fix(python-client): async=true was silently ignored on retain calls

The hand-written client wrapper passed `async_=retain_async` to RetainRequest, but the generated Pydantic model uses `var_async` as the Python field name (with `alias="async"`). The `async_` kwarg didn't match either the field name or the alias, so Pydantic silently ignored it — every retain call ran synchronously regardless of the flag. This has been broken since the client was first introduced (6073ac4f), not a regression. Also adds unit tests that verify the async field serializes correctly in the request JSON, preventing future regressions.
refactor(claude-code): remove recallTopK setting
2026-03-26 15:13:25 +01:00 · 2026-03-26 13:57:33 +01:00 · 2026-03-26 13:51:40 +01:00 · 2026-03-26 13:15:36 +01:00 · 2026-03-26 12:58:25 +01:00 · 2026-03-26 12:58:09 +01:00
597 changed files with 42352 additions and 3993 deletions
@@ -0,0 +1,15 @@
+{
+  "$schema": "https://anthropic.com/claude-code/marketplace.schema.json",
+  "name": "hindsight",
+  "description": "Official Hindsight integrations for Claude Code",
+  "owner": {
+    "name": "vectorize-io"
+  },
+  "plugins": [
+    {
+      "name": "hindsight-memory",
+      "description": "Automatic long-term memory for Claude Code via Hindsight",
+      "source": "./hindsight-integrations/claude-code"
+    }
+  ]
+}
@@ -31,8 +31,10 @@ jobs:
      run: |
        if [ -f "hindsight-integrations/${{ steps.info.outputs.integration }}/pyproject.toml" ]; then
          echo "type=python" >> $GITHUB_OUTPUT
-        else
+        elif [ -f "hindsight-integrations/${{ steps.info.outputs.integration }}/package.json" ]; then
          echo "type=typescript" >> $GITHUB_OUTPUT
+        else
+          echo "type=plugin" >> $GITHUB_OUTPUT
        fi

    # ── Python integrations (litellm, pydantic-ai, crewai) ──────────────────
@@ -45,7 +47,7 @@ jobs:

    - name: Set up Python
      if: steps.type.outputs.type == 'python'
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
      with:
        python-version-file: ".python-version"

@@ -63,6 +65,16 @@ jobs:

    # ── TypeScript integrations (ai-sdk, chat, openclaw) ────────────────────

+    # ── Plugin integrations (claude-code) — no package to publish ───────────
+
+    - name: Plugin release
+      if: steps.type.outputs.type == 'plugin'
+      run: |
+        echo "Plugin integration ${{ steps.info.outputs.integration }} v${{ steps.info.outputs.version }} — no package to publish."
+        echo "Users install via: claude plugin marketplace add vectorize-io/hindsight --sparse hindsight-integrations"
+
+    # ── TypeScript integrations (ai-sdk, chat, openclaw) ────────────────────
+
    - name: Set up Node.js
      if: steps.type.outputs.type == 'typescript'
      uses: actions/setup-node@v6
@@ -10,7 +10,103 @@ concurrency:
  cancel-in-progress: true

 jobs:
+  detect-changes:
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: read
+    outputs:
+      core: ${{ steps.filter.outputs.core }}
+      clients-ts: ${{ steps.filter.outputs.clients-ts }}
+      clients-python: ${{ steps.filter.outputs.clients-python }}
+      clients-rust: ${{ steps.filter.outputs.clients-rust }}
+      clients-go: ${{ steps.filter.outputs.clients-go }}
+      control-plane: ${{ steps.filter.outputs.control-plane }}
+      cli: ${{ steps.filter.outputs.cli }}
+      docker: ${{ steps.filter.outputs.docker }}
+      helm: ${{ steps.filter.outputs.helm }}
+      docs: ${{ steps.filter.outputs.docs }}
+      embed: ${{ steps.filter.outputs.embed }}
+      hindsight-all: ${{ steps.filter.outputs.hindsight-all }}
+      integration-tests: ${{ steps.filter.outputs.integration-tests }}
+      integrations-openclaw: ${{ steps.filter.outputs.integrations-openclaw }}
+      integrations-ai-sdk: ${{ steps.filter.outputs.integrations-ai-sdk }}
+      integrations-chat: ${{ steps.filter.outputs.integrations-chat }}
+      integrations-claude-code: ${{ steps.filter.outputs.integrations-claude-code }}
+      integrations-crewai: ${{ steps.filter.outputs.integrations-crewai }}
+      integrations-litellm: ${{ steps.filter.outputs.integrations-litellm }}
+      integrations-pydantic-ai: ${{ steps.filter.outputs.integrations-pydantic-ai }}
+      integrations-hermes: ${{ steps.filter.outputs.integrations-hermes }}
+      dev: ${{ steps.filter.outputs.dev }}
+      ci: ${{ steps.filter.outputs.ci }}
+    steps:
+    - uses: actions/checkout@v6
+
+    - uses: dorny/paths-filter@v3
+      id: filter
+      with:
+        filters: |
+          core:
+            - 'hindsight-api-slim/**'
+            - 'hindsight-api/**'
+            - 'scripts/**'
+            - '.python-version'
+            - '.env.example'
+          clients-ts:
+            - 'hindsight-clients/typescript/**'
+            - 'package.json'
+            - 'package-lock.json'
+          clients-python:
+            - 'hindsight-clients/python/**'
+          clients-rust:
+            - 'hindsight-clients/rust/**'
+          clients-go:
+            - 'hindsight-clients/go/**'
+          control-plane:
+            - 'hindsight-control-plane/**'
+            - 'package.json'
+            - 'package-lock.json'
+          cli:
+            - 'hindsight-cli/**'
+          docker:
+            - 'docker/**'
+          helm:
+            - 'helm/**'
+          docs:
+            - 'hindsight-docs/**'
+            - '*.md'
+          embed:
+            - 'hindsight-embed/**'
+          hindsight-all:
+            - 'hindsight-all/**'
+          integration-tests:
+            - 'hindsight-integration-tests/**'
+          integrations-openclaw:
+            - 'hindsight-integrations/openclaw/**'
+          integrations-ai-sdk:
+            - 'hindsight-integrations/ai-sdk/**'
+          integrations-chat:
+            - 'hindsight-integrations/chat/**'
+          integrations-claude-code:
+            - 'hindsight-integrations/claude-code/**'
+          integrations-crewai:
+            - 'hindsight-integrations/crewai/**'
+          integrations-litellm:
+            - 'hindsight-integrations/litellm/**'
+          integrations-pydantic-ai:
+            - 'hindsight-integrations/pydantic-ai/**'
+          integrations-hermes:
+            - 'hindsight-integrations/hermes/**'
+          dev:
+            - 'hindsight-dev/**'
+          ci:
+            - '.github/**'
+
  build-api-python-versions:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    strategy:
      matrix:
@@ -34,6 +130,11 @@ jobs:
      run: uv build

  build-typescript-client:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.clients-ts == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -53,6 +154,11 @@ jobs:
      run: npm run build --workspace=hindsight-clients/typescript

  build-openclaw-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-openclaw == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -75,7 +181,35 @@ jobs:
      working-directory: ./hindsight-integrations/openclaw
      run: npm run build

+  test-claude-code-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-claude-code == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v6
+
+    - name: Set up Python
+      uses: actions/setup-python@v6
+      with:
+        python-version: '3.11'
+
+    - name: Install pytest
+      run: pip install pytest
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/claude-code
+      run: python -m pytest tests/ -v
+
  build-ai-sdk-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-ai-sdk == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -99,6 +233,11 @@ jobs:
      run: npm run build

  test-ai-sdk-integration-deno:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-ai-sdk == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -123,6 +262,11 @@ jobs:
      run: npm run test:deno

  build-chat-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-chat == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -146,6 +290,12 @@ jobs:
      run: npm run build

  build-control-plane:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.control-plane == 'true' ||
+      needs.detect-changes.outputs.clients-ts == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -198,6 +348,11 @@ jobs:
        fi

  build-docs:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.docs == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -217,6 +372,12 @@ jobs:
      run: npm run build --workspace=hindsight-docs

  test-rust-cli:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.cli == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -337,6 +498,11 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  lint-helm-chart:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.helm == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -351,6 +517,13 @@ jobs:
      run: helm lint helm/hindsight

  build-docker-images:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.docker == 'true' ||
+      needs.detect-changes.outputs.control-plane == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    name: Build Docker (${{ matrix.name }})
    runs-on: ubuntu-latest
    strategy:
@@ -434,6 +607,11 @@ jobs:
      run: ./docker/test-image.sh "hindsight-${{ matrix.name }}:test" "${{ matrix.target }}"

  test-api:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -443,6 +621,9 @@ jobs:
      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
      COHERE_API_KEY: ${{ secrets.COHERE_API_KEY }}
      HINDSIGHT_API_EMBEDDINGS_OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+      AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+      AWS_REGION_NAME: ${{ secrets.AWS_REGION_NAME }}
      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
      # Prefer CPU-only PyTorch in CI (but keep PyPI for everything else)

@@ -499,6 +680,12 @@ jobs:
      run: uv run pytest tests -v

  test-python-client:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.clients-python == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -601,6 +788,12 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  test-typescript-client:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.clients-ts == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -708,6 +901,12 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  test-typescript-client-deno:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.clients-ts == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -821,6 +1020,11 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  build-rust-cli-arm64:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.cli == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-24.04-arm

    steps:
@@ -845,6 +1049,12 @@ jobs:
      run: cargo build --release --target aarch64-unknown-linux-gnu

  test-rust-client:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.clients-rust == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -951,6 +1161,12 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  test-go-client:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.clients-go == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -1055,6 +1271,13 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  test-openclaw-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.integrations-openclaw == 'true' ||
+      needs.detect-changes.outputs.embed == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -1162,6 +1385,12 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  test-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.integration-tests == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -1259,6 +1488,11 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  test-crewai-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-crewai == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -1288,6 +1522,11 @@ jobs:
      run: uv run pytest tests -v

  test-litellm-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-litellm == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -1317,6 +1556,11 @@ jobs:
      run: uv run pytest tests -v

  test-pydantic-ai-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-pydantic-ai == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest

    steps:
@@ -1345,7 +1589,46 @@ jobs:
      working-directory: ./hindsight-integrations/pydantic-ai
      run: uv run pytest tests -v

+  test-hermes-integration:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.integrations-hermes == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v6
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v7
+      with:
+        enable-cache: true
+        prune-cache: false
+
+    - name: Set up Python
+      uses: actions/setup-python@v6
+      with:
+        python-version-file: ".python-version"
+
+    - name: Build hermes integration
+      working-directory: ./hindsight-integrations/hermes
+      run: uv build
+
+    - name: Install dependencies
+      working-directory: ./hindsight-integrations/hermes
+      run: uv sync --frozen
+
+    - name: Run tests
+      working-directory: ./hindsight-integrations/hermes
+      run: uv run pytest tests -v
+
  test-pip-slim:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -1406,6 +1689,12 @@ jobs:
        cat /tmp/slim-api-server.log 2>/dev/null || true

  test-embed:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.embed == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -1459,6 +1748,12 @@ jobs:
      run: ./test.sh

  test-hindsight-all:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.hindsight-all == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -1512,6 +1807,16 @@ jobs:
      run: uv run pytest tests/ -v

  test-doc-examples:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.clients-ts == 'true' ||
+      needs.detect-changes.outputs.clients-python == 'true' ||
+      needs.detect-changes.outputs.clients-go == 'true' ||
+      needs.detect-changes.outputs.cli == 'true' ||
+      needs.detect-changes.outputs.docs == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    strategy:
      fail-fast: false
@@ -1651,6 +1956,12 @@ jobs:
        cat /tmp/api-server.log || echo "No API server log found"

  test-upgrade:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.dev == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    env:
      HINDSIGHT_API_LLM_PROVIDER: vertexai
@@ -1775,6 +2086,9 @@ jobs:
    - name: Run generate-clients
      run: ./scripts/generate-clients.sh

+    - name: Run generate-docs-skill
+      run: ./scripts/generate-docs-skill.sh
+
    - name: Run lint
      run: ./scripts/hooks/lint.sh

@@ -1789,6 +2103,7 @@ jobs:
          echo "Please run the following commands locally and commit the changes:"
          echo "  ./scripts/generate-openapi.sh"
          echo "  ./scripts/generate-clients.sh"
+          echo "  ./scripts/generate-docs-skill.sh"
          echo "  ./scripts/hooks/lint.sh"
          echo ""
          git diff --stat
@@ -1797,6 +2112,11 @@ jobs:
        echo "✓ All generated files are up to date"

  check-openapi-compatibility:
+    needs: [detect-changes]
+    if: >-
+      github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.core == 'true' ||
+      needs.detect-changes.outputs.ci == 'true'
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v6
@@ -7,6 +7,7 @@
 [![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-3nhbm4w29-LeSJ5Ixi6j8PdiYOCPlOgg)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![gitcgr](https://gitcgr.com/badge/vectorize-io/hindsight.svg)](https://gitcgr.com/vectorize-io/hindsight)
 ![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)
 <br/>
@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.4.19
-appVersion: "0.4.19"
+version: 0.4.20
+appVersion: "0.4.20"
 keywords:
  - ai
  - memory
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "hindsight-all-slim"
-version = "0.4.19"
+version = "0.4.20"
 description = "Hindsight: Agent Memory That Works Like Human Memory - Slim All-in-One Bundle"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -34,7 +34,6 @@ Using context manager:
 """

 import logging
-import os
 import threading
 from typing import Optional

@@ -140,13 +139,17 @@ class HindsightEmbedded:
                return

            if self._closed:
-                raise RuntimeError("Cannot use HindsightEmbedded after it has been closed")
+                raise RuntimeError(
+                    "Cannot use HindsightEmbedded after it has been closed"
+                )

            # Use embed manager interface for daemon management
            logger.info(f"Ensuring daemon is running for profile '{self.profile}'...")
            success = self._manager.ensure_running(self.config, self.profile)
            if not success:
-                raise RuntimeError(f"Failed to start daemon for profile '{self.profile}'")
+                raise RuntimeError(
+                    f"Failed to start daemon for profile '{self.profile}'"
+                )

            # Get daemon URL and create client
            daemon_url = self._manager.get_url(self.profile)
@@ -375,3 +378,45 @@ class HindsightEmbedded:
    def is_running(self) -> bool:
        """Check if the client is initialized."""
        return self._started and not self._closed and self._client is not None
+
+    def start_ui(self, ui_port: int | None = None, hostname: str = "0.0.0.0") -> bool:
+        """Start the control plane web UI.
+
+        The daemon is started automatically if not already running.
+
+        Args:
+            ui_port: Port for the UI. Defaults to daemon_port + 10000.
+            hostname: Hostname to bind to. Defaults to 0.0.0.0.
+
+        Returns:
+            True if UI started successfully.
+        """
+        self._ensure_started()
+        return self._manager.start_ui(self.profile, ui_port, hostname)
+
+    def stop_ui(self, ui_port: int | None = None) -> bool:
+        """Stop the control plane web UI.
+
+        Args:
+            ui_port: Port the UI is running on. Defaults to daemon_port + 10000.
+
+        Returns:
+            True if stopped successfully.
+        """
+        return self._manager.stop_ui(self.profile, ui_port)
+
+    def is_ui_running(self, ui_port: int | None = None) -> bool:
+        """Check if the control plane web UI is running.
+
+        Args:
+            ui_port: Port to check. Defaults to daemon_port + 10000.
+
+        Returns:
+            True if UI is running and responsive.
+        """
+        return self._manager.is_ui_running(self.profile, ui_port)
+
+    @property
+    def ui_url(self) -> str:
+        """Get the UI URL for this profile."""
+        return self._manager.get_ui_url(self.profile)
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "hindsight-all"
-version = "0.4.19"
+version = "0.4.20"
 description = "Hindsight: Agent Memory That Works Like Human Memory - All-in-One Bundle"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -46,4 +46,4 @@ __all__ = [
    "RemoteTEICrossEncoder",
    "LLMConfig",
 ]
-__version__ = "0.4.19"
+__version__ = "0.4.20"
@@ -11,6 +11,7 @@ block; see migrations.py for how this is handled safely.

 from collections.abc import Sequence

+import sqlalchemy as sa
 from alembic import context, op

 revision: str = "c1a2b3d4e5f6"
@@ -25,9 +26,21 @@ def _get_schema_prefix() -> str:


 def upgrade() -> None:
-    # pg_trgm ships with every standard PostgreSQL installation as a contrib module.
+    # pg_trgm ships with most PostgreSQL installations as a contrib module.
    # It enables fast similarity lookups via GIN indexes, used for entity name matching.
-    op.execute("CREATE EXTENSION IF NOT EXISTS pg_trgm")
+    # On managed services (e.g. Azure Flexible Server), the extension may not be
+    # available or may require manual enablement.  We gracefully skip the index
+    # creation if the extension cannot be loaded — the entity resolver will
+    # auto-detect and fall back to the "full" lookup strategy at runtime.  See #626.
+    conn = op.get_bind()
+    try:
+        conn.execute(sa.text("CREATE EXTENSION IF NOT EXISTS pg_trgm"))
+    except Exception:
+        # Extension not available (managed Postgres, insufficient privileges, etc.)
+        # Roll back the failed statement and skip index creation.
+        conn.execute(sa.text("ROLLBACK"))
+        conn.execute(sa.text("BEGIN"))
+        return

    schema = _get_schema_prefix()
    # GIN index on canonical_name enables sub-millisecond trigram similarity queries
@@ -72,6 +72,7 @@ def FieldWithDefault(default_factory: Callable, **kwargs) -> Any:

 from hindsight_api.config import get_config
 from hindsight_api.engine.memory_engine import Budget, _current_schema, _get_tiktoken_encoding, fq_table
+from hindsight_api.engine.providers.none_llm import LLMNotAvailableError
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, MemoryFact, TokenUsage
 from hindsight_api.engine.search.tags import TagGroup, TagsMatch
 from hindsight_api.extensions import HttpExtension, OperationValidationError, load_extension
@@ -169,6 +170,15 @@ class RecallRequest(BaseModel):
        "Each group is a leaf {tags, match} or compound {and: [...]}, {or: [...]}, {not: ...}.",
    )

+    @field_validator("query")
+    @classmethod
+    def validate_query_not_empty(cls, v: str) -> str:
+        from ..engine.search.retrieval import tokenize_query
+
+        if not tokenize_query(v):
+            raise ValueError("query must contain at least one word character after normalization")
+        return v
+
    @model_validator(mode="after")
    def validate_tags_exclusive(self) -> "RecallRequest":
        if self.tags is not None and self.tag_groups is not None:
@@ -415,6 +425,27 @@ class MemoryItem(BaseModel):
        default=None,
        description="Optional tags for visibility scoping. Memories with tags can be filtered during recall.",
    )
+
+    @field_validator("tags", mode="before")
+    @classmethod
+    def coerce_tags(cls, v):
+        """Coerce JSON-string tags to list.
+
+        MCP tool bridges sometimes serialize JSON arrays as strings during
+        transport, e.g. '["a", "b"]' instead of ["a", "b"]. This validator
+        parses such strings back into lists so the retain call succeeds.
+        A plain non-JSON string is wrapped in a single-element list.
+        """
+        if isinstance(v, str):
+            try:
+                parsed = json.loads(v)
+                if isinstance(parsed, list):
+                    return parsed
+            except (json.JSONDecodeError, TypeError):
+                pass
+            return [v]
+        return v
+
    observation_scopes: Literal["per_tag", "combined", "all_combinations"] | list[list[str]] | None = Field(
        default=None,
        title="ObservationScopes",
@@ -669,6 +700,25 @@ class ReflectRequest(BaseModel):
        description="Compound tag filter using boolean groups. Groups in the list are AND-ed. "
        "Each group is a leaf {tags, match} or compound {and: [...]}, {or: [...]}, {not: ...}.",
    )
+    fact_types: list[Literal["world", "experience", "observation"]] | None = Field(
+        default=None,
+        description="Filter which fact types are retrieved during reflect. None means all types (world, experience, observation).",
+    )
+    exclude_mental_models: bool = Field(
+        default=False,
+        description="If true, exclude all mental models from the reflect loop (skip search_mental_models tool).",
+    )
+    exclude_mental_model_ids: list[str] | None = Field(
+        default=None,
+        description="Exclude specific mental models by ID from the reflect loop.",
+    )
+
+    @field_validator("fact_types")
+    @classmethod
+    def validate_reflect_fact_types(cls, v: list[str] | None) -> list[str] | None:
+        if v is not None and len(v) == 0:
+            raise ValueError("fact_types must not be empty. Use null to include all fact types.")
+        return v

    @model_validator(mode="after")
    def validate_tags_exclusive(self) -> "ReflectRequest":
@@ -1435,6 +1485,25 @@ class MentalModelTrigger(BaseModel):
        default=False,
        description="If true, refresh this mental model after observations consolidation (real-time mode)",
    )
+    fact_types: list[Literal["world", "experience", "observation"]] | None = Field(
+        default=None,
+        description="Filter which fact types are retrieved during reflect. None means all types (world, experience, observation).",
+    )
+    exclude_mental_models: bool = Field(
+        default=False,
+        description="If true, exclude all mental models from the reflect loop (skip search_mental_models tool).",
+    )
+    exclude_mental_model_ids: list[str] | None = Field(
+        default=None,
+        description="Exclude specific mental models by ID from the reflect loop.",
+    )
+
+    @field_validator("fact_types")
+    @classmethod
+    def validate_fact_types(cls, v: list[str] | None) -> list[str] | None:
+        if v is not None and len(v) == 0:
+            raise ValueError("fact_types must not be empty. Use null to include all fact types.")
+        return v


 class MentalModelResponse(BaseModel):
@@ -1995,6 +2064,26 @@ def create_app(
    # This is required for mounted sub-applications where lifespan may not fire
    app.state.memory = memory

+    # ---------------------------------------------------------------------------
+    # Patch OpenAPI schema: align ValidationError with Pydantic v2 error format
+    # ---------------------------------------------------------------------------
+    # FastAPI auto-generates ValidationError with only loc/msg/type, but Pydantic
+    # v2 actually returns additional fields: input (the rejected value), ctx (extra
+    # context dict), and url (link to error docs). Without these in the spec,
+    # generated clients using strict JSON decoding break on real 422 responses.
+    _original_openapi = app.openapi
+
+    def _patched_openapi() -> dict[str, Any]:
+        schema = _original_openapi()
+        ve = schema.get("components", {}).get("schemas", {}).get("ValidationError")
+        if ve and "input" not in ve.get("properties", {}):
+            ve["properties"]["input"] = {"title": "Input"}
+            ve["properties"]["ctx"] = {"title": "Context", "type": "object"}
+            ve["properties"]["url"] = {"title": "URL", "type": "string"}
+        return schema
+
+    app.openapi = _patched_openapi  # type: ignore[assignment]
+
    # Add HTTP metrics middleware
    @app.middleware("http")
    async def http_metrics_middleware(request, call_next):
@@ -2505,6 +2594,9 @@ def _register_routes(app: FastAPI):
                    tags=request.tags,
                    tags_match=request.tags_match,
                    tag_groups=request.tag_groups,
+                    fact_types=request.fact_types,
+                    exclude_mental_models=request.exclude_mental_models,
+                    exclude_mental_model_ids=request.exclude_mental_model_ids,
                )

            # Build based_on (memories + mental_models + directives) if facts are requested
@@ -2580,6 +2672,14 @@ def _register_routes(app: FastAPI):
            raise HTTPException(status_code=e.status_code, detail=e.reason)
        except (AuthenticationError, HTTPException):
            raise
+        except LLMNotAvailableError as e:
+            raise HTTPException(status_code=400, detail=str(e))
+        except TimeoutError as e:
+            logger.error("Timeout in /v1/default/banks/%s/reflect: %s", bank_id, e)
+            raise HTTPException(
+                status_code=504,
+                detail=str(e) or "Reflect operation timed out. Consider reducing the budget or simplifying the query.",
+            )
        except Exception as e:
            import traceback

@@ -2940,6 +3040,8 @@ def _register_routes(app: FastAPI):
                request_context=request_context,
            )
            return AsyncOperationSubmitResponse(operation_id=result["operation_id"], status="queued")
+        except LLMNotAvailableError as e:
+            raise HTTPException(status_code=400, detail=str(e))
        except ValueError as e:
            raise HTTPException(status_code=404, detail=str(e))
        except (AuthenticationError, HTTPException):
@@ -83,6 +83,9 @@ _current_api_key: ContextVar[str | None] = ContextVar("current_api_key", default
 _current_tenant_id: ContextVar[str | None] = ContextVar("current_tenant_id", default=None)
 _current_api_key_id: ContextVar[str | None] = ContextVar("current_api_key_id", default=None)

+# Context variable for MCP pre-authentication flag (set when MCP_AUTH_TOKEN validates)
+_current_mcp_authenticated: ContextVar[bool] = ContextVar("current_mcp_authenticated", default=False)
+

 def get_current_bank_id() -> str | None:
    """Get the current bank_id from context."""
@@ -104,6 +107,11 @@ def get_current_api_key_id() -> str | None:
    return _current_api_key_id.get()


+def get_current_mcp_authenticated() -> bool:
+    """Get whether the request was pre-authenticated by MCP transport auth."""
+    return _current_mcp_authenticated.get()
+
+
 def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
    """
    Create and configure the Hindsight MCP server.
@@ -164,6 +172,7 @@ def create_mcp_server(memory: MemoryEngine, multi_bank: bool = True) -> FastMCP:
        api_key_resolver=get_current_api_key,  # Propagate API key for tenant auth
        tenant_id_resolver=get_current_tenant_id,  # Propagate tenant_id for usage metering
        api_key_id_resolver=get_current_api_key_id,  # Propagate api_key_id for usage metering
+        mcp_authenticated_resolver=get_current_mcp_authenticated,  # Propagate MCP pre-auth flag
        include_bank_id_param=multi_bank,
        tools=base_tools,
    )
@@ -312,6 +321,7 @@ class MCPMiddleware:
        tenant_context = None
        auth_tenant_id: str | None = None
        auth_api_key_id: str | None = None
+        mcp_pre_authenticated = False
        if MCP_AUTH_TOKEN:
            # Legacy authentication mode - validate against static token
            if not auth_token:
@@ -320,8 +330,9 @@ class MCPMiddleware:
            if auth_token != MCP_AUTH_TOKEN:
                await self._send_error(send, 401, "Invalid authentication token")
                return
-            # Legacy mode doesn't use tenant schemas
+            # Legacy mode: mark as pre-authenticated so tenant extension won't re-validate
            tenant_context = None
+            mcp_pre_authenticated = True
        else:
            # Use TenantExtension.authenticate_mcp() for auth
            try:
@@ -368,13 +379,15 @@ class MCPMiddleware:
        # - Header/env bank_id → multi-bank app (bank_id param, all tools)
        target_app = self.single_bank_app if bank_id_from_path else self.multi_bank_app

-        # Set bank_id, api_key, tenant_id, and api_key_id context
+        # Set bank_id, api_key, tenant_id, api_key_id, and mcp_authenticated 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
        # Store tenant_id and api_key_id from authentication for usage metering
        tenant_id_token = _current_tenant_id.set(auth_tenant_id) if auth_tenant_id else None
        api_key_id_token = _current_api_key_id.set(auth_api_key_id) if auth_api_key_id else None
+        # Store MCP pre-authentication flag to skip tenant re-validation
+        mcp_auth_token = _current_mcp_authenticated.set(mcp_pre_authenticated)
        try:
            new_scope = scope.copy()
            new_scope["path"] = new_path
@@ -419,6 +432,7 @@ class MCPMiddleware:
                _current_tenant_id.reset(tenant_id_token)
            if api_key_id_token is not None:
                _current_api_key_id.reset(api_key_id_token)
+            _current_mcp_authenticated.reset(mcp_auth_token)
            if schema_token is not None:
                _current_schema.reset(schema_token)

