feat(integrations): add hindsight-opencode-coding plugin

Reflect-only long-term memory for coding agents in OpenCode, with a git+chat backfill and (opt-in) live session write-back. - reflect + INJECT: on a task, reflect() the symptom and push the root-cause answer into the system prompt (no tools/recall). - backfill: every commit (full message + full diff, commit timestamp + git metadata) under a 'git' retain strategy; each chat as a JSON user/assistant transcript with custom extraction (<=2 coherent facts) under a 'chat' strategy; observations on; optional codebase knowledge pages. - live write-back (opt-in HINDSIGHT_RETAIN_SESSIONS): every N turns upsert the tool-filtered transcript under a stable conversation:<sessionID> document_id.
feat(knowledge-base): default pages to living-document trigger + 4096 tokens
2026-07-02 13:55:40 +02:00 · 2026-07-02 10:30:40 +02:00 · 2026-07-02 10:30:39 +02:00 · 2026-07-02 10:30:39 +02:00 · 2026-07-02 10:30:39 +02:00 · 2026-07-02 10:30:39 +02:00
750 changed files with 27527 additions and 60052 deletions
@@ -1,7 +1,7 @@
 {
  "$schema": "https://anthropic.com/claude-code/marketplace.schema.json",
  "name": "hindsight",
-  "version": "0.7.5",
+  "version": "0.7.2",
  "description": "Official Hindsight integrations for Claude Code",
  "owner": {
    "name": "vectorize-io"
@@ -11,11 +11,6 @@
      "name": "hindsight-memory",
      "description": "Automatic long-term memory for Claude Code via Hindsight",
      "source": "./hindsight-integrations/claude-code"
-    },
-    {
-      "name": "hindsight-zcode",
-      "description": "No-MCP long-term memory for ZCode via Hindsight hooks",
-      "source": "./hindsight-integrations/zcode"
    }
  ]
 }
@@ -78,11 +78,6 @@ results = await asyncio.gather(*tasks, return_exceptions=True)
 - **Authentication/tenancy is enforced inside each engine method, not assumed by the handler.** Every engine method that touches bank-scoped data must authenticate via `request_context` — typically `await self._authenticate_tenant(request_context)` (often indirectly through `get_bank_profile(...)`) — so the correct tenant schema is resolved before any query runs. Handlers must thread `request_context` through to the engine method; never query a tenant-scoped table assuming the schema is already set.
 - Engine methods return typed models (Pydantic/dataclass), not raw dicts (see Type Safety).

-### Database Locking
- **Never use PostgreSQL advisory locks** (`pg_advisory_lock`, `pg_try_advisory_lock`, `pg_advisory_xact_lock`, `pg_advisory_unlock`, …) in migrations, engine code, or anything else. Hindsight runs against connection poolers and managed/PG-compatible services where advisory locks are unreliable or unsupported: session-level locks silently leak or vanish when a pooler hands the session to another client, and callers can block forever on a lock the server never grants. Reject any new occurrence, including ones that look "safe" because they are transaction-scoped.
- The pre-existing usage in `hindsight_api/migrations.py` is grandfathered, not a precedent — it is tracked for removal. Don't copy it.
- Design the concurrency out instead of locking around it: give each process its own object to write (e.g. per-schema DDL rather than a shared `public.` object), make the operation idempotent, or use a real row/table constraint (`INSERT ... ON CONFLICT`, `SELECT ... FOR UPDATE` in a fixed order). See #2690 for a migration that reached for `pg_advisory_xact_lock` and had to be reverted.
-
 ### Branch Hygiene
 - **Always start new feature branches from `origin/main`** — rebase to ensure a clean base.
 - **Only include commits relevant to the PR/branch/feature** — no unrelated changes. If the branch contains commits that don't belong, they must be removed before merging.
@@ -209,14 +204,6 @@ in `hindsight-api-slim/hindsight_api/config.py`):
  The `test_bundled_template_matches_repo_root` sync test fails on drift; if the
  root file changed without re-copying, flag it as a **must fix**.

-### 11c. Check for advisory locks
-
-Grep the diff for `advisory` (`git diff main...HEAD | grep -in advisory`). Any new
-`pg_advisory_lock` / `pg_try_advisory_lock` / `pg_advisory_xact_lock` /
-`pg_advisory_unlock` call is a **must fix** — see Database Locking above. Point the
-author at the alternatives (per-process objects, idempotent DDL, row-level
-constraints) rather than just asking them to drop the lock.
-
 ### 12. Review against other coding standards

 Check the diff for violations of the standards listed above:
@@ -21,21 +21,6 @@ HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 # HINDSIGHT_API_LLM_TEMPERATURE_REFLECT=0.9
 # HINDSIGHT_API_LLM_TEMPERATURE_CONSOLIDATION=0.0

-# Grammar-enforce structured output (json_schema strict) instead of the soft
-# schema-in-prompt path. Helps weaker self-hosted models that emit prose preambles
-# or invalid JSON. The global override below applies to every operation;
-# per-operation overrides take precedence, in both directions -- set one to false
-# to opt that operation out while the global flag is on.
-# HINDSIGHT_API_LLM_STRICT_SCHEMA=false
-# HINDSIGHT_API_LLM_STRICT_SCHEMA_RETAIN=true
-# HINDSIGHT_API_LLM_STRICT_SCHEMA_REFLECT=true
-# HINDSIGHT_API_LLM_STRICT_SCHEMA_CONSOLIDATION=true
-
-# Diagnostic: on any LLM 4xx, log the exact assembled request ([LLM_4XX_DUMP]) --
-# serialized request config (message bodies stripped) + capped per-message previews.
-# For debugging otherwise-unreproducible rejected calls. Off by default.
-# HINDSIGHT_API_LLM_DEBUG_DUMP_4XX=false
-
 # Example: Anthropic Claude configuration
 # HINDSIGHT_API_LLM_PROVIDER=anthropic
 # HINDSIGHT_API_LLM_API_KEY=your-anthropic-api-key
@@ -74,15 +59,6 @@ HINDSIGHT_API_LLM_BASE_URL=https://api.openai.com/v1
 # HINDSIGHT_API_LLM_BASE_URL=http://localhost:1234/v1
 # HINDSIGHT_API_LLM_MODEL=qwen2.5-32b-instruct

-# Example: Ollama local configuration (native provider)
-# HINDSIGHT_API_LLM_PROVIDER=ollama
-# HINDSIGHT_API_LLM_BASE_URL=http://localhost:11434/v1
-# HINDSIGHT_API_LLM_MODEL=gemma3:12b
-# Native Ollama context-window override (num_ctx). Leave unset to let Ollama use
-# the model Modelfile / server default; set a positive integer only to force a
-# specific context size (e.g. 16384 to keep the previous request behavior).
-# HINDSIGHT_API_LLM_OLLAMA_NUM_CTX=16384
-
 # Multi-LLM strategies: configure extra LLMs by index alongside the primary above,
 # then pick a routing strategy. Unset = single primary LLM (default). Members are
 # numbered from 1; indices must be contiguous. Each operation can override with a
@@ -104,10 +80,6 @@ HINDSIGHT_API_LOG_LEVEL=info
 # Unset uses HINDSIGHT_API_RETAIN_CHUNK_SIZE as the structured-chunk limit.
 # HINDSIGHT_API_RETAIN_STRUCTURED_CHUNK_SIZE=

-# When true, a retain operation that hit any fact-extraction errors is marked
-# 'failed' (not 'completed'), surfacing silently-dropped facts. Default false.
-# HINDSIGHT_API_FAIL_ON_EXTRACTION_ERRORS=false
-
 # Dry-run extraction preview endpoint (POST /memories/dry-run-extract). Enabled by default; it makes
 # a real LLM call but stores nothing. Set to false to remove the endpoint (returns 404).
 # HINDSIGHT_API_ENABLE_DRY_RUN_EXTRACT=true
@@ -123,10 +95,7 @@ HINDSIGHT_API_LOG_LEVEL=info
 # HINDSIGHT_API_READ_DATABASE_URL=  # Optional read-replica URL. When set, recall queries (semantic, BM25, graph, temporal) flow through a separate pool against this URL, offloading the primary. Typically points to a read-only endpoint (CNPG's <cluster>-ro service or Aurora reader endpoint).
 # HINDSIGHT_API_MIGRATION_DATABASE_URL=  # Direct PostgreSQL URL for migrations (bypasses PgBouncer). Falls back to DATABASE_URL.
 # HINDSIGHT_API_DATABASE_SCHEMA=public  # PostgreSQL schema name (default: public)
-# HINDSIGHT_API_DB_MAX_PARALLEL_WORKERS_PER_GATHER=  # Optional cap on Postgres planner parallelism for this process's pool connections. Unset leaves the server default; 0 makes background/bulk queries run serially (useful on worker processes sharing a primary with latency-sensitive traffic).
 # HINDSIGHT_API_MIGRATION_CONCURRENCY=1  # Tenant schemas to migrate concurrently (PG only, each in its own process; per-schema work stays sequential). Each worker has ~1-2s startup cost + uses ~3 DB connections, so it only pays off with many schemas (tens+) or slow migrations; keep concurrency*3 <= spare max_connections. Default: 1 (sequential).
-# HINDSIGHT_API_OPERATION_RETENTION_DAYS=30  # Prune terminal operation rows, payloads, and metadata after this many days; 0 (the default) keeps them forever.
-# HINDSIGHT_API_OPERATION_CLEANUP_BATCH_SIZE=1000  # Maximum expired terminal rows deleted per tenant schema in each cleanup cycle; must be positive.

