPDATE {schema}memory_units
+        SET fact_type = 'observation'
+        WHERE fact_type = 'mental_model'
+    """)
+
+    # 2. Update the CHECK constraint - remove mental_model, keep observation
+    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD CONSTRAINT memory_units_fact_type_check
+        CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation'))
+    """)
+
+    # 3. Rename the index for observations (was for mental_models)
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_mental_models")
+    op.execute(f"""
+        CREATE INDEX IF NOT EXISTS idx_memory_units_observations
+        ON {schema}memory_units(bank_id, fact_type)
+        WHERE fact_type = 'observation'
+    """)
+
+    # 4. Update the unconsolidated index to not filter by fact_type since observations
+    # are now the consolidated type
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_unconsolidated")
+    op.execute(f"""
+        CREATE INDEX IF NOT EXISTS idx_memory_units_unconsolidated
+        ON {schema}memory_units (bank_id, created_at)
+        WHERE consolidated_at IS NULL AND fact_type IN ('experience', 'world')
+    """)
+
+    # 5. Rename reflections table to mental_models
+    op.execute(f"ALTER TABLE IF EXISTS {schema}reflections RENAME TO mental_models")
+
+    # 6. Rename indexes for mental_models (was reflections)
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_bank_id RENAME TO idx_mental_models_bank_id")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_embedding RENAME TO idx_mental_models_embedding")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_tags RENAME TO idx_mental_models_tags")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_reflections_text_search RENAME TO idx_mental_models_text_search")
+
+    # 7. Rename foreign key constraint
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        DROP CONSTRAINT IF EXISTS fk_reflections_bank_id
+    """)
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD CONSTRAINT fk_mental_models_bank_id
+        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
+    """)
+
+
+def downgrade() -> None:
+    """Reverse: observation -> mental_model and mental_models -> reflections."""
+    schema = _get_schema_prefix()
+
+    # 1. Rename mental_models table back to reflections
+    op.execute(f"ALTER TABLE IF EXISTS {schema}mental_models RENAME TO reflections")
+
+    # 2. Rename indexes back
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_mental_models_bank_id RENAME TO idx_reflections_bank_id")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_mental_models_embedding RENAME TO idx_reflections_embedding")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_mental_models_tags RENAME TO idx_reflections_tags")
+    op.execute(f"ALTER INDEX IF EXISTS {schema}idx_mental_models_text_search RENAME TO idx_reflections_text_search")
+
+    # 3. Rename foreign key back
+    op.execute(f"""
+        ALTER TABLE {schema}reflections
+        DROP CONSTRAINT IF EXISTS fk_mental_models_bank_id
+    """)
+    op.execute(f"""
+        ALTER TABLE {schema}reflections
+        ADD CONSTRAINT fk_reflections_bank_id
+        FOREIGN KEY (bank_id) REFERENCES {schema}banks(bank_id) ON DELETE CASCADE
+    """)
+
+    # 4. Update fact_type values: observation -> mental_model
+    op.execute(f"""
+        UPDATE {schema}memory_units
+        SET fact_type = 'mental_model'
+        WHERE fact_type = 'observation'
+    """)
+
+    # 5. Update the CHECK constraint back
+    op.execute(f"ALTER TABLE {schema}memory_units DROP CONSTRAINT IF EXISTS memory_units_fact_type_check")
+    op.execute(f"""
+        ALTER TABLE {schema}memory_units
+        ADD CONSTRAINT memory_units_fact_type_check
+        CHECK (fact_type IN ('world', 'experience', 'opinion', 'observation', 'mental_model'))
+    """)
+
+    # 6. Rename index back
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_memory_units_observations")
+    op.execute(f"""
+        CREATE INDEX IF NOT EXISTS idx_memory_units_mental_models
+        ON {schema}memory_units(bank_id, fact_type)
+        WHERE fact_type = 'mental_model'
+    """)

hindsight-api/hindsight_api/alembic/versions/u6p7q8r9s0t1_mental_models_text_id.py

@@ -0,0 +1,41 @@
+"""Change mental_models.id from UUID to TEXT
+
+Revision ID: u6p7q8r9s0t1
+Revises: t5o6p7q8r9s0
+Create Date: 2026-01-27
+
+This migration changes the mental_models.id column from UUID to TEXT
+to support user-defined text identifiers like 'team-communication' instead of UUIDs.
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "u6p7q8r9s0t1"
+down_revision: str | Sequence[str] | None = "t5o6p7q8r9s0"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Change mental_models.id from UUID to TEXT."""
+    schema = _get_schema_prefix()
+
+    # Change the id column type from UUID to TEXT
+    # Existing UUIDs will be converted to their string representation
+    op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN id TYPE TEXT USING id::TEXT")
+
+
+def downgrade() -> None:
+    """Revert mental_models.id from TEXT to UUID."""
+    schema = _get_schema_prefix()
+
+    # Note: This will fail if any id values are not valid UUIDs
+    op.execute(f"ALTER TABLE {schema}mental_models ALTER COLUMN id TYPE UUID USING id::UUID")

hindsight-api/hindsight_api/alembic/versions/v7q8r9s0t1u2_add_max_tokens_to_mental_models.py

@@ -0,0 +1,50 @@
+"""Add max_tokens and trigger columns to mental_models
+
+Revision ID: v7q8r9s0t1u2
+Revises: u6p7q8r9s0t1
+Create Date: 2026-01-27
+
+This migration adds:
+- max_tokens column: token limit for content generation during refresh
+- trigger column: JSONB for trigger settings (e.g., refresh_after_consolidation)
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+revision: str = "v7q8r9s0t1u2"
+down_revision: str | Sequence[str] | None = "u6p7q8r9s0t1"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _get_schema_prefix() -> str:
+    """Get schema prefix for table names (required for multi-tenant support)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def upgrade() -> None:
+    """Add max_tokens and trigger columns to mental_models."""
+    schema = _get_schema_prefix()
+
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD COLUMN IF NOT EXISTS max_tokens INT NOT NULL DEFAULT 2048
+    """)
+
+    # trigger column stores trigger settings as JSONB
+    # Default: refresh_after_consolidation = false (not "real time")
+    op.execute(f"""
+        ALTER TABLE {schema}mental_models
+        ADD COLUMN IF NOT EXISTS trigger JSONB NOT NULL DEFAULT '{{"refresh_after_consolidation": false}}'::jsonb
+    """)
+
+
+def downgrade() -> None:
+    """Remove max_tokens and trigger columns from mental_models."""
+    schema = _get_schema_prefix()
+
+    op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS max_tokens")
+    op.execute(f"ALTER TABLE {schema}mental_models DROP COLUMN IF EXISTS trigger")

hindsight-api/hindsight_api/api/mcp.py

@@ -1,4 +1,4 @@
-"""Hindsight MCP Server implementation using FastMCP."""
+"""Hindsight MCP Server implementation using FastMCP (HTTP transport)."""
 
 import json
 import logging
@@ -8,8 +8,7 @@ from contextvars import ContextVar
 from fastmcp import FastMCP
 
 from hindsight_api import MemoryEngine
-from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
-from hindsight_api.models import RequestContext
+from hindsight_api.mcp_tools import MCPToolsConfig, register_mcp_tools
 
 # Configure logging from HINDSIGHT_API_LOG_LEVEL environment variable
 _log_level_str = os.environ.get("HINDSIGHT_API_LOG_LEVEL", "info").lower()
@@ -30,15 +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.
@@ -52,200 +62,26 @@ def create_mcp_server(memory: MemoryEngine) -> FastMCP:
     # Use stateless_http=True for Claude Code compatibility
     mcp = FastMCP("hindsight-mcp-server", stateless_http=True)
 
-    @mcp.tool()
-    async def retain(
-        content: str,
-        context: str = "general",
-        async_processing: bool = True,
-        bank_id: str | None = None,
-    ) -> str:
-        """
-        Store important information to long-term memory.
+    # 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
+    )
 
-        Use this tool PROACTIVELY whenever the user shares:
-        - Personal facts, preferences, or interests
-        - Important events or milestones
-        - User history, experiences, or background
-        - Decisions, opinions, or stated preferences
-        - Goals, plans, or future intentions
-        - Relationships or people mentioned
-        - Work context, projects, or responsibilities
-
-        Args:
-            content: The fact/memory to store (be specific and include relevant details)
-            context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
-            async_processing: If True, queue for background processing and return immediately. If False, wait for completion. Default: True
-            bank_id: Optional bank to store in (defaults to session bank). Use for cross-bank operations.
-        """
-        try:
-            target_bank = bank_id or get_current_bank_id()
-            if target_bank is None:
-                return "Error: No bank_id configured"
-            contents = [{"content": content, "context": context}]
-            if async_processing:
-                # Queue for background processing and return immediately
-                result = await memory.submit_async_retain(
-                    bank_id=target_bank, contents=contents, request_context=RequestContext()
-                )
-                return f"Memory queued for background processing (operation_id: {result.get('operation_id', 'N/A')})"
-            else:
-                # Wait for completion
-                await memory.retain_batch_async(
-                    bank_id=target_bank,
-                    contents=contents,
-                    request_context=RequestContext(),
-                )
-                return f"Memory stored successfully in bank '{target_bank}'"
-        except Exception as e:
-            logger.error(f"Error storing memory: {e}", exc_info=True)
-            return f"Error: {str(e)}"
-
-    @mcp.tool()
-    async def recall(query: str, max_tokens: int = 4096, bank_id: str | None = None) -> str:
-        """
-        Search memories to provide personalized, context-aware responses.
-
-        Use this tool PROACTIVELY to:
-        - Check user's preferences before making suggestions
-        - Recall user's history to provide continuity
-        - Remember user's goals and context
-        - Personalize responses based on past interactions
-
-        Args:
-            query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
-            max_tokens: Maximum tokens in the response (default: 4096)
-            bank_id: Optional bank to search in (defaults to session bank). Use for cross-bank operations.
-        """
-        try:
-            target_bank = bank_id or get_current_bank_id()
-            if target_bank is None:
-                return "Error: No bank_id configured"
-            from hindsight_api.engine.memory_engine import Budget
-
-            recall_result = await memory.recall_async(
-                bank_id=target_bank,
-                query=query,
-                fact_type=list(VALID_RECALL_FACT_TYPES),
-                budget=Budget.HIGH,
-                max_tokens=max_tokens,
-                request_context=RequestContext(),
-            )
-
-            # Use model's JSON serialization
-            return recall_result.model_dump_json(indent=2)
-        except Exception as e:
-            logger.error(f"Error searching: {e}", exc_info=True)
-            return f'{{"error": "{e}", "results": []}}'
-
-    @mcp.tool()
-    async def reflect(query: str, context: str | None = None, budget: str = "low", bank_id: str | None = None) -> str:
-        """
-        Generate thoughtful analysis by synthesizing stored memories with the bank's personality.
-
-        WHEN TO USE THIS TOOL:
-        Use reflect when you need reasoned analysis, not just fact retrieval. This tool
-        thinks through the question using everything the bank knows and its personality traits.
-
-        EXAMPLES OF GOOD QUERIES:
-        - "What patterns have emerged in how I approach debugging?"
-        - "Based on my past decisions, what architectural style do I prefer?"
-        - "What might be the best approach for this problem given what you know about me?"
-        - "How should I prioritize these tasks based on my goals?"
-
-        HOW IT DIFFERS FROM RECALL:
-        - recall: Returns raw facts matching your search (fast lookup)
-        - reflect: Reasons across memories to form a synthesized answer (deeper analysis)
-
-        Use recall for "what did I say about X?" and reflect for "what should I do about X?"
-
-        Args:
-            query: The question or topic to reflect on
-            context: Optional context about why this reflection is needed
-            budget: Search budget - 'low', 'mid', or 'high' (default: 'low')
-            bank_id: Optional bank to reflect in (defaults to session bank). Use for cross-bank operations.
-        """
-        try:
-            target_bank = bank_id or get_current_bank_id()
-            if target_bank is None:
-                return "Error: No bank_id configured"
-            from hindsight_api.engine.memory_engine import Budget
-
-            # Map string budget to enum
-            budget_map = {"low": Budget.LOW, "mid": Budget.MID, "high": Budget.HIGH}
-            budget_enum = budget_map.get(budget.lower(), Budget.LOW)
-
-            reflect_result = await memory.reflect_async(
-                bank_id=target_bank,
-                query=query,
-                budget=budget_enum,
-                context=context,
-                request_context=RequestContext(),
-            )
-
-            return reflect_result.model_dump_json(indent=2)
-        except Exception as e:
-            logger.error(f"Error reflecting: {e}", exc_info=True)
-            return f'{{"error": "{e}", "text": ""}}'
-
-    @mcp.tool()
-    async def list_banks() -> str:
-        """
-        List all available memory banks.
-
-        Use this tool to discover what memory banks exist in the system.
-        Each bank is an isolated memory store (like a separate "brain").
-
-        Returns:
-            JSON list of banks with their IDs, names, dispositions, and backgrounds.
-        """
-        try:
-            banks = await memory.list_banks(request_context=RequestContext())
-            return json.dumps({"banks": banks}, indent=2)
-        except Exception as e:
-            logger.error(f"Error listing banks: {e}", exc_info=True)
-            return f'{{"error": "{e}", "banks": []}}'
-
-    @mcp.tool()
-    async def create_bank(bank_id: str, name: str | None = None, background: str | None = None) -> str:
-        """
-        Create a new memory bank or get an existing one.
-
-        Memory banks are isolated stores - each one is like a separate "brain" for a user/agent.
-        Banks are auto-created with default settings if they don't exist.
-
-        Args:
-            bank_id: Unique identifier for the bank (e.g., 'user-123', 'agent-alpha')
-            name: Optional human-friendly name for the bank
-            background: Optional background context about the bank's owner/purpose
-        """
-        try:
-            # get_bank_profile auto-creates bank if it doesn't exist
-            profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())
-
-            # Update name/background if provided
-            if name is not None or background is not None:
-                await memory.update_bank(
-                    bank_id,
-                    name=name,
-                    background=background,
-                    request_context=RequestContext(),
-                )
-                # Fetch updated profile
-                profile = await memory.get_bank_profile(bank_id, request_context=RequestContext())
-
-            # Serialize disposition if it's a Pydantic model
-            if "disposition" in profile and hasattr(profile["disposition"], "model_dump"):
-                profile["disposition"] = profile["disposition"].model_dump()
-            return json.dumps(profile, indent=2)
-        except Exception as e:
-            logger.error(f"Error creating bank: {e}", exc_info=True)
-            return f'{{"error": "{e}"}}'
+    register_mcp_tools(mcp, memory, config)
 
     return mcp
 
 
 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)
@@ -254,7 +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):
@@ -278,6 +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
@@ -312,8 +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
@@ -332,7 +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."""
@@ -356,6 +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

@@ -4,9 +4,12 @@ Centralized configuration for Hindsight API.
 All environment variables and their defaults are defined here.
 """
 
+import json
 import logging
 import os
+import sys
 from dataclasses import dataclass
+from datetime import datetime, timezone
 
 from dotenv import find_dotenv, load_dotenv
 
@@ -17,11 +20,15 @@ logger = logging.getLogger(__name__)
 
 # Environment variable names
 ENV_DATABASE_URL = "HINDSIGHT_API_DATABASE_URL"
+ENV_DATABASE_SCHEMA = "HINDSIGHT_API_DATABASE_SCHEMA"
 ENV_LLM_PROVIDER = "HINDSIGHT_API_LLM_PROVIDER"
 ENV_LLM_API_KEY = "HINDSIGHT_API_LLM_API_KEY"
 ENV_LLM_MODEL = "HINDSIGHT_API_LLM_MODEL"
 ENV_LLM_BASE_URL = "HINDSIGHT_API_LLM_BASE_URL"
 ENV_LLM_MAX_CONCURRENT = "HINDSIGHT_API_LLM_MAX_CONCURRENT"
+ENV_LLM_MAX_RETRIES = "HINDSIGHT_API_LLM_MAX_RETRIES"
+ENV_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_LLM_INITIAL_BACKOFF"
+ENV_LLM_MAX_BACKOFF = "HINDSIGHT_API_LLM_MAX_BACKOFF"
 ENV_LLM_TIMEOUT = "HINDSIGHT_API_LLM_TIMEOUT"
 ENV_LLM_GROQ_SERVICE_TIER = "HINDSIGHT_API_LLM_GROQ_SERVICE_TIER"
 
@@ -30,45 +37,93 @@ ENV_RETAIN_LLM_PROVIDER = "HINDSIGHT_API_RETAIN_LLM_PROVIDER"
 ENV_RETAIN_LLM_API_KEY = "HINDSIGHT_API_RETAIN_LLM_API_KEY"
 ENV_RETAIN_LLM_MODEL = "HINDSIGHT_API_RETAIN_LLM_MODEL"
 ENV_RETAIN_LLM_BASE_URL = "HINDSIGHT_API_RETAIN_LLM_BASE_URL"
+ENV_RETAIN_LLM_MAX_CONCURRENT = "HINDSIGHT_API_RETAIN_LLM_MAX_CONCURRENT"
+ENV_RETAIN_LLM_MAX_RETRIES = "HINDSIGHT_API_RETAIN_LLM_MAX_RETRIES"
+ENV_RETAIN_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_RETAIN_LLM_INITIAL_BACKOFF"
+ENV_RETAIN_LLM_MAX_BACKOFF = "HINDSIGHT_API_RETAIN_LLM_MAX_BACKOFF"
+ENV_RETAIN_LLM_TIMEOUT = "HINDSIGHT_API_RETAIN_LLM_TIMEOUT"
 
 ENV_REFLECT_LLM_PROVIDER = "HINDSIGHT_API_REFLECT_LLM_PROVIDER"
 ENV_REFLECT_LLM_API_KEY = "HINDSIGHT_API_REFLECT_LLM_API_KEY"
 ENV_REFLECT_LLM_MODEL = "HINDSIGHT_API_REFLECT_LLM_MODEL"
 ENV_REFLECT_LLM_BASE_URL = "HINDSIGHT_API_REFLECT_LLM_BASE_URL"
+ENV_REFLECT_LLM_MAX_CONCURRENT = "HINDSIGHT_API_REFLECT_LLM_MAX_CONCURRENT"
+ENV_REFLECT_LLM_MAX_RETRIES = "HINDSIGHT_API_REFLECT_LLM_MAX_RETRIES"
+ENV_REFLECT_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_REFLECT_LLM_INITIAL_BACKOFF"
+ENV_REFLECT_LLM_MAX_BACKOFF = "HINDSIGHT_API_REFLECT_LLM_MAX_BACKOFF"
+ENV_REFLECT_LLM_TIMEOUT = "HINDSIGHT_API_REFLECT_LLM_TIMEOUT"
+
+ENV_CONSOLIDATION_LLM_PROVIDER = "HINDSIGHT_API_CONSOLIDATION_LLM_PROVIDER"
+ENV_CONSOLIDATION_LLM_API_KEY = "HINDSIGHT_API_CONSOLIDATION_LLM_API_KEY"
+ENV_CONSOLIDATION_LLM_MODEL = "HINDSIGHT_API_CONSOLIDATION_LLM_MODEL"
+ENV_CONSOLIDATION_LLM_BASE_URL = "HINDSIGHT_API_CONSOLIDATION_LLM_BASE_URL"
+ENV_CONSOLIDATION_LLM_MAX_CONCURRENT = "HINDSIGHT_API_CONSOLIDATION_LLM_MAX_CONCURRENT"
+ENV_CONSOLIDATION_LLM_MAX_RETRIES = "HINDSIGHT_API_CONSOLIDATION_LLM_MAX_RETRIES"
+ENV_CONSOLIDATION_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_CONSOLIDATION_LLM_INITIAL_BACKOFF"
+ENV_CONSOLIDATION_LLM_MAX_BACKOFF = "HINDSIGHT_API_CONSOLIDATION_LLM_MAX_BACKOFF"
+ENV_CONSOLIDATION_LLM_TIMEOUT = "HINDSIGHT_API_CONSOLIDATION_LLM_TIMEOUT"
 
 ENV_EMBEDDINGS_PROVIDER = "HINDSIGHT_API_EMBEDDINGS_PROVIDER"
 ENV_EMBEDDINGS_LOCAL_MODEL = "HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL"
+ENV_EMBEDDINGS_LOCAL_FORCE_CPU = "HINDSIGHT_API_EMBEDDINGS_LOCAL_FORCE_CPU"
 ENV_EMBEDDINGS_TEI_URL = "HINDSIGHT_API_EMBEDDINGS_TEI_URL"
 ENV_EMBEDDINGS_OPENAI_API_KEY = "HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY"
 ENV_EMBEDDINGS_OPENAI_MODEL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_MODEL"
+ENV_EMBEDDINGS_OPENAI_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_OPENAI_BASE_URL"
 
 ENV_COHERE_API_KEY = "HINDSIGHT_API_COHERE_API_KEY"
 ENV_EMBEDDINGS_COHERE_MODEL = "HINDSIGHT_API_EMBEDDINGS_COHERE_MODEL"
+ENV_EMBEDDINGS_COHERE_BASE_URL = "HINDSIGHT_API_EMBEDDINGS_COHERE_BASE_URL"
 ENV_RERANKER_COHERE_MODEL = "HINDSIGHT_API_RERANKER_COHERE_MODEL"
+ENV_RERANKER_COHERE_BASE_URL = "HINDSIGHT_API_RERANKER_COHERE_BASE_URL"
+
+# LiteLLM gateway configuration (for embeddings and reranker via LiteLLM proxy)
+ENV_LITELLM_API_BASE = "HINDSIGHT_API_LITELLM_API_BASE"
+ENV_LITELLM_API_KEY = "HINDSIGHT_API_LITELLM_API_KEY"
+ENV_EMBEDDINGS_LITELLM_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_MODEL"
+ENV_RERANKER_LITELLM_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_MODEL"
 
 ENV_RERANKER_PROVIDER = "HINDSIGHT_API_RERANKER_PROVIDER"
 ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
+ENV_RERANKER_LOCAL_FORCE_CPU = "HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU"
+ENV_RERANKER_LOCAL_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT"
 ENV_RERANKER_TEI_URL = "HINDSIGHT_API_RERANKER_TEI_URL"
 ENV_RERANKER_TEI_BATCH_SIZE = "HINDSIGHT_API_RERANKER_TEI_BATCH_SIZE"
 ENV_RERANKER_TEI_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_TEI_MAX_CONCURRENT"
+ENV_RERANKER_MAX_CANDIDATES = "HINDSIGHT_API_RERANKER_MAX_CANDIDATES"
+ENV_RERANKER_FLASHRANK_MODEL = "HINDSIGHT_API_RERANKER_FLASHRANK_MODEL"
+ENV_RERANKER_FLASHRANK_CACHE_DIR = "HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR"
 
 ENV_HOST = "HINDSIGHT_API_HOST"
 ENV_PORT = "HINDSIGHT_API_PORT"
 ENV_LOG_LEVEL = "HINDSIGHT_API_LOG_LEVEL"
+ENV_LOG_FORMAT = "HINDSIGHT_API_LOG_FORMAT"
+ENV_WORKERS = "HINDSIGHT_API_WORKERS"
 ENV_MCP_ENABLED = "HINDSIGHT_API_MCP_ENABLED"
 ENV_GRAPH_RETRIEVER = "HINDSIGHT_API_GRAPH_RETRIEVER"
+ENV_MPFP_TOP_K_NEIGHBORS = "HINDSIGHT_API_MPFP_TOP_K_NEIGHBORS"
+ENV_RECALL_MAX_CONCURRENT = "HINDSIGHT_API_RECALL_MAX_CONCURRENT"
+ENV_RECALL_CONNECTION_BUDGET = "HINDSIGHT_API_RECALL_CONNECTION_BUDGET"
 ENV_MCP_LOCAL_BANK_ID = "HINDSIGHT_API_MCP_LOCAL_BANK_ID"
 ENV_MCP_INSTRUCTIONS = "HINDSIGHT_API_MCP_INSTRUCTIONS"
+ENV_MENTAL_MODEL_REFRESH_CONCURRENCY = "HINDSIGHT_API_MENTAL_MODEL_REFRESH_CONCURRENCY"
 
-# Observation thresholds
-ENV_OBSERVATION_MIN_FACTS = "HINDSIGHT_API_OBSERVATION_MIN_FACTS"
-ENV_OBSERVATION_TOP_ENTITIES = "HINDSIGHT_API_OBSERVATION_TOP_ENTITIES"
+# 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"
+
+# Observations settings (consolidated knowledge from facts)
+ENV_ENABLE_OBSERVATIONS = "HINDSIGHT_API_ENABLE_OBSERVATIONS"
+ENV_CONSOLIDATION_BATCH_SIZE = "HINDSIGHT_API_CONSOLIDATION_BATCH_SIZE"
+ENV_CONSOLIDATION_MAX_TOKENS = "HINDSIGHT_API_CONSOLIDATION_MAX_TOKENS"
 
 # Optimization flags
 ENV_SKIP_LLM_VERIFICATION = "HINDSIGHT_API_SKIP_LLM_VERIFICATION"
@@ -83,47 +138,83 @@ ENV_DB_POOL_MAX_SIZE = "HINDSIGHT_API_DB_POOL_MAX_SIZE"
 ENV_DB_COMMAND_TIMEOUT = "HINDSIGHT_API_DB_COMMAND_TIMEOUT"
 ENV_DB_ACQUIRE_TIMEOUT = "HINDSIGHT_API_DB_ACQUIRE_TIMEOUT"
 
-# Background task processing
-ENV_TASK_BATCH_SIZE = "HINDSIGHT_API_TASK_BATCH_SIZE"
-ENV_TASK_BATCH_INTERVAL = "HINDSIGHT_API_TASK_BATCH_INTERVAL"
+# Worker configuration (distributed task processing)
+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_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"
 
 # Default values
 DEFAULT_DATABASE_URL = "pg0"
+DEFAULT_DATABASE_SCHEMA = "public"
 DEFAULT_LLM_PROVIDER = "openai"
 DEFAULT_LLM_MODEL = "gpt-5-mini"
 DEFAULT_LLM_MAX_CONCURRENT = 32
+DEFAULT_LLM_MAX_RETRIES = 10  # Max retry attempts for LLM API calls
+DEFAULT_LLM_INITIAL_BACKOFF = 1.0  # Initial backoff in seconds for retry exponential backoff
+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)
 DEFAULT_EMBEDDINGS_OPENAI_MODEL = "text-embedding-3-small"
 DEFAULT_EMBEDDING_DIMENSION = 384
 
 DEFAULT_RERANKER_PROVIDER = "local"
 DEFAULT_RERANKER_LOCAL_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+DEFAULT_RERANKER_LOCAL_FORCE_CPU = False  # Force CPU mode for local reranker (avoids MPS/XPC issues on macOS)
+DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4  # Limit concurrent CPU-bound reranking to prevent thrashing
 DEFAULT_RERANKER_TEI_BATCH_SIZE = 128
 DEFAULT_RERANKER_TEI_MAX_CONCURRENT = 8
+DEFAULT_RERANKER_MAX_CANDIDATES = 300
+DEFAULT_RERANKER_FLASHRANK_MODEL = "ms-marco-MiniLM-L-12-v2"  # Best balance of speed and quality
+DEFAULT_RERANKER_FLASHRANK_CACHE_DIR = None  # Use default cache directory
 
 DEFAULT_EMBEDDINGS_COHERE_MODEL = "embed-english-v3.0"
 DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"
 
+# LiteLLM defaults
+DEFAULT_LITELLM_API_BASE = "http://localhost:4000"
+DEFAULT_EMBEDDINGS_LITELLM_MODEL = "text-embedding-3-small"
+DEFAULT_RERANKER_LITELLM_MODEL = "cohere/rerank-english-v3.0"
+
 DEFAULT_HOST = "0.0.0.0"
 DEFAULT_PORT = 8888
 DEFAULT_LOG_LEVEL = "info"
+DEFAULT_LOG_FORMAT = "text"  # Options: "text", "json"
+DEFAULT_WORKERS = 1
 DEFAULT_MCP_ENABLED = True
-DEFAULT_GRAPH_RETRIEVER = "bfs"  # Options: "bfs", "mpfp"
+DEFAULT_GRAPH_RETRIEVER = "link_expansion"  # Options: "link_expansion", "mpfp", "bfs"
+DEFAULT_MPFP_TOP_K_NEIGHBORS = 20  # Fan-out limit per node in MPFP graph traversal
+DEFAULT_RECALL_MAX_CONCURRENT = 32  # Max concurrent recall operations per worker
+DEFAULT_RECALL_CONNECTION_BUDGET = 4  # Max concurrent DB connections per recall operation
 DEFAULT_MCP_LOCAL_BANK_ID = "mcp"
-
-# Observation thresholds
-DEFAULT_OBSERVATION_MIN_FACTS = 5  # Min facts required to generate entity observations
-DEFAULT_OBSERVATION_TOP_ENTITIES = 5  # Max entities to process per retain batch
+DEFAULT_MENTAL_MODEL_REFRESH_CONCURRENCY = 8  # Max concurrent mental model refreshes
 
 # Retain settings
 DEFAULT_RETAIN_MAX_COMPLETION_TOKENS = 64000  # Max tokens for fact extraction LLM call
 DEFAULT_RETAIN_CHUNK_SIZE = 3000  # Max chars per chunk for fact extraction
 DEFAULT_RETAIN_EXTRACT_CAUSAL_LINKS = True  # Extract causal links between facts
-DEFAULT_RETAIN_EXTRACTION_MODE = "concise"  # Extraction mode: "concise" or "verbose"
-RETAIN_EXTRACTION_MODES = ("concise", "verbose")  # Allowed extraction modes
+DEFAULT_RETAIN_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")
+
+# Observations defaults (consolidated knowledge from facts)
+DEFAULT_ENABLE_OBSERVATIONS = True  # Observations enabled by default
+DEFAULT_CONSOLIDATION_BATCH_SIZE = 50  # Memories to load per batch (internal memory optimization)
+DEFAULT_CONSOLIDATION_MAX_TOKENS = 1024  # Max tokens for recall when finding related observations
 
 # Database migrations
 DEFAULT_RUN_MIGRATIONS_ON_STARTUP = True
@@ -134,9 +225,17 @@ DEFAULT_DB_POOL_MAX_SIZE = 100
 DEFAULT_DB_COMMAND_TIMEOUT = 60  # seconds
 DEFAULT_DB_ACQUIRE_TIMEOUT = 30  # seconds
 
-# Background task processing
-DEFAULT_TASK_BATCH_SIZE = 10
-DEFAULT_TASK_BATCH_INTERVAL = 1.0  # seconds
+# Worker configuration (distributed task processing)
+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_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
 
 # Default MCP tool descriptions (can be customized via env vars)
 DEFAULT_MCP_RETAIN_DESCRIPTION = """Store important information to long-term memory.
@@ -162,6 +261,36 @@ Use this tool PROACTIVELY to:
 EMBEDDING_DIMENSION = DEFAULT_EMBEDDING_DIMENSION
 
 
+class JsonFormatter(logging.Formatter):
+    """JSON formatter for structured logging.
+
+    Outputs logs in JSON format with a 'severity' field that cloud logging
+    systems (GCP, AWS CloudWatch, etc.) can parse to correctly categorize log levels.
+    """
+
+    SEVERITY_MAP = {
+        logging.DEBUG: "DEBUG",
+        logging.INFO: "INFO",
+        logging.WARNING: "WARNING",
+        logging.ERROR: "ERROR",
+        logging.CRITICAL: "CRITICAL",
+    }
+
+    def format(self, record: logging.LogRecord) -> str:
+        log_entry = {
+            "severity": self.SEVERITY_MAP.get(record.levelno, "DEFAULT"),
+            "message": record.getMessage(),
+            "timestamp": datetime.now(timezone.utc).isoformat(),
+            "logger": record.name,
+        }
+
+        # Add exception info if present
+        if record.exc_info:
+            log_entry["exception"] = self.formatException(record.exc_info)
+
+        return json.dumps(log_entry)
+
+
 def _validate_extraction_mode(mode: str) -> str:
     """Validate and normalize extraction mode."""
     mode_lower = mode.lower()
@@ -180,6 +309,7 @@ class HindsightConfig:
 
     # Database
     database_url: str
+    database_schema: str
 
     # LLM (default, used as fallback for per-operation config)
     llm_provider: str
@@ -187,49 +317,91 @@ class HindsightConfig:
     llm_model: str
     llm_base_url: str | None
     llm_max_concurrent: int
+    llm_max_retries: int
+    llm_initial_backoff: float
+    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
     retain_llm_model: str | None
     retain_llm_base_url: str | None
+    retain_llm_max_concurrent: int | None
+    retain_llm_max_retries: int | None
+    retain_llm_initial_backoff: float | None
+    retain_llm_max_backoff: float | None
+    retain_llm_timeout: float | None
 
     reflect_llm_provider: str | None
     reflect_llm_api_key: str | None
     reflect_llm_model: str | None
     reflect_llm_base_url: str | None
+    reflect_llm_max_concurrent: int | None
+    reflect_llm_max_retries: int | None
+    reflect_llm_initial_backoff: float | None
+    reflect_llm_max_backoff: float | None
+    reflect_llm_timeout: float | None
+
+    consolidation_llm_provider: str | None
+    consolidation_llm_api_key: str | None
+    consolidation_llm_model: str | None
+    consolidation_llm_base_url: str | None
+    consolidation_llm_max_concurrent: int | None
+    consolidation_llm_max_retries: int | None
+    consolidation_llm_initial_backoff: float | None
+    consolidation_llm_max_backoff: float | None
+    consolidation_llm_timeout: float | None
 
     # Embeddings
     embeddings_provider: str
     embeddings_local_model: str
+    embeddings_local_force_cpu: bool
     embeddings_tei_url: str | None
+    embeddings_openai_base_url: str | None
+    embeddings_cohere_base_url: str | None
 
     # Reranker
     reranker_provider: str
     reranker_local_model: str
+    reranker_local_force_cpu: bool
+    reranker_local_max_concurrent: int
     reranker_tei_url: str | None
     reranker_tei_batch_size: int
     reranker_tei_max_concurrent: int
+    reranker_max_candidates: int
+    reranker_cohere_base_url: str | None
 
     # Server
     host: str
     port: int
     log_level: str
+    log_format: str
     mcp_enabled: bool
 
     # Recall
     graph_retriever: str
-
-    # Observation thresholds
-    observation_min_facts: int
-    observation_top_entities: int
+    mpfp_top_k_neighbors: int
+    recall_max_concurrent: int
+    recall_connection_budget: int
+    mental_model_refresh_concurrency: int
 
     # Retain settings
     retain_max_completion_tokens: int
     retain_chunk_size: int
     retain_extract_causal_links: bool
     retain_extraction_mode: str
+    retain_custom_instructions: str | None
+
+    # Observations settings (consolidated knowledge from facts)
+    enable_observations: bool
+    consolidation_batch_size: int
+    consolidation_max_tokens: int
 
     # Optimization flags
     skip_llm_verification: bool
@@ -244,9 +416,17 @@ class HindsightConfig:
     db_command_timeout: int
     db_acquire_timeout: int
 
-    # Background task processing
-    task_batch_size: int
-    task_batch_interval: float
+    # Worker configuration (distributed task processing)
+    worker_enabled: bool
+    worker_id: str | None
+    worker_poll_interval_ms: int
+    worker_max_retries: int
+    worker_http_port: int
+    worker_max_slots: int
+    worker_consolidation_max_slots: int
+
+    # Reflect agent settings
+    reflect_max_iterations: int
 
     @classmethod
     def from_env(cls) -> "HindsightConfig":
@@ -254,49 +434,124 @@ class HindsightConfig:
         return cls(
             # Database
             database_url=os.getenv(ENV_DATABASE_URL, DEFAULT_DATABASE_URL),
+            database_schema=os.getenv(ENV_DATABASE_SCHEMA, DEFAULT_DATABASE_SCHEMA),
             # LLM
             llm_provider=os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER),
             llm_api_key=os.getenv(ENV_LLM_API_KEY),
             llm_model=os.getenv(ENV_LLM_MODEL, DEFAULT_LLM_MODEL),
             llm_base_url=os.getenv(ENV_LLM_BASE_URL) or None,
             llm_max_concurrent=int(os.getenv(ENV_LLM_MAX_CONCURRENT, str(DEFAULT_LLM_MAX_CONCURRENT))),
+            llm_max_retries=int(os.getenv(ENV_LLM_MAX_RETRIES, str(DEFAULT_LLM_MAX_RETRIES))),
+            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,
             retain_llm_model=os.getenv(ENV_RETAIN_LLM_MODEL) or None,
             retain_llm_base_url=os.getenv(ENV_RETAIN_LLM_BASE_URL) or None,
+            retain_llm_max_concurrent=int(os.getenv(ENV_RETAIN_LLM_MAX_CONCURRENT))
+            if os.getenv(ENV_RETAIN_LLM_MAX_CONCURRENT)
+            else None,
+            retain_llm_max_retries=int(os.getenv(ENV_RETAIN_LLM_MAX_RETRIES))
+            if os.getenv(ENV_RETAIN_LLM_MAX_RETRIES)
+            else None,
+            retain_llm_initial_backoff=float(os.getenv(ENV_RETAIN_LLM_INITIAL_BACKOFF))
+            if os.getenv(ENV_RETAIN_LLM_INITIAL_BACKOFF)
+            else None,
+            retain_llm_max_backoff=float(os.getenv(ENV_RETAIN_LLM_MAX_BACKOFF))
+            if os.getenv(ENV_RETAIN_LLM_MAX_BACKOFF)
+            else None,
+            retain_llm_timeout=float(os.getenv(ENV_RETAIN_LLM_TIMEOUT)) if os.getenv(ENV_RETAIN_LLM_TIMEOUT) else None,
             reflect_llm_provider=os.getenv(ENV_REFLECT_LLM_PROVIDER) or None,
             reflect_llm_api_key=os.getenv(ENV_REFLECT_LLM_API_KEY) or None,
             reflect_llm_model=os.getenv(ENV_REFLECT_LLM_MODEL) or None,
             reflect_llm_base_url=os.getenv(ENV_REFLECT_LLM_BASE_URL) or None,
+            reflect_llm_max_concurrent=int(os.getenv(ENV_REFLECT_LLM_MAX_CONCURRENT))
+            if os.getenv(ENV_REFLECT_LLM_MAX_CONCURRENT)
+            else None,
+            reflect_llm_max_retries=int(os.getenv(ENV_REFLECT_LLM_MAX_RETRIES))
+            if os.getenv(ENV_REFLECT_LLM_MAX_RETRIES)
+            else None,
+            reflect_llm_initial_backoff=float(os.getenv(ENV_REFLECT_LLM_INITIAL_BACKOFF))
+            if os.getenv(ENV_REFLECT_LLM_INITIAL_BACKOFF)
+            else None,
+            reflect_llm_max_backoff=float(os.getenv(ENV_REFLECT_LLM_MAX_BACKOFF))
+            if os.getenv(ENV_REFLECT_LLM_MAX_BACKOFF)
+            else None,
+            reflect_llm_timeout=float(os.getenv(ENV_REFLECT_LLM_TIMEOUT))
+            if os.getenv(ENV_REFLECT_LLM_TIMEOUT)
+            else None,
+            consolidation_llm_provider=os.getenv(ENV_CONSOLIDATION_LLM_PROVIDER) or None,
+            consolidation_llm_api_key=os.getenv(ENV_CONSOLIDATION_LLM_API_KEY) or None,
+            consolidation_llm_model=os.getenv(ENV_CONSOLIDATION_LLM_MODEL) or None,
+            consolidation_llm_base_url=os.getenv(ENV_CONSOLIDATION_LLM_BASE_URL) or None,
+            consolidation_llm_max_concurrent=int(os.getenv(ENV_CONSOLIDATION_LLM_MAX_CONCURRENT))
+            if os.getenv(ENV_CONSOLIDATION_LLM_MAX_CONCURRENT)
+            else None,
+            consolidation_llm_max_retries=int(os.getenv(ENV_CONSOLIDATION_LLM_MAX_RETRIES))
+            if os.getenv(ENV_CONSOLIDATION_LLM_MAX_RETRIES)
+            else None,
+            consolidation_llm_initial_backoff=float(os.getenv(ENV_CONSOLIDATION_LLM_INITIAL_BACKOFF))
+            if os.getenv(ENV_CONSOLIDATION_LLM_INITIAL_BACKOFF)
+            else None,
+            consolidation_llm_max_backoff=float(os.getenv(ENV_CONSOLIDATION_LLM_MAX_BACKOFF))
+            if os.getenv(ENV_CONSOLIDATION_LLM_MAX_BACKOFF)
+            else None,
+            consolidation_llm_timeout=float(os.getenv(ENV_CONSOLIDATION_LLM_TIMEOUT))
+            if os.getenv(ENV_CONSOLIDATION_LLM_TIMEOUT)
+            else None,
             # Embeddings
             embeddings_provider=os.getenv(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER),
             embeddings_local_model=os.getenv(ENV_EMBEDDINGS_LOCAL_MODEL, DEFAULT_EMBEDDINGS_LOCAL_MODEL),
+            embeddings_local_force_cpu=os.getenv(
+                ENV_EMBEDDINGS_LOCAL_FORCE_CPU, str(DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU)
+            ).lower()
+            in ("true", "1"),
             embeddings_tei_url=os.getenv(ENV_EMBEDDINGS_TEI_URL),
+            embeddings_openai_base_url=os.getenv(ENV_EMBEDDINGS_OPENAI_BASE_URL) or None,
+            embeddings_cohere_base_url=os.getenv(ENV_EMBEDDINGS_COHERE_BASE_URL) or None,
             # Reranker
             reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
             reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
+            reranker_local_force_cpu=os.getenv(
+                ENV_RERANKER_LOCAL_FORCE_CPU, str(DEFAULT_RERANKER_LOCAL_FORCE_CPU)
+            ).lower()
+            in ("true", "1"),
+            reranker_local_max_concurrent=int(
+                os.getenv(ENV_RERANKER_LOCAL_MAX_CONCURRENT, str(DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT))
+            ),
             reranker_tei_url=os.getenv(ENV_RERANKER_TEI_URL),
             reranker_tei_batch_size=int(os.getenv(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE))),
             reranker_tei_max_concurrent=int(
                 os.getenv(ENV_RERANKER_TEI_MAX_CONCURRENT, str(DEFAULT_RERANKER_TEI_MAX_CONCURRENT))
             ),
+            reranker_max_candidates=int(os.getenv(ENV_RERANKER_MAX_CANDIDATES, str(DEFAULT_RERANKER_MAX_CANDIDATES))),
+            reranker_cohere_base_url=os.getenv(ENV_RERANKER_COHERE_BASE_URL) or None,
             # Server
             host=os.getenv(ENV_HOST, DEFAULT_HOST),
             port=int(os.getenv(ENV_PORT, DEFAULT_PORT)),
             log_level=os.getenv(ENV_LOG_LEVEL, DEFAULT_LOG_LEVEL),
+            log_format=os.getenv(ENV_LOG_FORMAT, DEFAULT_LOG_FORMAT).lower(),
             mcp_enabled=os.getenv(ENV_MCP_ENABLED, str(DEFAULT_MCP_ENABLED)).lower() == "true",
             # Recall
             graph_retriever=os.getenv(ENV_GRAPH_RETRIEVER, DEFAULT_GRAPH_RETRIEVER),
+            mpfp_top_k_neighbors=int(os.getenv(ENV_MPFP_TOP_K_NEIGHBORS, str(DEFAULT_MPFP_TOP_K_NEIGHBORS))),
+            recall_max_concurrent=int(os.getenv(ENV_RECALL_MAX_CONCURRENT, str(DEFAULT_RECALL_MAX_CONCURRENT))),
+            recall_connection_budget=int(
+                os.getenv(ENV_RECALL_CONNECTION_BUDGET, str(DEFAULT_RECALL_CONNECTION_BUDGET))
+            ),
+            mental_model_refresh_concurrency=int(
+                os.getenv(ENV_MENTAL_MODEL_REFRESH_CONCURRENCY, str(DEFAULT_MENTAL_MODEL_REFRESH_CONCURRENCY))
+            ),
             # Optimization flags
             skip_llm_verification=os.getenv(ENV_SKIP_LLM_VERIFICATION, "false").lower() == "true",
             lazy_reranker=os.getenv(ENV_LAZY_RERANKER, "false").lower() == "true",
-            # Observation thresholds
-            observation_min_facts=int(os.getenv(ENV_OBSERVATION_MIN_FACTS, str(DEFAULT_OBSERVATION_MIN_FACTS))),
-            observation_top_entities=int(
-                os.getenv(ENV_OBSERVATION_TOP_ENTITIES, str(DEFAULT_OBSERVATION_TOP_ENTITIES))
-            ),
             # Retain settings
             retain_max_completion_tokens=int(
                 os.getenv(ENV_RETAIN_MAX_COMPLETION_TOKENS, str(DEFAULT_RETAIN_MAX_COMPLETION_TOKENS))
@@ -309,6 +564,15 @@ class HindsightConfig:
             retain_extraction_mode=_validate_extraction_mode(
                 os.getenv(ENV_RETAIN_EXTRACTION_MODE, DEFAULT_RETAIN_EXTRACTION_MODE)
             ),
+            retain_custom_instructions=os.getenv(ENV_RETAIN_CUSTOM_INSTRUCTIONS) or DEFAULT_RETAIN_CUSTOM_INSTRUCTIONS,
+            # Observations settings (consolidated knowledge from facts)
+            enable_observations=os.getenv(ENV_ENABLE_OBSERVATIONS, str(DEFAULT_ENABLE_OBSERVATIONS)).lower() == "true",
+            consolidation_batch_size=int(
+                os.getenv(ENV_CONSOLIDATION_BATCH_SIZE, str(DEFAULT_CONSOLIDATION_BATCH_SIZE))
+            ),
+            consolidation_max_tokens=int(
+                os.getenv(ENV_CONSOLIDATION_MAX_TOKENS, str(DEFAULT_CONSOLIDATION_MAX_TOKENS))
+            ),
             # Database migrations
             run_migrations_on_startup=os.getenv(ENV_RUN_MIGRATIONS_ON_STARTUP, "true").lower() == "true",
             # Database connection pool
@@ -316,9 +580,18 @@ class HindsightConfig:
             db_pool_max_size=int(os.getenv(ENV_DB_POOL_MAX_SIZE, str(DEFAULT_DB_POOL_MAX_SIZE))),
             db_command_timeout=int(os.getenv(ENV_DB_COMMAND_TIMEOUT, str(DEFAULT_DB_COMMAND_TIMEOUT))),
             db_acquire_timeout=int(os.getenv(ENV_DB_ACQUIRE_TIMEOUT, str(DEFAULT_DB_ACQUIRE_TIMEOUT))),
-            # Background task processing
-            task_batch_size=int(os.getenv(ENV_TASK_BATCH_SIZE, str(DEFAULT_TASK_BATCH_SIZE))),
-            task_batch_interval=float(os.getenv(ENV_TASK_BATCH_INTERVAL, str(DEFAULT_TASK_BATCH_INTERVAL))),
+            # Worker configuration
+            worker_enabled=os.getenv(ENV_WORKER_ENABLED, str(DEFAULT_WORKER_ENABLED)).lower() == "true",
+            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_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))),
         )
 
     def get_llm_base_url(self) -> str:
@@ -349,16 +622,32 @@ class HindsightConfig:
         return log_level_map.get(self.log_level.lower(), logging.INFO)
 
     def configure_logging(self) -> None:
-        """Configure Python logging based on the log level."""
-        logging.basicConfig(
-            level=self.get_python_log_level(),
-            format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-            force=True,  # Override any existing configuration
-        )
+        """Configure Python logging based on the log level and format.
+
+        When log_format is "json", outputs structured JSON logs with a severity
+        field that GCP Cloud Logging can parse for proper log level categorization.
+        """
+        root_logger = logging.getLogger()
+        root_logger.setLevel(self.get_python_log_level())
+
+        # Remove existing handlers
+        for handler in root_logger.handlers[:]:
+            root_logger.removeHandler(handler)
+
+        # Create handler writing to stdout (GCP treats stderr as ERROR)
+        handler = logging.StreamHandler(sys.stdout)
+        handler.setLevel(self.get_python_log_level())
+
+        if self.log_format == "json":
+            handler.setFormatter(JsonFormatter())
+        else:
+            handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(name)s - %(message)s"))
+
+        root_logger.addHandler(handler)
 
     def log_config(self) -> None:
         """Log the current configuration (without sensitive values)."""
-        logger.info(f"Database: {self.database_url}")
+        logger.info(f"Database: {self.database_url} (schema: {self.database_schema})")
         logger.info(f"LLM: provider={self.llm_provider}, model={self.llm_model}")
         if self.retain_llm_provider or self.retain_llm_model:
             retain_provider = self.retain_llm_provider or self.llm_provider
@@ -368,6 +657,10 @@ class HindsightConfig:
             reflect_provider = self.reflect_llm_provider or self.llm_provider
             reflect_model = self.reflect_llm_model or self.llm_model
             logger.info(f"LLM (reflect): provider={reflect_provider}, model={reflect_model}")
+        if self.consolidation_llm_provider or self.consolidation_llm_model:
+            consolidation_provider = self.consolidation_llm_provider or self.llm_provider
+            consolidation_model = self.consolidation_llm_model or self.llm_model
+            logger.info(f"LLM (consolidation): provider={consolidation_provider}, model={consolidation_model}")
         logger.info(f"Embeddings: provider={self.embeddings_provider}")
         logger.info(f"Reranker: provider={self.reranker_provider}")
         logger.info(f"Graph retriever: {self.graph_retriever}")

hindsight-api/hindsight_api/daemon.py

@@ -52,7 +52,10 @@ class IdleTimeoutMiddleware:
                 logger.info(f"Idle timeout reached ({self.idle_timeout}s), shutting down daemon")
                 # Give a moment for any in-flight requests
                 await asyncio.sleep(1)
-                os._exit(0)
+                # Send SIGTERM to ourselves to trigger graceful shutdown
+                import signal
+
+                os.kill(os.getpid(), signal.SIGTERM)
 
 
 class DaemonLock:

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

@@ -0,0 +1,5 @@
+"""Consolidation engine for automatic learning creation from memories."""
+
+from .consolidator import run_consolidation_job
+
+__all__ = ["run_consolidation_job"]