@@ -338,7 +338,9 @@ ENV_WORKER_CONSOLIDATION_MAX_SLOTS = "HINDSIGHT_API_WORKER_CONSOLIDATION_MAX_SLO
 # Reflect agent settings
 ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"
 ENV_REFLECT_MAX_CONTEXT_TOKENS = "HINDSIGHT_API_REFLECT_MAX_CONTEXT_TOKENS"
+ENV_REFLECT_WALL_TIMEOUT = "HINDSIGHT_API_REFLECT_WALL_TIMEOUT"
 ENV_REFLECT_MISSION = "HINDSIGHT_API_REFLECT_MISSION"
+ENV_REFLECT_SOURCE_FACTS_MAX_TOKENS = "HINDSIGHT_API_REFLECT_SOURCE_FACTS_MAX_TOKENS"

 # Disposition settings
 ENV_DISPOSITION_SKEPTICISM = "HINDSIGHT_API_DISPOSITION_SKEPTICISM"
@@ -353,7 +355,7 @@ DEFAULT_LLM_PROVIDER = "openai"
 # Provider-specific default models
 PROVIDER_DEFAULT_MODELS = {
    "openai": "gpt-4o-mini",
-    "anthropic": "claude-haiku-4-5-20251001",
+    "anthropic": "claude-haiku-4-5",
    "gemini": "gemini-2.5-flash",
    "groq": "openai/gpt-oss-120b",
    "minimax": "MiniMax-M2.7",
@@ -363,6 +365,9 @@ PROVIDER_DEFAULT_MODELS = {
    "openai-codex": "gpt-5.2-codex",
    "claude-code": "claude-sonnet-4-5-20250929",
    "mock": "mock-model",
+    "none": "none",
+    "litellm": "gpt-4o-mini",
+    "bedrock": "us.amazon.nova-2-lite-v1:0",
 }
 DEFAULT_LLM_MODEL = "gpt-4o-mini"  # Fallback if provider not in table
 DEFAULT_LLM_MAX_CONCURRENT = 32
@@ -499,6 +504,8 @@ DEFAULT_WORKER_CONSOLIDATION_MAX_SLOTS = 2  # Max concurrent consolidation tasks
 # Reflect agent settings
 DEFAULT_REFLECT_MAX_ITERATIONS = 10  # Max tool call iterations before forcing response
 DEFAULT_REFLECT_MAX_CONTEXT_TOKENS = 100_000  # Max accumulated context tokens before forcing final prompt
+DEFAULT_REFLECT_WALL_TIMEOUT = 300  # Wall-clock timeout in seconds for the entire reflect operation (5 minutes)
+DEFAULT_REFLECT_SOURCE_FACTS_MAX_TOKENS = -1  # Token budget for source facts in search_observations (-1 = disabled)

 # Disposition defaults (None = not set, fall back to bank DB value or 3)
 DEFAULT_DISPOSITION_SKEPTICISM = None
@@ -770,6 +777,7 @@ class HindsightConfig:

    # Reflect agent settings
    reflect_mission: str | None
+    reflect_source_facts_max_tokens: int

    # Disposition settings (hierarchical - can be overridden per bank; None = fall back to DB)
    disposition_skepticism: int | None
@@ -801,6 +809,7 @@ class HindsightConfig:
    # Reflect agent settings
    reflect_max_iterations: int
    reflect_max_context_tokens: int
+    reflect_wall_timeout: int

    # OpenTelemetry tracing configuration
    otel_traces_enabled: bool
@@ -867,6 +876,7 @@ class HindsightConfig:
        "observations_mission",
        # Reflect settings
        "reflect_mission",
+        "reflect_source_facts_max_tokens",
        # Disposition settings
        "disposition_skepticism",
        "disposition_literalism",
@@ -949,9 +959,19 @@ class HindsightConfig:
                f"Invalid text_search_extension: {self.text_search_extension}. Must be one of: {', '.join(valid_text_search)}"
            )

+        # When LLM provider is "none", force chunks-only mode and disable LLM-dependent features
+        if self.llm_provider == "none":
+            self.retain_extraction_mode = "chunks"
+            self.enable_observations = False
+            logger.info(
+                "LLM provider set to 'none': forcing retain_extraction_mode='chunks', "
+                "disabling observations/consolidation. Reflect will return HTTP 400."
+            )
+
        # RETAIN_MAX_COMPLETION_TOKENS must be greater than RETAIN_CHUNK_SIZE
        # to ensure the LLM has enough output capacity to extract facts from chunks
-        if self.retain_max_completion_tokens <= self.retain_chunk_size:
+        # (not applicable when provider is "none" since no LLM calls are made)
+        if self.llm_provider != "none" and self.retain_max_completion_tokens <= self.retain_chunk_size:
            raise ValueError(
                f"Invalid configuration: HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS "
                f"({self.retain_max_completion_tokens}) must be greater than "
@@ -1274,7 +1294,11 @@ class HindsightConfig:
            reflect_max_context_tokens=int(
                os.getenv(ENV_REFLECT_MAX_CONTEXT_TOKENS, str(DEFAULT_REFLECT_MAX_CONTEXT_TOKENS))
            ),
+            reflect_wall_timeout=int(os.getenv(ENV_REFLECT_WALL_TIMEOUT, str(DEFAULT_REFLECT_WALL_TIMEOUT))),
            reflect_mission=os.getenv(ENV_REFLECT_MISSION) or None,
+            reflect_source_facts_max_tokens=int(
+                os.getenv(ENV_REFLECT_SOURCE_FACTS_MAX_TOKENS, str(DEFAULT_REFLECT_SOURCE_FACTS_MAX_TOKENS))
+            ),
            # Disposition settings (None = fall back to DB value)
            disposition_skepticism=int(os.getenv(ENV_DISPOSITION_SKEPTICISM))
            if os.getenv(ENV_DISPOSITION_SKEPTICISM)
@@ -75,6 +75,7 @@ class EntityResolver:
        """
        self.pool = pool
        self.entity_lookup = entity_lookup
+        self._pg_trgm_checked = False
        # Keyed by asyncio task id so concurrent retain batches never mix their
        # pending updates.  flush_pending_stats() pops only the calling task's items.
        self._pending_stats: dict[int, list[_EntityStat]] = {}
@@ -85,6 +86,19 @@ class EntityResolver:
        task = asyncio.current_task()
        return id(task) if task is not None else 0

+    def discard_pending_stats(self) -> None:
+        """
+        Discard accumulated entity stats and co-occurrence counts for the current task.
+
+        Call this on any exception path between resolve_entities_batch /
+        link_units_to_entities_batch and flush_pending_stats() to prevent the
+        per-task dicts from growing unbounded when tasks fail before flushing.
+        Safe to call even if no entries exist for the current task.
+        """
+        key = self._task_key()
+        self._pending_stats.pop(key, None)
+        self._pending_cooccurrences.pop(key, None)
+
    async def flush_pending_stats(self) -> None:
        """
        Flush accumulated entity stats and co-occurrence counts for the current task.
@@ -202,6 +216,20 @@ class EntityResolver:
        taxonomy_lookup: set[str] | None = None,
    ) -> list[str]:
        if self.entity_lookup == "trigram":
+            # Auto-detect pg_trgm availability on first call and fall back to
+            # "full" strategy if the extension is not installed.  See #626.
+            if not self._pg_trgm_checked:
+                self._pg_trgm_checked = True
+                has_trgm = await conn.fetchval("SELECT EXISTS(SELECT 1 FROM pg_extension WHERE extname = 'pg_trgm')")
+                if not has_trgm:
+                    logger.warning(
+                        "pg_trgm extension is not available — falling back to 'full' "
+                        "entity lookup strategy. Install pg_trgm for faster entity "
+                        "resolution on large banks. See: "
+                        "https://github.com/vectorize-io/hindsight/issues/626"
+                    )
+                    self.entity_lookup = "full"
+                    return await self._resolve_entities_batch_full(conn, bank_id, entities_data, unit_event_date)
            return await self._resolve_entities_batch_trigram(conn, bank_id, entities_data, unit_event_date)
        return await self._resolve_entities_batch_full(conn, bank_id, entities_data, unit_event_date)

@@ -477,19 +505,42 @@ class EntityResolver:
            id_by_name: dict[str, str] = {row["name_lower"]: row["id"] for row in inserted_rows}

            # Fallback SELECT for names that conflicted (another worker won the race).
-            missing = [n for n, _ in sorted_groups if n not in id_by_name]
-            if missing:
+            #
+            # IMPORTANT: we must let PostgreSQL do the lowercasing on BOTH sides of the
+            # comparison.  Python's str.lower() and PostgreSQL's LOWER() differ for some
+            # Unicode characters — most notably Turkish İ (U+0130):
+            #   Python:     'İstanbul'.lower()  == 'i\u0307stanbul'  (i + combining dot, 2 chars)
+            #   PostgreSQL: LOWER('İstanbul')   == 'istanbul'         (plain i, 1 char)
+            # Passing a Python-lowercased name to "LOWER(canonical_name) = ANY($2::text[])"
+            # would fail to match the stored entity, leaving entity_id as None and causing
+            # a NOT NULL constraint violation on unit_entities.entity_id.
+            #
+            # Fix: pass the original (mixed-case) input names and use
+            # "LOWER(canonical_name) = ANY(SELECT LOWER(n) FROM unnest($2) AS n)" so
+            # PostgreSQL lowercases both sides identically.  The query also returns the
+            # original input_name so we can index id_by_name by Python's lower() of that
+            # name, which is what the assignment loop below uses as its lookup key.
+            missing_original = [g.name for name_lower, g in sorted_groups if name_lower not in id_by_name]
+            if missing_original:
                existing_rows = await conn.fetch(
                    f"""
-                    SELECT id, LOWER(canonical_name) AS name_lower
-                    FROM {fq_table("entities")}
-                    WHERE bank_id = $1 AND LOWER(canonical_name) = ANY($2::text[])
+                    SELECT e.id, LOWER(e.canonical_name) AS name_lower, inputs.input_name
+                    FROM {fq_table("entities")} e
+                    JOIN (
+                        SELECT LOWER(n) AS input_name_lower, n AS input_name
+                        FROM unnest($2::text[]) AS n
+                    ) AS inputs ON LOWER(e.canonical_name) = inputs.input_name_lower
+                    WHERE e.bank_id = $1
                    """,
                    bank_id,
-                    missing,
+                    missing_original,
                )
                for row in existing_rows:
                    id_by_name[row["name_lower"]] = row["id"]
+                    # Also index by Python's lower() of the original input name so the
+                    # assignment loop (which uses Python-lowercased keys) finds it even
+                    # when Python and PostgreSQL produce different lowercase strings.
+                    id_by_name[row["input_name"].lower()] = row["id"]

            # Assign entity IDs back and queue one stat per original mention so that
            # flush_pending_stats() increments mention_count by the true mention count,
@@ -125,7 +125,10 @@ _PROVIDERS_WITHOUT_API_KEY = frozenset(
        "openai-codex",
        "claude-code",
        "mock",
+        "none",
        "vertexai",
+        "litellm",
+        "bedrock",
    }
 )

@@ -172,7 +175,9 @@ def create_llm_provider(
        ClaudeCodeLLM,
        CodexLLM,
        GeminiLLM,
+        LiteLLMLLM,
        MockLLM,
+        NoneLLM,
        OpenAICompatibleLLM,
    )

@@ -205,6 +210,15 @@ def create_llm_provider(
            reasoning_effort=reasoning_effort,
        )

+    elif provider_lower == "none":
+        return NoneLLM(
+            provider=provider,
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            reasoning_effort=reasoning_effort,
+        )
+
    elif provider_lower in ("gemini", "vertexai"):
        return GeminiLLM(
            provider=provider,
@@ -227,6 +241,26 @@ def create_llm_provider(
            reasoning_effort=reasoning_effort,
        )

+    elif provider_lower == "litellm":
+        return LiteLLMLLM(
+            provider=provider,
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            reasoning_effort=reasoning_effort,
+        )
+
+    elif provider_lower == "bedrock":
+        # Bedrock is a first-class alias backed by LiteLLM with auto-prefixed model names
+        bedrock_model = model if model.startswith("bedrock/") else f"bedrock/{model}"
+        return LiteLLMLLM(
+            provider=provider,
+            api_key=api_key,
+            base_url=base_url,
+            model=bedrock_model,
+            reasoning_effort=reasoning_effort,
+        )
+
    elif provider_lower in ("openai", "groq", "ollama", "lmstudio", "minimax"):
        return OpenAICompatibleLLM(
            provider=provider,
@@ -296,7 +330,10 @@ class LLMProvider:
            "openai-codex",
            "claude-code",
            "mock",
+            "none",
            "minimax",
+            "litellm",
+            "bedrock",
        ]
        if self.provider not in valid_providers:
            raise ValueError(f"Invalid LLM provider: {self.provider}. Must be one of: {', '.join(valid_providers)}")
@@ -675,10 +712,13 @@ class LLMProvider:
        api_key = os.getenv("HINDSIGHT_API_LLM_API_KEY", "")

        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
-        # ollama (local), or vertexai (uses GCP service account credentials)
-        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
+        # ollama (local), vertexai (uses GCP service account credentials),
+        # or litellm (uses provider-specific auth, e.g. AWS credentials for Bedrock)
+        if not api_key and not requires_api_key(provider):
+            pass  # Provider handles its own auth
+        elif not api_key:
            raise ValueError(
-                "HINDSIGHT_API_LLM_API_KEY environment variable is required (unless using openai-codex or claude-code)"
+                "HINDSIGHT_API_LLM_API_KEY environment variable is required (unless using openai-codex, claude-code, or litellm)"
            )

        base_url = os.getenv("HINDSIGHT_API_LLM_BASE_URL", "")
@@ -692,12 +732,13 @@ class LLMProvider:
        provider = os.getenv("HINDSIGHT_API_ANSWER_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
        api_key = os.getenv("HINDSIGHT_API_ANSWER_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", ""))

-        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
-        # ollama (local), or vertexai (uses GCP service account credentials)
-        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
+        # API key not needed for providers with their own auth mechanisms
+        if not api_key and not requires_api_key(provider):
+            pass  # Provider handles its own auth
+        elif not api_key:
            raise ValueError(
                "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_ANSWER_LLM_API_KEY environment variable is required "
-                "(unless using openai-codex or claude-code)"
+                "(unless using openai-codex, claude-code, or litellm)"
            )

        base_url = os.getenv("HINDSIGHT_API_ANSWER_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
@@ -711,12 +752,13 @@ class LLMProvider:
        provider = os.getenv("HINDSIGHT_API_JUDGE_LLM_PROVIDER", os.getenv("HINDSIGHT_API_LLM_PROVIDER", "groq"))
        api_key = os.getenv("HINDSIGHT_API_JUDGE_LLM_API_KEY", os.getenv("HINDSIGHT_API_LLM_API_KEY", ""))

-        # API key not needed for openai-codex (uses OAuth), claude-code (uses Keychain OAuth),
-        # ollama (local), or vertexai (uses GCP service account credentials)
-        if not api_key and provider not in ("openai-codex", "claude-code", "ollama", "vertexai"):
+        # API key not needed for providers with their own auth mechanisms
+        if not api_key and not requires_api_key(provider):
+            pass  # Provider handles its own auth
+        elif not api_key:
            raise ValueError(
                "HINDSIGHT_API_LLM_API_KEY or HINDSIGHT_API_JUDGE_LLM_API_KEY environment variable is required "
-                "(unless using openai-codex or claude-code)"
+                "(unless using openai-codex, claude-code, or litellm)"
            )

        base_url = os.getenv("HINDSIGHT_API_JUDGE_LLM_BASE_URL", os.getenv("HINDSIGHT_API_LLM_BASE_URL", ""))
@@ -23,7 +23,7 @@ import asyncpg
 import httpx
 import tiktoken

-from ..config import get_config
+from ..config import DEFAULT_REFLECT_SOURCE_FACTS_MAX_TOKENS, get_config
 from ..metrics import get_metrics_collector
 from ..tracing import create_operation_span
 from ..utils import mask_network_location
@@ -67,6 +67,13 @@ def fq_table(table_name: str) -> str:
    return f"{get_current_schema()}.{table_name}"


+def _json_default(obj: Any) -> str:
+    """JSON serializer for types commonly carried through async task payloads."""
+    if isinstance(obj, datetime):
+        return obj.isoformat()
+    raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable")
+
+
 # Tables that must be schema-qualified (for runtime validation)
 _PROTECTED_TABLES = frozenset(
    [
@@ -321,6 +328,10 @@ class MemoryEngine(MemoryEngineInterface):
        # Apply defaults from config
        db_url = db_url or config.database_url
        memory_llm_provider = memory_llm_provider or config.llm_provider
+
+        # Force skip LLM verification when provider is "none" (no LLM to verify)
+        if memory_llm_provider == "none":
+            self._skip_llm_verification = True
        memory_llm_api_key = memory_llm_api_key or config.llm_api_key
        if not memory_llm_api_key and requires_api_key(memory_llm_provider):
            raise ValueError("LLM API key is required. Set HINDSIGHT_API_LLM_API_KEY environment variable.")
@@ -492,24 +503,28 @@ class MemoryEngine(MemoryEngineInterface):
        """The configured tenant extension, if any."""
        return self._tenant_extension

-    async def _validate_operation(self, validation_coro) -> None:
+    async def _validate_operation(self, validation_coro) -> "ValidationResult | None":
        """
        Run validation if an operation validator is configured.

        Args:
            validation_coro: Coroutine that returns a ValidationResult

+        Returns:
+            The ValidationResult (may contain enrichment fields), or None if no validator.
+
        Raises:
            OperationValidationError: If validation fails
        """
        if self._operation_validator is None:
-            return
+            return None

-        from hindsight_api.extensions import OperationValidationError
+        from hindsight_api.extensions import OperationValidationError, ValidationResult

        result = await validation_coro
        if not result.allowed:
            raise OperationValidationError(result.reason or "Operation not allowed", result.status_code)
+        return result

    async def _authenticate_tenant(self, request_context: "RequestContext | None") -> str:
        """
@@ -538,6 +553,12 @@ class MemoryEngine(MemoryEngineInterface):
        if request_context.internal:
            return _current_schema.get()

+        # For MCP requests already authenticated via MCP_AUTH_TOKEN, skip tenant re-validation.
+        # The MCP transport layer already verified the token; re-validating against the tenant
+        # extension would fail when MCP_AUTH_TOKEN and TENANT_API_KEY differ.
+        if request_context.mcp_authenticated:
+            return _current_schema.get()
+
        # Authenticate through tenant extension (always set, may be default no-auth extension)
        tenant_context = await self._tenant_extension.authenticate(request_context)

@@ -803,6 +824,11 @@ class MemoryEngine(MemoryEngineInterface):
        if not bank_id:
            raise ValueError("bank_id is required for consolidation task")

+        # Skip consolidation when LLM provider is "none"
+        if self._llm_config.provider == "none":
+            logger.info(f"[CONSOLIDATION] Skipping consolidation for bank {bank_id}: LLM provider is 'none'")
+            return {"memories_processed": 0, "skipped": True}
+
        from hindsight_api.models import RequestContext

        from .consolidation import run_consolidation_job
@@ -868,14 +894,23 @@ class MemoryEngine(MemoryEngineInterface):
        tags = mental_model.get("tags")
        tags_match = "all_strict" if tags else "any"

+        # Read reflect options from trigger (if stored)
+        trigger_data = mental_model.get("trigger") or {}
+        fact_types = trigger_data.get("fact_types")
+        exclude_mental_models = trigger_data.get("exclude_mental_models", False)
+        stored_exclude_ids: list[str] = trigger_data.get("exclude_mental_model_ids") or []
+
        # Run reflect to generate new content, excluding the mental model being refreshed
+        # Always add self to excluded IDs to prevent circular reference
        reflect_result = await self.reflect_async(
            bank_id=bank_id,
            query=source_query,
            request_context=internal_context,
            tags=tags,
            tags_match=tags_match,
-            exclude_mental_model_ids=[mental_model_id],
+            fact_types=fact_types,
+            exclude_mental_models=exclude_mental_models,
+            exclude_mental_model_ids=list({*stored_exclude_ids, mental_model_id}),
        )

        generated_content = reflect_result.text or "No content generated"
@@ -2030,7 +2065,9 @@ class MemoryEngine(MemoryEngineInterface):
                fact_type_override=fact_type_override,
                confidence_score=confidence_score,
            )
-            await self._validate_operation(self._operation_validator.validate_retain(ctx))
+            result = await self._validate_operation(self._operation_validator.validate_retain(ctx))
+            if result and result.contents is not None:
+                contents = result.contents

        # Apply batch-level document_id to contents that don't have their own (backwards compatibility)
        if document_id:
@@ -2226,6 +2263,11 @@ class MemoryEngine(MemoryEngineInterface):
            # Resolve bank-specific config for this operation
            resolved_config = await self._config_resolver.resolve_full_config(bank_id, request_context)

+            # Force chunks mode when LLM provider is "none" (no LLM available for fact extraction)
+            if self._llm_config.provider == "none":
+                resolved_config.retain_extraction_mode = "chunks"
+                resolved_config.enable_observations = False
+
            # Apply strategy overrides: explicit strategy > bank default strategy
            from hindsight_api.config_resolver import apply_strategy

@@ -2397,8 +2439,18 @@ class MemoryEngine(MemoryEngineInterface):
                max_entity_tokens=max_entity_tokens,
                include_chunks=include_chunks,
                max_chunk_tokens=max_chunk_tokens,
+                tags=tags,
+                tags_match=tags_match,
+                tag_groups=tag_groups,
            )
-            await self._validate_operation(self._operation_validator.validate_recall(ctx))
+            result = await self._validate_operation(self._operation_validator.validate_recall(ctx))
+            if result:
+                if result.tags is not None:
+                    tags = result.tags
+                if result.tags_match is not None:
+                    tags_match = result.tags_match
+                if result.tag_groups is not None:
+                    tag_groups = result.tag_groups

        # Map budget enum to thinking_budget number (default to MID if None)
        budget_mapping = {Budget.LOW: 100, Budget.MID: 300, Budget.HIGH: 1000}
@@ -3196,7 +3248,7 @@ class MemoryEngine(MemoryEngineInterface):
                            source_rows = await sf_conn.fetch(
                                f"""
                                SELECT id, text, fact_type, context, occurred_start, occurred_end,
-                                       mentioned_at, document_id, chunk_id, tags
+                                       mentioned_at, document_id, chunk_id, tags, metadata
                                FROM {fq_table("memory_units")}
                                WHERE id = ANY($1::uuid[])
                                """,
@@ -3217,6 +3269,7 @@ class MemoryEngine(MemoryEngineInterface):
                                    occurred_end=r["occurred_end"].isoformat() if r["occurred_end"] else None,
                                    mentioned_at=r["mentioned_at"].isoformat() if r["mentioned_at"] else None,
                                    document_id=r["document_id"],
+                                    metadata=r["metadata"],
                                    chunk_id=str(r["chunk_id"]) if r["chunk_id"] else None,
                                    tags=r["tags"] or None,
                                )
@@ -3295,6 +3348,7 @@ class MemoryEngine(MemoryEngineInterface):
                        occurred_end=result_dict.get("occurred_end"),
                        mentioned_at=result_dict.get("mentioned_at"),
                        document_id=result_dict.get("document_id"),
+                        metadata=result_dict.get("metadata"),
                        chunk_id=result_dict.get("chunk_id"),
                        tags=result_dict.get("tags"),
                        source_fact_ids=source_fact_ids_by_obs.get(result_id) if include_source_facts else None,
@@ -5113,6 +5167,8 @@ class MemoryEngine(MemoryEngineInterface):
        tags_match: TagsMatch = "any",
        tag_groups: list[TagGroup] | None = None,
        exclude_mental_model_ids: list[str] | None = None,
+        fact_types: list[str] | None = None,
+        exclude_mental_models: bool = False,
        _skip_span: bool = False,
    ) -> ReflectResult:
        """
@@ -5150,6 +5206,15 @@ class MemoryEngine(MemoryEngineInterface):
        if self._reflect_llm_config is None:
            raise ValueError("Memory LLM API key not set. Set HINDSIGHT_API_LLM_API_KEY environment variable.")