 # Vector Extension (Optional - uses pgvector by default)
 # Options: "pgvector" (default), "vchord", "pgvectorscale" (DiskANN)
@@ -146,11 +115,6 @@ HINDSIGHT_API_LOG_LEVEL=info
 # chinese_lindera/lindera(chinese), japanese_lindera/lindera(japanese),
 # korean_lindera/lindera(korean), ngram(min,max), edge_ngram(min,max)
 # HINDSIGHT_API_TEXT_SEARCH_EXTENSION_PG_SEARCH_TOKENIZER=
-# Optional cap on the number of terms in the native PostgreSQL BM25 tsquery.
-# Long queries OR-join every normalized token, which can match too much of a
-# large bank. 0 (default) keeps the historical uncapped behavior; a positive
-# value bounds only the native backend (other BM25 backends get the raw query).
-# HINDSIGHT_API_BM25_MAX_QUERY_TERMS=0

 # File Parser (Optional - uses markitdown by default)
 # HINDSIGHT_API_FILE_PARSER=markitdown
@@ -169,8 +133,6 @@ HINDSIGHT_API_LOG_LEVEL=info
 # HINDSIGHT_API_EMBEDDINGS_PROVIDER=local
 # For local provider:
 # HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=BAAI/bge-small-en-v1.5
-# Force CPU if local embeddings hit MPS/XPC instability on macOS:
-# HINDSIGHT_API_EMBEDDINGS_LOCAL_FORCE_CPU=false
 # For ONNX provider (local CPU embeddings without an Ollama/TEI sidecar):
 # HINDSIGHT_API_EMBEDDINGS_PROVIDER=onnx
 # HINDSIGHT_API_EMBEDDINGS_ONNX_MODEL_ID=intfloat/multilingual-e5-small
@@ -210,13 +172,8 @@ HINDSIGHT_API_LOG_LEVEL=info
 # Reranker Configuration (Optional - uses local by default)
 # Provider: "local" (default) or "tei" (HuggingFace Text Embeddings Inference)
 # HINDSIGHT_API_RERANKER_PROVIDER=local
-# Trusted gateway attribution (disabled by default). When enabled, remote
-# reranker requests include X-Hindsight-Bank-Id with the current bank ID.
-# HINDSIGHT_API_RERANKER_SEND_BANK_AS_HEADER=false
 # For local provider:
 # HINDSIGHT_API_RERANKER_LOCAL_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
-# Force CPU if the local reranker hits MPS/XPC instability on macOS:
-# HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU=false
 # For TEI provider:
 # HINDSIGHT_API_RERANKER_TEI_URL=http://localhost:8081

@@ -32,7 +32,6 @@ jobs:
      integration-tests: ${{ steps.filter.outputs.integration-tests }}
      integrations-openclaw: ${{ steps.filter.outputs.integrations-openclaw }}
      integrations-ai-sdk: ${{ steps.filter.outputs.integrations-ai-sdk }}
-      integrations-agentos: ${{ steps.filter.outputs.integrations-agentos }}
      integrations-agent-framework: ${{ steps.filter.outputs.integrations-agent-framework }}
      integrations-composio: ${{ steps.filter.outputs.integrations-composio }}
      integrations-chat: ${{ steps.filter.outputs.integrations-chat }}
@@ -42,7 +41,6 @@ jobs:
      integrations-github-copilot: ${{ steps.filter.outputs.integrations-github-copilot }}
      integrations-continue: ${{ steps.filter.outputs.integrations-continue }}
      integrations-cursor-cli: ${{ steps.filter.outputs.integrations-cursor-cli }}
-      integrations-zcode: ${{ steps.filter.outputs.integrations-zcode }}
      integrations-crewai: ${{ steps.filter.outputs.integrations-crewai }}
      integrations-litellm: ${{ steps.filter.outputs.integrations-litellm }}
      integrations-pydantic-ai: ${{ steps.filter.outputs.integrations-pydantic-ai }}
@@ -136,8 +134,6 @@ jobs:
            - 'hindsight-integrations/openclaw/**'
          integrations-ai-sdk:
            - 'hindsight-integrations/ai-sdk/**'
-          integrations-agentos:
-            - 'hindsight-integrations/agentos/**'
          integrations-agent-framework:
            - 'hindsight-integrations/agent-framework/**'
          integrations-composio:
@@ -184,8 +180,6 @@ jobs:
            - 'hindsight-integrations/cursor/**'
          integrations-zed:
            - 'hindsight-integrations/zed/**'
-          integrations-zcode:
-            - 'hindsight-integrations/zcode/**'
          integrations-n8n:
            - 'hindsight-integrations/n8n/**'
          integrations-zapier:
@@ -526,17 +520,22 @@ jobs:
      with:
        ref: ${{ github.event.pull_request.head.sha || '' }}

-    - name: Set up Node.js
-      uses: actions/setup-node@v6
+    - name: Set up Python
+      uses: actions/setup-python@v6
      with:
-        node-version: '22'
+        python-version: '3.11'
+
+    - name: Install package and pytest
+      working-directory: ./hindsight-integrations/zed
+      # Installs the package (incl. the zstandard runtime dep) so the threads.db
+      # reader tests can decompress Zed's zstd blobs.
+      run: pip install -e . pytest

    - name: Run tests
      working-directory: ./hindsight-integrations/zed
-      # Config-only integration with no dependencies — it uses Node's built-in
-      # test runner. The runtime MCP bridge is `npx mcp-remote` (Node), so this
-      # integration requires only Node.js (no Python).
-      run: npm test
+      # PR CI runs only the deterministic bucket; the real-LLM E2E bucket
+      # (requires_real_llm) needs a live Hindsight server and runs separately.
+      run: python -m pytest tests/ -v -m "not requires_real_llm"

  test-omo-integration:
    needs: [detect-changes]
@@ -703,43 +702,6 @@ jobs:
      working-directory: ./hindsight-integrations/cursor-cli
      run: uv run pytest tests -v

-  test-zcode-integration:
-    needs: [detect-changes]
-    if: >-
-      (github.event_name == 'workflow_dispatch' ||
-      needs.detect-changes.outputs.integrations-zcode == 'true' ||
-      needs.detect-changes.outputs.ci == 'true')
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-
-    steps:
-    - uses: actions/checkout@v6
-      with:
-        ref: ${{ github.event.pull_request.head.sha || '' }}
-
-    - 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 zcode integration
-      working-directory: ./hindsight-integrations/zcode
-      run: uv build
-
-    - name: Install dependencies
-      working-directory: ./hindsight-integrations/zcode
-      run: uv sync --frozen
-
-    - name: Run tests
-      working-directory: ./hindsight-integrations/zcode
-      run: uv run pytest tests -v
-
  build-ai-sdk-integration:
    needs: [detect-changes]
    if: >-
@@ -803,37 +765,6 @@ jobs:
      working-directory: ./hindsight-integrations/ai-sdk
      run: npm run test:deno

-  build-agentos-integration:
-    needs: [detect-changes]
-    if: >-
-      (github.event_name == 'workflow_dispatch' ||
-      needs.detect-changes.outputs.integrations-agentos == 'true' ||
-      needs.detect-changes.outputs.ci == 'true')
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-
-    steps:
-    - uses: actions/checkout@v6
-      with:
-        ref: ${{ github.event.pull_request.head.sha || '' }}
-
-    - name: Set up Node.js
-      uses: actions/setup-node@v6
-      with:
-        node-version: '22'
-
-    - name: Install dependencies
-      working-directory: ./hindsight-integrations/agentos
-      run: npm ci
-
-    - name: Run tests
-      working-directory: ./hindsight-integrations/agentos
-      run: npm test
-
-    - name: Build
-      working-directory: ./hindsight-integrations/agentos
-      run: npm run build
-
  test-opencode-integration:
    needs: [detect-changes]
    if: >-
@@ -1298,10 +1229,10 @@ jobs:

  build-docs:
    needs: [detect-changes]
-    # Keep the production docs build as an unconditional PR check. OpenAPI
-    # generation used to build the site again inside verify-generated-files;
-    # running the existing job for every PR preserves that coverage without
-    # serializing two full Docusaurus builds in the generated-files check.
+    if: >-
+      (github.event_name == 'workflow_dispatch' ||
+      needs.detect-changes.outputs.docs == 'true' ||
+      needs.detect-changes.outputs.ci == 'true')
    runs-on: ubuntu-latest
    timeout-minutes: 30

@@ -4840,8 +4771,7 @@ jobs:
        cd ../hindsight-embed && uv sync --frozen --index-strategy unsafe-best-match

    - name: Run generate-openapi
-      working-directory: hindsight-dev
-      run: uv run generate-openapi
+      run: ./scripts/generate-openapi.sh

    - name: Run generate-bank-template-schema
      run: ./scripts/generate-bank-template-schema.sh
@@ -4979,10 +4909,8 @@ jobs:
      - test-github-copilot-integration
      - test-codex-integration
      - test-cursor-cli-integration
-      - test-zcode-integration
      - build-ai-sdk-integration
      - test-ai-sdk-integration-deno
-      - build-agentos-integration
      - test-opencode-integration
      - test-eve-integration
      - test-omo-integration
@@ -41,7 +41,6 @@ nltk_data/
 logs/

 .DS_Store
-.sesskey

 # Generated docs files
 hindsight-docs/static/llms-full.txt
@@ -0,0 +1 @@
+fcac2839-1db5-432f-91e1-c5dac07d7290
@@ -41,43 +41,28 @@ RUN apt-get update && apt-get install -y \
    && rm -rf /var/lib/apt/lists/* \
    && pip install --no-cache-dir uv

-# Copy the workspace lock and member metadata before source code so dependency
-# installation stays cacheable while matching the versions tested in CI.
-COPY pyproject.toml uv.lock ./
-COPY hindsight-all/pyproject.toml ./hindsight-all/
-COPY hindsight-api/pyproject.toml ./hindsight-api/
+# Copy dependency files and README (required by pyproject.toml)
 COPY hindsight-api-slim/pyproject.toml ./api/
 COPY hindsight-api-slim/README.md ./api/
-COPY hindsight-all-slim/pyproject.toml ./hindsight-all-slim/
-COPY hindsight-dev/pyproject.toml ./hindsight-dev/
-COPY hindsight-clients/python/pyproject.toml ./hindsight-clients/python/
-COPY hindsight-embed/pyproject.toml ./hindsight-embed/
-RUN ln -s api hindsight-api-slim
+
+WORKDIR /app/api

 # Sync dependencies using appropriate extras based on INCLUDE_LOCAL_MODELS
 # local-ml: torch, sentence-transformers, transformers, einops, flashrank, mlx (optional)
 # embedded-db: pg0-embedded (always included for embedded PostgreSQL support)
 # ONNX Runtime embeddings are intentionally not bundled into the official
 # standalone image; install the local-onnx extra in custom images when needed.
-ENV UV_PROJECT_ENVIRONMENT=/app/api/.venv
 RUN if [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-        uv sync --locked --package hindsight-api-slim --no-install-package hindsight-api-slim --extra local-ml --extra embedded-db; \
+        uv sync --extra local-ml --extra embedded-db; \
    else \
-        uv sync --locked --package hindsight-api-slim --no-install-package hindsight-api-slim --extra embedded-db; \
+        uv sync --extra embedded-db; \
    fi

 # Copy source code (alembic migrations are inside hindsight_api/)
-WORKDIR /app/api
 COPY hindsight-api-slim/hindsight_api ./hindsight_api

-# Install the local package from the same validated lock after source is present.
-WORKDIR /app
-RUN if [ "$INCLUDE_LOCAL_MODELS" = "true" ]; then \
-        uv sync --locked --package hindsight-api-slim --extra local-ml --extra embedded-db; \
-    else \
-        uv sync --locked --package hindsight-api-slim --extra embedded-db; \
-    fi \
-    && uv pip check --python /app/api/.venv/bin/python
+# Install the local package (uv sync only installed dependencies, not the package itself)
+RUN uv pip install -e .