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

@@ -0,0 +1,962 @@
+"""Consolidation engine for automatic observation creation from memories.
+
+The consolidation engine runs as a background job after retain operations complete.
+It processes new memories and either:
+- Creates new observations from novel facts
+- Updates existing observations when new evidence supports/contradicts/refines them
+
+Observations are stored in memory_units with fact_type='observation' and include:
+- proof_count: Number of supporting memories
+- source_memory_ids: Array of memory UUIDs that contribute to this observation
+- history: JSONB tracking changes over time
+"""
+
+import json
+import logging
+import time
+import uuid
+from datetime import datetime, timezone
+from typing import TYPE_CHECKING, Any
+
+from ..memory_engine import fq_table
+from ..retain import embedding_utils
+from .prompts import (
+    CONSOLIDATION_SYSTEM_PROMPT,
+    CONSOLIDATION_USER_PROMPT,
+)
+
+if TYPE_CHECKING:
+    from asyncpg import Connection
+
+    from ...api.http import RequestContext
+    from ..memory_engine import MemoryEngine
+
+logger = logging.getLogger(__name__)
+
+
+class ConsolidationPerfLog:
+    """Performance logging for consolidation operations."""
+
+    def __init__(self, bank_id: str):
+        self.bank_id = bank_id
+        self.start_time = time.time()
+        self.lines: list[str] = []
+        self.timings: dict[str, float] = {}
+
+    def log(self, message: str) -> None:
+        """Add a log line."""
+        self.lines.append(message)
+
+    def record_timing(self, key: str, duration: float) -> None:
+        """Record a timing measurement."""
+        if key in self.timings:
+            self.timings[key] += duration
+        else:
+            self.timings[key] = duration
+
+    def flush(self) -> None:
+        """Flush all log lines to the logger."""
+        total_time = time.time() - self.start_time
+        header = f"\n{'=' * 60}\nCONSOLIDATION for bank {self.bank_id}"
+        footer = f"{'=' * 60}\nCONSOLIDATION COMPLETE: {total_time:.3f}s total\n{'=' * 60}"
+
+        log_output = header + "\n" + "\n".join(self.lines) + "\n" + footer
+        logger.info(log_output)
+
+
+async def run_consolidation_job(
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    request_context: "RequestContext",
+) -> dict[str, Any]:
+    """
+    Run consolidation job for a bank.
+
+    This is called after retain operations to consolidate new memories into mental models.
+
+    Args:
+        memory_engine: MemoryEngine instance
+        bank_id: Bank identifier
+        request_context: Request context for authentication
+
+    Returns:
+        Dict with consolidation results
+    """
+    from ...config import get_config
+
+    config = get_config()
+    perf = ConsolidationPerfLog(bank_id)
+    max_memories_per_batch = config.consolidation_batch_size
+
+    # Check if consolidation is enabled
+    if not config.enable_observations:
+        logger.debug(f"Consolidation disabled for bank {bank_id}")
+        return {"status": "disabled", "bank_id": bank_id}
+
+    pool = memory_engine._pool
+
+    # Get bank profile
+    async with pool.acquire() as conn:
+        t0 = time.time()
+        bank_row = await conn.fetchrow(
+            f"""
+            SELECT bank_id, name, mission
+            FROM {fq_table("banks")}
+            WHERE bank_id = $1
+            """,
+            bank_id,
+        )
+
+        if not bank_row:
+            logger.warning(f"Bank {bank_id} not found for consolidation")
+            return {"status": "bank_not_found", "bank_id": bank_id}
+
+        mission = bank_row["mission"] or "General memory consolidation"
+        perf.record_timing("fetch_bank", time.time() - t0)
+
+        # Count total unconsolidated memories for progress logging
+        total_count = await conn.fetchval(
+            f"""
+            SELECT COUNT(*)
+            FROM {fq_table("memory_units")}
+            WHERE bank_id = $1
+              AND consolidated_at IS NULL
+              AND fact_type IN ('experience', 'world')
+            """,
+            bank_id,
+        )
+
+    if total_count == 0:
+        logger.debug(f"No new memories to consolidate for bank {bank_id}")
+        return {"status": "no_new_memories", "bank_id": bank_id, "memories_processed": 0}
+
+    logger.info(f"[CONSOLIDATION] bank={bank_id} total_unconsolidated={total_count}")
+    perf.log(f"[1] Found {total_count} pending memories to consolidate")
+
+    # Process each memory with individual commits for crash recovery
+    stats = {
+        "memories_processed": 0,
+        "observations_created": 0,
+        "observations_updated": 0,
+        "observations_merged": 0,
+        "actions_executed": 0,
+        "skipped": 0,
+    }
+
+    batch_num = 0
+    last_progress_timings = {}  # Track timings at last progress log
+    while True:
+        batch_num += 1
+        batch_start = time.time()
+
+        # Snapshot timings at batch start for per-batch calculation
+        batch_start_timings = perf.timings.copy()
+
+        # Fetch next batch of unconsolidated memories
+        async with pool.acquire() as conn:
+            t0 = time.time()
+            memories = await conn.fetch(
+                f"""
+                SELECT id, text, fact_type, occurred_start, occurred_end, event_date, tags, mentioned_at
+                FROM {fq_table("memory_units")}
+                WHERE bank_id = $1
+                  AND consolidated_at IS NULL
+                  AND fact_type IN ('experience', 'world')
+                ORDER BY created_at ASC
+                LIMIT $2
+                """,
+                bank_id,
+                max_memories_per_batch,
+            )
+            perf.record_timing("fetch_memories", time.time() - t0)
+
+        if not memories:
+            break  # No more unconsolidated memories
+
+        for memory in memories:
+            mem_start = time.time()
+
+            # Process the memory (uses its own connection internally)
+            async with pool.acquire() as conn:
+                result = await _process_memory(
+                    conn=conn,
+                    memory_engine=memory_engine,
+                    bank_id=bank_id,
+                    memory=dict(memory),
+                    mission=mission,
+                    request_context=request_context,
+                    perf=perf,
+                )
+
+                # Mark memory as consolidated (committed immediately)
+                await conn.execute(
+                    f"""
+                    UPDATE {fq_table("memory_units")}
+                    SET consolidated_at = NOW()
+                    WHERE id = $1
+                    """,
+                    memory["id"],
+                )
+
+            mem_time = time.time() - mem_start
+            perf.record_timing("process_memory_total", mem_time)
+
+            stats["memories_processed"] += 1
+
+            action = result.get("action")
+            if action == "created":
+                stats["observations_created"] += 1
+                stats["actions_executed"] += 1
+            elif action == "updated":
+                stats["observations_updated"] += 1
+                stats["actions_executed"] += 1
+            elif action == "merged":
+                stats["observations_merged"] += 1
+                stats["actions_executed"] += 1
+            elif action == "multiple":
+                stats["observations_created"] += result.get("created", 0)
+                stats["observations_updated"] += result.get("updated", 0)
+                stats["observations_merged"] += result.get("merged", 0)
+                stats["actions_executed"] += result.get("total_actions", 0)
+            elif action == "skipped":
+                stats["skipped"] += 1
+
+            # Log progress periodically with timing breakdown
+            if stats["memories_processed"] % 10 == 0:
+                # Calculate timing deltas since last progress log
+                timing_parts = []
+                for key in ["recall", "llm", "embedding", "db_write"]:
+                    if key in perf.timings:
+                        delta = perf.timings[key] - last_progress_timings.get(key, 0)
+                        timing_parts.append(f"{key}={delta:.2f}s")
+
+                timing_str = f" | {', '.join(timing_parts)}" if timing_parts else ""
+                logger.info(
+                    f"[CONSOLIDATION] bank={bank_id} progress: "
+                    f"{stats['memories_processed']}/{total_count} memories processed{timing_str}"
+                )
+
+                # Update last progress snapshot
+                last_progress_timings = perf.timings.copy()
+
+        batch_time = time.time() - batch_start
+        perf.log(
+            f"[2] Batch {batch_num}: {len(memories)} memories in {batch_time:.3f}s "
+            f"(avg {batch_time / len(memories):.3f}s/memory)"
+        )
+
+        # Log timing breakdown after each batch (delta from batch start)
+        timing_parts = []
+        for key in ["recall", "llm", "embedding", "db_write"]:
+            if key in perf.timings:
+                delta = perf.timings[key] - batch_start_timings.get(key, 0)
+                timing_parts.append(f"{key}={delta:.3f}s")
+
+        if timing_parts:
+            avg_per_memory = batch_time / len(memories) if memories else 0
+            logger.info(
+                f"[CONSOLIDATION] bank={bank_id} batch {batch_num}/{len(memories)} memories: "
+                f"{', '.join(timing_parts)} | avg={avg_per_memory:.3f}s/memory"
+            )
+
+    # Build summary
+    perf.log(
+        f"[3] Results: {stats['memories_processed']} memories -> "
+        f"{stats['actions_executed']} actions "
+        f"({stats['observations_created']} created, "
+        f"{stats['observations_updated']} updated, "
+        f"{stats['observations_merged']} merged, "
+        f"{stats['skipped']} skipped)"
+    )
+
+    # Add timing breakdown
+    timing_parts = []
+    if "recall" in perf.timings:
+        timing_parts.append(f"recall={perf.timings['recall']:.3f}s")
+    if "llm" in perf.timings:
+        timing_parts.append(f"llm={perf.timings['llm']:.3f}s")
+    if "embedding" in perf.timings:
+        timing_parts.append(f"embedding={perf.timings['embedding']:.3f}s")
+    if "db_write" in perf.timings:
+        timing_parts.append(f"db_write={perf.timings['db_write']:.3f}s")
+
+    if timing_parts:
+        perf.log(f"[4] Timing breakdown: {', '.join(timing_parts)}")
+
+    # Trigger mental model refreshes for models with refresh_after_consolidation=true
+    mental_models_refreshed = await _trigger_mental_model_refreshes(
+        memory_engine=memory_engine,
+        bank_id=bank_id,
+        request_context=request_context,
+        perf=perf,
+    )
+    stats["mental_models_refreshed"] = mental_models_refreshed
+
+    perf.flush()
+
+    return {"status": "completed", "bank_id": bank_id, **stats}
+
+
+async def _trigger_mental_model_refreshes(
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    request_context: "RequestContext",
+    perf: ConsolidationPerfLog | None = None,
+) -> int:
+    """
+    Trigger refreshes for mental models with refresh_after_consolidation=true.
+
+    Args:
+        memory_engine: MemoryEngine instance
+        bank_id: Bank identifier
+        request_context: Request context for authentication
+        perf: Performance logging
+
+    Returns:
+        Number of mental models scheduled for refresh
+    """
+    pool = memory_engine._pool
+
+    # Find mental models with refresh_after_consolidation=true
+    async with pool.acquire() as conn:
+        rows = await conn.fetch(
+            f"""
+            SELECT id, name
+            FROM {fq_table("mental_models")}
+            WHERE bank_id = $1
+              AND (trigger->>'refresh_after_consolidation')::boolean = true
+            """,
+            bank_id,
+        )
+
+    if not rows:
+        return 0
+
+    if perf:
+        perf.log(f"[5] Triggering refresh for {len(rows)} mental models with refresh_after_consolidation=true")
+
+    # Submit refresh tasks for each mental model
+    refreshed_count = 0
+    for row in rows:
+        mental_model_id = row["id"]
+        try:
+            await memory_engine.submit_async_refresh_mental_model(
+                bank_id=bank_id,
+                mental_model_id=mental_model_id,
+                request_context=request_context,
+            )
+            refreshed_count += 1
+            logger.info(
+                f"[CONSOLIDATION] Triggered refresh for mental model {mental_model_id} "
+                f"(name: {row['name']}) in bank {bank_id}"
+            )
+        except Exception as e:
+            logger.warning(f"[CONSOLIDATION] Failed to trigger refresh for mental model {mental_model_id}: {e}")
+
+    return refreshed_count
+
+
+async def _process_memory(
+    conn: "Connection",
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    memory: dict[str, Any],
+    mission: str,
+    request_context: "RequestContext",
+    perf: ConsolidationPerfLog | None = None,
+) -> dict[str, Any]:
+    """
+    Process a single memory for consolidation using a SINGLE LLM call.
+
+    This function:
+    1. Finds related observations (can be empty)
+    2. Uses ONE LLM call to extract durable knowledge AND decide on actions
+    3. Executes array of actions (can be multiple creates/updates)
+
+    The LLM handles all cases:
+    - No related observations: returns create action(s) with extracted durable knowledge
+    - Related observations exist: returns update/create actions based on tag routing
+    - Purely ephemeral fact: returns empty array (skip)
+
+    Returns:
+        Dict with action summary: created/updated/merged counts
+    """
+    fact_text = memory["text"]
+    memory_id = memory["id"]
+    fact_tags = memory.get("tags") or []
+
+    # Find related observations using the full recall system (NO tag filtering)
+    t0 = time.time()
+    related_observations = await _find_related_observations(
+        conn=conn,
+        memory_engine=memory_engine,
+        bank_id=bank_id,
+        query=fact_text,
+        request_context=request_context,
+    )
+    if perf:
+        perf.record_timing("recall", time.time() - t0)
+
+    # Single LLM call handles ALL cases (with or without existing observations)
+    # Note: Tags are NOT passed to LLM - they are handled algorithmically
+    t0 = time.time()
+    actions = await _consolidate_with_llm(
+        memory_engine=memory_engine,
+        fact_text=fact_text,
+        observations=related_observations,  # Can be empty list
+        mission=mission,
+    )
+    if perf:
+        perf.record_timing("llm", time.time() - t0)
+
+    if not actions:
+        # LLM returned empty array - fact is purely ephemeral, skip
+        return {"action": "skipped", "reason": "no_durable_knowledge"}
+
+    # Execute all actions and collect results
+    results = []
+    for action in actions:
+        action_type = action.get("action")
+        if action_type == "update":
+            result = await _execute_update_action(
+                conn=conn,
+                memory_engine=memory_engine,
+                bank_id=bank_id,
+                memory_id=memory_id,
+                action=action,
+                observations=related_observations,
+                source_fact_tags=fact_tags,  # Pass source fact's tags for security
+                source_occurred_start=memory.get("occurred_start"),
+                source_occurred_end=memory.get("occurred_end"),
+                source_mentioned_at=memory.get("mentioned_at"),
+                perf=perf,
+            )
+            results.append(result)
+        elif action_type == "create":
+            result = await _execute_create_action(
+                conn=conn,
+                memory_engine=memory_engine,
+                bank_id=bank_id,
+                memory_id=memory_id,
+                action=action,
+                source_fact_tags=fact_tags,  # Pass source fact's tags for security
+                event_date=memory.get("event_date"),
+                occurred_start=memory.get("occurred_start"),
+                occurred_end=memory.get("occurred_end"),
+                mentioned_at=memory.get("mentioned_at"),
+                perf=perf,
+            )
+            results.append(result)
+
+    if not results:
+        # No valid actions executed
+        return {"action": "skipped", "reason": "no_valid_actions"}
+
+    # Summarize results
+    created = sum(1 for r in results if r.get("action") == "created")
+    updated = sum(1 for r in results if r.get("action") == "updated")
+    merged = sum(1 for r in results if r.get("action") == "merged")
+
+    if len(results) == 1:
+        return results[0]
+
+    return {
+        "action": "multiple",
+        "created": created,
+        "updated": updated,
+        "merged": merged,
+        "total_actions": len(results),
+    }
+
+
+async def _execute_update_action(
+    conn: "Connection",
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    memory_id: uuid.UUID,
+    action: dict[str, Any],
+    observations: list[dict[str, Any]],
+    source_fact_tags: list[str] | None = None,
+    source_occurred_start: datetime | None = None,
+    source_occurred_end: datetime | None = None,
+    source_mentioned_at: datetime | None = None,
+    perf: ConsolidationPerfLog | None = None,
+) -> dict[str, Any]:
+    """
+    Execute an update action on an existing observation.
+
+    Updates the observation text, adds to history, increments proof_count,
+    and updates temporal fields:
+    - occurred_start: uses LEAST to keep the earliest start time
+    - occurred_end: uses GREATEST to keep the most recent end time
+    - mentioned_at: uses GREATEST to keep the most recent mention time
+
+    SECURITY: Merges source fact's tags into the observation's existing tags.
+    This ensures all contributors can see the observation they contributed to.
+    For example, if Lisa's observation (tags=['user_lisa']) is updated with
+    Mike's fact (tags=['user_mike']), the observation will have both tags.
+    """
+    learning_id = action.get("learning_id")
+    new_text = action.get("text")
+    reason = action.get("reason", "Updated with new fact")
+
+    if not learning_id or not new_text:
+        return {"action": "skipped", "reason": "missing_learning_id_or_text"}
+
+    # Find the observation
+    model = next((m for m in observations if str(m["id"]) == learning_id), None)
+    if not model:
+        return {"action": "skipped", "reason": "learning_not_found"}
+
+    # Build history entry
+    history = list(model.get("history", []))
+    history.append(
+        {
+            "previous_text": model["text"],
+            "changed_at": datetime.now(timezone.utc).isoformat(),
+            "reason": reason,
+            "source_memory_id": str(memory_id),
+        }
+    )
+
+    # Update source_memory_ids
+    source_ids = list(model.get("source_memory_ids", []))
+    source_ids.append(memory_id)
+
+    # SECURITY: Merge source fact's tags into existing observation tags
+    # This ensures all contributors can see the observation they contributed to
+    existing_tags = set(model.get("tags", []) or [])
+    source_tags = set(source_fact_tags or [])
+    merged_tags = list(existing_tags | source_tags)  # Union of both tag sets
+    if source_tags and source_tags != existing_tags:
+        logger.debug(
+            f"Security: Merging tags for observation {learning_id}: "
+            f"existing={list(existing_tags)}, source={list(source_tags)}, merged={merged_tags}"
+        )
+
+    # Generate new embedding for updated text
+    t0 = time.time()
+    embeddings = await embedding_utils.generate_embeddings_batch(memory_engine.embeddings, [new_text])
+    embedding_str = str(embeddings[0]) if embeddings else None
+    if perf:
+        perf.record_timing("embedding", time.time() - t0)
+
+    # Update the observation
+    # - occurred_start: LEAST keeps the earliest start time across all source facts
+    # - occurred_end: GREATEST keeps the most recent end time across all source facts
+    # - mentioned_at: GREATEST keeps the most recent mention time
+    # - tags: merged from existing + source fact (for visibility)
+    t0 = time.time()
+    await conn.execute(
+        f"""
+        UPDATE {fq_table("memory_units")}
+        SET text = $1,
+            embedding = $2::vector,
+            history = $3,
+            source_memory_ids = $4,
+            proof_count = $5,
+            tags = $10,
+            updated_at = now(),
+            occurred_start = LEAST(occurred_start, COALESCE($7, occurred_start)),
+            occurred_end = GREATEST(occurred_end, COALESCE($8, occurred_end)),
+            mentioned_at = GREATEST(mentioned_at, COALESCE($9, mentioned_at))
+        WHERE id = $6
+        """,
+        new_text,
+        embedding_str,
+        json.dumps(history),
+        source_ids,
+        len(source_ids),
+        uuid.UUID(learning_id),
+        source_occurred_start,
+        source_occurred_end,
+        source_mentioned_at,
+        merged_tags,
+    )
+
+    # Create links from memory to observation
+    await _create_memory_links(conn, memory_id, uuid.UUID(learning_id))
+    if perf:
+        perf.record_timing("db_write", time.time() - t0)
+
+    logger.debug(f"Updated observation {learning_id} with memory {memory_id}")
+
+    return {"action": "updated", "observation_id": learning_id}
+
+
+async def _execute_create_action(
+    conn: "Connection",
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    memory_id: uuid.UUID,
+    action: dict[str, Any],
+    source_fact_tags: list[str] | None = None,
+    event_date: datetime | None = None,
+    occurred_start: datetime | None = None,
+    occurred_end: datetime | None = None,
+    mentioned_at: datetime | None = None,
+    perf: ConsolidationPerfLog | None = None,
+) -> dict[str, Any]:
+    """
+    Execute a create action for a new observation.
+
+    Creates a new observation with the specified text.
+    The text comes directly from the classify LLM - no second LLM call needed.
+
+    Tags are determined algorithmically (not by LLM):
+    - Observations always inherit their source fact's tags
+    - This ensures visibility scope is maintained (security)
+    """
+    text = action.get("text")
+
+    # Tags are determined algorithmically - always use source fact's tags
+    # This ensures private memories create private observations
+    tags = source_fact_tags or []
+
+    if not text:
+        return {"action": "skipped", "reason": "missing_text"}
+
+    # Use text directly from classify - skip the redundant LLM call
+    result = await _create_observation_directly(
+        conn=conn,
+        memory_engine=memory_engine,
+        bank_id=bank_id,
+        source_memory_id=memory_id,
+        observation_text=text,  # Text already processed by classify LLM
+        tags=tags,
+        event_date=event_date,
+        occurred_start=occurred_start,
+        occurred_end=occurred_end,
+        mentioned_at=mentioned_at,
+        perf=perf,
+    )
+
+    logger.debug(f"Created observation {result.get('observation_id')} from memory {memory_id} (tags: {tags})")
+
+    return result
+
+
+async def _create_memory_links(
+    conn: "Connection",
+    memory_id: uuid.UUID,
+    observation_id: uuid.UUID,
+) -> None:
+    """
+    Placeholder for observation link creation.
+
+    Observations do NOT get any memory_links copied from their source facts.
+    Instead, retrieval uses source_memory_ids to traverse:
+    - Entity connections: observation → source_memory_ids → unit_entities
+    - Semantic similarity: observations have their own embeddings
+    - Temporal proximity: observations have their own temporal fields
+
+    This avoids data duplication and ensures observations are always
+    connected via their source facts' relationships.
+
+    The memory_id and observation_id parameters are kept for interface
+    compatibility but no links are created.
+    """
+    # No links are created - observations rely on source_memory_ids for traversal
+    pass
+
+
+async def _find_related_observations(
+    conn: "Connection",
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    query: str,
+    request_context: "RequestContext",
+) -> list[dict[str, Any]]:
+    """
+    Find observations related to the given query using optimized recall.
+
+    IMPORTANT: We do NOT filter by tags here. Consolidation needs to see ALL
+    potentially related observations regardless of scope, so the LLM can
+    decide on tag routing (same scope update vs cross-scope create).
+
+    Uses max_tokens to naturally limit observations (no artificial count limit).
+    Includes source memories with dates for LLM context.
+
+    Returns:
+        List of related observations with their tags, source memories, and dates
+    """
+    # Use recall to find related observations with token budget
+    # max_tokens naturally limits how many observations are returned
+    from ...config import get_config
+
+    config = get_config()
+    recall_result = await memory_engine.recall_async(
+        bank_id=bank_id,
+        query=query,
+        max_tokens=config.consolidation_max_tokens,  # Token budget for observations (configurable)
+        fact_type=["observation"],  # Only retrieve observations
+        request_context=request_context,
+        _quiet=True,  # Suppress logging
+        # NO tags parameter - intentionally get ALL observations
+    )
+
+    # If no observations returned, return empty list
+    if not recall_result.results:
+        return []
+
+    # Batch fetch all observations in a single query (no artificial limit)
+    observation_ids = [uuid.UUID(obs.id) for obs in recall_result.results]
+
+    rows = await conn.fetch(
+        f"""
+        SELECT id, text, proof_count, history, tags, source_memory_ids, created_at, updated_at,
+               occurred_start, occurred_end, mentioned_at
+        FROM {fq_table("memory_units")}
+        WHERE id = ANY($1) AND bank_id = $2 AND fact_type = 'observation'
+        """,
+        observation_ids,
+        bank_id,
+    )
+
+    # Build results list preserving recall order
+    id_to_row = {row["id"]: row for row in rows}
+    results = []
+
+    for obs in recall_result.results:
+        obs_id = uuid.UUID(obs.id)
+        if obs_id not in id_to_row:
+            continue
+
+        row = id_to_row[obs_id]
+        history = row["history"]
+        if isinstance(history, str):
+            history = json.loads(history)
+        elif history is None:
+            history = []
+
+        # Fetch source memories to include their text and dates
+        source_memory_ids = row["source_memory_ids"] or []
+        source_memories = []
+
+        if source_memory_ids:
+            source_rows = await conn.fetch(
+                f"""
+                SELECT text, occurred_start, occurred_end, mentioned_at, event_date
+                FROM {fq_table("memory_units")}
+                WHERE id = ANY($1) AND bank_id = $2
+                ORDER BY created_at ASC
+                LIMIT 5
+                """,
+                source_memory_ids[:5],  # Limit to first 5 source memories for token efficiency
+                bank_id,
+            )
+
+            for src_row in source_rows:
+                source_memories.append(
+                    {
+                        "text": src_row["text"],
+                        "occurred_start": src_row["occurred_start"],
+                        "occurred_end": src_row["occurred_end"],
+                        "mentioned_at": src_row["mentioned_at"],
+                        "event_date": src_row["event_date"],
+                    }
+                )
+
+        results.append(
+            {
+                "id": row["id"],
+                "text": row["text"],
+                "proof_count": row["proof_count"] or 1,
+                "tags": row["tags"] or [],
+                "source_memories": source_memories,
+                "occurred_start": row["occurred_start"],
+                "occurred_end": row["occurred_end"],
+                "mentioned_at": row["mentioned_at"],
+                "created_at": row["created_at"],
+                "updated_at": row["updated_at"],
+            }
+        )
+
+    return results
+
+
+async def _consolidate_with_llm(
+    memory_engine: "MemoryEngine",
+    fact_text: str,
+    observations: list[dict[str, Any]],
+    mission: str,
+) -> list[dict[str, Any]]:
+    """
+    Single LLM call to extract durable knowledge and decide on consolidation actions.
+
+    This handles ALL cases:
+    - No related observations: extracts durable knowledge, returns create action
+    - Related observations exist: compares and returns update/create actions
+    - Purely ephemeral fact: returns empty array
+
+    Note: Tags are NOT handled by the LLM. They are determined algorithmically:
+    - CREATE: observation inherits source fact's tags
+    - UPDATE: observation merges source fact's tags with existing tags
+
+    Returns:
+        List of actions, each being:
+        - {"action": "update", "learning_id": "uuid", "text": "...", "reason": "..."}
+        - {"action": "create", "text": "...", "reason": "..."}
+        - [] if fact is purely ephemeral (no durable knowledge)
+    """
+    # Format observations as JSON with source memories and dates
+    if observations:
+        obs_list = []
+        for obs in observations:
+            obs_data = {
+                "id": str(obs["id"]),
+                "text": obs["text"],
+                "proof_count": obs["proof_count"],
+                "tags": obs["tags"],
+                "created_at": obs["created_at"].isoformat() if obs.get("created_at") else None,
+                "updated_at": obs["updated_at"].isoformat() if obs.get("updated_at") else None,
+            }
+
+            # Include temporal info if available
+            if obs.get("occurred_start"):
+                obs_data["occurred_start"] = obs["occurred_start"].isoformat()
+            if obs.get("occurred_end"):
+                obs_data["occurred_end"] = obs["occurred_end"].isoformat()
+            if obs.get("mentioned_at"):
+                obs_data["mentioned_at"] = obs["mentioned_at"].isoformat()
+
+            # Include source memories (up to 3 for brevity)
+            if obs.get("source_memories"):
+                obs_data["source_memories"] = [
+                    {
+                        "text": sm["text"],
+                        "event_date": sm["event_date"].isoformat() if sm.get("event_date") else None,
+                        "occurred_start": sm["occurred_start"].isoformat() if sm.get("occurred_start") else None,
+                    }
+                    for sm in obs["source_memories"][:3]  # Limit to 3 for token efficiency
+                ]
+
+            obs_list.append(obs_data)
+
+        observations_text = json.dumps(obs_list, indent=2)
+    else:
+        observations_text = "[]"
+
+    # Only include mission section if mission is set and not the default
+    mission_section = ""
+    if mission and mission != "General memory consolidation":
+        mission_section = f"""
+MISSION CONTEXT: {mission}
+
+Focus on DURABLE knowledge that serves this mission, not ephemeral state.
+"""
+
+    user_prompt = CONSOLIDATION_USER_PROMPT.format(
+        mission_section=mission_section,
+        fact_text=fact_text,
+        observations_text=observations_text,
+    )
+
+    messages = [
+        {"role": "system", "content": CONSOLIDATION_SYSTEM_PROMPT},
+        {"role": "user", "content": user_prompt},
+    ]
+
+    try:
+        result = await memory_engine._consolidation_llm_config.call(
+            messages=messages,
+            skip_validation=True,  # Raw JSON response
+            scope="consolidation",
+        )
+        # Parse JSON response - should be an array
+        if isinstance(result, str):
+            # 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
+        # Handle legacy single-action format for backward compatibility
+        if isinstance(result, dict):
+            if result.get("related_ids") and result.get("consolidated_text"):
+                # Convert old format to new format
+                return [
+                    {
+                        "action": "update",
+                        "learning_id": result["related_ids"][0],
+                        "text": result["consolidated_text"],
+                        "reason": result.get("reason", ""),
+                    }
+                ]
+            return []
+        return []
+    except Exception as e:
+        logger.warning(f"Error in consolidation LLM call: {e}")
+        return []
+
+
+async def _create_observation_directly(
+    conn: "Connection",
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    source_memory_id: uuid.UUID,
+    observation_text: str,
+    tags: list[str] | None = None,
+    event_date: datetime | None = None,
+    occurred_start: datetime | None = None,
+    occurred_end: datetime | None = None,
+    mentioned_at: datetime | None = None,
+    perf: ConsolidationPerfLog | None = None,
+) -> dict[str, Any]:
+    """
+    Create an observation directly with pre-processed text (no LLM call).
+
+    Used when the classify LLM has already provided the learning text.
+    This avoids the redundant second LLM call.
+    """
+    # Generate embedding for the observation (convert to string for pgvector)
+    t0 = time.time()
+    embeddings = await embedding_utils.generate_embeddings_batch(memory_engine.embeddings, [observation_text])
+    embedding_str = str(embeddings[0]) if embeddings else None
+    if perf:
+        perf.record_timing("embedding", time.time() - t0)
+
+    # Create the observation as a memory_unit
+    now = datetime.now(timezone.utc)
+    obs_event_date = event_date or now
+    obs_occurred_start = occurred_start or now
+    obs_occurred_end = occurred_end or now
+    obs_mentioned_at = mentioned_at or now
+    obs_tags = tags or []
+
+    t0 = time.time()
+    observation_id = uuid.uuid4()
+    row = await conn.fetchrow(
+        f"""
+        INSERT INTO {fq_table("memory_units")} (
+            id, bank_id, text, fact_type, embedding, proof_count, source_memory_ids, history,
+            tags, event_date, occurred_start, occurred_end, mentioned_at
+        )
+        VALUES ($1, $2, $3, 'observation', $4::vector, 1, $5, '[]'::jsonb, $6, $7, $8, $9, $10)
+        RETURNING id
+        """,
+        observation_id,
+        bank_id,
+        observation_text,
+        embedding_str,
+        [source_memory_id],
+        obs_tags,
+        obs_event_date,
+        obs_occurred_start,
+        obs_occurred_end,
+        obs_mentioned_at,
+    )
+
+    # Create links between memory and observation (includes entity links, memory_links)
+    await _create_memory_links(conn, source_memory_id, observation_id)
+    if perf:
+        perf.record_timing("db_write", time.time() - t0)
+
+    logger.debug(f"Created observation {observation_id} from memory {source_memory_id} (tags: {obs_tags})")
+
+    return {"action": "created", "observation_id": str(row["id"]), "tags": obs_tags}

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

@@ -0,0 +1,77 @@
+"""Prompts for the consolidation engine."""
+
+CONSOLIDATION_SYSTEM_PROMPT = """You are a memory consolidation system. Your job is to convert facts into durable knowledge (observations) and merge with existing knowledge when appropriate.
+
+You must output ONLY valid JSON with no markdown formatting, no code blocks, and no additional text.
+
+## EXTRACT DURABLE KNOWLEDGE, NOT EPHEMERAL STATE
+Facts often describe events or actions. Extract the DURABLE KNOWLEDGE implied by the fact, not the transient state.
+
+Examples of extracting durable knowledge:
+- "User moved to Room 203" -> "Room 203 exists" (location exists, not where user is now)
+- "User visited Acme Corp at Room 105" -> "Acme Corp is located in Room 105"
+- "User took the elevator to floor 3" -> "Floor 3 is accessible by elevator"
+- "User met Sarah at the lobby" -> "Sarah can be found at the lobby"
+
+DO NOT track current user position/state as knowledge - that changes constantly.
+DO track permanent facts learned from the user's actions.
+
+## PRESERVE SPECIFIC DETAILS
+Keep names, locations, numbers, and other specifics. Do NOT:
+- Abstract into general principles
+- Generate business insights
+- Make knowledge generic
+
+GOOD examples:
+- Fact: "John likes pizza" -> "John likes pizza"
+- Fact: "Alice works at Google" -> "Alice works at Google"
+
+BAD examples:
+- "John likes pizza" -> "Understanding dietary preferences helps..." (TOO ABSTRACT)
+- "User is at Room 203" -> "User is currently at Room 203" (EPHEMERAL STATE)
+
+## MERGE RULES (when comparing to existing observations):
+1. REDUNDANT: Same information worded differently → update existing
+2. CONTRADICTION: Opposite information about same topic → update with history (e.g., "used to X, now Y")
+3. UPDATE: New state replacing old state → update with history
+
+## CRITICAL RULES:
+- NEVER merge facts about DIFFERENT people
+- NEVER merge unrelated topics (food preferences vs work vs hobbies)
+- When merging contradictions, capture the CHANGE (before → after)
+- Keep observations focused on ONE specific topic per person
+- The "text" field MUST contain durable knowledge, not ephemeral state
+- Do NOT include "tags" in output - tags are handled automatically"""
+
+CONSOLIDATION_USER_PROMPT = """Analyze this new fact and consolidate into knowledge.
+{mission_section}
+NEW FACT: {fact_text}
+
+EXISTING OBSERVATIONS (JSON array with source memories and dates):
+{observations_text}
+
+Each observation includes:
+- id: unique identifier for updating
+- text: the observation content
+- proof_count: number of supporting memories
+- tags: visibility scope (handled automatically)
+- created_at/updated_at: when observation was created/modified
+- occurred_start/occurred_end: temporal range of source facts
+- source_memories: array of supporting facts with their text and dates
+
+Instructions:
+1. Extract DURABLE KNOWLEDGE from the new fact (not ephemeral state)
+2. Review source_memories in existing observations to understand evidence
+3. Check dates to detect contradictions or updates
+4. Compare with observations:
+   - Same topic → UPDATE with learning_id
+   - New topic → CREATE new observation
+   - Purely ephemeral → return []
+
+Output JSON array of actions:
+[
+  {{"action": "update", "learning_id": "uuid-from-observations", "text": "updated knowledge", "reason": "..."}},
+  {{"action": "create", "text": "new durable knowledge", "reason": "..."}}
+]
+
+Return [] if fact contains no durable knowledge."""

hindsight-api/hindsight_api/engine/cross_encoder.py

@@ -10,17 +10,32 @@ import asyncio
 import logging
 import os
 from abc import ABC, abstractmethod
+from concurrent.futures import ThreadPoolExecutor
 
 import httpx
 
 from ..config import (
+    DEFAULT_LITELLM_API_BASE,
     DEFAULT_RERANKER_COHERE_MODEL,
+    DEFAULT_RERANKER_FLASHRANK_CACHE_DIR,
+    DEFAULT_RERANKER_FLASHRANK_MODEL,
+    DEFAULT_RERANKER_LITELLM_MODEL,
+    DEFAULT_RERANKER_LOCAL_FORCE_CPU,
+    DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT,
     DEFAULT_RERANKER_LOCAL_MODEL,
     DEFAULT_RERANKER_PROVIDER,
     DEFAULT_RERANKER_TEI_BATCH_SIZE,
     DEFAULT_RERANKER_TEI_MAX_CONCURRENT,
     ENV_COHERE_API_KEY,
+    ENV_LITELLM_API_BASE,
+    ENV_LITELLM_API_KEY,
+    ENV_RERANKER_COHERE_BASE_URL,
     ENV_RERANKER_COHERE_MODEL,
+    ENV_RERANKER_FLASHRANK_CACHE_DIR,
+    ENV_RERANKER_FLASHRANK_MODEL,
+    ENV_RERANKER_LITELLM_MODEL,
+    ENV_RERANKER_LOCAL_FORCE_CPU,
+    ENV_RERANKER_LOCAL_MAX_CONCURRENT,
     ENV_RERANKER_LOCAL_MODEL,
     ENV_RERANKER_PROVIDER,
     ENV_RERANKER_TEI_BATCH_SIZE,
@@ -78,25 +93,37 @@ class LocalSTCrossEncoder(CrossEncoderModel):
     - Fast inference (~80ms for 100 pairs on CPU)
     - Small model (80MB)
     - Trained for passage re-ranking
+
+    Uses a dedicated thread pool to limit concurrent CPU-bound work.
     """
 
-    def __init__(self, model_name: str | None = None):
+    # Shared executor across all instances (one model loaded anyway)
+    _executor: ThreadPoolExecutor | None = None
+    _max_concurrent: int = 4  # Limit concurrent CPU-bound reranking calls
+
+    def __init__(self, model_name: str | None = None, max_concurrent: int = 4, force_cpu: bool = False):
         """
         Initialize local SentenceTransformers cross-encoder.
 
         Args:
             model_name: Name of the CrossEncoder model to use.
                        Default: cross-encoder/ms-marco-MiniLM-L-6-v2
+            max_concurrent: Maximum concurrent reranking calls (default: 2).
+                           Higher values may cause CPU thrashing under load.
+            force_cpu: Force CPU mode (avoids MPS/XPC issues on macOS in daemon mode).
+                      Default: False
         """
         self.model_name = model_name or DEFAULT_RERANKER_LOCAL_MODEL
+        self.force_cpu = force_cpu
         self._model = None
+        LocalSTCrossEncoder._max_concurrent = max_concurrent
 
     @property
     def provider_name(self) -> str:
         return "local"
 
     async def initialize(self) -> None:
-        """Load the cross-encoder model."""
+        """Load the cross-encoder model and initialize the executor."""
         if self._model is not None:
             return
 
@@ -109,13 +136,59 @@ class LocalSTCrossEncoder(CrossEncoderModel):
             )
 
         logger.info(f"Reranker: initializing local provider with model {self.model_name}")
-        self._model = CrossEncoder(self.model_name)
-        logger.info("Reranker: local provider initialized")
+
+        # Determine device based on hardware availability.
+        # We always set low_cpu_mem_usage=False to prevent lazy loading (meta tensors)
+        # which can cause issues when accelerate is installed but no GPU is available.
+        # Note: We do NOT use device_map because CrossEncoder internally calls .to(device)
+        # after loading, which conflicts with accelerate's device_map handling.
+        import torch
+
+        # Force CPU mode if configured (used in daemon mode to avoid MPS/XPC issues on macOS)
+        if self.force_cpu:
+            device = "cpu"
+            logger.info("Reranker: forcing CPU mode (HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU=1)")
+        else:
+            # Check for GPU (CUDA) or Apple Silicon (MPS)
+            # Wrap in try-except to gracefully handle any device detection issues
+            # (e.g., in CI environments or when PyTorch is built without GPU support)
+            device = "cpu"  # Default to CPU
+            try:
+                has_gpu = torch.cuda.is_available() or (
+                    hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
+                )
+                if has_gpu:
+                    device = None  # Let sentence-transformers auto-detect GPU/MPS
+            except Exception as e:
+                logger.warning(f"Failed to detect GPU/MPS, falling back to CPU: {e}")
+
+        self._model = CrossEncoder(
+            self.model_name,
+            device=device,
+            model_kwargs={"low_cpu_mem_usage": False},
+        )
+
+        # Initialize shared executor (limited workers naturally limits concurrency)
+        if LocalSTCrossEncoder._executor is None:
+            LocalSTCrossEncoder._executor = ThreadPoolExecutor(
+                max_workers=LocalSTCrossEncoder._max_concurrent,
+                thread_name_prefix="reranker",
+            )
+            logger.info(f"Reranker: local provider initialized (max_concurrent={LocalSTCrossEncoder._max_concurrent})")
+        else:
+            logger.info("Reranker: local provider initialized (using existing executor)")
+
+    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """Synchronous prediction wrapper for thread pool execution."""
+        scores = self._model.predict(pairs, show_progress_bar=False)
+        return scores.tolist() if hasattr(scores, "tolist") else list(scores)
 
     async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
         """
         Score query-document pairs for relevance.
 
+        Uses a dedicated thread pool with limited workers to prevent CPU thrashing.
+
         Args:
             pairs: List of (query, document) tuples to score
 
@@ -125,10 +198,13 @@ class LocalSTCrossEncoder(CrossEncoderModel):
         if self._model is None:
             raise RuntimeError("Reranker not initialized. Call initialize() first.")
 
-        # Run CPU-bound inference in thread pool to avoid blocking event loop
+        # Use dedicated executor - limited workers naturally limits concurrency
         loop = asyncio.get_event_loop()
-        scores = await loop.run_in_executor(None, lambda: self._model.predict(pairs, show_progress_bar=False))
-        return scores.tolist() if hasattr(scores, "tolist") else list(scores)
+        return await loop.run_in_executor(
+            LocalSTCrossEncoder._executor,
+            self._predict_sync,
+            pairs,
+        )
 
 
 class RemoteTEICrossEncoder(CrossEncoderModel):
@@ -141,8 +217,13 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
     Note: The TEI server must be running a cross-encoder/reranker model.
 
     Requests are made in parallel with configurable batch size and max concurrency (backpressure).
+    Uses a GLOBAL semaphore to limit concurrent requests across ALL recall operations.
     """
 
+    # Global semaphore shared across all instances and calls to prevent thundering herd
+    _global_semaphore: asyncio.Semaphore | None = None
+    _global_max_concurrent: int = DEFAULT_RERANKER_TEI_MAX_CONCURRENT
+
     def __init__(
         self,
         base_url: str,
@@ -159,7 +240,8 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
             base_url: Base URL of the TEI server (e.g., "http://localhost:8080")
             timeout: Request timeout in seconds (default: 30.0)
             batch_size: Maximum batch size for rerank requests (default: 128)
-            max_concurrent: Maximum concurrent requests for backpressure (default: 8)
+            max_concurrent: Maximum concurrent requests for backpressure (default: 8).
+                           This is a GLOBAL limit across all parallel recall operations.
             max_retries: Maximum number of retries for failed requests (default: 3)
             retry_delay: Initial delay between retries in seconds, doubles each retry (default: 0.5)
         """
@@ -172,6 +254,14 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
         self._async_client: httpx.AsyncClient | None = None
         self._model_id: str | None = None
 
+        # Update global semaphore if max_concurrent changed
+        if (
+            RemoteTEICrossEncoder._global_semaphore is None
+            or RemoteTEICrossEncoder._global_max_concurrent != max_concurrent
+        ):
+            RemoteTEICrossEncoder._global_max_concurrent = max_concurrent
+            RemoteTEICrossEncoder._global_semaphore = asyncio.Semaphore(max_concurrent)
+
     @property
     def provider_name(self) -> str:
         return "tei"
@@ -296,13 +386,13 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
                 batch_texts = texts[i : i + self.batch_size]
                 tasks_info.append((query, batch_indices, batch_texts))
 
-        # Run all requests in parallel with semaphore for backpressure
+        # Run all requests in parallel with GLOBAL semaphore for backpressure
+        # This ensures max_concurrent is respected across ALL parallel recall operations
         all_scores = [0.0] * len(pairs)
-        semaphore = asyncio.Semaphore(self.max_concurrent)
+        semaphore = RemoteTEICrossEncoder._global_semaphore
 
         tasks = [
-            self._rerank_query_group(self._async_client, semaphore, query, texts)
-            for query, _, texts in tasks_info
+            self._rerank_query_group(self._async_client, semaphore, query, texts) for query, _, texts in tasks_info
         ]
         results = await asyncio.gather(*tasks)
 
@@ -343,6 +433,7 @@ class CohereCrossEncoder(CrossEncoderModel):
         self,
         api_key: str,
         model: str = DEFAULT_RERANKER_COHERE_MODEL,
+        base_url: str | None = None,
         timeout: float = 60.0,
     ):
         """
@@ -351,10 +442,12 @@ class CohereCrossEncoder(CrossEncoderModel):
         Args:
             api_key: Cohere API key
             model: Cohere rerank model name (default: rerank-english-v3.0)
+            base_url: Custom base URL for Cohere-compatible API (e.g., Azure-hosted endpoint)
             timeout: Request timeout in seconds (default: 60.0)
         """
         self.api_key = api_key
         self.model = model
+        self.base_url = base_url
         self.timeout = timeout
         self._client = None
 
@@ -372,8 +465,14 @@ class CohereCrossEncoder(CrossEncoderModel):
         except ImportError:
             raise ImportError("cohere is required for CohereCrossEncoder. Install it with: pip install cohere")
 
-        logger.info(f"Reranker: initializing Cohere provider with model {self.model}")
-        self._client = cohere.Client(api_key=self.api_key, timeout=self.timeout)
+        base_url_msg = f" at {self.base_url}" if self.base_url else ""
+        logger.info(f"Reranker: initializing Cohere provider with model {self.model}{base_url_msg}")
+
+        # Build client kwargs, only including base_url if set (for Azure or custom endpoints)
+        client_kwargs = {"api_key": self.api_key, "timeout": self.timeout}
+        if self.base_url:
+            client_kwargs["base_url"] = self.base_url
+        self._client = cohere.Client(**client_kwargs)
         logger.info("Reranker: Cohere provider initialized")
 
     async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
@@ -428,33 +527,328 @@ class CohereCrossEncoder(CrossEncoderModel):
         return all_scores
 
 
+class RRFPassthroughCrossEncoder(CrossEncoderModel):
+    """
+    Passthrough cross-encoder that preserves RRF scores without neural reranking.
+
+    This is useful for:
+    - Testing retrieval quality without reranking overhead
+    - Deployments where reranking latency is unacceptable
+    - Debugging to isolate retrieval vs reranking issues
+    """
+
+    def __init__(self):
+        """Initialize RRF passthrough cross-encoder."""
+        pass
+
+    @property
+    def provider_name(self) -> str:
+        return "rrf"
+
+    async def initialize(self) -> None:
+        """No initialization needed."""
+        logger.info("Reranker: RRF passthrough provider initialized (neural reranking disabled)")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Return neutral scores - actual ranking uses RRF scores from retrieval.
+
+        Args:
+            pairs: List of (query, document) tuples (ignored)
+
+        Returns:
+            List of 0.5 scores (neutral, lets RRF scores dominate)
+        """
+        # Return neutral scores so RRF ranking is preserved
+        return [0.5] * len(pairs)
+
+
+class FlashRankCrossEncoder(CrossEncoderModel):
+    """
+    FlashRank cross-encoder implementation.
+
+    FlashRank is an ultra-lite reranking library that runs on CPU without
+    requiring PyTorch or Transformers. It's ideal for serverless deployments
+    with minimal cold-start overhead.
+
+    Available models:
+    - ms-marco-TinyBERT-L-2-v2: Fastest, ~4MB
+    - ms-marco-MiniLM-L-12-v2: Best quality, ~34MB (default)
+    - rank-T5-flan: Best zero-shot, ~110MB
+    - ms-marco-MultiBERT-L-12: Multi-lingual, ~150MB
+    """
+
+    # Shared executor for CPU-bound reranking
+    _executor: ThreadPoolExecutor | None = None
+    _max_concurrent: int = 4
+
+    def __init__(
+        self,
+        model_name: str | None = None,
+        cache_dir: str | None = None,
+        max_length: int = 512,
+        max_concurrent: int = 4,
+    ):
+        """
+        Initialize FlashRank cross-encoder.
+
+        Args:
+            model_name: FlashRank model name. Default: ms-marco-MiniLM-L-12-v2
+            cache_dir: Directory to cache downloaded models. Default: system cache
+            max_length: Maximum sequence length for reranking. Default: 512
+            max_concurrent: Maximum concurrent reranking calls. Default: 4
+        """
+        self.model_name = model_name or DEFAULT_RERANKER_FLASHRANK_MODEL
+        self.cache_dir = cache_dir or DEFAULT_RERANKER_FLASHRANK_CACHE_DIR
+        self.max_length = max_length
+        self._ranker = None
+        FlashRankCrossEncoder._max_concurrent = max_concurrent
+
+    @property
+    def provider_name(self) -> str:
+        return "flashrank"
+
+    async def initialize(self) -> None:
+        """Load the FlashRank model."""
+        if self._ranker is not None:
+            return
+
+        try:
+            from flashrank import Ranker
+        except ImportError:
+            raise ImportError("flashrank is required for FlashRankCrossEncoder. Install it with: pip install flashrank")
+
+        logger.info(f"Reranker: initializing FlashRank provider with model {self.model_name}")
+
+        # Initialize ranker with optional cache directory
+        ranker_kwargs = {"model_name": self.model_name, "max_length": self.max_length}
+        if self.cache_dir:
+            ranker_kwargs["cache_dir"] = self.cache_dir
+
+        self._ranker = Ranker(**ranker_kwargs)
+
+        # Initialize shared executor
+        if FlashRankCrossEncoder._executor is None:
+            FlashRankCrossEncoder._executor = ThreadPoolExecutor(
+                max_workers=FlashRankCrossEncoder._max_concurrent,
+                thread_name_prefix="flashrank",
+            )
+            logger.info(
+                f"Reranker: FlashRank provider initialized (max_concurrent={FlashRankCrossEncoder._max_concurrent})"
+            )
+        else:
+            logger.info("Reranker: FlashRank provider initialized (using existing executor)")
+
+    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """Synchronous predict - processes each query group."""
+        from flashrank import RerankRequest
+
+        if not pairs:
+            return []
+
+        # Group pairs by query
+        query_groups: dict[str, list[tuple[int, str]]] = {}
+        for idx, (query, text) in enumerate(pairs):
+            if query not in query_groups:
+                query_groups[query] = []
+            query_groups[query].append((idx, text))
+
+        all_scores = [0.0] * len(pairs)
+
+        for query, indexed_texts in query_groups.items():
+            # Build passages list for FlashRank
+            passages = [{"id": i, "text": text} for i, (_, text) in enumerate(indexed_texts)]
+            global_indices = [idx for idx, _ in indexed_texts]
+
+            # Create rerank request
+            request = RerankRequest(query=query, passages=passages)
+            results = self._ranker.rerank(request)
+
+            # Map scores back to original positions
+            for result in results:
+                local_idx = result["id"]
+                score = result["score"]
+                global_idx = global_indices[local_idx]
+                all_scores[global_idx] = score
+
+        return all_scores
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Score query-document pairs using FlashRank.
+
+        Args:
+            pairs: List of (query, document) tuples to score
+
+        Returns:
+            List of relevance scores (higher = more relevant)
+        """
+        if self._ranker is None:
+            raise RuntimeError("Reranker not initialized. Call initialize() first.")
+
+        # Run in thread pool to avoid blocking event loop
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(FlashRankCrossEncoder._executor, self._predict_sync, pairs)
+
+
+class LiteLLMCrossEncoder(CrossEncoderModel):
+    """
+    LiteLLM cross-encoder implementation using LiteLLM proxy's /rerank endpoint.
+
+    LiteLLM provides a unified interface for multiple reranking providers via
+    the Cohere-compatible /rerank endpoint.
+    See: https://docs.litellm.ai/docs/rerank
+
+    Supported providers via LiteLLM:
+    - Cohere (rerank-english-v3.0, etc.) - prefix with cohere/
+    - Together AI - prefix with together_ai/
+    - Azure AI - prefix with azure_ai/
+    - Jina AI - prefix with jina_ai/
+    - AWS Bedrock - prefix with bedrock/
+    - Voyage AI - prefix with voyage/
+    """
+
+    def __init__(
+        self,
+        api_base: str = DEFAULT_LITELLM_API_BASE,
+        api_key: str | None = None,
+        model: str = DEFAULT_RERANKER_LITELLM_MODEL,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize LiteLLM cross-encoder client.
+
+        Args:
+            api_base: Base URL of the LiteLLM proxy (default: http://localhost:4000)
+            api_key: API key for the LiteLLM proxy (optional, depends on proxy config)
+            model: Reranking model name (default: cohere/rerank-english-v3.0)
+                   Use provider prefix (e.g., cohere/, together_ai/, voyage/)
+            timeout: Request timeout in seconds (default: 60.0)
+        """
+        self.api_base = api_base.rstrip("/")
+        self.api_key = api_key
+        self.model = model
+        self.timeout = timeout
+        self._async_client: httpx.AsyncClient | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "litellm"
+
+    async def initialize(self) -> None:
+        """Initialize the async HTTP client."""
+        if self._async_client is not None:
+            return
+
+        logger.info(f"Reranker: initializing LiteLLM provider at {self.api_base} with model {self.model}")
+
+        headers = {"Content-Type": "application/json"}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+
+        self._async_client = httpx.AsyncClient(timeout=self.timeout, headers=headers)
+        logger.info("Reranker: LiteLLM provider initialized")
+
+    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
+        """
+        Score query-document pairs using the LiteLLM proxy's /rerank endpoint.
+
+        Args:
+            pairs: List of (query, document) tuples to score
+
+        Returns:
+            List of relevance scores
+        """
+        if self._async_client is None:
+            raise RuntimeError("Reranker not initialized. Call initialize() first.")
+
+        if not pairs:
+            return []
+
+        # Group pairs by query (LiteLLM rerank expects one query with multiple documents)
+        query_groups: dict[str, list[tuple[int, str]]] = {}
+        for idx, (query, text) in enumerate(pairs):
+            if query not in query_groups:
+                query_groups[query] = []
+            query_groups[query].append((idx, text))
+
+        all_scores = [0.0] * len(pairs)
+
+        for query, indexed_texts in query_groups.items():
+            texts = [text for _, text in indexed_texts]
+            indices = [idx for idx, _ in indexed_texts]
+
+            # LiteLLM /rerank follows Cohere API format
+            response = await self._async_client.post(
+                f"{self.api_base}/rerank",
+                json={
+                    "model": self.model,
+                    "query": query,
+                    "documents": texts,
+                    "top_n": len(texts),  # Return all scores
+                },
+            )
+            response.raise_for_status()
+            result = response.json()
+
+            # Map scores back to original positions
+            # Response format: {"results": [{"index": 0, "relevance_score": 0.9}, ...]}
+            for item in result.get("results", []):
+                original_idx = item["index"]
+                score = item.get("relevance_score", item.get("score", 0.0))
+                all_scores[indices[original_idx]] = score
+
+        return all_scores
+
+
 def create_cross_encoder_from_env() -> CrossEncoderModel:
     """
-    Create a CrossEncoderModel instance based on environment variables.
+    Create a CrossEncoderModel instance based on configuration.
 
-    See hindsight_api.config for environment variable names and defaults.
+    Reads configuration via get_config() to ensure consistency across the codebase.
 
     Returns:
         Configured CrossEncoderModel instance
     """
-    provider = os.environ.get(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER).lower()
+    from ..config import get_config
+
+    config = get_config()
+    provider = config.reranker_provider.lower()
 
     if provider == "tei":
-        url = os.environ.get(ENV_RERANKER_TEI_URL)
+        url = config.reranker_tei_url
         if not url:
             raise ValueError(f"{ENV_RERANKER_TEI_URL} is required when {ENV_RERANKER_PROVIDER} is 'tei'")
-        batch_size = int(os.environ.get(ENV_RERANKER_TEI_BATCH_SIZE, str(DEFAULT_RERANKER_TEI_BATCH_SIZE)))
-        max_concurrent = int(os.environ.get(ENV_RERANKER_TEI_MAX_CONCURRENT, str(DEFAULT_RERANKER_TEI_MAX_CONCURRENT)))
-        return RemoteTEICrossEncoder(base_url=url, batch_size=batch_size, max_concurrent=max_concurrent)
+        return RemoteTEICrossEncoder(
+            base_url=url,
+            batch_size=config.reranker_tei_batch_size,
+            max_concurrent=config.reranker_tei_max_concurrent,
+        )
     elif provider == "local":
-        model = os.environ.get(ENV_RERANKER_LOCAL_MODEL)
-        model_name = model or DEFAULT_RERANKER_LOCAL_MODEL
-        return LocalSTCrossEncoder(model_name=model_name)
+        return LocalSTCrossEncoder(
+            model_name=config.reranker_local_model,
+            max_concurrent=config.reranker_local_max_concurrent,
+            force_cpu=config.reranker_local_force_cpu,
+        )
     elif provider == "cohere":
         api_key = os.environ.get(ENV_COHERE_API_KEY)
         if not api_key:
             raise ValueError(f"{ENV_COHERE_API_KEY} is required when {ENV_RERANKER_PROVIDER} is 'cohere'")
         model = os.environ.get(ENV_RERANKER_COHERE_MODEL, DEFAULT_RERANKER_COHERE_MODEL)
-        return CohereCrossEncoder(api_key=api_key, model=model)
+        base_url = os.environ.get(ENV_RERANKER_COHERE_BASE_URL) or None
+        return CohereCrossEncoder(api_key=api_key, model=model, base_url=base_url)
+    elif provider == "flashrank":
+        model = os.environ.get(ENV_RERANKER_FLASHRANK_MODEL, DEFAULT_RERANKER_FLASHRANK_MODEL)
+        cache_dir = os.environ.get(ENV_RERANKER_FLASHRANK_CACHE_DIR, DEFAULT_RERANKER_FLASHRANK_CACHE_DIR)
+        return FlashRankCrossEncoder(model_name=model, cache_dir=cache_dir)
+    elif provider == "litellm":
+        api_base = os.environ.get(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE)
+        api_key = os.environ.get(ENV_LITELLM_API_KEY)
+        model = os.environ.get(ENV_RERANKER_LITELLM_MODEL, DEFAULT_RERANKER_LITELLM_MODEL)
+        return LiteLLMCrossEncoder(api_base=api_base, api_key=api_key, model=model)
+    elif provider == "rrf":
+        return RRFPassthroughCrossEncoder()
     else:
-        raise ValueError(f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere'")
+        raise ValueError(
+            f"Unknown reranker provider: {provider}. Supported: 'local', 'tei', 'cohere', 'flashrank', 'litellm', 'rrf'"
+        )

hindsight-api/hindsight_api/engine/db_budget.py

@@ -0,0 +1,284 @@
+"""
+Database connection budget management.
+
+Limits concurrent database connections per operation to prevent
+a single operation (e.g., recall with parallel queries) from
+exhausting the connection pool.
+"""
+
+import asyncio
+import logging
+import uuid
+from contextlib import asynccontextmanager
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, AsyncIterator
+
+if TYPE_CHECKING:
+    import asyncpg
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class OperationBudget:
+    """
+    Tracks connection budget for a single operation.
+
+    Each operation gets a semaphore limiting its concurrent connections.
+    """
+
+    operation_id: str
+    max_connections: int
+    semaphore: asyncio.Semaphore = field(init=False)
+    active_count: int = field(default=0, init=False)
+
+    def __post_init__(self):
+        self.semaphore = asyncio.Semaphore(self.max_connections)
+
+
+class ConnectionBudgetManager:
+    """
+    Manages per-operation connection budgets.
+
+    Usage:
+        manager = ConnectionBudgetManager(default_budget=4)
+
+        # Start an operation
+        async with manager.operation(max_connections=2) as op:
+            # Acquire connections within the budget
+            async with op.acquire(pool) as conn:
+                await conn.fetch(...)
+
+            # Multiple connections respect the budget
+            async with op.acquire(pool) as conn1, op.acquire(pool) as conn2:
+                # At most 2 concurrent connections for this operation
+                ...
+    """
+
+    def __init__(self, default_budget: int = 4):
+        """
+        Initialize the budget manager.
+
+        Args:
+            default_budget: Default max connections per operation
+        """
+        self.default_budget = default_budget
+        self._operations: dict[str, OperationBudget] = {}
+        self._lock = asyncio.Lock()
+
+    @asynccontextmanager
+    async def operation(
+        self,
+        max_connections: int | None = None,
+        operation_id: str | None = None,
+    ) -> AsyncIterator["BudgetedOperation"]:
+        """
+        Create a budgeted operation context.
+
+        Args:
+            max_connections: Max concurrent connections for this operation.
+                           Defaults to manager's default_budget.
+            operation_id: Optional custom operation ID. Auto-generated if not provided.
+
+        Yields:
+            BudgetedOperation context for acquiring connections
+        """
+        op_id = operation_id or f"op-{uuid.uuid4().hex[:12]}"
+        budget = max_connections or self.default_budget
+
+        async with self._lock:
+            if op_id in self._operations:
+                raise ValueError(f"Operation {op_id} already exists")
+            self._operations[op_id] = OperationBudget(op_id, budget)
+
+        try:
+            yield BudgetedOperation(self, op_id)
+        finally:
+            async with self._lock:
+                self._operations.pop(op_id, None)
+
+    def _get_budget(self, operation_id: str) -> OperationBudget:
+        """Get budget for an operation (internal use)."""
+        budget = self._operations.get(operation_id)
+        if not budget:
+            raise ValueError(f"Operation {operation_id} not found")
+        return budget
+
+
+class BudgetedOperation:
+    """
+    A single operation with connection budget.
+
+    Provides methods to acquire connections within the budget.
+    """
+
+    def __init__(self, manager: ConnectionBudgetManager, operation_id: str):
+        self._manager = manager
+        self.operation_id = operation_id
+
+    @property
+    def budget(self) -> OperationBudget:
+        """Get the budget for this operation."""
+        return self._manager._get_budget(self.operation_id)
+
+    @asynccontextmanager
+    async def acquire(self, pool: "asyncpg.Pool") -> AsyncIterator["asyncpg.Connection"]:
+        """
+        Acquire a connection within the operation's budget.
+
+        Blocks if the operation has reached its connection limit.
+
+        Args:
+            pool: asyncpg connection pool
+
+        Yields:
+            Database connection
+        """
+        budget = self.budget
+        async with budget.semaphore:
+            budget.active_count += 1
+            conn = await pool.acquire()
+            try:
+                yield conn
+            finally:
+                budget.active_count -= 1
+                await pool.release(conn)
+
+    def wrap_pool(self, pool: "asyncpg.Pool") -> "BudgetedPool":
+        """
+        Wrap a pool with this operation's budget.
+
+        The returned BudgetedPool can be passed to functions expecting a pool,
+        and all acquire() calls will be limited by this operation's budget.
+
+        Args:
+            pool: asyncpg connection pool to wrap
+
+        Returns:
+            BudgetedPool that limits connections to this operation's budget
+        """
+        return BudgetedPool(pool, self)
+
+    async def acquire_many(
+        self,
+        pool: "asyncpg.Pool",
+        count: int,
+    ) -> AsyncIterator[list["asyncpg.Connection"]]:
+        """
+        Acquire multiple connections within the budget.
+
+        Note: This acquires connections sequentially to respect the budget.
+        For parallel acquisition, use multiple acquire() calls with asyncio.gather().
+
+        Args:
+            pool: asyncpg connection pool
+            count: Number of connections to acquire
+
+        Yields:
+            List of database connections
+        """
+        connections = []
+        try:
+            for _ in range(count):
+                conn = await pool.acquire()
+                connections.append(conn)
+            yield connections
+        finally:
+            for conn in connections:
+                await pool.release(conn)
+
+
+# Global default manager instance
+_default_manager: ConnectionBudgetManager | None = None
+
+
+def get_budget_manager(default_budget: int = 4) -> ConnectionBudgetManager:
+    """
+    Get or create the global budget manager.
+
+    Args:
+        default_budget: Default max connections per operation
+
+    Returns:
+        Global ConnectionBudgetManager instance
+    """
+    global _default_manager
+    if _default_manager is None:
+        _default_manager = ConnectionBudgetManager(default_budget=default_budget)
+    return _default_manager
+
+
+@asynccontextmanager
+async def budgeted_operation(
+    max_connections: int | None = None,
+    operation_id: str | None = None,
+    default_budget: int = 4,
+) -> AsyncIterator[BudgetedOperation]:
+    """
+    Convenience function to create a budgeted operation.
+
+    Args:
+        max_connections: Max concurrent connections for this operation
+        operation_id: Optional custom operation ID
+        default_budget: Default budget if manager not yet created
+
+    Yields:
+        BudgetedOperation context
+
+    Example:
+        async with budgeted_operation(max_connections=2) as op:
+            async with op.acquire(pool) as conn:
+                await conn.fetch(...)
+    """
+    manager = get_budget_manager(default_budget)
+    async with manager.operation(max_connections, operation_id) as op:
+        yield op
+
+
+class BudgetedPool:
+    """
+    A pool wrapper that limits concurrent connection acquisitions.
+
+    This can be passed to functions expecting a pool, and acquire()
+    calls will be limited by the budget semaphore.
+
+    Usage:
+        async with budgeted_operation(max_connections=4) as op:
+            budgeted_pool = op.wrap_pool(pool)
+            # Pass budgeted_pool to functions that expect a pool
+            await some_function(budgeted_pool, ...)
+    """
+
+    def __init__(self, pool: "asyncpg.Pool", operation: BudgetedOperation):
+        self._pool = pool
+        self._operation = operation
+
+    async def acquire(self) -> "asyncpg.Connection":
+        """
+        Acquire a connection within the budget.
+
+        Note: Caller must release the connection when done.
+        Prefer using as context manager via acquire_with_retry or op.acquire().
+        """
+        budget = self._operation.budget
+        await budget.semaphore.acquire()
+        budget.active_count += 1
+        try:
+            return await self._pool.acquire()
+        except Exception:
+            budget.active_count -= 1
+            budget.semaphore.release()
+            raise
+
+    async def release(self, conn: "asyncpg.Connection") -> None:
+        """Release a connection back to the pool."""
+        budget = self._operation.budget
+        try:
+            await self._pool.release(conn)
+        finally:
+            budget.active_count -= 1
+            budget.semaphore.release()
+
+    def __getattr__(self, name):
+        """Proxy other attributes to the underlying pool."""
+        return getattr(self._pool, name)

hindsight-api/hindsight_api/engine/db_utils.py

@@ -83,11 +83,22 @@ async def acquire_with_retry(pool: asyncpg.Pool, max_retries: int = DEFAULT_MAX_
     Yields:
         An asyncpg connection
     """
+    import time
+
+    start = time.time()
 
     async def acquire():
         return await pool.acquire()
 
     conn = await retry_with_backoff(acquire, max_retries=max_retries)
+    acquire_time = time.time() - start
+
+    # Log slow connection acquisitions (indicates pool contention)
+    if acquire_time > 0.05:  # 50ms threshold
+        pool_size = pool.get_size()
+        pool_free = pool.get_idle_size()
+        logger.warning(f"[DB POOL] Slow acquire: {acquire_time:.3f}s | size={pool_size}, idle={pool_free}")
+
     try:
         yield conn
     finally:

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

@@ -0,0 +1,5 @@
+"""Directives module for hard rules injected into prompts."""
+
+from .models import Directive
+
+__all__ = ["Directive"]

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

@@ -0,0 +1,37 @@
+"""Pydantic models for directives."""
+
+from datetime import datetime, timezone
+from uuid import UUID
+
+from pydantic import BaseModel, Field
+
+
+class Directive(BaseModel):
+    """A directive is a hard rule injected into prompts.
+
+    Directives are user-defined rules that guide agent behavior. Unlike mental models
+    which are automatically consolidated from memories, directives are explicit
+    instructions that are always included in relevant prompts.
+
+    Examples:
+    - "Always respond in formal English"
+    - "Never share personal data with third parties"
+    - "Prefer conservative investment recommendations"
+    """
+
+    id: UUID = Field(description="Unique identifier")
+    bank_id: str = Field(description="Bank this directive belongs to")
+    name: str = Field(description="Human-readable name")
+    content: str = Field(description="The directive text to inject into prompts")
+    priority: int = Field(default=0, description="Higher priority directives are injected first")
+    is_active: bool = Field(default=True, description="Whether this directive is currently active")
+    tags: list[str] = Field(default_factory=list, description="Tags for filtering")
+    created_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc), description="When this directive was created"
+    )
+    updated_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc), description="When this directive was last updated"
+    )
+
+    class Config:
+        from_attributes = True

hindsight-api/hindsight_api/engine/embeddings.py

@@ -17,16 +17,25 @@ import httpx
 
 from ..config import (
     DEFAULT_EMBEDDINGS_COHERE_MODEL,
+    DEFAULT_EMBEDDINGS_LITELLM_MODEL,
+    DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU,
     DEFAULT_EMBEDDINGS_LOCAL_MODEL,
     DEFAULT_EMBEDDINGS_OPENAI_MODEL,
     DEFAULT_EMBEDDINGS_PROVIDER,
+    DEFAULT_LITELLM_API_BASE,
     ENV_COHERE_API_KEY,
+    ENV_EMBEDDINGS_COHERE_BASE_URL,
     ENV_EMBEDDINGS_COHERE_MODEL,
+    ENV_EMBEDDINGS_LITELLM_MODEL,
+    ENV_EMBEDDINGS_LOCAL_FORCE_CPU,
     ENV_EMBEDDINGS_LOCAL_MODEL,
     ENV_EMBEDDINGS_OPENAI_API_KEY,
+    ENV_EMBEDDINGS_OPENAI_BASE_URL,
     ENV_EMBEDDINGS_OPENAI_MODEL,
     ENV_EMBEDDINGS_PROVIDER,
     ENV_EMBEDDINGS_TEI_URL,
+    ENV_LITELLM_API_BASE,
+    ENV_LITELLM_API_KEY,
     ENV_LLM_API_KEY,
 )
 
@@ -85,15 +94,18 @@ class LocalSTEmbeddings(Embeddings):
     The embedding dimension is auto-detected from the model.
     """
 
-    def __init__(self, model_name: str | None = None):
+    def __init__(self, model_name: str | None = None, force_cpu: bool = False):
         """
         Initialize local SentenceTransformers embeddings.
 