+        # Block reflect when LLM provider is "none"
+        if self._llm_config.provider == "none":
+            from .providers.none_llm import LLMNotAvailableError
+
+            raise LLMNotAvailableError(
+                "Reflect requires an LLM provider. Current provider is set to 'none'. "
+                "Set HINDSIGHT_API_LLM_PROVIDER to a real provider (e.g., openai, anthropic, gemini)."
+            )
+
        # Authenticate tenant and set schema in context (for fq_table())
        await self._authenticate_tenant(request_context)

@@ -5188,6 +5253,7 @@ class MemoryEngine(MemoryEngineInterface):
        effective_budget = budget or Budget.LOW
        max_iterations = max(1, int(base_max_iterations * budget_multipliers.get(effective_budget, 1.0)))
        max_context_tokens = config.reflect_max_context_tokens
+        wall_timeout = config.reflect_wall_timeout

        # Run agentic loop - acquire connections only when needed for DB operations
        # (not held during LLM calls which can be slow)
@@ -5219,6 +5285,12 @@ class MemoryEngine(MemoryEngineInterface):
                    pending_consolidation=pending_consolidation,
                )

+        # Get reflect source facts config (hierarchical: env → tenant → bank)
+        config_dict = await self._config_resolver.get_bank_config(bank_id, request_context)
+        reflect_source_facts_max_tokens = config_dict.get(
+            "reflect_source_facts_max_tokens", DEFAULT_REFLECT_SOURCE_FACTS_MAX_TOKENS
+        )
+
        async def search_observations_fn(q: str, max_tokens: int = 5000) -> dict[str, Any]:
            return await tool_search_observations(
                self,
@@ -5231,8 +5303,14 @@ class MemoryEngine(MemoryEngineInterface):
                tag_groups=tag_groups,
                last_consolidated_at=last_consolidated_at,
                pending_consolidation=pending_consolidation,
+                source_facts_max_tokens=reflect_source_facts_max_tokens,
            )

+        # Determine which tools to enable based on fact_types and exclude_mental_models
+        include_observations = fact_types is None or "observation" in fact_types
+        recall_fact_types = [ft for ft in (fact_types or ["world", "experience"]) if ft in ("world", "experience")]
+        include_recall = bool(recall_fact_types)
+
        async def recall_fn(q: str, max_tokens: int = 4096, max_chunk_tokens: int = 1000) -> dict[str, Any]:
            return await tool_recall(
                self,
@@ -5244,6 +5322,7 @@ class MemoryEngine(MemoryEngineInterface):
                tags_match=tags_match,
                tag_groups=tag_groups,
                max_chunk_tokens=max_chunk_tokens,
+                fact_types=recall_fact_types if fact_types is not None else None,
            )

        async def expand_fn(memory_ids: list[str], depth: str) -> dict[str, Any]:
@@ -5266,15 +5345,17 @@ class MemoryEngine(MemoryEngineInterface):
        if directives:
            logger.info(f"[REFLECT {reflect_id}] Loaded {len(directives)} directives")

-        # Check if the bank has any mental models
-        async with pool.acquire() as conn:
-            mental_model_count = await conn.fetchval(
-                f"SELECT COUNT(*) FROM {fq_table('mental_models')} WHERE bank_id = $1",
-                bank_id,
-            )
-        has_mental_models = mental_model_count > 0
-        if has_mental_models:
-            logger.info(f"[REFLECT {reflect_id}] Bank has {mental_model_count} mental models")
+        # Check if the bank has any mental models (skip check if all mental models are excluded)
+        has_mental_models = False
+        if not exclude_mental_models:
+            async with pool.acquire() as conn:
+                mental_model_count = await conn.fetchval(
+                    f"SELECT COUNT(*) FROM {fq_table('mental_models')} WHERE bank_id = $1",
+                    bank_id,
+                )
+            has_mental_models = mental_model_count > 0
+            if has_mental_models:
+                logger.info(f"[REFLECT {reflect_id}] Bank has {mental_model_count} mental models")

        # Run the agent with parent span for reflect operation (skip if called from another operation)
        if not _skip_span:
@@ -5284,29 +5365,52 @@ class MemoryEngine(MemoryEngineInterface):
            span_context = None

        try:
-            agent_result = await run_reflect_agent(
-                llm_config=self._reflect_llm_config.with_config(resolved_reflect_config),
-                bank_id=bank_id,
-                query=query,
-                bank_profile=profile,
-                search_mental_models_fn=search_mental_models_fn,
-                search_observations_fn=search_observations_fn,
-                recall_fn=recall_fn,
-                expand_fn=expand_fn,
-                context=context,
-                max_iterations=max_iterations,
-                max_tokens=max_tokens,
-                response_schema=response_schema,
-                directives=directives,
-                has_mental_models=has_mental_models,
-                budget=effective_budget,
-                max_context_tokens=max_context_tokens,
-            )
+            try:
+                agent_result = await asyncio.wait_for(
+                    run_reflect_agent(
+                        llm_config=self._reflect_llm_config.with_config(resolved_reflect_config),
+                        bank_id=bank_id,
+                        query=query,
+                        bank_profile=profile,
+                        search_mental_models_fn=search_mental_models_fn,
+                        search_observations_fn=search_observations_fn,
+                        recall_fn=recall_fn,
+                        expand_fn=expand_fn,
+                        context=context,
+                        max_iterations=max_iterations,
+                        max_tokens=max_tokens,
+                        response_schema=response_schema,
+                        directives=directives,
+                        has_mental_models=has_mental_models,
+                        include_observations=include_observations,
+                        include_recall=include_recall,
+                        budget=effective_budget,
+                        max_context_tokens=max_context_tokens,
+                    ),
+                    timeout=wall_timeout,
+                )
+            except asyncio.TimeoutError:
+                total_time = time.time() - reflect_start
+                logger.error(
+                    "[REFLECT %s] Wall-clock timeout after %.1fs (limit: %ss) for query: %.50s...",
+                    reflect_id,
+                    total_time,
+                    wall_timeout,
+                    query,
+                )
+                raise TimeoutError(
+                    f"Reflect operation timed out after {wall_timeout} seconds. "
+                    f"Consider reducing the budget or simplifying the query."
+                )

            total_time = time.time() - reflect_start
            logger.info(
-                f"[REFLECT {reflect_id}] Complete: {len(agent_result.text)} chars, "
-                f"{agent_result.iterations} iterations, {agent_result.tools_called} tool calls | {total_time:.3f}s"
+                "[REFLECT %s] Complete: %d chars, %d iterations, %d tool calls | %.3fs",
+                reflect_id,
+                len(agent_result.text),
+                agent_result.iterations,
+                agent_result.tools_called,
+                total_time,
            )

            # Convert agent tool trace to ToolCallTrace objects
@@ -6430,6 +6534,12 @@ class MemoryEngine(MemoryEngineInterface):
            tags = mental_model.get("tags")
            tags_match = "all_strict" if tags else "any"

+            # Read reflect options from trigger (if stored)
+            trigger_data = mental_model.get("trigger") or {}
+            fact_types = trigger_data.get("fact_types")
+            exclude_mental_models = trigger_data.get("exclude_mental_models", False)
+            stored_exclude_ids: list[str] = trigger_data.get("exclude_mental_model_ids") or []
+
            # Run reflect with the source query, excluding the mental model being refreshed
            # Skip creating a nested "hindsight.reflect" span since we already have "hindsight.mental_model_refresh"
            reflect_result = await self.reflect_async(
@@ -6438,7 +6548,9 @@ class MemoryEngine(MemoryEngineInterface):
                request_context=request_context,
                tags=tags,
                tags_match=tags_match,
-                exclude_mental_model_ids=[mental_model_id],
+                fact_types=fact_types,
+                exclude_mental_models=exclude_mental_models,
+                exclude_mental_model_ids=list({*stored_exclude_ids, mental_model_id}),
                _skip_span=True,
            )

@@ -7407,21 +7519,10 @@ class MemoryEngine(MemoryEngineInterface):

        operation_id = uuid.uuid4()

-        # Insert operation record into database
-        async with acquire_with_retry(pool) as conn:
-            await conn.execute(
-                f"""
-                INSERT INTO {fq_table("async_operations")} (operation_id, bank_id, operation_type, result_metadata, status)
-                VALUES ($1, $2, $3, $4, $5)
-                """,
-                operation_id,
-                bank_id,
-                operation_type,
-                json.dumps(result_metadata or {}),
-                "pending",
-            )
-
-        # Build and submit task payload
+        # Build full payload before INSERT so task_payload is included atomically.
+        # Previously the INSERT omitted task_payload and a separate submit_task call
+        # did an UPDATE — a crash between the two left a null-payload row that the
+        # worker's claim query (task_payload IS NOT NULL) could never pick up.
        full_payload = {
            "type": task_type,
            "operation_id": str(operation_id),
@@ -7429,6 +7530,24 @@ class MemoryEngine(MemoryEngineInterface):
            **task_payload,
        }

+        # Insert operation record with task_payload in a single atomic statement
+        async with acquire_with_retry(pool) as conn:
+            await conn.execute(
+                f"""
+                INSERT INTO {fq_table("async_operations")} (operation_id, bank_id, operation_type, result_metadata, status, task_payload)
+                VALUES ($1, $2, $3, $4, $5, $6::jsonb)
+                """,
+                operation_id,
+                bank_id,
+                operation_type,
+                json.dumps(result_metadata or {}, default=_json_default),
+                "pending",
+                json.dumps(full_payload, default=_json_default),
+            )
+
+        # For SyncTaskBackend: executes the task immediately.
+        # For BrokerTaskBackend: does an idempotent UPDATE (payload already set above),
+        # kept for symmetry and to support any future notification mechanisms.
        await self._task_backend.submit_task(full_payload)

        logger.info(f"{operation_type} task queued for bank_id={bank_id}, operation_id={operation_id}")
@@ -7462,7 +7581,9 @@ class MemoryEngine(MemoryEngineInterface):
                contents=[dict(c) for c in contents],
                request_context=request_context,
            )
-            await self._validate_operation(self._operation_validator.validate_retain(ctx))
+            result = await self._validate_operation(self._operation_validator.validate_retain(ctx))
+            if result and result.contents is not None:
+                contents = result.contents

        # Validate no duplicate document_ids in the batch
        # Having duplicate document_ids causes race conditions in document upserts during parallel processing
@@ -7758,6 +7879,15 @@ class MemoryEngine(MemoryEngineInterface):
        Returns:
            Dict with operation_id
        """
+        # Block mental model refresh when LLM provider is "none"
+        if self._llm_config.provider == "none":
+            from .providers.none_llm import LLMNotAvailableError
+
+            raise LLMNotAvailableError(
+                "Mental model refresh requires an LLM provider. Current provider is set to 'none'. "
+                "Set HINDSIGHT_API_LLM_PROVIDER to a real provider (e.g., openai, anthropic, gemini)."
+            )
+
        await self._authenticate_tenant(request_context)

        # Pre-operation validation (credit check)
@@ -8,7 +8,18 @@ from .anthropic_llm import AnthropicLLM
 from .claude_code_llm import ClaudeCodeLLM
 from .codex_llm import CodexLLM
 from .gemini_llm import GeminiLLM
+from .litellm_llm import LiteLLMLLM
 from .mock_llm import MockLLM
+from .none_llm import NoneLLM
 from .openai_compatible_llm import OpenAICompatibleLLM

-__all__ = ["AnthropicLLM", "ClaudeCodeLLM", "CodexLLM", "GeminiLLM", "MockLLM", "OpenAICompatibleLLM"]
+__all__ = [
+    "AnthropicLLM",
+    "ClaudeCodeLLM",
+    "CodexLLM",
+    "GeminiLLM",
+    "LiteLLMLLM",
+    "MockLLM",
+    "NoneLLM",
+    "OpenAICompatibleLLM",
+]
@@ -0,0 +1,380 @@
+"""
+LiteLLM LLM provider for universal model support.
+
+This provider enables using 100+ LLM providers via the LiteLLM SDK, including:
+- AWS Bedrock (bedrock/anthropic.claude-3-5-sonnet-...)
+- Azure OpenAI (azure/gpt-4o)
+- Together AI (together_ai/meta-llama/...)
+- Any other LiteLLM-supported provider
+
+Uses litellm.acompletion() for async chat completions.
+Authentication for cloud providers (e.g., AWS Bedrock via boto3 credential chain)
+is handled automatically by LiteLLM.
+"""
+
+import asyncio
+import json
+import logging
+import time
+from typing import Any
+
+from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
+from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
+from hindsight_api.metrics import get_metrics_collector
+
+logger = logging.getLogger(__name__)
+
+
+class LiteLLMLLM(LLMInterface):
+    """
+    LLM provider using the LiteLLM SDK for universal model support.
+
+    Supports any model accessible via litellm.acompletion(), including AWS Bedrock,
+    Azure OpenAI, Together AI, Fireworks AI, and more.
+
+    Model names follow LiteLLM conventions with provider prefixes:
+    - bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
+    - azure/gpt-4o
+    - together_ai/meta-llama/Llama-3-70b-chat-hf
+    - fireworks_ai/accounts/fireworks/models/llama-v3p1-70b-instruct
+    """
+
+    def __init__(
+        self,
+        provider: str,
+        api_key: str,
+        base_url: str,
+        model: str,
+        reasoning_effort: str = "low",
+        timeout: float = 300.0,
+        **kwargs: Any,
+    ):
+        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
+        self.timeout = timeout
+        self._litellm: Any = None
+
+        try:
+            import litellm
+
+            self._litellm = litellm
+            # Suppress LiteLLM's verbose logging
+            litellm.suppress_debug_info = True  # type: ignore[assignment]
+            # Drop unsupported params instead of raising errors (e.g. tool_choice on some Bedrock models)
+            litellm.drop_params = True  # type: ignore[assignment]
+            logging.getLogger("LiteLLM").setLevel(logging.WARNING)
+            logger.info(f"LiteLLM SDK initialized for model: {self.model}")
+        except ImportError as e:
+            raise RuntimeError("LiteLLM SDK not installed. Run: uv add litellm or pip install litellm") from e
+
+    async def verify_connection(self) -> None:
+        try:
+            test_messages = [{"role": "user", "content": "test"}]
+            await self.call(
+                messages=test_messages,
+                max_completion_tokens=50,
+                temperature=0.0,
+                scope="verification",
+                max_retries=0,
+            )
+            logger.info("LiteLLM connection verified successfully")
+        except OutputTooLongError:
+            # Truncation is fine for verification — it means the connection works
+            logger.info("LiteLLM connection verified successfully (response truncated)")
+        except Exception as e:
+            logger.error(f"LiteLLM connection verification failed: {e}")
+            raise RuntimeError(f"Failed to verify LiteLLM connection: {e}") from e
+
+    def _build_common_kwargs(
+        self,
+        messages: list[dict[str, Any]],
+        max_completion_tokens: int | None = None,
+        temperature: float | None = None,
+    ) -> dict[str, Any]:
+        """Build common kwargs for litellm calls."""
+        kwargs: dict[str, Any] = {
+            "model": self.model,
+            "messages": messages,
+            "timeout": self.timeout,
+        }
+
+        if self.api_key:
+            kwargs["api_key"] = self.api_key
+        if self.base_url:
+            kwargs["api_base"] = self.base_url
+        if max_completion_tokens is not None:
+            kwargs["max_completion_tokens"] = max_completion_tokens
+        if temperature is not None:
+            kwargs["temperature"] = temperature
+
+        return kwargs
+
+    async def call(
+        self,
+        messages: list[dict[str, str]],
+        response_format: Any | None = None,
+        max_completion_tokens: int | None = None,
+        temperature: float | None = None,
+        scope: str = "memory",
+        max_retries: int = 10,
+        initial_backoff: float = 1.0,
+        max_backoff: float = 60.0,
+        skip_validation: bool = False,
+        strict_schema: bool = False,
+        return_usage: bool = False,
+    ) -> Any:
+        start_time = time.time()
+
+        call_kwargs = self._build_common_kwargs(messages, max_completion_tokens, temperature)
+
+        # Add JSON schema response format if provided
+        if response_format is not None and hasattr(response_format, "model_json_schema"):
+            schema = response_format.model_json_schema()
+            call_kwargs["response_format"] = {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": response_format.__name__ if hasattr(response_format, "__name__") else "response",
+                    "schema": schema,
+                    "strict": strict_schema,
+                },
+            }
+
+        last_exception = None
+
+        for attempt in range(max_retries + 1):
+            try:
+                response = await self._litellm.acompletion(**call_kwargs)
+
+                content = response.choices[0].message.content or ""
+                finish_reason = response.choices[0].finish_reason
+
+                # Check for length-limited output
+                if finish_reason == "length":
+                    raise OutputTooLongError("LiteLLM response was truncated due to token limit")
+
+                if response_format is not None:
+                    # Strip markdown code fences if present
+                    clean_content = content
+                    if "```json" in content:
+                        clean_content = content.split("```json")[1].split("```")[0].strip()
+                    elif "```" in content:
+                        clean_content = content.split("```")[1].split("```")[0].strip()
+
+                    try:
+                        json_data = json.loads(clean_content)
+                    except json.JSONDecodeError:
+                        json_data = json.loads(content)
+
+                    if skip_validation:
+                        result = json_data
+                    else:
+                        result = response_format.model_validate(json_data)
+                else:
+                    result = content
+
+                # Extract usage
+                input_tokens = getattr(response.usage, "prompt_tokens", 0) or 0
+                output_tokens = getattr(response.usage, "completion_tokens", 0) or 0
+                total_tokens = input_tokens + output_tokens
+
+                # Record metrics
+                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=input_tokens,
+                    output_tokens=output_tokens,
+                    success=True,
+                )
+
+                # Record trace span
+                from hindsight_api.tracing import _serialize_for_span, get_span_recorder
+
+                span_recorder = get_span_recorder()
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=_serialize_for_span(result),
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    duration=duration,
+                    finish_reason=finish_reason,
+                    error=None,
+                )
+
+                if duration > 10.0:
+                    logger.info(
+                        f"slow llm call: scope={scope}, model={self.provider}/{self.model}, "
+                        f"input_tokens={input_tokens}, output_tokens={output_tokens}, "
+                        f"time={duration:.3f}s"
+                    )
+
+                if return_usage:
+                    token_usage = TokenUsage(
+                        input_tokens=input_tokens,
+                        output_tokens=output_tokens,
+                        total_tokens=total_tokens,
+                    )
+                    return result, token_usage
+                return result
+
+            except OutputTooLongError:
+                raise
+
+            except json.JSONDecodeError as e:
+                last_exception = e
+                if attempt < max_retries:
+                    logger.warning("LiteLLM returned invalid JSON, retrying...")
+                    backoff = min(initial_backoff * (2**attempt), max_backoff)
+                    await asyncio.sleep(backoff)
+                    continue
+                else:
+                    logger.error(f"LiteLLM returned invalid JSON after {max_retries + 1} attempts")
+                    raise
+
+            except Exception as e:
+                error_str = str(e).lower()
+                # Fast fail on auth errors
+                if "401" in error_str or "403" in error_str or "unauthorized" in error_str:
+                    logger.error(f"LiteLLM auth error, not retrying: {e}")
+                    raise
+
+                last_exception = e
+                if attempt < max_retries:
+                    # Retry on rate limits, connection errors, server errors
+                    is_retryable = any(
+                        keyword in error_str
+                        for keyword in ("rate", "limit", "timeout", "connection", "500", "502", "503", "529")
+                    )
+                    if is_retryable:
+                        backoff = min(initial_backoff * (2**attempt), max_backoff)
+                        jitter = backoff * 0.2 * (2 * (time.time() % 1) - 1)
+                        await asyncio.sleep(backoff + jitter)
+                        continue
+
+                logger.error(f"LiteLLM API error after {attempt + 1} attempts: {e}")
+                raise
+
+        if last_exception:
+            raise last_exception
+        raise RuntimeError("LiteLLM call failed after all retries")
+
+    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:
+        start_time = time.time()
+
+        call_kwargs = self._build_common_kwargs(messages, max_completion_tokens, temperature)
+        call_kwargs["tools"] = tools
+        call_kwargs["tool_choice"] = tool_choice
+
+        last_exception = None
+        for attempt in range(max_retries + 1):
+            try:
+                response = await self._litellm.acompletion(**call_kwargs)
+
+                message = response.choices[0].message
+                content = message.content
+                finish_reason = response.choices[0].finish_reason
+
+                # Extract tool calls
+                tool_calls: list[LLMToolCall] = []
+                if message.tool_calls:
+                    for tc in message.tool_calls:
+                        arguments = tc.function.arguments
+                        if isinstance(arguments, str):
+                            arguments = json.loads(arguments)
+                        tool_calls.append(
+                            LLMToolCall(
+                                id=tc.id,
+                                name=tc.function.name,
+                                arguments=arguments,
+                            )
+                        )
+
+                # Extract usage
+                input_tokens = getattr(response.usage, "prompt_tokens", 0) or 0
+                output_tokens = getattr(response.usage, "completion_tokens", 0) or 0
+
+                # Record metrics
+                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=input_tokens,
+                    output_tokens=output_tokens,
+                    success=True,
+                )
+
+                # Record trace span
+                from hindsight_api.tracing import get_span_recorder
+
+                span_recorder = get_span_recorder()
+                tool_calls_dict = (
+                    [{"id": tc.id, "name": tc.name, "arguments": tc.arguments} for tc in tool_calls]
+                    if tool_calls
+                    else None
+                )
+                span_recorder.record_llm_call(
+                    provider=self.provider,
+                    model=self.model,
+                    scope=scope,
+                    messages=messages,
+                    response_content=content,
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                    duration=duration,
+                    finish_reason=finish_reason,
+                    error=None,
+                    tool_calls=tool_calls_dict,
+                )
+
+                return LLMToolCallResult(
+                    content=content,
+                    tool_calls=tool_calls,
+                    finish_reason=finish_reason or ("tool_calls" if tool_calls else "stop"),
+                    input_tokens=input_tokens,
+                    output_tokens=output_tokens,
+                )
+
+            except Exception as e:
+                error_str = str(e).lower()
+                if "401" in error_str or "403" in error_str or "unauthorized" in error_str:
+                    raise
+
+                last_exception = e
+                if attempt < max_retries:
+                    is_retryable = any(
+                        keyword in error_str
+                        for keyword in ("rate", "limit", "timeout", "connection", "500", "502", "503", "529")
+                    )
+                    if is_retryable:
+                        await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
+                        continue
+
+                logger.error(f"LiteLLM tool call error after {attempt + 1} attempts: {e}")
+                raise
+
+        if last_exception:
+            raise last_exception
+        raise RuntimeError("LiteLLM tool call failed after all retries")
+
+    async def cleanup(self) -> None:
+        """Clean up resources."""
+        pass
@@ -0,0 +1,78 @@
+"""
+No-op LLM provider for chunk-only storage mode.
+
+When the LLM provider is set to "none", the system operates without any LLM dependency.
+Retain uses chunks mode (no fact extraction), and reflect/consolidation are disabled.
+This provider acts as a safety net — if any code path unexpectedly tries to call the LLM,
+it raises a clear error instead of a confusing connection failure.
+"""
+
+import logging
+from typing import Any
+
+from ..llm_interface import LLMInterface
+from ..response_models import LLMToolCallResult
+
+logger = logging.getLogger(__name__)
+
+
+class LLMNotAvailableError(Exception):
+    """Raised when an operation requires an LLM but the provider is set to 'none'."""
+
+    pass
+
+
+class NoneLLM(LLMInterface):
+    """
+    No-op LLM provider that rejects all LLM calls.
+
+    Used when HINDSIGHT_API_LLM_PROVIDER=none to run Hindsight as a chunk store
+    with semantic search but without LLM-based features (fact extraction, reflect,
+    consolidation).
+    """
+
+    async def verify_connection(self) -> None:
+        """No-op — no LLM connection to verify."""
+        logger.debug("NoneLLM: no LLM connection to verify (provider=none)")
+
+    async def call(
+        self,
+        messages: list[dict[str, str]],
+        response_format: Any | None = None,
+        max_completion_tokens: int | None = None,
+        temperature: float | None = None,
+        scope: str = "memory",
+        max_retries: int = 10,
+        initial_backoff: float = 1.0,
+        max_backoff: float = 60.0,
+        skip_validation: bool = False,
+        strict_schema: bool = False,
+        return_usage: bool = False,
+    ) -> Any:
+        """Raise LLMNotAvailableError — no LLM is configured."""
+        raise LLMNotAvailableError(
+            "LLM provider is set to 'none'. This operation requires an LLM. "
+            "Set HINDSIGHT_API_LLM_PROVIDER to a real provider (e.g., openai, anthropic, gemini)."
+        )
+
+    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:
+        """Raise LLMNotAvailableError — no LLM is configured."""
+        raise LLMNotAvailableError(
+            "LLM provider is set to 'none'. This operation requires an LLM. "
+            "Set HINDSIGHT_API_LLM_PROVIDER to a real provider (e.g., openai, anthropic, gemini)."
+        )
+
+    async def cleanup(self) -> None:
+        """No-op — nothing to clean up."""
+        pass
@@ -24,6 +24,7 @@ import os
 import re
 import time
 from typing import Any
+from urllib.parse import parse_qs, urlparse, urlunparse

 import httpx
 from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinishReasonError
@@ -39,6 +40,25 @@ logger = logging.getLogger(__name__)
 DEFAULT_LLM_SEED = 4242


+def _strip_code_fences(content: str) -> str:
+    """Strip markdown code fences from LLM response if present.
+
+    Many LLM providers (MiniMax, some Ollama models, Claude via proxies)
+    wrap JSON responses in ```json ... ``` fences even when json_object
+    response format is requested. This strips the fences while preserving
+    the JSON content inside. Returns the original content unchanged if
+    no fences are detected.
+    """
+    if "```" not in content:
+        return content
+    try:
+        if "```json" in content:
+            return content.split("```json")[1].split("```")[0].strip()
+        return content.split("```")[1].split("```")[0].strip()
+    except (IndexError, ValueError):
+        return content
+
+
 class OpenAICompatibleLLM(LLMInterface):
    """
    LLM provider for OpenAI-compatible APIs.
@@ -108,10 +128,18 @@ class OpenAICompatibleLLM(LLMInterface):
        # Get timeout config
        self.timeout = timeout or float(os.getenv(ENV_LLM_TIMEOUT, str(DEFAULT_LLM_TIMEOUT)))

-        # Create OpenAI client
+        # Create OpenAI client — extract query params from base_url (e.g. Azure api-version)
        client_kwargs: dict[str, Any] = {"api_key": self.api_key, "max_retries": 0}
        if self.base_url:
-            client_kwargs["base_url"] = self.base_url
+            parsed = urlparse(self.base_url)
+            if parsed.query:
+                clean_url = urlunparse(parsed._replace(query=""))
+                client_kwargs["base_url"] = clean_url
+                default_query = {k: v[0] for k, v in parse_qs(parsed.query).items()}
+                client_kwargs["default_query"] = default_query
+                self.base_url = clean_url
+            else:
+                client_kwargs["base_url"] = self.base_url
        if self.timeout:
            client_kwargs["timeout"] = self.timeout

@@ -313,20 +341,14 @@ class OpenAICompatibleLLM(LLMInterface):
                        if len(content) < original_len:
                            logger.debug(f"Stripped {original_len - len(content)} chars of reasoning tokens")

-                    # For local models, they may wrap JSON in markdown code blocks
-                    if self.provider in ("lmstudio", "ollama"):
-                        clean_content = content
-                        if "```json" in content:
-                            clean_content = content.split("```json")[1].split("```")[0].strip()
-                        elif "```" in content:
-                            clean_content = content.split("```")[1].split("```")[0].strip()
-                        try:
-                            json_data = json.loads(clean_content)
-                        except json.JSONDecodeError:
-                            # Fallback to parsing raw content
-                            json_data = json.loads(content)
-                    else:
-                        # Log raw LLM response for debugging JSON parse issues
+                    # Strip markdown code fences if present — any provider may
+                    # produce these (confirmed with MiniMax, some Ollama models,
+                    # Claude via proxies). No-op when content is already bare JSON.
+                    clean_content = _strip_code_fences(content)
+                    try:
+                        json_data = json.loads(clean_content)
+                    except json.JSONDecodeError:
+                        # Fallback to parsing raw content in case stripping was wrong
                        try:
                            json_data = json.loads(content)
                        except json.JSONDecodeError as json_err:
@@ -721,26 +743,33 @@ class OpenAICompatibleLLM(LLMInterface):
                    result = response.json()
                    content = result.get("message", {}).get("content", "")

-                    # Parse JSON response
+                    # Strip markdown code fences if present (safety net —
+                    # Ollama with schema enforcement usually returns bare JSON,
+                    # but some models may still wrap in fences)
+                    clean_content = _strip_code_fences(content)
                    try:
-                        json_data = json.loads(content)
-                    except json.JSONDecodeError as json_err:
-                        content_preview = content[:500] if content else "<empty>"
-                        if content and len(content) > 700:
-                            content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
-                        logger.warning(
-                            f"Ollama JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
-                            f"  Model: ollama/{self.model}\n"
-                            f"  Content length: {len(content) if content else 0} chars\n"
-                            f"  Content preview: {content_preview!r}"
-                        )
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            last_exception = json_err
-                            continue
-                        else:
-                            raise
+                        json_data = json.loads(clean_content)
+                    except json.JSONDecodeError:
+                        # Fallback to raw content
+                        try:
+                            json_data = json.loads(content)
+                        except json.JSONDecodeError as json_err:
+                            content_preview = content[:500] if content else "<empty>"
+                            if content and len(content) > 700:
+                                content_preview = f"{content[:500]}...TRUNCATED...{content[-200:]}"
+                            logger.warning(
+                                f"Ollama JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {json_err}\n"
+                                f"  Model: ollama/{self.model}\n"
+                                f"  Content length: {len(content) if content else 0} chars\n"
+                                f"  Content preview: {content_preview!r}"
+                            )
+                            if attempt < max_retries:
+                                backoff = min(initial_backoff * (2**attempt), max_backoff)
+                                await asyncio.sleep(backoff)
+                                last_exception = json_err
+                                continue
+                            else:
+                                raise

                    # Extract token usage from Ollama response
                    duration = time.time() - start_time
@@ -316,6 +316,8 @@ async def run_reflect_agent(
    response_schema: dict | None = None,
    directives: list[dict[str, Any]] | None = None,
    has_mental_models: bool = False,
+    include_observations: bool = True,
+    include_recall: bool = True,
    budget: str | None = None,
    max_context_tokens: int = 100_000,
 ) -> ReflectAgentResult:
@@ -355,7 +357,14 @@ async def run_reflect_agent(
    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)
+    tools = get_reflect_tools(
+        directive_rules=directive_rules,
+        include_mental_models=has_mental_models,
+        include_observations=include_observations,
+        include_recall=include_recall,
+    )
+    # Build set of enabled tool names to guard against LLM hallucinating disabled tool calls
+    enabled_tools: frozenset[str] = frozenset(t["function"]["name"] for t in tools if t.get("type") == "function")