 # =============================================================================
 # Stage: SDK Builder (needed for Control Plane)
@@ -160,8 +145,6 @@ FROM python:3.11-slim AS api-only
 WORKDIR /app

 # Note: libicu version varies by Debian version - try common versions in order
-# Runtime images use uv directly; remove pip build tooling after installation so
-# vulnerable setuptools-vendored packages and wheel are not shipped in production.
 RUN apt-get update && apt-get install -y \
    curl \
    procps \
@@ -171,8 +154,7 @@ RUN apt-get update && apt-get install -y \
    libossp-uuid16 \
    && (apt-get install -y libicu72 2>/dev/null || apt-get install -y libicu74 2>/dev/null || apt-get install -y libicu76 2>/dev/null || true) \
    && rm -rf /var/lib/apt/lists/* \
-    && pip install --no-cache-dir uv \
-    && pip uninstall --yes setuptools wheel
+    && pip install --no-cache-dir uv

 RUN useradd -m -s /bin/bash hindsight

@@ -310,8 +292,6 @@ WORKDIR /app

 # Install Node.js, curl, uv, and system dependencies
 # Note: libicu version varies by Debian version - try common versions in order
-# Runtime images use uv directly; remove pip build tooling after installation so
-# vulnerable setuptools-vendored packages and wheel are not shipped in production.
 RUN apt-get update && apt-get install -y \
    curl \
    procps \
@@ -323,8 +303,7 @@ RUN apt-get update && apt-get install -y \
    && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \
    && apt-get install -y nodejs \
    && rm -rf /var/lib/apt/lists/* \
-    && pip install --no-cache-dir uv \
-    && pip uninstall --yes setuptools wheel
+    && pip install --no-cache-dir uv

 RUN useradd -m -s /bin/bash hindsight

@@ -18,10 +18,8 @@ import typer

 from ..config import DEFAULT_DATABASE_SCHEMA, HindsightConfig
 from ..engine.memory_engine import _current_schema
-from ..engine.retain.bank_utils import _vector_index_clause
 from ..engine.schema import fq_table_explicit as _fq_table
 from ..engine.transfer import export_bank
-from ..engine.vector_index_health import SchemaVectorIndexResult, repair_vector_indexes
 from ..extensions import TenantExtension, load_extension
 from ..pg0 import parse_pg0_url, resolve_database_url

@@ -58,6 +56,7 @@ BACKUP_TABLES = [
    "observation_history",
    "mental_models",
    "mental_model_history",
+    "knowledge_pages",
    "directives",
    "async_operations",
    "webhooks",
@@ -78,8 +77,7 @@ async def _admin_connect(db_url: str) -> asyncpg.Connection:
    is the only step needed to connect. JSON codecs are registered so ``jsonb``
    columns decode to Python objects (used by the export row dumps).
    """
-    _pg0 = parse_pg0_url(db_url)
-    is_pg0, instance_name = _pg0.is_pg0, _pg0.instance_name
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
    if is_pg0:
        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
    conn = await asyncpg.connect(await resolve_database_url(db_url))
@@ -180,8 +178,7 @@ async def _restore(database_url: str, input_path: Path, schema: str = "public")

 async def _run_backup(db_url: str, output: Path, schema: str = "public") -> dict[str, Any]:
    """Resolve database URL and run backup."""
-    _pg0 = parse_pg0_url(db_url)
-    is_pg0, instance_name = _pg0.is_pg0, _pg0.instance_name
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
    if is_pg0:
        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
    resolved_url = await resolve_database_url(db_url)
@@ -190,8 +187,7 @@ async def _run_backup(db_url: str, output: Path, schema: str = "public") -> dict

 async def _run_restore(db_url: str, input_file: Path, schema: str = "public") -> dict[str, Any]:
    """Resolve database URL and run restore."""
-    _pg0 = parse_pg0_url(db_url)
-    is_pg0, instance_name = _pg0.is_pg0, _pg0.instance_name
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
    if is_pg0:
        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
    resolved_url = await resolve_database_url(db_url)
@@ -266,8 +262,7 @@ async def _run_migration(
    """Resolve database URL and run migrations for one schema or all discovered schemas."""
    from ..migrations import run_migrations_for_schemas

-    _pg0 = parse_pg0_url(db_url)
-    is_pg0, instance_name = _pg0.is_pg0, _pg0.instance_name
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
    if is_pg0:
        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
    resolved_url = await resolve_database_url(db_url)
@@ -359,134 +354,6 @@ def run_db_migration(
    typer.echo(f"Database migrations completed successfully for {len(schemas)} schema(s)")


-async def _resolve_schemas(base_schema: str | None) -> list[str]:
-    """Base schema plus every discovered tenant schema, de-duplicated in order."""
-    schemas = [base_schema or DEFAULT_DATABASE_SCHEMA]
-    tenant_extension = load_extension("TENANT", TenantExtension)
-    if tenant_extension:
-        tenants = await tenant_extension.list_tenants()
-        schemas.extend(tenant.schema for tenant in tenants if tenant.schema)
-    return list(dict.fromkeys(schemas))
-
-
-async def _run_repair_bank(
-    db_url: str,
-    *,
-    base_schema: str,
-    schema: str | None,
-    bank_id: str | None,
-    dry_run: bool,
-) -> list[SchemaVectorIndexResult]:
-    """Reconcile per-(bank, fact_type) vector index coverage over a raw connection.
-
-    A single autocommit connection is used because ``CREATE INDEX CONCURRENTLY``
-    (used by ``repair_vector_indexes``) cannot run inside a transaction block.
-    """
-    schemas = [schema] if schema else await _resolve_schemas(base_schema)
-    index_clause = _vector_index_clause()
-    # Guarded by the command, but assert so this helper is never called for a
-    # backend without per-bank indexes.
-    assert index_clause is not None
-
-    conn = await _admin_connect(db_url)
-    try:
-        results = await repair_vector_indexes(conn, schemas, index_clause, dry_run=dry_run, bank_id=bank_id)
-        for result in results:
-            typer.echo(
-                f"  schema '{result.schema}': {result.banks_scanned} bank(s) scanned, "
-                f"{result.already_present} present, {result.created} created, "
-                f"{result.skipped} to-create (dry-run), {result.failed} failed"
-            )
-        return results
-    finally:
-        await conn.close()
-
-
-@app.command(name="repair-bank")
-def repair_bank(
-    bank_id: str | None = typer.Option(
-        None,
-        "--bank",
-        "-b",
-        help="Bank id to repair. Mutually exclusive with --all.",
-    ),
-    all_banks: bool = typer.Option(
-        False,
-        "--all",
-        help="Repair every bank in the base schema and all discovered tenant schemas.",
-    ),
-    schema: str | None = typer.Option(
-        None,
-        "--schema",
-        "-s",
-        help="Limit to a single schema. Defaults to the base schema plus discovered tenant schemas.",
-    ),
-    dry_run: bool = typer.Option(
-        False,
-        "--dry-run",
-        help="Report what would be repaired without creating or dropping any index.",
-    ),
-):
-    """Verify and repair a bank's per-(bank, fact_type) vector index coverage.
-
-    Per-bank partial vector indexes are created when a bank is first created
-    (instant on an empty bank). Banks that arrive populated — via logical
-    restore, a cross-version upgrade, or a vector-extension switch — never hit
-    that path, so their recall silently falls back to a global index +
-    post-filter (slower, under-returning). This command detects missing OR
-    invalid coverage (an INVALID leftover or an index whose access method
-    drifted counts as missing) and rebuilds it with CREATE INDEX CONCURRENTLY,
-    so it never blocks the live fleet. Idempotent and safe to re-run — the
-    escape hatch after a restore, upgrade, or backend switch.
-    """
-    if bool(bank_id) == all_banks:
-        typer.echo("Error: pass exactly one of --bank <id> or --all.", err=True)
-        raise typer.Exit(2)
-
-    config = HindsightConfig.from_env()
-    if not config.database_url:
-        typer.echo("Error: Database URL not configured.", err=True)
-        typer.echo("Set HINDSIGHT_API_DATABASE_URL environment variable.", err=True)
-        raise typer.Exit(1)
-
-    # Backend guard: backends with a single global vector index (AlloyDB ScaNN,
-    # Oracle) have no per-bank indexes to repair.
-    if _vector_index_clause() is None:
-        typer.echo("Configured vector backend does not use per-bank vector indexes — nothing to repair.")
-        return
-
-    target = f"bank '{bank_id}'" if bank_id else "all banks"
-    scope = f"schema '{schema}'" if schema else "base schema and all discovered tenant schemas"
-    typer.echo(f"Repairing per-bank vector indexes for {target} across {scope}...")
-    if dry_run:
-        typer.echo("Dry run: no indexes will be created or dropped.")
-
-    results = asyncio.run(
-        _run_repair_bank(
-            config.database_url,
-            base_schema=config.database_schema,
-            schema=schema,
-            bank_id=bank_id,
-            dry_run=dry_run,
-        )
-    )
-
-    total_banks = sum(r.banks_scanned for r in results)
-    total_present = sum(r.already_present for r in results)
-    total_created = sum(r.created for r in results)
-    total_skipped = sum(r.skipped for r in results)
-    total_failed = sum(r.failed for r in results)
-    typer.echo(
-        f"Done: {len(results)} schema(s), {total_banks} bank(s) scanned, "
-        f"{total_present} already present, {total_created} created, "
-        f"{total_skipped} to-create (dry-run), {total_failed} failed"
-    )
-    if total_failed:
-        failed_names = [name for r in results for name in r.failed_indexes]
-        typer.echo(f"Failed indexes (dropped, retry with a re-run): {', '.join(failed_names)}", err=True)
-        raise typer.Exit(1)
-
-
 async def _run_export_bank(db_url: str, bank_id: str, output: Path, schema: str, include_history: bool) -> int:
    """Export a whole bank to a ZIP archive."""
    conn = await _admin_connect(db_url)
@@ -494,14 +361,7 @@ async def _run_export_bank(db_url: str, bank_id: str, output: Path, schema: str,
        # export_bank resolves table names via fq_table (the _current_schema
        # contextvar); set it so the raw connection targets the right schema.
        _current_schema.set(schema)
-        # _admin_connect registers JSON codecs, so row dumps already contain
-        # decoded Python values (including JSON scalar strings).
-        data = await export_bank(
-            conn,
-            bank_id,
-            include_history=include_history,
-            bank_rows_json_encoding="decoded",
-        )
+        data = await export_bank(conn, bank_id, include_history=include_history)
    finally:
        await conn.close()