         Args:
             model_name: Name of the SentenceTransformer model to use.
                        Default: BAAI/bge-small-en-v1.5
+            force_cpu: Force CPU mode (avoids MPS/XPC issues on macOS in daemon mode).
+                      Default: False
         """
         self.model_name = model_name or DEFAULT_EMBEDDINGS_LOCAL_MODEL
+        self.force_cpu = force_cpu
         self._model = None
         self._dimension: int | None = None
 
@@ -121,11 +133,34 @@ class LocalSTEmbeddings(Embeddings):
             )
 
         logger.info(f"Embeddings: initializing local provider with model {self.model_name}")
-        # Disable lazy loading (meta tensors) which causes issues with newer transformers/accelerate
-        # Setting low_cpu_mem_usage=False and device_map=None ensures tensors are fully materialized
+
+        # Determine device based on hardware availability.
+        # We always set low_cpu_mem_usage=False to prevent lazy loading (meta tensors)
+        # which can cause issues when accelerate is installed but no GPU is available.
+        import torch
+
+        # Force CPU mode if configured (used in daemon mode to avoid MPS/XPC issues on macOS)
+        if self.force_cpu:
+            device = "cpu"
+            logger.info("Embeddings: forcing CPU mode")
+        else:
+            # Check for GPU (CUDA) or Apple Silicon (MPS)
+            # Wrap in try-except to gracefully handle any device detection issues
+            # (e.g., in CI environments or when PyTorch is built without GPU support)
+            device = "cpu"  # Default to CPU
+            try:
+                has_gpu = torch.cuda.is_available() or (
+                    hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
+                )
+                if has_gpu:
+                    device = None  # Let sentence-transformers auto-detect GPU/MPS
+            except Exception as e:
+                logger.warning(f"Failed to detect GPU/MPS, falling back to CPU: {e}")
+
         self._model = SentenceTransformer(
             self.model_name,
-            model_kwargs={"low_cpu_mem_usage": False, "device_map": None},
+            device=device,
+            model_kwargs={"low_cpu_mem_usage": False},
         )
 
         self._dimension = self._model.get_sentence_embedding_dimension()
@@ -143,6 +178,7 @@ class LocalSTEmbeddings(Embeddings):
         """
         if self._model is None:
             raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+
         embeddings = self._model.encode(texts, convert_to_numpy=True, show_progress_bar=False)
         return [emb.tolist() for emb in embeddings]
 
@@ -322,6 +358,7 @@ class OpenAIEmbeddings(Embeddings):
         self,
         api_key: str,
         model: str = DEFAULT_EMBEDDINGS_OPENAI_MODEL,
+        base_url: str | None = None,
         batch_size: int = 100,
         max_retries: int = 3,
     ):
@@ -331,11 +368,13 @@ class OpenAIEmbeddings(Embeddings):
         Args:
             api_key: OpenAI API key
             model: OpenAI embedding model name (default: text-embedding-3-small)
+            base_url: Custom base URL for OpenAI-compatible API (e.g., Azure OpenAI endpoint)
             batch_size: Maximum batch size for embedding requests (default: 100)
             max_retries: Maximum number of retries for failed requests (default: 3)
         """
         self.api_key = api_key
         self.model = model
+        self.base_url = base_url
         self.batch_size = batch_size
         self.max_retries = max_retries
         self._client = None
@@ -361,8 +400,14 @@ class OpenAIEmbeddings(Embeddings):
         except ImportError:
             raise ImportError("openai is required for OpenAIEmbeddings. Install it with: pip install openai")
 
-        logger.info(f"Embeddings: initializing OpenAI provider with model {self.model}")
-        self._client = OpenAI(api_key=self.api_key, max_retries=self.max_retries)
+        base_url_msg = f" at {self.base_url}" if self.base_url else ""
+        logger.info(f"Embeddings: initializing OpenAI provider with model {self.model}{base_url_msg}")
+
+        # Build client kwargs, only including base_url if set (for Azure or custom endpoints)
+        client_kwargs = {"api_key": self.api_key, "max_retries": self.max_retries}
+        if self.base_url:
+            client_kwargs["base_url"] = self.base_url
+        self._client = OpenAI(**client_kwargs)
 
         # Try to get dimension from known models, otherwise do a test embedding
         if self.model in self.MODEL_DIMENSIONS:
@@ -435,6 +480,7 @@ class CohereEmbeddings(Embeddings):
         self,
         api_key: str,
         model: str = DEFAULT_EMBEDDINGS_COHERE_MODEL,
+        base_url: str | None = None,
         batch_size: int = 96,
         timeout: float = 60.0,
         input_type: str = "search_document",
@@ -445,6 +491,7 @@ class CohereEmbeddings(Embeddings):
         Args:
             api_key: Cohere API key
             model: Cohere embedding model name (default: embed-english-v3.0)
+            base_url: Custom base URL for Cohere-compatible API (e.g., Azure-hosted endpoint)
             batch_size: Maximum batch size for embedding requests (default: 96, Cohere's limit)
             timeout: Request timeout in seconds (default: 60.0)
             input_type: Input type for embeddings (default: search_document).
@@ -452,6 +499,7 @@ class CohereEmbeddings(Embeddings):
         """
         self.api_key = api_key
         self.model = model
+        self.base_url = base_url
         self.batch_size = batch_size
         self.timeout = timeout
         self.input_type = input_type
@@ -478,8 +526,14 @@ class CohereEmbeddings(Embeddings):
         except ImportError:
             raise ImportError("cohere is required for CohereEmbeddings. Install it with: pip install cohere")
 
-        logger.info(f"Embeddings: initializing Cohere provider with model {self.model}")
-        self._client = cohere.Client(api_key=self.api_key, timeout=self.timeout)
+        base_url_msg = f" at {self.base_url}" if self.base_url else ""
+        logger.info(f"Embeddings: initializing Cohere provider with model {self.model}{base_url_msg}")
+
+        # Build client kwargs, only including base_url if set (for Azure or custom endpoints)
+        client_kwargs = {"api_key": self.api_key, "timeout": self.timeout}
+        if self.base_url:
+            client_kwargs["base_url"] = self.base_url
+        self._client = cohere.Client(**client_kwargs)
 
         # Try to get dimension from known models, otherwise do a test embedding
         if self.model in self.MODEL_DIMENSIONS:
@@ -491,7 +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})")
@@ -529,26 +583,147 @@ class CohereEmbeddings(Embeddings):
         return all_embeddings
 
 
+class LiteLLMEmbeddings(Embeddings):
+    """
+    LiteLLM embeddings implementation using LiteLLM proxy's /embeddings endpoint.
+
+    LiteLLM provides a unified interface for multiple embedding providers.
+    The proxy exposes an OpenAI-compatible /embeddings endpoint.
+    See: https://docs.litellm.ai/docs/embedding/supported_embedding
+
+    Supported providers via LiteLLM:
+    - OpenAI (text-embedding-3-small, text-embedding-ada-002, etc.)
+    - Cohere (embed-english-v3.0, etc.) - prefix with cohere/
+    - Vertex AI (textembedding-gecko, etc.) - prefix with vertex_ai/
+    - HuggingFace, Mistral, Voyage AI, etc.
+
+    The embedding dimension is auto-detected from the model at initialization.
+    """
+
+    def __init__(
+        self,
+        api_base: str = DEFAULT_LITELLM_API_BASE,
+        api_key: str | None = None,
+        model: str = DEFAULT_EMBEDDINGS_LITELLM_MODEL,
+        batch_size: int = 100,
+        timeout: float = 60.0,
+    ):
+        """
+        Initialize LiteLLM embeddings client.
+
+        Args:
+            api_base: Base URL of the LiteLLM proxy (default: http://localhost:4000)
+            api_key: API key for the LiteLLM proxy (optional, depends on proxy config)
+            model: Embedding model name (default: text-embedding-3-small)
+                   Use provider prefix for non-OpenAI models (e.g., cohere/embed-english-v3.0)
+            batch_size: Maximum batch size for embedding requests (default: 100)
+            timeout: Request timeout in seconds (default: 60.0)
+        """
+        self.api_base = api_base.rstrip("/")
+        self.api_key = api_key
+        self.model = model
+        self.batch_size = batch_size
+        self.timeout = timeout
+        self._client: httpx.Client | None = None
+        self._dimension: int | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "litellm"
+
+    @property
+    def dimension(self) -> int:
+        if self._dimension is None:
+            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+        return self._dimension
+
+    async def initialize(self) -> None:
+        """Initialize the HTTP client and detect embedding dimension."""
+        if self._client is not None:
+            return
+
+        logger.info(f"Embeddings: initializing LiteLLM provider at {self.api_base} with model {self.model}")
+
+        headers = {"Content-Type": "application/json"}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+
+        self._client = httpx.Client(timeout=self.timeout, headers=headers)
+
+        # Do a test embedding to detect dimension
+        try:
+            response = self._client.post(
+                f"{self.api_base}/embeddings",
+                json={"model": self.model, "input": ["test"]},
+            )
+            response.raise_for_status()
+            result = response.json()
+            if result.get("data") and len(result["data"]) > 0:
+                self._dimension = len(result["data"][0]["embedding"])
+            logger.info(f"Embeddings: LiteLLM provider initialized (model: {self.model}, dim: {self._dimension})")
+        except httpx.HTTPError as e:
+            raise RuntimeError(f"Failed to connect to LiteLLM proxy at {self.api_base}: {e}")
+
+    def encode(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings using the LiteLLM proxy.
+
+        Args:
+            texts: List of text strings to encode
+
+        Returns:
+            List of embedding vectors
+        """
+        if self._client is None:
+            raise RuntimeError("Embeddings not initialized. Call initialize() first.")
+
+        if not texts:
+            return []
+
+        all_embeddings = []
+
+        # Process in batches
+        for i in range(0, len(texts), self.batch_size):
+            batch = texts[i : i + self.batch_size]
+
+            response = self._client.post(
+                f"{self.api_base}/embeddings",
+                json={"model": self.model, "input": batch},
+            )
+            response.raise_for_status()
+            result = response.json()
+
+            # Sort by index to ensure correct order
+            batch_embeddings = sorted(result["data"], key=lambda x: x["index"])
+            all_embeddings.extend([e["embedding"] for e in batch_embeddings])
+
+        return all_embeddings
+
+
 def create_embeddings_from_env() -> Embeddings:
     """
-    Create an Embeddings instance based on environment variables.
+    Create an Embeddings instance based on configuration.
 
-    See hindsight_api.config for environment variable names and defaults.
+    Reads configuration via get_config() to ensure consistency across the codebase.
 
     Returns:
         Configured Embeddings instance
     """
-    provider = os.environ.get(ENV_EMBEDDINGS_PROVIDER, DEFAULT_EMBEDDINGS_PROVIDER).lower()
+    from ..config import get_config
+
+    config = get_config()
+    provider = config.embeddings_provider.lower()
 
     if provider == "tei":
-        url = os.environ.get(ENV_EMBEDDINGS_TEI_URL)
+        url = config.embeddings_tei_url
         if not url:
             raise ValueError(f"{ENV_EMBEDDINGS_TEI_URL} is required when {ENV_EMBEDDINGS_PROVIDER} is 'tei'")
         return RemoteTEIEmbeddings(base_url=url)
     elif provider == "local":
-        model = os.environ.get(ENV_EMBEDDINGS_LOCAL_MODEL)
-        model_name = model or DEFAULT_EMBEDDINGS_LOCAL_MODEL
-        return LocalSTEmbeddings(model_name=model_name)
+        return LocalSTEmbeddings(
+            model_name=config.embeddings_local_model,
+            force_cpu=config.embeddings_local_force_cpu,
+        )
     elif provider == "openai":
         # Use dedicated embeddings API key, or fall back to LLM API key
         api_key = os.environ.get(ENV_EMBEDDINGS_OPENAI_API_KEY) or os.environ.get(ENV_LLM_API_KEY)
@@ -558,12 +733,21 @@ def create_embeddings_from_env() -> Embeddings:
                 f"when {ENV_EMBEDDINGS_PROVIDER} is 'openai'"
             )
         model = os.environ.get(ENV_EMBEDDINGS_OPENAI_MODEL, DEFAULT_EMBEDDINGS_OPENAI_MODEL)
-        return OpenAIEmbeddings(api_key=api_key, model=model)
+        base_url = os.environ.get(ENV_EMBEDDINGS_OPENAI_BASE_URL) or None
+        return OpenAIEmbeddings(api_key=api_key, model=model, base_url=base_url)
     elif provider == "cohere":
         api_key = os.environ.get(ENV_COHERE_API_KEY)
         if not api_key:
             raise ValueError(f"{ENV_COHERE_API_KEY} is required when {ENV_EMBEDDINGS_PROVIDER} is 'cohere'")
         model = os.environ.get(ENV_EMBEDDINGS_COHERE_MODEL, DEFAULT_EMBEDDINGS_COHERE_MODEL)
-        return CohereEmbeddings(api_key=api_key, model=model)
+        base_url = os.environ.get(ENV_EMBEDDINGS_COHERE_BASE_URL) or None
+        return CohereEmbeddings(api_key=api_key, model=model, base_url=base_url)
+    elif provider == "litellm":
+        api_base = os.environ.get(ENV_LITELLM_API_BASE, DEFAULT_LITELLM_API_BASE)
+        api_key = os.environ.get(ENV_LITELLM_API_KEY)
+        model = os.environ.get(ENV_EMBEDDINGS_LITELLM_MODEL, DEFAULT_EMBEDDINGS_LITELLM_MODEL)
+        return LiteLLMEmbeddings(api_base=api_base, api_key=api_key, model=model)
     else:
-        raise ValueError(f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei', 'openai', 'cohere'")
+        raise ValueError(
+            f"Unknown embeddings provider: {provider}. Supported: 'local', 'tei', 'openai', 'cohere', 'litellm'"
+        )

hindsight-api/hindsight_api/engine/interface.py

@@ -160,14 +160,14 @@ class MemoryEngineInterface(ABC):
         request_context: "RequestContext",
     ) -> dict[str, Any]:
         """
-        Get bank profile including disposition and background.
+        Get bank profile including disposition and mission.
 
         Args:
             bank_id: The memory bank ID.
             request_context: Request context for authentication.
 
         Returns:
-            Bank profile dict.
+            Bank profile dict with bank_id, name, disposition, and mission.
         """
         ...
 
@@ -190,25 +190,44 @@ class MemoryEngineInterface(ABC):
         ...
 
     @abstractmethod
-    async def merge_bank_background(
+    async def merge_bank_mission(
         self,
         bank_id: str,
         new_info: str,
         *,
-        update_disposition: bool = True,
         request_context: "RequestContext",
     ) -> dict[str, Any]:
         """
-        Merge new background information into bank profile.
+        Merge new mission information into bank profile.
 
         Args:
             bank_id: The memory bank ID.
-            new_info: New background information to merge.
-            update_disposition: Whether to infer disposition from background.
+            new_info: New mission information to merge.
             request_context: Request context for authentication.
 
         Returns:
-            Updated background info.
+            Updated mission info.
+        """
+        ...
+
+    @abstractmethod
+    async def set_bank_mission(
+        self,
+        bank_id: str,
+        mission: str,
+        *,
+        request_context: "RequestContext",
+    ) -> dict[str, Any]:
+        """
+        Set the bank's mission (replaces existing).
+
+        Args:
+            bank_id: The memory bank ID.
+            mission: The mission text.
+            request_context: Request context for authentication.
+
+        Returns:
+            Dict with bank_id and mission.
         """
         ...
 
@@ -406,61 +425,20 @@ class MemoryEngineInterface(ABC):
         bank_id: str,
         *,
         limit: int = 100,
+        offset: int = 0,
         request_context: "RequestContext",
-    ) -> list[dict[str, Any]]:
+    ) -> dict[str, Any]:
         """
-        List entities for a bank.
+        List entities for a bank with pagination.
 
         Args:
             bank_id: The memory bank ID.
             limit: Maximum results.
+            offset: Offset for pagination.
             request_context: Request context for authentication.
 
         Returns:
-            List of entity dicts.
-        """
-        ...
-
-    @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.
+            Dict with items, total, limit, offset.
         """
         ...
 
@@ -516,7 +494,7 @@ class MemoryEngineInterface(ABC):
         bank_id: str,
         *,
         request_context: "RequestContext",
-    ) -> list[dict[str, Any]]:
+    ) -> dict[str, Any]:
         """
         List async operations for a bank.
 
@@ -525,7 +503,7 @@ class MemoryEngineInterface(ABC):
             request_context: Request context for authentication.
 
         Returns:
-            List of operation dicts with id, task_type, status, etc.
+            Dict with 'total' (int) and 'operations' (list of operation dicts).
         """
         ...
 
@@ -559,16 +537,16 @@ class MemoryEngineInterface(ABC):
         bank_id: str,
         *,
         name: str | None = None,
-        background: str | None = None,
+        mission: str | None = None,
         request_context: "RequestContext",
     ) -> dict[str, Any]:
         """
-        Update bank name and/or background.
+        Update bank name and/or mission.
 
         Args:
             bank_id: The memory bank ID.
             name: New bank name (optional).
-            background: New background text (optional, replaces existing).
+            mission: New mission text (optional, replaces existing).
             request_context: Request context for authentication.
 
         Returns:

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"
@@ -209,10 +272,10 @@ class LLMProvider:
             OutputTooLongError: If output exceeds token limits.
             Exception: Re-raises API errors after retries exhausted.
         """
-        queue_start_time = time.time()
+        semaphore_start = time.time()
         async with _global_llm_semaphore:
+            semaphore_wait_time = time.time() - semaphore_start
             start_time = time.time()
-            semaphore_wait_time = start_time - queue_start_time
 
             # Handle Mock provider (for testing)
             if self.provider == "mock":
@@ -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,
@@ -318,43 +381,46 @@ class LLMProvider:
 
             last_exception = None
 
+            # Prepare response format ONCE before the retry loop
+            # (to avoid appending schema to messages on every retry)
+            if response_format is not None:
+                schema = None
+                if hasattr(response_format, "model_json_schema"):
+                    schema = response_format.model_json_schema()
+
+                if strict_schema and schema is not None:
+                    # Use OpenAI's strict JSON schema enforcement
+                    # This guarantees all required fields are returned
+                    call_params["response_format"] = {
+                        "type": "json_schema",
+                        "json_schema": {
+                            "name": "response",
+                            "strict": True,
+                            "schema": schema,
+                        },
+                    }
+                else:
+                    # Soft enforcement: add schema to prompt and use json_object mode
+                    if schema is not None:
+                        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":
+                            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"]:
+                            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
+                        call_params["response_format"] = {"type": "json_object"}
+
             for attempt in range(max_retries + 1):
                 try:
                     if response_format is not None:
-                        schema = None
-                        if hasattr(response_format, "model_json_schema"):
-                            schema = response_format.model_json_schema()
-
-                        if strict_schema and schema is not None:
-                            # Use OpenAI's strict JSON schema enforcement
-                            # This guarantees all required fields are returned
-                            call_params["response_format"] = {
-                                "type": "json_schema",
-                                "json_schema": {
-                                    "name": "response",
-                                    "strict": True,
-                                    "schema": schema,
-                                },
-                            }
-                        else:
-                            # Soft enforcement: add schema to prompt and use json_object mode
-                            if schema is not None:
-                                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
-                                elif call_params["messages"]:
-                                    call_params["messages"][0]["content"] = (
-                                        schema_msg + "\n\n" + call_params["messages"][0]["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
-                                call_params["response_format"] = {"type": "json_object"}
-
-                        logger.debug(f"Sending request to {self.provider}/{self.model} (timeout={self.timeout})")
                         response = await self._client.chat.completions.create(**call_params)
-                        logger.debug(f"Received response from {self.provider}/{self.model}")
 
                         content = response.choices[0].message.content
 
@@ -467,13 +533,11 @@ class LLMProvider:
 
                 except APIConnectionError as e:
                     last_exception = e
+                    status_code = getattr(e, "status_code", None) or getattr(
+                        getattr(e, "response", None), "status_code", None
+                    )
+                    logger.warning(f"APIConnectionError (HTTP {status_code}), attempt {attempt + 1}: {str(e)[:200]}")
                     if attempt < max_retries:
-                        status_code = getattr(e, "status_code", None) or getattr(
-                            getattr(e, "response", None), "status_code", None
-                        )
-                        logger.warning(
-                            f"Connection error, retrying... (attempt {attempt + 1}/{max_retries + 1}) - status_code={status_code}, message={e}"
-                        )
                         backoff = min(initial_backoff * (2**attempt), max_backoff)
                         await asyncio.sleep(backoff)
                         continue
@@ -487,6 +551,45 @@ class LLMProvider:
                         logger.error(f"Auth error (HTTP {e.status_code}), not retrying: {str(e)}")
                         raise
 
+                    # Handle tool_use_failed error - model outputted in tool call format
+                    # Convert to expected JSON format and continue
+                    if e.status_code == 400 and response_format is not None:
+                        try:
+                            error_body = e.body if hasattr(e, "body") else {}
+                            if isinstance(error_body, dict):
+                                error_info: dict[str, Any] = error_body.get("error") or {}
+                                if error_info.get("code") == "tool_use_failed":
+                                    failed_gen = error_info.get("failed_generation", "")
+                                    if failed_gen:
+                                        # Parse the tool call format and convert to actions format
+                                        tool_call = json.loads(failed_gen)
+                                        tool_name = tool_call.get("name", "")
+                                        tool_args = tool_call.get("arguments", {})
+                                        # Convert to actions format: {"actions": [{"tool": "name", ...args}]}
+                                        converted = {"actions": [{"tool": tool_name, **tool_args}]}
+                                        if skip_validation:
+                                            result = converted
+                                        else:
+                                            result = response_format.model_validate(converted)
+
+                                        # Record metrics for this successful recovery
+                                        duration = time.time() - start_time
+                                        metrics = get_metrics_collector()
+                                        metrics.record_llm_call(
+                                            provider=self.provider,
+                                            model=self.model,
+                                            scope=scope,
+                                            duration=duration,
+                                            input_tokens=0,
+                                            output_tokens=0,
+                                            success=True,
+                                        )
+                                        if return_usage:
+                                            return result, TokenUsage(input_tokens=0, output_tokens=0, total_tokens=0)
+                                        return result
+                        except (json.JSONDecodeError, KeyError, TypeError):
+                            pass  # Failed to parse tool_use_failed, continue with normal retry
+
                     last_exception = e
                     if attempt < max_retries:
                         backoff = min(initial_backoff * (2**attempt), max_backoff)
@@ -497,14 +600,440 @@ class LLMProvider:
                         logger.error(f"API error after {max_retries + 1} attempts: {str(e)}")
                         raise
 
-                except Exception as e:
-                    logger.error(f"Unexpected error during LLM call: {type(e).__name__}: {str(e)}")
+                except Exception:
                     raise
 
             if last_exception:
                 raise last_exception
             raise RuntimeError("LLM call failed after all retries with no exception captured")
 
+    async def call_with_tools(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        max_completion_tokens: int | None = None,
+        temperature: float | None = None,
+        scope: str = "tools",
+        max_retries: int = 5,
+        initial_backoff: float = 1.0,
+        max_backoff: float = 30.0,
+        tool_choice: str | dict[str, Any] = "auto",
+    ) -> "LLMToolCallResult":
+        """
+        Make an LLM API call with tool/function calling support.
+
+        Args:
+            messages: List of message dicts. Can include tool results with role='tool'.
+            tools: List of tool definitions in OpenAI format.
+            max_completion_tokens: Maximum tokens in response.
+            temperature: Sampling temperature (0.0-2.0).
+            scope: Scope identifier for tracking.
+            max_retries: Maximum retry attempts.
+            initial_backoff: Initial backoff time in seconds.
+            max_backoff: Maximum backoff time in seconds.
+            tool_choice: How to choose tools - "auto", "none", "required", or {"type": "function", "function": {"name": "..."}}
+
+        Returns:
+            LLMToolCallResult with content and/or tool_calls.
+        """
+        from .response_models import LLMToolCall, LLMToolCallResult
+
+        async with _global_llm_semaphore:
+            start_time = time.time()
+
+            # Handle Mock provider
+            if self.provider == "mock":
+                return await self._call_with_tools_mock(messages, tools, scope)
+
+            # Handle Anthropic separately (uses different tool format)
+            if self.provider == "anthropic":
+                return await self._call_with_tools_anthropic(
+                    messages, tools, max_completion_tokens, max_retries, initial_backoff, max_backoff, start_time, scope
+                )
+
+            # 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
+                )
+
+            # OpenAI-compatible providers (OpenAI, Groq, Ollama, LMStudio)
+            call_params: dict[str, Any] = {
+                "model": self.model,
+                "messages": messages,
+                "tools": tools,
+                "tool_choice": tool_choice,
+            }
+
+            if max_completion_tokens is not None:
+                call_params["max_completion_tokens"] = max_completion_tokens
+            if temperature is not None:
+                call_params["temperature"] = temperature
+
+            # Provider-specific parameters
+            if self.provider == "groq":
+                call_params["seed"] = DEFAULT_LLM_SEED
+
+            last_exception = None
+
+            for attempt in range(max_retries + 1):
+                try:
+                    response = await self._client.chat.completions.create(**call_params)
+
+                    message = response.choices[0].message
+                    finish_reason = response.choices[0].finish_reason
+
+                    # Extract tool calls if present
+                    tool_calls: list[LLMToolCall] = []
+                    if message.tool_calls:
+                        for tc in message.tool_calls:
+                            try:
+                                args = json.loads(tc.function.arguments) if tc.function.arguments else {}
+                            except json.JSONDecodeError:
+                                args = {"_raw": tc.function.arguments}
+                            tool_calls.append(LLMToolCall(id=tc.id, name=tc.function.name, arguments=args))
+
+                    content = message.content
+
+                    # Record metrics
+                    duration = time.time() - start_time
+                    usage = response.usage
+                    input_tokens = usage.prompt_tokens or 0 if usage else 0
+                    output_tokens = usage.completion_tokens or 0 if usage else 0
+
+                    metrics = get_metrics_collector()
+                    metrics.record_llm_call(
+                        provider=self.provider,
+                        model=self.model,
+                        scope=scope,
+                        duration=duration,
+                        input_tokens=input_tokens,
+                        output_tokens=output_tokens,
+                        success=True,
+                    )
+
+                    return LLMToolCallResult(
+                        content=content,
+                        tool_calls=tool_calls,
+                        finish_reason=finish_reason,
+                        input_tokens=input_tokens,
+                        output_tokens=output_tokens,
+                    )
+
+                except APIConnectionError as e:
+                    last_exception = e
+                    if attempt < max_retries:
+                        await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
+                        continue
+                    raise
+
+                except APIStatusError as e:
+                    if e.status_code in (401, 403):
+                        raise
+                    last_exception = e
+                    if attempt < max_retries:
+                        await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
+                        continue
+                    raise
+
+                except Exception:
+                    raise
+
+            if last_exception:
+                raise last_exception
+            raise RuntimeError("Tool call failed after all retries")
+
+    async def _call_with_tools_mock(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        scope: str,
+    ) -> "LLMToolCallResult":
+        """Handle mock tool calls for testing."""
+        from .response_models import LLMToolCallResult
+
+        call_record = {
+            "provider": self.provider,
+            "model": self.model,
+            "messages": messages,
+            "tools": [t.get("function", {}).get("name") for t in tools],
+            "scope": scope,
+        }
+        self._mock_calls.append(call_record)
+
+        if self._mock_response is not None:
+            if isinstance(self._mock_response, LLMToolCallResult):
+                return self._mock_response
+            # Allow setting just tool calls as a list
+            if isinstance(self._mock_response, list):
+                from .response_models import LLMToolCall
+
+                return LLMToolCallResult(
+                    tool_calls=[
+                        LLMToolCall(id=f"mock_{i}", name=tc["name"], arguments=tc.get("arguments", {}))
+                        for i, tc in enumerate(self._mock_response)
+                    ],
+                    finish_reason="tool_calls",
+                )
+
+        return LLMToolCallResult(content="mock response", finish_reason="stop")
+
+    async def _call_with_tools_anthropic(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        max_completion_tokens: int | None,
+        max_retries: int,
+        initial_backoff: float,
+        max_backoff: float,
+        start_time: float,
+        scope: str,
+    ) -> "LLMToolCallResult":
+        """Handle Anthropic tool calling."""
+        from anthropic import APIConnectionError, APIStatusError
+
+        from .response_models import LLMToolCall, LLMToolCallResult
+
+        # Convert OpenAI tool format to Anthropic format
+        anthropic_tools = []
+        for tool in tools:
+            func = tool.get("function", {})
+            anthropic_tools.append(
+                {
+                    "name": func.get("name", ""),
+                    "description": func.get("description", ""),
+                    "input_schema": func.get("parameters", {"type": "object", "properties": {}}),
+                }
+            )
+
+        # Convert messages - handle tool results
+        system_prompt = None
+        anthropic_messages = []
+        for msg in messages:
+            role = msg.get("role", "user")
+            content = msg.get("content", "")
+
+            if role == "system":
+                system_prompt = (system_prompt + "\n\n" + content) if system_prompt else content
+            elif role == "tool":
+                # Anthropic uses tool_result blocks
+                anthropic_messages.append(
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "tool_result", "tool_use_id": msg.get("tool_call_id", ""), "content": content}
+                        ],
+                    }
+                )
+            elif role == "assistant" and msg.get("tool_calls"):
+                # Convert assistant tool calls
+                tool_use_blocks = []
+                for tc in msg["tool_calls"]:
+                    tool_use_blocks.append(
+                        {
+                            "type": "tool_use",
+                            "id": tc.get("id", ""),
+                            "name": tc.get("function", {}).get("name", ""),
+                            "input": json.loads(tc.get("function", {}).get("arguments", "{}")),
+                        }
+                    )
+                anthropic_messages.append({"role": "assistant", "content": tool_use_blocks})
+            else:
+                anthropic_messages.append({"role": role, "content": content})
+
+        call_params: dict[str, Any] = {
+            "model": self.model,
+            "messages": anthropic_messages,
+            "tools": anthropic_tools,
+            "max_tokens": max_completion_tokens or 4096,
+        }
+        if system_prompt:
+            call_params["system"] = system_prompt
+
+        last_exception = None
+        for attempt in range(max_retries + 1):
+            try:
+                response = await self._anthropic_client.messages.create(**call_params)
+
+                # Extract content and tool calls
+                content_parts = []
+                tool_calls: list[LLMToolCall] = []
+
+                for block in response.content:
+                    if block.type == "text":
+                        content_parts.append(block.text)
+                    elif block.type == "tool_use":
+                        tool_calls.append(LLMToolCall(id=block.id, name=block.name, arguments=block.input or {}))
+
+                content = "".join(content_parts) if content_parts else None
+                finish_reason = "tool_calls" if tool_calls else "stop"
+
+                # Extract token usage
+                input_tokens = response.usage.input_tokens or 0
+                output_tokens = response.usage.output_tokens or 0
+
+                # Record metrics
+                metrics = get_metrics_collector()
+                metrics.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    duration=time.time() - start_time,
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    success=True,
+                )
+
+                return LLMToolCallResult(
+                    content=content,
+                    tool_calls=tool_calls,
+                    finish_reason=finish_reason,
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                )
+
+            except (APIConnectionError, APIStatusError) as e:
+                if isinstance(e, APIStatusError) and e.status_code in (401, 403):
+                    raise
+                last_exception = e
+                if attempt < max_retries:
+                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
+                    continue
+                raise
+
+        if last_exception:
+            raise last_exception
+        raise RuntimeError("Anthropic tool call failed")
+
+    async def _call_with_tools_gemini(
+        self,
+        messages: list[dict[str, Any]],
+        tools: list[dict[str, Any]],
+        max_retries: int,
+        initial_backoff: float,
+        max_backoff: float,
+        start_time: float,
+        scope: str,
+    ) -> "LLMToolCallResult":
+        """Handle Gemini tool calling."""
+        from .response_models import LLMToolCall, LLMToolCallResult
+
+        # Convert tools to Gemini format
+        gemini_tools = []
+        for tool in tools:
+            func = tool.get("function", {})
+            gemini_tools.append(
+                genai_types.Tool(
+                    function_declarations=[
+                        genai_types.FunctionDeclaration(
+                            name=func.get("name", ""),
+                            description=func.get("description", ""),
+                            parameters=func.get("parameters"),
+                        )
+                    ]
+                )
+            )
+
+        # Convert messages
+        system_instruction = None
+        gemini_contents = []
+        for msg in messages:
+            role = msg.get("role", "user")
+            content = msg.get("content", "")
+
+            if role == "system":
+                system_instruction = (system_instruction + "\n\n" + content) if system_instruction else content
+            elif role == "tool":
+                # Gemini uses function_response
+                gemini_contents.append(
+                    genai_types.Content(
+                        role="user",
+                        parts=[
+                            genai_types.Part(
+                                function_response=genai_types.FunctionResponse(
+                                    name=msg.get("name", ""),
+                                    response={"result": content},
+                                )
+                            )
+                        ],
+                    )
+                )
+            elif role == "assistant":
+                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
+            else:
+                gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
+
+        config = genai_types.GenerateContentConfig(
+            system_instruction=system_instruction,
+            tools=gemini_tools,
+        )
+
+        last_exception = None
+        for attempt in range(max_retries + 1):
+            try:
+                response = await self._gemini_client.aio.models.generate_content(
+                    model=self.model,
+                    contents=gemini_contents,
+                    config=config,
+                )
+
+                # Extract content and tool calls
+                content = None
+                tool_calls: list[LLMToolCall] = []
+
+                if response.candidates and response.candidates[0].content:
+                    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"
+
+                # Record metrics
+                metrics = get_metrics_collector()
+                input_tokens = response.usage_metadata.prompt_token_count if response.usage_metadata else 0
+                output_tokens = response.usage_metadata.candidates_token_count if response.usage_metadata else 0
+                metrics.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    duration=time.time() - start_time,
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    success=True,
+                )
+
+                return LLMToolCallResult(
+                    content=content,
+                    tool_calls=tool_calls,
+                    finish_reason=finish_reason,
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                )
+
+            except genai_errors.APIError as e:
+                if e.code in (401, 403):
+                    raise
+                last_exception = e
+                if attempt < max_retries:
+                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
+                    continue
+                raise
+
+        if last_exception:
+            raise last_exception
+        raise RuntimeError("Gemini tool call failed")
+
     async def _call_anthropic(
         self,
         messages: list[dict[str, str]],
@@ -1042,6 +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/mental_models/__init__.py

@@ -0,0 +1,14 @@
+"""
+Mental models module for Hindsight.
+
+Mental models contain directives - hard rules that are injected into reflect prompts.
+Directives are user-defined and their observations are user-provided (not LLM-generated).
+
+Other types of consolidated knowledge are handled by:
+- Learnings: Automatic bottom-up consolidation from facts
+- Pinned Reflections: User-curated living documents
+"""
+
+from .models import MentalModel, MentalModelSubtype
+
+__all__ = ["MentalModel", "MentalModelSubtype"]

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

@@ -0,0 +1,53 @@
+"""
+Pydantic models for mental models.
+"""
+
+from datetime import datetime, timezone
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+class MentalModelSubtype(str, Enum):
+    """Subtype of mental model.
+
+    Currently only DIRECTIVE is supported. Other types of consolidated knowledge
+    are handled by:
+    - Learnings: Automatic bottom-up consolidation from facts
+    - Pinned Reflections: User-curated living documents
+    """
+
+    DIRECTIVE = "directive"  # User-defined hard rules, observations user-provided
+
+
+class MentalModel(BaseModel):
+    """
+    A mental model representing synthesized understanding.
+
+    Mental models are the agent's consolidated knowledge. Unlike raw facts,
+    mental models provide:
+    - A one-liner description for quick scanning/retrieval
+    - A full summary for deep understanding
+    - Links to related mental models
+    """
+
+    id: str = Field(description="Unique identifier within the bank")
+    bank_id: str = Field(description="Bank this mental model belongs to")
+    subtype: MentalModelSubtype = Field(description="How this model was created")
+    name: str = Field(description="Human-readable name")
+    description: str = Field(description="One-liner for quick scanning and retrieval matching")
+    summary: str | None = Field(default=None, description="Full synthesized understanding")
+
+    # References
+    entity_id: str | None = Field(default=None, description="Reference to entities table when type=entity")
+    source_facts: list[str] = Field(default_factory=list, description="Fact IDs used to generate summary")
+    links: list[str] = Field(default_factory=list, description="Related mental model IDs")
+
+    # Tags for scoped visibility (similar to document tags)
+    tags: list[str] = Field(default_factory=list, description="Tags for scoped visibility filtering")
+
+    # Timestamps
+    last_updated: datetime | None = Field(default=None, description="When summary was last regenerated")
+    created_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc), description="When this model was created"
+    )

hindsight-api/hindsight_api/engine/query_analyzer.py

@@ -84,7 +84,7 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
 
     Performance:
     - ~10-50ms per query
-    - No model loading required
+    - No model loading required (lazy import on first use)
     """
 
     def __init__(self):
@@ -112,8 +112,6 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
         Returns:
             QueryAnalysis with temporal_constraint if found
         """
-        self.load()
-
         if reference_date is None:
             reference_date = datetime.now()
 
@@ -123,6 +121,9 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
         if period_result is not None:
             return QueryAnalysis(temporal_constraint=period_result)
 
+        # Lazy load dateparser (only imports on first call, then cached)
+        self.load()
+
         # Use dateparser's search_dates to find temporal expressions
         settings = {
             "RELATIVE_BASE": reference_date,

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

@@ -0,0 +1,18 @@
+"""
+Reflect agent module for agentic reflection with tools.
+
+The reflect agent uses an iterative loop with tools to:
+1. Lookup mental models (existing knowledge)
+2. Recall facts (semantic + temporal search)
+3. Expand memories (get chunk/document context)
+"""
+
+from .agent import ReflectAgentResult, run_reflect_agent
+from .models import ReflectAction, ReflectActionBatch
+
+__all__ = [
+    "run_reflect_agent",
+    "ReflectAgentResult",
+    "ReflectAction",
+    "ReflectActionBatch",
+]

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

@@ -0,0 +1,933 @@
+"""
+Reflect agent - agentic loop for reflection with native tool calling.
+
+Uses hierarchical retrieval:
+1. search_mental_models - User-curated summaries (highest quality)
+2. search_observations - Consolidated knowledge with freshness
+3. recall - Raw facts as ground truth
+"""
+
+import asyncio
+import json
+import logging
+import re
+import time
+from typing import TYPE_CHECKING, Any, Awaitable, Callable
+
+from .models import DirectiveInfo, LLMCall, ReflectAgentResult, TokenUsageSummary, ToolCall
+from .prompts import FINAL_SYSTEM_PROMPT, _extract_directive_rules, build_final_prompt, build_system_prompt_for_tools
+from .tools_schema import get_reflect_tools
+
+
+def _build_directives_applied(directives: list[dict[str, Any]] | None) -> list[DirectiveInfo]:
+    """Build list of DirectiveInfo from directive mental models.
+
+    Handles multiple directive formats:
+    1. New format: directives have direct 'content' field
+    2. Fallback: directives have 'description' field
+    """
+    if not directives:
+        return []
+
+    result = []
+    for directive in directives:
+        directive_id = directive.get("id", "")
+        directive_name = directive.get("name", "")
+
+        # Get content from 'content' field or fallback to 'description'
+        content = directive.get("content", "") or directive.get("description", "")
+
+        result.append(DirectiveInfo(id=directive_id, name=directive_name, content=content))
+
+    return result
+
+
+if TYPE_CHECKING:
+    from ..llm_wrapper import LLMProvider
+    from ..response_models import LLMToolCall
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MAX_ITERATIONS = 10
+
+
+def _normalize_tool_name(name: str) -> str:
+    """Normalize tool name from various LLM output formats.
+
+    Some LLMs output tool names in non-standard formats:
+    - 'functions.done' (OpenAI-style prefix)
+    - 'call=functions.done' (some models)
+    - 'call=done' (some models)
+    - 'done<|channel|>commentary' (malformed special tokens appended)
+
+    Returns the normalized tool name (e.g., 'done', 'recall', etc.)
+    """
+    # Handle 'call=functions.name' or 'call=name' format
+    if name.startswith("call="):
+        name = name[len("call=") :]
+
+    # Handle 'functions.name' format
+    if name.startswith("functions."):
+        name = name[len("functions.") :]
+
+    # Handle malformed special tokens appended to tool name
+    # e.g., 'done<|channel|>commentary' -> 'done'
+    if "<|" in name:
+        name = name.split("<|")[0]
+
+    return name
+
+
+def _is_done_tool(name: str) -> bool:
+    """Check if the tool name represents the 'done' tool."""
+    return _normalize_tool_name(name) == "done"
+
+
+# Pattern to match done() call as text - handles done({...}) with nested JSON
+_DONE_CALL_PATTERN = re.compile(r"done\s*\(\s*\{.*$", re.DOTALL)
+
+# Patterns for leaked structured output in the answer field
+_LEAKED_JSON_SUFFIX = re.compile(
+    r'\s*```(?:json)?\s*\{[^}]*(?:"(?:observation_ids|memory_ids|mental_model_ids)"|\})\s*```\s*$',
+    re.DOTALL | re.IGNORECASE,
+)
+_LEAKED_JSON_OBJECT = re.compile(
+    r'\s*\{[^{]*"(?:observation_ids|memory_ids|mental_model_ids|answer)"[^}]*\}\s*$', re.DOTALL
+)
+_TRAILING_IDS_PATTERN = re.compile(
+    r"\s*(?:observation_ids|memory_ids|mental_model_ids)\s*[=:]\s*\[.*?\]\s*$", re.DOTALL | re.IGNORECASE
+)
+
+
+def _clean_answer_text(text: str) -> str:
+    """Clean up answer text by removing any done() tool call syntax.
+
+    Some LLMs output the done() call as text instead of a proper tool call.
+    This strips out patterns like: done({"answer": "...", ...})
+    """
+    # Remove done() call pattern from the end of the text
+    cleaned = _DONE_CALL_PATTERN.sub("", text).strip()
+    return cleaned if cleaned else text
+
+
+def _clean_done_answer(text: str) -> str:
+    """Clean up the answer field from a done() tool call.
+
+    Some LLMs leak structured output patterns into the answer text, such as:
+    - JSON code blocks with observation_ids/memory_ids at the end
+    - Raw JSON objects with these fields
+    - Plain text like "observation_ids: [...]"
+
+    This cleans those patterns while preserving the actual answer content.
+    """
+    if not text:
+        return text
+
+    cleaned = text
+
+    # Remove leaked JSON in code blocks at the end
+    cleaned = _LEAKED_JSON_SUFFIX.sub("", cleaned).strip()
+
+    # Remove leaked raw JSON objects at the end
+    cleaned = _LEAKED_JSON_OBJECT.sub("", cleaned).strip()
+
+    # Remove trailing ID patterns
+    cleaned = _TRAILING_IDS_PATTERN.sub("", cleaned).strip()
+
+    return cleaned if cleaned else text
+
+
+async def _generate_structured_output(
+    answer: str,
+    response_schema: dict,
+    llm_config: "LLMProvider",
+    reflect_id: str,
+) -> tuple[dict[str, Any] | None, int, int]:
+    """Generate structured output from an answer using the provided JSON schema.
+
+    Args:
+        answer: The text answer to extract structured data from
+        response_schema: JSON Schema for the expected output structure
+        llm_config: LLM provider for making the extraction call
+        reflect_id: Reflect ID for logging
+
+    Returns:
+        Tuple of (structured_output, input_tokens, output_tokens).
+        structured_output is None if generation fails.
+    """
+    try:
+        from typing import Any as TypingAny
+
+        from pydantic import create_model
+
+        def _json_schema_type_to_python(field_schema: dict) -> type:
+            """Map JSON schema type to Python type for better LLM guidance."""
+            json_type = field_schema.get("type", "string")
+            if json_type == "array":
+                return list
+            elif json_type == "object":
+                return dict
+            elif json_type == "integer":
+                return int
+            elif json_type == "number":
+                return float
+            elif json_type == "boolean":
+                return bool
+            else:
+                return str
+
+        # Build fields from JSON schema properties
+        schema_props = response_schema.get("properties", {})
+        required_fields = set(response_schema.get("required", []))
+        fields: dict[str, TypingAny] = {}
+        for field_name, field_schema in schema_props.items():
+            field_type = _json_schema_type_to_python(field_schema)
+            default = ... if field_name in required_fields else None
+            fields[field_name] = (field_type, default)
+
+        if not fields:
+            logger.warning(f"[REFLECT {reflect_id}] No fields found in response_schema, skipping structured output")
+            return None, 0, 0
+
+        DynamicModel = create_model("StructuredResponse", **fields)
+
+        # Include the full schema in the prompt for better LLM guidance
+        schema_str = json.dumps(response_schema, indent=2)
+
+        # Build field descriptions for the prompt
+        field_descriptions = []
+        for field_name, field_schema in schema_props.items():
+            field_type = field_schema.get("type", "string")
+            field_desc = field_schema.get("description", "")
+            is_required = field_name in required_fields
+            req_marker = " (REQUIRED)" if is_required else " (optional)"
+            field_descriptions.append(f"- {field_name} ({field_type}){req_marker}: {field_desc}")
+        fields_text = "\n".join(field_descriptions)
+
+        # Call LLM with the answer to extract structured data
+        structured_prompt = f"""Your task is to extract specific information from the answer below and format it as JSON.
+
+ANSWER TO EXTRACT FROM:
+\"\"\"
+{answer}
+\"\"\"
+
+REQUIRED OUTPUT FORMAT - Extract the following fields from the answer above:
+{fields_text}
+
+JSON Schema:
+```json
+{schema_str}
+```
+
+INSTRUCTIONS:
+1. Read the answer carefully and identify the information that matches each field
+2. Extract the ACTUAL content from the answer - do NOT leave fields empty if information is present
+3. For string fields: use the exact text or a clear summary from the answer
+4. For array fields: return a JSON array (e.g., ["item1", "item2"]), NOT a string
+5. For required fields: you MUST provide a value extracted from the answer
+6. Return ONLY the JSON object, no explanation
+
+OUTPUT:"""
+
+        structured_result, usage = await llm_config.call(
+            messages=[
+                {
+                    "role": "system",
+                    "content": "You are a precise data extraction assistant. Extract information from text and return it as valid JSON matching the provided schema. Always extract actual content - never return empty strings for required fields if information is available.",
+                },
+                {"role": "user", "content": structured_prompt},
+            ],
+            response_format=DynamicModel,
+            scope="reflect_structured",
+            skip_validation=True,  # We'll handle the dict ourselves
+            return_usage=True,
+        )
+
+        # Convert to dict
+        if hasattr(structured_result, "model_dump"):
+            structured_output = structured_result.model_dump()
+        elif isinstance(structured_result, dict):
+            structured_output = structured_result
+        else:
+            # Try to parse as JSON
+            structured_output = json.loads(str(structured_result))
+
+        # Validate that required fields have non-empty values
+        for field_name in required_fields:
+            value = structured_output.get(field_name)
+            if value is None or value == "" or value == []:
+                logger.warning(f"[REFLECT {reflect_id}] Required field '{field_name}' is empty in structured output")
+
+        logger.info(f"[REFLECT {reflect_id}] Generated structured output with {len(structured_output)} fields")
+        return structured_output, usage.input_tokens, usage.output_tokens
+
+    except Exception as e:
+        logger.warning(f"[REFLECT {reflect_id}] Failed to generate structured output: {e}")
+        return None, 0, 0
+
+
+async def run_reflect_agent(
+    llm_config: "LLMProvider",
+    bank_id: str,
+    query: str,
+    bank_profile: dict[str, Any],
+    search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
+    context: str | None = None,
+    max_iterations: int = DEFAULT_MAX_ITERATIONS,
+    max_tokens: int | None = None,
+    response_schema: dict | None = None,
+    directives: list[dict[str, Any]] | None = None,
+    has_mental_models: bool = False,
+    budget: str | None = None,
+) -> ReflectAgentResult:
+    """
+    Execute the reflect agent loop using native tool calling.
+
+    The agent uses hierarchical retrieval:
+    1. search_mental_models - User-curated summaries (try first)
+    2. search_observations - Consolidated knowledge with freshness
+    3. recall - Raw facts as ground truth
+
+    Args:
+        llm_config: LLM provider for agent calls
+        bank_id: Bank identifier
+        query: Question to answer
+        bank_profile: Bank profile with name and mission
+        search_mental_models_fn: Tool callback for searching mental models (query, max_results) -> result
+        search_observations_fn: Tool callback for searching observations (query, max_results) -> result
+        recall_fn: Tool callback for recall (query, max_tokens) -> result
+        expand_fn: Tool callback for expand (memory_ids, depth) -> result
+        context: Optional additional context
+        max_iterations: Maximum number of iterations before forcing response
+        max_tokens: Maximum tokens for the final response
+        response_schema: Optional JSON Schema for structured output in final response
+        directives: Optional list of directive mental models to inject as hard rules
+
+    Returns:
+        ReflectAgentResult with final answer and metadata
+    """
+    reflect_id = f"{bank_id[:8]}-{int(time.time() * 1000) % 100000}"
+    start_time = time.time()
+
+    # Build directives_applied for the trace
+    directives_applied = _build_directives_applied(directives)
+
+    # Extract directive rules for tool schema (if any)
+    directive_rules = _extract_directive_rules(directives) if directives else None
+
+    # Get tools for this agent (with directive compliance field if directives exist)
+    tools = get_reflect_tools(directive_rules=directive_rules)
+
+    # Build initial messages (directives are injected into system prompt at START and END)
+    system_prompt = build_system_prompt_for_tools(
+        bank_profile, context, directives=directives, has_mental_models=has_mental_models, budget=budget
+    )
+    messages: list[dict[str, Any]] = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": query},
+    ]
+
+    # Tracking
+    total_tools_called = 0
+    tool_trace: list[ToolCall] = []
+    tool_trace_summary: list[dict[str, Any]] = []
+    llm_trace: list[dict[str, Any]] = []
+    context_history: list[dict[str, Any]] = []  # For final prompt fallback
+
+    # Token usage tracking - accumulate across all LLM calls
+    total_input_tokens = 0
+    total_output_tokens = 0
+
+    # Track available IDs for validation (prevents hallucinated citations)
+    available_memory_ids: set[str] = set()
+    available_mental_model_ids: set[str] = set()
+    available_observation_ids: set[str] = set()
+
+    def _get_llm_trace() -> list[LLMCall]:
+        return [
+            LLMCall(
+                scope=c["scope"],
+                duration_ms=c["duration_ms"],
+                input_tokens=c.get("input_tokens", 0),
+                output_tokens=c.get("output_tokens", 0),
+            )
+            for c in llm_trace
+        ]
+
+    def _get_usage() -> TokenUsageSummary:
+        return TokenUsageSummary(
+            input_tokens=total_input_tokens,
+            output_tokens=total_output_tokens,
+            total_tokens=total_input_tokens + total_output_tokens,
+        )
+
+    def _log_completion(answer: str, iterations: int, forced: bool = False):
+        elapsed_ms = int((time.time() - start_time) * 1000)
+        tools_summary = (
+            ", ".join(
+                f"{t['tool']}({t['input_summary']})={t['duration_ms']}ms/{t.get('output_chars', 0)}c"
+                for t in tool_trace_summary
+            )
+            or "none"
+        )
+        llm_summary = ", ".join(f"{c['scope']}={c['duration_ms']}ms" for c in llm_trace) or "none"
+        total_llm_ms = sum(c["duration_ms"] for c in llm_trace)
+        total_tools_ms = sum(t["duration_ms"] for t in tool_trace_summary)
+
+        answer_preview = answer[:100] + "..." if len(answer) > 100 else answer
+        mode = "forced" if forced else "done"
+        logger.info(
+            f"[REFLECT {reflect_id}] {mode} | "
+            f"query='{query[:50]}...' | "
+            f"iterations={iterations} | "
+            f"llm=[{llm_summary}] ({total_llm_ms}ms) | "
+            f"tools=[{tools_summary}] ({total_tools_ms}ms) | "
+            f"answer='{answer_preview}' | "
+            f"total={elapsed_ms}ms"
+        )
+
+    for iteration in range(max_iterations):
+        is_last = iteration == max_iterations - 1
+
+        if is_last:
+            # Force text response on last iteration - no tools
+            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            llm_start = time.time()
+            response, usage = await llm_config.call(
+                messages=[
+                    {"role": "system", "content": FINAL_SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                scope="reflect_agent_final",
+                max_completion_tokens=max_tokens,
+                return_usage=True,
+            )
+            llm_duration = int((time.time() - llm_start) * 1000)
+            total_input_tokens += usage.input_tokens
+            total_output_tokens += usage.output_tokens
+            llm_trace.append(
+                {
+                    "scope": "final",
+                    "duration_ms": llm_duration,
+                    "input_tokens": usage.input_tokens,
+                    "output_tokens": usage.output_tokens,
+                }
+            )
+            answer = _clean_answer_text(response.strip())
+
+            # Generate structured output if schema provided
+            structured_output = None
+            if response_schema and answer:
+                structured_output, struct_in, struct_out = await _generate_structured_output(
+                    answer, response_schema, llm_config, reflect_id
+                )
+                total_input_tokens += struct_in
+                total_output_tokens += struct_out
+
+            _log_completion(answer, iteration + 1, forced=True)
+            return ReflectAgentResult(
+                text=answer,
+                structured_output=structured_output,
+                iterations=iteration + 1,
+                tools_called=total_tools_called,
+                tool_trace=tool_trace,
+                llm_trace=_get_llm_trace(),
+                usage=_get_usage(),
+                directives_applied=directives_applied,
+            )
+
+        # Call LLM with tools
+        llm_start = time.time()
+
+        try:
+            result = await llm_config.call_with_tools(
+                messages=messages,
+                tools=tools,
+                scope="reflect_agent",
+                tool_choice="required" if iteration == 0 else "auto",  # Force tool use on first iteration
+            )
+            llm_duration = int((time.time() - llm_start) * 1000)
+            total_input_tokens += result.input_tokens
+            total_output_tokens += result.output_tokens
+            llm_trace.append(
+                {
+                    "scope": f"agent_{iteration + 1}",
+                    "duration_ms": llm_duration,
+                    "input_tokens": result.input_tokens,
+                    "output_tokens": result.output_tokens,
+                }
+            )
+
+        except Exception as e:
+            err_duration = int((time.time() - llm_start) * 1000)
+            logger.warning(f"[REFLECT {reflect_id}] LLM error on iteration {iteration + 1}: {e} ({err_duration}ms)")
+            llm_trace.append({"scope": f"agent_{iteration + 1}_err", "duration_ms": err_duration})
+            # Guardrail: If no evidence gathered yet, retry
+            has_gathered_evidence = (
+                bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
+            )
+            if not has_gathered_evidence and iteration < max_iterations - 1:
+                continue
+            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            llm_start = time.time()
+            response, usage = await llm_config.call(
+                messages=[
+                    {"role": "system", "content": FINAL_SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                scope="reflect_agent_final",
+                max_completion_tokens=max_tokens,
+                return_usage=True,
+            )
+            llm_duration = int((time.time() - llm_start) * 1000)
+            total_input_tokens += usage.input_tokens
+            total_output_tokens += usage.output_tokens
+            llm_trace.append(
+                {
+                    "scope": "final",
+                    "duration_ms": llm_duration,
+                    "input_tokens": usage.input_tokens,
+                    "output_tokens": usage.output_tokens,
+                }
+            )
+            answer = _clean_answer_text(response.strip())
+
+            # Generate structured output if schema provided
+            structured_output = None
+            if response_schema and answer:
+                structured_output, struct_in, struct_out = await _generate_structured_output(
+                    answer, response_schema, llm_config, reflect_id
+                )
+                total_input_tokens += struct_in
+                total_output_tokens += struct_out
+
+            _log_completion(answer, iteration + 1, forced=True)
+            return ReflectAgentResult(
+                text=answer,
+                structured_output=structured_output,
+                iterations=iteration + 1,
+                tools_called=total_tools_called,
+                tool_trace=tool_trace,
+                llm_trace=_get_llm_trace(),
+                usage=_get_usage(),
+                directives_applied=directives_applied,
+            )
+
+        # No tool calls - LLM wants to respond with text
+        if not result.tool_calls:
+            if result.content:
+                answer = _clean_answer_text(result.content.strip())
+
+                # Generate structured output if schema provided
+                structured_output = None
+                if response_schema and answer:
+                    structured_output, struct_in, struct_out = await _generate_structured_output(
+                        answer, response_schema, llm_config, reflect_id
+                    )
+                    total_input_tokens += struct_in
+                    total_output_tokens += struct_out
+
+                _log_completion(answer, iteration + 1)
+                return ReflectAgentResult(
+                    text=answer,
+                    structured_output=structured_output,
+                    iterations=iteration + 1,
+                    tools_called=total_tools_called,
+                    tool_trace=tool_trace,
+                    llm_trace=_get_llm_trace(),
+                    usage=_get_usage(),
+                    directives_applied=directives_applied,
+                )
+            # Empty response, force final
+            prompt = build_final_prompt(query, context_history, bank_profile, context)
+            llm_start = time.time()
+            response, usage = await llm_config.call(
+                messages=[
+                    {"role": "system", "content": FINAL_SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                scope="reflect_agent_final",
+                max_completion_tokens=max_tokens,
+                return_usage=True,
+            )
+            llm_duration = int((time.time() - llm_start) * 1000)
+            total_input_tokens += usage.input_tokens
+            total_output_tokens += usage.output_tokens
+            llm_trace.append(
+                {
+                    "scope": "final",
+                    "duration_ms": llm_duration,
+                    "input_tokens": usage.input_tokens,
+                    "output_tokens": usage.output_tokens,
+                }
+            )
+            answer = _clean_answer_text(response.strip())
+
+            # Generate structured output if schema provided
+            structured_output = None
+            if response_schema and answer:
+                structured_output, struct_in, struct_out = await _generate_structured_output(
+                    answer, response_schema, llm_config, reflect_id
+                )
+                total_input_tokens += struct_in
+                total_output_tokens += struct_out
+
+            _log_completion(answer, iteration + 1, forced=True)
+            return ReflectAgentResult(
+                text=answer,
+                structured_output=structured_output,
+                iterations=iteration + 1,
+                tools_called=total_tools_called,
+                tool_trace=tool_trace,
+                llm_trace=_get_llm_trace(),
+                usage=_get_usage(),
+                directives_applied=directives_applied,
+            )
+
+        # Check for done tool call (handle various LLM output formats)
+        done_call = next((tc for tc in result.tool_calls if _is_done_tool(tc.name)), None)
+        if done_call:
+            # Guardrail: Require evidence before done
+            has_gathered_evidence = (
+                bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
+            )
+            if not has_gathered_evidence and iteration < max_iterations - 1:
+                # Add assistant message and fake tool result asking for evidence
+                messages.append(
+                    {
+                        "role": "assistant",
+                        "tool_calls": [_tool_call_to_dict(done_call)],
+                    }
+                )
+                messages.append(
+                    {
+                        "role": "tool",
+                        "tool_call_id": done_call.id,
+                        "name": done_call.name,  # Required by Gemini
+                        "content": json.dumps(
+                            {
+                                "error": "You must search for information first. Use search_mental_models(), search_observations(), or recall() before providing your final answer."
+                            }
+                        ),
+                    }
+                )
+                continue
+
+            # Process done tool
+            return await _process_done_tool(
+                done_call,
+                available_memory_ids,
+                available_mental_model_ids,
+                available_observation_ids,
+                iteration + 1,
+                total_tools_called,
+                tool_trace,
+                _get_llm_trace(),
+                _get_usage(),
+                _log_completion,
+                reflect_id,
+                directives_applied=directives_applied,
+                llm_config=llm_config,
+                response_schema=response_schema,
+            )
+
+        # Execute other tools in parallel (exclude done tool in all its format variants)
+        other_tools = [tc for tc in result.tool_calls if not _is_done_tool(tc.name)]
+        if other_tools:
+            # Add assistant message with tool calls
+            messages.append(
+                {
+                    "role": "assistant",
+                    "tool_calls": [_tool_call_to_dict(tc) for tc in other_tools],
+                }
+            )
+
+            # Execute tools in parallel
+            tool_tasks = [
+                _execute_tool_with_timing(
+                    tc,
+                    search_mental_models_fn,
+                    search_observations_fn,
+                    recall_fn,
+                    expand_fn,
+                )
+                for tc in other_tools
+            ]
+            tool_results = await asyncio.gather(*tool_tasks, return_exceptions=True)
+            total_tools_called += len(other_tools)
+
+            # Process results and add to messages
+            for tc, result_data in zip(other_tools, tool_results):
+                if isinstance(result_data, Exception):
+                    # Tool execution failed - send error back to LLM so it can try again
+                    logger.warning(f"[REFLECT {reflect_id}] Tool {tc.name} failed with exception: {result_data}")
+                    output = {"error": f"Tool execution failed: {result_data}"}
+                    duration_ms = 0
+                else:
+                    output, duration_ms = result_data
+
+                # Normalize tool name for consistent tracking
+                normalized_tool_name = _normalize_tool_name(tc.name)
+
+                # Check if tool returned an error response - log but continue (LLM will see the error)
+                if isinstance(output, dict) and "error" in output:
+                    logger.warning(
+                        f"[REFLECT {reflect_id}] Tool {normalized_tool_name} returned error: {output['error']}"
+                    )
+
+                # Track available IDs from tool results (only for successful responses)
+                if (
+                    normalized_tool_name == "search_mental_models"
+                    and isinstance(output, dict)
+                    and "mental_models" in output
+                ):
+                    for mm in output["mental_models"]:
+                        if "id" in mm:
+                            available_mental_model_ids.add(mm["id"])
+
+                if (
+                    normalized_tool_name == "search_observations"
+                    and isinstance(output, dict)
+                    and "observations" in output
+                ):
+                    for obs in output["observations"]:
+                        if "id" in obs:
+                            available_observation_ids.add(obs["id"])
+
+                if normalized_tool_name == "recall" and isinstance(output, dict) and "memories" in output:
+                    for memory in output["memories"]:
+                        if "id" in memory:
+                            available_memory_ids.add(memory["id"])
+
+                # Add tool result message
+                messages.append(
+                    {
+                        "role": "tool",
+                        "tool_call_id": tc.id,
+                        "name": tc.name,  # Required by Gemini
+                        "content": json.dumps(output, default=str),
+                    }
+                )
+
+                # Track for logging and context history
+                input_dict = {"tool": tc.name, **tc.arguments}
+                input_summary = _summarize_input(tc.name, tc.arguments)
+
+                # Extract reason from tool arguments (if provided)
+                tool_reason = tc.arguments.get("reason")
+
+                tool_trace.append(
+                    ToolCall(
+                        tool=tc.name,
+                        reason=tool_reason,
+                        input=input_dict,
+                        output=output,
+                        duration_ms=duration_ms,
+                        iteration=iteration + 1,
+                    )
+                )
+
+                try:
+                    output_chars = len(json.dumps(output))
+                except (TypeError, ValueError):
+                    output_chars = len(str(output))
+
+                tool_trace_summary.append(
+                    {
+                        "tool": tc.name,
+                        "input_summary": input_summary,
+                        "duration_ms": duration_ms,
+                        "output_chars": output_chars,
+                    }
+                )
+
+                # Keep context history for fallback final prompt
+                context_history.append({"tool": tc.name, "input": input_dict, "output": output})
+
+    # Should not reach here
+    answer = "I was unable to formulate a complete answer within the iteration limit."
+    _log_completion(answer, max_iterations, forced=True)
+    return ReflectAgentResult(
+        text=answer,
+        iterations=max_iterations,
+        tools_called=total_tools_called,
+        tool_trace=tool_trace,
+        llm_trace=_get_llm_trace(),
+        usage=_get_usage(),
+        directives_applied=directives_applied,
+    )
+
+
+def _tool_call_to_dict(tc: "LLMToolCall") -> dict[str, Any]:
+    """Convert LLMToolCall to OpenAI message format."""
+    return {
+        "id": tc.id,
+        "type": "function",
+        "function": {
+            "name": tc.name,
+            "arguments": json.dumps(tc.arguments),
+        },
+    }
+
+
+async def _process_done_tool(
+    done_call: "LLMToolCall",
+    available_memory_ids: set[str],
+    available_mental_model_ids: set[str],
+    available_observation_ids: set[str],
+    iterations: int,
+    total_tools_called: int,
+    tool_trace: list[ToolCall],
+    llm_trace: list[LLMCall],
+    usage: TokenUsageSummary,
+    log_completion: Callable,
+    reflect_id: str,
+    directives_applied: list[DirectiveInfo],
+    llm_config: "LLMProvider | None" = None,
+    response_schema: dict | None = None,
+) -> ReflectAgentResult:
+    """Process the done tool call and return the result."""
+    args = done_call.arguments
+
+    # Extract and clean the answer - some LLMs leak structured output into the answer text
+    raw_answer = args.get("answer", "").strip()
+    answer = _clean_done_answer(raw_answer) if raw_answer else ""
+    if not answer:
+        answer = "No answer provided."
+
+    # Validate IDs (only include IDs that were actually retrieved)
+    used_memory_ids = [mid for mid in args.get("memory_ids", []) if mid in available_memory_ids]
+    used_mental_model_ids = [mid for mid in args.get("mental_model_ids", []) if mid in available_mental_model_ids]
+    used_observation_ids = [oid for oid in args.get("observation_ids", []) if oid in available_observation_ids]
+
+    # Generate structured output if schema provided
+    structured_output = None
+    final_usage = usage
+    if response_schema and llm_config and answer:
+        structured_output, struct_in, struct_out = await _generate_structured_output(
+            answer, response_schema, llm_config, reflect_id
+        )
+        # Add structured output tokens to usage
+        final_usage = TokenUsageSummary(
+            input_tokens=usage.input_tokens + struct_in,
+            output_tokens=usage.output_tokens + struct_out,
+            total_tokens=usage.total_tokens + struct_in + struct_out,
+        )
+
+    log_completion(answer, iterations)
+    return ReflectAgentResult(
+        text=answer,
+        structured_output=structured_output,
+        iterations=iterations,
+        tools_called=total_tools_called,
+        tool_trace=tool_trace,
+        llm_trace=llm_trace,
+        usage=final_usage,
+        used_memory_ids=used_memory_ids,
+        used_mental_model_ids=used_mental_model_ids,
+        used_observation_ids=used_observation_ids,
+        directives_applied=directives_applied,
+    )
+
+
+async def _execute_tool_with_timing(
+    tc: "LLMToolCall",
+    search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
+) -> tuple[dict[str, Any], int]:
+    """Execute a tool call and return result with timing."""
+    start = time.time()
+    result = await _execute_tool(
+        tc.name,
+        tc.arguments,
+        search_mental_models_fn,
+        search_observations_fn,
+        recall_fn,
+        expand_fn,
+    )
+    duration_ms = int((time.time() - start) * 1000)
+    return result, duration_ms
+
+
+async def _execute_tool(
+    tool_name: str,
+    args: dict[str, Any],
+    search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    recall_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
+    expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
+) -> dict[str, Any]:
+    """Execute a single tool by name."""
+    # Normalize tool name for various LLM output formats
+    tool_name = _normalize_tool_name(tool_name)
+
+    if tool_name == "search_mental_models":
+        query = args.get("query")
+        if not query:
+            return {"error": "search_mental_models requires a query parameter"}
+        max_results = args.get("max_results") or 5
+        return await search_mental_models_fn(query, max_results)
+
+    elif tool_name == "search_observations":
+        query = args.get("query")
+        if not query:
+            return {"error": "search_observations requires a query parameter"}
+        max_tokens = max(args.get("max_tokens") or 5000, 1000)  # Default 5000, min 1000
+        return await search_observations_fn(query, max_tokens)
+
+    elif tool_name == "recall":
+        query = args.get("query")
+        if not query:
+            return {"error": "recall requires a query parameter"}
+        max_tokens = max(args.get("max_tokens") or 2048, 1000)  # Default 2048, min 1000
+        return await recall_fn(query, max_tokens)
+
+    elif tool_name == "expand":
+        memory_ids = args.get("memory_ids", [])
+        if not memory_ids:
+            return {"error": "expand requires memory_ids"}
+        depth = args.get("depth", "chunk")
+        return await expand_fn(memory_ids, depth)
+
+    else:
+        return {"error": f"Unknown tool: {tool_name}"}
+
+
+def _summarize_input(tool_name: str, args: dict[str, Any]) -> str:
+    """Create a summary of tool input for logging, showing all params."""
+    if tool_name == "search_mental_models":
+        query = args.get("query", "")
+        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
+        max_results = args.get("max_results") or 5
+        return f"(query={query_preview}, max_results={max_results})"
+    elif tool_name == "search_observations":
+        query = args.get("query", "")
+        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
+        max_tokens = max(args.get("max_tokens") or 5000, 1000)
+        return f"(query={query_preview}, max_tokens={max_tokens})"
+    elif tool_name == "recall":
+        query = args.get("query", "")
+        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
+        # Show actual value used (default 2048, min 1000)
+        max_tokens = max(args.get("max_tokens") or 2048, 1000)
+        return f"(query={query_preview}, max_tokens={max_tokens})"
+    elif tool_name == "expand":
+        memory_ids = args.get("memory_ids", [])
+        depth = args.get("depth", "chunk")
+        return f"(memory_ids=[{len(memory_ids)} ids], depth={depth})"
+    elif tool_name == "done":
+        answer = args.get("answer", "")
+        answer_preview = f"'{answer[:30]}...'" if len(answer) > 30 else f"'{answer}'"
+        memory_ids = args.get("memory_ids", [])
+        mental_model_ids = args.get("mental_model_ids", [])
+        observation_ids = args.get("observation_ids", [])
+        return (
+            f"(answer={answer_preview}, mem={len(memory_ids)}, mm={len(mental_model_ids)}, obs={len(observation_ids)})"
+        )
+    return str(args)