    # Build initial messages (directives are injected into system prompt at START and END)
    system_prompt = build_system_prompt_for_tools(
@@ -538,19 +547,18 @@ async def run_reflect_agent(
        llm_start = time.time()

        # Determine tool_choice for this iteration.
-        # Force the full hierarchical retrieval path before allowing auto:
-        # With mental models:
-        #   0 → search_mental_models, 1 → search_observations, 2 → recall, 3+ → auto
-        # Without mental models:
-        #   0 → search_observations, 1 → recall, 2+ → auto
-        if iteration == 0 and has_mental_models:
-            iter_tool_choice: str | dict = {"type": "function", "function": {"name": "search_mental_models"}}
-        elif iteration == 0:
-            iter_tool_choice = {"type": "function", "function": {"name": "search_observations"}}
-        elif iteration == 1 and has_mental_models:
-            iter_tool_choice = {"type": "function", "function": {"name": "search_observations"}}
-        elif iteration == 1 or (iteration == 2 and has_mental_models):
-            iter_tool_choice = {"type": "function", "function": {"name": "recall"}}
+        # Force the full hierarchical retrieval path (only for enabled tools) before allowing auto.
+        # Build the forced sequence from the tools that are actually enabled.
+        forced_sequence = []
+        if has_mental_models:
+            forced_sequence.append("search_mental_models")
+        if include_observations:
+            forced_sequence.append("search_observations")
+        if include_recall:
+            forced_sequence.append("recall")
+
+        if iteration < len(forced_sequence):
+            iter_tool_choice: str | dict = {"type": "function", "function": {"name": forced_sequence[iteration]}}
        else:
            iter_tool_choice = "auto"

@@ -769,7 +777,17 @@ async def run_reflect_agent(
        # 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
+            # Partition into enabled vs hallucinated (not in enabled_tools set)
+            allowed_tools = []
+            hallucinated_tools = []
+            for tc in other_tools:
+                norm = _normalize_tool_name(tc.name)
+                if enabled_tools is not None and norm not in enabled_tools and norm not in ("done", "expand"):
+                    hallucinated_tools.append(tc)
+                else:
+                    allowed_tools.append(tc)
+
+            # Build assistant message with all tool calls (LLM requires them for history)
            messages.append(
                {
                    "role": "assistant",
@@ -777,6 +795,23 @@ async def run_reflect_agent(
                }
            )

+            # Immediately reject hallucinated tool calls without adding to trace
+            for tc in hallucinated_tools:
+                messages.append(
+                    {
+                        "role": "tool",
+                        "tool_call_id": tc.id,
+                        "name": tc.name,
+                        "content": json.dumps(
+                            {
+                                "error": f"Tool '{_normalize_tool_name(tc.name)}' is not available. Use only the tools provided to you."
+                            }
+                        ),
+                    }
+                )
+
+            other_tools = allowed_tools
+
            # Execute tools in parallel
            tool_tasks = [
                _execute_tool_with_timing(
@@ -785,6 +820,7 @@ async def run_reflect_agent(
                    search_observations_fn,
                    recall_fn,
                    expand_fn,
+                    enabled_tools=enabled_tools,
                )
                for tc in other_tools
            ]
@@ -974,6 +1010,7 @@ async def _execute_tool_with_timing(
    search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
    expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
+    enabled_tools: frozenset[str] | None = None,
 ) -> tuple[dict[str, Any], int]:
    """Execute a tool call and return result with timing."""
    from hindsight_api.tracing import get_tracer
@@ -1007,6 +1044,7 @@ async def _execute_tool_with_timing(
                search_observations_fn,
                recall_fn,
                expand_fn,
+                enabled_tools=enabled_tools,
            )

            # Set success attributes
@@ -1046,11 +1084,16 @@ async def _execute_tool(
    search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
    expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
+    enabled_tools: frozenset[str] | None = None,
 ) -> dict[str, Any]:
    """Execute a single tool by name."""
    # Normalize tool name for various LLM output formats
    tool_name = _normalize_tool_name(tool_name)

+    # Guard against LLMs hallucinating calls to tools that were not provided
+    if enabled_tools is not None and tool_name not in enabled_tools and tool_name not in ("done", "expand"):
+        return {"error": f"Tool '{tool_name}' is not available. Use only the tools provided to you."}
+
    if tool_name == "search_mental_models":
        query = args.get("query")
        if not query:
@@ -134,9 +134,10 @@ async def tool_search_observations(
    tag_groups: "list | None" = None,
    last_consolidated_at: datetime | None = None,
    pending_consolidation: int = 0,
+    source_facts_max_tokens: int = -1,
 ) -> dict[str, Any]:
    """
-    Search consolidated observations using recall with include_source_facts.
+    Search consolidated observations using recall.

    Observations are auto-generated from memories. Returns freshness info
    so the agent knows if it should also verify with recall().
@@ -151,10 +152,16 @@ async def tool_search_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
+        source_facts_max_tokens: Token budget for source facts (-1 = disabled, 0+ = enabled with limit)

    Returns:
        Dict with matching observations including freshness info and source memories
    """
+    include_source_facts = source_facts_max_tokens != -1
+    recall_kwargs: dict[str, Any] = {}
+    if include_source_facts and source_facts_max_tokens > 0:
+        recall_kwargs["max_source_facts_tokens"] = source_facts_max_tokens
+
    result = await memory_engine.recall_async(
        bank_id=bank_id,
        query=query,
@@ -165,10 +172,10 @@ async def tool_search_observations(
        tags=tags,
        tags_match=tags_match,
        tag_groups=tag_groups,
-        include_source_facts=True,
-        max_source_facts_tokens=-1,  # No token limit — include all source facts
+        include_source_facts=include_source_facts,
        _connection_budget=1,
        _quiet=True,
+        **recall_kwargs,
    )

    is_stale = pending_consolidation > 0
@@ -200,6 +207,7 @@ async def tool_recall(
    tag_groups: "list | None" = None,
    connection_budget: int = 1,
    max_chunk_tokens: int = 1000,
+    fact_types: list[str] | None = None,
 ) -> dict[str, Any]:
    """
    Search memories using TEMPR retrieval.
@@ -217,15 +225,18 @@ async def tool_recall(
        tags_match: How to match tags - "any" (OR), "all" (AND), or "exact"
        connection_budget: Max DB connections for this recall (default 1 for internal ops)
        max_chunk_tokens: Maximum tokens for raw source chunk text (default 1000, always included)
+        fact_types: Optional filter for fact types to retrieve. Defaults to ["experience", "world"].

    Returns:
        Dict with list of matching memories including raw chunk text
    """
+    # Only world/experience are valid for raw recall (observation is handled by search_observations)
+    recall_fact_type = [ft for ft in (fact_types or ["experience", "world"]) if ft in ("world", "experience")]
    include_chunks = True
    result = await memory_engine.recall_async(
        bank_id=bank_id,
        query=query,
-        fact_type=["experience", "world"],
+        fact_type=recall_fact_type,
        max_tokens=max_tokens,
        enable_trace=False,
        request_context=request_context,
@@ -227,7 +227,12 @@ def _build_done_tool_with_directives(directive_rules: list[str]) -> dict:
    }


-def get_reflect_tools(directive_rules: list[str] | None = None) -> list[dict]:
+def get_reflect_tools(
+    directive_rules: list[str] | None = None,
+    include_mental_models: bool = True,
+    include_observations: bool = True,
+    include_recall: bool = True,
+) -> list[dict]:
    """
    Get the list of tools for the reflect agent.

@@ -239,16 +244,23 @@ def get_reflect_tools(directive_rules: list[str] | None = None) -> list[dict]:
    Args:
        directive_rules: Optional list of directive rule strings. If provided,
                        the done() tool will require directive compliance confirmation.
+        include_mental_models: Whether to include the search_mental_models tool.
+        include_observations: Whether to include the search_observations tool.
+        include_recall: Whether to include the recall tool.

    Returns:
        List of tool definitions in OpenAI format
    """
-    tools = [
-        TOOL_SEARCH_MENTAL_MODELS,
-        TOOL_SEARCH_OBSERVATIONS,
-        TOOL_RECALL,
-        TOOL_EXPAND,
-    ]
+    tools = []
+
+    if include_mental_models:
+        tools.append(TOOL_SEARCH_MENTAL_MODELS)
+    if include_observations:
+        tools.append(TOOL_SEARCH_OBSERVATIONS)
+    if include_recall:
+        tools.append(TOOL_RECALL)
+
+    tools.append(TOOL_EXPAND)

    # Use directive-aware done tool if directives are present
    if directive_rules:
@@ -8,7 +8,7 @@ API stability even if internal models change.

 from typing import Any

-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, field_validator

 # Valid fact types for recall operations (excludes 'opinion' which is deprecated)
 VALID_RECALL_FACT_TYPES = frozenset(["world", "experience", "observation"])
@@ -159,6 +159,19 @@ class MemoryFact(BaseModel):
    mentioned_at: str | None = Field(None, description="ISO format date when the fact was mentioned/learned")
    document_id: str | None = Field(None, description="ID of the document this memory belongs to")
    metadata: dict[str, str] | None = Field(None, description="User-defined metadata")
+
+    @field_validator("metadata", mode="before")
+    @classmethod
+    def parse_metadata(cls, v: Any) -> dict[str, str] | None:
+        """Parse metadata from JSON string if needed (asyncpg may return JSONB as str)."""
+        if v is None:
+            return None
+        if isinstance(v, str):
+            import json
+
+            return json.loads(v)
+        return v
+
    chunk_id: str | None = Field(
        None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)"
    )
@@ -1083,30 +1083,16 @@ async def _extract_facts_from_chunk(
                        logger.warning(f"Skipping fact {i}: missing 'what' field")
                        continue

-                # Critical field: fact_type
-                # LLM uses "assistant" but we convert to "experience" for storage
-                original_fact_type = llm_fact.get("fact_type")
-                fact_type = original_fact_type
-
-                # Convert "assistant" → "experience" for storage
-                if fact_type == "assistant":
+                # Critical field: fact_type — "assistant" maps to "experience", everything else is "world".
+                # If fact_type is unexpected, fall back to fact_kind before defaulting to "world".
+                raw_fact_type = llm_fact.get("fact_type")
+                if raw_fact_type == "assistant":
                    fact_type = "experience"
-
-                # Validate fact_type (after conversion)
-                if fact_type not in ["world", "experience", "opinion"]:
-                    # Try to fix common mistakes - check if they swapped fact_type and fact_kind
-                    fact_kind = llm_fact.get("fact_kind")
-                    if fact_kind == "assistant":
-                        fact_type = "experience"
-                    elif fact_kind in ["world", "experience", "opinion"]:
-                        fact_type = fact_kind
-                    else:
-                        # Default to 'world' if we can't determine
-                        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})"
-                        )
+                elif raw_fact_type == "world":
+                    fact_type = "world"
+                else:
+                    raw_fact_kind = llm_fact.get("fact_kind")
+                    fact_type = "experience" if raw_fact_kind == "assistant" else "world"

                # Get fact_kind for temporal handling (but don't store it)
                fact_kind = llm_fact.get("fact_kind", "conversation")
@@ -1754,23 +1740,17 @@ async def extract_facts_from_contents_batch_api(
            who = get_value("who")
            why = get_value("why")

-            # Critical field: fact_type
-            original_fact_type = llm_fact.get("fact_type")
-            fact_type = original_fact_type
-
-            # Convert "assistant" → "experience"
-            if fact_type == "assistant":
+            # Critical field: fact_type — only "assistant" maps to "experience", everything else is "world"
+            # Critical field: fact_type — "assistant" maps to "experience", everything else is "world".
+            # If fact_type is unexpected, fall back to fact_kind before defaulting to "world".
+            raw_fact_type = llm_fact.get("fact_type")
+            if raw_fact_type == "assistant":
                fact_type = "experience"
-
-            # Validate fact_type
-            if fact_type not in ["world", "experience", "opinion"]:
-                fact_kind = llm_fact.get("fact_kind")
-                if fact_kind == "assistant":
-                    fact_type = "experience"
-                elif fact_kind in ["world", "experience", "opinion"]:
-                    fact_type = fact_kind
-                else:
-                    fact_type = "world"
+            elif raw_fact_type == "world":
+                fact_type = "world"
+            else:
+                raw_fact_kind = llm_fact.get("fact_kind")
+                fact_type = "experience" if raw_fact_kind == "assistant" else "world"

            # Build combined fact text
            combined_parts = [what]
@@ -1933,7 +1913,7 @@ async def extract_facts_from_contents_batch_api(
        for fact_from_llm in chunk_facts:
            extracted_fact = ExtractedFactType(
                fact_text=fact_from_llm.fact,
-                fact_type=fact_from_llm.fact_type,
+                fact_type="experience" if fact_from_llm.fact_type == "assistant" else "world",
                entities=[e.text for e in (fact_from_llm.entities or [])],
                occurred_start=_parse_datetime(fact_from_llm.occurred_start) if fact_from_llm.occurred_start else None,
                occurred_end=_parse_datetime(fact_from_llm.occurred_end) if fact_from_llm.occurred_end else None,
@@ -2110,7 +2090,7 @@ async def extract_facts_from_contents(
                    # mentioned_at is always the event_date (when the conversation/document occurred)
                    extracted_fact = ExtractedFactType(
                        fact_text=fact_from_llm.fact,
-                        fact_type=fact_from_llm.fact_type,
+                        fact_type="experience" if fact_from_llm.fact_type == "assistant" else "world",
                        entities=[e.text for e in (fact_from_llm.entities or [])],
                        # occurred_start/end: from LLM only, leave None if not provided
                        occurred_start=_parse_datetime(fact_from_llm.occurred_start)
@@ -81,9 +81,15 @@ async def insert_facts_batch(
        if fact.entities:
            signal_parts.extend(e.name for e in fact.entities)
        if fact.occurred_start:
-            signal_parts.append(fact.occurred_start.strftime("%B %-d %Y"))
+            try:
+                signal_parts.append(fact.occurred_start.strftime("%B %d %Y").lstrip("0").replace(" 0", " "))
+            except (ValueError, AttributeError):
+                pass
        if fact.occurred_end and fact.occurred_end != fact.occurred_start:
-            signal_parts.append(fact.occurred_end.strftime("%B %-d %Y"))
+            try:
+                signal_parts.append(fact.occurred_end.strftime("%B %d %Y").lstrip("0").replace(" 0", " "))
+            except (ValueError, AttributeError):
+                pass
        text_signals_list.append(" ".join(signal_parts) if signal_parts else None)

    # Batch insert all facts
@@ -292,6 +292,10 @@ async def retain_batch(
            pf.document_id = None
            pf.chunk_id = None

+        # Discard any leftover pending stats from a previous failed attempt so
+        # retries don't double-count or accumulate unbounded state.
+        entity_resolver.discard_pending_stats()
+
        async with acquire_with_retry(pool) as conn:
            async with conn.transaction():
                # Handle document tracking for all documents
@@ -231,7 +231,7 @@ class BFSGraphRetriever(GraphRetriever):
                    f"""
                    SELECT mu.id, mu.text, mu.context, mu.occurred_start, mu.occurred_end,
                           mu.mentioned_at, mu.fact_type,
-                           mu.document_id, mu.chunk_id, mu.tags,
+                           mu.document_id, mu.chunk_id, mu.tags, mu.metadata,
                           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
@@ -449,7 +449,7 @@ async def fetch_memory_units_by_ids(
        rows = await conn.fetch(
            f"""
            SELECT id, text, context, event_date, occurred_start, occurred_end,
-                   mentioned_at, fact_type, document_id, chunk_id, tags
+                   mentioned_at, fact_type, document_id, chunk_id, tags, metadata
            FROM {fq_table("memory_units")}
            WHERE id = ANY($1::uuid[])
              AND fact_type = $2
@@ -153,6 +153,8 @@ class CrossEncoderReranker:

        # Normalize scores using sigmoid to [0, 1] range
        # Cross-encoder returns logits which can be negative
+        import math
+
        import numpy as np

        def sigmoid(x):
@@ -163,11 +165,20 @@ class CrossEncoderReranker:
        # Create ScoredResult objects with cross-encoder scores
        scored_results = []
        for candidate, raw_score, norm_score in zip(candidates, scores, normalized_scores):
+            # Sanitize NaN scores (cross-encoder can return NaN for certain inputs).
+            # NaN propagates through all downstream scoring and Pydantic serializes
+            # NaN as JSON null, which breaks clients expecting numeric values.
+            raw = float(raw_score)
+            norm = float(norm_score)
+            if math.isnan(raw):
+                raw = 0.0
+            if math.isnan(norm):
+                norm = 0.0
            scored_result = ScoredResult(
                candidate=candidate,
-                cross_encoder_score=float(raw_score),
-                cross_encoder_score_normalized=float(norm_score),
-                weight=float(norm_score),  # Initial weight is just cross-encoder score
+                cross_encoder_score=raw,
+                cross_encoder_score_normalized=norm,
+                weight=norm,  # Initial weight is just cross-encoder score
            )
            scored_results.append(scored_result)

@@ -10,6 +10,7 @@ Implements:

 import asyncio
 import logging
+import re
 from dataclasses import dataclass, field
 from datetime import UTC, datetime
 from typing import Optional
@@ -26,6 +27,15 @@ from .types import MPFPTimings, RetrievalResult
 logger = logging.getLogger(__name__)


+def tokenize_query(query_text: str) -> list[str]:
+    """Normalize query text and split into BM25 tokens.
+
+    Strips punctuation, lowercases, and splits on whitespace.
+    Returns an empty list when the query contains no word characters.
+    """
+    return re.sub(r"[^\w\s]", " ", query_text.lower()).split()
+
+
@dataclass
 class ParallelRetrievalResult:
    """Result from parallel retrieval across all methods."""
@@ -129,30 +139,31 @@ async def retrieve_semantic_bm25_combined(
    Returns:
        Dict mapping fact_type -> (semantic_results, bm25_results)
    """
-    import re
-
    result_dict: dict[str, tuple[list[RetrievalResult], list[RetrievalResult]]] = {ft: ([], []) for ft in fact_types}

-    sanitized_text = re.sub(r"[^\w\s]", " ", query_text.lower())
-    tokens = [token for token in sanitized_text.split() if token]
+    tokens = tokenize_query(query_text)

    # Over-fetch for HNSW approximation; semantic results trimmed to limit in Python.
    hnsw_fetch = max(limit * 5, 100)

    cols = (
        "id, text, context, event_date, occurred_start, occurred_end, mentioned_at, "
-        "fact_type, document_id, chunk_id, tags"
+        "fact_type, document_id, chunk_id, tags, metadata"
    )
    table = fq_table("memory_units")

    # --- Parameter layout ---
    # $1 = query_emb_str  (semantic arms)
    # $2 = bank_id
-    # $3 = limit          (BM25 LIMIT; semantic uses inlined hnsw_fetch literal)
-    # $4 = bm25_text      (only when tokens present)
-    # $N = tags           (N=4 when no tokens, N=5 when tokens present)
-    # $M+ = tag_groups params (one per leaf, starting after tags param)
-    tags_param_idx = 5 if tokens else 4
+    # When tokens present:
+    #   $3 = limit          (BM25 LIMIT; semantic uses inlined hnsw_fetch literal)
+    #   $4 = bm25_text
+    #   $5 = tags           (if present)
+    #   $6+ = tag_groups params (one per leaf)
+    # When no tokens ($3 is skipped — not included in params to avoid type inference gap):
+    #   $3 = tags           (if present)
+    #   $4+ = tag_groups params (one per leaf)
+    tags_param_idx = 5 if tokens else 3
    tags_clause = build_tags_where_clause_simple(tags, tags_param_idx, match=tags_match)

    # tag_groups params start immediately after the tags param slot
@@ -222,9 +233,10 @@ async def retrieve_semantic_bm25_combined(

    query = "\nUNION ALL\n".join(arms)

-    params: list = [query_emb_str, bank_id, limit]
+    params: list = [query_emb_str, bank_id]
    if tokens:
-        params.append(bm25_text_param)
+        params.append(limit)  # $3: BM25 LIMIT (only referenced when tokens are present)
+        params.append(bm25_text_param)  # $4
    if tags:
        params.append(tags)
    params.extend(groups_params)
@@ -331,7 +343,7 @@ async def retrieve_temporal_combined(
              {groups_clause}
        ),
        sim_ranked AS (
-            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+            SELECT mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.metadata,
                   1 - (mu.embedding <=> $1::vector) AS similarity,
                   ROW_NUMBER() OVER (PARTITION BY mu.fact_type ORDER BY mu.embedding <=> $1::vector) AS sim_rn
            FROM date_ranked dr
@@ -339,7 +351,7 @@ async def retrieve_temporal_combined(
            WHERE dr.rn <= 50
              AND (1 - (mu.embedding <=> $1::vector)) >= $6
        )
-        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags, similarity
+        SELECT id, text, context, event_date, occurred_start, occurred_end, mentioned_at, fact_type, document_id, chunk_id, tags, metadata, similarity
        FROM sim_ranked
        WHERE sim_rn <= 10
        """,
@@ -437,7 +449,7 @@ async def retrieve_temporal_combined(
            # bank_id on memory_units lets the planner use idx_memory_units_bank_fact_type.
            neighbors = await conn.fetch(
                f"""
-                SELECT src.from_unit_id, mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags,
+                SELECT src.from_unit_id, mu.id, mu.text, mu.context, mu.event_date, mu.occurred_start, mu.occurred_end, mu.mentioned_at, mu.fact_type, mu.document_id, mu.chunk_id, mu.tags, mu.metadata,
                       l.weight, l.link_type,
                       1 - (mu.embedding <=> $1::vector) AS similarity
                FROM unnest($2::uuid[]) AS src(from_unit_id)
@@ -47,6 +47,7 @@ class RetrievalResult:
    document_id: str | None = None
    chunk_id: str | None = None
    tags: list[str] | None = None  # Visibility scope tags
+    metadata: dict[str, str] | None = None  # User-provided metadata

    # Retrieval-specific scores (only one will be set depending on retrieval method)
    similarity: float | None = None  # Semantic retrieval
@@ -70,6 +71,7 @@ class RetrievalResult:
            document_id=row.get("document_id"),
            chunk_id=row.get("chunk_id"),
            tags=row.get("tags"),
+            metadata=row.get("metadata"),
            similarity=row.get("similarity"),
            bm25_score=row.get("bm25_score"),
            activation=row.get("activation"),
@@ -153,6 +155,7 @@ class ScoredResult:
            "document_id": self.retrieval.document_id,
            "chunk_id": self.retrieval.chunk_id,
            "tags": self.retrieval.tags,
+            "metadata": self.retrieval.metadata,
            "semantic_similarity": self.retrieval.similarity,
            "bm25_score": self.retrieval.bm25_score,
        }
@@ -11,6 +11,7 @@ if TYPE_CHECKING:
    from hindsight_api.engine.memory_engine import Budget
    from hindsight_api.engine.response_models import RecallResult as RecallResultModel
    from hindsight_api.engine.response_models import ReflectResult
+    from hindsight_api.engine.search.tags import TagGroup, TagsMatch
    from hindsight_api.models import RequestContext


@@ -25,17 +26,51 @@ class OperationValidationError(Exception):

@dataclass
 class ValidationResult:
-    """Result of an operation validation."""
+    """Result of an operation validation.
+
+    Validators return this to accept or reject an operation. When accepting,
+    validators can optionally return modified data that the engine will use
+    instead of the original request parameters. This enables context enrichment
+    (e.g., injecting tags or tag_groups).
+    """

    allowed: bool
    reason: str | None = None
    status_code: int = 403  # Default to Forbidden
+    # Optional enrichment fields — returned by validator, used by engine if present.
+    # None means "no modification" (engine uses original values).
+    contents: list[dict] | None = None  # Enriched retain contents (e.g., injected tags/strategy)
+    tags: list[str] | None = None  # Enriched recall tags
+    tags_match: "TagsMatch | None" = None  # Enriched recall tags match mode
+    tag_groups: "list[TagGroup] | None" = None  # Enriched recall tag_groups

    @classmethod
    def accept(cls) -> "ValidationResult":
-        """Create an accepted validation result."""
+        """Create an accepted validation result (no enrichment)."""
        return cls(allowed=True)

+    @classmethod
+    def accept_with(
+        cls,
+        *,
+        contents: list[dict] | None = None,
+        tags: list[str] | None = None,
+        tags_match: "TagsMatch | None" = None,
+        tag_groups: "list[TagGroup] | None" = None,
+    ) -> "ValidationResult":
+        """Create an accepted validation result with enriched data.
+
+        The engine will use the returned values instead of the original request
+        parameters. Only non-None fields are applied; None means "keep original".
+        """
+        return cls(
+            allowed=True,
+            contents=contents,
+            tags=tags,
+            tags_match=tags_match,
+            tag_groups=tag_groups,
+        )
+
    @classmethod
    def reject(cls, reason: str, status_code: int = 403) -> "ValidationResult":
        """Create a rejected validation result with a reason and HTTP status code."""
@@ -52,10 +87,12 @@ class RetainContext:
    """Context for a retain operation validation (pre-operation).

    Contains ALL user-provided parameters for the retain operation.
+    To enrich contents (e.g., inject tags or strategy), return them
+    via ValidationResult.accept_with(contents=...).
    """

    bank_id: str
-    contents: list[dict]  # List of {content, context, event_date, document_id}
+    contents: list[dict]  # List of {content, context, event_date, document_id, tags, strategy}
    request_context: "RequestContext"
    document_id: str | None = None
    fact_type_override: str | None = None
@@ -67,6 +104,8 @@ class RecallContext:
    """Context for a recall operation validation (pre-operation).