@@ -613,8 +473,7 @@ def import_bank_command(

 async def _decommission_worker(db_url: str, worker_id: str, schema: str = "public") -> int:
    """Release all tasks owned by a worker, setting them back to pending status."""
-    _pg0 = parse_pg0_url(db_url)
-    is_pg0, instance_name = _pg0.is_pg0, _pg0.instance_name
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
    if is_pg0:
        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
    resolved_url = await resolve_database_url(db_url)
@@ -673,8 +532,7 @@ def decommission_worker(

 async def _decommission_all_workers(db_url: str, schema: str = "public") -> list[dict[str, Any]]:
    """Release all processing tasks from all workers, setting them back to pending status."""
-    _pg0 = parse_pg0_url(db_url)
-    is_pg0, instance_name = _pg0.is_pg0, _pg0.instance_name
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
    if is_pg0:
        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
    resolved_url = await resolve_database_url(db_url)
@@ -739,8 +597,7 @@ def decommission_workers(

 async def _worker_status(db_url: str, schema: str = "public") -> list[dict[str, Any]]:
    """Get all processing tasks grouped by worker with their last update time."""
-    _pg0 = parse_pg0_url(db_url)
-    is_pg0, instance_name = _pg0.is_pg0, _pg0.instance_name
+    is_pg0, instance_name, _ = parse_pg0_url(db_url)
    if is_pg0:
        typer.echo(f"Starting embedded PostgreSQL (instance: {instance_name})...")
    resolved_url = await resolve_database_url(db_url)
@@ -96,8 +96,7 @@ def get_database_url() -> str:
        # for the sync engine used during migrations.
        database_url = to_libpq_url(database_url)

-    # Alembic stores options through ConfigParser, where '%' is interpolation.
-    config.set_main_option("sqlalchemy.url", database_url.replace("%", "%%"))
+    config.set_main_option("sqlalchemy.url", database_url)
    return database_url