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

@@ -0,0 +1,109 @@
+"""
+Pydantic models for the reflect agent.
+"""
+
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+
+
+class ObservationSection(BaseModel):
+    """A section within an observation with its supporting memories."""
+
+    title: str = Field(description="Section header (can be empty for intro)")
+    text: str = Field(description="Section content - no headers, use lists/tables/bold")
+    memory_ids: list[str] = Field(default_factory=list, description="Memory IDs supporting this section")
+
+
+class ReflectAction(BaseModel):
+    """Single action the reflect agent can take."""
+
+    tool: Literal["list_observations", "get_observation", "recall", "expand", "done"] = Field(
+        description="Tool to invoke: list_observations, get_observation, recall, expand, or done"
+    )
+    # Tool-specific parameters
+    observation_id: str | None = Field(default=None, description="Observation ID for get_observation")
+    query: str | None = Field(default=None, description="Search query for recall")
+    max_tokens: int | None = Field(default=None, description="Max tokens for recall results (default 2048)")
+    memory_ids: list[str] | None = Field(default=None, description="Memory unit IDs for expand (batched)")
+    depth: Literal["chunk", "document"] | None = Field(default=None, description="Expansion depth for expand")
+    observation_sections: list[ObservationSection] | None = Field(
+        default=None, description="Observation sections for done action (when output_mode=observations)"
+    )
+    # Plain text answer fields (for output_mode=answer)
+    answer: str | None = Field(default=None, description="Plain text answer for done action (no markdown)")
+    answer_memory_ids: list[str] | None = Field(
+        default=None, description="Memory IDs supporting the answer", alias="memory_ids"
+    )
+    answer_model_ids: list[str] | None = Field(
+        default=None, description="Mental model IDs supporting the answer", alias="model_ids"
+    )
+    reasoning: str | None = Field(default=None, description="Brief reasoning for this action")
+
+
+class ReflectActionBatch(BaseModel):
+    """Batch of actions for parallel execution."""
+
+    actions: list[ReflectAction] = Field(description="List of actions to execute in parallel")
+
+
+class ToolCall(BaseModel):
+    """A single tool call made during reflect."""
+
+    tool: str = Field(description="Tool name: lookup, recall, expand")
+    reason: str | None = Field(default=None, description="Agent's reasoning for making this tool call")
+    input: dict = Field(description="Tool input parameters")
+    output: dict = Field(description="Tool output/result")
+    duration_ms: int = Field(description="Execution time in milliseconds")
+    iteration: int = Field(default=0, description="Iteration number (1-based) when this tool was called")
+
+
+class LLMCall(BaseModel):
+    """A single LLM call made during reflect."""
+
+    scope: str = Field(description="Call scope: agent_1, agent_2, final, etc.")
+    duration_ms: int = Field(description="Execution time in milliseconds")
+    input_tokens: int = Field(default=0, description="Input tokens used")
+    output_tokens: int = Field(default=0, description="Output tokens used")
+
+
+class DirectiveInfo(BaseModel):
+    """Information about a directive that was applied during reflect."""
+
+    id: str = Field(description="Directive mental model ID")
+    name: str = Field(description="Directive name")
+    content: str = Field(description="Directive content")
+
+
+class TokenUsageSummary(BaseModel):
+    """Total token usage across all LLM calls."""
+
+    input_tokens: int = Field(default=0, description="Total input tokens used")
+    output_tokens: int = Field(default=0, description="Total output tokens used")
+    total_tokens: int = Field(default=0, description="Total tokens (input + output)")
+
+
+class ReflectAgentResult(BaseModel):
+    """Result from the reflect agent."""
+
+    text: str = Field(description="Final answer text")
+    structured_output: dict[str, Any] | None = Field(
+        default=None, description="Structured output parsed according to provided response_schema"
+    )
+    iterations: int = Field(default=0, description="Number of iterations taken")
+    tools_called: int = Field(default=0, description="Total number of tool calls made")
+    tool_trace: list[ToolCall] = Field(default_factory=list, description="Trace of all tool calls made")
+    llm_trace: list[LLMCall] = Field(default_factory=list, description="Trace of all LLM calls made")
+    usage: TokenUsageSummary = Field(
+        default_factory=TokenUsageSummary, description="Total token usage across all LLM calls"
+    )
+    used_memory_ids: list[str] = Field(default_factory=list, description="Validated memory IDs actually used in answer")
+    used_mental_model_ids: list[str] = Field(
+        default_factory=list, description="Validated mental model IDs actually used in answer"
+    )
+    used_observation_ids: list[str] = Field(
+        default_factory=list, description="Validated observation IDs actually used in answer"
+    )
+    directives_applied: list[DirectiveInfo] = Field(
+        default_factory=list, description="Directive mental models that affected this reflection"
+    )

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

@@ -0,0 +1,186 @@
+"""
+Models and utilities for evidence-grounded observations with computed trends.
+
+Observations are part of mental models and represent patterns/beliefs derived
+from memories. Each observation must be grounded in specific evidence (quotes)
+from memories, and trends are computed algorithmically from evidence timestamps.
+"""
+
+from datetime import datetime, timedelta, timezone
+from enum import Enum
+
+from pydantic import BaseModel, Field, computed_field, field_validator
+
+
+class Trend(str, Enum):
+    """Computed trend for an observation based on evidence timestamps.
+
+    Trends indicate how an observation's evidence is distributed over time:
+    - STABLE: Evidence spread across time, continues to present
+    - STRENGTHENING: More/denser evidence recently than before
+    - WEAKENING: Evidence mostly old, sparse recently
+    - NEW: All evidence within recent window
+    - STALE: No evidence in recent window (may no longer apply)
+    """
+
+    STABLE = "stable"
+    STRENGTHENING = "strengthening"
+    WEAKENING = "weakening"
+    NEW = "new"
+    STALE = "stale"
+
+
+class ObservationEvidence(BaseModel):
+    """A single piece of evidence supporting an observation.
+
+    Each evidence item must include an exact quote from the source memory
+    to ensure observations are grounded and verifiable.
+    """
+
+    memory_id: str = Field(description="ID of the memory unit this evidence comes from")
+    quote: str = Field(description="Exact quote from the memory supporting the observation")
+    relevance: str = Field(default="", description="Brief explanation of how this quote supports the observation")
+    timestamp: datetime = Field(description="When the source memory was created")
+
+    @field_validator("timestamp", mode="before")
+    @classmethod
+    def ensure_timezone_aware(cls, v: datetime | str | None) -> datetime:
+        """Ensure timestamp is always timezone-aware UTC."""
+        if v is None:
+            return datetime.now(timezone.utc)
+        if isinstance(v, str):
+            # Parse ISO format string, handling 'Z' suffix
+            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
+        if isinstance(v, datetime):
+            if v.tzinfo is None:
+                return v.replace(tzinfo=timezone.utc)
+            return v
+        raise ValueError(f"Invalid timestamp type: {type(v)}")
+
+
+class Observation(BaseModel):
+    """A single observation within a mental model.
+
+    Observations represent patterns, preferences, beliefs, or other insights
+    derived from memories. Each observation must be grounded in evidence
+    with exact quotes from source memories.
+    """
+
+    title: str = Field(description="Short summary title for the observation (5-10 words)")
+    content: str = Field(description="The observation content - detailed explanation of what we believe to be true")
+    evidence: list[ObservationEvidence] = Field(default_factory=list, description="Supporting evidence with quotes")
+    created_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc), description="When this observation was first created"
+    )
+
+    @field_validator("created_at", mode="before")
+    @classmethod
+    def ensure_created_at_timezone_aware(cls, v: datetime | str | None) -> datetime:
+        """Ensure created_at is always timezone-aware UTC."""
+        if v is None:
+            return datetime.now(timezone.utc)
+        if isinstance(v, str):
+            v = datetime.fromisoformat(v.replace("Z", "+00:00"))
+        if isinstance(v, datetime):
+            if v.tzinfo is None:
+                return v.replace(tzinfo=timezone.utc)
+            return v
+        raise ValueError(f"Invalid created_at type: {type(v)}")
+
+    @computed_field
+    @property
+    def trend(self) -> Trend:
+        """Compute trend from evidence timestamps."""
+        return compute_trend(self.evidence)
+
+    @computed_field
+    @property
+    def evidence_span(self) -> dict[str, str | None]:
+        """Get the time span covered by evidence."""
+        if not self.evidence:
+            return {"from": None, "to": None}
+        timestamps = [e.timestamp for e in self.evidence]
+        return {
+            "from": min(timestamps).isoformat(),
+            "to": max(timestamps).isoformat(),
+        }
+
+    @computed_field
+    @property
+    def evidence_count(self) -> int:
+        """Number of evidence items supporting this observation."""
+        return len(self.evidence)
+
+
+def compute_trend(
+    evidence: list[ObservationEvidence],
+    now: datetime | None = None,
+    recent_days: int = 30,
+    old_days: int = 90,
+) -> Trend:
+    """Compute the trend for an observation based on evidence timestamps.
+
+    The trend indicates how the evidence is distributed over time:
+    - STABLE: Evidence spread across time, continues to present
+    - STRENGTHENING: More evidence recently than historically
+    - WEAKENING: Evidence mostly old, sparse recently
+    - NEW: All evidence is recent (within recent_days)
+    - STALE: No evidence in recent window
+
+    Args:
+        evidence: List of evidence items with timestamps
+        now: Reference time for calculations (defaults to current UTC time)
+        recent_days: Number of days to consider "recent" (default 30)
+        old_days: Number of days to consider "old" (default 90)
+
+    Returns:
+        Computed Trend enum value
+    """
+    if now is None:
+        now = datetime.now(timezone.utc)
+
+    # Ensure now is timezone-aware
+    if now.tzinfo is None:
+        now = now.replace(tzinfo=timezone.utc)
+
+    if not evidence:
+        return Trend.STALE
+
+    recent_cutoff = now - timedelta(days=recent_days)
+    old_cutoff = now - timedelta(days=old_days)
+
+    # Normalize timestamps to UTC for comparison
+    def normalize_ts(ts: datetime) -> datetime:
+        if ts.tzinfo is None:
+            return ts.replace(tzinfo=timezone.utc)
+        return ts
+
+    recent = [e for e in evidence if normalize_ts(e.timestamp) > recent_cutoff]
+    old = [e for e in evidence if normalize_ts(e.timestamp) < old_cutoff]
+    middle = [e for e in evidence if old_cutoff <= normalize_ts(e.timestamp) <= recent_cutoff]
+
+    # No recent evidence = stale
+    if not recent:
+        return Trend.STALE
+
+    # All evidence is recent = new
+    if not old and not middle:
+        return Trend.NEW
+
+    # Compare density (evidence per day)
+    recent_density = len(recent) / recent_days if recent_days > 0 else 0
+    older_period = old_days - recent_days
+    older_density = (len(old) + len(middle)) / older_period if older_period > 0 else 0
+
+    # Avoid division by zero
+    if older_density == 0:
+        return Trend.NEW
+
+    ratio = recent_density / older_density
+
+    if ratio > 1.5:
+        return Trend.STRENGTHENING
+    elif ratio < 0.5:
+        return Trend.WEAKENING
+    else:
+        return Trend.STABLE

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

@@ -0,0 +1,483 @@
+"""
+System prompts for the reflect agent.
+
+The reflect agent uses hierarchical retrieval:
+1. search_mental_models - User-curated summaries (highest quality)
+2. search_observations - Consolidated knowledge with freshness awareness
+3. recall - Raw facts as ground truth fallback
+"""
+
+import json
+from typing import Any
+
+
+def _extract_directive_rules(directives: list[dict[str, Any]]) -> list[str]:
+    """
+    Extract directive rules as a list of strings.
+
+    Args:
+        directives: List of directives with name and content
+
+    Returns:
+        List of directive rule strings
+    """
+    rules = []
+    for directive in directives:
+        directive_name = directive.get("name", "")
+        # New format: directives have direct content field
+        content = directive.get("content", "")
+        if content:
+            if directive_name:
+                rules.append(f"**{directive_name}**: {content}")
+            else:
+                rules.append(content)
+        else:
+            # Legacy format: check for observations
+            observations = directive.get("observations", [])
+            if observations:
+                for obs in observations:
+                    # Support both Pydantic Observation objects and dicts
+                    if hasattr(obs, "title"):
+                        title = obs.title
+                        obs_content = obs.content
+                    else:
+                        title = obs.get("title", "")
+                        obs_content = obs.get("content", "")
+                    if title and obs_content:
+                        rules.append(f"**{title}**: {obs_content}")
+                    elif obs_content:
+                        rules.append(obs_content)
+            elif directive_name:
+                # Fallback to description
+                desc = directive.get("description", "")
+                if desc:
+                    rules.append(f"**{directive_name}**: {desc}")
+    return rules
+
+
+def build_directives_section(directives: list[dict[str, Any]]) -> str:
+    """
+    Build the directives section for the system prompt.
+
+    Directives are hard rules that MUST be followed in all responses.
+
+    Args:
+        directives: List of directive mental models with observations
+    """
+    if not directives:
+        return ""
+
+    rules = _extract_directive_rules(directives)
+    if not rules:
+        return ""
+
+    parts = [
+        "## DIRECTIVES (MANDATORY)",
+        "These are hard rules you MUST follow in ALL responses:",
+        "",
+    ]
+
+    for rule in rules:
+        parts.append(f"- {rule}")
+
+    parts.extend(
+        [
+            "",
+            "NEVER violate these directives, even if other context suggests otherwise.",
+            "IMPORTANT: Do NOT explain or justify how you handled directives in your answer. Just follow them silently.",
+            "",
+        ]
+    )
+    return "\n".join(parts)
+
+
+def build_directives_reminder(directives: list[dict[str, Any]]) -> str:
+    """
+    Build a reminder section for directives to place at the end of the prompt.
+
+    Args:
+        directives: List of directive mental models with observations
+    """
+    if not directives:
+        return ""
+
+    rules = _extract_directive_rules(directives)
+    if not rules:
+        return ""
+
+    parts = [
+        "",
+        "## REMINDER: MANDATORY DIRECTIVES",
+        "Before responding, ensure your answer complies with ALL of these directives:",
+        "",
+    ]
+
+    for i, rule in enumerate(rules, 1):
+        parts.append(f"{i}. {rule}")
+
+    parts.append("")
+    parts.append("Your response will be REJECTED if it violates any directive above.")
+    parts.append("Do NOT include any commentary about how you handled directives - just follow them.")
+    return "\n".join(parts)
+
+
+def build_system_prompt_for_tools(
+    bank_profile: dict[str, Any],
+    context: str | None = None,
+    directives: list[dict[str, Any]] | None = None,
+    has_mental_models: bool = False,
+    budget: str | None = None,
+) -> str:
+    """
+    Build the system prompt for tool-calling reflect agent.
+
+    The agent uses hierarchical retrieval:
+    1. search_mental_models - User-curated summaries (try first, if available)
+    2. search_observations - Consolidated knowledge with freshness
+    3. recall - Raw facts as ground truth
+
+    Args:
+        bank_profile: Bank profile with name and mission
+        context: Optional additional context
+        directives: Optional list of directive mental models to inject as hard rules
+        has_mental_models: Whether the bank has any mental models (skip if not)
+        budget: Search depth budget - "low", "mid", or "high". Controls exploration thoroughness.
+    """
+    name = bank_profile.get("name", "Assistant")
+    mission = bank_profile.get("mission", "")
+
+    parts = []
+
+    # Inject directives at the VERY START for maximum prominence
+    if directives:
+        parts.append(build_directives_section(directives))
+
+    parts.extend(
+        [
+            "You are a reflection agent that answers questions by reasoning over retrieved memories.",
+            "",
+        ]
+    )
+
+    parts.extend(
+        [
+            "## CRITICAL RULES",
+            "- You must NEVER fabricate information that has no basis in retrieved data",
+            "- You SHOULD synthesize, infer, and reason from the retrieved memories",
+            "- You MUST search before saying you don't have information",
+            "",
+            "## How to Reason",
+            "- If memories mention someone did an activity, you can infer they likely enjoyed it",
+            "- Synthesize a coherent narrative from related memories",
+            "- Be a thoughtful interpreter, not just a literal repeater",
+            "- When the exact answer isn't stated, use what IS stated to give the best answer",
+            "",
+            "## HIERARCHICAL RETRIEVAL STRATEGY",
+            "",
+        ]
+    )
+
+    # Build retrieval levels based on what's available
+    if has_mental_models:
+        parts.extend(
+            [
+                "You have access to THREE levels of knowledge. Use them in this order:",
+                "",
+                "### 1. MENTAL MODELS (search_mental_models) - Try First",
+                "- User-curated summaries about specific topics",
+                "- HIGHEST quality - manually created and maintained",
+                "- If a relevant mental model exists and is FRESH, it may fully answer the question",
+                "- Check `is_stale` field - if stale, also verify with lower levels",
+                "",
+                "### 2. OBSERVATIONS (search_observations) - Second Priority",
+                "- Auto-consolidated knowledge from memories",
+                "- Check `is_stale` field - if stale, ALSO use recall() to verify",
+                "- Good for understanding patterns and summaries",
+                "",
+                "### 3. RAW FACTS (recall) - Ground Truth",
+                "- Individual memories (world facts and experiences)",
+                "- Use when: no mental models/observations exist, they're stale, or you need specific details",
+                "- This is the source of truth that other levels are built from",
+                "",
+            ]
+        )
+    else:
+        parts.extend(
+            [
+                "You have access to TWO levels of knowledge. Use them in this order:",
+                "",
+                "### 1. OBSERVATIONS (search_observations) - Try First",
+                "- Auto-consolidated knowledge from memories",
+                "- Check `is_stale` field - if stale, ALSO use recall() to verify",
+                "- Good for understanding patterns and summaries",
+                "",
+                "### 2. RAW FACTS (recall) - Ground Truth",
+                "- Individual memories (world facts and experiences)",
+                "- Use when: no observations exist, they're stale, or you need specific details",
+                "- This is the source of truth that observations are built from",
+                "",
+            ]
+        )
+
+    parts.extend(
+        [
+            "## Query Strategy",
+            "recall() uses semantic search. NEVER just echo the user's question - decompose it into targeted searches:",
+            "",
+            "BAD: User asks 'recurring lesson themes between students' → recall('recurring lesson themes between students')",
+            "GOOD: Break it down into component searches:",
+            "  1. recall('lessons') - find all lesson-related memories",
+            "  2. recall('teaching sessions') - alternative phrasing",
+            "  3. recall('student progress') - find student-related memories",
+            "",
+            "Think: What ENTITIES and CONCEPTS does this question involve? Search for each separately.",
+            "",
+        ]
+    )
+
+    # Add budget guidance
+    if budget:
+        budget_lower = budget.lower()
+        if budget_lower == "low":
+            parts.extend(
+                [
+                    "## RESEARCH DEPTH: SHALLOW (Quick Response)",
+                    "- Prioritize speed over completeness",
+                    "- If mental models or observations provide a reasonable answer, stop there",
+                    "- Only dig deeper if the initial results are clearly insufficient",
+                    "- Prefer a quick overview rather than exhaustive details",
+                    "- Answer promptly with available information",
+                    "",
+                ]
+            )
+        elif budget_lower == "mid":
+            parts.extend(
+                [
+                    "## RESEARCH DEPTH: MODERATE (Balanced)",
+                    "- Balance thoroughness with efficiency",
+                    "- Check multiple sources when the question warrants it",
+                    "- Verify stale data if it's central to the answer",
+                    "- Don't over-explore, but ensure reasonable coverage",
+                    "",
+                ]
+            )
+        elif budget_lower == "high":
+            parts.extend(
+                [
+                    "## RESEARCH DEPTH: DEEP (Thorough Exploration)",
+                    "- Explore comprehensively before answering",
+                    "- Search across all available knowledge levels",
+                    "- Use multiple query variations to ensure coverage",
+                    "- Verify information across different retrieval levels",
+                    "- Use expand() to get full context on important memories",
+                    "- Take time to synthesize a complete, well-researched answer",
+                    "",
+                ]
+            )
+
+    parts.append("## Workflow")
+
+    if has_mental_models:
+        parts.extend(
+            [
+                "1. First, try search_mental_models() - check if a curated summary exists",
+                "2. If no mental model or it's stale, try search_observations() for consolidated knowledge",
+                "3. If observations are stale OR you need specific details, use recall() for raw facts",
+                "4. Use expand() if you need more context on specific memories",
+                "5. When ready, call done() with your answer and supporting IDs",
+            ]
+        )
+    else:
+        parts.extend(
+            [
+                "1. First, try search_observations() - check for consolidated knowledge",
+                "2. If observations are stale OR you need specific details, use recall() for raw facts",
+                "3. Use expand() if you need more context on specific memories",
+                "4. When ready, call done() with your answer and supporting IDs",
+            ]
+        )
+
+    parts.extend(
+        [
+            "",
+            "## Output Format: Plain Text Answer",
+            "Call done() with a plain text 'answer' field.",
+            "- Do NOT use markdown formatting",
+            "- NEVER include memory IDs, UUIDs, or 'Memory references' in the answer text",
+            "- Put IDs ONLY in the memory_ids/mental_model_ids/observation_ids arrays, not in the answer",
+        ]
+    )
+
+    parts.append("")
+    parts.append(f"## Memory Bank: {name}")
+
+    if mission:
+        parts.append(f"Mission: {mission}")
+
+    # Disposition traits
+    disposition = bank_profile.get("disposition", {})
+    if disposition:
+        traits = []
+        if "skepticism" in disposition:
+            traits.append(f"skepticism={disposition['skepticism']}")
+        if "literalism" in disposition:
+            traits.append(f"literalism={disposition['literalism']}")
+        if "empathy" in disposition:
+            traits.append(f"empathy={disposition['empathy']}")
+        if traits:
+            parts.append(f"Disposition: {', '.join(traits)}")
+
+    if context:
+        parts.append(f"\n## Additional Context\n{context}")
+
+    # Add directive reminder at the END for recency effect
+    if directives:
+        parts.append(build_directives_reminder(directives))
+
+    return "\n".join(parts)
+
+
+def build_agent_prompt(
+    query: str,
+    context_history: list[dict],
+    bank_profile: dict,
+    additional_context: str | None = None,
+) -> str:
+    """Build the user prompt for the reflect agent."""
+    parts = []
+
+    # Bank identity
+    name = bank_profile.get("name", "Assistant")
+    mission = bank_profile.get("mission", "")
+
+    parts.append(f"## Memory Bank Context\nName: {name}")
+    if mission:
+        parts.append(f"Mission: {mission}")
+
+    # Disposition traits if present
+    disposition = bank_profile.get("disposition", {})
+    if disposition:
+        traits = []
+        if "skepticism" in disposition:
+            traits.append(f"skepticism={disposition['skepticism']}")
+        if "literalism" in disposition:
+            traits.append(f"literalism={disposition['literalism']}")
+        if "empathy" in disposition:
+            traits.append(f"empathy={disposition['empathy']}")
+        if traits:
+            parts.append(f"Disposition: {', '.join(traits)}")
+
+    # Additional context from caller
+    if additional_context:
+        parts.append(f"\n## Additional Context\n{additional_context}")
+
+    # Tool call history
+    if context_history:
+        parts.append("\n## Tool Results (synthesize and reason from this data)")
+        for i, entry in enumerate(context_history, 1):
+            tool = entry["tool"]
+            output = entry["output"]
+            # Format as proper JSON for LLM readability
+            try:
+                output_str = json.dumps(output, indent=2, default=str)
+            except (TypeError, ValueError):
+                output_str = str(output)
+            parts.append(f"\n### Call {i}: {tool}\n```json\n{output_str}\n```")
+
+    # The question
+    parts.append(f"\n## Question\n{query}")
+
+    # Instructions
+    if context_history:
+        parts.append(
+            "\n## Instructions\n"
+            "Based on the tool results above, either call more tools or provide your final answer. "
+            "Synthesize and reason from the data - make reasonable inferences when helpful. "
+            "If you have related information, use it to give the best possible answer."
+        )
+    else:
+        parts.append(
+            "\n## Instructions\n"
+            "Start by searching for relevant information using the hierarchical retrieval strategy:\n"
+            "1. Try search_mental_models() first for curated summaries\n"
+            "2. Try search_observations() for consolidated knowledge\n"
+            "3. Use recall() for specific details or to verify stale data"
+        )
+
+    return "\n".join(parts)
+
+
+def build_final_prompt(
+    query: str,
+    context_history: list[dict],
+    bank_profile: dict,
+    additional_context: str | None = None,
+) -> str:
+    """Build the final prompt when forcing a text response (no tools)."""
+    parts = []
+
+    # Bank identity
+    name = bank_profile.get("name", "Assistant")
+    mission = bank_profile.get("mission", "")
+
+    parts.append(f"## Memory Bank Context\nName: {name}")
+    if mission:
+        parts.append(f"Mission: {mission}")
+
+    # Disposition traits if present
+    disposition = bank_profile.get("disposition", {})
+    if disposition:
+        traits = []
+        if "skepticism" in disposition:
+            traits.append(f"skepticism={disposition['skepticism']}")
+        if "literalism" in disposition:
+            traits.append(f"literalism={disposition['literalism']}")
+        if "empathy" in disposition:
+            traits.append(f"empathy={disposition['empathy']}")
+        if traits:
+            parts.append(f"Disposition: {', '.join(traits)}")
+
+    # Additional context from caller
+    if additional_context:
+        parts.append(f"\n## Additional Context\n{additional_context}")
+
+    # Tool call history
+    if context_history:
+        parts.append("\n## Retrieved Data (synthesize and reason from this data)")
+        for entry in context_history:
+            tool = entry["tool"]
+            output = entry["output"]
+            # Format as proper JSON for LLM readability
+            try:
+                output_str = json.dumps(output, indent=2, default=str)
+            except (TypeError, ValueError):
+                output_str = str(output)
+            parts.append(f"\n### From {tool}:\n```json\n{output_str}\n```")
+    else:
+        parts.append("\n## Retrieved Data\nNo data was retrieved.")
+
+    # The question
+    parts.append(f"\n## Question\n{query}")
+
+    # Final instructions
+    parts.append(
+        "\n## Instructions\n"
+        "Provide a thoughtful answer by synthesizing and reasoning from the retrieved data above. "
+        "You can make reasonable inferences from the memories, but don't completely fabricate information."
+        "If the exact answer isn't stated, use what IS stated to give the best possible answer. "
+        "Only say 'I don't have information' if the retrieved data is truly unrelated to the question."
+    )
+
+    return "\n".join(parts)
+
+
+FINAL_SYSTEM_PROMPT = """You are a thoughtful assistant that synthesizes answers from retrieved memories.
+
+Your approach:
+- Reason over the retrieved memories to answer the question
+- Make reasonable inferences when the exact answer isn't explicitly stated
+- Connect related memories to form a complete picture
+- Be helpful - if you have related information, use it to give the best possible answer
+
+Only say "I don't have information" if the retrieved data is truly unrelated to the question.
+Do NOT fabricate information that has no basis in the retrieved data."""

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

@@ -0,0 +1,437 @@
+"""
+Tool implementations for the reflect agent.
+
+Implements hierarchical retrieval:
+1. search_mental_models - User-curated stored reflect responses (highest quality)
+2. search_observations - Consolidated knowledge with freshness
+3. recall - Raw facts as ground truth
+"""
+
+import logging
+import uuid
+from datetime import datetime, timedelta, timezone
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from asyncpg import Connection
+
+    from ...api.http import RequestContext
+    from ..memory_engine import MemoryEngine
+
+logger = logging.getLogger(__name__)
+
+# Observation is considered stale if not updated in this many days
+STALE_THRESHOLD_DAYS = 7
+
+
+async def tool_search_mental_models(
+    conn: "Connection",
+    bank_id: str,
+    query: str,
+    query_embedding: list[float],
+    max_results: int = 5,
+    tags: list[str] | None = None,
+    tags_match: str = "any",
+    exclude_ids: list[str] | None = None,
+) -> dict[str, Any]:
+    """
+    Search user-curated mental models by semantic similarity.
+
+    Mental models are high-quality, manually created summaries about specific topics.
+    They should be searched FIRST as they represent the most reliable synthesized knowledge.
+
+    Args:
+        conn: Database connection
+        bank_id: Bank identifier
+        query: Search query (for logging/tracing)
+        query_embedding: Pre-computed embedding for semantic search
+        max_results: Maximum number of mental models to return
+        tags: Optional tags to filter mental models
+        tags_match: How to match tags - "any" (OR), "all" (AND)
+        exclude_ids: Optional list of mental model IDs to exclude (e.g., when refreshing a mental model)
+
+    Returns:
+        Dict with matching mental models including content and freshness info
+    """
+    from ..memory_engine import fq_table
+
+    # Build filters dynamically
+    filters = ""
+    params: list[Any] = [bank_id, str(query_embedding), max_results]
+    next_param = 4
+
+    if tags:
+        if tags_match == "all":
+            filters += f" AND tags @> ${next_param}::varchar[]"
+        else:
+            filters += f" AND (tags && ${next_param}::varchar[] OR tags IS NULL OR tags = '{{}}')"
+        params.append(tags)
+        next_param += 1
+
+    if exclude_ids:
+        filters += f" AND id != ALL(${next_param}::text[])"
+        params.append(exclude_ids)
+        next_param += 1
+
+    # Search mental models by embedding similarity
+    rows = await conn.fetch(
+        f"""
+        SELECT
+            id, name, content,
+            tags, created_at, last_refreshed_at,
+            1 - (embedding <=> $2::vector) as relevance
+        FROM {fq_table("mental_models")}
+        WHERE bank_id = $1 AND embedding IS NOT NULL {filters}
+        ORDER BY embedding <=> $2::vector
+        LIMIT $3
+        """,
+        *params,
+    )
+
+    now = datetime.now(timezone.utc)
+    mental_models = []
+
+    for row in rows:
+        last_refreshed_at = row["last_refreshed_at"]
+        if last_refreshed_at and last_refreshed_at.tzinfo is None:
+            last_refreshed_at = last_refreshed_at.replace(tzinfo=timezone.utc)
+
+        # Calculate freshness
+        is_stale = False
+        if last_refreshed_at:
+            age = now - last_refreshed_at
+            is_stale = age > timedelta(days=STALE_THRESHOLD_DAYS)
+
+        mental_models.append(
+            {
+                "id": str(row["id"]),
+                "name": row["name"],
+                "content": row["content"],
+                "tags": row["tags"] or [],
+                "relevance": round(row["relevance"], 4),
+                "updated_at": last_refreshed_at.isoformat() if last_refreshed_at else None,
+                "is_stale": is_stale,
+            }
+        )
+
+    return {
+        "query": query,
+        "count": len(mental_models),
+        "mental_models": mental_models,
+    }
+
+
+async def tool_search_observations(
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    query: str,
+    request_context: "RequestContext",
+    max_tokens: int = 5000,
+    tags: list[str] | None = None,
+    tags_match: str = "any",
+    last_consolidated_at: datetime | None = None,
+    pending_consolidation: int = 0,
+) -> dict[str, Any]:
+    """
+    Search consolidated observations using recall with include_observations.
+
+    Observations are auto-generated from memories. Returns freshness info
+    so the agent knows if it should also verify with recall().
+
+    Args:
+        memory_engine: Memory engine instance
+        bank_id: Bank identifier
+        query: Search query
+        request_context: Request context for authentication
+        max_tokens: Maximum tokens for results (default 5000)
+        tags: Optional tags to filter observations
+        tags_match: How to match tags - "any" (OR), "all" (AND)
+        last_consolidated_at: When consolidation last ran (for staleness check)
+        pending_consolidation: Number of memories waiting to be consolidated
+
+    Returns:
+        Dict with matching observations including freshness info
+    """
+    from ..memory_engine import fq_table
+
+    # Use recall to search observations (they come back in results field when fact_type=["observation"])
+    result = await memory_engine.recall_async(
+        bank_id=bank_id,
+        query=query,
+        fact_type=["observation"],  # Only retrieve observations
+        max_tokens=max_tokens,  # Token budget controls how many observations are returned
+        enable_trace=False,
+        request_context=request_context,
+        tags=tags,
+        tags_match=tags_match,
+        _connection_budget=1,
+        _quiet=True,
+    )
+
+    observations = []
+
+    # When fact_type=["observation"], results come back in `results` field as MemoryFact objects
+    # We need to fetch additional fields (proof_count, source_memory_ids) from the database
+    if result.results:
+        obs_ids = [m.id for m in result.results]
+
+        # Fetch proof_count and source_memory_ids for these observations
+        pool = await memory_engine._get_pool()
+        async with pool.acquire() as conn:
+            obs_rows = await conn.fetch(
+                f"""
+                SELECT id, proof_count, source_memory_ids
+                FROM {fq_table("memory_units")}
+                WHERE id = ANY($1::uuid[])
+                """,
+                obs_ids,
+            )
+            obs_data = {str(row["id"]): row for row in obs_rows}
+
+        for m in result.results:
+            # Get additional data from DB lookup
+            extra = obs_data.get(m.id, {})
+            proof_count = extra.get("proof_count", 1) if extra else 1
+            source_ids = extra.get("source_memory_ids", []) if extra else []
+            # Convert UUIDs to strings
+            source_memory_ids = [str(sid) for sid in (source_ids or [])]
+
+            # Determine staleness
+            is_stale = False
+            staleness_reason = None
+            if pending_consolidation > 0:
+                is_stale = True
+                staleness_reason = f"{pending_consolidation} memories pending consolidation"
+
+            observations.append(
+                {
+                    "id": str(m.id),
+                    "text": m.text,
+                    "proof_count": proof_count,
+                    "source_memory_ids": source_memory_ids,
+                    "tags": m.tags or [],
+                    "is_stale": is_stale,
+                    "staleness_reason": staleness_reason,
+                }
+            )
+
+    # Return freshness info (more understandable than raw pending_consolidation count)
+    if pending_consolidation == 0:
+        freshness = "up_to_date"
+    elif pending_consolidation < 10:
+        freshness = "slightly_stale"
+    else:
+        freshness = "stale"
+
+    return {
+        "query": query,
+        "count": len(observations),
+        "observations": observations,
+        "freshness": freshness,
+    }
+
+
+async def tool_recall(
+    memory_engine: "MemoryEngine",
+    bank_id: str,
+    query: str,
+    request_context: "RequestContext",
+    max_tokens: int = 2048,
+    max_results: int = 50,
+    tags: list[str] | None = None,
+    tags_match: str = "any",
+    connection_budget: int = 1,
+) -> dict[str, Any]:
+    """
+    Search memories using TEMPR retrieval.
+
+    This is the ground truth - raw facts and experiences.
+    Use when mental models/observations don't exist, are stale, or need verification.
+
+    Args:
+        memory_engine: Memory engine instance
+        bank_id: Bank identifier
+        query: Search query
+        request_context: Request context for authentication
+        max_tokens: Maximum tokens for results (default 2048)
+        max_results: Maximum number of results
+        tags: Filter by tags (includes untagged memories)
+        tags_match: How to match tags - "any" (OR), "all" (AND), or "exact"
+        connection_budget: Max DB connections for this recall (default 1 for internal ops)
+
+    Returns:
+        Dict with list of matching memories
+    """
+    result = await memory_engine.recall_async(
+        bank_id=bank_id,
+        query=query,
+        fact_type=["experience", "world"],  # Exclude opinions and observations
+        max_tokens=max_tokens,
+        enable_trace=False,
+        request_context=request_context,
+        tags=tags,
+        tags_match=tags_match,
+        _connection_budget=connection_budget,
+        _quiet=True,  # Suppress logging for internal operations
+    )
+
+    memories = []
+    for m in result.results[:max_results]:
+        memories.append(
+            {
+                "id": str(m.id),
+                "text": m.text,
+                "type": m.fact_type,
+                "entities": m.entities or [],
+                "occurred": m.occurred_start,  # Already ISO format string
+            }
+        )
+
+    return {
+        "query": query,
+        "count": len(memories),
+        "memories": memories,
+    }
+
+
+async def tool_expand(
+    conn: "Connection",
+    bank_id: str,
+    memory_ids: list[str],
+    depth: str,
+) -> dict[str, Any]:
+    """
+    Expand multiple memories to get chunk or document context.
+
+    Args:
+        conn: Database connection
+        bank_id: Bank identifier
+        memory_ids: List of memory unit IDs
+        depth: "chunk" or "document"
+
+    Returns:
+        Dict with results array, each containing memory, chunk, and optionally document data
+    """
+    from ..memory_engine import fq_table
+
+    if not memory_ids:
+        return {"error": "memory_ids is required and must not be empty"}
+
+    # Validate and convert UUIDs
+    valid_uuids: list[uuid.UUID] = []
+    errors: dict[str, str] = {}
+    for mid in memory_ids:
+        try:
+            valid_uuids.append(uuid.UUID(mid))
+        except ValueError:
+            errors[mid] = f"Invalid memory_id format: {mid}"
+
+    if not valid_uuids:
+        return {"error": "No valid memory IDs provided", "details": errors}
+
+    # Batch fetch all memory units
+    memories = await conn.fetch(
+        f"""
+        SELECT id, text, chunk_id, document_id, fact_type, context
+        FROM {fq_table("memory_units")}
+        WHERE id = ANY($1) AND bank_id = $2
+        """,
+        valid_uuids,
+        bank_id,
+    )
+    memory_map = {row["id"]: row for row in memories}
+
+    # Collect chunk_ids and document_ids for batch fetching
+    chunk_ids = [m["chunk_id"] for m in memories if m["chunk_id"]]
+    doc_ids_from_chunks: set[str] = set()
+    doc_ids_direct: set[str] = set()
+
+    # Batch fetch all chunks
+    chunk_map: dict[str, Any] = {}
+    if chunk_ids:
+        chunks = await conn.fetch(
+            f"""
+            SELECT chunk_id, chunk_text, chunk_index, document_id
+            FROM {fq_table("chunks")}
+            WHERE chunk_id = ANY($1)
+            """,
+            chunk_ids,
+        )
+        chunk_map = {row["chunk_id"]: row for row in chunks}
+        if depth == "document":
+            doc_ids_from_chunks = {c["document_id"] for c in chunks if c["document_id"]}
+
+    # Collect direct document IDs (memories without chunks)
+    if depth == "document":
+        for m in memories:
+            if not m["chunk_id"] and m["document_id"]:
+                doc_ids_direct.add(m["document_id"])
+
+    # Batch fetch all documents
+    doc_map: dict[str, Any] = {}
+    all_doc_ids = list(doc_ids_from_chunks | doc_ids_direct)
+    if all_doc_ids:
+        docs = await conn.fetch(
+            f"""
+            SELECT id, original_text, metadata, retain_params
+            FROM {fq_table("documents")}
+            WHERE id = ANY($1) AND bank_id = $2
+            """,
+            all_doc_ids,
+            bank_id,
+        )
+        doc_map = {row["id"]: row for row in docs}
+
+    # Build results
+    results: list[dict[str, Any]] = []
+    for mid, mem_uuid in zip(memory_ids, valid_uuids):
+        if mid in errors:
+            results.append({"memory_id": mid, "error": errors[mid]})
+            continue
+
+        memory = memory_map.get(mem_uuid)
+        if not memory:
+            results.append({"memory_id": mid, "error": f"Memory not found: {mid}"})
+            continue
+
+        item: dict[str, Any] = {
+            "memory_id": mid,
+            "memory": {
+                "id": str(memory["id"]),
+                "text": memory["text"],
+                "type": memory["fact_type"],
+                "context": memory["context"],
+            },
+        }
+
+        # Add chunk if available
+        if memory["chunk_id"] and memory["chunk_id"] in chunk_map:
+            chunk = chunk_map[memory["chunk_id"]]
+            item["chunk"] = {
+                "id": chunk["chunk_id"],
+                "text": chunk["chunk_text"],
+                "index": chunk["chunk_index"],
+                "document_id": chunk["document_id"],
+            }
+            # Add document if depth=document
+            if depth == "document" and chunk["document_id"] in doc_map:
+                doc = doc_map[chunk["document_id"]]
+                item["document"] = {
+                    "id": doc["id"],
+                    "full_text": doc["original_text"],
+                    "metadata": doc["metadata"],
+                    "retain_params": doc["retain_params"],
+                }
+        elif memory["document_id"] and depth == "document" and memory["document_id"] in doc_map:
+            # No chunk, but has document_id
+            doc = doc_map[memory["document_id"]]
+            item["document"] = {
+                "id": doc["id"],
+                "full_text": doc["original_text"],
+                "metadata": doc["metadata"],
+                "retain_params": doc["retain_params"],
+            }
+
+        results.append(item)
+
+    return {"results": results, "count": len(results)}