    Contains ALL user-provided parameters for the recall operation.
+    To enrich tag filters (e.g., inject tag_groups), return them
+    via ValidationResult.accept_with(tag_groups=...).
    """

    bank_id: str
@@ -81,6 +120,9 @@ class RecallContext:
    max_entity_tokens: int = 500
    include_chunks: bool = False
    max_chunk_tokens: int = 8192
+    tags: list[str] | None = None
+    tags_match: "TagsMatch" = "any"
+    tag_groups: "list[TagGroup] | None" = None


@dataclass
@@ -13,6 +13,7 @@ Stop with Ctrl+C.
 import argparse
 import asyncio
 import atexit
+import dataclasses
 import os
 import signal
 import sys
@@ -152,178 +153,7 @@ def main():
    # Configure Python logging based on log level
    # Update config with CLI override if provided
    if args.log_level != config.log_level:
-        config = HindsightConfig(
-            database_url=config.database_url,
-            database_schema=config.database_schema,
-            vector_extension=config.vector_extension,
-            text_search_extension=config.text_search_extension,
-            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_groq_service_tier=config.llm_groq_service_tier,
-            llm_openai_service_tier=config.llm_openai_service_tier,
-            llm_vertexai_project_id=config.llm_vertexai_project_id,
-            llm_vertexai_region=config.llm_vertexai_region,
-            llm_vertexai_service_account_key=config.llm_vertexai_service_account_key,
-            llm_gemini_safety_settings=config.llm_gemini_safety_settings,
-            retain_llm_provider=config.retain_llm_provider,
-            retain_llm_api_key=config.retain_llm_api_key,
-            retain_llm_model=config.retain_llm_model,
-            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_local_trust_remote_code=config.embeddings_local_trust_remote_code,
-            embeddings_tei_url=config.embeddings_tei_url,
-            embeddings_openai_base_url=config.embeddings_openai_base_url,
-            embeddings_cohere_api_key=config.embeddings_cohere_api_key,
-            embeddings_cohere_model=config.embeddings_cohere_model,
-            embeddings_cohere_base_url=config.embeddings_cohere_base_url,
-            embeddings_litellm_api_base=config.embeddings_litellm_api_base,
-            embeddings_litellm_api_key=config.embeddings_litellm_api_key,
-            embeddings_litellm_model=config.embeddings_litellm_model,
-            embeddings_litellm_sdk_api_key=config.embeddings_litellm_sdk_api_key,
-            embeddings_litellm_sdk_model=config.embeddings_litellm_sdk_model,
-            embeddings_litellm_sdk_api_base=config.embeddings_litellm_sdk_api_base,
-            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_local_trust_remote_code=config.reranker_local_trust_remote_code,
-            reranker_local_fp16=config.reranker_local_fp16,
-            reranker_local_bucket_batching=config.reranker_local_bucket_batching,
-            reranker_local_batch_size=config.reranker_local_batch_size,
-            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_api_key=config.reranker_cohere_api_key,
-            reranker_cohere_model=config.reranker_cohere_model,
-            reranker_cohere_base_url=config.reranker_cohere_base_url,
-            reranker_litellm_api_base=config.reranker_litellm_api_base,
-            reranker_litellm_api_key=config.reranker_litellm_api_key,
-            reranker_litellm_model=config.reranker_litellm_model,
-            reranker_litellm_max_tokens_per_doc=config.reranker_litellm_max_tokens_per_doc,
-            reranker_litellm_sdk_api_key=config.reranker_litellm_sdk_api_key,
-            reranker_litellm_sdk_model=config.reranker_litellm_sdk_model,
-            reranker_litellm_sdk_api_base=config.reranker_litellm_sdk_api_base,
-            reranker_zeroentropy_api_key=config.reranker_zeroentropy_api_key,
-            reranker_zeroentropy_model=config.reranker_zeroentropy_model,
-            host=args.host,
-            port=args.port,
-            base_path=config.base_path,
-            log_level=args.log_level,
-            log_format=config.log_format,
-            mcp_enabled=config.mcp_enabled,
-            mcp_enabled_tools=config.mcp_enabled_tools,
-            enable_bank_config_api=config.enable_bank_config_api,
-            graph_retriever=config.graph_retriever,
-            mpfp_top_k_neighbors=config.mpfp_top_k_neighbors,
-            recall_max_concurrent=config.recall_max_concurrent,
-            recall_connection_budget=config.recall_connection_budget,
-            recall_max_query_tokens=config.recall_max_query_tokens,
-            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_mission=config.retain_mission,
-            retain_custom_instructions=config.retain_custom_instructions,
-            retain_default_strategy=config.retain_default_strategy,
-            retain_strategies=config.retain_strategies,
-            retain_batch_tokens=config.retain_batch_tokens,
-            retain_entity_lookup=config.retain_entity_lookup,
-            retain_batch_enabled=config.retain_batch_enabled,
-            retain_batch_poll_interval_seconds=config.retain_batch_poll_interval_seconds,
-            file_storage_type=config.file_storage_type,
-            file_storage_s3_bucket=config.file_storage_s3_bucket,
-            file_storage_s3_region=config.file_storage_s3_region,
-            file_storage_s3_endpoint=config.file_storage_s3_endpoint,
-            file_storage_s3_access_key_id=config.file_storage_s3_access_key_id,
-            file_storage_s3_secret_access_key=config.file_storage_s3_secret_access_key,
-            file_storage_gcs_bucket=config.file_storage_gcs_bucket,
-            file_storage_gcs_service_account_key=config.file_storage_gcs_service_account_key,
-            file_storage_azure_container=config.file_storage_azure_container,
-            file_storage_azure_account_name=config.file_storage_azure_account_name,
-            file_storage_azure_account_key=config.file_storage_azure_account_key,
-            file_parser=config.file_parser,
-            file_parser_allowlist=config.file_parser_allowlist,
-            file_parser_iris_token=config.file_parser_iris_token,
-            file_parser_iris_org_id=config.file_parser_iris_org_id,
-            file_conversion_max_batch_size_mb=config.file_conversion_max_batch_size_mb,
-            file_conversion_max_batch_size=config.file_conversion_max_batch_size,
-            enable_file_upload_api=config.enable_file_upload_api,
-            file_delete_after_retain=config.file_delete_after_retain,
-            enable_observations=config.enable_observations,
-            enable_observation_history=config.enable_observation_history,
-            enable_mental_model_history=config.enable_mental_model_history,
-            consolidation_batch_size=config.consolidation_batch_size,
-            consolidation_llm_batch_size=config.consolidation_llm_batch_size,
-            consolidation_max_tokens=config.consolidation_max_tokens,
-            consolidation_source_facts_max_tokens=config.consolidation_source_facts_max_tokens,
-            consolidation_source_facts_max_tokens_per_observation=config.consolidation_source_facts_max_tokens_per_observation,
-            observations_mission=config.observations_mission,
-            entity_labels=config.entity_labels,
-            entities_allow_free_form=config.entities_allow_free_form,
-            skip_llm_verification=config.skip_llm_verification,
-            lazy_reranker=config.lazy_reranker,
-            run_migrations_on_startup=config.run_migrations_on_startup,
-            db_pool_min_size=config.db_pool_min_size,
-            db_pool_max_size=config.db_pool_max_size,
-            db_command_timeout=config.db_command_timeout,
-            db_acquire_timeout=config.db_acquire_timeout,
-            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,
-            reflect_max_context_tokens=config.reflect_max_context_tokens,
-            reflect_mission=config.reflect_mission,
-            disposition_skepticism=config.disposition_skepticism,
-            disposition_literalism=config.disposition_literalism,
-            disposition_empathy=config.disposition_empathy,
-            mental_model_refresh_concurrency=config.mental_model_refresh_concurrency,
-            otel_traces_enabled=config.otel_traces_enabled,
-            otel_exporter_otlp_endpoint=config.otel_exporter_otlp_endpoint,
-            otel_exporter_otlp_headers=config.otel_exporter_otlp_headers,
-            otel_service_name=config.otel_service_name,
-            otel_deployment_environment=config.otel_deployment_environment,
-            webhook_url=config.webhook_url,
-            webhook_secret=config.webhook_secret,
-            webhook_event_types=config.webhook_event_types,
-            webhook_delivery_poll_interval_seconds=config.webhook_delivery_poll_interval_seconds,
-        )
+        config = dataclasses.replace(config, host=args.host, port=args.port, log_level=args.log_level)
    config.configure_logging()
    if not args.daemon:
        config.log_config()
@@ -381,15 +211,27 @@ def main():
    # 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
+    # Check for uvloop/winloop availability
+    import sys

-        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")
+    loop_impl = "asyncio"
+    if sys.platform == "win32":
+        try:
+            import winloop
+
+            winloop.install()  # Patches asyncio globally — uvicorn uses "asyncio" but gets winloop
+            loop_impl = "asyncio"  # Tell uvicorn "asyncio" — it's now winloop underneath
+            print("winloop installed as asyncio event loop policy (Windows uvloop port)")
+        except ImportError:
+            print("winloop not installed, using default asyncio event loop")
+    else:
+        try:
+            import uvloop  # noqa: F401
+
+            loop_impl = "uvloop"
+            print("uvloop available, will use for event loop")
+        except ImportError:
+            print("uvloop not installed, using default asyncio event loop")

    uvicorn_config = {
        "app": "hindsight_api.server:app" if use_import_string else app,
@@ -42,6 +42,9 @@ class MCPToolsConfig:
    # How to resolve api_key_id for usage metering (set by MCP middleware after auth)
    api_key_id_resolver: Callable[[], str | None] | None = None

+    # How to resolve mcp_authenticated flag (set when MCP_AUTH_TOKEN validates)
+    mcp_authenticated_resolver: Callable[[], bool] | None = None
+
    # Whether to include bank_id as a parameter on tools (for multi-bank support)
    include_bank_id_param: bool = False

@@ -64,7 +67,10 @@ def _get_request_context(config: MCPToolsConfig) -> RequestContext:
    api_key = config.api_key_resolver() if config.api_key_resolver else None
    tenant_id = config.tenant_id_resolver() if config.tenant_id_resolver else None
    api_key_id = config.api_key_id_resolver() if config.api_key_id_resolver else None
-    return RequestContext(api_key=api_key, tenant_id=tenant_id, api_key_id=api_key_id)
+    mcp_authenticated = config.mcp_authenticated_resolver() if config.mcp_authenticated_resolver else False
+    return RequestContext(
+        api_key=api_key, tenant_id=tenant_id, api_key_id=api_key_id, mcp_authenticated=mcp_authenticated
+    )


 def parse_timestamp(timestamp: str) -> datetime | None:
@@ -95,6 +101,7 @@ def build_content_dict(
    tags: list[str] | None = None,
    metadata: dict[str, str] | None = None,
    document_id: str | None = None,
+    strategy: str | None = None,
 ) -> tuple[dict[str, Any], str | None]:
    """Build a content dict for retain operations.

@@ -105,10 +112,24 @@ def build_content_dict(
        tags: Optional tags for scoped visibility filtering
        metadata: Optional key-value metadata to attach to the memory
        document_id: Optional document ID to associate the memory with
+        strategy: Optional named retain strategy override (e.g., 'exact', 'verbose')

    Returns:
        Tuple of (content_dict, error_message). error_message is None if successful.
    """
+    # Coerce tags from JSON string to list if needed.
+    # MCP tool bridges sometimes serialize JSON arrays as strings during
+    # transport, e.g. '["a", "b"]' instead of ["a", "b"].
+    if isinstance(tags, str):
+        try:
+            parsed = json.loads(tags)
+            if isinstance(parsed, list):
+                tags = parsed
+        except (json.JSONDecodeError, TypeError):
+            pass
+        if isinstance(tags, str):
+            tags = [tags]
+
    content_dict: dict[str, Any] = {"content": content, "context": context}

    if timestamp:
@@ -124,6 +145,8 @@ def build_content_dict(
        content_dict["metadata"] = metadata
    if document_id is not None:
        content_dict["document_id"] = document_id
+    if strategy is not None:
+        content_dict["strategy"] = strategy

    return content_dict, None

@@ -353,6 +376,7 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
            metadata: dict[str, str] | None = None,
            document_id: str | None = None,
            bank_id: str | None = None,
+            strategy: str | None = None,
        ) -> dict:
            """
            Args:
@@ -363,12 +387,13 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                metadata: Optional key-value metadata to attach (e.g., {'source': 'slack', 'channel': 'general'})
                document_id: Optional document ID to associate this memory with
                bank_id: Optional bank to store in (defaults to session bank). Use for cross-bank operations.
+                strategy: Optional named retain strategy (e.g., 'exact' for verbatim storage). Strategies are defined in the bank config.
            """
            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, tags, metadata, document_id)
+            content_dict, error = build_content_dict(content, context, timestamp, tags, metadata, document_id, strategy)
            if error:
                return {"status": "error", "message": error}

@@ -402,6 +427,7 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
            tags: list[str] | None = None,
            metadata: dict[str, str] | None = None,
            document_id: str | None = None,
+            strategy: str | None = None,
        ) -> dict:
            """
            Args:
@@ -411,12 +437,13 @@ def _register_retain(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfig)
                tags: Optional tags for scoped visibility filtering (e.g., ['project:alpha', 'user:123'])
                metadata: Optional key-value metadata to attach (e.g., {'source': 'slack', 'channel': 'general'})
                document_id: Optional document ID to associate this memory with
+                strategy: Optional named retain strategy (e.g., 'exact' for verbatim storage). Strategies are defined in the bank config.
            """
            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, tags, metadata, document_id)
+            content_dict, error = build_content_dict(content, context, timestamp, tags, metadata, document_id, strategy)
            if error:
                return {"status": "error", "message": error}

@@ -13,7 +13,12 @@ This module provides metrics for:

 import logging
 import os
-import resource
+import types
+
+try:
+    import resource
+except ImportError:
+    resource: types.ModuleType | None = None  # Windows doesn't have resource module
 import threading
 import time
 from contextlib import contextmanager
@@ -455,6 +460,8 @@ class MetricsCollector(MetricsCollectorBase):

    def _setup_process_metrics(self):
        """Set up observable gauges for process metrics."""
+        if resource is None:
+            return  # Skip process metrics on Windows

        def get_cpu_times(_options):
            """Get process CPU times."""
@@ -21,6 +21,7 @@ class RequestContext:
    api_key_id: str | None = None  # UUID of the API key used for authentication
    tenant_id: str | None = None  # Tenant identifier (set by extension after auth)
    internal: bool = False  # True for background/internal operations (skips extension auth)
+    mcp_authenticated: bool = False  # True when MCP transport auth already validated (skips tenant re-auth)
    user_initiated: bool = False  # True for async operations that originated from a user request
    allowed_bank_ids: list[str] | None = None  # None = unrestricted (all banks)

@@ -302,19 +302,106 @@ class WorkerPoller:
        )

    async def _mark_failed(self, operation_id: str, error_message: str, schema: str | None):
-        """Mark a task as failed with error message."""
+        """Mark a task as failed with error message, then propagate to parent if applicable."""
        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 with self._pool.acquire() as conn:
+            async with conn.transaction():
+                await conn.execute(
+                    f"""
+                    UPDATE {table}
+                    SET status = 'failed', error_message = $2, completed_at = now(), updated_at = now()
+                    WHERE operation_id = $1
+                    """,
+                    operation_id,
+                    error_message,
+                )
+                await self._maybe_update_parent_operation(operation_id, schema, conn)
+
+    async def _maybe_update_parent_operation(self, child_operation_id: str, schema: str | None, conn) -> None:
+        """If this operation is a child of a batch_retain, update the parent status when all siblings are done.
+
+        Must be called within an active transaction that has already updated the child's status.
+        The memory engine has an equivalent method that runs inside task execution transactions.
+        This poller-level version handles the case where a task fails via an unhandled exception
+        that bypasses the memory engine's own failure path (e.g. a DB constraint violation that
+        rolls back the engine's transaction before it can update the parent).
+        """
+        import json
+        import uuid
+
+        table = fq_table("async_operations", schema)
+
+        try:
+            row = await conn.fetchrow(
+                f"SELECT result_metadata, bank_id FROM {table} WHERE operation_id = $1",
+                uuid.UUID(child_operation_id),
+            )
+            if not row:
+                return
+
+            result_metadata = row["result_metadata"] or {}
+            if isinstance(result_metadata, str):
+                result_metadata = json.loads(result_metadata)
+            parent_operation_id = result_metadata.get("parent_operation_id")
+            if not parent_operation_id:
+                return
+
+            bank_id = row["bank_id"]
+
+            # Lock parent to prevent concurrent sibling updates
+            parent_row = await conn.fetchrow(
+                f"SELECT operation_id FROM {table} WHERE operation_id = $1 AND bank_id = $2 FOR UPDATE",
+                uuid.UUID(parent_operation_id),
+                bank_id,
+            )
+            if not parent_row:
+                return
+
+            # Check whether all siblings are done
+            siblings = await conn.fetch(
+                f"""
+                SELECT status FROM {table}
+                WHERE bank_id = $1
+                  AND result_metadata::jsonb @> $2::jsonb
+                """,
+                bank_id,
+                json.dumps({"parent_operation_id": parent_operation_id}),
+            )
+            if not siblings or not all(s["status"] in ("completed", "failed") for s in siblings):
+                return
+
+            any_failed = any(s["status"] == "failed" for s in siblings)
+            if any_failed:
+                await conn.execute(
+                    f"""
+                    UPDATE {table}
+                    SET status = 'failed', error_message = $2, updated_at = now()
+                    WHERE operation_id = $1
+                    """,
+                    uuid.UUID(parent_operation_id),
+                    "One or more sub-batches failed",
+                )
+            else:
+                await conn.execute(
+                    f"""
+                    UPDATE {table}
+                    SET status = 'completed', updated_at = now(), completed_at = now()
+                    WHERE operation_id = $1
+                    """,
+                    uuid.UUID(parent_operation_id),
+                )
+            logger.info(
+                f"Poller updated parent operation {parent_operation_id} to "
+                f"{'failed' if any_failed else 'completed'} (all siblings done)"
+            )
+        except Exception as e:
+            # Log but don't re-raise — the child has already been marked failed,
+            # which is the critical state change. A stuck parent will be caught on
+            # the next run or via monitoring.
+            logger.error(f"Failed to update parent operation for child {child_operation_id}: {e}")

    async def _schedule_retry(self, operation_id: str, retry_at: "Any", error_message: str, schema: str | None):
        """Reset task to pending with a future retry timestamp."""
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"

 [project]
 name = "hindsight-api-slim"
-version = "0.4.19"
+version = "0.4.20"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -40,12 +40,13 @@ dependencies = [
    "anthropic>=0.40.0",
    "typer>=0.9.0",
    "cohere>=5.0.0",
-    "litellm>=1.0.0",
+    "litellm>=1.0.0,<=1.82.6", # 1.82.7+ contains a supply chain attack (malicious .pth credential stealer)
    "markitdown[pdf,docx,pptx,xlsx,xls]>=0.1.4", # File to markdown conversion
    "obstore>=0.4.0", # S3/GCS/Azure object storage client (Rust-backed)
-    "uvloop>=0.22.1",
+    "winloop>=0.1.0; sys_platform == 'win32'",
+    "uvloop>=0.22.1; sys_platform != 'win32'",
    # Transitive dependency security fixes
-    "pyasn1>=0.6.2", # DoS vulnerability fix
+    "pyasn1>=0.6.3", # DoS vulnerability fix
    "urllib3>=2.6.3", # Decompression-bomb safeguards bypass fix
    "langchain-core>=1.2.11", # Serialization injection + SSRF vulnerability fix
    "langsmith>=0.6.3", # SSRF via tracing header injection fix
@@ -53,9 +54,13 @@ dependencies = [
    "pillow>=12.1.1", # Out-of-bounds write in PSD image loading fix
    "cryptography>=46.0.5", # Subgroup attack vulnerability fix
    "filelock>=3.20.1", # TOCTOU race condition fix
-    "authlib>=1.6.6", # Account takeover vulnerability fix
+    "authlib>=1.6.9", # Account takeover/JWS header injection vulnerability fix
+    "pyjwt>=2.12.0", # Accepts unknown crit header extensions fix
+    "orjson>=3.11.6", # Unbounded recursion DoS fix
+    "tornado>=6.5.5", # DoS multipart/incomplete cookie validation fix
    "aiohttp>=3.13.3", # Multiple DoS vulnerabilities
-    "claude-agent-sdk>=0.1.27; sys_platform == 'darwin'",
+    "claude-agent-sdk>=0.1.27",
+    "boto3>=1.42.74",
 ]

 [project.optional-dependencies]
@@ -48,7 +48,8 @@ def pg0_db_url(db_url, tmp_path_factory, worker_id):
    Session-scoped fixture that ensures pg0 is running, migrations are applied,
    and returns the database URL.

-    If HINDSIGHT_API_DATABASE_URL is set, uses that directly (no pg0 management).
+    If HINDSIGHT_API_DATABASE_URL is a plain postgresql:// URL, uses it directly.
+    If HINDSIGHT_API_DATABASE_URL is a pg0:// URL, resolves it to a real URL first.
    Otherwise, starts pg0 once for the entire test session.

    Uses filelock to ensure only one pytest-xdist worker starts pg0.
@@ -58,10 +59,23 @@ def pg0_db_url(db_url, tmp_path_factory, worker_id):
    Note: We don't stop pg0 at the end because pytest-xdist runs workers in separate
    processes that share the same pg0 instance. pg0 will persist for the next test run.
    """
-    if db_url:
-        # Use provided database URL directly
+    from hindsight_api.pg0 import parse_pg0_url as _parse_pg0_url
+
+    # Determine pg0 instance name/port from db_url (if it's a pg0:// URL) or use defaults
+    if db_url and not _parse_pg0_url(db_url)[0]:
+        # Plain postgresql:// URL - use it directly but still run migrations
+        from hindsight_api.migrations import run_migrations
+        run_migrations(db_url)
        return db_url

+    if db_url:
+        _, pg0_name, pg0_port = _parse_pg0_url(db_url)
+        pg0_instance_name = pg0_name or DEFAULT_PG0_INSTANCE_NAME
+        pg0_instance_port = pg0_port or DEFAULT_PG0_PORT
+    else:
+        pg0_instance_name = DEFAULT_PG0_INSTANCE_NAME
+        pg0_instance_port = DEFAULT_PG0_PORT
+
    # Get shared temp dir for coordination between xdist workers
    if worker_id == "master":
        # Running without xdist (-n 0 or no -n flag)
@@ -71,8 +85,8 @@ def pg0_db_url(db_url, tmp_path_factory, worker_id):
        root_tmp_dir = tmp_path_factory.getbasetemp().parent

    # Use a lock file to ensure only one worker starts pg0
-    lock_file = root_tmp_dir / "pg0_setup.lock"
-    url_file = root_tmp_dir / "pg0_url.txt"
+    lock_file = root_tmp_dir / f"pg0_setup_{pg0_instance_name}.lock"
+    url_file = root_tmp_dir / f"pg0_url_{pg0_instance_name}.txt"

    with filelock.FileLock(str(lock_file)):
        if url_file.exists():
@@ -80,7 +94,7 @@ def pg0_db_url(db_url, tmp_path_factory, worker_id):
            url = url_file.read_text().strip()
        else:
            # First worker - start pg0
-            pg0 = EmbeddedPostgres(name=DEFAULT_PG0_INSTANCE_NAME, port=DEFAULT_PG0_PORT)
+            pg0 = EmbeddedPostgres(name=pg0_instance_name, port=pg0_instance_port)

            # Run ensure_running in a new event loop
            loop = asyncio.new_event_loop()
@@ -0,0 +1,114 @@
+"""
+Tests for EntityResolver edge cases.
+"""
+
+import uuid
+from datetime import datetime, timezone
+
+import asyncpg
+import pytest
+
+from hindsight_api.engine.entity_resolver import EntityResolver
+from hindsight_api.pg0 import resolve_database_url
+
+# ---------------------------------------------------------------------------
+# Unit tests for discard_pending_stats() — no database required
+# ---------------------------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_discard_pending_stats_clears_both_dicts():
+    """discard_pending_stats() must remove entries for the current task from
+    both _pending_stats and _pending_cooccurrences."""
+    resolver = EntityResolver(pool=None)  # type: ignore[arg-type]
+    key = resolver._task_key()
+
+    resolver._pending_stats[key] = [object()]  # type: ignore[list-item]
+    resolver._pending_cooccurrences[key] = [object()]  # type: ignore[list-item]
+
+    resolver.discard_pending_stats()
+
+    assert key not in resolver._pending_stats
+    assert key not in resolver._pending_cooccurrences
+
+
+@pytest.mark.asyncio
+async def test_discard_pending_stats_is_idempotent():
+    """Calling discard_pending_stats() when nothing is pending must not raise."""
+    resolver = EntityResolver(pool=None)  # type: ignore[arg-type]
+    resolver.discard_pending_stats()
+    resolver.discard_pending_stats()  # second call — still safe
+
+
+@pytest.mark.asyncio
+async def test_discard_pending_stats_does_not_affect_other_task_keys():
+    """discard_pending_stats() must only remove the current task's entries,
+    leaving entries keyed under other task IDs untouched."""
+    resolver = EntityResolver(pool=None)  # type: ignore[arg-type]
+    other_key = -1  # A fake key that can never be a real task id
+
+    resolver._pending_stats[other_key] = [object()]  # type: ignore[list-item]
+    resolver._pending_cooccurrences[other_key] = [object()]  # type: ignore[list-item]
+
+    resolver.discard_pending_stats()  # discards current task's key only
+
+    assert other_key in resolver._pending_stats, "other task's stats must be preserved"
+    assert other_key in resolver._pending_cooccurrences, "other task's cooccurrences must be preserved"
+
+
+@pytest.mark.asyncio
+async def test_resolve_entities_batch_handles_unicode_lower_conflicts(pg0_db_url):
+    """
+    Existing entities with PostgreSQL/Python lowercase mismatches should resolve
+    to the conflicted row instead of leaving a missing entity_id.
+    """
+    resolved_url = await resolve_database_url(pg0_db_url)
+    pool = await asyncpg.create_pool(resolved_url, min_size=1, max_size=2, command_timeout=30)
+    bank_id = f"test-entity-resolver-{uuid.uuid4().hex[:8]}"
+    event_date = datetime(2024, 1, 15, tzinfo=timezone.utc)
+    resolver = EntityResolver(pool=pool, entity_lookup="full")
+
+    try:
+        async with pool.acquire() as conn:
+            existing_entity_id = await conn.fetchval(
+                """
+                INSERT INTO entities (bank_id, canonical_name, first_seen, last_seen, mention_count)
+                VALUES ($1, $2, $3, $3, 1)
+                RETURNING id
+                """,
+                bank_id,
+                "İstanbul",
+                event_date,
+            )
+
+            resolved_ids = await resolver.resolve_entities_batch(
+                bank_id=bank_id,
+                entities_data=[
+                    {
+                        "text": "istanbul",
+                        "nearby_entities": [],
+                        "event_date": event_date,
+                    }
+                ],
+                context="unicode case mismatch",
+                unit_event_date=event_date,
+                conn=conn,
+            )
+
+            entity_rows = await conn.fetch(
+                """
+                SELECT id, canonical_name
+                FROM entities
+                WHERE bank_id = $1
+                ORDER BY canonical_name
+                """,
+                bank_id,
+            )
+
+        assert resolved_ids == [existing_entity_id]
+        assert len(entity_rows) == 1
+        assert entity_rows[0]["id"] == existing_entity_id
+        assert entity_rows[0]["canonical_name"] == "İstanbul"
+    finally:
+        await pool.execute("DELETE FROM entities WHERE bank_id = $1", bank_id)
+        await pool.close()
@@ -0,0 +1,163 @@
+"""
+Unit tests for EntityResolver pg_trgm auto-detection (PR #626/#649).
+
+These tests verify:
+1. When entity_lookup="trigram" and pg_trgm IS available, the trigram path is used.
+2. When entity_lookup="trigram" and pg_trgm is NOT available, the resolver falls back
+   to entity_lookup="full" and uses the full-scan path.
+3. The pg_trgm check is only performed once (_pg_trgm_checked flag prevents re-checking).
+4. When entity_lookup="full" from the start, the trgm check is never performed.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from hindsight_api.engine.entity_resolver import EntityResolver
+
+
+def _make_conn(pg_trgm_available: bool) -> MagicMock:
+    """Create a minimal mock asyncpg connection for the pg_trgm availability check."""
+    conn = MagicMock()
+    conn.fetchval = AsyncMock(return_value=pg_trgm_available)
+    conn.fetch = AsyncMock(return_value=[])
+    conn.executemany = AsyncMock()
+    conn.fetchrow = AsyncMock(return_value=None)
+    return conn
+
+
+def _make_resolver(entity_lookup: str = "trigram") -> EntityResolver:
+    """Return an EntityResolver with a None pool (not needed for unit tests)."""
+    return EntityResolver(pool=None, entity_lookup=entity_lookup)  # type: ignore[arg-type]
+
+
+class TestPgTrgmAutoDetection:
+    """Unit tests for pg_trgm detection logic inside _resolve_entities_batch_impl."""
+
+    @pytest.mark.asyncio
+    async def test_falls_back_to_full_when_pg_trgm_unavailable(self):
+        """When pg_trgm is absent the resolver switches to 'full' and calls the full-scan path."""
+        resolver = _make_resolver(entity_lookup="trigram")
+        conn = _make_conn(pg_trgm_available=False)
+
+        with (
+            patch.object(resolver, "_resolve_entities_batch_full", new=AsyncMock(return_value=[])) as mock_full,
+            patch.object(resolver, "_resolve_entities_batch_trigram", new=AsyncMock(return_value=[])) as mock_trgm,
+        ):
+            await resolver._resolve_entities_batch_impl(
+                conn=conn,
+                bank_id="test-bank",
+                entities_data=[],
+                context="",
+                unit_event_date=None,
+            )
+
+        # Trigram path must NOT be called
+        mock_trgm.assert_not_called()
+        # Full-scan path must be called as the fallback
+        mock_full.assert_called_once()
+        # Strategy is permanently downgraded
+        assert resolver.entity_lookup == "full"
+        assert resolver._pg_trgm_checked is True
+
+    @pytest.mark.asyncio
+    async def test_uses_trigram_when_pg_trgm_available(self):
+        """When pg_trgm is present the trigram path is used."""
+        resolver = _make_resolver(entity_lookup="trigram")
+        conn = _make_conn(pg_trgm_available=True)
+
+        with (
+            patch.object(resolver, "_resolve_entities_batch_full", new=AsyncMock(return_value=[])) as mock_full,
+            patch.object(resolver, "_resolve_entities_batch_trigram", new=AsyncMock(return_value=[])) as mock_trgm,
+        ):
+            await resolver._resolve_entities_batch_impl(
+                conn=conn,
+                bank_id="test-bank",
+                entities_data=[],
+                context="",
+                unit_event_date=None,
+            )
+
+        mock_trgm.assert_called_once()
+        mock_full.assert_not_called()
+        assert resolver.entity_lookup == "trigram"
+        assert resolver._pg_trgm_checked is True
+
+    @pytest.mark.asyncio
+    async def test_pg_trgm_check_performed_only_once(self):
+        """The fetchval check is only issued on the first call; subsequent calls skip it."""
+        resolver = _make_resolver(entity_lookup="trigram")
+        conn = _make_conn(pg_trgm_available=True)
+
+        with patch.object(resolver, "_resolve_entities_batch_trigram", new=AsyncMock(return_value=[])):
+            # First call — check is issued
+            await resolver._resolve_entities_batch_impl(
+                conn=conn,
+                bank_id="test-bank",
+                entities_data=[],
+                context="",
+                unit_event_date=None,
+            )
+            # Second call — check must NOT be issued again
+            await resolver._resolve_entities_batch_impl(
+                conn=conn,
+                bank_id="test-bank",
+                entities_data=[],
+                context="",
+                unit_event_date=None,
+            )
+
+        # fetchval (the pg_trgm availability query) should be called exactly once
+        assert conn.fetchval.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_full_strategy_skips_pg_trgm_check(self):
+        """When entity_lookup='full' from the start, no pg_trgm check is ever issued."""
+        resolver = _make_resolver(entity_lookup="full")
+        conn = _make_conn(pg_trgm_available=False)
+
+        with patch.object(resolver, "_resolve_entities_batch_full", new=AsyncMock(return_value=[])):
+            await resolver._resolve_entities_batch_impl(
+                conn=conn,
+                bank_id="test-bank",
+                entities_data=[],
+                context="",
+                unit_event_date=None,
+            )
+
+        # fetchval should never be called when entity_lookup is already "full"
+        conn.fetchval.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_fallback_is_sticky_across_calls(self):
+        """After falling back to 'full', subsequent calls also use the full path."""
+        resolver = _make_resolver(entity_lookup="trigram")
+        conn = _make_conn(pg_trgm_available=False)
+
+        with (
+            patch.object(resolver, "_resolve_entities_batch_full", new=AsyncMock(return_value=[])) as mock_full,
+            patch.object(resolver, "_resolve_entities_batch_trigram", new=AsyncMock(return_value=[])) as mock_trgm,
+        ):
+            # First call triggers the fallback
+            await resolver._resolve_entities_batch_impl(
+                conn=conn,
+                bank_id="b",
+                entities_data=[],
+                context="",
+                unit_event_date=None,
+            )
+            # Second call — _pg_trgm_checked is True so no re-check; entity_lookup=="full"
+            await resolver._resolve_entities_batch_impl(
+                conn=conn,
+                bank_id="b",
+                entities_data=[],
+                context="",
+                unit_event_date=None,
+            )
+
+        # Trigram path is never called
+        mock_trgm.assert_not_called()
+        # Full-scan path is called both times
+        assert mock_full.call_count == 2
+        # pg_trgm check was issued exactly once
+        assert conn.fetchval.call_count == 1
@@ -5,6 +5,7 @@ End-to-end tests for file retain (upload, convert, retain) functionality.
 import asyncio
 import io
 import json
+from datetime import datetime, timezone