@@ -0,0 +1,52 @@
+"""Add managed flag to knowledge_pages.
+
+The knowledge base is managed by clients (CRUD over folders/pages). ``managed``
+lets a client tag a node as system-owned vs. hand-authored; it carries no
+server-side behaviour.
+
+Revision ID: a5b6c7d8e9f0
+Revises: a9b8c7d6e5f4
+Create Date: 2026-06-26
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+from hindsight_api.alembic._dialect import run_for_dialect
+
+revision: str = "a5b6c7d8e9f0"
+down_revision: str | Sequence[str] | None = "a9b8c7d6e5f4"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _pg_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def _pg_upgrade() -> None:
+    schema = _pg_schema_prefix()
+    op.execute(f"ALTER TABLE {schema}knowledge_pages ADD COLUMN IF NOT EXISTS managed BOOLEAN NOT NULL DEFAULT false")
+
+
+def _pg_downgrade() -> None:
+    schema = _pg_schema_prefix()
+    op.execute(f"ALTER TABLE {schema}knowledge_pages DROP COLUMN IF EXISTS managed")
+
+
+def _oracle_upgrade() -> None:
+    op.execute("ALTER TABLE knowledge_pages ADD (managed NUMBER(1) DEFAULT 0 NOT NULL)")
+
+
+def _oracle_downgrade() -> None:
+    op.execute("ALTER TABLE knowledge_pages DROP COLUMN managed")
+
+
+def upgrade() -> None:
+    run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
+
+
+def downgrade() -> None:
+    run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -1,82 +0,0 @@
-"""Add indexes for terminal cleanup and newest-first operation listing.
-
-Revision ID: a8c1e4f7b0d3
-Revises: e7c3a9f1b2d5
-Create Date: 2026-07-14
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-from hindsight_api.alembic._dialect import run_for_dialect
-
-revision: str = "a8c1e4f7b0d3"
-down_revision: str | Sequence[str] | None = "e7c3a9f1b2d5"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _pg_schema_prefix() -> str:
-    """Schema-qualifier for PostgreSQL multi-tenant migration runs."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def _pg_upgrade() -> None:
-    schema = _pg_schema_prefix()
-    # These can be large tables in long-running installations. Concurrent DDL
-    # keeps operation submission, polling, and status reads available.
-    with op.get_context().autocommit_block():
-        op.execute(
-            "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_async_operations_terminal_cleanup "
-            f"ON {schema}async_operations (updated_at, operation_id) "
-            "WHERE status IN ('completed', 'failed', 'cancelled')"
-        )
-        op.execute(
-            "CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_async_operations_bank_created_desc "
-            f"ON {schema}async_operations (bank_id, created_at DESC)"
-        )
-
-
-def _pg_downgrade() -> None:
-    schema = _pg_schema_prefix()
-    with op.get_context().autocommit_block():
-        op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_async_operations_bank_created_desc")
-        op.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}idx_async_operations_terminal_cleanup")
-
-
-def _oracle_create_index(sql: str) -> None:
-    """Create an index idempotently for rerun-safe Oracle migrations."""
-    block = (
-        "BEGIN "
-        "EXECUTE IMMEDIATE :stmt; "
-        "EXCEPTION WHEN OTHERS THEN "
-        "IF SQLCODE = -955 THEN NULL; ELSE RAISE; END IF; "
-        "END;"
-    )
-    op.get_bind().exec_driver_sql(block, {"stmt": sql})
-
-
-def _oracle_upgrade() -> None:
-    # Oracle migrations run with CURRENT_SCHEMA set to each tenant, so table
-    # and index names intentionally remain unqualified here.
-    _oracle_create_index(
-        "CREATE INDEX idx_async_operations_terminal_cleanup ON async_operations (updated_at, operation_id, status)"
-    )
-    _oracle_create_index(
-        "CREATE INDEX idx_async_operations_bank_created_desc ON async_operations (bank_id, created_at DESC)"
-    )
-
-
-def _oracle_downgrade() -> None:
-    op.execute("DROP INDEX idx_async_operations_bank_created_desc")
-    op.execute("DROP INDEX idx_async_operations_terminal_cleanup")
-
-
-def upgrade() -> None:
-    run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
-
-
-def downgrade() -> None:
-    run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -0,0 +1,110 @@
+"""Add knowledge_pages table (knowledge-base hierarchy).
+
+The knowledge base organizes synthesized mental models into a navigable tree of
+**folders** and **pages**. A page references the mental model that holds its
+content (``mental_model_id``); a folder is a pure container (``mental_model_id``
+NULL). Hierarchy is a single self-referential ``parent_id`` so folders can nest
+arbitrarily. Content stays in ``mental_models`` — this table is metadata + tree
+structure only.
+
+Revision ID: a9b8c7d6e5f4
+Revises: b57a7c9e0d13
+Create Date: 2026-06-25
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+from hindsight_api.alembic._dialect import run_for_dialect
+
+revision: str = "a9b8c7d6e5f4"
+down_revision: str | Sequence[str] | None = "b57a7c9e0d13"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _pg_schema_prefix() -> str:
+    """Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def _pg_upgrade() -> None:
+    schema = _pg_schema_prefix()
+    # parent_id self-FK cascades so deleting a folder row removes its whole
+    # subtree of rows in one shot. The mental_model FK is composite (matches the
+    # mental_models (id, bank_id) PK) and cascades too, so deleting a page's
+    # mental model removes the page row — folders skip the FK because a NULL
+    # column in a composite FK is not enforced (MATCH SIMPLE).
+    op.execute(
+        f"""
+        CREATE TABLE IF NOT EXISTS {schema}knowledge_pages (
+            id VARCHAR(64) NOT NULL,
+            bank_id TEXT NOT NULL,
+            parent_id VARCHAR(64),
+            kind VARCHAR(16) NOT NULL,
+            name TEXT NOT NULL,
+            mental_model_id VARCHAR(64),
+            sort_order INTEGER NOT NULL DEFAULT 0,
+            created_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            updated_at TIMESTAMP WITH TIME ZONE NOT NULL DEFAULT now(),
+            CONSTRAINT pk_knowledge_pages PRIMARY KEY (id),
+            CONSTRAINT ck_knowledge_pages_kind CHECK (kind IN ('folder', 'page')),
+            CONSTRAINT fk_kp_bank FOREIGN KEY (bank_id)
+                REFERENCES {schema}banks(bank_id) ON DELETE CASCADE,
+            CONSTRAINT fk_kp_parent FOREIGN KEY (parent_id)
+                REFERENCES {schema}knowledge_pages(id) ON DELETE CASCADE,
+            CONSTRAINT fk_kp_mm FOREIGN KEY (mental_model_id, bank_id)
+                REFERENCES {schema}mental_models(id, bank_id) ON DELETE CASCADE
+        )
+        """
+    )
+    op.execute(
+        f"CREATE INDEX IF NOT EXISTS idx_kp_bank_parent ON {schema}knowledge_pages (bank_id, parent_id, sort_order)"
+    )
+
+
+def _pg_downgrade() -> None:
+    schema = _pg_schema_prefix()
+    op.execute(f"DROP INDEX IF EXISTS {schema}idx_kp_bank_parent")
+    op.execute(f"DROP TABLE IF EXISTS {schema}knowledge_pages")
+
+
+def _oracle_upgrade() -> None:
+    op.execute(
+        """
+        CREATE TABLE IF NOT EXISTS knowledge_pages (
+            id VARCHAR2(64) NOT NULL,
+            bank_id VARCHAR2(256) NOT NULL,
+            parent_id VARCHAR2(64),
+            kind VARCHAR2(16) NOT NULL,
+            name CLOB NOT NULL,
+            mental_model_id VARCHAR2(64),
+            sort_order NUMBER DEFAULT 0 NOT NULL,
+            created_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
+            updated_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
+            CONSTRAINT pk_knowledge_pages PRIMARY KEY (id),
+            CONSTRAINT ck_knowledge_pages_kind CHECK (kind IN ('folder', 'page')),
+            CONSTRAINT fk_kp_bank FOREIGN KEY (bank_id)
+                REFERENCES banks(bank_id) ON DELETE CASCADE,
+            CONSTRAINT fk_kp_parent FOREIGN KEY (parent_id)
+                REFERENCES knowledge_pages(id) ON DELETE CASCADE,
+            CONSTRAINT fk_kp_mm FOREIGN KEY (mental_model_id, bank_id)
+                REFERENCES mental_models(id, bank_id) ON DELETE CASCADE
+        )
+        """
+    )
+    op.execute("CREATE INDEX idx_kp_bank_parent ON knowledge_pages (bank_id, parent_id, sort_order)")
+
+
+def _oracle_downgrade() -> None:
+    op.execute("DROP TABLE knowledge_pages CASCADE CONSTRAINTS")
+
+
+def upgrade() -> None:
+    run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
+
+
+def downgrade() -> None:
+    run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -1,259 +0,0 @@
-"""Install the maintenance discovery routines into the configured schema.
-
-The three discovery routines driving the background maintenance loop —
-``banks_needing_consolidation()``, ``schemas_with_expired_rows(...)`` and
-``mental_models_with_cron()`` — were installed into ``public`` and gated on the
-run being the base run (no ``target_schema``) or an explicit
-``target_schema='public'`` run (``e5f6a7b8c9d0`` → ``b2d4f6a8c1e3`` →
-``c7e9f1a3b5d2``, ``f4d1c2b3a5e6``).
-
-That leaves a **single-tenant deployment migrated into a dedicated, non-**
-``public`` **schema** (``HINDSIGHT_API_DATABASE_SCHEMA=<non-public>``) with no
-routines at all: the runtime migrates only that one schema, so ``target_schema``
-is never falsy or ``public``, the gate never opens, and the maintenance loop
-logs, forever::
-
-    function public.banks_needing_consolidation() does not exist
-    function public.schemas_with_expired_rows(...) does not exist
-
-The revision is stamped applied, so redeploying the same version does not help
-(issue #2638; #2056 only fixed the ``public``/base-run case).
-
-**The bug was the hardcoded literal, not the gating.** These routines are
-database-global — each enumerates ``pg_class`` across every schema and dispatches
-per schema — so exactly one copy should exist, and the maintenance loop calls the
-one in ``get_config().database_schema`` (see ``fq_routine``). The old gate
-installed into whichever schema was named ``public`` instead of whichever schema
-the deployment is actually configured to use. Comparing ``target_schema`` against
-the configured schema instead of the literal fixes #2638 at the source.
-
-That also keeps the property the gate existed for: exactly one migration run
-satisfies the predicate, so concurrent per-schema runs never issue competing
-``CREATE OR REPLACE`` against the same ``pg_proc`` row and cannot hit
-``tuple concurrently updated``. No cross-process coordination is required — in
-particular no advisory lock, which is unusable here because Hindsight runs behind
-connection poolers and managed PG services (see #2817).
-
-Runs targeting any *other* schema drop the routines from that schema rather than
-merely skipping. An earlier revision of this migration installed a copy into
-every schema it touched, which left one dead duplicate per tenant on any database
-that ran it; the drop makes the next migration pass clean those up instead of
-leaving them behind forever.
-
-PostgreSQL only: the maintenance loop and worker poller are PG-only, so the
-Oracle slot is intentionally absent (mirrors ``e5f6a7b8c9d0``).
-
-Revision ID: b6d2f8a4c1e7
-Revises: a8c1e4f7b0d3
-Create Date: 2026-07-20
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-from hindsight_api.alembic._dialect import run_for_dialect
-from hindsight_api.config import get_config
-
-revision: str = "b6d2f8a4c1e7"
-down_revision: str | Sequence[str] | None = "a8c1e4f7b0d3"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _configured_schema() -> str:
-    """The one schema this deployment's routines live in and are called from."""
-    return get_config().database_schema or "public"
-
-
-def _target_schema() -> str | None:
-    return context.config.get_main_option("target_schema")
-
-
-def _is_install_run() -> bool:
-    """True for the single run that owns the routines.
-
-    The base run (no ``target_schema``) and the run targeting the configured
-    schema are the same deployment-level run; every other target is a tenant
-    schema that must not carry its own copy.
-    """
-    target = _target_schema()
-    return not target or target == _configured_schema()
-
-
-def _prefix(schema: str | None) -> str:
-    """Qualifier for ``schema``, or ``""`` to fall back to ``search_path``."""
-    return f'"{schema}".' if schema else ""
-
-
-def _pg_upgrade() -> None:
-    if not _is_install_run():
-        _drop_stray_copies()
-        return
-    schema = _prefix(_target_schema())
-
-    op.execute(
-        f"""
-        CREATE OR REPLACE FUNCTION {schema}banks_needing_consolidation()
-        RETURNS TABLE(schema_name text, bank_id text)
-        LANGUAGE plpgsql STABLE
-        AS $fn$
-        DECLARE
-            sch text;
-        BEGIN
-            FOR sch IN
-                SELECT n.nspname
-                FROM pg_class c
-                JOIN pg_namespace n ON n.oid = c.relnamespace
-                WHERE c.relname = 'memory_units' AND c.relkind = 'r'
-            LOOP
-                BEGIN
-                    RETURN QUERY EXECUTE format($q$
-                        SELECT %1$L::text, m.bank_id
-                        FROM %1$I.memory_units m
-                        JOIN %1$I.banks b ON b.bank_id = m.bank_id
-                        WHERE m.consolidated_at IS NULL
-                          AND m.consolidation_failed_at IS NULL
-                          AND m.fact_type IN ('experience', 'world')
-                          AND COALESCE(b.config -> 'enable_auto_consolidation', 'true'::jsonb) <> 'false'::jsonb
-                          AND NOT EXISTS (
-                              SELECT 1 FROM %1$I.async_operations o
-                              WHERE o.bank_id = m.bank_id
-                                AND o.operation_type = 'consolidation'
-                                AND o.status IN ('pending', 'processing')
-                          )
-                        GROUP BY m.bank_id
-                    $q$, sch);
-                EXCEPTION
-                    -- Schema or its tables vanished between the pg_class
-                    -- snapshot and this query (tenant dropped or migrating).
-                    WHEN undefined_table OR invalid_schema_name OR undefined_column THEN
-                        CONTINUE;
-                END;
-            END LOOP;
-        END;
-        $fn$;
-        """
-    )
-
-    op.execute(
-        f"""
-        CREATE OR REPLACE FUNCTION {schema}schemas_with_expired_rows(
-            p_table text, p_ts_col text, p_days int
-        )
-        RETURNS SETOF text
-        LANGUAGE plpgsql STABLE
-        AS $fn$
-        DECLARE
-            sch text;
-            has_expired boolean;
-        BEGIN
-            IF p_days IS NULL OR p_days <= 0 THEN
-                RETURN;
-            END IF;
-            FOR sch IN
-                SELECT n.nspname
-                FROM pg_class c
-                JOIN pg_namespace n ON n.oid = c.relnamespace
-                WHERE c.relname = p_table AND c.relkind = 'r'
-            LOOP
-                BEGIN
-                    EXECUTE format(
-                        'SELECT EXISTS (SELECT 1 FROM %I.%I WHERE %I < NOW() - make_interval(days => $1))',
-                        sch, p_table, p_ts_col
-                    ) INTO has_expired USING p_days;
-                EXCEPTION
-                    -- Schema or its table vanished mid-scan; skip it.
-                    WHEN undefined_table OR invalid_schema_name OR undefined_column THEN
-                        CONTINUE;
-                END;
-                IF has_expired THEN
-                    RETURN NEXT sch;
-                END IF;
-            END LOOP;
-        END;
-        $fn$;
-        """
-    )
-
-    op.execute(
-        f"""
-        CREATE OR REPLACE FUNCTION {schema}mental_models_with_cron()
-        RETURNS TABLE(schema_name text, bank_id text, mental_model_id text,
-                     refresh_cron text, last_refreshed_at timestamptz)