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

@@ -0,0 +1,250 @@
+"""
+Tool schema definitions for the reflect agent.
+
+These are OpenAI-format tool definitions used with native tool calling.
+The reflect agent uses a hierarchical retrieval strategy:
+1. search_mental_models - User-curated stored reflect responses (highest quality, if applicable)
+2. search_observations - Consolidated knowledge with freshness awareness
+3. recall - Raw facts (world/experience) as ground truth fallback
+"""
+
+# Tool definitions in OpenAI format
+
+TOOL_SEARCH_MENTAL_MODELS = {
+    "type": "function",
+    "function": {
+        "name": "search_mental_models",
+        "description": (
+            "Search user-curated mental models (stored reflect responses). These are high-quality, manually created "
+            "summaries about specific topics. Use FIRST when the question might be covered by an "
+            "existing mental model. Returns mental models with their content and last refresh time."
+        ),
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "reason": {
+                    "type": "string",
+                    "description": "Brief explanation of why you're making this search (for debugging)",
+                },
+                "query": {
+                    "type": "string",
+                    "description": "Search query to find relevant mental models",
+                },
+                "max_results": {
+                    "type": "integer",
+                    "description": "Maximum number of mental models to return (default 5)",
+                },
+            },
+            "required": ["reason", "query"],
+        },
+    },
+}
+
+TOOL_SEARCH_OBSERVATIONS = {
+    "type": "function",
+    "function": {
+        "name": "search_observations",
+        "description": (
+            "Search consolidated observations (auto-generated knowledge). These are automatically "
+            "synthesized from memories. Returns observations with freshness info (updated_at, is_stale). "
+            "If an observation is STALE, you should ALSO use recall() to verify with current facts."
+        ),
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "reason": {
+                    "type": "string",
+                    "description": "Brief explanation of why you're making this search (for debugging)",
+                },
+                "query": {
+                    "type": "string",
+                    "description": "Search query to find relevant observations",
+                },
+                "max_tokens": {
+                    "type": "integer",
+                    "description": "Maximum tokens for results (default 5000). Use higher values for broader searches.",
+                },
+            },
+            "required": ["reason", "query"],
+        },
+    },
+}
+
+TOOL_RECALL = {
+    "type": "function",
+    "function": {
+        "name": "recall",
+        "description": (
+            "Search raw memories (facts and experiences). This is the ground truth data. "
+            "Use when: (1) no reflections/mental models exist, (2) mental models are stale, "
+            "(3) you need specific details not in synthesized knowledge. "
+            "Returns individual memory facts with their timestamps."
+        ),
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "reason": {
+                    "type": "string",
+                    "description": "Brief explanation of why you're making this search (for debugging)",
+                },
+                "query": {
+                    "type": "string",
+                    "description": "Search query string",
+                },
+                "max_tokens": {
+                    "type": "integer",
+                    "description": "Optional limit on result size (default 2048). Use higher values for broader searches.",
+                },
+            },
+            "required": ["reason", "query"],
+        },
+    },
+}
+
+TOOL_EXPAND = {
+    "type": "function",
+    "function": {
+        "name": "expand",
+        "description": "Get more context for one or more memories. Memory hierarchy: memory -> chunk -> document.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "reason": {
+                    "type": "string",
+                    "description": "Brief explanation of why you need more context (for debugging)",
+                },
+                "memory_ids": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Array of memory IDs from recall results (batch multiple for efficiency)",
+                },
+                "depth": {
+                    "type": "string",
+                    "enum": ["chunk", "document"],
+                    "description": "chunk: surrounding text chunk, document: full source document",
+                },
+            },
+            "required": ["reason", "memory_ids", "depth"],
+        },
+    },
+}
+
+TOOL_DONE_ANSWER = {
+    "type": "function",
+    "function": {
+        "name": "done",
+        "description": "Signal completion with your final answer. Use this when you have gathered enough information to answer the question.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "answer": {
+                    "type": "string",
+                    "description": "Your response as plain text. Do NOT use markdown formatting. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                },
+                "memory_ids": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Array of memory IDs that support your answer (put IDs here, NOT in answer text)",
+                },
+                "mental_model_ids": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Array of mental model IDs that support your answer",
+                },
+                "observation_ids": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "Array of observation IDs that support your answer",
+                },
+            },
+            "required": ["answer"],
+        },
+    },
+}
+
+
+def _build_done_tool_with_directives(directive_rules: list[str]) -> dict:
+    """
+    Build the done tool schema with directive compliance field.
+
+    When directives are present, adds a required field that forces the agent
+    to confirm compliance with each directive before submitting.
+
+    Args:
+        directive_rules: List of directive rule strings
+    """
+    # Build rules list for description
+    rules_list = "\n".join(f"  {i + 1}. {rule}" for i, rule in enumerate(directive_rules))
+
+    # Build the tool with directive compliance field
+    return {
+        "type": "function",
+        "function": {
+            "name": "done",
+            "description": (
+                "Signal completion with your final answer. IMPORTANT: You must confirm directive compliance before submitting. "
+                "Your answer will be REJECTED if it violates any directive."
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "answer": {
+                        "type": "string",
+                        "description": "Your response as plain text. Do NOT use markdown formatting. NEVER include memory IDs, UUIDs, or 'Memory references' in this text - put IDs only in memory_ids array.",
+                    },
+                    "memory_ids": {
+                        "type": "array",
+                        "items": {"type": "string"},
+                        "description": "Array of memory IDs that support your answer (put IDs here, NOT in answer text)",
+                    },
+                    "mental_model_ids": {
+                        "type": "array",
+                        "items": {"type": "string"},
+                        "description": "Array of mental model IDs that support your answer",
+                    },
+                    "observation_ids": {
+                        "type": "array",
+                        "items": {"type": "string"},
+                        "description": "Array of observation IDs that support your answer",
+                    },
+                    "directive_compliance": {
+                        "type": "string",
+                        "description": f"REQUIRED: Confirm your answer complies with ALL directives. List each directive and how your answer follows it:\n{rules_list}\n\nFormat: 'Directive 1: [how answer complies]. Directive 2: [how answer complies]...'",
+                    },
+                },
+                "required": ["answer", "directive_compliance"],
+            },
+        },
+    }
+
+
+def get_reflect_tools(directive_rules: list[str] | None = None) -> list[dict]:
+    """
+    Get the list of tools for the reflect agent.
+
+    The tools support a hierarchical retrieval strategy:
+    1. search_mental_models - User-curated stored reflect responses (try first)
+    2. search_observations - Consolidated knowledge with freshness
+    3. recall - Raw facts as ground truth
+
+    Args:
+        directive_rules: Optional list of directive rule strings. If provided,
+                        the done() tool will require directive compliance confirmation.
+
+    Returns:
+        List of tool definitions in OpenAI format
+    """
+    tools = [
+        TOOL_SEARCH_MENTAL_MODELS,
+        TOOL_SEARCH_OBSERVATIONS,
+        TOOL_RECALL,
+        TOOL_EXPAND,
+    ]
+
+    # Use directive-aware done tool if directives are present
+    if directive_rules:
+        tools.append(_build_done_tool_with_directives(directive_rules))
+    else:
+        tools.append(TOOL_DONE_ANSWER)
+
+    return tools

hindsight-api/hindsight_api/engine/response_models.py

@@ -10,8 +10,63 @@ from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
-# Valid fact types for recall operations (excludes 'observation' which is internal)
-VALID_RECALL_FACT_TYPES = frozenset(["world", "experience", "opinion"])
+# Valid fact types for recall operations (excludes 'opinion' which is deprecated)
+VALID_RECALL_FACT_TYPES = frozenset(["world", "experience", "observation"])
+
+
+class LLMToolCall(BaseModel):
+    """A tool call requested by the LLM."""
+
+    id: str = Field(description="Unique identifier for this tool call")
+    name: str = Field(description="Name of the tool to call")
+    arguments: dict[str, Any] = Field(description="Arguments to pass to the tool")
+
+
+class LLMToolCallResult(BaseModel):
+    """Result from an LLM call that may include tool calls."""
+
+    content: str | None = Field(default=None, description="Text content if any")
+    tool_calls: list[LLMToolCall] = Field(default_factory=list, description="Tool calls requested by the LLM")
+    finish_reason: str | None = Field(default=None, description="Reason the LLM stopped: 'stop', 'tool_calls', etc.")
+    input_tokens: int = Field(default=0, description="Input tokens used in this call")
+    output_tokens: int = Field(default=0, description="Output tokens used in this call")
+
+
+class ToolCallTrace(BaseModel):
+    """A single tool call made during reflect."""
+
+    tool: str = Field(description="Tool name: lookup, recall, learn, expand")
+    reason: str | None = Field(default=None, description="Agent's reasoning for making this tool call")
+    input: dict = Field(description="Tool input parameters")
+    output: dict = Field(description="Tool output/result")
+    duration_ms: int = Field(description="Execution time in milliseconds")
+    iteration: int = Field(default=0, description="Iteration number (1-based) when this tool was called")
+
+
+class LLMCallTrace(BaseModel):
+    """A single LLM call made during reflect."""
+
+    scope: str = Field(description="Call scope: agent_1, agent_2, final, etc.")
+    duration_ms: int = Field(description="Execution time in milliseconds")
+
+
+class ObservationRef(BaseModel):
+    """Reference to an observation accessed during reflect."""
+
+    id: str = Field(description="Observation ID")
+    name: str = Field(description="Observation name")
+    type: str = Field(description="Observation type: entity, concept, event")
+    subtype: str = Field(description="Observation subtype: structural, emergent, learned")
+    description: str = Field(description="Brief description")
+    summary: str | None = Field(default=None, description="Full summary (when looked up in detail)")
+
+
+class DirectiveRef(BaseModel):
+    """Reference to a directive that was applied during reflect."""
+
+    id: str = Field(description="Directive mental model ID")
+    name: str = Field(description="Directive name")
+    content: str = Field(description="Directive content")
 
 
 class TokenUsage(BaseModel):
@@ -85,6 +140,7 @@ class MemoryFact(BaseModel):
                 "metadata": {"source": "slack"},
                 "chunk_id": "bank123_session_abc123_0",
                 "activation": 0.95,
+                "tags": ["user_a", "session_123"],
             }
         }
     )
@@ -102,6 +158,7 @@ class MemoryFact(BaseModel):
     chunk_id: str | None = Field(
         None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)"
     )
+    tags: list[str] | None = Field(None, description="Visibility scope tags associated with this fact")
 
 
 class ChunkInfo(BaseModel):
@@ -112,6 +169,28 @@ class ChunkInfo(BaseModel):
     truncated: bool = Field(default=False, description="Whether the chunk was truncated due to token limits")
 
 
+class ObservationResult(BaseModel):
+    """An observation result from recall (consolidated knowledge synthesized from facts)."""
+
+    id: str = Field(description="Unique observation ID")
+    text: str = Field(description="The observation text")
+    proof_count: int = Field(description="Number of facts supporting this observation")
+    relevance: float = Field(default=0.0, description="Relevance score to the query")
+    tags: list[str] | None = Field(default=None, description="Tags for visibility scoping")
+    source_memory_ids: list[str] = Field(
+        default_factory=list, description="IDs of facts that contribute to this observation"
+    )
+
+
+class MentalModelResult(BaseModel):
+    """A mental model result from recall (stored reflect response)."""
+
+    id: str = Field(description="Unique mental model ID")
+    name: str = Field(description="Human-readable name")
+    content: str = Field(description="The synthesized content")
+    relevance: float = Field(default=0.0, description="Relevance score to the query")
+
+
 class RecallResult(BaseModel):
     """
     Result from a recall operation.
@@ -175,8 +254,15 @@ class ReflectResult(BaseModel):
                     ],
                     "experience": [],
                     "opinion": [],
+                    "mental_models": [],
+                    "directives": [
+                        {
+                            "id": "directive-123",
+                            "name": "Response Style",
+                            "rules": ["Always be concise"],
+                        }
+                    ],
                 },
-                "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},
             }
@@ -184,10 +270,9 @@ class ReflectResult(BaseModel):
     )
 
     text: str = Field(description="The formulated answer text")
-    based_on: dict[str, list[MemoryFact]] = Field(
-        description="Facts used to formulate the answer, organized by type (world, experience, opinion)"
+    based_on: dict[str, Any] = Field(
+        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.",
@@ -196,24 +281,18 @@ class ReflectResult(BaseModel):
         default=None,
         description="Token usage metrics for the LLM calls made during this reflect operation.",
     )
-
-
-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}
-        }
+    tool_trace: list[ToolCallTrace] = Field(
+        default_factory=list,
+        description="Trace of tool calls made during reflection. Only present when include.tool_calls is enabled.",
+    )
+    llm_trace: list[LLMCallTrace] = Field(
+        default_factory=list,
+        description="Trace of LLM calls made during reflection. Only present when include.tool_calls is enabled.",
+    )
+    directives_applied: list[DirectiveRef] = Field(
+        default_factory=list,
+        description="Directive mental models that were applied during this reflection.",
     )
-
-    text: str = Field(description="The opinion text")
-    confidence: float = Field(description="Confidence score between 0.0 and 1.0")
 
 
 class EntityObservation(BaseModel):
@@ -259,3 +338,32 @@ class EntityState(BaseModel):
     observations: list[EntityObservation] = Field(
         default_factory=list, description="List of observations about this entity"
     )
+
+
+class MentalModel(BaseModel):
+    """
+    A manually configured mental model for tracking specific topics/areas.
+
+    Mental models are user-defined focus areas that the agent should track
+    and maintain summaries for, unlike auto-extracted entities.
+    """
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "example": {
+                "id": "team-dynamics",
+                "name": "Team Dynamics",
+                "description": "Track how the team collaborates, communication patterns, conflicts, and resolutions",
+                "summary": "The team has strong collaboration...",
+                "summary_updated_at": "2024-01-15T10:30:00Z",
+                "created_at": "2024-01-10T08:00:00Z",
+            }
+        }
+    )
+
+    id: str = Field(description="Unique identifier (alphanumeric lowercase)")
+    name: str = Field(description="Display name for the mental model")
+    description: str = Field(description="Prompt/directions for what to track and summarize")
+    summary: str | None = Field(None, description="Generated summary based on relevant facts")
+    summary_updated_at: str | None = Field(None, description="ISO format date when summary was last updated")
+    created_at: str = Field(description="ISO format date when the mental model was created")

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

@@ -1,5 +1,5 @@
 """
-bank profile utilities for disposition and background management.
+bank profile utilities for disposition and mission management.
 """
 
 import json
@@ -27,19 +27,18 @@ class BankProfile(TypedDict):
 
     name: str
     disposition: DispositionTraits
-    background: str
+    mission: str
 
 
-class BackgroundMergeResponse(BaseModel):
-    """LLM response for background merge with disposition inference."""
+class MissionMergeResponse(BaseModel):
+    """LLM response for mission merge."""
 
-    background: str = Field(description="Merged background in first person perspective")
-    disposition: DispositionTraits = Field(description="Inferred disposition traits (skepticism, literalism, empathy)")
+    mission: str = Field(description="Merged mission in first person perspective")
 
 
 async def get_bank_profile(pool, bank_id: str) -> BankProfile:
     """
-    Get bank profile (name, disposition + background).
+    Get bank profile (name, disposition + mission).
     Auto-creates bank with default values if not exists.
 
     Args:
@@ -47,13 +46,13 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
         bank_id: bank IDentifier
 
     Returns:
-        BankProfile with name, typed DispositionTraits, and background
+        BankProfile with name, typed DispositionTraits, and mission
     """
     async with acquire_with_retry(pool) as conn:
         # Try to get existing bank
         row = await conn.fetchrow(
             f"""
-            SELECT name, disposition, background
+            SELECT name, disposition, mission
             FROM {fq_table("banks")} WHERE bank_id = $1
             """,
             bank_id,
@@ -66,13 +65,15 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
                 disposition_data = json.loads(disposition_data)
 
             return BankProfile(
-                name=row["name"], disposition=DispositionTraits(**disposition_data), background=row["background"]
+                name=row["name"],
+                disposition=DispositionTraits(**disposition_data),
+                mission=row["mission"] or "",
             )
 
         # Bank doesn't exist, create with defaults
         await conn.execute(
             f"""
-            INSERT INTO {fq_table("banks")} (bank_id, name, disposition, background)
+            INSERT INTO {fq_table("banks")} (bank_id, name, disposition, mission)
             VALUES ($1, $2, $3::jsonb, $4)
             ON CONFLICT (bank_id) DO NOTHING
             """,
@@ -82,7 +83,7 @@ async def get_bank_profile(pool, bank_id: str) -> BankProfile:
             "",
         )
 
-        return BankProfile(name=bank_id, disposition=DispositionTraits(**DEFAULT_DISPOSITION), background="")
+        return BankProfile(name=bank_id, disposition=DispositionTraits(**DEFAULT_DISPOSITION), mission="")
 
 
 async def update_bank_disposition(pool, bank_id: str, disposition: dict[str, int]) -> None:
@@ -110,244 +111,121 @@ async def update_bank_disposition(pool, bank_id: str, disposition: dict[str, int
         )
 
 
-async def merge_bank_background(pool, llm_config, bank_id: str, new_info: str, update_disposition: bool = True) -> dict:
+async def set_bank_mission(pool, bank_id: str, mission: str) -> None:
     """
-    Merge new background information with existing background using LLM.
-    Normalizes to first person ("I") and resolves conflicts.
-    Optionally infers disposition traits from the merged background.
+    Set bank mission (replacing any existing mission).
 
     Args:
         pool: Database connection pool
-        llm_config: LLM configuration for background merging
         bank_id: bank IDentifier
-        new_info: New background information to add/merge
-        update_disposition: If True, infer Big Five traits from background (default: True)
+        mission: The mission text
+    """
+    # Ensure bank exists first
+    await get_bank_profile(pool, bank_id)
+
+    async with acquire_with_retry(pool) as conn:
+        await conn.execute(
+            f"""
+            UPDATE {fq_table("banks")}
+            SET mission = $2,
+                updated_at = NOW()
+            WHERE bank_id = $1
+            """,
+            bank_id,
+            mission,
+        )
+
+
+async def merge_bank_mission(pool, llm_config, bank_id: str, new_info: str) -> dict:
+    """
+    Merge new mission information with existing mission using LLM.
+    Normalizes to first person ("I") and resolves conflicts.
+
+    Args:
+        pool: Database connection pool
+        llm_config: LLM configuration for mission merging
+        bank_id: bank IDentifier
+        new_info: New mission information to add/merge
 
     Returns:
-        Dict with 'background' (str) and optionally 'disposition' (dict) keys
+        Dict with 'mission' (str) key
     """
     # Get current profile
     profile = await get_bank_profile(pool, bank_id)
-    current_background = profile["background"]
+    current_mission = profile["mission"]
 
-    # Use LLM to merge backgrounds and optionally infer disposition
-    result = await _llm_merge_background(llm_config, current_background, new_info, infer_disposition=update_disposition)
+    # Use LLM to merge missions
+    result = await _llm_merge_mission(llm_config, current_mission, new_info)
 
-    merged_background = result["background"]
-    inferred_disposition = result.get("disposition")
+    merged_mission = result["mission"]
 
     # Update in database
     async with acquire_with_retry(pool) as conn:
-        if inferred_disposition:
-            # Update both background and disposition
-            await conn.execute(
-                f"""
-                UPDATE {fq_table("banks")}
-                SET background = $2,
-                    disposition = $3::jsonb,
-                    updated_at = NOW()
-                WHERE bank_id = $1
-                """,
-                bank_id,
-                merged_background,
-                json.dumps(inferred_disposition),
-            )
-        else:
-            # Update only background
-            await conn.execute(
-                f"""
-                UPDATE {fq_table("banks")}
-                SET background = $2,
-                    updated_at = NOW()
-                WHERE bank_id = $1
-                """,
-                bank_id,
-                merged_background,
-            )
+        await conn.execute(
+            f"""
+            UPDATE {fq_table("banks")}
+            SET mission = $2,
+                updated_at = NOW()
+            WHERE bank_id = $1
+            """,
+            bank_id,
+            merged_mission,
+        )
 
-    response = {"background": merged_background}
-    if inferred_disposition:
-        response["disposition"] = inferred_disposition
-
-    return response
+    return {"mission": merged_mission}
 
 
-async def _llm_merge_background(llm_config, current: str, new_info: str, infer_disposition: bool = False) -> dict:
+async def _llm_merge_mission(llm_config, current: str, new_info: str) -> dict:
     """
-    Use LLM to intelligently merge background information.
-    Optionally infer Big Five disposition traits from the merged background.
+    Use LLM to intelligently merge mission information.
 
     Args:
         llm_config: LLM configuration to use
-        current: Current background text
+        current: Current mission text
         new_info: New information to merge
-        infer_disposition: If True, also infer disposition traits
 
     Returns:
-        Dict with 'background' (str) and optionally 'disposition' (dict) keys
+        Dict with 'mission' (str) key
     """
-    if infer_disposition:
-        prompt = f"""You are helping maintain a memory bank's background/profile and infer their disposition. You MUST respond with ONLY valid JSON.
+    prompt = f"""You are helping maintain an agent's mission statement.
 
-Current background: {current if current else "(empty)"}
+Current mission: {current if current else "(empty)"}
 
 New information to add: {new_info}
 
 Instructions:
-1. Merge the new information with the current background
-2. If there are conflicts (e.g., different birthplaces), the NEW information overwrites the old
-3. Keep additions that don't conflict
-4. Output in FIRST PERSON ("I") perspective
-5. Be concise - keep merged background under 500 characters
-6. Infer disposition traits from the merged background (each 1-5 integer):
-   - Skepticism: 1-5 (1=trusting, takes things at face value; 5=skeptical, questions everything)
-   - Literalism: 1-5 (1=flexible interpretation, reads between lines; 5=literal, exact interpretation)
-   - Empathy: 1-5 (1=detached, focuses on facts; 5=empathetic, considers emotional context)
-
-CRITICAL: You MUST respond with ONLY a valid JSON object. No markdown, no code blocks, no explanations. Just the JSON.
-
-Format:
-{{
-  "background": "the merged background text in first person",
-  "disposition": {{
-    "skepticism": 3,
-    "literalism": 3,
-    "empathy": 3
-  }}
-}}
-
-Trait inference examples:
-- "I'm a lawyer" → skepticism: 4, literalism: 5, empathy: 2
-- "I'm a therapist" → skepticism: 2, literalism: 2, empathy: 5
-- "I'm an engineer" → skepticism: 3, literalism: 4, empathy: 3
-- "I've been burned before by trusting people" → skepticism: 5, literalism: 3, empathy: 3
-- "I try to understand what people really mean" → skepticism: 3, literalism: 2, empathy: 4
-- "I take contracts very seriously" → skepticism: 4, literalism: 5, empathy: 2"""
-    else:
-        prompt = f"""You are helping maintain a memory bank's background/profile.
-
-Current background: {current if current else "(empty)"}
-
-New information to add: {new_info}
-
-Instructions:
-1. Merge the new information with the current background
-2. If there are conflicts (e.g., different birthplaces), the NEW information overwrites the old
+1. Merge the new information with the current mission
+2. If there are conflicts, the NEW information overwrites the old
 3. Keep additions that don't conflict
 4. Output in FIRST PERSON ("I") perspective
 5. Be concise - keep it under 500 characters
-6. Return ONLY the merged background text, no explanations
+6. Return ONLY the merged mission text, no explanations
 
-Merged background:"""
+Merged mission:"""
 
     try:
-        # Prepare messages
         messages = [{"role": "user", "content": prompt}]
 
-        if infer_disposition:
-            # Use structured output with Pydantic model for disposition inference
-            try:
-                parsed = await llm_config.call(
-                    messages=messages,
-                    response_format=BackgroundMergeResponse,
-                    scope="bank_background",
-                    temperature=0.3,
-                    max_completion_tokens=8192,
-                )
-                logger.info(f"Successfully got structured response: background={parsed.background[:100]}")
-
-                # Convert Pydantic model to dict format
-                return {"background": parsed.background, "disposition": parsed.disposition.model_dump()}
-            except Exception as e:
-                logger.warning(f"Structured output failed, falling back to manual parsing: {e}")
-                # Fall through to manual parsing below
-
-        # Manual parsing fallback or non-disposition merge
         content = await llm_config.call(
-            messages=messages, scope="bank_background", temperature=0.3, max_completion_tokens=8192
+            messages=messages, scope="bank_mission", temperature=0.3, max_completion_tokens=8192
         )
 
-        logger.info(f"LLM response for background merge (first 500 chars): {content[:500]}")
+        logger.info(f"LLM response for mission merge (first 500 chars): {content[:500]}")
 
-        if infer_disposition:
-            # Parse JSON response - try multiple extraction methods
-            result = None
-
-            # Method 1: Direct parse
-            try:
-                result = json.loads(content)
-                logger.info("Successfully parsed JSON directly")
-            except json.JSONDecodeError:
-                pass
-
-            # Method 2: Extract from markdown code blocks
-            if result is None:
-                # Remove markdown code blocks
-                code_block_match = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", content, re.DOTALL)
-                if code_block_match:
-                    try:
-                        result = json.loads(code_block_match.group(1))
-                        logger.info("Successfully extracted JSON from markdown code block")
-                    except json.JSONDecodeError:
-                        pass
-
-            # Method 3: Find nested JSON structure
-            if result is None:
-                # Look for JSON object with nested structure
-                json_match = re.search(
-                    r'\{[^{}]*"background"[^{}]*"disposition"[^{}]*\{[^{}]*\}[^{}]*\}', content, re.DOTALL
-                )
-                if json_match:
-                    try:
-                        result = json.loads(json_match.group())
-                        logger.info("Successfully extracted JSON using nested pattern")
-                    except json.JSONDecodeError:
-                        pass
-
-            # All parsing methods failed - use fallback
-            if result is None:
-                logger.warning(f"Failed to extract JSON from LLM response. Raw content: {content[:200]}")
-                # Fallback: use new_info as background with default disposition
-                return {
-                    "background": new_info if new_info else current if current else "",
-                    "disposition": DEFAULT_DISPOSITION.copy(),
-                }
-
-            # Validate disposition values
-            disposition = result.get("disposition", {})
-            for key in ["skepticism", "literalism", "empathy"]:
-                if key not in disposition:
-                    disposition[key] = 3  # Default to neutral
-                else:
-                    # Clamp to [1, 5] and convert to int
-                    disposition[key] = max(1, min(5, int(disposition[key])))
-
-            result["disposition"] = disposition
-
-            # Ensure background exists
-            if "background" not in result or not result["background"]:
-                result["background"] = new_info if new_info else ""
-
-            return result
-        else:
-            # Just background merge
-            merged = content
-            if not merged or merged.lower() in ["(empty)", "none", "n/a"]:
-                merged = new_info if new_info else ""
-            return {"background": merged}
+        merged = content.strip()
+        if not merged or merged.lower() in ["(empty)", "none", "n/a"]:
+            merged = new_info if new_info else ""
+        return {"mission": merged}
 
     except Exception as e:
-        logger.error(f"Error merging background with LLM: {e}")
+        logger.error(f"Error merging mission with LLM: {e}")
         # Fallback: just append new info
         if current:
             merged = f"{current} {new_info}".strip()
         else:
             merged = new_info
 
-        result = {"background": merged}
-        if infer_disposition:
-            result["disposition"] = DEFAULT_DISPOSITION.copy()
-        return result
+        return {"mission": merged}
 
 
 async def list_banks(pool) -> list:
@@ -358,12 +236,12 @@ async def list_banks(pool) -> list:
         pool: Database connection pool
 
     Returns:
-        List of dicts with bank_id, name, disposition, background, created_at, updated_at
+        List of dicts with bank_id, name, disposition, mission, created_at, updated_at
     """
     async with acquire_with_retry(pool) as conn:
         rows = await conn.fetch(
             f"""
-            SELECT bank_id, name, disposition, background, created_at, updated_at
+            SELECT bank_id, name, disposition, mission, created_at, updated_at
             FROM {fq_table("banks")}
             ORDER BY updated_at DESC
             """
@@ -381,7 +259,7 @@ async def list_banks(pool) -> list:
                     "bank_id": row["bank_id"],
                     "name": row["name"],
                     "disposition": disposition_data,
-                    "background": row["background"],
+                    "mission": row["mission"] or "",
                     "created_at": row["created_at"].isoformat() if row["created_at"] else None,
                     "updated_at": row["updated_at"].isoformat() if row["updated_at"] else None,
                 }

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

@@ -114,11 +114,8 @@ class CausalRelation(BaseModel):
     """Causal relationship from this fact to a previous fact (stored format)."""
 
     target_fact_index: int = Field(description="Index of the related fact in the facts array (0-based).")
-    relation_type: Literal["caused_by", "enabled_by", "prevented_by"] = Field(
-        description="How this fact relates to the target: "
-        "'caused_by' = this fact was caused by the target, "
-        "'enabled_by' = this fact was enabled by the target, "
-        "'prevented_by' = this fact was prevented by the target"
+    relation_type: Literal["caused_by"] = Field(
+        description="How this fact relates to the target: 'caused_by' = this fact was caused by the target"
     )
     strength: float = Field(
         description="Strength of relationship (0.0 to 1.0)",
@@ -141,11 +138,8 @@ class FactCausalRelation(BaseModel):
         "MUST be less than this fact's position in the list. "
         "Example: if this is fact #5, target_index can only be 0, 1, 2, 3, or 4."
     )
-    relation_type: Literal["caused_by", "enabled_by", "prevented_by"] = Field(
-        description="How this fact relates to the target fact: "
-        "'caused_by' = this fact was caused by the target fact, "
-        "'enabled_by' = this fact was enabled by the target fact, "
-        "'prevented_by' = this fact was blocked/prevented by the target fact"
+    relation_type: Literal["caused_by"] = Field(
+        description="How this fact relates to the target fact: 'caused_by' = this fact was caused by the target fact"
     )
     strength: float = Field(
         description="Strength of relationship (0.0 to 1.0). 1.0 = strong, 0.5 = moderate",
@@ -438,34 +432,15 @@ def _chunk_conversation(turns: list[dict], max_chars: int) -> list[str]:
 # FACT EXTRACTION PROMPTS
 # =============================================================================
 
-# Concise extraction prompt (default) - selective, high-quality facts
-CONCISE_FACT_EXTRACTION_PROMPT = """Extract SIGNIFICANT facts from text. Be SELECTIVE - only extract facts worth remembering long-term.
+# Base prompt template (shared by concise and custom modes)
+# Uses {extraction_guidelines} placeholder for mode-specific instructions
+_BASE_FACT_EXTRACTION_PROMPT = """Extract SIGNIFICANT facts from text. Be SELECTIVE - only extract facts worth remembering long-term.
 
-LANGUAGE RULE (CRITICAL): Output facts in the EXACT SAME language as the input text. If input is Japanese, output Japanese. If input is Chinese, output Chinese. NEVER translate to English. Preserve original language completely.
+LANGUAGE REQUIREMENT: Detect the language of the input text. All extracted facts, entity names, descriptions, and other output MUST be in the SAME language as the input. Do not translate to another language.
 
 {fact_types_instruction}
 
-══════════════════════════════════════════════════════════════════════════
-SELECTIVITY - CRITICAL (Reduces 90% of unnecessary output)
-══════════════════════════════════════════════════════════════════════════
-
-ONLY extract facts that are:
-✅ Personal info: names, relationships, roles, background
-✅ Preferences: likes, dislikes, habits, interests (e.g., "Alice likes coffee")
-✅ Significant events: milestones, decisions, achievements, changes
-✅ Plans/goals: future intentions, deadlines, commitments
-✅ Expertise: skills, knowledge, certifications, experience
-✅ Important context: projects, problems, constraints
-✅ Sensory/emotional details: feelings, sensations, perceptions that provide context
-✅ Observations: descriptions of people, places, things with specific details
-
-DO NOT extract:
-❌ Generic greetings: "how are you", "hello", pleasantries without substance
-❌ Pure filler: "thanks", "sounds good", "ok", "got it", "sure"
-❌ Process chatter: "let me check", "one moment", "I'll look into it"
-❌ Repeated info: if already stated, don't extract again
-
-CONSOLIDATE related statements into ONE fact when possible.
+{extraction_guidelines}
 
 ══════════════════════════════════════════════════════════════════════════
 FACT FORMAT - BE CONCISE
@@ -513,7 +488,33 @@ ENTITIES
 ══════════════════════════════════════════════════════════════════════════
 
 Include: people names, organizations, places, key objects, abstract concepts (career, friendship, etc.)
-Always include "user" when fact is about the user.
+Always include "user" when fact is about the user.{examples}"""
+
+# Concise mode guidelines
+_CONCISE_GUIDELINES = """══════════════════════════════════════════════════════════════════════════
+SELECTIVITY - CRITICAL (Reduces 90% of unnecessary output)
+══════════════════════════════════════════════════════════════════════════
+
+ONLY extract facts that are:
+✅ Personal info: names, relationships, roles, background
+✅ Preferences: likes, dislikes, habits, interests (e.g., "Alice likes coffee")
+✅ Significant events: milestones, decisions, achievements, changes
+✅ Plans/goals: future intentions, deadlines, commitments
+✅ Expertise: skills, knowledge, certifications, experience
+✅ Important context: projects, problems, constraints
+✅ Sensory/emotional details: feelings, sensations, perceptions that provide context
+✅ Observations: descriptions of people, places, things with specific details
+
+DO NOT extract:
+❌ Generic greetings: "how are you", "hello", pleasantries without substance
+❌ Pure filler: "thanks", "sounds good", "ok", "got it", "sure"
+❌ Process chatter: "let me check", "one moment", "I'll look into it"
+❌ Repeated info: if already stated, don't extract again
+
+CONSOLIDATE related statements into ONE fact when possible."""
+
+# Concise mode examples
+_CONCISE_EXAMPLES = """
 
 ══════════════════════════════════════════════════════════════════════════
 EXAMPLES
@@ -539,6 +540,20 @@ QUALITY OVER QUANTITY
 
 Ask: "Would this be useful to recall in 6 months?" If no, skip it."""
 
+# Assembled concise prompt (backward compatible - exact same output as before)
+CONCISE_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
+    fact_types_instruction="{fact_types_instruction}",
+    extraction_guidelines=_CONCISE_GUIDELINES,
+    examples=_CONCISE_EXAMPLES,
+)
+
+# Custom prompt uses same base but without examples
+CUSTOM_FACT_EXTRACTION_PROMPT = _BASE_FACT_EXTRACTION_PROMPT.format(
+    fact_types_instruction="{fact_types_instruction}",
+    extraction_guidelines="{custom_instructions}",
+    examples="",  # No examples for custom mode
+)
+
 
 # Verbose extraction prompt - detailed, comprehensive facts (legacy mode)
 VERBOSE_FACT_EXTRACTION_PROMPT = """Extract facts from text into structured format with FIVE required dimensions - BE EXTREMELY DETAILED.
@@ -662,7 +677,7 @@ CAUSAL RELATIONSHIPS
 ══════════════════════════════════════════════════════════════════════════
 
 Link facts with causal_relations (max 2 per fact). target_index must be < this fact's index.
-Types: "caused_by", "enabled_by", "prevented_by"
+Type: "caused_by" (this fact was caused by the target fact)
 
 Example: "Lost job → couldn't pay rent → moved apartment"
 - Fact 0: Lost job, causal_relations: null
@@ -678,7 +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).
@@ -686,17 +700,15 @@ async def _extract_facts_from_chunk(
     Note: event_date parameter is kept for backward compatibility but not used in prompt.
     The LLM extracts temporal information from the context string instead.
     """
-    memory_bank_context = f"\n- Your name: {agent_name}" if agent_name and extract_opinions else ""
+    import logging
 
-    # Determine which fact types to extract based on the flag
+    from openai import BadRequestError
+
+    logger = logging.getLogger(__name__)
+
+    # 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()
@@ -704,13 +716,27 @@ async def _extract_facts_from_chunk(
     extract_causal_links = config.retain_extract_causal_links
 
     # Select base prompt based on extraction mode
-    if extraction_mode == "verbose":
+    if extraction_mode == "custom":
+        # Custom mode: inject user-provided guidelines
+        if not config.retain_custom_instructions:
+            logger.warning(
+                "extraction_mode='custom' but HINDSIGHT_API_RETAIN_CUSTOM_INSTRUCTIONS not set. "
+                "Falling back to 'concise' mode."
+            )
+            base_prompt = CONCISE_FACT_EXTRACTION_PROMPT
+            prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
+        else:
+            base_prompt = CUSTOM_FACT_EXTRACTION_PROMPT
+            prompt = base_prompt.format(
+                fact_types_instruction=fact_types_instruction,
+                custom_instructions=config.retain_custom_instructions,
+            )
+    elif extraction_mode == "verbose":
         base_prompt = VERBOSE_FACT_EXTRACTION_PROMPT
+        prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
     else:
         base_prompt = CONCISE_FACT_EXTRACTION_PROMPT
-
-    # Format the prompt with fact types instruction
-    prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
+        prompt = base_prompt.format(fact_types_instruction=fact_types_instruction)
 
     # Build the full prompt with or without causal relationships section
     # Select appropriate response schema based on extraction mode and causal links
@@ -723,12 +749,6 @@ async def _extract_facts_from_chunk(
     else:
         response_schema = FactExtractionResponseNoCausal
 
-    import logging
-
-    from openai import BadRequestError
-
-    logger = logging.getLogger(__name__)
-
     # Retry logic for JSON validation errors
     max_retries = 2
     last_error = None
@@ -741,7 +761,6 @@ async def _extract_facts_from_chunk(
     # Format event_date with day of week for better temporal reasoning
     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()})
@@ -753,12 +772,28 @@ Text:
     usage = TokenUsage()  # Track cumulative usage across retries
     for attempt in range(max_retries):
         try:
+            # Use retain-specific overrides if set, otherwise fall back to global LLM config
+            max_retries = (
+                config.retain_llm_max_retries if config.retain_llm_max_retries is not None else config.llm_max_retries
+            )
+            initial_backoff = (
+                config.retain_llm_initial_backoff
+                if config.retain_llm_initial_backoff is not None
+                else config.llm_initial_backoff
+            )
+            max_backoff = (
+                config.retain_llm_max_backoff if config.retain_llm_max_backoff is not None else config.llm_max_backoff
+            )
+
             extraction_response_json, call_usage = await llm_config.call(
                 messages=[{"role": "system", "content": prompt}, {"role": "user", "content": user_message}],
                 response_format=response_schema,
                 scope="memory_extract_facts",
                 temperature=0.1,
                 max_completion_tokens=config.retain_max_completion_tokens,
+                max_retries=max_retries,
+                initial_backoff=initial_backoff,
+                max_backoff=max_backoff,
                 skip_validation=True,  # Get raw JSON, we'll validate leniently
                 return_usage=True,
             )
@@ -823,7 +858,8 @@ Text:
 
                 # Critical field: fact_type
                 # LLM uses "assistant" but we convert to "experience" for storage
-                fact_type = llm_fact.get("fact_type")
+                original_fact_type = llm_fact.get("fact_type")
+                fact_type = original_fact_type
 
                 # Convert "assistant" → "experience" for storage
                 if fact_type == "assistant":
@@ -840,7 +876,10 @@ Text:
                     else:
                         # Default to 'world' if we can't determine
                         fact_type = "world"
-                        logger.warning(f"Fact {i}: defaulting to fact_type='world'")
+                        logger.warning(
+                            f"Fact {i}: defaulting to fact_type='world' "
+                            f"(original fact_type={original_fact_type!r}, fact_kind={fact_kind!r})"
+                        )
 
                 # Get fact_kind for temporal handling (but don't store it)
                 fact_kind = llm_fact.get("fact_kind", "conversation")
@@ -980,7 +1019,6 @@ 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.
@@ -996,7 +1034,6 @@ 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)
@@ -1015,7 +1052,6 @@ 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
@@ -1060,7 +1096,6 @@ 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,
@@ -1070,7 +1105,6 @@ async def _extract_facts_with_auto_split(
                 context=context,
                 llm_config=llm_config,
                 agent_name=agent_name,
-                extract_opinions=extract_opinions,
             ),
         ]
 
@@ -1094,7 +1128,6 @@ 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.
@@ -1111,7 +1144,6 @@ 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:
@@ -1139,7 +1171,6 @@ 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)
     ]
@@ -1171,7 +1202,7 @@ 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.
@@ -1186,7 +1217,6 @@ 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)
@@ -1205,7 +1235,6 @@ 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)
 
@@ -1268,6 +1297,7 @@ async def extract_facts_from_contents(
                         # mentioned_at: always the event_date (when the conversation/document occurred)
                         mentioned_at=content.event_date,
                         metadata=content.metadata,
+                        tags=content.tags,
                     )
 
                     extracted_facts.append(extracted_fact)

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

@@ -41,10 +41,10 @@ async def insert_facts_batch(
     contexts = []
     fact_types = []
     confidence_scores = []
-    access_counts = []
     metadata_jsons = []
     chunk_ids = []
     document_ids = []
+    tags_list = []
 
     for fact in facts:
         fact_texts.append(fact.fact_text)
@@ -60,21 +60,35 @@ async def insert_facts_batch(
         fact_types.append(fact.fact_type)
         # confidence_score is only for opinion facts
         confidence_scores.append(1.0 if fact.fact_type == "opinion" else None)
-        access_counts.append(0)  # Initial access count
         metadata_jsons.append(json.dumps(fact.metadata))
         chunk_ids.append(fact.chunk_id)
         # Use per-fact document_id if available, otherwise fallback to batch-level document_id
         document_ids.append(fact.document_id if fact.document_id else document_id)
+        # Convert tags to JSON string for proper batch insertion (PostgreSQL unnest doesn't handle 2D arrays well)
+        tags_list.append(json.dumps(fact.tags if fact.tags else []))
 
     # Batch insert all facts
+    # Note: tags are passed as JSON strings and converted back to varchar[] via jsonb_array_elements_text + array_agg
     results = await conn.fetch(
         f"""
-        INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                 context, fact_type, confidence_score, access_count, metadata, chunk_id, document_id)
-        SELECT $1, * FROM unnest(
-            $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-            $8::text[], $9::text[], $10::float[], $11::int[], $12::jsonb[], $13::text[], $14::text[]
+        WITH input_data AS (
+            SELECT * FROM unnest(
+                $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                $8::text[], $9::text[], $10::float[], $11::jsonb[], $12::text[], $13::text[], $14::jsonb[]
+            ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                   context, fact_type, confidence_score, metadata, chunk_id, document_id, tags_json)
         )
+        INSERT INTO {fq_table("memory_units")} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                 context, fact_type, confidence_score, metadata, chunk_id, document_id, tags)
+        SELECT
+            $1,
+            text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+            context, fact_type, confidence_score, metadata, chunk_id, document_id,
+            COALESCE(
+                (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
+                '{{}}'::varchar[]
+            )
+        FROM input_data
         RETURNING id
         """,
         bank_id,
@@ -87,10 +101,10 @@ async def insert_facts_batch(
         contexts,
         fact_types,
         confidence_scores,
-        access_counts,
         metadata_jsons,
         chunk_ids,
         document_ids,
+        tags_list,
     )
 
     unit_ids = [str(row["id"]) for row in results]
@@ -109,7 +123,7 @@ async def ensure_bank_exists(conn, bank_id: str) -> None:
     """
     await conn.execute(
         f"""
-        INSERT INTO {fq_table("banks")} (bank_id, disposition, background)
+        INSERT INTO {fq_table("banks")} (bank_id, disposition, mission)
         VALUES ($1, $2::jsonb, $3)
         ON CONFLICT (bank_id) DO UPDATE
         SET updated_at = NOW()
@@ -121,7 +135,13 @@ async def ensure_bank_exists(conn, bank_id: str) -> None:
 
 
 async def handle_document_tracking(
-    conn, bank_id: str, document_id: str, combined_content: str, is_first_batch: bool, retain_params: dict | None = None
+    conn,
+    bank_id: str,
+    document_id: str,
+    combined_content: str,
+    is_first_batch: bool,
+    retain_params: dict | None = None,
+    document_tags: list[str] | None = None,
 ) -> None:
     """
     Handle document tracking in the database.
@@ -133,6 +153,7 @@ async def handle_document_tracking(
         combined_content: Combined content text from all content items
         is_first_batch: Whether this is the first batch (for chunked operations)
         retain_params: Optional parameters passed during retain (context, event_date, etc.)
+        document_tags: Optional list of tags to associate with the document
     """
     import hashlib
 
@@ -149,13 +170,14 @@ async def handle_document_tracking(
     # Insert document (or update if exists from concurrent operations)
     await conn.execute(
         f"""
-        INSERT INTO {fq_table("documents")} (id, bank_id, original_text, content_hash, metadata, retain_params)
-        VALUES ($1, $2, $3, $4, $5, $6)
+        INSERT INTO {fq_table("documents")} (id, bank_id, original_text, content_hash, metadata, retain_params, tags)
+        VALUES ($1, $2, $3, $4, $5, $6, $7)
         ON CONFLICT (id, bank_id) DO UPDATE
         SET original_text = EXCLUDED.original_text,
             content_hash = EXCLUDED.content_hash,
             metadata = EXCLUDED.metadata,
             retain_params = EXCLUDED.retain_params,
+            tags = EXCLUDED.tags,
             updated_at = NOW()
         """,
         document_id,
@@ -164,4 +186,5 @@ async def handle_document_tracking(
         content_hash,
         json.dumps({}),  # Empty metadata dict
         json.dumps(retain_params) if retain_params else None,
+        document_tags or [],
     )

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

@@ -754,17 +754,14 @@ async def create_causal_links_batch(
         causal_relations_per_fact: List of causal relations for each fact.
             Each element is a list of dicts with:
             - target_fact_index: Index into unit_ids for the target fact
-            - relation_type: "causes", "caused_by", "enables", or "prevents"
+            - relation_type: "caused_by"
             - strength: Float in [0.0, 1.0] representing relationship strength
 
     Returns:
         Number of causal links created
 
-    Causal link types:
-    - "causes": This fact directly causes the target fact (forward causation)
-    - "caused_by": This fact was caused by the target fact (backward causation)
-    - "enables": This fact enables/allows the target fact (enablement)
-    - "prevents": This fact prevents/blocks the target fact (prevention)
+    Causal link type:
+    - "caused_by": This fact was caused by the target fact
     """
     if not unit_ids or not causal_relations_per_fact:
         return 0
@@ -787,8 +784,8 @@ async def create_causal_links_batch(
                 relation_type = relation["relation_type"]
                 strength = relation.get("strength", 1.0)
 
-                # Validate relation_type - must match database constraint
-                valid_types = {"causes", "caused_by", "enables", "prevents"}
+                # Validate relation_type - only "caused_by" is supported (DB constraint)
+                valid_types = {"caused_by"}
                 if relation_type not in valid_types:
                     logger.error(
                         f"Invalid relation_type '{relation_type}' (type: {type(relation_type).__name__}) "

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

@@ -1,254 +0,0 @@
-"""
-Observation regeneration for retain pipeline.
-
-Regenerates entity observations as part of the retain transaction.
-"""
-
-import logging
-import time
-import uuid
-from datetime import UTC, datetime
-
-from ...config import get_config
-from ..memory_engine import fq_table
-from ..search import observation_utils
-from . import embedding_utils
-from .types import EntityLink
-
-logger = logging.getLogger(__name__)
-
-
-def utcnow():
-    """Get current UTC time."""
-    return datetime.now(UTC)
-
-
-# Simple dataclass-like container for facts (avoid importing from memory_engine)
-class MemoryFactForObservation:
-    def __init__(self, id: str, text: str, fact_type: str, context: str, occurred_start: str | None):
-        self.id = id
-        self.text = text
-        self.fact_type = fact_type
-        self.context = context
-        self.occurred_start = occurred_start
-
-
-async def regenerate_observations_batch(
-    conn, embeddings_model, llm_config, bank_id: str, entity_links: list[EntityLink], log_buffer: list[str] = None
-) -> None:
-    """
-    Regenerate observations for top entities in this batch.
-
-    Called INSIDE the retain transaction for atomicity - if observations
-    fail, the entire retain batch is rolled back.
-
-    Args:
-        conn: Database connection (from the retain transaction)
-        embeddings_model: Embeddings model for generating observation embeddings
-        llm_config: LLM configuration for observation extraction
-        bank_id: Bank identifier
-        entity_links: Entity links from this batch
-        log_buffer: Optional log buffer for timing
-    """
-    config = get_config()
-    TOP_N_ENTITIES = config.observation_top_entities
-    MIN_FACTS_THRESHOLD = config.observation_min_facts
-
-    if not entity_links:
-        return
-
-    # Count mentions per entity in this batch
-    entity_mention_counts: dict[str, int] = {}
-    for link in entity_links:
-        if link.entity_id:
-            entity_id = str(link.entity_id)
-            entity_mention_counts[entity_id] = entity_mention_counts.get(entity_id, 0) + 1
-
-    if not entity_mention_counts:
-        return
-
-    # Sort by mention count descending and take top N
-    sorted_entities = sorted(entity_mention_counts.items(), key=lambda x: x[1], reverse=True)
-    entities_to_process = [e[0] for e in sorted_entities[:TOP_N_ENTITIES]]
-
-    obs_start = time.time()
-
-    # Convert to UUIDs
-    entity_uuids = [uuid.UUID(eid) if isinstance(eid, str) else eid for eid in entities_to_process]
-
-    # Batch query for entity names
-    entity_rows = await conn.fetch(
-        f"""
-        SELECT id, canonical_name FROM {fq_table("entities")}
-        WHERE id = ANY($1) AND bank_id = $2
-        """,
-        entity_uuids,
-        bank_id,
-    )
-    entity_names = {row["id"]: row["canonical_name"] for row in entity_rows}
-
-    # Batch query for fact counts
-    fact_counts = await conn.fetch(
-        f"""
-        SELECT ue.entity_id, COUNT(*) as cnt
-        FROM {fq_table("unit_entities")} ue
-        JOIN {fq_table("memory_units")} mu ON ue.unit_id = mu.id
-        WHERE ue.entity_id = ANY($1) AND mu.bank_id = $2
-        GROUP BY ue.entity_id
-        """,
-        entity_uuids,
-        bank_id,
-    )
-    entity_fact_counts = {row["entity_id"]: row["cnt"] for row in fact_counts}
-
-    # Filter entities that meet the threshold
-    entities_with_names = []
-    for entity_id in entities_to_process:
-        entity_uuid = uuid.UUID(entity_id) if isinstance(entity_id, str) else entity_id
-        if entity_uuid not in entity_names:
-            continue
-        fact_count = entity_fact_counts.get(entity_uuid, 0)
-        if fact_count >= MIN_FACTS_THRESHOLD:
-            entities_with_names.append((entity_id, entity_names[entity_uuid]))
-
-    if not entities_with_names:
-        return
-
-    # Process entities SEQUENTIALLY (asyncpg doesn't allow concurrent queries on same connection)
-    # We must use the same connection to stay in the retain transaction
-    total_observations = 0
-
-    for entity_id, entity_name in entities_with_names:
-        try:
-            obs_ids = await _regenerate_entity_observations(
-                conn, embeddings_model, llm_config, bank_id, entity_id, entity_name
-            )
-            total_observations += len(obs_ids)
-        except Exception as e:
-            logger.error(f"[OBSERVATIONS] Error processing entity {entity_id}: {e}")
-
-    obs_time = time.time() - obs_start
-    if log_buffer is not None:
-        log_buffer.append(
-            f"[11] Observations: {total_observations} observations for {len(entities_with_names)} entities in {obs_time:.3f}s"
-        )
-
-
-async def _regenerate_entity_observations(
-    conn, embeddings_model, llm_config, bank_id: str, entity_id: str, entity_name: str
-) -> list[str]:
-    """
-    Regenerate observations for a single entity.
-
-    Uses the provided connection (part of retain transaction).
-
-    Args:
-        conn: Database connection (from the retain transaction)
-        embeddings_model: Embeddings model
-        llm_config: LLM configuration
-        bank_id: Bank identifier
-        entity_id: Entity UUID
-        entity_name: Canonical name of the entity
-
-    Returns:
-        List of created observation IDs
-    """
-    entity_uuid = uuid.UUID(entity_id) if isinstance(entity_id, str) else entity_id
-
-    # Get all facts mentioning this entity (exclude observations themselves)
-    rows = await conn.fetch(
-        f"""
-        SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.fact_type
-        FROM {fq_table("memory_units")} mu
-        JOIN {fq_table("unit_entities")} ue ON mu.id = ue.unit_id
-        WHERE mu.bank_id = $1
-          AND ue.entity_id = $2
-          AND mu.fact_type IN ('world', 'experience')
-        ORDER BY mu.occurred_start DESC
-        LIMIT 50
-        """,
-        bank_id,
-        entity_uuid,
-    )
-
-    if not rows:
-        return []
-
-    # Convert to fact objects for observation extraction
-    facts = []
-    for row in rows:
-        occurred_start = row["occurred_start"].isoformat() if row["occurred_start"] else None
-        facts.append(
-            MemoryFactForObservation(
-                id=str(row["id"]),
-                text=row["text"],
-                fact_type=row["fact_type"],
-                context=row["context"],
-                occurred_start=occurred_start,
-            )
-        )
-
-    # Extract observations using LLM
-    observations = await observation_utils.extract_observations_from_facts(llm_config, entity_name, facts)
-
-    if not observations:
-        return []
-
-    # Delete old observations for this entity
-    await conn.execute(
-        f"""
-        DELETE FROM {fq_table("memory_units")}
-        WHERE id IN (
-            SELECT mu.id
-            FROM {fq_table("memory_units")} mu
-            JOIN {fq_table("unit_entities")} ue ON mu.id = ue.unit_id
-            WHERE mu.bank_id = $1
-              AND mu.fact_type = 'observation'
-              AND ue.entity_id = $2
-        )
-        """,
-        bank_id,
-        entity_uuid,
-    )
-
-    # Generate embeddings for new observations
-    embeddings = await embedding_utils.generate_embeddings_batch(embeddings_model, observations)
-
-    # Insert new observations
-    current_time = utcnow()
-    created_ids = []
-
-    for obs_text, embedding in zip(observations, embeddings):
-        result = await conn.fetchrow(
-            f"""
-            INSERT INTO {fq_table("memory_units")} (
-                bank_id, text, embedding, context, event_date,
-                occurred_start, occurred_end, mentioned_at,
-                fact_type, access_count
-            )
-            VALUES ($1, $2, $3, $4, $5, $6, $7, $8, 'observation', 0)
-            RETURNING id
-            """,
-            bank_id,
-            obs_text,
-            str(embedding),
-            f"observation about {entity_name}",
-            current_time,
-            current_time,
-            current_time,
-            current_time,
-        )
-        obs_id = str(result["id"])
-        created_ids.append(obs_id)
-
-        # Link observation to entity
-        await conn.execute(
-            f"""
-            INSERT INTO {fq_table("unit_entities")} (unit_id, entity_id)
-            VALUES ($1, $2)
-            """,
-            uuid.UUID(obs_id),
-            entity_uuid,
-        )
-
-    return created_ids

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

@@ -27,9 +27,8 @@ from . import (
     fact_extraction,
     fact_storage,
     link_creation,
-    observation_regeneration,
 )
-from .types import ExtractedFact, ProcessedFact, RetainContent, RetainContentDict
+from .types import EntityLink, ExtractedFact, ProcessedFact, RetainContent, RetainContentDict
 
 logger = logging.getLogger(__name__)
 
@@ -39,7 +38,6 @@ async def retain_batch(
     embeddings_model,
     llm_config,
     entity_resolver,
-    task_backend,
     format_date_fn,
     duplicate_checker_fn,
     bank_id: str,
@@ -48,6 +46,7 @@ async def retain_batch(
     is_first_batch: bool = True,
     fact_type_override: str | None = None,
     confidence_score: float | None = None,
+    document_tags: list[str] | None = None,
 ) -> tuple[list[list[str]], TokenUsage]:
     """
     Process a batch of content through the retain pipeline.
@@ -57,7 +56,6 @@ async def retain_batch(
         embeddings_model: Embeddings model for generating embeddings
         llm_config: LLM configuration for fact extraction
         entity_resolver: Entity resolver for entity processing
-        task_backend: Task backend for background jobs
         format_date_fn: Function to format datetime to readable string
         duplicate_checker_fn: Function to check for duplicate facts
         bank_id: Bank identifier
@@ -66,6 +64,7 @@ async def retain_batch(
         is_first_batch: Whether this is the first batch
         fact_type_override: Override fact type for all facts
         confidence_score: Confidence score for opinions
+        document_tags: Tags applied to all items in this batch
 
     Returns:
         Tuple of (unit ID lists, token usage for fact extraction)
@@ -87,22 +86,23 @@ async def retain_batch(
     # Convert dicts to RetainContent objects
     contents = []
     for item in contents_dicts:
+        # Merge item-level tags with document-level tags
+        item_tags = item.get("tags", []) or []
+        merged_tags = list(set(item_tags + (document_tags or [])))
         content = RetainContent(
             content=item["content"],
             context=item.get("context", ""),
             event_date=item.get("event_date") or utcnow(),
             metadata=item.get("metadata", {}),
             entities=item.get("entities", []),
+            tags=merged_tags,
         )
         contents.append(content)
 
     # 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"
     )
@@ -130,7 +130,7 @@ async def retain_batch(
                         if first_item.get("metadata"):
                             retain_params["metadata"] = first_item["metadata"]
                     await fact_storage.handle_document_tracking(
-                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params
+                        conn, bank_id, document_id, combined_content, is_first_batch, retain_params, document_tags
                     )
                 else:
                     # Check for per-item document_ids
@@ -158,7 +158,7 @@ async def retain_batch(
                             if first_item.get("metadata"):
                                 retain_params["metadata"] = first_item["metadata"]
                         await fact_storage.handle_document_tracking(
-                            conn, bank_id, doc_id, combined_content, is_first_batch, retain_params
+                            conn, bank_id, doc_id, combined_content, is_first_batch, retain_params, document_tags
                         )
 
         total_time = time.time() - start_time
@@ -224,7 +224,7 @@ async def retain_batch(
                         retain_params["metadata"] = first_item["metadata"]
 
                 await fact_storage.handle_document_tracking(
-                    conn, bank_id, document_id, combined_content, is_first_batch, retain_params
+                    conn, bank_id, document_id, combined_content, is_first_batch, retain_params, document_tags
                 )
                 document_ids_added.append(document_id)
                 doc_id_mapping[None] = document_id  # For backwards compatibility
@@ -268,7 +268,13 @@ async def retain_batch(
                                     retain_params["metadata"] = first_item["metadata"]
 
                             await fact_storage.handle_document_tracking(
-                                conn, bank_id, actual_doc_id, combined_content, is_first_batch, retain_params
+                                conn,
+                                bank_id,
+                                actual_doc_id,
+                                combined_content,
+                                is_first_batch,
+                                retain_params,
+                                document_tags,
                             )
                             document_ids_added.append(actual_doc_id)
 
@@ -395,17 +401,9 @@ async def retain_batch(
             causal_link_count = await link_creation.create_causal_links_batch(conn, unit_ids, non_duplicate_facts)
             log_buffer.append(f"[10] Causal links: {causal_link_count} links in {time.time() - step_start:.3f}s")
 
-            # Regenerate observations INSIDE transaction for atomicity
-            await observation_regeneration.regenerate_observations_batch(
-                conn, embeddings_model, llm_config, bank_id, entity_links, log_buffer
-            )
-
             # Map results back to original content items
             result_unit_ids = _map_results_to_contents(contents, extracted_facts, is_duplicate_flags, unit_ids)
 
-        # Trigger background tasks AFTER transaction commits (opinion reinforcement only)
-        await _trigger_background_tasks(task_backend, bank_id, unit_ids, non_duplicate_facts)
-
         # Log final summary
         total_time = time.time() - start_time
         log_buffer.append(f"{'=' * 60}")
@@ -447,24 +445,3 @@ def _map_results_to_contents(
         result_unit_ids.append(content_unit_ids)
 
     return result_unit_ids
-
-
-async def _trigger_background_tasks(
-    task_backend,
-    bank_id: str,
-    unit_ids: list[str],
-    facts: list[ProcessedFact],
-) -> None:
-    """Trigger opinion reinforcement as background task (after transaction commits)."""
-    # Trigger opinion reinforcement if there are entities
-    fact_entities = [[e.name for e in fact.entities] for fact in facts]
-    if any(fact_entities):
-        await task_backend.submit_task(
-            {
-                "type": "reinforce_opinion",
-                "bank_id": bank_id,
-                "created_unit_ids": unit_ids,
-                "unit_texts": [fact.fact_text for fact in facts],
-                "unit_entities": fact_entities,
-            }
-        )

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

@@ -21,6 +21,7 @@ class RetainContentDict(TypedDict, total=False):
         metadata: Custom key-value metadata (optional)
         document_id: Document ID for this content item (optional)
         entities: User-provided entities to merge with extracted entities (optional)
+        tags: Visibility scope tags for this content item (optional)
     """
 
     content: str  # Required
@@ -29,6 +30,7 @@ class RetainContentDict(TypedDict, total=False):
     metadata: dict[str, str]
     document_id: str
     entities: list[dict[str, str]]  # [{"text": "...", "type": "..."}]
+    tags: list[str]  # Visibility scope tags
 
 
 def _now_utc() -> datetime:
@@ -49,6 +51,7 @@ class RetainContent:
     event_date: datetime = field(default_factory=_now_utc)
     metadata: dict[str, str] = field(default_factory=dict)
     entities: list[dict[str, str]] = field(default_factory=list)  # User-provided entities
+    tags: list[str] = field(default_factory=list)  # Visibility scope tags
 
 
 @dataclass
@@ -83,10 +86,10 @@ class CausalRelation:
     """
     Causal relationship between facts.
 