 import pytest
 from httpx import ASGITransport, AsyncClient
@@ -471,6 +472,77 @@ async def test_file_conversion_creates_separate_retain_operation(memory_no_llm_v
    assert len(doc["original_text"]) > 0


+@pytest.mark.asyncio
+async def test_async_file_retain_serializes_datetime_timestamp(memory_no_llm_verify, sample_txt_content):
+    """Async file retain should accept Python datetimes in task payloads."""
+    from hindsight_api.engine.parsers.base import FileParser
+    from hindsight_api.models import RequestContext
+
+    bank_id = f"test_file_timestamp_bank_{datetime.now(timezone.utc).timestamp()}"
+    timestamp = datetime(2024, 1, 15, 10, 30, tzinfo=timezone.utc)
+
+    context = RequestContext(internal=True)
+    await memory_no_llm_verify.get_bank_profile(bank_id, request_context=context)
+
+    class MockFile:
+        def __init__(self, content, filename, content_type):
+            self.content = content
+            self.filename = filename
+            self.content_type = content_type
+
+        async def read(self):
+            return self.content
+
+    class TimestampParser(FileParser):
+        async def convert(self, file_data: bytes, filename: str) -> str:
+            return file_data.decode("utf-8")
+
+        def supports(self, filename: str, content_type: str | None = None) -> bool:
+            return filename.endswith(".txt")
+
+        def name(self) -> str:
+            return "timestamp_parser"
+
+    memory_no_llm_verify._parser_registry.register(TimestampParser())
+
+    mock_file = MockFile(sample_txt_content, "timestamped.txt", "text/plain")
+
+    result = await memory_no_llm_verify.submit_async_file_retain(
+        bank_id=bank_id,
+        file_items=[
+            {
+                "file": mock_file,
+                "document_id": "timestamped_doc",
+                "context": "timestamp test",
+                "metadata": {},
+                "tags": [],
+                "timestamp": timestamp,
+                "parser": ["timestamp_parser"],
+            }
+        ],
+        document_tags=None,
+        request_context=context,
+    )
+
+    operation_id = result["operation_ids"][0]
+    pool = await memory_no_llm_verify._get_pool()
+    from hindsight_api.engine.memory_engine import get_current_schema
+
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow(
+            f"""
+            SELECT status, task_payload->>'timestamp' AS timestamp
+            FROM {get_current_schema()}.async_operations
+            WHERE operation_id = $1
+            """,
+            operation_id,
+        )
+
+    assert row is not None
+    assert row["status"] == "completed"
+    assert row["timestamp"] == "2024-01-15T10:30:00+00:00"
+
+
@pytest.mark.asyncio
 async def test_file_conversion_failure_sets_status_to_failed(memory_no_llm_verify, sample_txt_content):
    """Test that when file conversion fails, the operation status is set to 'failed' not 'completed'."""
@@ -89,7 +89,7 @@ async def test_hierarchical_fields_categorization():
    assert "entity_labels" in configurable

    # Verify count is correct
-    assert len(configurable) == 19
+    assert len(configurable) == 20

    # Verify credential fields (NEVER exposed)
    assert "llm_api_key" in credentials
@@ -402,7 +402,7 @@ async def test_config_get_bank_config_no_static_or_credential_fields_leak(memory
            assert field in config, f"Expected configurable field '{field}' missing from config"

        # Should have a small number of configurable fields (not hundreds)
-        assert len(config) < 20, f"Too many fields returned: {len(config)}"
+        assert len(config) < 25, f"Too many fields returned: {len(config)}"

    finally:
        await memory.delete_bank(bank_id, request_context=request_context)
@@ -43,6 +43,8 @@ MODEL_MATRIX = [
    ("claude-code", "claude-sonnet-4-20250514"),
    # OpenAI Codex (uses MCP with Codex-specific models)
    ("openai-codex", "gpt-5.2-codex"),
+    # Bedrock models (via LiteLLM)
+    ("bedrock", "us.amazon.nova-2-lite-v1:0"),
    # Mock provider (for testing)
    ("mock", "mock"),
 ]
@@ -78,6 +80,12 @@ def should_skip_provider(provider: str, model: str = "") -> tuple[bool, str]:
    if provider == "ollama" and "gemma" in model.lower():
        return True, f"Ollama {model} does not support tool calling"

+    # Bedrock needs AWS credentials
+    if provider == "bedrock":
+        if not os.getenv("AWS_ACCESS_KEY_ID"):
+            return True, "No AWS credentials available (set AWS_ACCESS_KEY_ID)"
+        return False, ""
+
    # Other providers need an API key
    if provider not in ("ollama", "claude-code", "openai-codex", "mock"):
        api_key = get_api_key_for_provider(provider)
@@ -227,7 +235,7 @@ async def test_llm_provider_api_methods(provider: str, model: str):

@pytest.mark.parametrize("provider,model", MODEL_MATRIX)
@pytest.mark.asyncio
-@pytest.mark.timeout(300)
+@pytest.mark.timeout(600)  # 600s: some providers (e.g., bedrock via litellm) need extra time for fact extraction
 async def test_llm_provider_memory_operations(provider: str, model: str):
    """
    Test LLM provider with actual memory operations: fact extraction and reflect.
@@ -4,6 +4,9 @@ This test verifies that /mcp/ and /mcp/{bank_id}/ expose different tool sets,
 and that URLs with or without trailing slashes both work (no 307 redirect).
 """

+import json
+from unittest.mock import patch
+
 import httpx
 import pytest
 from mcp.client.session import ClientSession
@@ -278,3 +281,93 @@ async def test_mcp_bank_named_messages_routes_to_single_bank(memory):

                    assert "retain" in tools
                    assert "list_banks" not in tools, "Bank 'messages' should route to single-bank mode"
+
+
+@pytest.mark.asyncio
+async def test_mcp_tool_execution_with_different_mcp_and_tenant_tokens(memory):
+    """Test that MCP tool calls work when MCP_AUTH_TOKEN and TENANT_API_KEY differ.
+
+    Regression test for https://github.com/vectorize-io/hindsight/issues/627
+    When both HINDSIGHT_API_MCP_AUTH_TOKEN and ApiKeyTenantExtension are configured
+    with different values, tool calls should succeed because MCP transport auth
+    already validated the token — the tenant extension should not re-validate.
+    """
+    from httpx import ASGITransport
+
+    from hindsight_api.api import create_app
+    from hindsight_api.extensions import ApiKeyTenantExtension
+
+    mcp_token = "mcp-secret-token"
+    tenant_key = "tenant-secret-key"
+
+    # Configure ApiKeyTenantExtension with a different key than the MCP token
+    tenant_ext = ApiKeyTenantExtension({"api_key": tenant_key})
+    memory._tenant_extension = tenant_ext
+
+    # Patch MCP_AUTH_TOKEN so the MCP middleware uses legacy auth
+    with patch("hindsight_api.api.mcp.MCP_AUTH_TOKEN", mcp_token):
+        app = create_app(memory, mcp_api_enabled=True, initialize_memory=False)
+
+        async with app.router.lifespan_context(app):
+            # Pass auth header via the httpx client (streamable_http_client doesn't accept headers)
+            async with httpx.AsyncClient(
+                transport=ASGITransport(app=app),
+                base_url="http://test",
+                headers={"Authorization": f"Bearer {mcp_token}"},
+            ) as http_client:
+                async with streamable_http_client("http://test/mcp/", http_client=http_client) as (
+                    read_stream,
+                    write_stream,
+                    _,
+                ):
+                    async with ClientSession(read_stream, write_stream) as session:
+                        await session.initialize()
+
+                        # list_tools should work
+                        tools_result = await session.list_tools()
+                        tool_names = {t.name for t in tools_result.tools}
+                        assert "get_bank" in tool_names
+
+                        # Tool execution should work (this was failing before the fix)
+                        result = await session.call_tool("list_banks", arguments={})
+                        assert result is not None
+                        assert len(result.content) > 0
+                        parsed = json.loads(result.content[0].text)
+                        assert "banks" in parsed
+                        assert "error" not in parsed, f"Tool call failed with: {parsed.get('error')}"
+
+
+@pytest.mark.asyncio
+async def test_mcp_rejects_wrong_mcp_token_even_if_matches_tenant_key(memory):
+    """Test that an invalid MCP token is rejected even if it matches the tenant key.
+
+    When MCP_AUTH_TOKEN is set, the MCP middleware should validate against that token,
+    not the tenant API key.
+    """
+    from httpx import ASGITransport
+
+    from hindsight_api.api import create_app
+    from hindsight_api.extensions import ApiKeyTenantExtension
+
+    mcp_token = "mcp-secret-token"
+    tenant_key = "tenant-secret-key"
+
+    tenant_ext = ApiKeyTenantExtension({"api_key": tenant_key})
+    memory._tenant_extension = tenant_ext
+
+    with patch("hindsight_api.api.mcp.MCP_AUTH_TOKEN", mcp_token):
+        app = create_app(memory, mcp_api_enabled=True, initialize_memory=False)
+
+        async with app.router.lifespan_context(app):
+            async with httpx.AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as http_client:
+                # Try connecting with the tenant key (wrong for MCP auth)
+                response = await http_client.post(
+                    "http://test/mcp/",
+                    headers={
+                        "Authorization": f"Bearer {tenant_key}",
+                        "Content-Type": "application/json",
+                        "Accept": "application/json, text/event-stream",
+                    },
+                    json={"jsonrpc": "2.0", "id": 1, "method": "initialize", "params": {}},
+                )
+                assert response.status_code == 401
@@ -0,0 +1,232 @@
+"""
+Tests for the 'none' LLM provider mode.
+
+Verifies that when HINDSIGHT_API_LLM_PROVIDER=none:
+- Retain defaults to chunks mode (no LLM calls)
+- Reflect returns 400
+- Mental model refresh returns 400
+- Consolidation is skipped
+- NoneLLM.call() raises LLMNotAvailableError
+"""
+
+import os
+from datetime import datetime, timezone
+
+import httpx
+import pytest
+import pytest_asyncio
+
+from hindsight_api import LLMConfig, LocalSTEmbeddings, MemoryEngine, RequestContext
+from hindsight_api.api import create_app
+from hindsight_api.engine.cross_encoder import LocalSTCrossEncoder
+from hindsight_api.engine.memory_engine import Budget
+from hindsight_api.engine.providers.none_llm import LLMNotAvailableError, NoneLLM
+from hindsight_api.engine.query_analyzer import DateparserQueryAnalyzer
+from hindsight_api.engine.task_backend import SyncTaskBackend
+
+
+@pytest.fixture(scope="function")
+def request_context():
+    return RequestContext()
+
+
+@pytest_asyncio.fixture(scope="function")
+async def none_memory(pg0_db_url, embeddings, cross_encoder, query_analyzer):
+    """MemoryEngine with provider=none."""
+    mem = MemoryEngine(
+        db_url=pg0_db_url,
+        memory_llm_provider="none",
+        memory_llm_api_key=None,
+        memory_llm_model="none",
+        embeddings=embeddings,
+        cross_encoder=cross_encoder,
+        query_analyzer=query_analyzer,
+        pool_min_size=1,
+        pool_max_size=5,
+        run_migrations=False,
+        task_backend=SyncTaskBackend(),
+    )
+    await mem.initialize()
+    yield mem
+    try:
+        if mem._pool and not mem._pool._closing:
+            await mem.close()
+    except Exception:
+        pass
+
+
+@pytest_asyncio.fixture
+async def none_api_client(none_memory):
+    """HTTP test client backed by a none-provider MemoryEngine."""
+    app = create_app(none_memory, initialize_memory=False)
+    transport = httpx.ASGITransport(app=app)
+    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
+        yield client
+
+
+# -- Unit tests for NoneLLM ---------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_none_llm_call_raises():
+    """NoneLLM.call() should raise LLMNotAvailableError."""
+    llm = NoneLLM(provider="none", api_key="", base_url="", model="none")
+    with pytest.raises(LLMNotAvailableError):
+        await llm.call(messages=[{"role": "user", "content": "hello"}])
+
+
+@pytest.mark.asyncio
+async def test_none_llm_call_with_tools_raises():
+    """NoneLLM.call_with_tools() should raise LLMNotAvailableError."""
+    llm = NoneLLM(provider="none", api_key="", base_url="", model="none")
+    with pytest.raises(LLMNotAvailableError):
+        await llm.call_with_tools(
+            messages=[{"role": "user", "content": "hello"}],
+            tools=[{"type": "function", "function": {"name": "test", "parameters": {}}}],
+        )
+
+
+@pytest.mark.asyncio
+async def test_none_llm_verify_connection_succeeds():
+    """NoneLLM.verify_connection() should be a no-op."""
+    llm = NoneLLM(provider="none", api_key="", base_url="", model="none")
+    await llm.verify_connection()  # Should not raise
+
+
+# -- Config validation tests ---------------------------------------------------
+
+
+def test_config_forces_chunks_mode():
+    """When provider is 'none', config.validate() forces retain_extraction_mode='chunks'."""
+    from hindsight_api.config import HindsightConfig
+
+    config = HindsightConfig.from_env()
+    # Override to none for test
+    config.llm_provider = "none"
+    config.retain_extraction_mode = "facts"
+    config.enable_observations = True
+    config.validate()
+
+    assert config.retain_extraction_mode == "chunks"
+    assert config.enable_observations is False
+
+
+# -- Integration tests (require database) -------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_retain_works_with_none_provider(none_memory, request_context):
+    """Retain should work with provider=none, storing chunks without LLM calls."""
+    bank_id = f"test_none_retain_{datetime.now(timezone.utc).timestamp()}"
+
+    unit_ids = await none_memory.retain_async(
+        bank_id=bank_id,
+        content="Alice is a software engineer. She works at TechCorp and loves Python.",
+        context="team info",
+        request_context=request_context,
+    )
+
+    assert len(unit_ids) > 0, "Should store chunks even without an LLM"
+
+
+@pytest.mark.asyncio
+async def test_recall_works_with_none_provider(none_memory, request_context):
+    """Recall should work with provider=none (uses embeddings, not LLM)."""
+    bank_id = f"test_none_recall_{datetime.now(timezone.utc).timestamp()}"
+
+    await none_memory.retain_async(
+        bank_id=bank_id,
+        content="Alice is a software engineer at TechCorp.",
+        context="team info",
+        request_context=request_context,
+    )
+
+    result = await none_memory.recall_async(
+        bank_id=bank_id,
+        query="Who is Alice?",
+        budget=Budget.LOW,
+        request_context=request_context,
+    )
+
+    assert len(result.results) > 0, "Should find results via semantic search"
+
+
+@pytest.mark.asyncio
+async def test_reflect_raises_with_none_provider(none_memory, request_context):
+    """Reflect should raise LLMNotAvailableError with provider=none."""
+    bank_id = f"test_none_reflect_{datetime.now(timezone.utc).timestamp()}"
+
+    with pytest.raises(LLMNotAvailableError):
+        await none_memory.reflect_async(
+            bank_id=bank_id,
+            query="What do you know?",
+            request_context=request_context,
+        )
+
+
+@pytest.mark.asyncio
+async def test_consolidation_skipped_with_none_provider(none_memory, request_context):
+    """Consolidation handler should skip when provider=none."""
+    result = await none_memory._handle_consolidation({"bank_id": "test_bank"})
+    assert result["skipped"] is True
+    assert result["memories_processed"] == 0
+
+
+@pytest.mark.asyncio
+async def test_mental_model_refresh_raises_with_none_provider(none_memory, request_context):
+    """Mental model refresh should raise LLMNotAvailableError with provider=none."""
+    bank_id = f"test_none_mm_{datetime.now(timezone.utc).timestamp()}"
+
+    with pytest.raises(LLMNotAvailableError):
+        await none_memory.submit_async_refresh_mental_model(
+            bank_id=bank_id,
+            mental_model_id="fake-id",
+            request_context=request_context,
+        )
+
+
+# -- HTTP API tests -----------------------------------------------------------
+
+
+@pytest.mark.asyncio
+async def test_http_reflect_returns_400(none_api_client):
+    """Reflect endpoint should return 400 when LLM provider is none."""
+    bank_id = f"test_none_http_{datetime.now(timezone.utc).timestamp()}"
+
+    response = await none_api_client.post(
+        f"/v1/default/banks/{bank_id}/reflect",
+        json={"query": "What do you know?"},
+    )
+    assert response.status_code == 400
+    assert "none" in response.json()["detail"].lower()
+
+
+@pytest.mark.asyncio
+async def test_http_retain_works(none_api_client):
+    """Retain endpoint should work with provider=none (chunks mode)."""
+    bank_id = f"test_none_http_retain_{datetime.now(timezone.utc).timestamp()}"
+
+    response = await none_api_client.post(
+        f"/v1/default/banks/{bank_id}/memories",
+        json={"items": [{"content": "Hello world", "context": "test"}]},
+    )
+    assert response.status_code == 200
+
+
+@pytest.mark.asyncio
+async def test_http_recall_works(none_api_client):
+    """Recall endpoint should work with provider=none."""
+    bank_id = f"test_none_http_recall_{datetime.now(timezone.utc).timestamp()}"
+
+    # Retain first
+    await none_api_client.post(
+        f"/v1/default/banks/{bank_id}/memories",
+        json={"items": [{"content": "Alice is an engineer.", "context": "test"}]},
+    )
+
+    # Recall
+    response = await none_api_client.post(
+        f"/v1/default/banks/{bank_id}/memories/recall",
+        json={"query": "Alice"},
+    )
+    assert response.status_code == 200
@@ -97,8 +97,8 @@ def test_per_operation_provider_default_model():

        # Retain should use Anthropic default
        assert (
-            config.retain_llm_model == "claude-haiku-4-5-20251001"
-        ), f"Expected claude-haiku-4-5-20251001, got {config.retain_llm_model}"
+            config.retain_llm_model == "claude-haiku-4-5"
+        ), f"Expected claude-haiku-4-5, got {config.retain_llm_model}"

    finally:
        clear_config_cache()
@@ -5,8 +5,10 @@ These tests verify:
 1. Tool name normalization for various LLM output formats
 2. Recovery from unknown tool calls
 3. Recovery from tool execution errors
+4. Wall-clock timeout enforcement
 """

+import asyncio
 from unittest.mock import AsyncMock, MagicMock

 import pytest
@@ -416,6 +418,32 @@ class TestReflectAgentMocked:
        assert result is not None
        assert result.iterations == 3

+    @pytest.mark.asyncio
+    async def test_wall_clock_timeout(self, mock_llm: MagicMock, mock_functions: dict[str, AsyncMock]) -> None:
+        """Test that asyncio.wait_for can enforce a wall-clock timeout on run_reflect_agent."""
+
+        async def slow_llm_call(*args: object, **kwargs: object) -> LLMToolCallResult:
+            await asyncio.sleep(10)  # Simulate a slow LLM call
+            return LLMToolCallResult(
+                tool_calls=[LLMToolCall(id="1", name="recall", arguments={"query": "test"})],
+                finish_reason="tool_calls",
+            )
+
+        mock_llm.call_with_tools.side_effect = slow_llm_call
+
+        with pytest.raises(asyncio.TimeoutError):
+            await asyncio.wait_for(
+                run_reflect_agent(
+                    llm_config=mock_llm,
+                    bank_id="test-bank",
+                    query="test query",
+                    bank_profile={"name": "Test", "mission": "Testing"},
+                    max_iterations=5,
+                    **mock_functions,
+                ),
+                timeout=0.1,  # Very short timeout to trigger quickly
+            )
+

 class TestContextOverflowHelpers:
    """Unit tests for context-overflow detection helpers."""
@@ -0,0 +1,129 @@
+"""
+Tests for reflect search_observations source_facts_max_tokens configuration.
+
+Verifies that the source_facts_max_tokens parameter correctly controls
+whether source facts are included in search_observations recall calls.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from hindsight_api.engine.reflect.tools import tool_search_observations
+from hindsight_api.engine.response_models import RecallResult
+
+
+def _make_mock_engine(recall_result=None):
+    """Create a mock memory engine with a recall_async method."""
+    if recall_result is None:
+        recall_result = RecallResult(results=[], source_facts={})
+    engine = MagicMock()
+    engine.recall_async = AsyncMock(return_value=recall_result)
+    return engine
+
+
+@pytest.fixture
+def mock_request_context():
+    return MagicMock()
+
+
+class TestSearchObservationsSourceFacts:
+    """Test source_facts_max_tokens parameter in tool_search_observations."""
+
+    @pytest.mark.asyncio
+    async def test_default_disables_source_facts(self, mock_request_context):
+        """Default source_facts_max_tokens=-1 should disable source facts."""
+        engine = _make_mock_engine()
+
+        await tool_search_observations(
+            engine, "bank-1", "test query", mock_request_context
+        )
+
+        engine.recall_async.assert_called_once()
+        call_kwargs = engine.recall_async.call_args.kwargs
+        assert call_kwargs["include_source_facts"] is False
+        assert "max_source_facts_tokens" not in call_kwargs
+
+    @pytest.mark.asyncio
+    async def test_zero_enables_source_facts_unlimited(self, mock_request_context):
+        """source_facts_max_tokens=0 should enable source facts with no token limit."""
+        engine = _make_mock_engine()
+
+        await tool_search_observations(
+            engine, "bank-1", "test query", mock_request_context,
+            source_facts_max_tokens=0,
+        )
+
+        engine.recall_async.assert_called_once()
+        call_kwargs = engine.recall_async.call_args.kwargs
+        assert call_kwargs["include_source_facts"] is True
+        assert "max_source_facts_tokens" not in call_kwargs
+
+    @pytest.mark.asyncio
+    async def test_positive_enables_source_facts_with_limit(self, mock_request_context):
+        """source_facts_max_tokens>0 should enable source facts with a token budget."""
+        engine = _make_mock_engine()
+
+        await tool_search_observations(
+            engine, "bank-1", "test query", mock_request_context,
+            source_facts_max_tokens=5000,
+        )
+
+        engine.recall_async.assert_called_once()
+        call_kwargs = engine.recall_async.call_args.kwargs
+        assert call_kwargs["include_source_facts"] is True
+        assert call_kwargs["max_source_facts_tokens"] == 5000
+
+    @pytest.mark.asyncio
+    async def test_negative_one_disables_source_facts(self, mock_request_context):
+        """Explicit -1 should disable source facts (same as default)."""
+        engine = _make_mock_engine()
+
+        await tool_search_observations(
+            engine, "bank-1", "test query", mock_request_context,
+            source_facts_max_tokens=-1,
+        )
+
+        engine.recall_async.assert_called_once()
+        call_kwargs = engine.recall_async.call_args.kwargs
+        assert call_kwargs["include_source_facts"] is False
+        assert "max_source_facts_tokens" not in call_kwargs
+
+
+class TestReflectSourceFactsConfig:
+    """Test that reflect_source_facts_max_tokens is properly wired in HindsightConfig."""
+
+    def test_config_field_exists(self):
+        """reflect_source_facts_max_tokens should be a valid config field."""
+        from hindsight_api.config import HindsightConfig
+
+        import dataclasses
+
+        field_names = {f.name for f in dataclasses.fields(HindsightConfig)}
+        assert "reflect_source_facts_max_tokens" in field_names
+
+    def test_config_is_configurable(self):
+        """reflect_source_facts_max_tokens should be a configurable (per-bank) field."""
+        from hindsight_api.config import HindsightConfig
+
+        assert "reflect_source_facts_max_tokens" in HindsightConfig.get_configurable_fields()
+
+    def test_default_value_is_disabled(self):
+        """Default should be -1 (disabled)."""
+        from hindsight_api.config import DEFAULT_REFLECT_SOURCE_FACTS_MAX_TOKENS
+
+        assert DEFAULT_REFLECT_SOURCE_FACTS_MAX_TOKENS == -1
+
+    def test_env_var_constant_exists(self):
+        """Env var constant should be defined."""
+        from hindsight_api.config import ENV_REFLECT_SOURCE_FACTS_MAX_TOKENS
+
+        assert ENV_REFLECT_SOURCE_FACTS_MAX_TOKENS == "HINDSIGHT_API_REFLECT_SOURCE_FACTS_MAX_TOKENS"
+
+    @patch.dict("os.environ", {"HINDSIGHT_API_REFLECT_SOURCE_FACTS_MAX_TOKENS": "8000"})
+    def test_from_env_reads_value(self):
+        """from_env should parse the env var."""
+        from hindsight_api.config import HindsightConfig
+
+        config = HindsightConfig.from_env()
+        assert config.reflect_source_facts_max_tokens == 8000
@@ -485,3 +485,206 @@ class TestReflectUsesMentalModels:

        # Cleanup
        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+class TestMentalModelReflectOptions:
+    """Tests for fact_types and exclude_mental_models options stored in the trigger field."""
+
+    @pytest.mark.asyncio
+    async def test_trigger_stores_fact_types(self, memory: MemoryEngine, request_context):
+        """Trigger field persists fact_types and returns them via get_mental_model."""
+        bank_id = f"test-mm-trigger-ft-{uuid.uuid4().hex[:8]}"
+        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+        mm = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Observations only",
+            source_query="Summarize observations",
+            content="content",
+            trigger={"refresh_after_consolidation": False, "fact_types": ["observation"]},
+            request_context=request_context,
+        )
+
+        fetched = await memory.get_mental_model(bank_id=bank_id, mental_model_id=mm["id"], request_context=request_context)
+        assert fetched["trigger"]["fact_types"] == ["observation"]
+        assert fetched["trigger"]["refresh_after_consolidation"] is False
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_trigger_stores_exclude_mental_models(self, memory: MemoryEngine, request_context):
+        """Trigger field persists exclude_mental_models flag."""
+        bank_id = f"test-mm-trigger-em-{uuid.uuid4().hex[:8]}"
+        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+        mm = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="No mental models",
+            source_query="Summarize raw facts",
+            content="content",
+            trigger={"refresh_after_consolidation": False, "exclude_mental_models": True},
+            request_context=request_context,
+        )
+
+        fetched = await memory.get_mental_model(bank_id=bank_id, mental_model_id=mm["id"], request_context=request_context)
+        assert fetched["trigger"]["exclude_mental_models"] is True
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_trigger_stores_exclude_mental_model_ids(self, memory: MemoryEngine, request_context):
+        """Trigger field persists exclude_mental_model_ids list."""
+        bank_id = f"test-mm-trigger-eid-{uuid.uuid4().hex[:8]}"
+        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+        excluded_ids = ["mm-abc", "mm-xyz"]
+        mm = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Exclude some models",
+            source_query="Summarize",
+            content="content",
+            trigger={"refresh_after_consolidation": False, "exclude_mental_model_ids": excluded_ids},
+            request_context=request_context,
+        )
+
+        fetched = await memory.get_mental_model(bank_id=bank_id, mental_model_id=mm["id"], request_context=request_context)
+        assert fetched["trigger"]["exclude_mental_model_ids"] == excluded_ids
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_update_trigger_reflect_options(self, memory: MemoryEngine, request_context):
+        """update_mental_model persists updated trigger reflect options."""
+        bank_id = f"test-mm-trigger-upd-{uuid.uuid4().hex[:8]}"
+        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+        mm = await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Initially no filter",
+            source_query="Summarize",
+            content="content",
+            trigger={"refresh_after_consolidation": False},
+            request_context=request_context,
+        )
+
+        updated = await memory.update_mental_model(
+            bank_id=bank_id,
+            mental_model_id=mm["id"],
+            trigger={
+                "refresh_after_consolidation": True,
+                "fact_types": ["world", "experience"],
+                "exclude_mental_models": False,
+                "exclude_mental_model_ids": ["mm-skip"],
+            },
+            request_context=request_context,
+        )
+
+        assert updated["trigger"]["refresh_after_consolidation"] is True
+        assert updated["trigger"]["fact_types"] == ["world", "experience"]
+        assert updated["trigger"]["exclude_mental_models"] is False
+        assert updated["trigger"]["exclude_mental_model_ids"] == ["mm-skip"]
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+class TestReflectFactTypeFiltering:
+    """Tests for fact_types and exclude_mental_models filtering in reflect_async."""
+
+    @pytest.mark.asyncio
+    async def test_exclude_mental_models_skips_search_mental_models_tool(
+        self, memory: MemoryEngine, request_context
+    ):
+        """When exclude_mental_models=True, search_mental_models is never called."""
+        bank_id = f"test-reflect-exmm-{uuid.uuid4().hex[:8]}"
+        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+        # Create a mental model so the bank has one
+        await memory.create_mental_model(
+            bank_id=bank_id,
+            name="Existing Model",
+            source_query="Q",
+            content="Some content about the team",
+            request_context=request_context,
+        )
+
+        result = await memory.reflect_async(
+            bank_id=bank_id,
+            query="Tell me about the team",
+            request_context=request_context,
+            exclude_mental_models=True,
+        )
+
+        tool_names = [tc.tool for tc in result.tool_trace]
+        assert "search_mental_models" not in tool_names, (
+            f"search_mental_models should be excluded but found in: {tool_names}"
+        )
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_exclude_observations_via_fact_types(self, memory: MemoryEngine, request_context):
+        """When fact_types excludes observation, search_observations is never called."""
+        bank_id = f"test-reflect-exobs-{uuid.uuid4().hex[:8]}"
+        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+        result = await memory.reflect_async(
+            bank_id=bank_id,
+            query="Tell me something",
+            request_context=request_context,
+            fact_types=["world", "experience"],
+        )
+
+        tool_names = [tc.tool for tc in result.tool_trace]
+        assert "search_observations" not in tool_names, (
+            f"search_observations should be excluded but found in: {tool_names}"
+        )
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+    @pytest.mark.asyncio
+    async def test_observation_only_fact_types_skips_recall(self, memory: MemoryEngine, request_context):
+        """When fact_types=['observation'], recall is never called."""
+        bank_id = f"test-reflect-obsonly-{uuid.uuid4().hex[:8]}"
+        await memory.get_bank_profile(bank_id=bank_id, request_context=request_context)
+
+        result = await memory.reflect_async(
+            bank_id=bank_id,
+            query="Tell me something",
+            request_context=request_context,
+            fact_types=["observation"],
+        )
+
+        tool_names = [tc.tool for tc in result.tool_trace]
+        assert "recall" not in tool_names, f"recall should be excluded but found in: {tool_names}"
+
+        await memory.delete_bank(bank_id, request_context=request_context)
+
+
+class TestReflectRequestValidation:
+    """Tests for ReflectRequest and MentalModelTrigger validation via the HTTP API."""
+
+    @pytest.mark.asyncio
+    async def test_reflect_empty_fact_types_rejected(self, api_client, test_bank_id):
+        """Passing fact_types=[] to reflect must return 422."""
+        await api_client.get(f"/v1/default/banks/{test_bank_id}/profile")
+
+        response = await api_client.post(
+            f"/v1/default/banks/{test_bank_id}/reflect",
+            json={"query": "test", "fact_types": []},
+        )
+        assert response.status_code == 422
+
+    @pytest.mark.asyncio
+    async def test_create_mental_model_empty_fact_types_rejected(self, api_client, test_bank_id):
+        """Passing fact_types=[] inside trigger must return 422."""
+        await api_client.get(f"/v1/default/banks/{test_bank_id}/profile")
+
+        response = await api_client.post(
+            f"/v1/default/banks/{test_bank_id}/mental-models",
+            json={
+                "name": "Test",
+                "source_query": "Q",
+                "trigger": {"refresh_after_consolidation": False, "fact_types": []},
+            },
+        )
+        assert response.status_code == 422
@@ -814,34 +814,36 @@ async def test_context_with_batch(memory, request_context):
@pytest.mark.asyncio
 async def test_metadata_storage_and_retrieval(memory, request_context):
    """
-    Test that user-defined metadata is preserved.
+    Test that user-defined metadata passed during retain is returned on recall.
    Metadata allows arbitrary key-value data to be stored with facts.
    """
    bank_id = f"test_metadata_{datetime.now(timezone.utc).timestamp()}"

    try:
-        # Store content with custom metadata
        custom_metadata = {
            "source": "slack",
            "channel": "engineering",
            "importance": "high",
-            "tags": "product,launch"
        }

-        # Note: retain_async doesn't directly support metadata parameter
-        # Metadata would need to be supported in the API layer
-        # For now, we test that the system handles content without errors
-        unit_ids = await memory.retain_async(
+        # Use retain_batch_async which supports the metadata parameter
+        unit_ids_list = await memory.retain_batch_async(
            bank_id=bank_id,
-            content="The product launch is scheduled for March 1st.",
-            context="planning meeting",
-            event_date=datetime(2024, 1, 15, tzinfo=timezone.utc),
+            contents=[
+                {
+                    "content": "The product launch is scheduled for March 1st.",
+                    "context": "planning meeting",
+                    "event_date": datetime(2024, 1, 15, tzinfo=timezone.utc),
+                    "metadata": custom_metadata,
+                }
+            ],
            request_context=request_context,
        )

-        assert len(unit_ids) > 0, "Should create memory units"
+        assert len(unit_ids_list) > 0, "Should create memory units"
+        assert len(unit_ids_list[0]) > 0, "Should have at least one unit ID"

-        # Recall to verify storage worked
+        # Recall and verify metadata is returned
        result = await memory.recall_async(
            bank_id=bank_id,
            query="When is the product launch?",
@@ -853,8 +855,12 @@ async def test_metadata_storage_and_retrieval(memory, request_context):

        assert len(result.results) > 0, "Should recall stored facts"

-        print("✓ Successfully stored and retrieved facts")
-        print("  (Note: Metadata support depends on API implementation)")
+        # Verify metadata is present on recalled facts
+        fact = result.results[0]
+        assert fact.metadata is not None, "Metadata should not be null on recall"
+        assert fact.metadata.get("source") == "slack"
+        assert fact.metadata.get("channel") == "engineering"
+        assert fact.metadata.get("importance") == "high"

    finally:
        await memory.delete_bank(bank_id, request_context=request_context)
@@ -0,0 +1,92 @@
+"""Tests for _strip_code_fences helper in OpenAI-compatible LLM provider."""
+
+import pytest
+
+from hindsight_api.engine.providers.openai_compatible_llm import _strip_code_fences
+
+
+class TestStripCodeFences:
+    """Test markdown code fence stripping from LLM responses."""
+
+    def test_bare_json_unchanged(self):
+        """Bare JSON passes through unchanged."""
+        content = '{"facts": [{"what": "test"}]}'
+        assert _strip_code_fences(content) == content
+
+    def test_json_fence_stripped(self):
+        """```json ... ``` fences are stripped."""
+        content = '```json\n{"facts": [{"what": "test"}]}\n```'
+        assert _strip_code_fences(content) == '{"facts": [{"what": "test"}]}'
+
+    def test_plain_fence_stripped(self):
+        """``` ... ``` fences without language tag are stripped."""
+        content = '```\n{"facts": [{"what": "test"}]}\n```'
+        assert _strip_code_fences(content) == '{"facts": [{"what": "test"}]}'
+
+    def test_fence_with_trailing_whitespace(self):
+        """Fences with extra whitespace are handled."""
+        content = '```json\n{"facts": []}\n```\n'
+        result = _strip_code_fences(content)
+        assert result == '{"facts": []}'
+
+    def test_fence_with_leading_whitespace(self):
+        """Content with leading whitespace before fence."""
+        content = '  ```json\n{"facts": []}\n```'
+        # The function checks for ``` in content, not startswith
+        result = _strip_code_fences(content)
+        assert '{"facts": []}' in result
+
+    def test_no_fences_no_change(self):
+        """Content without any backticks passes through."""
+        content = "Just some text without fences"
+        assert _strip_code_fences(content) == content
+
+    def test_empty_string(self):
+        """Empty string passes through."""
+        assert _strip_code_fences("") == ""
+
+    def test_multiline_json(self):
+        """Multi-line JSON inside fences is preserved."""
+        content = '```json\n{\n  "facts": [\n    {"what": "line1"},\n    {"what": "line2"}\n  ]\n}\n```'
+        result = _strip_code_fences(content)
+        assert '"line1"' in result
+        assert '"line2"' in result
+        assert "```" not in result
+
+    def test_malformed_fence_returns_original(self):
+        """Malformed fences (missing closing) return something parseable."""
+        content = '```json\n{"facts": []}'
+        result = _strip_code_fences(content)
+        # Should attempt to strip and return best effort
+        assert isinstance(result, str)
+
+    def test_minimax_style_response(self):
+        """Real-world MiniMax response format."""
+        content = (
+            "```json\n"
+            "{\n"
+            '  "facts": [\n'
+            "    {\n"
+            '      "what": "Sebastian switched the Hindsight extraction LLM",\n'
+            '      "when": "2026-03-21",\n'
+            '      "where": "N/A",\n'
+            '      "who": "Sebastian",\n'
+            '      "why": "MiniMax wraps JSON in code fences",\n'
+            '      "fact_kind": "event",\n'
+            '      "fact_type": "world",\n'
+            '      "entities": [{"text": "Sebastian"}, {"text": "Hindsight"}],\n'
+            '      "labels": {"source_type": "stated", "domain": ["infrastructure"]}\n'
+            "    }\n"
+            "  ]\n"
+            "}\n"
+            "```"
+        )
+        result = _strip_code_fences(content)
+        assert not result.startswith("```")
+        assert not result.endswith("```")
+        # Should be valid JSON
+        import json
+
+        parsed = json.loads(result)
+        assert len(parsed["facts"]) == 1
+        assert parsed["facts"][0]["who"] == "Sebastian"
@@ -0,0 +1,269 @@
+"""
+Unit tests for ValidationResult.accept_with() enrichment (PR #639).
+
+These tests verify:
+1. The accept_with() factory creates an accepted result with the correct enrichment fields.
+2. The engine applies enrichment to retain contents and recall tags/tag_groups.
+3. RecallContext carries tags/tags_match/tag_groups so validators can read filter state.
+"""
+
+import pytest
+
+from hindsight_api.extensions import (
+    OperationValidatorExtension,
+    RecallContext,
+    ReflectContext,
+    RetainContext,
+    ValidationResult,
+)
+from hindsight_api.models import RequestContext
+
+# ---------------------------------------------------------------------------
+# Pure unit tests for ValidationResult factory methods
+# ---------------------------------------------------------------------------
+
+
+class TestValidationResultAcceptWith:
+    """Unit tests for the accept_with() factory — no DB needed."""
+
+    def test_accept_is_allowed_with_no_enrichment(self):
+        result = ValidationResult.accept()
+        assert result.allowed is True
+        assert result.contents is None
+        assert result.tags is None
+        assert result.tags_match is None
+        assert result.tag_groups is None
+
+    def test_accept_with_contents(self):
+        contents = [{"content": "enriched text", "tags": ["injected"]}]
+        result = ValidationResult.accept_with(contents=contents)
+        assert result.allowed is True
+        assert result.contents == contents
+        assert result.tags is None
+        assert result.tag_groups is None
+
+    def test_accept_with_tags(self):
+        result = ValidationResult.accept_with(tags=["alpha", "beta"])
+        assert result.allowed is True
+        assert result.tags == ["alpha", "beta"]
+        assert result.contents is None
+        assert result.tag_groups is None
+
+    def test_accept_with_tags_match(self):
+        result = ValidationResult.accept_with(tags=["x"], tags_match="all")
+        assert result.allowed is True
+        assert result.tags_match == "all"
+
+    def test_accept_with_tag_groups(self):
+        tag_groups = [{"tags": ["env:prod"], "match": "all"}]
+        result = ValidationResult.accept_with(tag_groups=tag_groups)
+        assert result.allowed is True
+        assert result.tag_groups == tag_groups
+
+    def test_accept_with_all_fields(self):
+        contents = [{"content": "c"}]
+        tags = ["t1"]
+        tag_groups = [{"tags": ["g1"]}]
+        result = ValidationResult.accept_with(
+            contents=contents,
+            tags=tags,
+            tags_match="any",
+            tag_groups=tag_groups,
+        )
+        assert result.allowed is True
+        assert result.contents == contents
+        assert result.tags == tags
+        assert result.tags_match == "any"
+        assert result.tag_groups == tag_groups
+
+    def test_reject_ignores_enrichment_fields(self):
+        """reject() always sets allowed=False and leaves enrichment fields at their defaults."""
+        result = ValidationResult.reject("not allowed", status_code=403)
+        assert result.allowed is False
+        assert result.reason == "not allowed"
+        assert result.status_code == 403
+        assert result.contents is None
+        assert result.tags is None
+
+    def test_none_fields_mean_no_modification(self):
+        """None enrichment fields must not overwrite engine defaults."""
+        result = ValidationResult.accept_with(tags=None, tag_groups=None)
+        assert result.tags is None
+        assert result.tag_groups is None
+        # Engine should interpret None as "keep original" — we verify the contract here.
+
+
+# ---------------------------------------------------------------------------
+# Integration tests: engine applies enrichment from validator
+# ---------------------------------------------------------------------------
+
+
+class _ContentEnrichingValidator(OperationValidatorExtension):
+    """Validator that injects a tag into every retain content item."""
+
+    def __init__(self, injected_tag: str):
+        super().__init__({})
+        self.injected_tag = injected_tag
+
+    async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
+        enriched = []
+        for item in ctx.contents:
+            new_item = dict(item)
+            new_item.setdefault("tags", [])
+            new_item["tags"] = list(new_item["tags"]) + [self.injected_tag]
+            enriched.append(new_item)
+        return ValidationResult.accept_with(contents=enriched)
+
+    async def validate_recall(self, ctx: RecallContext) -> ValidationResult:
+        return ValidationResult.accept()
+
+    async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
+        return ValidationResult.accept()
+
+
+class _TagEnrichingValidator(OperationValidatorExtension):
+    """Validator that injects tags into every recall operation."""
+
+    def __init__(self, forced_tags: list[str]):
+        super().__init__({})
+        self.forced_tags = forced_tags
+
+    async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
+        return ValidationResult.accept()
+
+    async def validate_recall(self, ctx: RecallContext) -> ValidationResult:
+        return ValidationResult.accept_with(tags=self.forced_tags, tags_match="all")
+
+    async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
+        return ValidationResult.accept()
+
+
+class _RecallContextCapturingValidator(OperationValidatorExtension):
+    """Validator that captures the RecallContext for inspection."""
+
+    def __init__(self):
+        super().__init__({})
+        self.captured: list[RecallContext] = []
+
+    async def validate_retain(self, ctx: RetainContext) -> ValidationResult:
+        return ValidationResult.accept()
+
+    async def validate_recall(self, ctx: RecallContext) -> ValidationResult:
+        self.captured.append(ctx)
+        return ValidationResult.accept()
+
+    async def validate_reflect(self, ctx: ReflectContext) -> ValidationResult:
+        return ValidationResult.accept()
+
+
+@pytest.fixture
+def memory_with_content_enricher(memory):
+    validator = _ContentEnrichingValidator(injected_tag="validator-injected")
+    memory._operation_validator = validator
+    return memory, validator
+
+
+@pytest.fixture
+def memory_with_tag_enricher(memory):
+    validator = _TagEnrichingValidator(forced_tags=["forced-tag"])
+    memory._operation_validator = validator
+    return memory, validator
+
+
+@pytest.fixture
+def memory_with_recall_context_capture(memory):
+    validator = _RecallContextCapturingValidator()
+    memory._operation_validator = validator
+    return memory, validator
+
+
+class TestRetainContentEnrichment:
+    """Engine applies enriched contents returned by validate_retain."""
+
+    @pytest.mark.asyncio
+    async def test_enriched_contents_are_used_for_retain(self, memory_with_content_enricher):
+        """When validator returns accept_with(contents=...), engine uses those contents."""
+        memory, validator = memory_with_content_enricher
+        bank_id = "test-retain-enrichment"
+        ctx = RequestContext()
+
+        # Retain without any tags — validator should inject "validator-injected"
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[{"content": "Alice is an engineer."}],
+            request_context=ctx,
+        )
+
+        # Retrieve facts tagged with the injected tag to confirm enrichment was applied
+        result = await memory.recall_async(
+            bank_id=bank_id,
+            query="Alice",
+            tags=["validator-injected"],
+            request_context=ctx,
+        )
+        # The fact should be retrievable via the injected tag
+        assert result is not None
+
+
+class TestRecallTagEnrichment:
+    """Engine applies enriched tags returned by validate_recall."""
+
+    @pytest.mark.asyncio
+    async def test_enriched_tags_filter_recall_results(self, memory_with_tag_enricher):
+        """When validator returns accept_with(tags=...), engine filters recall by those tags."""
+        memory, validator = memory_with_tag_enricher
+        bank_id = "test-recall-tag-enrichment"
+        ctx = RequestContext()
+
+        # Retain one fact with the forced tag and one without
+        await memory.retain_batch_async(
+            bank_id=bank_id,
+            contents=[{"content": "Bob is a designer.", "tags": ["forced-tag"]}],
+            request_context=ctx,
+        )
+
+        # recall is called without tags but validator injects "forced-tag" + match=all
+        result = await memory.recall_async(
+            bank_id=bank_id,
+            query="Bob",
+            request_context=ctx,
+        )
+        # Should still get a result — the injected tag matches the stored fact
+        assert result is not None
+
+
+class TestRecallContextContainsTagFields:
+    """RecallContext passed to validate_recall carries tag filter state."""
+
+    @pytest.mark.asyncio
+    async def test_recall_context_carries_tags(self, memory_with_recall_context_capture):
+        """tags, tags_match, and tag_groups are present in RecallContext."""
+        memory, validator = memory_with_recall_context_capture
+        bank_id = "test-recall-ctx-tags"
+        ctx = RequestContext()
+
+        await memory.recall_async(
+            bank_id=bank_id,
+            query="test",
+            tags=["env:prod"],
+            tags_match="all",
+            request_context=ctx,
+        )
+
+        assert len(validator.captured) == 1
+        rc = validator.captured[0]
+        assert rc.tags == ["env:prod"]
+        assert rc.tags_match == "all"
+
+    @pytest.mark.asyncio
+    async def test_recall_context_tags_default_to_none(self, memory_with_recall_context_capture):
+        """When caller provides no tags, RecallContext.tags is None."""
+        memory, validator = memory_with_recall_context_capture
+        bank_id = "test-recall-ctx-no-tags"
+        ctx = RequestContext()
+
+        await memory.recall_async(bank_id=bank_id, query="test", request_context=ctx)
+
+        assert len(validator.captured) == 1
+        rc = validator.captured[0]
+        assert rc.tags is None
@@ -58,10 +58,14 @@ async def pool(pg0_db_url):
 async def clean_operations(pool):
    """Clean up async_operations table before and after tests."""
    # Clean before test - covers both 'test-worker-' and 'test_worker_recovery' patterns
-    await pool.execute("DELETE FROM async_operations WHERE bank_id LIKE 'test-worker-%' OR bank_id LIKE 'test_worker_%'")
+    await pool.execute(
+        "DELETE FROM async_operations WHERE bank_id LIKE 'test-worker-%' OR bank_id LIKE 'test_worker_%'"
+    )
    yield
    # Clean after test
-    await pool.execute("DELETE FROM async_operations WHERE bank_id LIKE 'test-worker-%' OR bank_id LIKE 'test_worker_%'")
+    await pool.execute(
+        "DELETE FROM async_operations WHERE bank_id LIKE 'test-worker-%' OR bank_id LIKE 'test_worker_%'"
+    )


 class TestBrokerTaskBackend:
@@ -387,9 +391,7 @@ class TestWorkerPoller:
            "SELECT status, error_message, retry_count FROM async_operations WHERE operation_id = $1",
            op_id,
        )
-        assert row["status"] == "failed", (
-            f"Expected 'failed' for plain exception, got '{row['status']}'"
-        )
+        assert row["status"] == "failed", f"Expected 'failed' for plain exception, got '{row['status']}'"
        assert row["error_message"] is not None
        assert row["retry_count"] == 0  # not incremented; plain exception = immediate fail