-        LANGUAGE plpgsql STABLE
-        AS $fn$
-        DECLARE
-            sch text;
-        BEGIN
-            FOR sch IN
-                SELECT n.nspname
-                FROM pg_class c
-                JOIN pg_namespace n ON n.oid = c.relnamespace
-                WHERE c.relname = 'mental_models' AND c.relkind = 'r'
-            LOOP
-                BEGIN
-                    RETURN QUERY EXECUTE format($q$
-                        SELECT %1$L::text, mm.bank_id::text, mm.id::text,
-                               mm.trigger->>'refresh_cron', mm.last_refreshed_at
-                        FROM %1$I.mental_models mm
-                        WHERE COALESCE(mm.trigger->>'refresh_cron', '') <> ''
-                          AND NOT EXISTS (
-                              SELECT 1 FROM %1$I.async_operations o
-                              WHERE o.bank_id = mm.bank_id
-                                AND o.operation_type = 'refresh_mental_model'
-                                AND o.status IN ('pending', 'processing')
-                                AND o.task_payload->>'mental_model_id' = mm.id::text
-                          )
-                    $q$, sch);
-                EXCEPTION
-                    -- Schema or its tables vanished between the pg_class
-                    -- snapshot and this query (tenant dropped or migrating).
-                    WHEN undefined_table OR invalid_schema_name OR undefined_column THEN
-                        CONTINUE;
-                END;
-            END LOOP;
-        END;
-        $fn$;
-        """
-    )
-
-
-def _drop_routines(schema: str | None) -> None:
-    prefix = _prefix(schema)
-    op.execute(f"DROP FUNCTION IF EXISTS {prefix}mental_models_with_cron()")
-    op.execute(f"DROP FUNCTION IF EXISTS {prefix}schemas_with_expired_rows(text, text, int)")
-    op.execute(f"DROP FUNCTION IF EXISTS {prefix}banks_needing_consolidation()")
-
-
-def _drop_stray_copies() -> None:
-    """Remove per-tenant duplicates left by the first cut of this migration.
-
-    That version installed a copy into every schema it touched, so a database
-    that ran it carries one dead duplicate per tenant — only the copy in the
-    configured schema is ever called. Dropping here means the next migration pass
-    cleans them up; without it they would persist for the life of the database.
-
-    Safe on a database that never had them: ``DROP FUNCTION IF EXISTS`` is a
-    no-op, and this branch never runs for the configured schema.
-    """
-    _drop_routines(_target_schema())
-
-
-def _pg_downgrade() -> None:
-    # Only drop what this migration uniquely owns. When the configured schema is
-    # ``public`` the copies there belong to e5f6a7b8c9d0 / f4d1c2b3a5e6, which are
-    # still applied at this point and drop them on their own downgrade — removing
-    # them here would strand those migrations without the functions they claim to
-    # have installed.
-    if not _is_install_run() or _configured_schema() == "public":
-        return
-    _drop_routines(_target_schema())
-
-
-def upgrade() -> None:
-    run_for_dialect(pg=_pg_upgrade)
-
-
-def downgrade() -> None:
-    run_for_dialect(pg=_pg_downgrade)
@@ -0,0 +1,71 @@
+"""Unique page name per folder in knowledge_pages.
+
+The folder curator can fire concurrently (folder-create trigger + the
+post-consolidation sweep), and an in-process lock can't serialize runs that
+execute in different threads/loops. A partial unique index on
+(bank_id, parent, lower(name)) for pages makes duplicate-named pages in the same
+folder impossible at the DB level — the second concurrent insert fails and the
+curator treats it as "already exists".
+
+PostgreSQL only: the Oracle ``name`` column is a CLOB and cannot back a
+functional unique index; Oracle relies on the in-process serialization instead.
+
+Revision ID: c3d4e5f6a7b8
+Revises: a5b6c7d8e9f0
+Create Date: 2026-06-26
+"""
+
+from collections.abc import Sequence
+
+from alembic import context, op
+
+from hindsight_api.alembic._dialect import run_for_dialect
+
+revision: str = "c3d4e5f6a7b8"
+down_revision: str | Sequence[str] | None = "a5b6c7d8e9f0"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def _pg_schema_prefix() -> str:
+    schema = context.config.get_main_option("target_schema")
+    return f'"{schema}".' if schema else ""
+
+
+def _pg_upgrade() -> None:
+    schema = _pg_schema_prefix()
+    # First drop any pre-existing duplicate pages (created by the racy curator
+    # before this guard existed), keeping the earliest row of each duplicate set,
+    # so the unique index can be built. Their backing mental models are left in
+    # place (harmless orphans).
+    op.execute(
+        f"""
+        DELETE FROM {schema}knowledge_pages a
+        USING {schema}knowledge_pages b
+        WHERE a.kind = 'page' AND b.kind = 'page'
+          AND a.bank_id = b.bank_id
+          AND COALESCE(a.parent_id, '') = COALESCE(b.parent_id, '')
+          AND lower(a.name) = lower(b.name)
+          AND a.ctid > b.ctid
+        """
+    )
+    # COALESCE(parent_id, '') so root-level pages (NULL parent) are also unique by
+    # name — NULLs would otherwise compare distinct and allow duplicates.
+    op.execute(
+        "CREATE UNIQUE INDEX IF NOT EXISTS uq_kp_folder_pagename "
+        f"ON {schema}knowledge_pages (bank_id, COALESCE(parent_id, ''), lower(name)) "
+        "WHERE kind = 'page'"
+    )
+
+
+def _pg_downgrade() -> None:
+    schema = _pg_schema_prefix()
+    op.execute(f"DROP INDEX IF EXISTS {schema}uq_kp_folder_pagename")
+
+
+def upgrade() -> None:
+    run_for_dialect(pg=_pg_upgrade)  # oracle slot intentionally absent (CLOB name)
+
+
+def downgrade() -> None:
+    run_for_dialect(pg=_pg_downgrade)
@@ -1,150 +0,0 @@
-"""Add the ``schemas_with_expired_operations`` cross-tenant discovery routine.
-
-The worker's terminal-operation cleanup (``a8c1e4f7b0d3``) opens a connection
-and a prune transaction against *every* tenant schema on every cleanup cycle,
-whether or not that tenant has anything to prune. At thousands of tenants that
-is a per-cycle query storm whose cost is paid entirely by idle schemas.
-
-This is the same problem ``public.schemas_with_expired_rows`` already solves for
-the ``audit_log`` / ``llm_requests`` retention sweeps (``e5f6a7b8c9d0``): one
-round-trip returns just the schemas that actually hold expired rows, and the
-caller then does real work only there. ``async_operations`` needs its own
-routine rather than reusing that one because eligibility is not "row older than
-N days" — pending and processing rows are never prunable, so the status filter
-has to be part of the predicate.
-
-Install policy mirrors ``b6d2f8a4c1e7`` (#2638/#2824), the current behaviour for
-the sibling routines: the routine is database-global — it enumerates ``pg_class``
-across every schema and dispatches per schema — so exactly one copy should exist,
-installed into the schema this deployment is *configured* to use and called from
-there via ``fq_routine``. Gating on the literal ``"public"`` instead of the
-configured schema is what left single-tenant deployments in a dedicated
-non-``public`` schema without the routine (#2638).
-
-Exactly one migration run satisfies that predicate, so concurrent per-schema runs
-never issue competing ``CREATE OR REPLACE`` against the same ``pg_proc`` row and
-cannot hit ``tuple concurrently updated``. No cross-process coordination is
-required — in particular no advisory lock, which is unusable here because
-Hindsight runs behind connection poolers and managed PG services (see #2817).
-
-Each per-schema probe runs in its own ``BEGIN ... EXCEPTION`` block so a tenant
-dropped mid-scan is skipped instead of aborting the sweep (see ``c7e9f1a3b5d2``).
-
-Revision ID: d7b2f8a1c934
-Revises: b6d2f8a4c1e7
-Create Date: 2026-07-20
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-from hindsight_api.alembic._dialect import run_for_dialect
-from hindsight_api.config import get_config
-
-revision: str = "d7b2f8a1c934"
-down_revision: str | Sequence[str] | None = "b6d2f8a4c1e7"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _configured_schema() -> str:
-    """The one schema this deployment's routines live in and are called from."""
-    return get_config().database_schema or "public"
-
-
-def _target_schema() -> str | None:
-    return context.config.get_main_option("target_schema")
-
-
-def _is_install_run() -> bool:
-    """True for the single run that owns the routine (mirrors b6d2f8a4c1e7)."""
-    target = _target_schema()
-    return not target or target == _configured_schema()
-
-
-def _prefix(schema: str | None) -> str:
-    """Qualifier for ``schema``, or ``""`` to fall back to ``search_path``."""
-    return f'"{schema}".' if schema else ""
-
-
-def _drop_routine(schema: str | None) -> None:
-    op.execute(f"DROP FUNCTION IF EXISTS {_prefix(schema)}schemas_with_expired_operations(int)")
-
-
-def _pg_upgrade() -> None:
-    if not _is_install_run():
-        # Tenant schemas must not carry their own copy: the routine is
-        # database-global and only the configured schema's copy is ever called.
-        # Dropping (rather than skipping) also cleans up after any interim build
-        # of this branch that installed per-schema copies.
-        _drop_routine(_target_schema())
-        return
-    schema = _prefix(_target_schema())
-    op.execute(
-        f"""
-        CREATE OR REPLACE FUNCTION {schema}schemas_with_expired_operations(p_days int)
-        RETURNS SETOF text
-        LANGUAGE plpgsql STABLE
-        AS $fn$
-        DECLARE
-            sch text;
-            has_expired boolean;
-        BEGIN
-            -- Zero (or negative) retention means "keep forever": report nothing
-            -- so the caller skips the sweep entirely.
-            IF p_days IS NULL OR p_days <= 0 THEN
-                RETURN;
-            END IF;
-            FOR sch IN
-                SELECT n.nspname
-                FROM pg_class c
-                JOIN pg_namespace n ON n.oid = c.relnamespace
-                WHERE c.relname = 'async_operations' AND c.relkind = 'r'
-            LOOP
-                BEGIN
-                    -- Matches the worker's prune predicate: only terminal rows
-                    -- are eligible, so a schema holding nothing but pending or
-                    -- processing work is correctly reported as having nothing
-                    -- to prune. Uses idx_async_operations_terminal_cleanup.
-                    EXECUTE format(
-                        'SELECT EXISTS ('
-                        '  SELECT 1 FROM %I.async_operations'
-                        '  WHERE status IN (''completed'', ''failed'', ''cancelled'')'
-                        '    AND updated_at < NOW() - make_interval(days => $1)'
-                        ')',
-                        sch
-                    ) INTO has_expired USING p_days;
-                EXCEPTION
-                    -- Schema or its table vanished between the pg_class
-                    -- snapshot and this probe (tenant dropped or migrating).
-                    WHEN undefined_table OR invalid_schema_name OR undefined_column THEN
-                        CONTINUE;
-                END;
-                IF has_expired THEN
-                    RETURN NEXT sch;
-                END IF;
-            END LOOP;
-        END;
-        $fn$;
-        """
-    )
-
-
-def _pg_downgrade() -> None:
-    # This migration is the sole creator of this routine — no older migration
-    # owns a copy the way e5f6a7b8c9d0 owns the public sibling routines — so the
-    # install run's own copy is always ours to drop.
-    if not _is_install_run():
-        return
-    _drop_routine(_target_schema())
-
-
-def upgrade() -> None:
-    # Oracle slot intentionally absent: this mirrors the PostgreSQL-only
-    # maintenance routines, and the Oracle worker keeps its per-schema sweep.
-    run_for_dialect(pg=_pg_upgrade)
-
-
-def downgrade() -> None:
-    run_for_dialect(pg=_pg_downgrade)
@@ -1,96 +0,0 @@
-"""Drop the search_vector column from the curation archive (invalidated_memory_units).
-
-The archive is cold storage, never a recall surface, and carries no text-search
-index. Like ``embedding`` (dropped in d4f6a8c2e1b3), ``search_vector`` is a
-recall-surface column whose type follows the configured text-search backend, so
-it has no business living on the archive. Earlier curation code copied the live
-row's ``search_vector`` into ``invalidated_memory_units`` on invalidate; the
-engine now leaves it out on invalidate and recomputes it on revert, so the
-column is dead weight.
-
-Dropping it removes a latent failure mode (#2503): under a non-native backend
-(pgroonga / pg_textsearch / pg_search / vchord) ``ensure_text_search_extension``
-reconciles ``memory_units.search_vector`` to ``text`` / ``bm25vector`` but never
-touched the archive, which the ``LIKE memory_units`` clone (c9a1b2d3e4f5) created
-as ``tsvector``. The type mismatch then broke the curation INSERT … SELECT
-round-trip:
-
-    column "search_vector" is of type tsvector but expression is of type text
-
-With no column at all, there is nothing to mismatch. Unlike ``embedding`` (whose
-creation sites already omit it), the ``LIKE`` clone still adds ``search_vector``,
-so this migration does real work on both fresh and existing PostgreSQL databases.
-
-DROP COLUMN is a metadata-only operation on both PostgreSQL and Oracle 23ai (no
-table rewrite), so it is cheap even across many tenant schemas. The downgrade
-re-adds an empty ``tsvector`` column (its original creation type).
-
-Revision ID: e7c3a9f1b2d5
-Revises: b57a7c9e0d13
-Create Date: 2026-07-02
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-from hindsight_api.alembic._dialect import run_for_dialect
-
-revision: str = "e7c3a9f1b2d5"
-down_revision: str | Sequence[str] | None = "b57a7c9e0d13"
-branch_labels: str | Sequence[str] | None = None
-depends_on: str | Sequence[str] | None = None
-
-
-def _pg_schema_prefix() -> str:
-    """Schema-qualifier for raw SQL on PG (multi-tenant search_path)."""
-    schema = context.config.get_main_option("target_schema")
-    return f'"{schema}".' if schema else ""
-
-
-def _pg_upgrade() -> None:
-    schema = _pg_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}invalidated_memory_units DROP COLUMN IF EXISTS search_vector")
-
-
-def _pg_downgrade() -> None:
-    schema = _pg_schema_prefix()
-    # Re-add as the original tsvector creation type; comes back empty regardless.
-    op.execute(f"ALTER TABLE {schema}invalidated_memory_units ADD COLUMN IF NOT EXISTS search_vector tsvector")
-
-
-def _oracle_upgrade() -> None:
-    # Oracle has no `DROP COLUMN IF EXISTS`; swallow ORA-00904 (column does not
-    # exist) so the migration is idempotent and safe on a schema whose baseline
-    # may already omit the column.
-    op.execute(
-        """
-        BEGIN
-            EXECUTE IMMEDIATE 'ALTER TABLE invalidated_memory_units DROP COLUMN search_vector';
-        EXCEPTION WHEN OTHERS THEN
-            IF SQLCODE != -904 THEN RAISE; END IF;
-        END;
-        """
-    )
-
-
-def _oracle_downgrade() -> None:
-    # Swallow ORA-01430 (column already exists) for idempotency. Oracle stores
-    # search_vector as CLOB (see the Oracle baseline), so re-add it as CLOB.
-    op.execute(
-        """
-        BEGIN
-            EXECUTE IMMEDIATE 'ALTER TABLE invalidated_memory_units ADD (search_vector CLOB)';
-        EXCEPTION WHEN OTHERS THEN
-            IF SQLCODE != -1430 THEN RAISE; END IF;
-        END;
-        """
-    )
-
-
-def upgrade() -> None:
-    run_for_dialect(pg=_pg_upgrade, oracle=_oracle_upgrade)
-
-
-def downgrade() -> None:
-    run_for_dialect(pg=_pg_downgrade, oracle=_oracle_downgrade)
@@ -18,6 +18,7 @@ from typing import Any, Literal, TypeVar
 from fastapi import Depends, FastAPI, File, Form, Header, HTTPException, Query, Request, UploadFile
 from fastapi.middleware.gzip import GZipMiddleware