-    Represents how one fact causes, enables, or prevents another.
+    Represents how one fact was caused by another.
     """
 
-    relation_type: str  # "causes", "enables", "prevents", "caused_by"
+    relation_type: str  # "caused_by"
     target_fact_index: int  # Index of the target fact in the batch
     strength: float = 1.0  # Strength of the causal relationship
 
@@ -113,6 +116,7 @@ class ExtractedFact:
     context: str = ""
     mentioned_at: datetime | None = None
     metadata: dict[str, str] = field(default_factory=dict)
+    tags: list[str] = field(default_factory=list)  # Visibility scope tags
 
 
 @dataclass
@@ -158,6 +162,9 @@ class ProcessedFact:
     # Track which content this fact came from (for user entity merging)
     content_index: int = 0
 
+    # Visibility scope tags
+    tags: list[str] = field(default_factory=list)
+
     @property
     def is_duplicate(self) -> bool:
         """Check if this fact was marked as a duplicate."""
@@ -201,6 +208,7 @@ class ProcessedFact:
             causal_relations=extracted_fact.causal_relations,
             chunk_id=chunk_id,
             content_index=extracted_fact.content_index,
+            tags=extracted_fact.tags,
         )
 
 
@@ -232,6 +240,7 @@ class RetainBatch:
     document_id: str | None = None
     fact_type_override: str | None = None
     confidence_score: float | None = None
+    document_tags: list[str] = field(default_factory=list)  # Tags applied to all items
 
     # Extracted data (populated during processing)
     extracted_facts: list[ExtractedFact] = field(default_factory=list)

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

@@ -11,7 +11,8 @@ from abc import ABC, abstractmethod
 
 from ..db_utils import acquire_with_retry
 from ..memory_engine import fq_table
-from .types import RetrievalResult
+from .tags import TagsMatch, filter_results_by_tags
+from .types import MPFPTimings, RetrievalResult
 
 logger = logging.getLogger(__name__)
 
@@ -42,7 +43,10 @@ class GraphRetriever(ABC):
         query_text: str | None = None,
         semantic_seeds: list[RetrievalResult] | None = None,
         temporal_seeds: list[RetrievalResult] | None = None,
-    ) -> list[RetrievalResult]:
+        adjacency=None,  # TypedAdjacency, optional pre-loaded graph
+        tags: list[str] | None = None,  # Visibility scope tags for filtering
+        tags_match: TagsMatch = "any",  # How to match tags: 'any' (OR) or 'all' (AND)
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
         """
         Retrieve relevant facts via graph traversal.
 
@@ -55,9 +59,11 @@ class GraphRetriever(ABC):
             query_text: Original query text (optional, for some strategies)
             semantic_seeds: Pre-computed semantic entry points (from semantic retrieval)
             temporal_seeds: Pre-computed temporal entry points (from temporal retrieval)
+            adjacency: Pre-loaded typed adjacency graph (optional, for MPFP)
+            tags: Optional list of tags for visibility filtering (OR matching)
 
         Returns:
-            List of RetrievalResult objects with activation scores set
+            Tuple of (List of RetrievalResult with activation scores, optional timing info)
         """
         pass
 
@@ -111,7 +117,10 @@ class BFSGraphRetriever(GraphRetriever):
         query_text: str | None = None,
         semantic_seeds: list[RetrievalResult] | None = None,
         temporal_seeds: list[RetrievalResult] | None = None,
-    ) -> list[RetrievalResult]:
+        adjacency=None,  # Not used by BFS
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
         """
         Retrieve facts using BFS spreading activation.
 
@@ -122,11 +131,14 @@ class BFSGraphRetriever(GraphRetriever):
         4. Return visited nodes up to budget
 
         Note: BFS finds its own entry points via embedding search.
-        The semantic_seeds and temporal_seeds parameters are accepted
+        The semantic_seeds, temporal_seeds, and adjacency parameters are accepted
         for interface compatibility but not used.
         """
         async with acquire_with_retry(pool) as conn:
-            return await self._retrieve_with_conn(conn, query_embedding_str, bank_id, fact_type, budget)
+            results = await self._retrieve_with_conn(
+                conn, query_embedding_str, bank_id, fact_type, budget, tags=tags, tags_match=tags_match
+            )
+            return results, None
 
     async def _retrieve_with_conn(
         self,
@@ -135,33 +147,46 @@ class BFSGraphRetriever(GraphRetriever):
         bank_id: str,
         fact_type: str,
         budget: int,
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
     ) -> list[RetrievalResult]:
         """Internal implementation with connection."""
+        from .tags import build_tags_where_clause_simple
+
+        tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
+        params = [query_embedding_str, bank_id, fact_type, self.entry_point_threshold, self.entry_point_limit]
+        if tags:
+            params.append(tags)
 
         # Step 1: Find entry points
         entry_points = await conn.fetch(
             f"""
             SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, access_count, embedding, fact_type, document_id, chunk_id,
+                   mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
                    1 - (embedding <=> $1::vector) AS similarity
             FROM {fq_table("memory_units")}
             WHERE bank_id = $2
               AND embedding IS NOT NULL
               AND fact_type = $3
               AND (1 - (embedding <=> $1::vector)) >= $4
+              {tags_clause}
             ORDER BY embedding <=> $1::vector
             LIMIT $5
             """,
-            query_embedding_str,
-            bank_id,
-            fact_type,
-            self.entry_point_threshold,
-            self.entry_point_limit,
+            *params,
         )
 
         if not entry_points:
+            logger.debug(
+                f"[BFS] No entry points found for fact_type={fact_type} (tags={tags}, tags_match={tags_match})"
+            )
             return []
 
+        logger.debug(
+            f"[BFS] Found {len(entry_points)} entry points for fact_type={fact_type} "
+            f"(tags={tags}, tags_match={tags_match})"
+        )
+
         # Step 2: BFS spreading activation
         visited = set()
         results = []
@@ -191,8 +216,8 @@ class BFSGraphRetriever(GraphRetriever):
                 neighbors = await conn.fetch(
                     f"""
                     SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end,
-                           mu.mentioned_at, mu.access_count, mu.embedding, mu.fact_type,
-                           mu.document_id, mu.chunk_id,
+                           mu.mentioned_at, mu.embedding, mu.fact_type,
+                           mu.document_id, mu.chunk_id, mu.tags,
                            ml.weight, ml.link_type, ml.from_unit_id
                     FROM {fq_table("memory_links")} ml
                     JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
@@ -232,4 +257,8 @@ class BFSGraphRetriever(GraphRetriever):
                             neighbor_result = RetrievalResult.from_db_row(dict(n))
                             queue.append((neighbor_result, new_activation))
 
+        # Apply tags filtering (BFS may traverse into memories that don't match tags criteria)
+        if tags:
+            results = filter_results_by_tags(results, tags, match=tags_match)
+
         return results

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

@@ -0,0 +1,391 @@
+"""
+Link Expansion graph retrieval.
+
+A simple, fast graph retrieval that expands from seeds via:
+1. Entity links: Find facts sharing entities with seeds (filtered by entity frequency)
+2. Causal links: Find facts causally linked to seeds (top-k by weight)
+
+Characteristics:
+- 2-3 DB queries (seed finding + parallel entity/causal expansion)
+- Sublinear: only touches connected facts via indexes
+- No iteration, no propagation, no normalization
+- Target: <100ms
+"""
+
+import logging
+import time
+
+from ..db_utils import acquire_with_retry
+from ..memory_engine import fq_table
+from .graph_retrieval import GraphRetriever
+from .tags import TagsMatch, filter_results_by_tags
+from .types import MPFPTimings, RetrievalResult
+
+logger = logging.getLogger(__name__)
+
+
+async def _find_semantic_seeds(
+    conn,
+    query_embedding_str: str,
+    bank_id: str,
+    fact_type: str,
+    limit: int = 20,
+    threshold: float = 0.3,
+    tags: list[str] | None = None,
+    tags_match: TagsMatch = "any",
+) -> list[RetrievalResult]:
+    """Find semantic seeds via embedding search."""
+    from .tags import build_tags_where_clause_simple
+
+    tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
+    params = [query_embedding_str, bank_id, fact_type, threshold, limit]
+    if tags:
+        params.append(tags)
+
+    rows = await conn.fetch(
+        f"""
+        SELECT id, text, context, event_date, occurred_start, occurred_end,
+               mentioned_at, embedding, fact_type, document_id, chunk_id, tags,
+               1 - (embedding <=> $1::vector) AS similarity
+        FROM {fq_table("memory_units")}
+        WHERE bank_id = $2
+          AND embedding IS NOT NULL
+          AND fact_type = $3
+          AND (1 - (embedding <=> $1::vector)) >= $4
+          {tags_clause}
+        ORDER BY embedding <=> $1::vector
+        LIMIT $5
+        """,
+        *params,
+    )
+    return [RetrievalResult.from_db_row(dict(r)) for r in rows]
+
+
+class LinkExpansionRetriever(GraphRetriever):
+    """
+    Graph retrieval via direct link expansion from seeds.
+
+    Expands through entity co-occurrence and causal links in a single query.
+    Fast and simple alternative to MPFP.
+    """
+
+    def __init__(
+        self,
+        max_entity_frequency: int = 500,
+        causal_weight_threshold: float = 0.3,
+        causal_limit_per_seed: int = 10,
+    ):
+        """
+        Initialize link expansion retriever.
+
+        Args:
+            max_entity_frequency: Skip entities appearing in more than this many facts
+            causal_weight_threshold: Minimum weight for causal links
+            causal_limit_per_seed: Max causal links to follow per seed
+        """
+        self.max_entity_frequency = max_entity_frequency
+        self.causal_weight_threshold = causal_weight_threshold
+        self.causal_limit_per_seed = causal_limit_per_seed
+
+    @property
+    def name(self) -> str:
+        return "link_expansion"
+
+    async def retrieve(
+        self,
+        pool,
+        query_embedding_str: str,
+        bank_id: str,
+        fact_type: str,
+        budget: int,
+        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
+        adjacency=None,
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
+        """
+        Retrieve facts by expanding links from seeds.
+
+        Args:
+            pool: Database connection pool
+            query_embedding_str: Query embedding (unused, kept for interface)
+            bank_id: Memory bank ID
+            fact_type: Fact type to filter
+            budget: Maximum results to return
+            query_text: Original query text (unused)
+            semantic_seeds: Pre-computed semantic entry points
+            temporal_seeds: Pre-computed temporal entry points
+            adjacency: Unused, kept for interface compatibility
+            tags: Optional list of tags for visibility filtering (OR matching)
+
+        Returns:
+            Tuple of (results, timings)
+        """
+        start_time = time.time()
+        timings = MPFPTimings(fact_type=fact_type)
+
+        # Use single connection for all queries to reduce pool pressure
+        # (queries are fast ~50ms each, connection acquisition is the bottleneck)
+        async with acquire_with_retry(pool) as conn:
+            # Find seeds if not provided
+            if semantic_seeds:
+                all_seeds = list(semantic_seeds)
+            else:
+                seeds_start = time.time()
+                all_seeds = await _find_semantic_seeds(
+                    conn,
+                    query_embedding_str,
+                    bank_id,
+                    fact_type,
+                    limit=20,
+                    threshold=0.3,
+                    tags=tags,
+                    tags_match=tags_match,
+                )
+                timings.seeds_time = time.time() - seeds_start
+                logger.debug(
+                    f"[LinkExpansion] Found {len(all_seeds)} semantic seeds for fact_type={fact_type} "
+                    f"(tags={tags}, tags_match={tags_match})"
+                )
+
+            # Add temporal seeds if provided
+            if temporal_seeds:
+                all_seeds.extend(temporal_seeds)
+
+            if not all_seeds:
+                return [], timings
+
+            seed_ids = list({s.id for s in all_seeds})
+            timings.pattern_count = len(seed_ids)
+
+            # Run entity and causal expansion sequentially on same connection
+            query_start = time.time()
+
+            # For observations, traverse through source_memory_ids to find entity connections.
+            # Observations don't have direct unit_entities - they inherit entities via their
+            # source world/experience facts.
+            #
+            # Path: observation → source_memory_ids → world fact → entities →
+            #       ALL world facts with those entities → their observations (excluding seeds)
+            if fact_type == "observation":
+                # Debug: Check what source_memory_ids exist on seed observations
+                debug_sources = await conn.fetch(
+                    f"""
+                    SELECT id, source_memory_ids
+                    FROM {fq_table("memory_units")}
+                    WHERE id = ANY($1::uuid[])
+                    """,
+                    seed_ids,
+                )
+                source_ids_found = []
+                for row in debug_sources:
+                    if row["source_memory_ids"]:
+                        source_ids_found.extend(row["source_memory_ids"])
+                logger.debug(
+                    f"[LinkExpansion] observation graph: {len(seed_ids)} seeds, "
+                    f"{len(source_ids_found)} source_memory_ids found"
+                )
+
+                entity_rows = await conn.fetch(
+                    f"""
+                    WITH seed_sources AS (
+                        -- Get source memory IDs from seed observations
+                        SELECT DISTINCT unnest(source_memory_ids) AS source_id
+                        FROM {fq_table("memory_units")}
+                        WHERE id = ANY($1::uuid[])
+                          AND source_memory_ids IS NOT NULL
+                    ),
+                    source_entities AS (
+                        -- Get entities from those source memories (filtered by frequency)
+                        SELECT DISTINCT ue.entity_id
+                        FROM seed_sources ss
+                        JOIN {fq_table("unit_entities")} ue ON ss.source_id = ue.unit_id
+                        JOIN {fq_table("entities")} e ON ue.entity_id = e.id
+                        WHERE e.mention_count < $2
+                    ),
+                    all_connected_sources AS (
+                        -- Find ALL world facts sharing those entities (don't exclude seed sources)
+                        -- The exclusion happens at the observation level, not the source level
+                        SELECT DISTINCT other_ue.unit_id AS source_id
+                        FROM source_entities se
+                        JOIN {fq_table("unit_entities")} other_ue ON se.entity_id = other_ue.entity_id
+                    )
+                    -- Find observations derived from connected source memories
+                    -- Only exclude the actual seed observations
+                    SELECT
+                        mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                        mu.occurred_end, mu.mentioned_at, mu.embedding,
+                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                        COUNT(DISTINCT cs.source_id)::float AS score
+                    FROM all_connected_sources cs
+                    JOIN {fq_table("memory_units")} mu
+                        ON mu.source_memory_ids @> ARRAY[cs.source_id]
+                    WHERE mu.fact_type = 'observation'
+                      AND mu.id != ALL($1::uuid[])
+                    GROUP BY mu.id
+                    ORDER BY score DESC
+                    LIMIT $3
+                    """,
+                    seed_ids,
+                    self.max_entity_frequency,
+                    budget,
+                )
+                logger.debug(f"[LinkExpansion] observation graph: found {len(entity_rows)} connected observations")
+            else:
+                # For world/experience facts, use direct entity lookup
+                entity_rows = await conn.fetch(
+                    f"""
+                    SELECT
+                        mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                        mu.occurred_end, mu.mentioned_at, mu.embedding,
+                        mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                        COUNT(*)::float AS score
+                    FROM {fq_table("unit_entities")} seed_ue
+                    JOIN {fq_table("entities")} e ON seed_ue.entity_id = e.id
+                    JOIN {fq_table("unit_entities")} other_ue ON seed_ue.entity_id = other_ue.entity_id
+                    JOIN {fq_table("memory_units")} mu ON other_ue.unit_id = mu.id
+                    WHERE seed_ue.unit_id = ANY($1::uuid[])
+                      AND e.mention_count < $2
+                      AND mu.id != ALL($1::uuid[])
+                      AND mu.fact_type = $3
+                    GROUP BY mu.id
+                    ORDER BY score DESC
+                    LIMIT $4
+                    """,
+                    seed_ids,
+                    self.max_entity_frequency,
+                    fact_type,
+                    budget,
+                )
+
+            causal_rows = await conn.fetch(
+                f"""
+                SELECT DISTINCT ON (mu.id)
+                    mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                    mu.occurred_end, mu.mentioned_at, mu.embedding,
+                    mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                    ml.weight + 1.0 AS score
+                FROM {fq_table("memory_links")} ml
+                JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
+                WHERE ml.from_unit_id = ANY($1::uuid[])
+                  AND ml.link_type IN ('causes', 'caused_by', 'enables', 'prevents')
+                  AND ml.weight >= $2
+                  AND mu.fact_type = $3
+                ORDER BY mu.id, ml.weight DESC
+                LIMIT $4
+                """,
+                seed_ids,
+                self.causal_weight_threshold,
+                fact_type,
+                budget,
+            )
+
+            # Fallback: semantic/temporal/entity links from memory_links table
+            # These are secondary to entity links (via unit_entities) and causal links
+            # Weight is halved (0.5x) to prioritize primary link types
+            # Check both directions: seeds -> others AND others -> seeds
+            fallback_rows = await conn.fetch(
+                f"""
+                WITH outgoing AS (
+                    -- Links FROM seeds TO other facts
+                    SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                           mu.occurred_end, mu.mentioned_at, mu.embedding,
+                           mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                           ml.weight
+                    FROM {fq_table("memory_links")} ml
+                    JOIN {fq_table("memory_units")} mu ON ml.to_unit_id = mu.id
+                    WHERE ml.from_unit_id = ANY($1::uuid[])
+                      AND ml.link_type IN ('semantic', 'temporal', 'entity')
+                      AND ml.weight >= $2
+                      AND mu.fact_type = $3
+                      AND mu.id != ALL($1::uuid[])
+                ),
+                incoming AS (
+                    -- Links FROM other facts TO seeds (reverse direction)
+                    SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start,
+                           mu.occurred_end, mu.mentioned_at, mu.embedding,
+                           mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                           ml.weight
+                    FROM {fq_table("memory_links")} ml
+                    JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
+                    WHERE ml.to_unit_id = ANY($1::uuid[])
+                      AND ml.link_type IN ('semantic', 'temporal', 'entity')
+                      AND ml.weight >= $2
+                      AND mu.fact_type = $3
+                      AND mu.id != ALL($1::uuid[])
+                ),
+                combined AS (
+                    SELECT * FROM outgoing
+                    UNION ALL
+                    SELECT * FROM incoming
+                )
+                SELECT DISTINCT ON (id)
+                    id, text, context, event_date, occurred_start,
+                    occurred_end, mentioned_at, embedding,
+                    fact_type, document_id, chunk_id, tags,
+                    (MAX(weight) * 0.5) AS score
+                FROM combined
+                GROUP BY id, text, context, event_date, occurred_start,
+                         occurred_end, mentioned_at, embedding,
+                         fact_type, document_id, chunk_id, tags
+                ORDER BY id, score DESC
+                LIMIT $4
+                """,
+                seed_ids,
+                self.causal_weight_threshold,
+                fact_type,
+                budget,
+            )
+
+            timings.edge_load_time = time.time() - query_start
+            timings.db_queries = 3
+            timings.edge_count = len(entity_rows) + len(causal_rows) + len(fallback_rows)
+
+        # Merge results, taking max score per fact
+        # Priority: entity links (unit_entities) > causal links > fallback links
+        score_map: dict[str, float] = {}
+        row_map: dict[str, dict] = {}
+
+        for row in entity_rows:
+            fact_id = str(row["id"])
+            score_map[fact_id] = max(score_map.get(fact_id, 0), row["score"])
+            row_map[fact_id] = dict(row)
+
+        for row in causal_rows:
+            fact_id = str(row["id"])
+            score_map[fact_id] = max(score_map.get(fact_id, 0), row["score"])
+            if fact_id not in row_map:
+                row_map[fact_id] = dict(row)
+
+        for row in fallback_rows:
+            fact_id = str(row["id"])
+            score_map[fact_id] = max(score_map.get(fact_id, 0), row["score"])
+            if fact_id not in row_map:
+                row_map[fact_id] = dict(row)
+
+        # Sort by score and limit
+        sorted_ids = sorted(score_map.keys(), key=lambda x: score_map[x], reverse=True)[:budget]
+        rows = [row_map[fact_id] for fact_id in sorted_ids]
+
+        # Convert to results
+        results = []
+        for row in rows:
+            result = RetrievalResult.from_db_row(dict(row))
+            result.activation = row["score"]
+            results.append(result)
+
+        # Apply tags filtering (graph expansion may reach untagged memories)
+        if tags:
+            results = filter_results_by_tags(results, tags, match=tags_match)
+
+        timings.result_count = len(results)
+        timings.traverse = time.time() - start_time
+
+        logger.debug(
+            f"LinkExpansion: {len(results)} results from {len(seed_ids)} seeds "
+            f"in {timings.traverse * 1000:.1f}ms (query: {timings.edge_load_time * 1000:.1f}ms)"
+        )
+
+        return results, timings

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

@@ -9,6 +9,7 @@ propagation from Approximate PPR.
 
 Key properties:
 - Sublinear in graph size (threshold pruning bounds active nodes)
+- Lazy edge loading: only loads edges for frontier nodes, not entire graph
 - Predefined patterns capture different retrieval intents
 - All patterns run in parallel, results fused via RRF
 - No LLM in the loop during traversal
@@ -22,7 +23,8 @@ from dataclasses import dataclass, field
 from ..db_utils import acquire_with_retry
 from ..memory_engine import fq_table
 from .graph_retrieval import GraphRetriever
-from .types import RetrievalResult
+from .tags import TagsMatch
+from .types import MPFPTimings, RetrievalResult
 
 logger = logging.getLogger(__name__)
 
@@ -41,11 +43,27 @@ class EdgeTarget:
 
 
 @dataclass
-class TypedAdjacency:
-    """Adjacency lists split by edge type."""
+class EdgeCache:
+    """
+    Cache for lazily-loaded edges.
 
-    # edge_type -> from_node_id -> list of (to_node_id, weight)
+    Grows per-hop as edges are loaded for frontier nodes.
+    Shared across patterns to avoid redundant loads.
+    Loads ALL edge types at once to minimize DB queries.
+    Thread-safe via asyncio lock to prevent redundant concurrent loads.
+    """
+
+    # edge_type -> from_node_id -> list of EdgeTarget
     graphs: dict[str, dict[str, list[EdgeTarget]]] = field(default_factory=dict)
+    # Track which nodes have been fully loaded (all edge types)
+    _fully_loaded: set[str] = field(default_factory=set)
+    # Timing stats
+    db_queries: int = 0
+    edge_load_time: float = 0.0
+    # Detailed hop timing for debugging
+    hop_details: list[dict] = field(default_factory=list)
+    # Lock to prevent redundant concurrent loads
+    _lock: asyncio.Lock = field(default_factory=asyncio.Lock)
 
     def get_neighbors(self, edge_type: str, node_id: str) -> list[EdgeTarget]:
         """Get neighbors for a node via a specific edge type."""
@@ -63,6 +81,31 @@ class TypedAdjacency:
 
         return [EdgeTarget(node_id=n.node_id, weight=n.weight / total) for n in neighbors]
 
+    def is_fully_loaded(self, node_id: str) -> bool:
+        """Check if all edges for this node have been loaded."""
+        return node_id in self._fully_loaded
+
+    def get_uncached(self, node_ids: list[str]) -> list[str]:
+        """Get node IDs that haven't been fully loaded yet."""
+        return [n for n in node_ids if not self.is_fully_loaded(n)]
+
+    def add_all_edges(self, edges_by_type: dict[str, dict[str, list[EdgeTarget]]], all_queried: list[str]):
+        """
+        Add loaded edges to the cache (all edge types at once).
+
+        Args:
+            edges_by_type: Dict mapping edge_type -> from_node_id -> list of EdgeTarget
+            all_queried: All node IDs that were queried (marks them as fully loaded)
+        """
+        for edge_type, edges in edges_by_type.items():
+            if edge_type not in self.graphs:
+                self.graphs[edge_type] = {}
+            for node_id, neighbors in edges.items():
+                self.graphs[edge_type][node_id] = neighbors
+
+        # Mark all queried nodes as fully loaded (even if they have no edges)
+        self._fully_loaded.update(all_queried)
+
 
 @dataclass
 class PatternResult:
@@ -109,66 +152,249 @@ class SeedNode:
 
 
 # -----------------------------------------------------------------------------
-# Core Algorithm
+# Lazy Edge Loading
 # -----------------------------------------------------------------------------
 
 
-def mpfp_traverse(
-    seeds: list[SeedNode],
-    pattern: list[str],
-    adjacency: TypedAdjacency,
-    config: MPFPConfig,
-) -> PatternResult:
+async def load_all_edges_for_frontier(
+    pool,
+    node_ids: list[str],
+    top_k_per_type: int = 20,
+) -> dict[str, dict[str, list[EdgeTarget]]]:
     """
-    Forward Push traversal following a meta-path pattern.
+    Load top-k edges per (node, edge_type) for frontier nodes.
+
+    Uses a LATERAL join to efficiently fetch only the top-k edges per type,
+    avoiding loading hundreds of entity edges when only 20 are needed.
+
+    Requires composite index: (from_unit_id, link_type, weight DESC)
 
     Args:
-        seeds: Entry point nodes with initial scores
-        pattern: Sequence of edge types to follow
-        adjacency: Typed adjacency structure
-        config: Algorithm parameters
+        pool: Database connection pool
+        node_ids: Frontier node IDs to load edges for
+        top_k_per_type: Max edges to load per (node, link_type) pair
 
     Returns:
-        PatternResult with accumulated scores per node
+        Dict mapping edge_type -> from_node_id -> list of EdgeTarget
+    """
+    if not node_ids:
+        return {}
+
+    async with acquire_with_retry(pool) as conn:
+        # Use LATERAL join to get top-k per (from_node, link_type)
+        # This leverages the composite index for efficient early termination
+        rows = await conn.fetch(
+            f"""
+            WITH frontier(node_id) AS (SELECT unnest($1::uuid[]))
+            SELECT f.node_id as from_unit_id, lt.link_type, edges.to_unit_id, edges.weight
+            FROM frontier f
+            CROSS JOIN (VALUES ('semantic'), ('temporal'), ('entity'), ('causes'), ('caused_by')) AS lt(link_type)
+            CROSS JOIN LATERAL (
+                SELECT ml.to_unit_id, ml.weight
+                FROM {fq_table("memory_links")} ml
+                WHERE ml.from_unit_id = f.node_id
+                  AND ml.link_type = lt.link_type
+                  AND ml.weight >= 0.1
+                ORDER BY ml.weight DESC
+                LIMIT $2
+            ) edges
+            """,
+            node_ids,
+            top_k_per_type,
+        )
+
+    # Group by edge_type -> from_node -> neighbors
+    result: dict[str, dict[str, list[EdgeTarget]]] = defaultdict(lambda: defaultdict(list))
+    for row in rows:
+        edge_type = row["link_type"]
+        from_id = str(row["from_unit_id"])
+        to_id = str(row["to_unit_id"])
+        weight = row["weight"]
+        result[edge_type][from_id].append(EdgeTarget(node_id=to_id, weight=weight))
+
+    # Convert nested defaultdicts to regular dicts
+    return {edge_type: dict(edges) for edge_type, edges in result.items()}
+
+
+# -----------------------------------------------------------------------------
+# Core Algorithm (Async with Lazy Loading)
+# -----------------------------------------------------------------------------
+
+
+@dataclass
+class PatternState:
+    """State for a pattern traversal between hops."""
+
+    pattern: list[str]
+    hop_index: int
+    scores: dict[str, float]
+    frontier: dict[str, float]
+
+
+def _init_pattern_state(seeds: list[SeedNode], pattern: list[str]) -> PatternState:
+    """Initialize pattern state from seeds."""
+    if not seeds:
+        return PatternState(pattern=pattern, hop_index=0, scores={}, frontier={})
+
+    total_seed_score = sum(s.score for s in seeds)
+    if total_seed_score == 0:
+        total_seed_score = len(seeds)
+
+    frontier = {s.node_id: s.score / total_seed_score for s in seeds}
+    return PatternState(pattern=pattern, hop_index=0, scores={}, frontier=frontier)
+
+
+def _execute_hop(state: PatternState, cache: EdgeCache, config: MPFPConfig) -> set[str]:
+    """
+    Execute ONE hop of traversal, return frontier nodes for next hop.
+
+    This is a pure function that uses cached edges (no DB access).
+    Returns set of uncached nodes needed for next hop.
+    """
+    if state.hop_index >= len(state.pattern):
+        return set()
+
+    edge_type = state.pattern[state.hop_index]
+
+    # Collect active nodes above threshold
+    active_nodes = [node_id for node_id, mass in state.frontier.items() if mass >= config.threshold]
+    if not active_nodes:
+        state.frontier = {}
+        return set()
+
+    # Propagate mass using cached edges
+    next_frontier: dict[str, float] = {}
+    uncached_for_next: set[str] = set()
+
+    for node_id, mass in state.frontier.items():
+        if mass < config.threshold:
+            continue
+
+        # Keep α portion for this node
+        state.scores[node_id] = state.scores.get(node_id, 0) + config.alpha * mass
+
+        # Push (1-α) to neighbors
+        push_mass = (1 - config.alpha) * mass
+        neighbors = cache.get_normalized_neighbors(edge_type, node_id, config.top_k_neighbors)
+
+        for neighbor in neighbors:
+            next_frontier[neighbor.node_id] = next_frontier.get(neighbor.node_id, 0) + push_mass * neighbor.weight
+            # Track if we'll need edges for this node in the next hop
+            if not cache.is_fully_loaded(neighbor.node_id):
+                uncached_for_next.add(neighbor.node_id)
+
+    state.frontier = next_frontier
+    state.hop_index += 1
+
+    return uncached_for_next
+
+
+def _finalize_pattern(state: PatternState, config: MPFPConfig) -> PatternResult:
+    """Finalize pattern by adding remaining frontier mass to scores."""
+    for node_id, mass in state.frontier.items():
+        if mass >= config.threshold:
+            state.scores[node_id] = state.scores.get(node_id, 0) + mass
+
+    return PatternResult(pattern=state.pattern, scores=state.scores)
+
+
+async def mpfp_traverse_hop_synchronized(
+    pool,
+    pattern_jobs: list[tuple[list[SeedNode], list[str]]],
+    config: MPFPConfig,
+    cache: EdgeCache,
+) -> list[PatternResult]:
+    """
+    Execute ALL patterns with hop-synchronized edge loading.
+
+    Instead of running each pattern independently (causing multiple DB queries),
+    this function:
+    1. Runs hop 1 for ALL patterns (using pre-warmed seed edges)
+    2. Collects ALL unique hop-2 frontier nodes across patterns
+    3. Pre-warms hop-2 edges in ONE query
+    4. Runs hop 2 for ALL patterns
+
+    This reduces DB queries from O(patterns * hops) to O(hops).
+
+    Args:
+        pool: Database connection pool
+        pattern_jobs: List of (seeds, pattern) tuples
+        config: Algorithm parameters
+        cache: Shared edge cache (should be pre-warmed with seed edges)
+
+    Returns:
+        List of PatternResult for each pattern
+    """
+    import time
+
+    # Initialize all pattern states
+    states = [_init_pattern_state(seeds, pattern) for seeds, pattern in pattern_jobs]
+
+    # Determine max hops (all patterns should be same length, but be safe)
+    max_hops = max((len(p) for _, p in pattern_jobs), default=0)
+
+    # Detailed timing for debugging
+    hop_times: list[dict] = []
+
+    # Execute hop-by-hop across ALL patterns
+    for hop in range(max_hops):
+        hop_start = time.time()
+        hop_timing = {"hop": hop, "patterns_executed": 0, "uncached_count": 0, "load_time": 0.0}
+
+        # Execute this hop for all patterns, collect uncached nodes for next hop
+        all_uncached: set[str] = set()
+        exec_start = time.time()
+        for state in states:
+            if state.hop_index < len(state.pattern):
+                uncached = _execute_hop(state, cache, config)
+                all_uncached.update(uncached)
+                hop_timing["patterns_executed"] += 1
+        hop_timing["exec_time"] = time.time() - exec_start
+
+        # Pre-warm edges for ALL uncached nodes before next hop
+        hop_timing["uncached_count"] = len(all_uncached)
+        if all_uncached:
+            uncached_list = list(all_uncached - cache._fully_loaded)
+            hop_timing["uncached_after_filter"] = len(uncached_list)
+            if uncached_list:
+                load_start = time.time()
+                edges_by_type = await load_all_edges_for_frontier(pool, uncached_list, config.top_k_neighbors)
+                hop_timing["load_time"] = time.time() - load_start
+                cache.edge_load_time += hop_timing["load_time"]
+                cache.db_queries += 1
+                cache.add_all_edges(edges_by_type, uncached_list)
+                hop_timing["edges_loaded"] = sum(
+                    len(neighbors) for edges in edges_by_type.values() for neighbors in edges.values()
+                )
+
+        hop_timing["total_time"] = time.time() - hop_start
+        hop_times.append(hop_timing)
+
+    # Store hop timing details in cache for logging
+    cache.hop_details = hop_times
+
+    # Finalize all patterns
+    return [_finalize_pattern(state, config) for state in states]
+
+
+async def mpfp_traverse_async(
+    pool,
+    seeds: list[SeedNode],
+    pattern: list[str],
+    config: MPFPConfig,
+    cache: EdgeCache,
+) -> PatternResult:
+    """
+    Async Forward Push traversal with lazy edge loading.
+
+    NOTE: For better performance with multiple patterns, use mpfp_traverse_hop_synchronized().
+    This function is kept for single-pattern use cases.
     """
     if not seeds:
         return PatternResult(pattern=pattern, scores={})
 
-    scores: dict[str, float] = {}
-
-    # Initialize frontier with seed masses (normalized)
-    total_seed_score = sum(s.score for s in seeds)
-    if total_seed_score == 0:
-        total_seed_score = len(seeds)  # fallback to uniform
-
-    frontier: dict[str, float] = {s.node_id: s.score / total_seed_score for s in seeds}
-
-    # Follow pattern hop by hop
-    for edge_type in pattern:
-        next_frontier: dict[str, float] = {}
-
-        for node_id, mass in frontier.items():
-            if mass < config.threshold:
-                continue
-
-            # Keep α portion for this node
-            scores[node_id] = scores.get(node_id, 0) + config.alpha * mass
-
-            # Push (1-α) to neighbors
-            push_mass = (1 - config.alpha) * mass
-            neighbors = adjacency.get_normalized_neighbors(edge_type, node_id, config.top_k_neighbors)
-
-            for neighbor in neighbors:
-                next_frontier[neighbor.node_id] = next_frontier.get(neighbor.node_id, 0) + push_mass * neighbor.weight
-
-        frontier = next_frontier
-
-    # Final frontier nodes get their remaining mass
-    for node_id, mass in frontier.items():
-        if mass >= config.threshold:
-            scores[node_id] = scores.get(node_id, 0) + mass
-
-    return PatternResult(pattern=pattern, scores=scores)
+    results = await mpfp_traverse_hop_synchronized(pool, [(seeds, pattern)], config, cache)
+    return results[0] if results else PatternResult(pattern=pattern, scores={})
 
 
 def rrf_fusion(
@@ -210,38 +436,6 @@ def rrf_fusion(
 # -----------------------------------------------------------------------------
 
 
-async def load_typed_adjacency(pool, bank_id: str) -> TypedAdjacency:
-    """
-    Load all edges for a bank, split by edge type.
-
-    Single query, then organize in-memory for fast traversal.
-    """
-    async with acquire_with_retry(pool) as conn:
-        rows = await conn.fetch(
-            f"""
-            SELECT ml.from_unit_id, ml.to_unit_id, ml.link_type, ml.weight
-            FROM {fq_table("memory_links")} ml
-            JOIN {fq_table("memory_units")} mu ON ml.from_unit_id = mu.id
-            WHERE mu.bank_id = $1
-              AND ml.weight >= 0.1
-            ORDER BY ml.from_unit_id, ml.weight DESC
-            """,
-            bank_id,
-        )
-
-    graphs: dict[str, dict[str, list[EdgeTarget]]] = defaultdict(lambda: defaultdict(list))
-
-    for row in rows:
-        from_id = str(row["from_unit_id"])
-        to_id = str(row["to_unit_id"])
-        link_type = row["link_type"]
-        weight = row["weight"]
-
-        graphs[link_type][from_id].append(EdgeTarget(node_id=to_id, weight=weight))
-
-    return TypedAdjacency(graphs=dict(graphs))
-
-
 async def fetch_memory_units_by_ids(
     pool,
     node_ids: list[str],
@@ -255,7 +449,7 @@ async def fetch_memory_units_by_ids(
         rows = await conn.fetch(
             f"""
             SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, access_count, embedding, fact_type, document_id, chunk_id
+                   mentioned_at, embedding, fact_type, document_id, chunk_id, tags
             FROM {fq_table("memory_units")}
             WHERE id = ANY($1::uuid[])
               AND fact_type = $2
@@ -274,10 +468,10 @@ async def fetch_memory_units_by_ids(
 
 class MPFPGraphRetriever(GraphRetriever):
     """
-    Graph retrieval using Meta-Path Forward Push.
+    Graph retrieval using Meta-Path Forward Push with lazy edge loading.
 
     Runs predefined patterns in parallel from semantic and temporal seeds,
-    then fuses results via RRF.
+    loading edges on-demand per hop instead of loading entire graph upfront.
     """
 
     def __init__(self, config: MPFPConfig | None = None):
@@ -287,7 +481,13 @@ class MPFPGraphRetriever(GraphRetriever):
         Args:
             config: Algorithm configuration (uses defaults if None)
         """
-        self.config = config or MPFPConfig()
+        if config is None:
+            # Read top_k_neighbors from global config
+            from ...config import get_config
+
+            global_config = get_config()
+            config = MPFPConfig(top_k_neighbors=global_config.mpfp_top_k_neighbors)
+        self.config = config
 
     @property
     def name(self) -> str:
@@ -303,9 +503,12 @@ class MPFPGraphRetriever(GraphRetriever):
         query_text: str | None = None,
         semantic_seeds: list[RetrievalResult] | None = None,
         temporal_seeds: list[RetrievalResult] | None = None,
-    ) -> list[RetrievalResult]:
+        adjacency=None,  # Ignored - kept for interface compatibility
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
+    ) -> tuple[list[RetrievalResult], MPFPTimings | None]:
         """
-        Retrieve facts using MPFP algorithm.
+        Retrieve facts using MPFP algorithm with lazy edge loading.
 
         Args:
             pool: Database connection pool
@@ -316,12 +519,15 @@ class MPFPGraphRetriever(GraphRetriever):
             query_text: Original query text (optional)
             semantic_seeds: Pre-computed semantic entry points
             temporal_seeds: Pre-computed temporal entry points
+            adjacency: Ignored (kept for interface compatibility)
+            tags: Optional list of tags for visibility filtering (OR matching)
 
         Returns:
-            List of RetrievalResult with activation scores
+            Tuple of (List of RetrievalResult with activation scores, MPFPTimings)
         """
-        # Load typed adjacency (could cache per bank_id with TTL)
-        adjacency = await load_typed_adjacency(pool, bank_id)
+        import time
+
+        timings = MPFPTimings(fact_type=fact_type)
 
         # Convert seeds to SeedNode format
         semantic_seed_nodes = self._convert_seeds(semantic_seeds, "similarity")
@@ -329,54 +535,88 @@ class MPFPGraphRetriever(GraphRetriever):
 
         # If no semantic seeds provided, fall back to finding our own
         if not semantic_seed_nodes:
-            semantic_seed_nodes = await self._find_semantic_seeds(pool, query_embedding_str, bank_id, fact_type)
+            seeds_start = time.time()
+            semantic_seed_nodes = await self._find_semantic_seeds(
+                pool, query_embedding_str, bank_id, fact_type, tags=tags, tags_match=tags_match
+            )
+            timings.seeds_time = time.time() - seeds_start
+            logger.debug(
+                f"[MPFP] Found {len(semantic_seed_nodes)} semantic seeds for fact_type={fact_type} (tags={tags}, tags_match={tags_match})"
+            )
 
-        # Run all patterns in parallel
-        tasks = []
+        # Collect all pattern jobs
+        pattern_jobs = []
 
         # Patterns from semantic seeds
         for pattern in self.config.patterns_semantic:
             if semantic_seed_nodes:
-                tasks.append(
-                    asyncio.to_thread(
-                        mpfp_traverse,
-                        semantic_seed_nodes,
-                        pattern,
-                        adjacency,
-                        self.config,
-                    )
-                )
+                pattern_jobs.append((semantic_seed_nodes, pattern))
 
         # Patterns from temporal seeds
         for pattern in self.config.patterns_temporal:
             if temporal_seed_nodes:
-                tasks.append(
-                    asyncio.to_thread(
-                        mpfp_traverse,
-                        temporal_seed_nodes,
-                        pattern,
-                        adjacency,
-                        self.config,
-                    )
-                )
+                pattern_jobs.append((temporal_seed_nodes, pattern))
 
-        if not tasks:
-            return []
+        if not pattern_jobs:
+            logger.debug(
+                f"[MPFP] No pattern jobs (semantic_seeds={len(semantic_seed_nodes)}, temporal_seeds={len(temporal_seed_nodes)})"
+            )
+            return [], timings
 
-        # Gather pattern results
-        pattern_results = await asyncio.gather(*tasks)
+        timings.pattern_count = len(pattern_jobs)
+
+        # Shared edge cache across all patterns
+        cache = EdgeCache()
+
+        # Pre-warm cache with ALL seed node edges BEFORE running patterns
+        # This prevents redundant DB queries at hop 1
+        all_seed_ids = list({s.node_id for seeds, _ in pattern_jobs for s in seeds})
+        if all_seed_ids:
+            import time as time_module
+
+            prewarm_start = time_module.time()
+            edges_by_type = await load_all_edges_for_frontier(pool, all_seed_ids, self.config.top_k_neighbors)
+            cache.edge_load_time += time_module.time() - prewarm_start
+            cache.db_queries += 1
+            cache.add_all_edges(edges_by_type, all_seed_ids)
+
+        # Run all patterns with HOP-SYNCHRONIZED edge loading
+        # This batches hop-2 edge loads across ALL patterns into ONE query
+        # Reduces DB queries from O(patterns * hops) to O(hops)
+        step_start = time.time()
+        pattern_results = await mpfp_traverse_hop_synchronized(pool, pattern_jobs, self.config, cache)
+        timings.traverse = time.time() - step_start
+
+        # Record edge loading stats from cache
+        timings.edge_count = sum(len(neighbors) for g in cache.graphs.values() for neighbors in g.values())
+        timings.db_queries = cache.db_queries
+        timings.edge_load_time = cache.edge_load_time
+        timings.hop_details = cache.hop_details
 
         # Fuse results
+        step_start = time.time()
         fused = rrf_fusion(pattern_results, top_k=budget)
+        timings.fusion = time.time() - step_start
 
         if not fused:
-            return []
+            logger.debug(f"[MPFP] No fused results after RRF fusion (pattern_count={len(pattern_results)})")
+            return [], timings
 
-        # Get top result IDs (don't exclude seeds - they may be highly relevant)
+        # Get top result IDs
         result_ids = [node_id for node_id, score in fused][:budget]
 
         # Fetch full details
+        step_start = time.time()
         results = await fetch_memory_units_by_ids(pool, result_ids, fact_type)
+        timings.fetch = time.time() - step_start
+
+        # Filter results by tags (graph traversal may have picked up unfiltered memories)
+        if tags:
+            from .tags import filter_results_by_tags
+
+            results = filter_results_by_tags(results, tags, match=tags_match)
+
+        timings.result_count = len(results)
 
         # Add activation scores from fusion
         score_map = {node_id: score for node_id, score in fused}
@@ -386,7 +626,7 @@ class MPFPGraphRetriever(GraphRetriever):
         # Sort by activation
         results.sort(key=lambda r: r.activation or 0, reverse=True)
 
-        return results
+        return results, timings
 
     def _convert_seeds(
         self,
@@ -414,8 +654,17 @@ class MPFPGraphRetriever(GraphRetriever):
         fact_type: str,
         limit: int = 20,
         threshold: float = 0.3,
+        tags: list[str] | None = None,
+        tags_match: TagsMatch = "any",
     ) -> list[SeedNode]:
         """Fallback: find semantic seeds via embedding search."""
+        from .tags import build_tags_where_clause_simple
+
+        tags_clause = build_tags_where_clause_simple(tags, 6, match=tags_match)
+        params = [query_embedding_str, bank_id, fact_type, threshold, limit]
+        if tags:
+            params.append(tags)
+
         async with acquire_with_retry(pool) as conn:
             rows = await conn.fetch(
                 f"""
@@ -425,14 +674,11 @@ class MPFPGraphRetriever(GraphRetriever):
                   AND embedding IS NOT NULL
                   AND fact_type = $3
                   AND (1 - (embedding <=> $1::vector)) >= $4
+                  {tags_clause}
                 ORDER BY embedding <=> $1::vector
                 LIMIT $5
                 """,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                threshold,
-                limit,
+                *params,
             )
 
         return [SeedNode(node_id=str(r["id"]), score=r["similarity"]) for r in rows]

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

@@ -1,125 +0,0 @@
-"""
-Observation utilities for generating entity observations from facts.
-
-Observations are objective facts synthesized from multiple memory facts
-about an entity, without personality influence.
-"""
-
-import logging
-
-from pydantic import BaseModel, Field
-
-from ..response_models import MemoryFact
-
-logger = logging.getLogger(__name__)
-
-
-class Observation(BaseModel):
-    """An observation about an entity."""
-
-    observation: str = Field(description="The observation text - a factual statement about the entity")
-
-
-class ObservationExtractionResponse(BaseModel):
-    """Response containing extracted observations."""
-
-    observations: list[Observation] = Field(default_factory=list, description="List of observations about the entity")
-
-
-def format_facts_for_observation_prompt(facts: list[MemoryFact]) -> str:
-    """Format facts as text for observation extraction prompt."""
-    import json
-
-    if not facts:
-        return "[]"
-    formatted = []
-    for fact in facts:
-        fact_obj = {"text": fact.text}
-
-        # Add context if available
-        if fact.context:
-            fact_obj["context"] = fact.context
-
-        # Add occurred_start if available
-        if fact.occurred_start:
-            fact_obj["occurred_at"] = fact.occurred_start
-
-        formatted.append(fact_obj)
-
-    return json.dumps(formatted, indent=2)
-
-
-def build_observation_prompt(
-    entity_name: str,
-    facts_text: str,
-) -> str:
-    """Build the observation extraction prompt for the LLM."""
-    return f"""Based on the following facts about "{entity_name}", generate a list of key observations.
-
-FACTS ABOUT {entity_name.upper()}:
-{facts_text}
-
-Your task: Synthesize the facts into clear, objective observations about {entity_name}.
-
-GUIDELINES:
-1. Each observation should be a factual statement about {entity_name}
-2. Combine related facts into single observations where appropriate
-3. Be objective - do not add opinions, judgments, or interpretations
-4. Focus on what we KNOW about {entity_name}, not what we assume
-5. Include observations about: identity, characteristics, roles, relationships, activities
-6. Write in third person (e.g., "John is..." not "I think John is...")
-7. If there are conflicting facts, note the most recent or most supported one
-
-EXAMPLES of good observations:
-- "John works at Google as a software engineer"
-- "John is detail-oriented and methodical in his approach"
-- "John collaborates frequently with Sarah on the AI project"
-- "John joined the company in 2023"
-
-EXAMPLES of bad observations (avoid these):
-- "John seems like a good person" (opinion/judgment)
-- "John probably likes his job" (assumption)
-- "I believe John is reliable" (first-person opinion)
-
-Generate 3-7 observations based on the available facts. If there are very few facts, generate fewer observations."""
-
-
-def get_observation_system_message() -> str:
-    """Get the system message for observation extraction."""
-    return "You are an objective observer synthesizing facts about an entity. Generate clear, factual observations without opinions or personality influence. Be concise and accurate."
-
-
-async def extract_observations_from_facts(llm_config, entity_name: str, facts: list[MemoryFact]) -> list[str]:
-    """
-    Extract observations from facts about an entity using LLM.
-
-    Args:
-        llm_config: LLM configuration to use
-        entity_name: Name of the entity to generate observations about
-        facts: List of facts mentioning the entity
-
-    Returns:
-        List of observation strings
-    """
-    if not facts:
-        return []
-
-    facts_text = format_facts_for_observation_prompt(facts)
-    prompt = build_observation_prompt(entity_name, facts_text)
-
-    try:
-        result = await llm_config.call(
-            messages=[
-                {"role": "system", "content": get_observation_system_message()},
-                {"role": "user", "content": prompt},
-            ],
-            response_format=ObservationExtractionResponse,
-            scope="memory_extract_observation",
-        )
-
-        observations = [op.observation for op in result.observations]
-        return observations
-
-    except Exception as e:
-        logger.warning(f"Failed to extract observations for {entity_name}: {str(e)}")
-        return []

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

@@ -1,159 +0,0 @@
-"""
-Scoring functions for memory search and retrieval.
-
-Includes recency weighting, frequency weighting, temporal proximity,
-and similarity calculations used in memory activation and ranking.
-"""
-
-from datetime import datetime
-
-
-def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
-    """
-    Calculate cosine similarity between two vectors.
-
-    Args:
-        vec1: First vector
-        vec2: Second vector
-
-    Returns:
-        Similarity score between 0 and 1
-    """
-    if len(vec1) != len(vec2):
-        raise ValueError("Vectors must have same dimension")
-
-    dot_product = sum(a * b for a, b in zip(vec1, vec2))
-    magnitude1 = sum(a * a for a in vec1) ** 0.5
-    magnitude2 = sum(b * b for b in vec2) ** 0.5
-
-    if magnitude1 == 0 or magnitude2 == 0:
-        return 0.0
-
-    return dot_product / (magnitude1 * magnitude2)
-
-
-def calculate_recency_weight(days_since: float, half_life_days: float = 365.0) -> float:
-    """
-    Calculate recency weight using logarithmic decay.
-
-    This provides much better differentiation over long time periods compared to
-    exponential decay. Uses a log-based decay where the half-life parameter controls
-    when memories reach 50% weight.
-
-    Examples:
-        - Today (0 days): 1.0
-        - 1 year (365 days): ~0.5 (with default half_life=365)
-        - 2 years (730 days): ~0.33
-        - 5 years (1825 days): ~0.17
-        - 10 years (3650 days): ~0.09
-
-    This ensures that 2-year-old and 5-year-old memories have meaningfully
-    different weights, unlike exponential decay which makes them both ~0.
-
-    Args:
-        days_since: Number of days since the memory was created
-        half_life_days: Number of days for weight to reach 0.5 (default: 1 year)
-
-    Returns:
-        Weight between 0 and 1
-    """
-    import math
-
-    # Logarithmic decay: 1 / (1 + log(1 + days_since/half_life))
-    # This decays much slower than exponential, giving better long-term differentiation
-    normalized_age = days_since / half_life_days
-    return 1.0 / (1.0 + math.log1p(normalized_age))
-
-
-def calculate_frequency_weight(access_count: int, max_boost: float = 2.0) -> float:
-    """
-    Calculate frequency weight based on access count.
-
-    Frequently accessed memories are weighted higher.
-    Uses logarithmic scaling to avoid over-weighting.
-
-    Args:
-        access_count: Number of times the memory was accessed
-        max_boost: Maximum multiplier for frequently accessed memories
-
-    Returns:
-        Weight between 1.0 and max_boost
-    """
-    import math
-
-    if access_count <= 0:
-        return 1.0
-
-    # Logarithmic scaling: log(access_count + 1) / log(10)
-    # This gives: 0 accesses = 1.0, 9 accesses ~= 1.5, 99 accesses ~= 2.0
-    normalized = math.log(access_count + 1) / math.log(10)
-    return 1.0 + min(normalized, max_boost - 1.0)
-
-
-def calculate_temporal_anchor(occurred_start: datetime, occurred_end: datetime) -> datetime:
-    """
-    Calculate a single temporal anchor point from a temporal range.
-
-    Used for spreading activation - we need a single representative date
-    to calculate temporal proximity between facts. This simplifies the
-    range-to-range distance problem.
-
-    Strategy: Use midpoint of the range for balanced representation.
-
-    Args:
-        occurred_start: Start of temporal range
-        occurred_end: End of temporal range
-
-    Returns:
-        Single datetime representing the temporal anchor (midpoint)
-
-    Examples:
-        - Point event (July 14): start=July 14, end=July 14 → anchor=July 14
-        - Month range (February): start=Feb 1, end=Feb 28 → anchor=Feb 14
-        - Year range (2023): start=Jan 1, end=Dec 31 → anchor=July 1
-    """
-    # Calculate midpoint
-    time_delta = occurred_end - occurred_start
-    midpoint = occurred_start + (time_delta / 2)
-    return midpoint
-
-
-def calculate_temporal_proximity(anchor_a: datetime, anchor_b: datetime, half_life_days: float = 30.0) -> float:
-    """
-    Calculate temporal proximity between two temporal anchors.
-
-    Used for spreading activation to determine how "close" two facts are
-    in time. Uses logarithmic decay so that temporal similarity doesn't
-    drop off too quickly.
-
-    Args:
-        anchor_a: Temporal anchor of first fact
-        anchor_b: Temporal anchor of second fact
-        half_life_days: Number of days for proximity to reach 0.5
-                       (default: 30 days = 1 month)
-
-    Returns:
-        Proximity score in [0, 1] where:
-        - 1.0 = same day
-        - 0.5 = ~half_life days apart
-        - 0.0 = very distant in time
-
-    Examples:
-        - Same day: 1.0
-        - 1 week apart (half_life=30): ~0.7
-        - 1 month apart (half_life=30): ~0.5
-        - 1 year apart (half_life=30): ~0.2
-    """
-    import math
-
-    days_apart = abs((anchor_a - anchor_b).days)
-
-    if days_apart == 0:
-        return 1.0
-
-    # Logarithmic decay: 1 / (1 + log(1 + days_apart/half_life))
-    # Similar to calculate_recency_weight but for proximity between events
-    normalized_distance = days_apart / half_life_days
-    proximity = 1.0 / (1.0 + math.log1p(normalized_distance))
-
-    return proximity

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

@@ -0,0 +1,172 @@
+"""
+Tags filtering utilities for retrieval.
+
+Provides SQL building functions for filtering memories by tags.
+Supports four matching modes via TagsMatch enum:
+- "any": OR matching, includes untagged memories (default, backward compatible)
+- "all": AND matching, includes untagged memories
+- "any_strict": OR matching, excludes untagged memories
+- "all_strict": AND matching, excludes untagged memories
+
+OR matching (any/any_strict): Memory matches if ANY of its tags overlap with request tags
+AND matching (all/all_strict): Memory matches if ALL request tags are present in its tags
+"""
+
+from typing import Literal
+
+TagsMatch = Literal["any", "all", "any_strict", "all_strict"]
+
+
+def _parse_tags_match(match: TagsMatch) -> tuple[str, bool]:
+    """
+    Parse TagsMatch into operator and include_untagged flag.
+
+    Returns:
+        Tuple of (operator, include_untagged)
+        - operator: "&&" for any/any_strict, "@>" for all/all_strict
+        - include_untagged: True for any/all, False for any_strict/all_strict
+    """
+    if match == "any":
+        return "&&", True
+    elif match == "all":
+        return "@>", True
+    elif match == "any_strict":
+        return "&&", False
+    elif match == "all_strict":
+        return "@>", False
+    else:
+        # Default to "any" behavior
+        return "&&", True
+
+
+def build_tags_where_clause(
+    tags: list[str] | None,
+    param_offset: int = 1,
+    table_alias: str = "",
+    match: TagsMatch = "any",
+) -> tuple[str, list, int]:
+    """
+    Build a SQL WHERE clause for filtering by tags.
+
+    Supports four matching modes:
+    - "any" (default): OR matching, includes untagged memories
+    - "all": AND matching, includes untagged memories
+    - "any_strict": OR matching, excludes untagged memories
+    - "all_strict": AND matching, excludes untagged memories
+
+    Args:
+        tags: List of tags to filter by. If None or empty, returns empty clause (no filtering).
+        param_offset: Starting parameter number for SQL placeholders (default 1).
+        table_alias: Optional table alias prefix (e.g., "mu." for "memory_units mu").
+        match: Matching mode. Defaults to "any".
+
+    Returns:
+        Tuple of (sql_clause, params, next_param_offset):
+        - sql_clause: SQL WHERE clause string
+        - params: List of parameter values to bind
+        - next_param_offset: Next available parameter number
+
+    Example:
+        >>> clause, params, next_offset = build_tags_where_clause(['user_a'], 3, 'mu.', 'any_strict')
+        >>> print(clause)  # "AND mu.tags IS NOT NULL AND mu.tags != '{}' AND mu.tags && $3"
+    """
+    if not tags:
+        return "", [], param_offset
+
+    column = f"{table_alias}tags" if table_alias else "tags"
+    operator, include_untagged = _parse_tags_match(match)
+
+    if include_untagged:
+        # Include untagged memories (NULL or empty array) OR matching tags
+        clause = f"AND ({column} IS NULL OR {column} = '{{}}' OR {column} {operator} ${param_offset})"
+    else:
+        # Strict: only memories with matching tags (exclude NULL and empty)
+        clause = f"AND {column} IS NOT NULL AND {column} != '{{}}' AND {column} {operator} ${param_offset}"
+
+    return clause, [tags], param_offset + 1
+
+
+def build_tags_where_clause_simple(
+    tags: list[str] | None,
+    param_num: int,
+    table_alias: str = "",
+    match: TagsMatch = "any",
+) -> str:
+    """
+    Build a simple SQL WHERE clause for tags filtering.
+
+    This is a convenience version that returns just the clause string,
+    assuming the caller will add the tags array to their params list.
+
+    Args:
+        tags: List of tags to filter by. If None or empty, returns empty string.
+        param_num: Parameter number to use in the clause.
+        table_alias: Optional table alias prefix.
+        match: Matching mode. Defaults to "any".
+
+    Returns:
+        SQL clause string or empty string.
+    """
+    if not tags:
+        return ""
+
+    column = f"{table_alias}tags" if table_alias else "tags"
+    operator, include_untagged = _parse_tags_match(match)
+
+    if include_untagged:
+        # Include untagged memories (NULL or empty array) OR matching tags
+        return f"AND ({column} IS NULL OR {column} = '{{}}' OR {column} {operator} ${param_num})"
+    else:
+        # Strict: only memories with matching tags (exclude NULL and empty)
+        return f"AND {column} IS NOT NULL AND {column} != '{{}}' AND {column} {operator} ${param_num}"
+
+
+def filter_results_by_tags(
+    results: list,
+    tags: list[str] | None,
+    match: TagsMatch = "any",
+) -> list:
+    """
+    Filter retrieval results by tags in Python (for post-processing).
+
+    Used when SQL filtering isn't possible (e.g., graph traversal results).
+
+    Args:
+        results: List of RetrievalResult objects with a 'tags' attribute.
+        tags: List of tags to filter by. If None or empty, returns all results.
+        match: Matching mode. Defaults to "any".
+
+    Returns:
+        Filtered list of results.
+    """
+    if not tags:
+        return results
+
+    _, include_untagged = _parse_tags_match(match)
+    is_any_match = match in ("any", "any_strict")
+
+    tags_set = set(tags)
+    filtered = []
+
+    for result in results:
+        result_tags = getattr(result, "tags", None)
+
+        # Check if untagged
+        is_untagged = result_tags is None or len(result_tags) == 0
+
+        if is_untagged:
+            if include_untagged:
+                filtered.append(result)
+            # else: skip untagged
+        else:
+            result_tags_set = set(result_tags)
+            if is_any_match:
+                # Any overlap
+                if result_tags_set & tags_set:
+                    filtered.append(result)
+            else:
+                # All tags must be present
+                if tags_set <= result_tags_set:
+                    filtered.append(result)
+
+    return filtered

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

@@ -3,31 +3,13 @@ Think operation utilities for formulating answers based on agent and world facts
 """
 
 import logging
-import re
 from datetime import datetime
 
-from pydantic import BaseModel, Field
-
 from ..response_models import DispositionTraits, MemoryFact
 
 logger = logging.getLogger(__name__)
 
 
-class Opinion(BaseModel):
-    """An opinion formed by the bank."""
-
-    opinion: str = Field(description="The opinion or perspective with reasoning included")
-    confidence: float = Field(description="Confidence score for this opinion (0.0 to 1.0, where 1.0 is very confident)")
-
-
-class OpinionExtractionResponse(BaseModel):
-    """Response containing extracted opinions."""
-
-    opinions: list[Opinion] = Field(
-        default_factory=list, description="List of opinions formed with their supporting reasons and confidence scores"
-    )
-
-
 def describe_trait_level(value: int) -> str:
     """Convert trait value (1-5) to descriptive text."""
     levels = {1: "very low", 2: "low", 3: "moderate", 4: "high", 5: "very high"}
@@ -93,17 +75,46 @@ def format_facts_for_prompt(facts: list[MemoryFact]) -> str:
     return json.dumps(formatted, indent=2)
 
 
+def format_entity_summaries_for_prompt(entities: dict) -> str:
+    """Format entity summaries for inclusion in the reflect prompt.
+
+    Args:
+        entities: Dict mapping entity name to EntityState objects
+
+    Returns:
+        Formatted string with entity summaries, or empty string if no summaries
+    """
+    if not entities:
+        return ""
+
+    summaries = []
+    for name, state in entities.items():
+        # Get summary from observations (summary is stored as single observation)
+        if state.observations:
+            summary_text = state.observations[0].text
+            summaries.append(f"## {name}\n{summary_text}")
+
+    if not summaries:
+        return ""
+
+    return "\n\n".join(summaries)
+
+
 def build_think_prompt(
     agent_facts_text: str,
     world_facts_text: str,
-    opinion_facts_text: str,
     query: str,
     name: str,
     disposition: DispositionTraits,
     background: str,
     context: str | None = None,
+    entity_summaries_text: str | None = None,
 ) -> str:
-    """Build the think prompt for the LLM."""
+    """Build the think prompt for the LLM.
+
+    Note: opinion_facts_text parameter removed - opinions are now stored as mental models
+    and included via entity_summaries_text.
+    """
     disposition_desc = build_disposition_description(disposition)
 
     name_section = f"""
@@ -125,6 +136,14 @@ Your background:
 ADDITIONAL CONTEXT:
 {context}
 
+"""
+
+    entity_section = ""
+    if entity_summaries_text:
+        entity_section = f"""
+KEY PEOPLE, PLACES & THINGS I KNOW ABOUT:
+{entity_summaries_text}
+
 """
 
     return f"""Here's what I know and have experienced:
@@ -135,14 +154,11 @@ MY IDENTITY & EXPERIENCES:
 WHAT I KNOW ABOUT THE WORLD:
 {world_facts_text}
 
-MY EXISTING OPINIONS & BELIEFS:
-{opinion_facts_text}
-
-{context_section}{name_section}{disposition_desc}{background_section}
+{entity_section}{context_section}{name_section}{disposition_desc}{background_section}
 
 QUESTION: {query}
 
-Based on everything I know, believe, and who I am (including my name, disposition and background), here's what I genuinely think about this question. I'll draw on my experiences, knowledge, opinions, and personal traits to give you my honest perspective."""
+Based on everything I know, believe, and who I am (including my name, disposition and background), here's what I genuinely think about this question. I'll draw on my experiences, knowledge, and personal traits to give you my honest perspective."""
 
 
 def get_system_message(disposition: DispositionTraits) -> str:
@@ -175,122 +191,11 @@ def get_system_message(disposition: DispositionTraits) -> str:
     return f"You are a person with your own thoughts, experiences, opinions, and disposition. When asked a question, respond naturally like a human would - share what you genuinely think based on what you know and have experienced. {disposition_instruction} Be direct, express your views confidently, and use 'I think', 'I believe', 'in my view', etc. Respond in plain text without markdown formatting. IMPORTANT: Detect the language of the question and respond in the SAME language. Do not translate to English if the question is in another language."
 