@@ -1265,7 +1267,9 @@ async def test_worker_fire_and_forget_nonblocking(pool, clean_operations):
    for i in range(2):
        op_id = uuid.uuid4()
        task_ids.append(str(op_id))
-        payload = json.dumps({"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id})
+        payload = json.dumps(
+            {"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id}
+        )
        await pool.execute(
            """
            INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload)
@@ -1294,7 +1298,9 @@ async def test_worker_fire_and_forget_nonblocking(pool, clean_operations):
        for i in range(2):
            op_id = uuid.uuid4()
            task_ids.append(str(op_id))
-            payload = json.dumps({"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id})
+            payload = json.dumps(
+                {"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id}
+            )
            await pool.execute(
                """
                INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload)
@@ -1364,7 +1370,9 @@ async def test_worker_slot_limits_enforced(pool, clean_operations):
    await _ensure_bank(pool, bank_id)
    for i in range(10):
        op_id = uuid.uuid4()
-        payload = json.dumps({"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id})
+        payload = json.dumps(
+            {"type": "test", "operation_type": "retain", "operation_id": str(op_id), "bank_id": bank_id}
+        )
        await pool.execute(
            """
            INSERT INTO async_operations (operation_id, bank_id, operation_type, status, task_payload)
@@ -1396,7 +1404,7 @@ async def test_worker_slot_limits_enforced(pool, clean_operations):
        completed = 0
        while completed < 10 and len(tasks_started) < 10:
            # Release the next batch
-            events_to_release = list(task_events.values())[completed:completed+3]
+            events_to_release = list(task_events.values())[completed : completed + 3]
            for event in events_to_release:
                event.set()
            completed += len(events_to_release)
@@ -1417,3 +1425,223 @@ async def test_worker_slot_limits_enforced(pool, clean_operations):
            await asyncio.wait_for(poll_task, timeout=1.0)
        except asyncio.CancelledError:
            pass
+
+
+class TestMarkFailedParentPropagation:
+    """Tests for _mark_failed parent propagation in WorkerPoller.
+
+    When a child retain operation fails via an unhandled exception, the memory
+    engine's transaction is rolled back entirely — including any call to
+    _maybe_update_parent_operation inside the engine. The poller's fallback
+    _mark_failed must detect this and finalise the parent batch_retain itself.
+    """
+
+    async def _insert_op(
+        self,
+        pool,
+        *,
+        op_id: "uuid.UUID",
+        bank_id: str,
+        operation_type: str,
+        status: str,
+        result_metadata: dict | None = None,
+    ) -> None:
+        meta_json = json.dumps(result_metadata if result_metadata is not None else {})
+        await pool.execute(
+            """
+            INSERT INTO async_operations
+                (operation_id, bank_id, operation_type, status, result_metadata)
+            VALUES ($1, $2, $3, $4, $5::jsonb)
+            """,
+            op_id,
+            bank_id,
+            operation_type,
+            status,
+            meta_json,
+        )
+
+    @pytest.mark.asyncio
+    async def test_mark_failed_finalises_parent_when_last_sibling_fails(self, pool, clean_operations):
+        """When the last pending child fails, parent batch_retain is marked failed."""
+        from hindsight_api.worker import WorkerPoller
+
+        bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(pool, bank_id)
+
+        parent_id = uuid.uuid4()
+        child1_id = uuid.uuid4()
+        child2_id = uuid.uuid4()
+
+        # Parent batch_retain still pending
+        await self._insert_op(pool, op_id=parent_id, bank_id=bank_id, operation_type="batch_retain", status="pending")
+
+        # child1 already completed
+        await self._insert_op(
+            pool,
+            op_id=child1_id,
+            bank_id=bank_id,
+            operation_type="retain",
+            status="completed",
+            result_metadata={"parent_operation_id": str(parent_id)},
+        )
+
+        # child2 still processing — this is the one that will fail
+        await self._insert_op(
+            pool,
+            op_id=child2_id,
+            bank_id=bank_id,
+            operation_type="retain",
+            status="processing",
+            result_metadata={"parent_operation_id": str(parent_id)},
+        )
+
+        poller = WorkerPoller(pool=pool, worker_id="test-worker-1", executor=lambda x: None)
+        await poller._mark_failed(str(child2_id), "DB constraint violation", schema=None)
+
+        # child2 must be failed
+        child2_row = await pool.fetchrow(
+            "SELECT status, error_message FROM async_operations WHERE operation_id = $1", child2_id
+        )
+        assert child2_row["status"] == "failed"
+        assert "DB constraint violation" in child2_row["error_message"]
+
+        # parent must now be failed (all siblings done, at least one failed)
+        parent_row = await pool.fetchrow("SELECT status FROM async_operations WHERE operation_id = $1", parent_id)
+        assert parent_row["status"] == "failed", (
+            f"Parent should be 'failed' when last sibling fails, got '{parent_row['status']}'"
+        )
+
+    @pytest.mark.asyncio
+    async def test_mark_failed_finalises_parent_when_last_sibling_is_sole_child(self, pool, clean_operations):
+        """When the only child fails, parent batch_retain becomes failed."""
+        from hindsight_api.worker import WorkerPoller
+
+        bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(pool, bank_id)
+
+        parent_id = uuid.uuid4()
+        child_id = uuid.uuid4()
+
+        await self._insert_op(pool, op_id=parent_id, bank_id=bank_id, operation_type="batch_retain", status="pending")
+        await self._insert_op(
+            pool,
+            op_id=child_id,
+            bank_id=bank_id,
+            operation_type="retain",
+            status="processing",
+            result_metadata={"parent_operation_id": str(parent_id)},
+        )
+
+        poller = WorkerPoller(pool=pool, worker_id="test-worker-1", executor=lambda x: None)
+        await poller._mark_failed(str(child_id), "unexpected error", schema=None)
+
+        parent_row = await pool.fetchrow("SELECT status FROM async_operations WHERE operation_id = $1", parent_id)
+        assert parent_row["status"] == "failed"
+
+    @pytest.mark.asyncio
+    async def test_mark_failed_does_not_finalise_parent_when_siblings_still_pending(self, pool, clean_operations):
+        """Parent is NOT updated while other siblings are still processing/pending."""
+        from hindsight_api.worker import WorkerPoller
+
+        bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(pool, bank_id)
+
+        parent_id = uuid.uuid4()
+        child1_id = uuid.uuid4()
+        child2_id = uuid.uuid4()
+
+        await self._insert_op(pool, op_id=parent_id, bank_id=bank_id, operation_type="batch_retain", status="pending")
+
+        # child1 is the one failing
+        await self._insert_op(
+            pool,
+            op_id=child1_id,
+            bank_id=bank_id,
+            operation_type="retain",
+            status="processing",
+            result_metadata={"parent_operation_id": str(parent_id)},
+        )
+        # child2 is still pending — not done yet
+        await self._insert_op(
+            pool,
+            op_id=child2_id,
+            bank_id=bank_id,
+            operation_type="retain",
+            status="pending",
+            result_metadata={"parent_operation_id": str(parent_id)},
+        )
+
+        poller = WorkerPoller(pool=pool, worker_id="test-worker-1", executor=lambda x: None)
+        await poller._mark_failed(str(child1_id), "early failure", schema=None)
+
+        # child1 is failed
+        child1_row = await pool.fetchrow("SELECT status FROM async_operations WHERE operation_id = $1", child1_id)
+        assert child1_row["status"] == "failed"
+
+        # parent must still be pending (child2 not done)
+        parent_row = await pool.fetchrow("SELECT status FROM async_operations WHERE operation_id = $1", parent_id)
+        assert parent_row["status"] == "pending", (
+            f"Parent should remain 'pending' while siblings are outstanding, got '{parent_row['status']}'"
+        )
+
+    @pytest.mark.asyncio
+    async def test_mark_failed_no_parent_is_safe(self, pool, clean_operations):
+        """Operations without a parent (no result_metadata parent_operation_id) fail cleanly."""
+        from hindsight_api.worker import WorkerPoller
+
+        bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(pool, bank_id)
+
+        op_id = uuid.uuid4()
+        await self._insert_op(pool, op_id=op_id, bank_id=bank_id, operation_type="retain", status="processing")
+
+        poller = WorkerPoller(pool=pool, worker_id="test-worker-1", executor=lambda x: None)
+        # Must not raise
+        await poller._mark_failed(str(op_id), "standalone failure", schema=None)
+
+        row = await pool.fetchrow("SELECT status FROM async_operations WHERE operation_id = $1", op_id)
+        assert row["status"] == "failed"
+
+    @pytest.mark.asyncio
+    async def test_unhandled_exception_via_execute_task_propagates_to_parent(self, pool, clean_operations):
+        """End-to-end: executor raises a plain exception, poller calls _mark_failed,
+        which then resolves the parent batch_retain to failed."""
+        from hindsight_api.worker import WorkerPoller
+        from hindsight_api.worker.poller import ClaimedTask
+
+        bank_id = f"test-worker-{uuid.uuid4().hex[:8]}"
+        await _ensure_bank(pool, bank_id)
+
+        parent_id = uuid.uuid4()
+        child_id = uuid.uuid4()
+
+        await self._insert_op(pool, op_id=parent_id, bank_id=bank_id, operation_type="batch_retain", status="pending")
+        await self._insert_op(
+            pool,
+            op_id=child_id,
+            bank_id=bank_id,
+            operation_type="retain",
+            status="processing",
+            result_metadata={"parent_operation_id": str(parent_id)},
+        )
+
+        async def crashing_executor(task_dict):
+            raise RuntimeError("Simulated DB constraint violation — transaction rolled back")
+
+        poller = WorkerPoller(pool=pool, worker_id="test-worker-1", executor=crashing_executor)
+
+        task_dict = {"type": "retain", "operation_id": str(child_id), "bank_id": bank_id}
+        claimed_task = ClaimedTask(operation_id=str(child_id), task_dict=task_dict, schema=None)
+        await poller.execute_task(claimed_task)
+
+        completed = await poller.wait_for_active_tasks(timeout=5.0)
+        assert completed, "Task did not complete within timeout"
+
+        child_row = await pool.fetchrow("SELECT status FROM async_operations WHERE operation_id = $1", child_id)
+        assert child_row["status"] == "failed"
+
+        parent_row = await pool.fetchrow("SELECT status FROM async_operations WHERE operation_id = $1", parent_id)
+        assert parent_row["status"] == "failed", (
+            f"Parent batch_retain should be 'failed' after child fails via unhandled exception, "
+            f"got '{parent_row['status']}'"
+        )
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "hindsight-api"
-version = "0.4.19"
+version = "0.4.20"
 description = "Hindsight: Agent Memory That Works Like Human Memory"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -15,5 +15,11 @@ dependencies = [
 [tool.uv.sources]
 hindsight-api-slim = { workspace = true }

+[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"
+
 [tool.setuptools]
 packages = []
@@ -1,6 +1,6 @@
 [package]
 name = "hindsight-cli"
-version = "0.4.19"
+version = "0.4.20"
 edition = "2021"
 authors = ["Hindsight Team"]
 description = "A beautiful CLI for Hindsight - semantic memory system"
@@ -601,6 +601,13 @@ impl ApiClient {
        })
    }

+    pub fn get_mental_model_history(&self, bank_id: &str, mental_model_id: &str, _verbose: bool) -> Result<serde_json::Value> {
+        self.runtime.block_on(async {
+            let response = self.client.get_mental_model_history(bank_id, mental_model_id, None).await?;
+            Ok(response.into_inner())
+        })
+    }
+
    // --- Directive Methods ---

    pub fn list_directives(&self, bank_id: &str, _verbose: bool) -> Result<types::DirectiveListResponse> {
@@ -717,6 +717,13 @@ pub fn set_config(
    llm_model: Option<String>,
    llm_api_key: Option<String>,
    llm_base_url: Option<String>,
+    retain_mission: Option<String>,
+    retain_extraction_mode: Option<String>,
+    observations_mission: Option<String>,
+    reflect_mission: Option<String>,
+    disposition_skepticism: Option<i64>,
+    disposition_literalism: Option<i64>,
+    disposition_empathy: Option<i64>,
    verbose: bool,
    output_format: OutputFormat,
 ) -> Result<()> {
@@ -736,9 +743,30 @@ pub fn set_config(
    if let Some(base_url) = llm_base_url {
        updates.insert("llm_base_url".to_string(), serde_json::Value::String(base_url));
    }
+    if let Some(mission) = retain_mission {
+        updates.insert("retain_mission".to_string(), serde_json::Value::String(mission));
+    }
+    if let Some(mode) = retain_extraction_mode {
+        updates.insert("retain_extraction_mode".to_string(), serde_json::Value::String(mode));
+    }
+    if let Some(mission) = observations_mission {
+        updates.insert("observations_mission".to_string(), serde_json::Value::String(mission));
+    }
+    if let Some(mission) = reflect_mission {
+        updates.insert("reflect_mission".to_string(), serde_json::Value::String(mission));
+    }
+    if let Some(skepticism) = disposition_skepticism {
+        updates.insert("disposition_skepticism".to_string(), serde_json::Value::Number(skepticism.into()));
+    }
+    if let Some(literalism) = disposition_literalism {
+        updates.insert("disposition_literalism".to_string(), serde_json::Value::Number(literalism.into()));
+    }
+    if let Some(empathy) = disposition_empathy {
+        updates.insert("disposition_empathy".to_string(), serde_json::Value::Number(empathy.into()));
+    }

    if updates.is_empty() {
-        return Err(anyhow!("No config updates provided. Use --llm-provider, --llm-model, --llm-api-key, or --llm-base-url".to_string()));
+        return Err(anyhow!("No config updates provided. Use --llm-provider, --llm-model, --retain-mission, --observations-mission, or other flags".to_string()));
    }

    let spinner = if output_format == OutputFormat::Pretty {
@@ -149,11 +149,12 @@ pub fn update(
    directive_id: &str,
    name: Option<String>,
    content: Option<String>,
+    is_active: Option<bool>,
    verbose: bool,
    output_format: OutputFormat,
 ) -> Result<()> {
-    if name.is_none() && content.is_none() {
-        anyhow::bail!("At least one of --name or --content must be provided");
+    if name.is_none() && content.is_none() && is_active.is_none() {
+        anyhow::bail!("At least one of --name, --content, or --is-active must be provided");
    }

    let spinner = if output_format == OutputFormat::Pretty {
@@ -165,7 +166,7 @@ pub fn update(
    let request = types::UpdateDirectiveRequest {
        name,
        content,
-        is_active: None,
+        is_active,
        priority: None,
        tags: None,
    };
@@ -363,6 +363,9 @@ impl App {
                            tags: None,
                            tags_match: TagsMatch::Any,
                            tag_groups: None,
+                            fact_types: None,
+                            exclude_mental_models: false,
+                            exclude_mental_model_ids: None,
                        };

                        let result = client.reflect(&bank_id, &request, false)
@@ -9,7 +9,7 @@ use crate::output::{self, OutputFormat};
 use crate::ui;

 // Import types from generated client
-use hindsight_client::types::{Budget, ChunkIncludeOptions, IncludeOptions, TagsMatch};
+use hindsight_client::types::{Budget, ChunkIncludeOptions, FactsIncludeOptions, IncludeOptions, ReflectIncludeOptions, TagsMatch};
 use serde::Deserialize;
 use serde_json;

@@ -43,6 +43,16 @@ fn parse_budget(budget: &str) -> Budget {
    }
 }

+// Helper function to parse tags_match string to TagsMatch enum
+fn parse_tags_match(tags_match: &Option<String>) -> TagsMatch {
+    match tags_match.as_deref().unwrap_or("any").to_lowercase().as_str() {
+        "all" => TagsMatch::All,
+        "any_strict" => TagsMatch::AnyStrict,
+        "all_strict" => TagsMatch::AllStrict,
+        _ => TagsMatch::Any,
+    }
+}
+
 /// List memory units with pagination and optional filters
 pub fn list(
    client: &ApiClient,
@@ -250,6 +260,8 @@ pub fn recall(
    trace: bool,
    include_chunks: bool,
    chunk_max_tokens: i64,
+    tags: Vec<String>,
+    tags_match: Option<String>,
    verbose: bool,
    output_format: OutputFormat,
 ) -> Result<()> {
@@ -280,8 +292,8 @@ pub fn recall(
        trace,
        query_timestamp: None,
        include,
-        tags: None,
-        tags_match: TagsMatch::Any,
+        tags: if tags.is_empty() { None } else { Some(tags) },
+        tags_match: parse_tags_match(&tags_match),
        tag_groups: None,
    };

@@ -312,6 +324,9 @@ pub fn reflect(
    context: Option<String>,
    max_tokens: Option<i64>,
    schema_path: Option<PathBuf>,
+    tags: Vec<String>,
+    tags_match: Option<String>,
+    include_facts: bool,
    verbose: bool,
    output_format: OutputFormat,
 ) -> Result<()> {
@@ -332,16 +347,28 @@ pub fn reflect(
        None
    };

+    let include = if include_facts {
+        Some(ReflectIncludeOptions {
+            facts: Some(FactsIncludeOptions(serde_json::Map::new())),
+            tool_calls: None,
+        })
+    } else {
+        None
+    };
+
    let request = ReflectRequest {
        query,
        budget: Some(parse_budget(&budget)),
        context,
        max_tokens: max_tokens.unwrap_or(4096),
-        include: None,
+        include,
        response_schema,
-        tags: None,
-        tags_match: TagsMatch::Any,
+        tags: if tags.is_empty() { None } else { Some(tags) },
+        tags_match: parse_tags_match(&tags_match),
        tag_groups: None,
+        fact_types: None,
+        exclude_mental_models: false,
+        exclude_mental_model_ids: None,
    };

    let response = client.reflect(agent_id, &request, verbose);
@@ -272,6 +272,55 @@ pub fn refresh(
    }
 }

+/// Get the change history of a mental model
+pub fn history(
+    client: &ApiClient,
+    bank_id: &str,
+    mental_model_id: &str,
+    verbose: bool,
+    output_format: OutputFormat,
+) -> Result<()> {
+    let spinner = if output_format == OutputFormat::Pretty {
+        Some(ui::create_spinner("Fetching mental model history..."))
+    } else {
+        None
+    };
+
+    let response = client.get_mental_model_history(bank_id, mental_model_id, verbose);
+
+    if let Some(mut sp) = spinner {
+        sp.finish();
+    }
+
+    match response {
+        Ok(history) => {
+            if output_format == OutputFormat::Pretty {
+                ui::print_section_header(&format!("History: {}", mental_model_id));
+
+                if let Some(entries) = history.as_array() {
+                    if entries.is_empty() {
+                        println!("  {}", ui::dim("No history entries found."));
+                    } else {
+                        for entry in entries {
+                            let changed_at = entry.get("changed_at").and_then(|v| v.as_str()).unwrap_or("unknown");
+                            let previous = entry.get("previous_content").and_then(|v| v.as_str()).unwrap_or("(none)");
+                            println!("  {} {}", ui::dim("Changed at:"), changed_at);
+                            let preview: String = previous.chars().take(80).collect();
+                            let ellipsis = if previous.len() > 80 { "..." } else { "" };
+                            println!("  {} {}{}", ui::dim("Previous:"), ui::dim(&preview), ellipsis);
+                            println!();
+                        }
+                    }
+                }
+            } else {
+                output::print_output(&history, output_format)?;
+            }
+            Ok(())
+        }
+        Err(e) => Err(e),
+    }
+}
+
 // Helper function to print mental model details
 fn print_mental_model_detail(mental_model: &types::MentalModelResponse) {
    ui::print_section_header(&mental_model.name);
@@ -310,6 +310,34 @@ enum BankCommands {
        /// LLM base URL override
        #[arg(long)]
        llm_base_url: Option<String>,
+
+        /// Retain mission: what to focus on during fact extraction
+        #[arg(long)]
+        retain_mission: Option<String>,
+
+        /// Retain extraction mode (concise, verbose, custom)
+        #[arg(long)]
+        retain_extraction_mode: Option<String>,
+
+        /// Observations mission: what to synthesize into durable observations
+        #[arg(long)]
+        observations_mission: Option<String>,
+
+        /// Reflect mission: first-person identity for reflect operations
+        #[arg(long)]
+        reflect_mission: Option<String>,
+
+        /// Disposition skepticism trait (1-5)
+        #[arg(long, value_parser = clap::value_parser!(i64).range(1..=5))]
+        disposition_skepticism: Option<i64>,
+
+        /// Disposition literalism trait (1-5)
+        #[arg(long, value_parser = clap::value_parser!(i64).range(1..=5))]
+        disposition_literalism: Option<i64>,
+
+        /// Disposition empathy trait (1-5)
+        #[arg(long, value_parser = clap::value_parser!(i64).range(1..=5))]
+        disposition_empathy: Option<i64>,
    },

    /// Reset bank configuration to defaults (remove all overrides)
@@ -387,6 +415,14 @@ enum MemoryCommands {
        /// Maximum tokens for chunks (only used with --include-chunks)
        #[arg(long, default_value = "8192")]
        chunk_max_tokens: i64,
+
+        /// Filter by tags (comma-separated, e.g. user:alice,team)
+        #[arg(long, value_delimiter = ',')]
+        tags: Vec<String>,
+
+        /// Tag matching mode: any, all, any_strict, all_strict (default: any)
+        #[arg(long)]
+        tags_match: Option<String>,
    },

    /// Generate answers using bank identity (reflect/reasoning)
@@ -412,6 +448,18 @@ enum MemoryCommands {
        /// Path to JSON schema file for structured output
        #[arg(short = 's', long)]
        schema: Option<PathBuf>,
+
+        /// Filter by tags (comma-separated, e.g. user:alice,team)
+        #[arg(long, value_delimiter = ',')]
+        tags: Vec<String>,
+
+        /// Tag matching mode: any, all, any_strict, all_strict (default: any)
+        #[arg(long)]
+        tags_match: Option<String>,
+
+        /// Include source facts (based_on) in the response
+        #[arg(long)]
+        include_facts: bool,
    },

    /// Store (retain) a single memory
@@ -678,6 +726,15 @@ enum MentalModelCommands {
        /// Mental model ID
        mental_model_id: String,
    },
+
+    /// Get the change history of a mental model
+    History {
+        /// Bank ID
+        bank_id: String,
+
+        /// Mental model ID
+        mental_model_id: String,
+    },
 }

 #[derive(Subcommand)]
@@ -724,6 +781,10 @@ enum DirectiveCommands {
        /// New content
        #[arg(long)]
        content: Option<String>,
+
+        /// Enable or disable the directive
+        #[arg(long)]
+        is_active: Option<bool>,
    },

    /// Delete a directive
@@ -821,8 +882,8 @@ fn run() -> Result<()> {
            BankCommands::Config { bank_id, overrides_only } => {
                commands::bank::config(&client, &bank_id, overrides_only, verbose, output_format)
            }
-            BankCommands::SetConfig { bank_id, llm_provider, llm_model, llm_api_key, llm_base_url } => {
-                commands::bank::set_config(&client, &bank_id, llm_provider, llm_model, llm_api_key, llm_base_url, verbose, output_format)
+            BankCommands::SetConfig { bank_id, llm_provider, llm_model, llm_api_key, llm_base_url, retain_mission, retain_extraction_mode, observations_mission, reflect_mission, disposition_skepticism, disposition_literalism, disposition_empathy } => {
+                commands::bank::set_config(&client, &bank_id, llm_provider, llm_model, llm_api_key, llm_base_url, retain_mission, retain_extraction_mode, observations_mission, reflect_mission, disposition_skepticism, disposition_literalism, disposition_empathy, verbose, output_format)
            }
            BankCommands::ResetConfig { bank_id, yes } => {
                commands::bank::reset_config(&client, &bank_id, yes, verbose, output_format)
@@ -837,11 +898,11 @@ fn run() -> Result<()> {
            MemoryCommands::Get { bank_id, memory_id } => {
                commands::memory::get(&client, &bank_id, &memory_id, verbose, output_format)
            }
-            MemoryCommands::Recall { bank_id, query, fact_type, budget, max_tokens, trace, include_chunks, chunk_max_tokens } => {
-                commands::memory::recall(&client, &bank_id, query, fact_type, budget, max_tokens, trace, include_chunks, chunk_max_tokens, verbose, output_format)
+            MemoryCommands::Recall { bank_id, query, fact_type, budget, max_tokens, trace, include_chunks, chunk_max_tokens, tags, tags_match } => {
+                commands::memory::recall(&client, &bank_id, query, fact_type, budget, max_tokens, trace, include_chunks, chunk_max_tokens, tags, tags_match, verbose, output_format)
            }
-            MemoryCommands::Reflect { bank_id, query, budget, context, max_tokens, schema } => {
-                commands::memory::reflect(&client, &bank_id, query, budget, context, max_tokens, schema, verbose, output_format)
+            MemoryCommands::Reflect { bank_id, query, budget, context, max_tokens, schema, tags, tags_match, include_facts } => {
+                commands::memory::reflect(&client, &bank_id, query, budget, context, max_tokens, schema, tags, tags_match, include_facts, verbose, output_format)
            }
            MemoryCommands::Retain { bank_id, content, doc_id, context, r#async } => {
                commands::memory::retain(&client, &bank_id, content, doc_id, context, r#async, verbose, output_format)
@@ -930,6 +991,9 @@ fn run() -> Result<()> {
            MentalModelCommands::Refresh { bank_id, mental_model_id } => {
                commands::mental_model::refresh(&client, &bank_id, &mental_model_id, verbose, output_format)
            }
+            MentalModelCommands::History { bank_id, mental_model_id } => {
+                commands::mental_model::history(&client, &bank_id, &mental_model_id, verbose, output_format)
+            }
        },

        // Directive commands
@@ -943,8 +1007,8 @@ fn run() -> Result<()> {
            DirectiveCommands::Create { bank_id, name, content } => {
                commands::directive::create(&client, &bank_id, &name, &content, verbose, output_format)
            }
-            DirectiveCommands::Update { bank_id, directive_id, name, content } => {
-                commands::directive::update(&client, &bank_id, &directive_id, name, content, verbose, output_format)
+            DirectiveCommands::Update { bank_id, directive_id, name, content, is_active } => {
+                commands::directive::update(&client, &bank_id, &directive_id, name, content, is_active, verbose, output_format)
            }
            DirectiveCommands::Delete { bank_id, directive_id, yes } => {
                commands::directive::delete(&client, &bank_id, &directive_id, yes, verbose, output_format)
@@ -7,7 +7,7 @@ info:
    name: Apache 2.0
    url: https://www.apache.org/licenses/LICENSE-2.0.html
  title: Hindsight HTTP API
-  version: 0.4.19
+  version: 0.4.20
 servers:
 - url: /
 paths:
@@ -3884,12 +3884,18 @@ components:
          loc:
          - ValidationError_loc_inner
          - ValidationError_loc_inner
+          input: ""
+          ctx: "{}"
          type: type
+          url: url
        - msg: msg
          loc:
          - ValidationError_loc_inner
          - ValidationError_loc_inner
+          input: ""
+          ctx: "{}"
          type: type
+          url: url
      properties:
        detail:
          items:
@@ -4074,6 +4080,13 @@ components:
          id: id
          trigger:
            refresh_after_consolidation: false
+            fact_types:
+            - world
+            - world
+            exclude_mental_model_ids:
+            - exclude_mental_model_ids
+            - exclude_mental_model_ids
+            exclude_mental_models: false
          last_refreshed_at: last_refreshed_at
          content: content
          tags:
@@ -4089,6 +4102,13 @@ components:
          id: id
          trigger:
            refresh_after_consolidation: false
+            fact_types:
+            - world
+            - world
+            exclude_mental_model_ids:
+            - exclude_mental_model_ids
+            - exclude_mental_model_ids
+            exclude_mental_models: false
          last_refreshed_at: last_refreshed_at
          content: content
          tags:
@@ -4115,6 +4135,13 @@ components:
        id: id
        trigger:
          refresh_after_consolidation: false
+          fact_types:
+          - world
+          - world
+          exclude_mental_model_ids:
+          - exclude_mental_model_ids
+          - exclude_mental_model_ids
+          exclude_mental_models: false
        last_refreshed_at: last_refreshed_at
        content: content
        tags:
@@ -4169,6 +4196,13 @@ components:
      description: Trigger settings for a mental model.
      example:
        refresh_after_consolidation: false
+        fact_types:
+        - world
+        - world
+        exclude_mental_model_ids:
+        - exclude_mental_model_ids
+        - exclude_mental_model_ids
+        exclude_mental_models: false
      properties:
        refresh_after_consolidation:
          default: false
@@ -4176,6 +4210,26 @@ components:
            \ (real-time mode)"
          title: Refresh After Consolidation
          type: boolean
+        fact_types:
+          items:
+            enum:
+            - world
+            - experience
+            - observation
+            type: string
+          nullable: true
+          type: array
+        exclude_mental_models:
+          default: false
+          description: "If true, exclude all mental models from the reflect loop (skip\
+            \ search_mental_models tool)."
+          title: Exclude Mental Models
+          type: boolean
+        exclude_mental_model_ids:
+          items:
+            type: string
+          nullable: true
+          type: array
      title: MentalModelTrigger
    OperationResponse:
      description: Response model for a single async operation.
@@ -4684,6 +4738,26 @@ components:
            $ref: '#/components/schemas/RecallRequest_tag_groups_inner'
          nullable: true
          type: array
+        fact_types:
+          items:
+            enum:
+            - world
+            - experience
+            - observation
+            type: string
+          nullable: true
+          type: array
+        exclude_mental_models:
+          default: false
+          description: "If true, exclude all mental models from the reflect loop (skip\
+            \ search_mental_models tool)."
+          title: Exclude Mental Models
+          type: boolean
+        exclude_mental_model_ids:
+          items:
+            type: string
+          nullable: true
+          type: array
      required:
      - query
      title: ReflectRequest
@@ -5134,7 +5208,10 @@ components:
        loc:
        - ValidationError_loc_inner
        - ValidationError_loc_inner
+        input: ""
+        ctx: "{}"
        type: type
+        url: url
      properties:
        loc:
          items:
@@ -5146,6 +5223,13 @@ components:
        type:
          title: Error Type
          type: string
+        input: {}
+        ctx:
+          title: Context
+          type: object
+        url:
+          title: URL
+          type: string
      required:
      - loc
      - msg
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -41,7 +41,7 @@ var (
 	queryDescape    = strings.NewReplacer( "%5B", "[", "%5D", "]" )
 )

-// APIClient manages communication with the Hindsight HTTP API API v0.4.19
+// APIClient manages communication with the Hindsight HTTP API API v0.4.20
 // In most cases there should be only one, shared, APIClient.
 type APIClient struct {
 	cfg    *Configuration
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
@@ -3,7 +3,7 @@ Hindsight HTTP API

 HTTP API for Hindsight

-API version: 0.4.19
+API version: 0.4.20
 */

 // Code generated by OpenAPI Generator (https://openapi-generator.tech); DO NOT EDIT.
--- a/Show More
+++ b/Show More