+from hindsight_api.api import okf
 from hindsight_api.api.disconnect import ClientDisconnectCancellationMiddleware, get_scope_cancellation_token
 from hindsight_api.cancellation import OperationCancelledError
 from hindsight_api.engine.audit import (
@@ -52,7 +53,6 @@ from fastapi.routing import APIRoute
 from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator

 from hindsight_api import MemoryEngine
-from hindsight_api.config import RETAIN_EXTRACTION_MODES


 def _annotation_is_nullable(annotation: Any) -> bool:
@@ -1246,7 +1246,7 @@ class CreateBankRequest(BaseModel):
    )
    retain_extraction_mode: str | None = Field(
        default=None,
-        description="Fact extraction mode: 'concise' (default), 'verbose', 'custom', 'verbatim', or 'chunks'.",
+        description="Fact extraction mode: 'concise' (default), 'verbose', or 'custom'.",
    )
    retain_custom_instructions: str | None = Field(
        default=None,
@@ -1434,7 +1434,6 @@ class ListMemoryUnitsResponse(BaseModel):
                        "date": "2024-01-15T10:30:00Z",
                        "type": "world",
                        "entities": "Alice (PERSON), Google (ORGANIZATION)",
-                        "metadata": {"source": "slack", "channel": "engineering"},
                    }
                ],
                "total": 150,
@@ -1668,8 +1667,8 @@ class UpdateMemoryRequest(BaseModel):

    @model_validator(mode="after")
    def _require_an_edit(self) -> "UpdateMemoryRequest":
-        has_value_edit = any(
-            v is not None
+        if all(
+            v is None
            for v in (
                self.text,
                self.context,
@@ -1679,9 +1678,7 @@ class UpdateMemoryRequest(BaseModel):
                self.entities,
                self.state,
            )
-        )
-        has_date_clear = bool({"occurred_start", "occurred_end"} & self.model_fields_set)
-        if not has_value_edit and not has_date_clear:
+        ):
            raise ValueError("Provide at least one field to update.")
        if self.state is not None and self.state not in ("valid", "invalidated"):
            raise ValueError("state must be 'valid' or 'invalidated'.")
@@ -2114,6 +2111,150 @@ class MentalModelListResponse(BaseModel):
    items: list[MentalModelResponse]


+# =========================================================================
+# KNOWLEDGE BASE (folders + pages over mental models, projected to OKF)
+# =========================================================================
+
+
+class KnowledgeNode(BaseModel):
+    """A node in the knowledge-base tree — a folder or a page.
+
+    Pages carry ``description``/``tags`` from their backing mental model. The
+    knowledge base is client-managed (CRUD); ``managed`` lets a client tag a node
+    as system-owned vs. hand-authored.
+    """
+
+    id: str
+    kind: Literal["folder", "page"]
+    name: str
+    parent_id: str | None = None
+    mental_model_id: str | None = Field(default=None, description="Backing mental model id (pages only).")
+    managed: bool = Field(default=False, description="Client-set flag: true = system-owned, false = hand-authored.")
+    description: str | None = Field(default=None, description="Page source query (OKF `description`).")
+    tags: list[str] = FieldWithDefault(list)
+    timestamp: str | None = Field(default=None, description="Last refresh (page) or last update (folder).")
+    children: list["KnowledgeNode"] = FieldWithDefault(list)
+
+
+class KnowledgeTreeResponse(BaseModel):
+    """The knowledge base as a nested folder/page tree."""
+
+    roots: list[KnowledgeNode]
+
+
+class CreateFolderRequest(BaseModel):
+    """Create a folder under an optional parent folder."""
+
+    name: str
+    parent_id: str | None = None
+
+
+class CreatePageRequest(BaseModel):
+    """Create a page (a mental model + tree node) under an optional parent folder."""
+
+    name: str
+    source_query: str
+    parent_id: str | None = None
+    tags: list[str] | None = None
+    max_tokens: int | None = None
+    trigger: MentalModelTrigger | None = None
+
+
+class UpdateNodeRequest(BaseModel):
+    """Rename and/or move a node. Each field applies only when present."""
+
+    name: str | None = None
+    parent_id: str | None = None
+
+
+class CreateKnowledgePageResponse(BaseModel):
+    """Result of creating a page: the node id, its mental model, and the refresh op."""
+
+    page_id: str
+    mental_model_id: str
+    operation_id: str | None = None
+
+
+class KnowledgePageResponse(BaseModel):
+    """A knowledge page rendered as an OKF document."""
+
+    id: str
+    name: str
+    type: str = Field(description="OKF document type — from a `type:<x>` tag, else 'knowledge-page'.")
+    description: str | None = Field(default=None, description="The source query that rebuilds the page.")
+    tags: list[str] = FieldWithDefault(list)
+    timestamp: str | None = Field(default=None, description="Last refresh time (falls back to creation).")
+    body: str | None = Field(default=None, description="The page's synthesized markdown body.")
+    markdown: str = Field(description="The full OKF document: YAML frontmatter + markdown body.")
+
+
+class KnowledgePageGraphResponse(BaseModel):
+    """Constellation graph of knowledge pages linked by shared tags."""
+
+    nodes: list[dict[str, Any]]
+    edges: list[dict[str, Any]]
+    total_pages: int
+    total_edges: int
+
+
+class KnowledgePageBundleFile(BaseModel):
+    """One file in a portable OKF bundle."""
+
+    path: str
+    content: str
+
+
+class KnowledgePageBundleResponse(BaseModel):
+    """A portable OKF bundle — a flat set of markdown files (index + pages + logs)."""
+
+    files: list[KnowledgePageBundleFile]
+
+
+def _knowledge_node_model(node: dict[str, Any]) -> KnowledgeNode:
+    """Project an engine node dict into a (childless) KnowledgeNode."""
+    is_page = node.get("kind") == "page"
+    return KnowledgeNode(
+        id=node["id"],
+        kind=node["kind"],
+        name=node["name"],
+        parent_id=node.get("parent_id"),
+        mental_model_id=node.get("mental_model_id"),
+        managed=bool(node.get("managed")),
+        description=node.get("source_query") if is_page else None,
+        tags=list(node.get("tags") or []) if is_page else [],
+        timestamp=(node.get("last_refreshed_at") if is_page else node.get("updated_at")),
+    )
+
+
+def _build_knowledge_tree(nodes: list[dict[str, Any]]) -> list[KnowledgeNode]:
+    """Assemble the flat node list into a nested tree of roots."""
+    models = {n["id"]: _knowledge_node_model(n) for n in nodes}
+    roots: list[KnowledgeNode] = []
+    for node in nodes:
+        model = models[node["id"]]
+        parent_id = node.get("parent_id")
+        if parent_id and parent_id in models:
+            models[parent_id].children.append(model)
+        else:
+            roots.append(model)
+    return roots
+
+
+def _knowledge_page_response(node: dict[str, Any]) -> KnowledgePageResponse:
+    """Project a page node (with merged mental-model content) into an OKF document."""
+    page = okf.page_type(node.get("tags"))
+    return KnowledgePageResponse(
+        id=node["id"],
+        name=node["name"],
+        type=page.type,
+        description=node.get("source_query"),
+        tags=page.display_tags,
+        timestamp=node.get("last_refreshed_at") or node.get("created_at"),
+        body=node.get("content"),
+        markdown=okf.render_document(node),
+    )
+
+
 class CreateMentalModelRequest(BaseModel):
    """Request model for creating a mental model."""