 
-async def extract_opinions_from_text(llm_config, text: str, query: str) -> list[Opinion]:
-    """
-    Extract opinions with reasons and confidence from text using LLM.
-
-    Args:
-        llm_config: LLM configuration to use
-        text: Text to extract opinions from
-        query: The original query that prompted this response
-
-    Returns:
-        List of Opinion objects with text and confidence
-    """
-    extraction_prompt = f"""Extract any NEW opinions or perspectives from the answer below and rewrite them in FIRST-PERSON as if YOU are stating the opinion directly.
-
-ORIGINAL QUESTION:
-{query}
-
-ANSWER PROVIDED:
-{text}
-
-Your task: Find opinions in the answer and rewrite them AS IF YOU ARE THE ONE SAYING THEM.
-
-An opinion is a judgment, viewpoint, or conclusion that goes beyond just stating facts.
-
-IMPORTANT: Do NOT extract statements like:
-- "I don't have enough information"
-- "The facts don't contain information about X"
-- "I cannot answer because..."
-
-ONLY extract actual opinions about substantive topics.
-
-CRITICAL FORMAT REQUIREMENTS:
-1. **ALWAYS start with first-person phrases**: "I think...", "I believe...", "In my view...", "I've come to believe...", "Previously I thought... but now..."
-2. **NEVER use third-person**: Do NOT say "The speaker thinks..." or "They believe..." - always use "I"
-3. Include the reasoning naturally within the statement
-4. Provide a confidence score (0.0 to 1.0)
-
-CORRECT Examples (✓ FIRST-PERSON):
-- "I think Alice is more reliable because she consistently delivers on time and writes clean code"
-- "Previously I thought all engineers were equal, but now I feel that experience and track record really matter"
-- "I believe reliability is best measured by consistent output over time"
-- "I've come to believe that track records are more important than potential"
-
-WRONG Examples (✗ THIRD-PERSON - DO NOT USE):
-- "The speaker thinks Alice is more reliable"
-- "They believe reliability matters"
-- "It is believed that Alice is better"
-
-If no genuine opinions are expressed (e.g., the response just says "I don't know"), return an empty list."""
-
-    try:
-        result = await llm_config.call(
-            messages=[
-                {
-                    "role": "system",
-                    "content": "You are converting opinions from text into first-person statements. Always use 'I think', 'I believe', 'I feel', etc. NEVER use third-person like 'The speaker' or 'They'.",
-                },
-                {"role": "user", "content": extraction_prompt},
-            ],
-            response_format=OpinionExtractionResponse,
-            scope="memory_extract_opinion",
-        )
-
-        # Format opinions with confidence score and convert to first-person
-        formatted_opinions = []
-        for op in result.opinions:
-            # Convert third-person to first-person if needed
-            opinion_text = op.opinion
-
-            # Replace common third-person patterns with first-person
-            def singularize_verb(verb):
-                if verb.endswith("es"):
-                    return verb[:-1]  # believes -> believe
-                elif verb.endswith("s"):
-                    return verb[:-1]  # thinks -> think
-                return verb
-
-            # Pattern: "The speaker/user [verb]..." -> "I [verb]..."
-            match = re.match(
-                r"^(The speaker|The user|They|It is believed) (believes?|thinks?|feels?|says|asserts?|considers?)(\s+that)?(.*)$",
-                opinion_text,
-                re.IGNORECASE,
-            )
-            if match:
-                verb = singularize_verb(match.group(2))
-                that_part = match.group(3) or ""  # Keep " that" if present
-                rest = match.group(4)
-                opinion_text = f"I {verb}{that_part}{rest}"
-
-            # If still doesn't start with first-person, prepend "I believe that "
-            first_person_starters = [
-                "I think",
-                "I believe",
-                "I feel",
-                "In my view",
-                "I've come to believe",
-                "Previously I",
-            ]
-            if not any(opinion_text.startswith(starter) for starter in first_person_starters):
-                opinion_text = "I believe that " + opinion_text[0].lower() + opinion_text[1:]
-
-            formatted_opinions.append(Opinion(opinion=opinion_text, confidence=op.confidence))
-
-        return formatted_opinions
-
-    except Exception as e:
-        logger.warning(f"Failed to extract opinions: {str(e)}")
-        return []
-
-
 async def reflect(
     llm_config,
     query: str,
     experience_facts: list[str] = None,
     world_facts: list[str] = None,
-    opinion_facts: list[str] = None,
     name: str = "Assistant",
     disposition: DispositionTraits = None,
     background: str = "",
@@ -307,7 +212,6 @@ async def reflect(
         query: Question to answer
         experience_facts: List of experience/agent fact strings
         world_facts: List of world fact strings
-        opinion_facts: List of opinion fact strings
         name: Name of the agent/persona
         disposition: Disposition traits (defaults to neutral)
         background: Background information
@@ -328,18 +232,15 @@ async def reflect(
 
     agent_results = to_memory_facts(experience_facts or [], "experience")
     world_results = to_memory_facts(world_facts or [], "world")
-    opinion_results = to_memory_facts(opinion_facts or [], "opinion")
 
     # Format facts for prompt
     agent_facts_text = format_facts_for_prompt(agent_results)
     world_facts_text = format_facts_for_prompt(world_results)
-    opinion_facts_text = format_facts_for_prompt(opinion_results)
 
     # Build prompt
     prompt = build_think_prompt(
         agent_facts_text=agent_facts_text,
         world_facts_text=world_facts_text,
-        opinion_facts_text=opinion_facts_text,
         query=query,
         name=name,
         disposition=disposition,

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

@@ -11,6 +11,13 @@ from typing import Any, Literal
 from pydantic import BaseModel, Field
 
 
+class TemporalConstraint(BaseModel):
+    """Detected temporal constraint from query analysis."""
+
+    start: datetime | None = Field(default=None, description="Start of temporal range")
+    end: datetime | None = Field(default=None, description="End of temporal range")
+
+
 class QueryInfo(BaseModel):
     """Information about the search query."""
 
@@ -19,6 +26,11 @@ class QueryInfo(BaseModel):
     timestamp: datetime = Field(description="When the query was executed")
     budget: int = Field(description="Maximum nodes to explore")
     max_tokens: int = Field(description="Maximum tokens to return in results")
+    tags: list[str] | None = Field(default=None, description="Tags filter applied to recall")
+    tags_match: str | None = Field(default=None, description="Tags matching mode: any, all, any_strict, all_strict")
+    temporal_constraint: TemporalConstraint | None = Field(
+        default=None, description="Detected temporal range from query"
+    )
 
 
 class EntryPoint(BaseModel):
@@ -73,7 +85,6 @@ class NodeVisit(BaseModel):
     text: str = Field(description="Memory unit text content")
     context: str = Field(description="Memory unit context")
     event_date: datetime | None = Field(default=None, description="When the memory occurred")
-    access_count: int = Field(description="Number of times accessed before this search")
 
     # How this node was reached
     is_entry_point: bool = Field(description="Whether this is an entry point")

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

@@ -22,6 +22,7 @@ from .trace import (
     SearchPhaseMetrics,
     SearchSummary,
     SearchTrace,
+    TemporalConstraint,
     WeightComponents,
 )
 
@@ -45,7 +46,14 @@ class SearchTracer:
         json_output = trace.to_json()
     """
 
-    def __init__(self, query: str, budget: int, max_tokens: int):
+    def __init__(
+        self,
+        query: str,
+        budget: int,
+        max_tokens: int,
+        tags: list[str] | None = None,
+        tags_match: str | None = None,
+    ):
         """
         Initialize tracer.
 
@@ -53,10 +61,14 @@ class SearchTracer:
             query: Search query text
             budget: Maximum nodes to explore
             max_tokens: Maximum tokens to return in results
+            tags: Tags filter applied to recall
+            tags_match: Tags matching mode (any, all, any_strict, all_strict)
         """
         self.query_text = query
         self.budget = budget
         self.max_tokens = max_tokens
+        self.tags = tags
+        self.tags_match = tags_match
 
         # Trace data
         self.query_embedding: list[float] | None = None
@@ -66,6 +78,9 @@ class SearchTracer:
         self.pruned: list[PruningDecision] = []
         self.phase_metrics: list[SearchPhaseMetrics] = []
 
+        # Temporal constraint detected from query
+        self.temporal_constraint: TemporalConstraint | None = None
+
         # New 4-way retrieval tracking
         self.retrieval_results: list[RetrievalMethodResults] = []
         self.rrf_merged: list[RRFMergeResult] = []
@@ -88,6 +103,11 @@ class SearchTracer:
         """Record the query embedding."""
         self.query_embedding = embedding
 
+    def record_temporal_constraint(self, start: datetime | None, end: datetime | None):
+        """Record the detected temporal constraint from query analysis."""
+        if start is not None or end is not None:
+            self.temporal_constraint = TemporalConstraint(start=start, end=end)
+
     def add_entry_point(self, node_id: str, text: str, similarity: float, rank: int):
         """
         Record an entry point.
@@ -116,7 +136,6 @@ class SearchTracer:
         text: str,
         context: str,
         event_date: datetime | None,
-        access_count: int,
         is_entry_point: bool,
         parent_node_id: str | None,
         link_type: Literal["temporal", "semantic", "entity"] | None,
@@ -135,7 +154,6 @@ class SearchTracer:
             text: Memory unit text
             context: Memory unit context
             event_date: When the memory occurred
-            access_count: Access count before this search
             is_entry_point: Whether this is an entry point
             parent_node_id: Node that led here (None for entry points)
             link_type: Type of link from parent
@@ -174,7 +192,6 @@ class SearchTracer:
             text=text,
             context=context,
             event_date=event_date,
-            access_count=access_count,
             is_entry_point=is_entry_point,
             parent_node_id=parent_node_id,
             link_type=link_type,
@@ -313,8 +330,8 @@ class SearchTracer:
                 RetrievalResult(
                     rank=rank,
                     node_id=doc_id,
-                    text=data.get("text", ""),
-                    context=data.get("context", ""),
+                    text=data.get("text") or "",
+                    context=data.get("context") or "",
                     event_date=data.get("event_date"),
                     fact_type=data.get("fact_type") or fact_type,
                     score=score,
@@ -428,6 +445,9 @@ class SearchTracer:
             timestamp=datetime.now(UTC),
             budget=self.budget,
             max_tokens=self.max_tokens,
+            tags=self.tags,
+            tags_match=self.tags_match,
+            temporal_constraint=self.temporal_constraint,
         )
 
         # Create summary

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

@@ -10,6 +10,24 @@ from datetime import datetime
 from typing import Any
 
 
+@dataclass
+class MPFPTimings:
+    """Timing breakdown for a single MPFP retrieval call."""
+
+    fact_type: str
+    edge_count: int = 0  # Total edges loaded
+    db_queries: int = 0  # Number of DB queries for edge loading
+    edge_load_time: float = 0.0  # Time spent loading edges from DB
+    traverse: float = 0.0  # Total traversal time (includes edge loading)
+    pattern_count: int = 0  # Number of patterns executed
+    fusion: float = 0.0  # Time for RRF fusion
+    fetch: float = 0.0  # Time to fetch memory unit details
+    seeds_time: float = 0.0  # Time to find semantic seeds (if fallback used)
+    result_count: int = 0  # Number of results returned
+    # Detailed per-hop timing: list of {hop, exec_time, uncached, load_time, edges_loaded, total_time}
+    hop_details: list[dict] = field(default_factory=list)
+
+
 @dataclass
 class RetrievalResult:
     """
@@ -28,8 +46,8 @@ class RetrievalResult:
     mentioned_at: datetime | None = None
     document_id: str | None = None
     chunk_id: str | None = None
-    access_count: int = 0
     embedding: list[float] | None = None
+    tags: list[str] | None = None  # Visibility scope tags
 
     # Retrieval-specific scores (only one will be set depending on retrieval method)
     similarity: float | None = None  # Semantic retrieval
@@ -52,8 +70,8 @@ class RetrievalResult:
             mentioned_at=row.get("mentioned_at"),
             document_id=row.get("document_id"),
             chunk_id=row.get("chunk_id"),
-            access_count=row.get("access_count", 0),
             embedding=row.get("embedding"),
+            tags=row.get("tags"),
             similarity=row.get("similarity"),
             bm25_score=row.get("bm25_score"),
             activation=row.get("activation"),
@@ -136,8 +154,8 @@ class ScoredResult:
             "mentioned_at": self.retrieval.mentioned_at,
             "document_id": self.retrieval.document_id,
             "chunk_id": self.retrieval.chunk_id,
-            "access_count": self.retrieval.access_count,
             "embedding": self.retrieval.embedding,
+            "tags": self.retrieval.tags,
             "semantic_similarity": self.retrieval.similarity,
             "bm25_score": self.retrieval.bm25_score,
         }

hindsight-api/hindsight_api/engine/task_backend.py

@@ -1,31 +1,40 @@
 """
-Abstract task backend for running async tasks.
+Task backend for distributed task processing.
 
-This provides an abstraction that can be adapted to different execution models:
-- AsyncIO queue (default implementation)
-- Pub/Sub architectures (future)
-- Message brokers (future)
+This provides an abstraction for task storage and execution:
+- BrokerTaskBackend: Uses PostgreSQL as broker (production)
+- SyncTaskBackend: Executes tasks immediately (testing/embedded)
 """
 
-import asyncio
+import json
 import logging
 from abc import ABC, abstractmethod
 from collections.abc import Awaitable, Callable
-from typing import Any
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    import asyncpg
 
 logger = logging.getLogger(__name__)
 
 
+def fq_table(table: str, schema: str | None = None) -> str:
+    """Get fully-qualified table name with optional schema prefix."""
+    if schema:
+        return f'"{schema}".{table}'
+    return table
+
+
 class TaskBackend(ABC):
     """
     Abstract base class for task execution backends.
 
     Implementations must:
     1. Store/publish task events (as serializable dicts)
-    2. Execute tasks through a provided executor callback
+    2. Execute tasks through a provided executor callback (optional)
 
     The backend treats tasks as pure dictionaries that can be serialized
-    and sent over the network. The executor (typically MemoryEngine.execute_task)
+    and stored in the database. The executor (typically MemoryEngine.execute_task)
     receives the dict and routes it to the appropriate handler.
     """
 
@@ -46,7 +55,7 @@ class TaskBackend(ABC):
     @abstractmethod
     async def initialize(self):
         """
-        Initialize the backend (e.g., start workers, connect to broker).
+        Initialize the backend (e.g., connect to database).
         """
         pass
 
@@ -63,7 +72,7 @@ class TaskBackend(ABC):
     @abstractmethod
     async def shutdown(self):
         """
-        Shutdown the backend gracefully (e.g., stop workers, close connections).
+        Shutdown the backend gracefully.
         """
         pass
 
@@ -93,9 +102,8 @@ class SyncTaskBackend(TaskBackend):
     """
     Synchronous task backend that executes tasks immediately.
 
-    This is useful for embedded/CLI usage where we don't want background
-    workers that prevent clean exit. Tasks are executed inline rather than
-    being queued.
+    This is useful for tests and embedded/CLI usage where we don't want
+    background workers. Tasks are executed inline rather than being queued.
     """
 
     async def initialize(self):
@@ -121,198 +129,129 @@ class SyncTaskBackend(TaskBackend):
         logger.debug("SyncTaskBackend shutdown")
 
 
-class AsyncIOQueueBackend(TaskBackend):
+class BrokerTaskBackend(TaskBackend):
     """
-    Task backend implementation using asyncio queues.
+    Task backend using PostgreSQL as broker.
 
-    This is the default implementation that uses in-process asyncio queues
-    and a periodic consumer worker.
+    submit_task() stores task_payload in async_operations table.
+    Actual polling and execution is handled separately by WorkerPoller.
+
+    This backend is used by the API to store tasks. Workers poll
+    the database separately to claim and execute tasks.
     """
 
-    def __init__(self, batch_size: int = 10, batch_interval: float = 1.0):
+    def __init__(
+        self,
+        pool_getter: Callable[[], "asyncpg.Pool"],
+        schema: str | None = None,
+        schema_getter: Callable[[], str | None] | None = None,
+    ):
         """
-        Initialize AsyncIO queue backend.
+        Initialize the broker task backend.
 
         Args:
-            batch_size: Maximum number of tasks to process in one batch
-            batch_interval: Maximum time (seconds) to wait before processing batch
+            pool_getter: Callable that returns the asyncpg connection pool
+            schema: Database schema for multi-tenant support (optional, static)
+            schema_getter: Callable that returns current schema dynamically (optional).
+                          If set, takes precedence over static schema for submit_task.
         """
         super().__init__()
-        self._queue: asyncio.Queue | None = None
-        self._worker_task: asyncio.Task | None = None
-        self._shutdown_event: asyncio.Event | None = None
-        self._batch_size = batch_size
-        self._batch_interval = batch_interval
-        self._in_flight_count = 0
-        self._in_flight_lock = asyncio.Lock()
+        self._pool_getter = pool_getter
+        self._schema = schema
+        self._schema_getter = schema_getter
 
     async def initialize(self):
-        """Initialize the queue and start the worker."""
-        if self._initialized:
-            return
-
-        self._queue = asyncio.Queue()
-        self._shutdown_event = asyncio.Event()
-        self._worker_task = asyncio.create_task(self._worker())
+        """Initialize the backend."""
         self._initialized = True
-        logger.info("AsyncIOQueueBackend initialized")
+        logger.info("BrokerTaskBackend initialized")
 
     async def submit_task(self, task_dict: dict[str, Any]):
         """
-        Submit a task by putting it in the queue.
+        Store task payload in async_operations table.
+
+        The task_dict should contain an 'operation_id' if updating an existing
+        operation record, otherwise a new operation will be created.
 
         Args:
-            task_dict: Task dictionary to execute
+            task_dict: Task dictionary to store (must be JSON serializable)
         """
         if not self._initialized:
             await self.initialize()
 
-        await self._queue.put(task_dict)
+        pool = self._pool_getter()
+        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)
+
+        schema = self._schema_getter() if self._schema_getter else self._schema
+        table = fq_table("async_operations", schema)
+
+        if operation_id:
+            # Update existing operation with task payload
+            await pool.execute(
+                f"""
+                UPDATE {table}
+                SET task_payload = $1::jsonb, updated_at = now()
+                WHERE operation_id = $2
+                """,
+                payload_json,
+                operation_id,
+            )
+            logger.debug(f"Updated task payload for operation {operation_id}")
+        else:
+            # Insert new operation (for tasks without pre-created records)
+            # e.g., access_count_update tasks
+            import uuid
+
+            new_id = uuid.uuid4()
+            await pool.execute(
+                f"""
+                INSERT INTO {table} (operation_id, bank_id, operation_type, status, task_payload)
+                VALUES ($1, $2, $3, 'pending', $4::jsonb)
+                """,
+                new_id,
+                bank_id,
+                task_type,
+                payload_json,
+            )
+            logger.debug(f"Created new operation {new_id} for task type {task_type}")
+
+    async def shutdown(self):
+        """Shutdown the backend."""
+        self._initialized = False
+        logger.info("BrokerTaskBackend shutdown")
 
     async def wait_for_pending_tasks(self, timeout: float = 120.0):
         """
-        Wait for all pending tasks in the queue and in-flight tasks to complete.
+        Wait for pending tasks to be processed.
 
-        This is useful in tests to ensure background tasks complete before assertions.
+        In the broker model, this polls the database to check if tasks
+        for this process have been completed. This is useful in tests
+        when worker_enabled=True (API processes its own tasks).
 
         Args:
-            timeout: Maximum time to wait in seconds (default 120s for long-running tasks)
+            timeout: Maximum time to wait in seconds
         """
-        if not self._initialized or self._queue is None:
-            return
+        import asyncio
+
+        pool = self._pool_getter()
+        schema = self._schema_getter() if self._schema_getter else self._schema
+        table = fq_table("async_operations", schema)
 
-        # Wait for queue to be empty AND no in-flight tasks
         start_time = asyncio.get_event_loop().time()
         while asyncio.get_event_loop().time() - start_time < timeout:
-            async with self._in_flight_lock:
-                in_flight = self._in_flight_count
+            # Check if there are any pending tasks with payloads
+            count = await pool.fetchval(
+                f"""
+                SELECT COUNT(*) FROM {table}
+                WHERE status = 'pending' AND task_payload IS NOT NULL
+                """
+            )
 
-            if self._queue.empty() and in_flight == 0:
-                # Queue is empty and no tasks in flight, we're done
+            if count == 0:
                 return
 
-            # Wait a bit before checking again
             await asyncio.sleep(0.5)
 
-    async def shutdown(self):
-        """Shutdown the worker and drain the queue."""
-        if not self._initialized:
-            return
-
-        logger.info("Shutting down AsyncIOQueueBackend...")
-
-        # Signal shutdown
-        self._shutdown_event.set()
-
-        # Cancel worker
-        if self._worker_task is not None:
-            self._worker_task.cancel()
-            try:
-                await self._worker_task
-            except asyncio.CancelledError:
-                pass  # Worker cancelled successfully
-
-        self._initialized = False
-        logger.info("AsyncIOQueueBackend shutdown complete")
-
-    async def _execute_task_with_tracking(self, task_dict: dict[str, Any]):
-        """Execute a task and track its in-flight status."""
-        async with self._in_flight_lock:
-            self._in_flight_count += 1
-        try:
-            await self._execute_task(task_dict)
-        finally:
-            async with self._in_flight_lock:
-                self._in_flight_count -= 1
-
-    async def _execute_task_no_tracking(self, task_dict: dict[str, Any]):
-        """Execute a task without in-flight tracking (tracking done at batch level)."""
-        await self._execute_task(task_dict)
-
-    def _get_queue_stats(self) -> tuple[int, dict[str, int]]:
-        """Get current queue size and bank_id distribution."""
-        queue_size = self._queue.qsize() if self._queue else 0
-        bank_distribution: dict[str, int] = {}
-
-        if queue_size > 0 and self._queue:
-            # Peek at queue items without removing them
-            # Note: This is a snapshot and may not be perfectly accurate due to concurrency
-            try:
-                # Access internal deque for logging purposes only
-                items = list(self._queue._queue)  # type: ignore[attr-defined]
-                for item in items:
-                    bank_id = item.get("bank_id", "unknown")
-                    bank_distribution[bank_id] = bank_distribution.get(bank_id, 0) + 1
-            except Exception:
-                pass  # Queue access failed, return empty distribution
-
-        return queue_size, bank_distribution
-
-    async def _worker(self):
-        """
-        Background worker that processes tasks in batches.
-
-        Collects tasks for up to batch_interval seconds or batch_size items,
-        then processes them.
-        """
-        while not self._shutdown_event.is_set():
-            try:
-                # Collect tasks for batching
-                tasks = []
-                deadline = asyncio.get_event_loop().time() + self._batch_interval
-
-                while len(tasks) < self._batch_size and asyncio.get_event_loop().time() < deadline:
-                    try:
-                        remaining_time = max(0.1, deadline - asyncio.get_event_loop().time())
-                        task_dict = await asyncio.wait_for(self._queue.get(), timeout=remaining_time)
-                        # Track task as in-flight immediately when picked up from queue
-                        # This prevents wait_for_pending_tasks from returning too early
-                        async with self._in_flight_lock:
-                            self._in_flight_count += 1
-                        tasks.append(task_dict)
-                    except TimeoutError:
-                        break
-
-                # Process batch
-                if tasks:
-                    # Log batch start with queue stats
-                    queue_size, bank_distribution = self._get_queue_stats()
-
-                    # Summarize batch by task type and bank
-                    batch_summary: dict[str, dict[str, int]] = {}
-                    for task_dict in tasks:
-                        task_type = task_dict.get("type", "unknown")
-                        bank_id = task_dict.get("bank_id", "unknown")
-                        if task_type not in batch_summary:
-                            batch_summary[task_type] = {}
-                        batch_summary[task_type][bank_id] = batch_summary[task_type].get(bank_id, 0) + 1
-
-                    # Build log message
-                    batch_parts = []
-                    for task_type, banks in sorted(batch_summary.items()):
-                        bank_str = ", ".join(f"{b}:{c}" for b, c in sorted(banks.items()))
-                        batch_parts.append(f"{task_type}[{bank_str}]")
-                    batch_str = ", ".join(batch_parts)
-
-                    if queue_size > 0:
-                        pending_str = ", ".join(f"{k}:{v}" for k, v in sorted(bank_distribution.items()))
-                        logger.info(
-                            f"Processing {len(tasks)} tasks: {batch_str} (pending={queue_size} [{pending_str}])"
-                        )
-                    else:
-                        logger.info(f"Processing {len(tasks)} tasks: {batch_str}")
-
-                    # Execute tasks concurrently (in_flight already tracked when picked up)
-                    await asyncio.gather(
-                        *[self._execute_task_no_tracking(task_dict) for task_dict in tasks], return_exceptions=True
-                    )
-
-                    # Decrement in_flight count after all tasks complete
-                    async with self._in_flight_lock:
-                        self._in_flight_count -= len(tasks)
-
-            except asyncio.CancelledError:
-                break
-            except Exception as e:
-                logger.error(f"Worker error: {e}")
-                await asyncio.sleep(1)  # Backoff on error
+        logger.warning(f"Timeout waiting for pending tasks after {timeout}s")

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:
@@ -65,154 +62,3 @@ async def extract_facts(
         return [], chunks
 
     return facts, chunks
-
-
-def cosine_similarity(vec1: list[float], vec2: list[float]) -> float:
-    """
-    Calculate cosine similarity between two vectors.
-
-    Args:
-        vec1: First vector
-        vec2: Second vector
-
-    Returns:
-        Similarity score between 0 and 1
-    """
-    if len(vec1) != len(vec2):
-        raise ValueError("Vectors must have same dimension")
-
-    dot_product = sum(a * b for a, b in zip(vec1, vec2))
-    magnitude1 = sum(a * a for a in vec1) ** 0.5
-    magnitude2 = sum(b * b for b in vec2) ** 0.5
-
-    if magnitude1 == 0 or magnitude2 == 0:
-        return 0.0
-
-    return dot_product / (magnitude1 * magnitude2)
-
-
-def calculate_recency_weight(days_since: float, half_life_days: float = 365.0) -> float:
-    """
-    Calculate recency weight using logarithmic decay.
-
-    This provides much better differentiation over long time periods compared to
-    exponential decay. Uses a log-based decay where the half-life parameter controls
-    when memories reach 50% weight.
-
-    Examples:
-        - Today (0 days): 1.0
-        - 1 year (365 days): ~0.5 (with default half_life=365)
-        - 2 years (730 days): ~0.33
-        - 5 years (1825 days): ~0.17
-        - 10 years (3650 days): ~0.09
-
-    This ensures that 2-year-old and 5-year-old memories have meaningfully
-    different weights, unlike exponential decay which makes them both ~0.
-
-    Args:
-        days_since: Number of days since the memory was created
-        half_life_days: Number of days for weight to reach 0.5 (default: 1 year)
-
-    Returns:
-        Weight between 0 and 1
-    """
-    import math
-
-    # Logarithmic decay: 1 / (1 + log(1 + days_since/half_life))
-    # This decays much slower than exponential, giving better long-term differentiation
-    normalized_age = days_since / half_life_days
-    return 1.0 / (1.0 + math.log1p(normalized_age))
-
-
-def calculate_frequency_weight(access_count: int, max_boost: float = 2.0) -> float:
-    """
-    Calculate frequency weight based on access count.
-
-    Frequently accessed memories are weighted higher.
-    Uses logarithmic scaling to avoid over-weighting.
-
-    Args:
-        access_count: Number of times the memory was accessed
-        max_boost: Maximum multiplier for frequently accessed memories
-
-    Returns:
-        Weight between 1.0 and max_boost
-    """
-    import math
-
-    if access_count <= 0:
-        return 1.0
-
-    # Logarithmic scaling: log(access_count + 1) / log(10)
-    # This gives: 0 accesses = 1.0, 9 accesses ~= 1.5, 99 accesses ~= 2.0
-    normalized = math.log(access_count + 1) / math.log(10)
-    return 1.0 + min(normalized, max_boost - 1.0)
-
-
-def calculate_temporal_anchor(occurred_start: datetime, occurred_end: datetime) -> datetime:
-    """
-    Calculate a single temporal anchor point from a temporal range.
-
-    Used for spreading activation - we need a single representative date
-    to calculate temporal proximity between facts. This simplifies the
-    range-to-range distance problem.
-
-    Strategy: Use midpoint of the range for balanced representation.
-
-    Args:
-        occurred_start: Start of temporal range
-        occurred_end: End of temporal range
-
-    Returns:
-        Single datetime representing the temporal anchor (midpoint)
-
-    Examples:
-        - Point event (July 14): start=July 14, end=July 14 → anchor=July 14
-        - Month range (February): start=Feb 1, end=Feb 28 → anchor=Feb 14
-        - Year range (2023): start=Jan 1, end=Dec 31 → anchor=July 1
-    """
-    # Calculate midpoint
-    time_delta = occurred_end - occurred_start
-    midpoint = occurred_start + (time_delta / 2)
-    return midpoint
-
-
-def calculate_temporal_proximity(anchor_a: datetime, anchor_b: datetime, half_life_days: float = 30.0) -> float:
-    """
-    Calculate temporal proximity between two temporal anchors.
-
-    Used for spreading activation to determine how "close" two facts are
-    in time. Uses logarithmic decay so that temporal similarity doesn't
-    drop off too quickly.
-
-    Args:
-        anchor_a: Temporal anchor of first fact
-        anchor_b: Temporal anchor of second fact
-        half_life_days: Number of days for proximity to reach 0.5
-                       (default: 30 days = 1 month)
-
-    Returns:
-        Proximity score in [0, 1] where:
-        - 1.0 = same day
-        - 0.5 = ~half_life days apart
-        - 0.0 = very distant in time
-
-    Examples:
-        - Same day: 1.0
-        - 1 week apart (half_life=30): ~0.7
-        - 1 month apart (half_life=30): ~0.5
-        - 1 year apart (half_life=30): ~0.2
-    """
-    import math
-
-    days_apart = abs((anchor_a - anchor_b).days)
-
-    if days_apart == 0:
-        return 1.0
-
-    # Logarithmic decay: 1 / (1 + log(1 + days_apart/half_life))
-    # Similar to calculate_recency_weight but for proximity between events
-    normalized_distance = days_apart / half_life_days
-    proximity = 1.0 / (1.0 + math.log1p(normalized_distance))
-
-    return proximity

hindsight-api/hindsight_api/extensions/__init__.py

@@ -21,6 +21,10 @@ from hindsight_api.extensions.context import DefaultExtensionContext, ExtensionC
 from hindsight_api.extensions.http import HttpExtension
 from hindsight_api.extensions.loader import load_extension
 from hindsight_api.extensions.operation_validator import (
+    # Consolidation operation
+    ConsolidateContext,
+    ConsolidateResult,
+    # Core operations
     OperationValidationError,
     OperationValidatorExtension,
     RecallContext,
@@ -33,6 +37,7 @@ from hindsight_api.extensions.operation_validator import (
 )
 from hindsight_api.extensions.tenant import (
     AuthenticationError,
+    Tenant,
     TenantContext,
     TenantExtension,
 )
@@ -47,7 +52,7 @@ __all__ = [
     "DefaultExtensionContext",
     # HTTP Extension
     "HttpExtension",
-    # Operation Validator
+    # Operation Validator - Core
     "OperationValidationError",
     "OperationValidatorExtension",
     "RecallContext",
@@ -57,10 +62,14 @@ __all__ = [
     "RetainContext",
     "RetainResult",
     "ValidationResult",
+    # Operation Validator - Consolidation
+    "ConsolidateContext",
+    "ConsolidateResult",
     # Tenant/Auth
     "ApiKeyTenantExtension",
     "AuthenticationError",
     "RequestContext",
+    "Tenant",
     "TenantContext",
     "TenantExtension",
 ]

hindsight-api/hindsight_api/extensions/builtin/tenant.py

@@ -1,6 +1,7 @@
 """Built-in tenant extension implementations."""
 
-from hindsight_api.extensions.tenant import AuthenticationError, TenantContext, TenantExtension
+from hindsight_api.config import get_config
+from hindsight_api.extensions.tenant import AuthenticationError, Tenant, TenantContext, TenantExtension
 from hindsight_api.models import RequestContext
 
 
@@ -10,11 +11,13 @@ class ApiKeyTenantExtension(TenantExtension):
 
     This is a simple implementation that:
     1. Validates the API key matches HINDSIGHT_API_TENANT_API_KEY
-    2. Returns 'public' as the schema for all authenticated requests
+    2. Returns the configured schema (HINDSIGHT_API_DATABASE_SCHEMA, default 'public')
+       for all authenticated requests
 
     Configuration:
         HINDSIGHT_API_TENANT_EXTENSION=hindsight_api.extensions.builtin.tenant:ApiKeyTenantExtension
         HINDSIGHT_API_TENANT_API_KEY=your-secret-key
+        HINDSIGHT_API_DATABASE_SCHEMA=your-schema (optional, defaults to 'public')
 
     For multi-tenant setups with separate schemas per tenant, implement a custom
     TenantExtension that looks up the schema based on the API key or token claims.
@@ -27,7 +30,11 @@ class ApiKeyTenantExtension(TenantExtension):
             raise ValueError("HINDSIGHT_API_TENANT_API_KEY is required when using ApiKeyTenantExtension")
 
     async def authenticate(self, context: RequestContext) -> TenantContext:
-        """Validate API key and return public schema context."""
+        """Validate API key and return configured schema context."""
         if context.api_key != self.expected_api_key:
             raise AuthenticationError("Invalid API key")
-        return TenantContext(schema_name="public")
+        return TenantContext(schema_name=get_config().database_schema)
+
+    async def list_tenants(self) -> list[Tenant]:
+        """Return configured schema for single-tenant setup."""
+        return [Tenant(schema=get_config().database_schema)]

hindsight-api/hindsight_api/extensions/operation_validator.py

@@ -1,4 +1,4 @@
-"""Operation Validator Extension for validating retain/recall/reflect operations."""
+"""Operation Validator Extension for validating retain/recall/reflect/consolidate operations."""
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
@@ -97,6 +97,19 @@ class ReflectContext:
     context: str | None = None
 
 
+# =============================================================================
+# Consolidation Pre-operation Context
+# =============================================================================
+
+
+@dataclass
+class ConsolidateContext:
+    """Context for a consolidation operation validation (pre-operation)."""
+
+    bank_id: str
+    request_context: "RequestContext"
+
+
 # =============================================================================
 # Post-operation Contexts (includes results)
 # =============================================================================
@@ -164,9 +177,28 @@ class ReflectResultContext:
     error: str | None = None
 
 
+# =============================================================================
+# Consolidation Post-operation Context
+# =============================================================================
+
+
+@dataclass
+class ConsolidateResult:
+    """Result context for post-consolidation hook."""
+
+    bank_id: str
+    request_context: "RequestContext"
+    # Result
+    processed: int = 0
+    created: int = 0
+    updated: int = 0
+    success: bool = True
+    error: str | None = None
+
+
 class OperationValidatorExtension(Extension, ABC):
     """
-    Validates and hooks into retain/recall/reflect operations.
+    Validates and hooks into retain/recall/reflect/consolidate operations.
 
     This extension allows implementing custom logic such as:
     - Rate limiting (pre-operation)
@@ -185,9 +217,13 @@ class OperationValidatorExtension(Extension, ABC):
         -> config = {"max_requests": "100"}
 
     Hook execution order:
-        1. validate_retain/validate_recall/validate_reflect (pre-operation)
+        1. validate_* (pre-operation)
         2. [operation executes]
-        3. on_retain_complete/on_recall_complete/on_reflect_complete (post-operation)
+        3. on_*_complete (post-operation)
+
+    Supported operations:
+        - retain, recall, reflect (core memory operations)
+        - consolidate (mental models consolidation)
     """
 
     # =========================================================================
@@ -325,3 +361,44 @@ class OperationValidatorExtension(Extension, ABC):
                 - error: Error message (if failed)
         """
         pass
+
+    # =========================================================================
+    # Consolidation - Pre-operation validation hook (optional - override to implement)
+    # =========================================================================
+
+    async def validate_consolidate(self, ctx: ConsolidateContext) -> ValidationResult:
+        """
+        Validate a consolidation operation before execution.
+
+        Override to implement custom validation logic for consolidation.
+
+        Args:
+            ctx: Context containing:
+                - bank_id: Bank identifier
+                - request_context: Request context with auth info
+
+        Returns:
+            ValidationResult indicating whether the operation is allowed.
+        """
+        return ValidationResult.accept()
+
+    # =========================================================================
+    # Consolidation - Post-operation hook (optional - override to implement)
+    # =========================================================================
+
+    async def on_consolidate_complete(self, result: ConsolidateResult) -> None:
+        """
+        Called after a consolidation operation completes (success or failure).
+
+        Override to implement post-operation logic such as usage tracking or audit logging.
+
+        Args:
+            result: Result context containing:
+                - bank_id: Bank identifier
+                - processed: Number of memories processed
+                - created: Number of mental models created
+                - updated: Number of mental models updated
+                - success: Whether the operation succeeded
+                - error: Error message (if failed)
+        """
+        pass

hindsight-api/hindsight_api/extensions/tenant.py

@@ -28,6 +28,18 @@ class TenantContext:
     schema_name: str
 
 
+@dataclass
+class Tenant:
+    """
+    Represents a tenant for worker discovery.
+
+    Used by list_tenants() to return tenant information including
+    the PostgreSQL schema name for database operations.
+    """
+
+    schema: str
+
+
 class TenantExtension(Extension, ABC):
     """
     Extension for multi-tenancy and API key authentication.
@@ -61,3 +73,17 @@ class TenantExtension(Extension, ABC):
             AuthenticationError: If authentication fails.
         """
         ...
+
+    @abstractmethod
+    async def list_tenants(self) -> list[Tenant]:
+        """
+        List all tenants that should be processed by workers.
+
+        This method is used by the worker to discover all tenants that need
+        task polling. Workers will poll for pending tasks in each tenant's schema.
+
+        Returns:
+            List of Tenant objects containing schema information.
+            For single-tenant setups, return [Tenant(schema="public")].
+        """
+        ...

hindsight-api/hindsight_api/main.py

@@ -23,7 +23,7 @@ import uvicorn
 from . import MemoryEngine
 from .api import create_app
 from .banner import print_banner
-from .config import HindsightConfig, get_config
+from .config import DEFAULT_WORKERS, ENV_WORKERS, HindsightConfig, get_config
 from .daemon import (
     DEFAULT_DAEMON_PORT,
     DEFAULT_IDLE_TIMEOUT,
@@ -95,7 +95,12 @@ def main():
 
     # Development options
     parser.add_argument("--reload", action="store_true", help="Enable auto-reload on code changes (development only)")
-    parser.add_argument("--workers", type=int, default=1, help="Number of worker processes (default: 1)")
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=int(os.getenv(ENV_WORKERS, str(DEFAULT_WORKERS))),
+        help=f"Number of worker processes (env: {ENV_WORKERS}, default: {DEFAULT_WORKERS})",
+    )
 
     # Access log options
     parser.add_argument("--access-log", action="store_true", help="Enable access log")
@@ -165,39 +170,78 @@ def main():
     if args.log_level != config.log_level:
         config = HindsightConfig(
             database_url=config.database_url,
+            database_schema=config.database_schema,
             llm_provider=config.llm_provider,
             llm_api_key=config.llm_api_key,
             llm_model=config.llm_model,
             llm_base_url=config.llm_base_url,
             llm_max_concurrent=config.llm_max_concurrent,
+            llm_max_retries=config.llm_max_retries,
+            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,
             retain_llm_base_url=config.retain_llm_base_url,
+            retain_llm_max_concurrent=config.retain_llm_max_concurrent,
+            retain_llm_max_retries=config.retain_llm_max_retries,
+            retain_llm_initial_backoff=config.retain_llm_initial_backoff,
+            retain_llm_max_backoff=config.retain_llm_max_backoff,
+            retain_llm_timeout=config.retain_llm_timeout,
             reflect_llm_provider=config.reflect_llm_provider,
             reflect_llm_api_key=config.reflect_llm_api_key,
             reflect_llm_model=config.reflect_llm_model,
             reflect_llm_base_url=config.reflect_llm_base_url,
+            reflect_llm_max_concurrent=config.reflect_llm_max_concurrent,
+            reflect_llm_max_retries=config.reflect_llm_max_retries,
+            reflect_llm_initial_backoff=config.reflect_llm_initial_backoff,
+            reflect_llm_max_backoff=config.reflect_llm_max_backoff,
+            reflect_llm_timeout=config.reflect_llm_timeout,
+            consolidation_llm_provider=config.consolidation_llm_provider,
+            consolidation_llm_api_key=config.consolidation_llm_api_key,
+            consolidation_llm_model=config.consolidation_llm_model,
+            consolidation_llm_base_url=config.consolidation_llm_base_url,
+            consolidation_llm_max_concurrent=config.consolidation_llm_max_concurrent,
+            consolidation_llm_max_retries=config.consolidation_llm_max_retries,
+            consolidation_llm_initial_backoff=config.consolidation_llm_initial_backoff,
+            consolidation_llm_max_backoff=config.consolidation_llm_max_backoff,
+            consolidation_llm_timeout=config.consolidation_llm_timeout,
             embeddings_provider=config.embeddings_provider,
             embeddings_local_model=config.embeddings_local_model,
+            embeddings_local_force_cpu=config.embeddings_local_force_cpu,
             embeddings_tei_url=config.embeddings_tei_url,
+            embeddings_openai_base_url=config.embeddings_openai_base_url,
+            embeddings_cohere_base_url=config.embeddings_cohere_base_url,
             reranker_provider=config.reranker_provider,
             reranker_local_model=config.reranker_local_model,
+            reranker_local_force_cpu=config.reranker_local_force_cpu,
+            reranker_local_max_concurrent=config.reranker_local_max_concurrent,
             reranker_tei_url=config.reranker_tei_url,
             reranker_tei_batch_size=config.reranker_tei_batch_size,
             reranker_tei_max_concurrent=config.reranker_tei_max_concurrent,
+            reranker_max_candidates=config.reranker_max_candidates,
+            reranker_cohere_base_url=config.reranker_cohere_base_url,
             host=args.host,
             port=args.port,
             log_level=args.log_level,
+            log_format=config.log_format,
             mcp_enabled=config.mcp_enabled,
             graph_retriever=config.graph_retriever,
-            observation_min_facts=config.observation_min_facts,
-            observation_top_entities=config.observation_top_entities,
+            mpfp_top_k_neighbors=config.mpfp_top_k_neighbors,
+            recall_max_concurrent=config.recall_max_concurrent,
+            recall_connection_budget=config.recall_connection_budget,
             retain_max_completion_tokens=config.retain_max_completion_tokens,
             retain_chunk_size=config.retain_chunk_size,
             retain_extract_causal_links=config.retain_extract_causal_links,
             retain_extraction_mode=config.retain_extraction_mode,
+            retain_custom_instructions=config.retain_custom_instructions,
+            enable_observations=config.enable_observations,
+            consolidation_batch_size=config.consolidation_batch_size,
+            consolidation_max_tokens=config.consolidation_max_tokens,
             skip_llm_verification=config.skip_llm_verification,
             lazy_reranker=config.lazy_reranker,
             run_migrations_on_startup=config.run_migrations_on_startup,
@@ -205,8 +249,15 @@ def main():
             db_pool_max_size=config.db_pool_max_size,
             db_command_timeout=config.db_command_timeout,
             db_acquire_timeout=config.db_acquire_timeout,
-            task_batch_size=config.task_batch_size,
-            task_batch_interval=config.task_batch_interval,
+            worker_enabled=config.worker_enabled,
+            worker_id=config.worker_id,
+            worker_poll_interval_ms=config.worker_poll_interval_ms,
+            worker_max_retries=config.worker_max_retries,
+            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,
         )
     config.configure_logging()
     if not args.daemon:
@@ -263,14 +314,27 @@ def main():
         app = idle_middleware
 
     # Prepare uvicorn config
+    # When using workers or reload, we must use import string so each worker can import the app
+    use_import_string = args.workers > 1 or args.reload
+    # Check for uvloop availability
+    try:
+        import uvloop  # noqa: F401
+
+        loop_impl = "uvloop"
+        print("uvloop available, will use for event loop")
+    except ImportError:
+        loop_impl = "asyncio"
+        print("uvloop not installed, using default asyncio event loop")
+
     uvicorn_config = {
-        "app": app,
+        "app": "hindsight_api.server:app" if use_import_string else app,
         "host": args.host,
         "port": args.port,
         "log_level": args.log_level,
         "access_log": args.access_log,
         "proxy_headers": args.proxy_headers,
         "ws": "wsproto",  # Use wsproto instead of websockets to avoid deprecation warnings
+        "loop": loop_impl,  # Explicitly set event loop implementation
     }
 
     # Add optional parameters if provided
@@ -303,6 +367,7 @@ def main():
     # Start idle checker in daemon mode
     if idle_middleware is not None:
         # Start the idle checker in a background thread with its own event loop
+        import logging
         import threading
 
         def run_idle_checker():
@@ -313,12 +378,12 @@ def main():
                 loop = asyncio.new_event_loop()
                 asyncio.set_event_loop(loop)
                 loop.run_until_complete(idle_middleware._check_idle())
-            except Exception:
-                pass
+            except Exception as e:
+                logging.error(f"Idle checker error: {e}", exc_info=True)
 
         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_local.py

@@ -44,7 +44,6 @@ import os
 import sys
 
 from mcp.server.fastmcp import FastMCP
-from mcp.types import Icon
 
 from hindsight_api.config import (
     DEFAULT_MCP_LOCAL_BANK_ID,
@@ -53,6 +52,7 @@ from hindsight_api.config import (
     ENV_MCP_INSTRUCTIONS,
     ENV_MCP_LOCAL_BANK_ID,
 )
+from hindsight_api.mcp_tools import MCPToolsConfig, register_mcp_tools
 
 # Configure logging - default to warning to avoid polluting stderr during MCP init
 # MCP clients interpret stderr output as errors, so we suppress INFO logs by default
@@ -85,9 +85,6 @@ def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
     """
     # Import here to avoid slow startup if just checking --help
     from hindsight_api import MemoryEngine
-    from hindsight_api.engine.memory_engine import Budget
-    from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
-    from hindsight_api.models import RequestContext
 
     # Create memory engine with pg0 embedded database if not provided
     if memory is None:
@@ -105,55 +102,17 @@ def create_local_mcp_server(bank_id: str, memory=None) -> FastMCP:
 
     mcp = FastMCP("hindsight")
 
-    @mcp.tool(description=retain_description)
-    async def retain(content: str, context: str = "general") -> dict:
-        """
-        Args:
-            content: The fact/memory to store (be specific and include relevant details)
-            context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
-        """
-        import asyncio
+    # Configure and register tools using shared module
+    config = MCPToolsConfig(
+        bank_id_resolver=lambda: bank_id,
+        include_bank_id_param=False,  # Local MCP uses fixed bank_id
+        tools={"retain", "recall"},  # Local MCP only has retain and recall
+        retain_description=retain_description,
+        recall_description=recall_description,
+        retain_fire_and_forget=True,  # Local MCP uses fire-and-forget pattern
+    )
 
-        async def _retain():
-            try:
-                await memory.retain_batch_async(
-                    bank_id=bank_id,
-                    contents=[{"content": content, "context": context}],
-                    request_context=RequestContext(),
-                )
-            except Exception as e:
-                logger.error(f"Error storing memory: {e}", exc_info=True)
-
-        # Fire and forget - don't block on memory storage
-        asyncio.create_task(_retain())
-        return {"status": "accepted", "message": "Memory storage initiated"}
-
-    @mcp.tool(description=recall_description)
-    async def recall(query: str, max_tokens: int = 4096, budget: str = "low") -> dict:
-        """
-        Args:
-            query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
-            max_tokens: Maximum tokens to return in results (default: 4096)
-            budget: Search budget level - "low", "mid", or "high" (default: "low")
-        """
-        try:
-            # Map string budget to enum
-            budget_map = {"low": Budget.LOW, "mid": Budget.MID, "high": Budget.HIGH}
-            budget_enum = budget_map.get(budget.lower(), Budget.LOW)
-
-            search_result = await memory.recall_async(
-                bank_id=bank_id,
-                query=query,
-                fact_type=list(VALID_RECALL_FACT_TYPES),
-                budget=budget_enum,
-                max_tokens=max_tokens,
-                request_context=RequestContext(),
-            )
-
-            return search_result.model_dump()
-        except Exception as e:
-            logger.error(f"Error searching: {e}", exc_info=True)
-            return {"error": str(e), "results": []}
+    register_mcp_tools(mcp, memory, config)
 
     return mcp
 

hindsight-api/hindsight_api/mcp_tools.py

@@ -0,0 +1,513 @@
+"""Shared MCP tool implementations for Hindsight.
+
+This module provides the core tool logic used by both:
+- mcp_local.py (stdio transport for Claude Code)
+- api/mcp.py (HTTP transport for API server)
+"""
+
+import json
+import logging
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Any, Callable
+
+from fastmcp import FastMCP
+
+from hindsight_api import MemoryEngine
+from hindsight_api.config import (
+    DEFAULT_MCP_RECALL_DESCRIPTION,
+    DEFAULT_MCP_RETAIN_DESCRIPTION,
+)
+from hindsight_api.engine.memory_engine import Budget
+from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES
+from hindsight_api.models import RequestContext
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class MCPToolsConfig:
+    """Configuration for MCP tools registration."""
+
+    # 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
+
+    # Which tools to register
+    tools: set[str] | None = None  # None means all tools
+
+    # Custom descriptions (if None, uses defaults)
+    retain_description: str | None = None
+    recall_description: str | None = None
+
+    # Retain behavior
+    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.
+
+    Args:
+        timestamp: ISO format timestamp (e.g., '2024-01-15T10:30:00Z')
+
+    Returns:
+        Parsed datetime or None if invalid
+
+    Raises:
+        ValueError: If timestamp format is invalid
+    """
+    try:
+        return datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
+    except ValueError as e:
+        raise ValueError(
+            f"Invalid timestamp format '{timestamp}'. "
+            "Expected ISO format like '2024-01-15T10:30:00' or '2024-01-15T10:30:00Z'"
+        ) from e
+
+
+def build_content_dict(
+    content: str,
+    context: str,
+    timestamp: str | None = None,
+) -> tuple[dict[str, Any], str | None]:
+    """Build a content dict for retain operations.
+
+    Args:
+        content: The memory content
+        context: Category for the memory
+        timestamp: Optional ISO timestamp
+
+    Returns:
+        Tuple of (content_dict, error_message). error_message is None if successful.
+    """
+    content_dict: dict[str, Any] = {"content": content, "context": context}
+
+    if timestamp:
+        try:
+            parsed_timestamp = parse_timestamp(timestamp)
+            content_dict["event_date"] = parsed_timestamp
+        except ValueError as e:
+            return {}, str(e)
+
+    return content_dict, None
+
+
+def register_mcp_tools(
+    mcp: FastMCP,
+    memory: MemoryEngine,
+    config: MCPToolsConfig,
+) -> None:
+    """Register MCP tools on a FastMCP server.
+
+    Args:
+        mcp: FastMCP server instance
+        memory: MemoryEngine instance
+        config: Tool configuration
+    """
+    tools_to_register = config.tools or {"retain", "recall", "reflect", "list_banks", "create_bank"}
+
+    if "retain" in tools_to_register:
+        _register_retain(mcp, memory, config)
+
+    if "recall" in tools_to_register:
+        _register_recall(mcp, memory, config)
+
+    if "reflect" in tools_to_register:
+        _register_reflect(mcp, memory, config)
+
+    if "list_banks" in tools_to_register:
+        _register_list_banks(mcp, memory, config)
+
+    if "create_bank" in tools_to_register:
+        _register_create_bank(mcp, memory, config)
+
+
+def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
+    """Register the retain tool."""
+    description = config.retain_description or DEFAULT_MCP_RETAIN_DESCRIPTION
+
+    if config.include_bank_id_param:
+        if config.retain_fire_and_forget:
+
+            @mcp.tool(description=description)
+            async def retain(
+                content: str,
+                context: str = "general",
+                timestamp: str | None = None,
+                bank_id: str | None = None,
+            ) -> dict:
+                """
+                Args:
+                    content: The fact/memory to store (be specific and include relevant details)
+                    context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
+                    timestamp: When this event/fact occurred (ISO format, e.g., '2024-01-15T10:30:00Z'). Useful for timeline tracking.
+                    bank_id: Optional bank to store in (defaults to session bank). Use for cross-bank operations.
+                """
+                import asyncio
+
+                target_bank = bank_id or config.bank_id_resolver()
+                if target_bank is None:
+                    return {"status": "error", "message": "No bank_id configured"}
+
+                content_dict, error = build_content_dict(content, context, timestamp)
+                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=request_context,
+                        )
+                    except Exception as e:
+                        logger.error(f"Error storing memory: {e}", exc_info=True)
+
+                asyncio.create_task(_retain())
+                return {"status": "accepted", "message": "Memory storage initiated"}
+
+        else:
+
+            @mcp.tool(description=description)
+            async def retain(
+                content: str,
+                context: str = "general",
+                timestamp: str | None = None,
+                async_processing: bool = True,
+                bank_id: str | None = None,
+            ) -> str:
+                """
+                Args:
+                    content: The fact/memory to store (be specific and include relevant details)
+                    context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
+                    timestamp: When this event/fact occurred (ISO format, e.g., '2024-01-15T10:30:00Z'). Useful for timeline tracking.
+                    async_processing: If True, queue for background processing and return immediately. If False, wait for completion. Default: True
+                    bank_id: Optional bank to store in (defaults to session bank). Use for cross-bank operations.
+                """
+                try:
+                    target_bank = bank_id or config.bank_id_resolver()
+                    if target_bank is None:
+                        return "Error: No bank_id configured"
+
+                    content_dict, error = build_content_dict(content, context, timestamp)
+                    if error:
+                        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=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=request_context,
+                        )
+                        return f"Memory stored successfully in bank '{target_bank}'"
+                except Exception as e:
+                    logger.error(f"Error storing memory: {e}", exc_info=True)
+                    return f"Error: {str(e)}"
+
+    else:
+        # No bank_id param - use fixed bank from resolver
+
+        @mcp.tool(description=description)
+        async def retain(
+            content: str,
+            context: str = "general",
+            timestamp: str | None = None,
+        ) -> dict:
+            """
+            Args:
+                content: The fact/memory to store (be specific and include relevant details)
+                context: Category for the memory (e.g., 'preferences', 'work', 'hobbies', 'family'). Default: 'general'
+                timestamp: When this event/fact occurred (ISO format, e.g., '2024-01-15T10:30:00Z'). Useful for timeline tracking.
+            """
+            import asyncio
+
+            target_bank = config.bank_id_resolver()
+            if target_bank is None:
+                return {"status": "error", "message": "No bank_id configured"}
+
+            content_dict, error = build_content_dict(content, context, timestamp)
+            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=request_context,
+                    )
+                except Exception as e:
+                    logger.error(f"Error storing memory: {e}", exc_info=True)
+
+            asyncio.create_task(_retain())
+            return {"status": "accepted", "message": "Memory storage initiated"}
+
+
+def _register_recall(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
+    """Register the recall tool."""
+    description = config.recall_description or DEFAULT_MCP_RECALL_DESCRIPTION
+
+    if config.include_bank_id_param:
+
+        @mcp.tool(description=description)
+        async def recall(
+            query: str,
+            max_tokens: int = 4096,
+            bank_id: str | None = None,
+        ) -> str | dict:
+            """
+            Args:
+                query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
+                max_tokens: Maximum tokens to return in results (default: 4096)
+                bank_id: Optional bank to search in (defaults to session bank). Use for cross-bank operations.
+            """
+            try:
+                target_bank = bank_id or config.bank_id_resolver()
+                if target_bank is None:
+                    return "Error: No bank_id configured"
+
+                recall_result = await memory.recall_async(
+                    bank_id=target_bank,
+                    query=query,
+                    fact_type=list(VALID_RECALL_FACT_TYPES),
+                    budget=Budget.HIGH,
+                    max_tokens=max_tokens,
+                    request_context=_get_request_context(config),
+                )
+
+                return recall_result.model_dump_json(indent=2)
+            except Exception as e:
+                logger.error(f"Error searching: {e}", exc_info=True)
+                return f'{{"error": "{e}", "results": []}}'
+
+    else:
+
+        @mcp.tool(description=description)
+        async def recall(
+            query: str,
+            max_tokens: int = 4096,
+        ) -> dict:
+            """
+            Args:
+                query: Natural language search query (e.g., "user's food preferences", "what projects is user working on")
+                max_tokens: Maximum tokens to return in results (default: 4096)
+            """
+            try:
+                target_bank = config.bank_id_resolver()
+                if target_bank is None:
+                    return {"error": "No bank_id configured", "results": []}
+
+                recall_result = await memory.recall_async(
+                    bank_id=target_bank,
+                    query=query,
+                    fact_type=list(VALID_RECALL_FACT_TYPES),
+                    budget=Budget.HIGH,
+                    max_tokens=max_tokens,
+                    request_context=_get_request_context(config),
+                )
+
+                return recall_result.model_dump()
+            except Exception as e:
+                logger.error(f"Error searching: {e}", exc_info=True)
+                return {"error": str(e), "results": []}
+
+
+def _register_reflect(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
+    """Register the reflect tool."""
+
+    if config.include_bank_id_param:
+
+        @mcp.tool()
+        async def reflect(
+            query: str,
+            context: str | None = None,
+            budget: str = "low",
+            bank_id: str | None = None,
+        ) -> str:
+            """
+            Generate thoughtful analysis by synthesizing stored memories with the bank's personality.
+
+            WHEN TO USE THIS TOOL:
+            Use reflect when you need reasoned analysis, not just fact retrieval. This tool
+            thinks through the question using everything the bank knows and its personality traits.
+
+            EXAMPLES OF GOOD QUERIES:
+            - "What patterns have emerged in how I approach debugging?"
+            - "Based on my past decisions, what architectural style do I prefer?"
+            - "What might be the best approach for this problem given what you know about me?"
+            - "How should I prioritize these tasks based on my goals?"
+
+            HOW IT DIFFERS FROM RECALL:
+            - recall: Returns raw facts matching your search (fast lookup)
+            - reflect: Reasons across memories to form a synthesized answer (deeper analysis)
+
+            Use recall for "what did I say about X?" and reflect for "what should I do about X?"
+
+            Args:
+                query: The question or topic to reflect on
+                context: Optional context about why this reflection is needed
+                budget: Search budget - 'low', 'mid', or 'high' (default: 'low')
+                bank_id: Optional bank to reflect in (defaults to session bank). Use for cross-bank operations.
+            """
+            try:
+                target_bank = bank_id or config.bank_id_resolver()
+                if target_bank is None:
+                    return "Error: No bank_id configured"
+
+                budget_map = {"low": Budget.LOW, "mid": Budget.MID, "high": Budget.HIGH}
+                budget_enum = budget_map.get(budget.lower(), Budget.LOW)
+
+                reflect_result = await memory.reflect_async(
+                    bank_id=target_bank,
+                    query=query,
+                    budget=budget_enum,
+                    context=context,
+                    request_context=_get_request_context(config),
+                )
+
+                return reflect_result.model_dump_json(indent=2)
+            except Exception as e:
+                logger.error(f"Error reflecting: {e}", exc_info=True)
+                return f'{{"error": "{e}", "text": ""}}'
+
+    else:
+
+        @mcp.tool()
+        async def reflect(
+            query: str,
+            context: str | None = None,
+            budget: str = "low",
+        ) -> dict:
+            """
+            Generate thoughtful analysis by synthesizing stored memories with the bank's personality.
+
+            WHEN TO USE THIS TOOL:
+            Use reflect when you need reasoned analysis, not just fact retrieval. This tool
+            thinks through the question using everything the bank knows and its personality traits.
+
+            EXAMPLES OF GOOD QUERIES:
+            - "What patterns have emerged in how I approach debugging?"
+            - "Based on my past decisions, what architectural style do I prefer?"
+            - "What might be the best approach for this problem given what you know about me?"
+            - "How should I prioritize these tasks based on my goals?"
+
+            HOW IT DIFFERS FROM RECALL:
+            - recall: Returns raw facts matching your search (fast lookup)
+            - reflect: Reasons across memories to form a synthesized answer (deeper analysis)
+
+            Use recall for "what did I say about X?" and reflect for "what should I do about X?"
+
+            Args:
+                query: The question or topic to reflect on
+                context: Optional context about why this reflection is needed
+                budget: Search budget - 'low', 'mid', or 'high' (default: 'low')
+            """
+            try:
+                target_bank = config.bank_id_resolver()
+                if target_bank is None:
+                    return {"error": "No bank_id configured", "text": ""}
+
+                budget_map = {"low": Budget.LOW, "mid": Budget.MID, "high": Budget.HIGH}
+                budget_enum = budget_map.get(budget.lower(), Budget.LOW)
+
+                reflect_result = await memory.reflect_async(
+                    bank_id=target_bank,
+                    query=query,
+                    budget=budget_enum,
+                    context=context,
+                    request_context=_get_request_context(config),
+                )
+
+                return reflect_result.model_dump()
+            except Exception as e:
+                logger.error(f"Error reflecting: {e}", exc_info=True)
+                return {"error": str(e), "text": ""}
+
+
+def _register_list_banks(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
+    """Register the list_banks tool."""
+
+    @mcp.tool()
+    async def list_banks() -> str:
+        """
+        List all available memory banks.
+
+        Use this tool to discover what memory banks exist in the system.
+        Each bank is an isolated memory store (like a separate "brain").
+
+        Returns:
+            JSON list of banks with their IDs, names, dispositions, and missions.
+        """
+        try:
+            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)
+            return f'{{"error": "{e}", "banks": []}}'
+
+
+def _register_create_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig) -> None:
+    """Register the create_bank tool."""
+
+    @mcp.tool()
+    async def create_bank(bank_id: str, name: str | None = None, mission: str | None = None) -> str:
+        """
+        Create a new memory bank or get an existing one.
+
+        Memory banks are isolated stores - each one is like a separate "brain" for a user/agent.
+        Banks are auto-created with default settings if they don't exist.
+
+        Args:
+            bank_id: Unique identifier for the bank (e.g., 'user-123', 'agent-alpha')
+            name: Optional human-friendly name for the bank
+            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=request_context)
+
+            # Update name/mission if provided
+            if name is not None or mission is not None:
+                await memory.update_bank(
+                    bank_id,
+                    name=name,
+                    mission=mission,
+                    request_context=request_context,
+                )
+                # Fetch updated profile
+                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"):
+                profile["disposition"] = profile["disposition"].model_dump()
+            return json.dumps(profile, indent=2)
+        except Exception as e:
+            logger.error(f"Error creating bank: {e}", exc_info=True)
+            return f'{{"error": "{e}"}}'

hindsight-api/hindsight_api/metrics.py

@@ -6,11 +6,18 @@ This module provides metrics for:
 - Token usage (input/output) per operation
 - Per-bank granularity via labels
 - LLM call latency and token usage with scope dimension
+- HTTP request metrics (latency, count by endpoint/method/status)
+- Process metrics (CPU, memory, file descriptors, threads)
+- Database connection pool metrics
 """
 
 import logging
+import os
+import resource
+import threading
 import time
 from contextlib import contextmanager
+from typing import TYPE_CHECKING, Callable
 
 from opentelemetry import metrics
 from opentelemetry.exporter.prometheus import PrometheusMetricReader
@@ -18,6 +25,18 @@ from opentelemetry.sdk.metrics import MeterProvider
 from opentelemetry.sdk.metrics.view import ExplicitBucketHistogramAggregation, View
 from opentelemetry.sdk.resources import Resource
 
+if TYPE_CHECKING:
+    import asyncpg
+
+
+def _get_tenant() -> str:
+    """Get current tenant (schema) from context for metrics labeling."""
+    # Import here to avoid circular imports
+    from hindsight_api.engine.memory_engine import get_current_schema
+
+    return get_current_schema()
+
+
 # Custom bucket boundaries for operation duration (in seconds)
 # Fine granularity in 0-30s range where most operations complete
 DURATION_BUCKETS = (0.1, 0.25, 0.5, 0.75, 1.0, 2.0, 3.0, 5.0, 7.5, 10.0, 15.0, 20.0, 30.0, 60.0, 120.0)
@@ -25,6 +44,9 @@ DURATION_BUCKETS = (0.1, 0.25, 0.5, 0.75, 1.0, 2.0, 3.0, 5.0, 7.5, 10.0, 15.0, 2
 # LLM duration buckets (finer granularity for faster LLM calls)
 LLM_DURATION_BUCKETS = (0.1, 0.25, 0.5, 1.0, 2.0, 3.0, 5.0, 10.0, 15.0, 30.0, 60.0, 120.0)
 
+# HTTP request duration buckets (millisecond-level for fast endpoints)
+HTTP_DURATION_BUCKETS = (0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0, 30.0)
+
 
 def get_token_bucket(token_count: int) -> str:
     """
@@ -107,9 +129,17 @@ def initialize_metrics(service_name: str = "hindsight-api", service_version: str
         aggregation=ExplicitBucketHistogramAggregation(boundaries=LLM_DURATION_BUCKETS),
     )
 
+    # Create view with custom bucket boundaries for HTTP request duration histogram
+    http_duration_view = View(
+        instrument_name="hindsight.http.duration",
+        aggregation=ExplicitBucketHistogramAggregation(boundaries=HTTP_DURATION_BUCKETS),
+    )
+
     # Create meter provider with Prometheus exporter and custom views
     provider = MeterProvider(
-        resource=resource, metric_readers=[prometheus_reader], views=[duration_view, llm_duration_view]
+        resource=resource,
+        metric_readers=[prometheus_reader],
+        views=[duration_view, llm_duration_view, http_duration_view],
     )
 
     # Set the global meter provider
@@ -159,7 +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
@@ -167,6 +197,15 @@ class MetricsCollectorBase:
         """
         raise NotImplementedError
 
+    @contextmanager
+    def record_http_request(self, method: str, endpoint: str, status_code_getter: Callable[[], int]):
+        """Context manager to record HTTP request metrics."""
+        raise NotImplementedError
+
+    def set_db_pool(self, pool: "asyncpg.Pool"):
+        """Set the database pool for metrics collection."""
+        pass
+
 
 class NoOpMetricsCollector(MetricsCollectorBase):
     """No-op metrics collector that does nothing. Used when metrics are disabled."""
@@ -196,6 +235,11 @@ class NoOpMetricsCollector(MetricsCollectorBase):
         """No-op LLM call recording."""
         pass
 
+    @contextmanager
+    def record_http_request(self, method: str, endpoint: str, status_code_getter: Callable[[], int]):
+        """No-op HTTP request recording."""
+        yield
+
 
 class MetricsCollector(MetricsCollectorBase):
     """
@@ -238,6 +282,27 @@ class MetricsCollector(MetricsCollectorBase):
             name="hindsight.llm.calls.total", description="Total number of LLM API calls", unit="calls"
         )
 
+        # HTTP request metrics
+        self.http_request_duration = self.meter.create_histogram(
+            name="hindsight.http.duration", description="Duration of HTTP requests in seconds", unit="s"
+        )
+
+        self.http_requests_total = self.meter.create_counter(
+            name="hindsight.http.requests.total", description="Total number of HTTP requests", unit="requests"
+        )
+
+        self.http_requests_in_progress = self.meter.create_up_down_counter(
+            name="hindsight.http.requests.in_progress",
+            description="Number of HTTP requests in progress",
+            unit="requests",
+        )
+
+        # Process metrics (observable gauges - collected on scrape)
+        self._setup_process_metrics()
+
+        # DB pool metrics holder (set via set_db_pool)
+        self._db_pool: "asyncpg.Pool | None" = None
+
     @contextmanager
     def record_operation(
         self,
@@ -256,7 +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)
@@ -267,6 +332,7 @@ class MetricsCollector(MetricsCollectorBase):
             "operation": operation,
             "bank_id": bank_id,
             "source": source,
+            "tenant": _get_tenant(),
         }
         if budget:
             attributes["budget"] = budget
@@ -305,7 +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
@@ -317,6 +383,7 @@ class MetricsCollector(MetricsCollectorBase):
             "model": model,
             "scope": scope,
             "success": str(success).lower(),
+            "tenant": _get_tenant(),
         }
 
         # Record duration
@@ -340,6 +407,200 @@ class MetricsCollector(MetricsCollectorBase):
             }
             self.llm_tokens_output.add(output_tokens, output_attributes)
 
+    @contextmanager
+    def record_http_request(self, method: str, endpoint: str, status_code_getter: Callable[[], int]):
+        """
+        Context manager to record HTTP request metrics.
+
+        Usage:
+            status_code = [200]  # Use list for mutability
+            with metrics.record_http_request("GET", "/api/banks", lambda: status_code[0]):
+                # ... handle request
+                status_code[0] = response.status_code
+
+        Args:
+            method: HTTP method (GET, POST, etc.)
+            endpoint: Request endpoint path
+            status_code_getter: Callable that returns the status code after request completes
+        """
+        start_time = time.time()
+        base_attributes = {"method": method, "endpoint": endpoint}
+
+        # Track in-progress
+        self.http_requests_in_progress.add(1, base_attributes)
+
+        try:
+            yield
+        finally:
+            duration = time.time() - start_time
+            status_code = status_code_getter()
+            status_class = f"{status_code // 100}xx"
+
+            # Get tenant from context (may be set during request processing)
+            tenant = _get_tenant()
+
+            attributes = {
+                **base_attributes,
+                "status_code": str(status_code),
+                "status_class": status_class,
+                "tenant": tenant,
+            }
+
+            # Record duration and count
+            self.http_request_duration.record(duration, attributes)
+            self.http_requests_total.add(1, attributes)
+
+            # Decrement in-progress
+            self.http_requests_in_progress.add(-1, base_attributes)
+
+    def _setup_process_metrics(self):
+        """Set up observable gauges for process metrics."""
+
+        def get_cpu_times(_options):
+            """Get process CPU times."""
+            try:
+                rusage = resource.getrusage(resource.RUSAGE_SELF)
+                yield metrics.Observation(rusage.ru_utime, {"type": "user"})
+                yield metrics.Observation(rusage.ru_stime, {"type": "system"})
+            except Exception:
+                pass
+
+        def get_memory_usage(_options):
+            """Get process memory usage in bytes."""
+            try:
+                rusage = resource.getrusage(resource.RUSAGE_SELF)
+                # ru_maxrss is in kilobytes on Linux, bytes on macOS
+                max_rss = rusage.ru_maxrss
+                if os.uname().sysname == "Linux":
+                    max_rss *= 1024  # Convert KB to bytes
+                yield metrics.Observation(max_rss, {"type": "rss_max"})
+            except Exception:
+                pass
+
+        def get_open_file_descriptors(_options):
+            """Get number of open file descriptors."""
+            try:
+                # Try to count open FDs by checking /proc on Linux
+                if os.path.exists("/proc/self/fd"):
+                    count = len(os.listdir("/proc/self/fd"))
+                    yield metrics.Observation(count)
+                else:
+                    # Fallback: use resource limits
+                    soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
+                    yield metrics.Observation(soft, {"limit": "soft"})
+            except Exception:
+                pass
+
+        def get_thread_count(_options):
+            """Get number of active threads."""
+            try:
+                yield metrics.Observation(threading.active_count())
+            except Exception:
+                pass
+
+        # Create observable gauges
+        self.meter.create_observable_gauge(
+            name="hindsight.process.cpu.seconds",
+            callbacks=[get_cpu_times],
+            description="Process CPU time in seconds",
+            unit="s",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.process.memory.bytes",
+            callbacks=[get_memory_usage],
+            description="Process memory usage in bytes",
+            unit="By",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.process.open_fds",
+            callbacks=[get_open_file_descriptors],
+            description="Number of open file descriptors",
+            unit="{fds}",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.process.threads",
+            callbacks=[get_thread_count],
+            description="Number of active threads",
+            unit="{threads}",
+        )
+
+    def set_db_pool(self, pool: "asyncpg.Pool"):
+        """
+        Set the database pool for metrics collection.
+
+        Args:
+            pool: asyncpg connection pool instance
+        """
+        self._db_pool = pool
+        self._setup_db_pool_metrics()
+
+    def _setup_db_pool_metrics(self):
+        """Set up observable gauges for database pool metrics."""
+
+        def get_pool_size(_options):
+            """Get current pool size."""
+            if self._db_pool is not None:
+                try:
+                    yield metrics.Observation(self._db_pool.get_size())
+                except Exception:
+                    pass
+
+        def get_pool_free_size(_options):
+            """Get number of free connections in pool."""
+            if self._db_pool is not None:
+                try:
+                    yield metrics.Observation(self._db_pool.get_idle_size())
+                except Exception:
+                    pass
+
+        def get_pool_min_size(_options):
+            """Get pool minimum size."""
+            if self._db_pool is not None:
+                try:
+                    yield metrics.Observation(self._db_pool.get_min_size())
+                except Exception:
+                    pass
+
+        def get_pool_max_size(_options):
+            """Get pool maximum size."""
+            if self._db_pool is not None:
+                try:
+                    yield metrics.Observation(self._db_pool.get_max_size())
+                except Exception:
+                    pass
+
+        # Create observable gauges for pool metrics
+        self.meter.create_observable_gauge(
+            name="hindsight.db.pool.size",
+            callbacks=[get_pool_size],
+            description="Current number of connections in the pool",
+            unit="{connections}",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.db.pool.idle",
+            callbacks=[get_pool_free_size],
+            description="Number of idle connections in the pool",
+            unit="{connections}",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.db.pool.min",
+            callbacks=[get_pool_min_size],
+            description="Minimum pool size",
+            unit="{connections}",
+        )
+
+        self.meter.create_observable_gauge(
+            name="hindsight.db.pool.max",
+            callbacks=[get_pool_max_size],
+            description="Maximum pool size",
+            unit="{connections}",
+        )
+
 
 # Global metrics collector instance (defaults to no-op)
 _metrics_collector: MetricsCollectorBase = NoOpMetricsCollector()

hindsight-api/hindsight_api/migrations.py

@@ -22,6 +22,7 @@ from pathlib import Path
 
 from alembic import command
 from alembic.config import Config
+from alembic.script.revision import ResolutionError
 from sqlalchemy import create_engine, text
 
 logger = logging.getLogger(__name__)
@@ -78,7 +79,18 @@ def _run_migrations_internal(database_url: str, script_location: str, schema: st
         alembic_cfg.set_main_option("target_schema", schema)
 
     # Run migrations
-    command.upgrade(alembic_cfg, "head")
+    try:
+        command.upgrade(alembic_cfg, "head")
+    except ResolutionError as e:
+        # This happens during rolling deployments when a newer version of the code
+        # has already run migrations, and this older replica doesn't have the new
+        # migration files. The database is already at a newer revision than we know.
+        # This is safe to ignore - the newer code has already applied its migrations.
+        logger.warning(
+            f"Database is at a newer migration revision than this code version knows about. "
+            f"This is expected during rolling deployments. Skipping migrations. Error: {e}"
+        )
+        return
 
     logger.info(f"Database migrations completed successfully for schema '{schema_name}'")
 

hindsight-api/hindsight_api/models.py

@@ -95,7 +95,6 @@ class MemoryUnit(Base):
     mentioned_at: Mapped[datetime | None] = mapped_column(TIMESTAMP(timezone=True))  # When fact was mentioned
     fact_type: Mapped[str] = mapped_column(Text, nullable=False, server_default="world")
     confidence_score: Mapped[float | None] = mapped_column(Float)
-    access_count: Mapped[int] = mapped_column(Integer, server_default="0")
     unit_metadata: Mapped[dict] = mapped_column(
         "metadata", JSONB, server_default=sql_text("'{}'::jsonb")
     )  # User-defined metadata (str->str)
@@ -131,7 +130,6 @@ class MemoryUnit(Base):
         Index("idx_memory_units_document_id", "document_id"),
         Index("idx_memory_units_event_date", "event_date", postgresql_ops={"event_date": "DESC"}),
         Index("idx_memory_units_bank_date", "bank_id", "event_date", postgresql_ops={"event_date": "DESC"}),
-        Index("idx_memory_units_access_count", "access_count", postgresql_ops={"access_count": "DESC"}),
         Index("idx_memory_units_fact_type", "fact_type"),
         Index("idx_memory_units_bank_fact_type", "bank_id", "fact_type"),
         Index(

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/server.py

@@ -7,6 +7,7 @@ This module provides the ASGI app for uvicorn import string usage:
 For CLI usage, use the hindsight-api command instead.
 """
 
+import logging
 import os
 import warnings
 
@@ -17,6 +18,12 @@ warnings.filterwarnings("ignore", message="websockets.server.WebSocketServerProt
 from hindsight_api import MemoryEngine
 from hindsight_api.api import create_app
 from hindsight_api.config import get_config
+from hindsight_api.extensions import (
+    DefaultExtensionContext,
+    OperationValidatorExtension,
+    TenantExtension,
+    load_extension,
+)
 
 # Disable tokenizers parallelism to avoid warnings
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -25,12 +32,42 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false"
 config = get_config()
 config.configure_logging()
 
+# Load operation validator extension if configured
+operation_validator = load_extension("OPERATION_VALIDATOR", OperationValidatorExtension)
+if operation_validator:
+    logging.info(f"Loaded operation validator: {operation_validator.__class__.__name__}")
+
+# Load tenant extension if configured
+tenant_extension = load_extension("TENANT", TenantExtension)
+if tenant_extension:
+    logging.info(f"Loaded tenant extension: {tenant_extension.__class__.__name__}")
+
 # Create app at module level (required for uvicorn import string)
 # MemoryEngine reads configuration from environment variables automatically
-_memory = MemoryEngine()
+# Note: run_migrations=True by default, but migrations are idempotent so safe with workers
+_memory = MemoryEngine(
+    operation_validator=operation_validator,
+    tenant_extension=tenant_extension,
+    run_migrations=config.run_migrations_on_startup,
+)
+
+# Set extension context on tenant extension (needed for schema provisioning)
+if tenant_extension:
+    extension_context = DefaultExtensionContext(
+        database_url=config.database_url,
+        memory_engine=_memory,
+    )
+    tenant_extension.set_context(extension_context)
+    logging.info("Extension context set on tenant extension")
 
 # Create unified app with both HTTP and optionally MCP
-app = create_app(memory=_memory, http_api_enabled=True, mcp_api_enabled=config.mcp_enabled, mcp_mount_path="/mcp")
+app = create_app(
+    memory=_memory,
+    http_api_enabled=True,
+    mcp_api_enabled=config.mcp_enabled,
+    mcp_mount_path="/mcp",
+    initialize_memory=True,
+)
 
 
 if __name__ == "__main__":

hindsight-api/hindsight_api/worker/__init__.py

@@ -0,0 +1,11 @@
+"""
+Worker package for distributed task processing.
+
+This package provides:
+- WorkerPoller: Polls PostgreSQL for pending tasks and executes them
+- main: CLI entry point for hindsight-worker
+"""
+
+from .poller import WorkerPoller
+
+__all__ = ["WorkerPoller"]

hindsight-api/hindsight_api/worker/main.py

@@ -0,0 +1,296 @@
+"""
+Command-line interface for Hindsight Worker.
+
+Run the worker with:
+    hindsight-worker
+
+Stop with Ctrl+C (graceful shutdown).
+"""
+
+import argparse
+import asyncio
+import atexit
+import logging
+import os
+import signal
+import socket
+import sys
+import warnings
+
+from ..config import get_config
+from ..engine.task_backend import SyncTaskBackend
+from .poller import WorkerPoller
+
+# Filter deprecation warnings from third-party libraries
+warnings.filterwarnings("ignore", message="websockets.legacy is deprecated")
+warnings.filterwarnings("ignore", message="websockets.server.WebSocketServerProtocol is deprecated")
+
+# Disable tokenizers parallelism to avoid warnings
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+logger = logging.getLogger(__name__)
+
+
+def create_worker_app(poller: WorkerPoller, memory):
+    """Create a minimal FastAPI app for worker metrics and health."""
+    from fastapi import FastAPI
+    from fastapi.responses import JSONResponse, Response
+    from prometheus_client import CONTENT_TYPE_LATEST, generate_latest
+
+    from ..metrics import create_metrics_collector, get_metrics_collector, initialize_metrics
+
+    app = FastAPI(
+        title="Hindsight Worker",
+        description="Worker process for distributed task execution",
+    )
+
+    # Initialize OpenTelemetry metrics
+    try:
+        prometheus_reader = initialize_metrics(service_name="hindsight-worker", service_version="1.0.0")
+        create_metrics_collector()
+        app.state.prometheus_reader = prometheus_reader
+        logger.info("Metrics initialized - available at /metrics endpoint")
+    except Exception as e:
+        logger.warning(f"Failed to initialize metrics: {e}. Metrics will be disabled.")
+        app.state.prometheus_reader = None
+
+    # Set up DB pool metrics if available
+    metrics_collector = get_metrics_collector()
+    if memory._pool is not None and hasattr(metrics_collector, "set_db_pool"):
+        metrics_collector.set_db_pool(memory._pool)
+        logger.info("DB pool metrics configured")
+
+    @app.get(
+        "/health",
+        summary="Health check endpoint",
+        description="Returns worker health status including database connectivity",
+        tags=["Monitoring"],
+    )
+    async def health_endpoint():
+        """Health check endpoint."""
+        health = await memory.health_check()
+        health["worker_id"] = poller.worker_id
+        health["is_shutdown"] = poller.is_shutdown
+        status_code = 200 if health.get("status") == "healthy" else 503
+        return JSONResponse(content=health, status_code=status_code)
+
+    @app.get(
+        "/metrics",
+        summary="Prometheus metrics endpoint",
+        description="Exports metrics in Prometheus format for scraping",
+        tags=["Monitoring"],
+    )
+    async def metrics_endpoint():
+        """Return Prometheus metrics."""
+        metrics_data = generate_latest()
+        return Response(content=metrics_data, media_type=CONTENT_TYPE_LATEST)
+
+    @app.get(
+        "/",
+        summary="Worker info",
+        description="Basic worker information",
+        tags=["Info"],
+    )
+    async def root():
+        """Return basic worker info."""
+        return {
+            "service": "hindsight-worker",
+            "worker_id": poller.worker_id,
+            "is_shutdown": poller.is_shutdown,
+        }
+
+    return app
+
+
+def main():
+    """Main entry point for the hindsight-worker CLI."""
+    # Load configuration from environment
+    config = get_config()
+
+    parser = argparse.ArgumentParser(
+        prog="hindsight-worker",
+        description="Hindsight Worker - distributed task processor",
+    )
+
+    # Worker options
+    parser.add_argument(
+        "--worker-id",
+        default=config.worker_id or socket.gethostname(),
+        help="Worker identifier (default: hostname, env: HINDSIGHT_API_WORKER_ID)",
+    )
+    parser.add_argument(
+        "--poll-interval",
+        type=int,
+        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(
+        "--max-retries",
+        type=int,
+        default=config.worker_max_retries,
+        help=f"Max retries before marking failed (default: {config.worker_max_retries}, env: HINDSIGHT_API_WORKER_MAX_RETRIES)",
+    )
+
+    # HTTP server options
+    parser.add_argument(
+        "--http-port",
+        type=int,
+        default=config.worker_http_port,
+        help=f"HTTP port for metrics/health endpoints (default: {config.worker_http_port}, env: HINDSIGHT_API_WORKER_HTTP_PORT)",
+    )
+    parser.add_argument(
+        "--http-host",
+        default="0.0.0.0",
+        help="HTTP host to bind (default: 0.0.0.0)",
+    )
+
+    # Logging options
+    parser.add_argument(
+        "--log-level",
+        default=config.log_level,
+        choices=["critical", "error", "warning", "info", "debug", "trace"],
+        help=f"Log level (default: {config.log_level}, env: HINDSIGHT_API_LOG_LEVEL)",
+    )
+
+    args = parser.parse_args()
+
+    # Configure logging
+    config.configure_logging()
+
+    # Import MemoryEngine here to avoid circular imports
+    from .. import MemoryEngine
+
+    print(f"Starting Hindsight Worker: {args.worker_id}")
+    print(f"  Poll interval: {args.poll_interval}ms")
+    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()
+
+    # Global references for cleanup
+    memory = None
+    poller = None
+
+    async def run():
+        nonlocal memory, poller
+        import uvicorn
+
+        from ..extensions import TenantExtension, load_extension
+
+        # Load tenant extension BEFORE creating MemoryEngine so it can
+        # set correct schema context during task execution. Without this,
+        # _authenticate_tenant sees no extension and resets schema to "public",
+        # causing worker writes to land in the wrong schema.
+        tenant_extension = load_extension("TENANT", TenantExtension)
+
+        # Initialize MemoryEngine
+        # Workers use SyncTaskBackend because they execute tasks directly,
+        # they don't need to store tasks (they poll from DB)
+        memory = MemoryEngine(
+            run_migrations=False,  # Workers don't run migrations
+            task_backend=SyncTaskBackend(),
+            tenant_extension=tenant_extension,
+        )
+
+        await memory.initialize()
+
+        print(f"Database connected: {config.database_url}")
+
+        if tenant_extension:
+            print("Tenant extension loaded - schemas will be discovered dynamically on each poll")
+        else:
+            print("No tenant extension configured, using public schema only")
+
+        # Create a single poller that handles all schemas dynamically
+        poller = WorkerPoller(
+            pool=memory._pool,
+            worker_id=args.worker_id,
+            executor=memory.execute_task,
+            poll_interval_ms=args.poll_interval,
+            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
+        app = create_worker_app(poller, memory)
+
+        # Setup signal handlers for graceful shutdown
+        shutdown_requested = asyncio.Event()
+
+        def signal_handler(signum, frame):
+            print(f"\nReceived signal {signum}, initiating graceful shutdown...")
+            shutdown_requested.set()
+
+        signal.signal(signal.SIGINT, signal_handler)
+        signal.signal(signal.SIGTERM, signal_handler)
+
+        # Create uvicorn config and server
+        uvicorn_config = uvicorn.Config(
+            app,
+            host=args.http_host,
+            port=args.http_port,
+            log_level="info",  # Reduce uvicorn noise
+            access_log=False,
+        )
+        server = uvicorn.Server(uvicorn_config)
+
+        # Run the poller and HTTP server concurrently
+        poller_task = asyncio.create_task(poller.run())
+        http_task = asyncio.create_task(server.serve())
+
+        print(f"Worker started. Metrics available at http://{args.http_host}:{args.http_port}/metrics")
+
+        # Wait for shutdown signal
+        await shutdown_requested.wait()
+
+        # Graceful shutdown
+        print("Shutting down HTTP server...")
+        server.should_exit = True
+
+        print("Waiting for poller to finish...")
+        await poller.shutdown_graceful(timeout=30.0)
+        poller_task.cancel()
+        try:
+            await poller_task
+        except asyncio.CancelledError:
+            pass
+
+        # Wait for HTTP server to finish
+        try:
+            await asyncio.wait_for(http_task, timeout=5.0)
+        except asyncio.TimeoutError:
+            http_task.cancel()
+            try:
+                await http_task
+            except asyncio.CancelledError:
+                pass
+
+        # Close memory engine
+        await memory.close()
+        print("Worker shutdown complete")
+
+    def cleanup():
+        """Synchronous cleanup for atexit."""
+        if memory is not None and memory._pg0 is not None:
+            try:
+                loop = asyncio.new_event_loop()
+                loop.run_until_complete(memory._pg0.stop())
+                loop.close()
+                print("\npg0 stopped.")
+            except Exception as e:
+                print(f"\nError stopping pg0: {e}")
+
+    atexit.register(cleanup)
+
+    try:
+        asyncio.run(run())
+    except KeyboardInterrupt:
+        print("\nWorker interrupted")
+        sys.exit(0)
+
+
+if __name__ == "__main__":
+    main()

hindsight-api/hindsight_api/worker/poller.py

@@ -0,0 +1,615 @@
+"""
+Worker poller for distributed task execution.
+
+Polls PostgreSQL for pending tasks and executes them using
+FOR UPDATE SKIP LOCKED for safe concurrent claiming.
+"""
+
+import asyncio
+import json
+import logging
+import time
+import traceback
+from collections.abc import Awaitable, Callable
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    import asyncpg
+
+    from hindsight_api.extensions.tenant import TenantExtension
+
+logger = logging.getLogger(__name__)
+
+# Progress logging interval in seconds
+PROGRESS_LOG_INTERVAL = 30
+
+
+def fq_table(table: str, schema: str | None = None) -> str:
+    """Get fully-qualified table name with optional schema prefix."""
+    if schema:
+        return f'"{schema}".{table}'
+    return table
+
+
+@dataclass
+class ClaimedTask:
+    """A task claimed from the database with its schema context."""
+
+    operation_id: str
+    task_dict: dict[str, Any]
+    schema: str | None
+
+
+class WorkerPoller:
+    """
+    Polls PostgreSQL for pending tasks and executes them.
+
+    Uses FOR UPDATE SKIP LOCKED for safe distributed claiming,
+    allowing multiple workers to process tasks without conflicts.
+
+    Supports dynamic multi-tenant discovery via tenant_extension.
+    """
+
+    def __init__(
+        self,
+        pool: "asyncpg.Pool",
+        worker_id: str,
+        executor: Callable[[dict[str, Any]], Awaitable[None]],
+        poll_interval_ms: int = 500,
+        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.
+
+        Args:
+            pool: asyncpg connection pool
+            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)
+            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._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, 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."""
+        if self._tenant_extension is not None:
+            tenants = await self._tenant_extension.list_tenants()
+            # Convert "public" to None for SQL compatibility, keep others as-is
+            return [t.schema if t.schema != "public" else None for t in tenants]
+        # 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 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.
+
+        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_total = total_available
+        remaining_consolidation = consolidation_available
+
+        for schema in schemas:
+            if remaining_total <= 0:
+                break
+
+            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_total -= len(tasks)
+
+        return all_tasks
+
+    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():
+                # 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}
+                    WHERE status = 'pending'
+                      AND task_payload IS NOT NULL
+                      AND operation_type != 'consolidation'
+                    ORDER BY created_at
+                    LIMIT $1
+                    FOR UPDATE SKIP LOCKED
+                    """,
+                    limit,
+                )
+
+                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 all_rows]
+                await conn.execute(
+                    f"""
+                    UPDATE {table}
+                    SET status = 'processing', worker_id = $1, claimed_at = now(), updated_at = now()
+                    WHERE operation_id = ANY($2)
+                    """,
+                    self._worker_id,
+                    operation_ids,
+                )
+
+                # Parse and return task payloads with schema context
+                return [
+                    ClaimedTask(
+                        operation_id=str(row["operation_id"]),
+                        task_dict=json.loads(row["task_payload"]),
+                        schema=schema,
+                    )
+                    for row in all_rows
+                ]
+
+    async def _mark_completed(self, operation_id: str, schema: str | None):
+        """Mark a task as completed."""
+        table = fq_table("async_operations", schema)
+        await self._pool.execute(
+            f"""
+            UPDATE {table}
+            SET status = 'completed', completed_at = now(), updated_at = now()
+            WHERE operation_id = $1
+            """,
+            operation_id,
+        )
+
+    async def _mark_failed(self, operation_id: str, error_message: str, schema: str | None):
+        """Mark a task as failed with error message."""
+        table = fq_table("async_operations", schema)
+        # Truncate error message if too long (max 5000 chars in schema)
+        error_message = error_message[:5000] if len(error_message) > 5000 else error_message
+        await self._pool.execute(
+            f"""
+            UPDATE {table}
+            SET status = 'failed', error_message = $2, completed_at = now(), updated_at = now()
+            WHERE operation_id = $1
+            """,
+            operation_id,
+            error_message,
+        )
+
+    async def _retry_or_fail(self, operation_id: str, error_message: str, schema: str | None):
+        """Increment retry count or mark as failed if max retries exceeded."""
+        table = fq_table("async_operations", schema)
+
+        # Get current retry count
+        row = await self._pool.fetchrow(
+            f"SELECT retry_count FROM {table} WHERE operation_id = $1",
+            operation_id,
+        )
+
+        if row is None:
+            logger.warning(f"Operation {operation_id} not found, cannot retry")
+            return
+
+        retry_count = row["retry_count"]
+
+        if retry_count >= self._max_retries:
+            # Max retries exceeded, mark as failed
+            await self._mark_failed(
+                operation_id, f"Max retries ({self._max_retries}) exceeded. Last error: {error_message}", schema
+            )
+            logger.error(f"Task {operation_id} failed after {retry_count} retries")
+        else:
+            # Increment retry and reset to pending
+            await self._pool.execute(
+                f"""
+                UPDATE {table}
+                SET status = 'pending', worker_id = NULL, claimed_at = NULL,
+                    retry_count = retry_count + 1, updated_at = now()
+                WHERE operation_id = $1
+                """,
+                operation_id,
+            )
+            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 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, 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})")
+            if task.schema:
+                task.task_dict["_schema"] = task.schema
+            await self._executor(task.task_dict)
+            await self._mark_completed(task.operation_id, task.schema)
+            logger.debug(f"Task {task.operation_id} completed successfully")
+        except Exception as e:
+            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)
+
+    async def recover_own_tasks(self) -> int:
+        """
+        Recover tasks that were assigned to this worker but not completed.
+
+        This handles the case where a worker crashes while processing tasks.
+        On startup, we reset any tasks stuck in 'processing' for this worker_id
+        back to 'pending' so they can be picked up again.
+
+        If tenant_extension is configured, recovers across all tenant schemas.
+
+        Returns:
+            Number of tasks recovered
+        """
+        schemas = await self._get_schemas()
+        total_count = 0
+
+        for schema in schemas:
+            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,
+                )
+
+                # 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")
+        return total_count
+
+    async def run(self):
+        """
+        Main polling loop with fire-and-forget task execution.
+
+        Continuously polls for pending tasks, spawns them as background tasks,
+        and immediately continues polling (up to slot limits).
+        """
+        await self.recover_own_tasks()
+
+        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 (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 "
+                        f"({consolidation_count} consolidation): {types_str} (schemas: {schemas_str})"
+                    )
+
+                    # Spawn tasks as background jobs (fire-and-forget)
+                    for task in tasks:
+                        await self.execute_task(task)
+
+                    # 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()
+
+            except asyncio.CancelledError:
+                logger.info(f"Worker {self._worker_id} polling loop cancelled")
+                break
+            except Exception as e:
+                logger.error(f"Worker {self._worker_id} error in polling loop: {e}")
+                traceback.print_exc()
+                # Backoff on error
+                await asyncio.sleep(1)
+
+        logger.info(f"Worker {self._worker_id} polling loop stopped")
+
+    async def shutdown_graceful(self, timeout: float = 30.0):
+        """
+        Signal shutdown and wait for current tasks to complete.
+
+        Args:
+            timeout: Maximum time to wait for in-flight tasks (seconds)
+        """
+        logger.info(f"Worker {self._worker_id} initiating graceful shutdown")
+        self._shutdown.set()
+
+        # Wait for in-flight tasks to complete
+        start_time = asyncio.get_event_loop().time()
+        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")
+
+            # 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."""
+        now = time.time()
+        if now - self._last_progress_log < PROGRESS_LOG_INTERVAL:
+            return
+
+        self._last_progress_log = now
+
+        try:
+            # Get local active tasks
+            async with self._in_flight_lock:
+                in_flight = self._in_flight_count
+                in_flight_by_type = dict(self._in_flight_by_type)
+                active_tasks = dict(self._active_tasks)
+
+            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():
+                key = (op_type, bank_id)
+                task_groups[key] = task_groups.get(key, 0) + 1
+
+            processing_info = [f"{op}:{bank}({cnt})" for (op, bank), cnt in task_groups.items()]
+            processing_str = ", ".join(processing_info[:10]) if processing_info else "none"
+            if len(processing_info) > 10:
+                processing_str += f" +{len(processing_info) - 10} more"
+
+            # Get global stats from DB
+            schemas = await self._get_schemas()
+            global_pending = 0
+            all_worker_counts: dict[str, int] = {}
+
+            async with self._pool.acquire() as conn:
+                for schema in schemas:
+                    table = fq_table("async_operations", schema)
+
+                    row = await conn.fetchrow(f"SELECT COUNT(*) as count FROM {table} WHERE status = 'pending'")
+                    global_pending += row["count"] if row else 0
+
+                    worker_rows = await conn.fetch(
+                        f"""
+                        SELECT worker_id, COUNT(*) as count
+                        FROM {table}
+                        WHERE status = 'processing'
+                        GROUP BY worker_id
+                        """
+                    )
+                    for wr in worker_rows:
+                        wid = wr["worker_id"] or "unknown"
+                        all_worker_counts[wid] = all_worker_counts.get(wid, 0) + wr["count"]
+
+            other_workers = []
+            for wid, cnt in all_worker_counts.items():
+                if wid != self._worker_id:
+                    other_workers.append(f"{wid}:{cnt}")
+            others_str = ", ".join(other_workers) if other_workers else "none"
+
+            schemas_str = ", ".join(s or "public" for s in schemas)
+            logger.info(
+                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}"
+            )
+
+        except Exception as e:
+            logger.debug(f"Failed to log progress stats: {e}")
+
+    @property
+    def worker_id(self) -> str:
+        """Get the worker ID."""
+        return self._worker_id
+
+    @property
+    def is_shutdown(self) -> bool:
+        """Check if shutdown has been signaled."""
+        return self._shutdown.is_set()

hindsight-api/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "hindsight-api"
-version = "0.2.1"
+version = "0.4.2"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -25,7 +25,7 @@ dependencies = [
     "psycopg2-binary>=2.9.11",
     "tiktoken>=0.12.0",
     "httpx>=0.27.0",
-    "fastmcp>=2.3.0",
+    "fastmcp>=2.14.0",  # CVE-2025-66416
     "pg0-embedded>=0.11.0",
     "python-dateutil>=2.8.0",
     "opentelemetry-api>=1.20.0",
@@ -34,13 +34,23 @@ 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",
+    "flashrank>=0.2.0",
     # Local ML models for embeddings/reranking - can be excluded in Docker with INCLUDE_LOCAL_MODELS=false
-    "sentence-transformers>=3.0.0,<3.3.0",
-    "transformers>=4.30.0,<4.46.0",
-    "torch>=2.0.0",
+    "sentence-transformers>=3.3.0",
+    "transformers>=4.53.0",  # Security fixes for ReDoS vulnerabilities
+    "torch>=2.6.0",  # CVE fix for remote code execution
+    "uvloop>=0.22.1",
+    # Transitive dependency security fixes
+    "pyasn1>=0.6.2",  # DoS vulnerability fix
+    "urllib3>=2.6.3",  # Decompression-bomb safeguards bypass fix
+    "langchain-core>=1.2.5",  # Serialization injection vulnerability fix
+    "filelock>=3.20.1",  # TOCTOU race condition fix
+    "authlib>=1.6.6",  # Account takeover vulnerability fix
+    "aiohttp>=3.13.3",  # Multiple DoS vulnerabilities
 ]
 
 [project.optional-dependencies]
@@ -49,11 +59,12 @@ test = [
     "pytest-asyncio>=0.21.0",
     "pytest-timeout>=2.4.0",
     "pytest-xdist>=3.0.0",
-    "filelock>=3.0.0",
+    "filelock>=3.20.1",  # TOCTOU race condition fix
 ]
 
 [project.scripts]
 hindsight-api = "hindsight_api.main:main"
+hindsight-worker = "hindsight_api.worker.main:main"
 hindsight-local-mcp = "hindsight_api.mcp_local:main"
 hindsight-admin = "hindsight_api.admin.cli:main"
 
@@ -95,7 +106,7 @@ dev = [
     "pytest-timeout>=2.4.0",
     "pytest-xdist>=3.8.0",
     "python-dotenv>=1.2.1",
-    "filelock>=3.0.0",
+    "filelock>=3.20.1",  # TOCTOU race condition fix
     "ruff>=0.8.0",
     "ty>=0.0.1",
 ]
@@ -131,6 +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/conftest.py

@@ -12,6 +12,7 @@ from hindsight_api import MemoryEngine, LLMConfig, LocalSTEmbeddings, RequestCon
 
 from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder
 from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
+from hindsight_api.engine.task_backend import SyncTaskBackend
 from hindsight_api.pg0 import EmbeddedPostgres
 
 # Default pg0 instance configuration for tests
@@ -115,16 +116,65 @@ def llm_config():
 
 
 @pytest.fixture(scope="session")
-def embeddings():
+def embeddings(tmp_path_factory, worker_id):
+    """
+    Session-scoped embeddings fixture with filelock to prevent race conditions.
 
-    return LocalSTEmbeddings()
+    When pytest-xdist runs multiple workers in parallel, they all try to load
+    models from the HuggingFace cache simultaneously, which can cause race
+    conditions and meta tensor errors. We use a filelock to serialize model
+    initialization across workers.
+    """
+    # Get shared temp dir for coordination between xdist workers
+    if worker_id == "master":
+        root_tmp_dir = tmp_path_factory.getbasetemp()
+    else:
+        root_tmp_dir = tmp_path_factory.getbasetemp().parent
 
+    lock_file = root_tmp_dir / "embeddings_init.lock"
+
+    emb = LocalSTEmbeddings()
+
+    # Serialize model initialization across workers
+    with filelock.FileLock(str(lock_file)):
+        loop = asyncio.new_event_loop()
+        try:
+            loop.run_until_complete(emb.initialize())
+        finally:
+            loop.close()
+
+    return emb
 
 
 @pytest.fixture(scope="session")
-def cross_encoder():
+def cross_encoder(tmp_path_factory, worker_id):
+    """
+    Session-scoped cross-encoder fixture with filelock to prevent race conditions.
 
-    return LocalSTCrossEncoder()
+    When pytest-xdist runs multiple workers in parallel, they all try to load
+    models from the HuggingFace cache simultaneously, which can cause race
+    conditions and meta tensor errors. We use a filelock to serialize model
+    initialization across workers.
+    """
+    # Get shared temp dir for coordination between xdist workers
+    if worker_id == "master":
+        root_tmp_dir = tmp_path_factory.getbasetemp()
+    else:
+        root_tmp_dir = tmp_path_factory.getbasetemp().parent
+
+    lock_file = root_tmp_dir / "cross_encoder_init.lock"
+
+    ce = LocalSTCrossEncoder()
+
+    # Serialize model initialization across workers
+    with filelock.FileLock(str(lock_file)):
+        loop = asyncio.new_event_loop()
+        try:
+            loop.run_until_complete(ce.initialize())
+        finally:
+            loop.close()
+
+    return ce
 
 @pytest.fixture(scope="session")
 def query_analyzer():
@@ -147,6 +197,7 @@ async def memory(pg0_db_url, embeddings, cross_encoder, query_analyzer):
     Uses pg0_db_url (a postgresql:// URL) directly, so MemoryEngine won't try to
     manage pg0 lifecycle - that's handled by the session-scoped pg0_db_url fixture.
     Migrations are disabled here since they're run once at session scope in pg0_db_url.
+    Uses SyncTaskBackend so async tasks execute immediately (no worker needed).
     """
     mem = MemoryEngine(
         db_url=pg0_db_url,  # Direct postgresql:// URL, not pg0://
@@ -160,6 +211,7 @@ async def memory(pg0_db_url, embeddings, cross_encoder, query_analyzer):
         pool_min_size=1,
         pool_max_size=5,
         run_migrations=False,  # Migrations already run at session scope
+        task_backend=SyncTaskBackend(),  # Execute tasks immediately in tests
     )
     await mem.initialize()
     yield mem

hindsight-api/tests/test_agents_api.py

@@ -1,5 +1,5 @@
 """
-Tests for agent management API (profile, disposition, background).
+Tests for agent management API (profile, disposition).
 """
 import pytest
 import uuid
@@ -25,15 +25,12 @@ class TestAgentProfile:
 
         assert profile is not None
         assert "disposition" in profile
-        assert "background" in profile
 
         disposition = profile["disposition"]
         assert disposition.skepticism == 3
         assert disposition.literalism == 3
         assert disposition.empathy == 3
 
-        assert profile["background"] == ""
-
     @pytest.mark.asyncio
     async def test_update_agent_disposition(self, memory: MemoryEngine, request_context):
         """Test updating agent disposition traits."""
@@ -76,63 +73,10 @@ class TestAgentProfile:
         for agent in agents:
             assert "bank_id" in agent
             assert "disposition" in agent
-            assert "background" in agent
             assert "created_at" in agent
             assert "updated_at" in agent
 
 
-class TestAgentBackground:
-    """Tests for agent background management."""
-
-    @pytest.mark.asyncio
-    async def test_merge_agent_background(self, memory: MemoryEngine, request_context):
-        """Test merging agent background information."""
-        bank_id = unique_agent_id("test_profile_merge")
-
-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
-        assert profile["background"] == ""
-
-        result1 = await memory.merge_bank_background(
-            bank_id,
-            "I was born in Texas",
-            update_disposition=False,
-            request_context=request_context,
-        )
-        assert "Texas" in result1["background"]
-
-        result2 = await memory.merge_bank_background(
-            bank_id,
-            "I have 10 years of startup experience",
-            update_disposition=False,
-            request_context=request_context,
-        )
-        assert "Texas" in result2["background"] or "startup" in result2["background"]
-
-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
-        assert final_profile["background"] != ""
-
-    @pytest.mark.asyncio
-    async def test_merge_background_handles_conflicts(self, memory: MemoryEngine, request_context):
-        """Test that merging background handles conflicts (new overwrites old)."""
-        bank_id = unique_agent_id("test_profile_conflict")
-
-        result1 = await memory.merge_bank_background(
-            bank_id,
-            "I was born in Colorado",
-            update_disposition=False,
-            request_context=request_context,
-        )
-        assert "Colorado" in result1["background"]
-
-        result2 = await memory.merge_bank_background(
-            bank_id,
-            "You were born in Texas",
-            update_disposition=False,
-            request_context=request_context,
-        )
-        assert "Texas" in result2["background"]
-
-
 class TestAgentEndpoint:
     """Tests for agent PUT endpoint logic."""
 
@@ -147,7 +91,6 @@ class TestAgentEndpoint:
                 literalism=5,
                 empathy=2
             ),
-            background="I am a creative software engineer"
         )
 
         profile = await memory.get_bank_profile(bank_id, request_context=request_context)
@@ -159,55 +102,10 @@ class TestAgentEndpoint:
                 request_context=request_context,
             )
 
-        if request.background is not None:
-            pool = await memory._get_pool()
-            async with pool.acquire() as conn:
-                await conn.execute(
-                    """
-                    UPDATE banks
-                    SET background = $2,
-                        updated_at = NOW()
-                    WHERE bank_id = $1
-                    """,
-                    bank_id,
-                    request.background
-                )
-
         final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
 
         assert final_profile["disposition"].skepticism == 4
         assert final_profile["disposition"].literalism == 5
-        assert final_profile["background"] == "I am a creative software engineer"
-
-    @pytest.mark.asyncio
-    async def test_put_agent_partial_update(self, memory: MemoryEngine, request_context):
-        """Test updating only background."""
-        bank_id = unique_agent_id("test_put_partial")
-
-        request = CreateBankRequest(
-            background="I am a data scientist"
-        )
-
-        profile = await memory.get_bank_profile(bank_id, request_context=request_context)
-
-        if request.background is not None:
-            pool = await memory._get_pool()
-            async with pool.acquire() as conn:
-                await conn.execute(
-                    """
-                    UPDATE banks
-                    SET background = $2,
-                        updated_at = NOW()
-                    WHERE bank_id = $1
-                    """,
-                    bank_id,
-                    request.background
-                )
-
-        final_profile = await memory.get_bank_profile(bank_id, request_context=request_context)
-
-        assert final_profile["disposition"].skepticism == 3  # Default
-        assert final_profile["background"] == "I am a data scientist"
 
 
 class TestAgentDispositionIntegration:
@@ -225,13 +123,6 @@ class TestAgentDispositionIntegration:
         }
         await memory.update_bank_disposition(bank_id, disposition, request_context=request_context)
 
-        await memory.merge_bank_background(
-            bank_id,
-            "I am a creative artist who values innovation over tradition",
-            update_disposition=False,
-            request_context=request_context,
-        )
-
         await memory.retain_batch_async(
             bank_id=bank_id,
             contents=[

hindsight-api/tests/test_custom_embedding_dimension.py

@@ -9,17 +9,18 @@ Includes tests for:
 
 import asyncio
 import os
-import pytest
 from datetime import datetime
+
+import pytest
 from sqlalchemy import create_engine, text
 
 from hindsight_api import MemoryEngine, RequestContext
-from hindsight_api.engine.embeddings import LocalSTEmbeddings, OpenAIEmbeddings, CohereEmbeddings
-from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder, CohereCrossEncoder
+from hindsight_api.engine.cross_encoder import CohereCrossEncoder, LocalSTCrossEncoder
+from hindsight_api.engine.embeddings import CohereEmbeddings, LocalSTEmbeddings, OpenAIEmbeddings
 from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
-from hindsight_api.extensions import TenantExtension, TenantContext
-from hindsight_api.migrations import run_migrations, ensure_embedding_dimension
-
+from hindsight_api.engine.task_backend import SyncTaskBackend
+from hindsight_api.extensions import TenantContext, TenantExtension
+from hindsight_api.migrations import ensure_embedding_dimension, run_migrations
 
 # =============================================================================
 # Shared Utilities
@@ -35,6 +36,11 @@ class SchemaTenantExtension(TenantExtension):
     async def authenticate(self, request_context: RequestContext) -> TenantContext:
         return TenantContext(schema_name=self.schema_name)
 
+    async def list_tenants(self) -> list:
+        from hindsight_api.extensions.tenant import Tenant
+
+        return [Tenant(schema=self.schema_name)]
+
 
 def get_test_schema(prefix: str, worker_id: str) -> str:
     """Get unique schema name per xdist worker."""
@@ -323,6 +329,7 @@ class TestOpenAIEmbeddings:
             pool_max_size=3,
             run_migrations=False,
             tenant_extension=SchemaTenantExtension(schema_name),
+            task_backend=SyncTaskBackend(),
         )
 
         try:
@@ -392,6 +399,7 @@ class TestOpenAIEmbeddings:
             pool_max_size=3,
             run_migrations=False,
             tenant_extension=SchemaTenantExtension(schema_name),
+            task_backend=SyncTaskBackend(),
         )
 
         try:
@@ -559,6 +567,7 @@ class TestCohereIntegration:
             pool_max_size=3,
             run_migrations=False,
             tenant_extension=SchemaTenantExtension(schema_name),
+            task_backend=SyncTaskBackend(),
         )
 
         try:

hindsight-api/tests/test_extensions.py

@@ -24,6 +24,9 @@ from hindsight_api.extensions import (
     TenantExtension,
     ValidationResult,
     load_extension,
+    # Consolidation operation
+    ConsolidateContext,
+    ConsolidateResult,
 )
 
 
@@ -128,14 +131,18 @@ class TrackingValidator(OperationValidatorExtension):
 
     def __init__(self, config: dict):
         super().__init__(config)
-        # Pre-hook tracking
+        # Pre-hook tracking - Core operations
         self.pre_retain_calls: list[RetainContext] = []
         self.pre_recall_calls: list[RecallContext] = []
         self.pre_reflect_calls: list[ReflectContext] = []
-        # Post-hook tracking
+        # Post-hook tracking - Core operations
         self.post_retain_calls: list[RetainResult] = []
         self.post_recall_calls: list[RecallResult] = []
         self.post_reflect_calls: list[ReflectResultContext] = []
+        # Pre-hook tracking - Consolidation
+        self.pre_consolidate_calls: list[ConsolidateContext] = []
+        # Post-hook tracking - Consolidation
+        self.post_consolidate_calls: list[ConsolidateResult] = []
 
     async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
         self.pre_retain_calls.append(ctx)
@@ -158,6 +165,14 @@ class TrackingValidator(OperationValidatorExtension):
     async def on_reflect_complete(self, result: ReflectResultContext) -> None:
         self.post_reflect_calls.append(result)
 
+    # Consolidation hooks
+    async def validate_consolidate(self, ctx: ConsolidateContext) -> ValidationResult:
+        self.pre_consolidate_calls.append(ctx)
+        return ValidationResult.accept()
+
+    async def on_consolidate_complete(self, result: ConsolidateResult) -> None:
+        self.post_consolidate_calls.append(result)
+
 
 class TestMemoryEngineValidation:
     """Tests for validation integration with MemoryEngine.