@@ -2207,8 +2348,7 @@ class BankTemplateConfig(BaseModel):
    reflect_mission: str | None = Field(default=None, description="Mission/context for Reflect operations")
    retain_mission: str | None = Field(default=None, description="Steers what gets extracted during retain")
    retain_extraction_mode: str | None = Field(
-        default=None,
-        description="Fact extraction mode: 'concise' (default), 'verbose', 'custom', 'verbatim', or 'chunks'",
+        default=None, description="Fact extraction mode: 'concise' (default), 'verbose', or 'custom'"
    )
    retain_custom_instructions: str | None = Field(
        default=None, description="Custom extraction prompt (when mode='custom')"
@@ -2434,10 +2574,10 @@ def validate_bank_template(manifest: "BankTemplateManifest") -> list[str]:
    if manifest.bank:
        bank = manifest.bank
        if bank.retain_extraction_mode is not None:
-            if bank.retain_extraction_mode not in RETAIN_EXTRACTION_MODES:
+            valid_modes = ("concise", "verbose", "custom", "chunks")
+            if bank.retain_extraction_mode not in valid_modes:
                errors.append(
-                    "bank.retain_extraction_mode: "
-                    f"must be one of {RETAIN_EXTRACTION_MODES}, got '{bank.retain_extraction_mode}'"
+                    f"bank.retain_extraction_mode: must be one of {valid_modes}, got '{bank.retain_extraction_mode}'"
                )
        if bank.retain_custom_instructions and bank.retain_extraction_mode != "custom":
            errors.append("bank.retain_custom_instructions: requires retain_extraction_mode='custom'")
@@ -2730,24 +2870,6 @@ class RetryOperationResponse(BaseModel):
    operation_id: str


-class DeleteOperationResponse(BaseModel):
-    """Response model for delete operation endpoint."""
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "example": {
-                "success": True,
-                "message": "Operation 550e8400-e29b-41d4-a716-446655440000 deleted",
-                "operation_id": "550e8400-e29b-41d4-a716-446655440000",
-            }
-        }
-    )
-
-    success: bool
-    message: str
-    operation_id: str
-
-
 class ChildOperationStatus(BaseModel):
    """Status of a child operation (for batch operations)."""

@@ -2839,7 +2961,7 @@ class FeaturesInfo(BaseModel):
    file_upload_api: bool = Field(description="Whether file upload/conversion API is enabled")
    document_export_api: bool = Field(description="Whether the document export endpoint is enabled")
    document_import_api: bool = Field(description="Whether the document import endpoint is enabled")
-    audit_log: bool = Field(description="Whether audit logging is enabled by default (overridable per bank)")
+    audit_log: bool = Field(description="Whether audit logging is enabled")
    llm_trace: bool = Field(description="Whether per-bank LLM request tracing is enabled")
    store_document_text: bool = Field(
        description="Whether raw source text is persisted. When false, document/chunk source text is not stored."
@@ -3009,15 +3131,10 @@ def _make_audited_http(audit_logger_getter: Callable[[], AuditLogger | None]):
            @wraps(func)
            async def wrapper(*args, **kwargs):
                al = audit_logger_getter()
-                # Cheap bank-independent pre-filter first, then the per-bank
-                # decision (audit_log_enabled is overridable per bank).
-                if al is None or not al.action_allowed(action):
+                if al is None or not al.is_enabled(action):
                    return await func(*args, **kwargs)

                bank_id = kwargs.get("bank_id")
-                if not await al.should_log(action, bank_id, kwargs.get("request_context")):
-                    return await func(*args, **kwargs)
-
                started_at = _dt.now(_tz.utc)

                req_data = None
@@ -3514,8 +3631,8 @@ def _register_routes(app: FastAPI):
        Returns version info and feature flags that can be used by clients
        to determine which capabilities are available.

-        Note: the observations and audit_log flags show the global default.
-        Individual banks may override these via bank-specific configuration.
+        Note: observations flag shows the global default. Individual banks
+        may override this setting via bank-specific configuration.
        """
        from hindsight_api import __version__
        from hindsight_api.config import _get_raw_config
@@ -3611,8 +3728,6 @@ def _register_routes(app: FastAPI):
        consolidation_state: str | None = None,
        state: str | None = None,
        document_id: str | None = None,
-        tags: list[str] | None = Query(default=None),
-        tags_match: TagsMatch = Query(default="any"),
        limit: int = Query(default=100, ge=0),
        offset: int = Query(default=0, ge=0),
        request_context: RequestContext = Depends(get_request_context),
@@ -3629,10 +3744,6 @@ def _register_routes(app: FastAPI):
            q: Search query for full-text search (searches text and context)
            consolidation_state: Filter by consolidation state for source memories
                (world/experience). One of 'failed', 'pending', or 'done'.
-            tags: Optional list of tag names to filter by.
-            tags_match: How to combine tags: 'any' (OR, default) or 'all' (AND) both
-                also include untagged memories; 'any_strict'/'all_strict' exclude
-                untagged; 'exact' matches the tag set exactly.
            limit: Maximum number of results (default: 100)
            offset: Offset for pagination (default: 0)
        """
@@ -3644,8 +3755,6 @@ def _register_routes(app: FastAPI):
                consolidation_state=consolidation_state,
                state=state,
                document_id=document_id,
-                tags=tags,
-                tags_match=tags_match,
                limit=limit,
                offset=offset,
                request_context=request_context,
@@ -3778,7 +3887,6 @@ def _register_routes(app: FastAPI):
        operation_id="update_memory",
        tags=["Memory"],
    )
-    @audited("update_memory")
    async def api_update_memory(
        bank_id: str,
        memory_id: str,
@@ -3787,23 +3895,13 @@ def _register_routes(app: FastAPI):
    ):
        """Curate a single memory unit (edit text / invalidate / revert)."""
        try:
-            occurred_start = (
-                ""
-                if "occurred_start" in request.model_fields_set and request.occurred_start is None
-                else request.occurred_start
-            )
-            occurred_end = (
-                ""
-                if "occurred_end" in request.model_fields_set and request.occurred_end is None
-                else request.occurred_end
-            )
            data = await app.state.memory.update_memory_unit(
                bank_id=bank_id,
                memory_id=memory_id,
                text=request.text,
                context=request.context,
-                occurred_start=occurred_start,
-                occurred_end=occurred_end,
+                occurred_start=request.occurred_start,
+                occurred_end=request.occurred_end,
                new_fact_type=request.fact_type,
                entities=request.entities,
                state=request.state,
@@ -4828,6 +4926,333 @@ def _register_routes(app: FastAPI):
            logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/mental-models/{mental_model_id}: {error_detail}")
            raise HTTPException(status_code=500, detail=str(e))

+    # =========================================================================
+    # KNOWLEDGE BASE ENDPOINTS (folders + pages, Open Knowledge Format)
+    # =========================================================================
+    # A hierarchy of folders and pages over mental models. Pages project to OKF
+    # documents (markdown body + YAML frontmatter); see api/okf.py. The static
+    # sub-paths (/tree, /folders, /pages, /graph, /export) are declared before
+    # the /pages/{id} and /nodes/{id} path-parameter routes so they win.
+
+    @app.get(
+        "/v1/default/banks/{bank_id}/knowledge-base/tree",
+        response_model=KnowledgeTreeResponse,
+        summary="Get the knowledge-base tree",
+        description="Return the knowledge base as a nested tree of folders and pages.",
+        operation_id="get_knowledge_base_tree",
+        tags=["Knowledge Base"],
+    )
+    async def api_knowledge_base_tree(
+        bank_id: str,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Return the folder/page tree for a bank."""
+        try:
+            nodes = await app.state.memory.list_knowledge_nodes(bank_id=bank_id, request_context=request_context)
+            return KnowledgeTreeResponse(roots=_build_knowledge_tree(nodes))
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in GET /v1/default/banks/{bank_id}/knowledge-base/tree: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post(
+        "/v1/default/banks/{bank_id}/knowledge-base/folders",
+        response_model=KnowledgeNode,
+        status_code=201,
+        summary="Create a knowledge-base folder",
+        description="Create a folder, optionally nested under a parent folder.",
+        operation_id="create_knowledge_folder",
+        tags=["Knowledge Base"],
+    )
+    async def api_create_knowledge_folder(
+        bank_id: str,
+        body: CreateFolderRequest,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Create a folder node."""
+        try:
+            node = await app.state.memory.create_knowledge_folder(
+                bank_id=bank_id,
+                name=body.name,
+                parent_id=body.parent_id,
+                request_context=request_context,
+            )
+            return _knowledge_node_model(node)
+        except ValueError as e:
+            raise HTTPException(status_code=400, detail=str(e))
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in POST /v1/default/banks/{bank_id}/knowledge-base/folders: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.post(
+        "/v1/default/banks/{bank_id}/knowledge-base/pages",
+        response_model=CreateKnowledgePageResponse,
+        status_code=201,
+        summary="Create a knowledge-base page",
+        description="Create a page (a mental model + tree node). Content is generated asynchronously; "
+        "use the returned operation_id to track completion.",
+        operation_id="create_knowledge_page",
+        tags=["Knowledge Base"],
+    )
+    async def api_create_knowledge_page(
+        bank_id: str,
+        body: CreatePageRequest,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Create a page node (async content generation)."""
+        try:
+            node = await app.state.memory.create_knowledge_page(
+                bank_id=bank_id,
+                name=body.name,
+                source_query=body.source_query,
+                content="Generating content...",
+                parent_id=body.parent_id,
+                tags=body.tags if body.tags else None,
+                max_tokens=body.max_tokens,
+                trigger=body.trigger.model_dump() if body.trigger else None,
+                request_context=request_context,
+            )
+            if node is None:
+                raise HTTPException(status_code=409, detail=f"A page named '{body.name}' already exists in this folder")
+            result = await app.state.memory.submit_async_refresh_mental_model(
+                bank_id=bank_id,
+                mental_model_id=node["mental_model_id"],
+                request_context=request_context,
+            )
+            return CreateKnowledgePageResponse(
+                page_id=node["id"],
+                mental_model_id=node["mental_model_id"],
+                operation_id=result["operation_id"],
+            )
+        except ValueError as e:
+            raise HTTPException(status_code=400, detail=str(e))
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in POST /v1/default/banks/{bank_id}/knowledge-base/pages: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.get(
+        "/v1/default/banks/{bank_id}/knowledge-base/graph",
+        response_model=KnowledgePageGraphResponse,
+        summary="Knowledge-base constellation graph",
+        description="Return pages as nodes linked by shared tags, for the constellation view.",
+        operation_id="get_knowledge_base_graph",
+        tags=["Knowledge Base"],
+    )
+    async def api_knowledge_base_graph(
+        bank_id: str,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Return the shared-tag constellation graph for a bank's pages."""
+        try:
+            nodes = await app.state.memory.list_knowledge_nodes(bank_id=bank_id, request_context=request_context)
+            pages = [n for n in nodes if n.get("kind") == "page"]
+            # Cluster the constellation by parent folder (the knowledge base's own
+            # structure) rather than by the retired type: tag.
+            folder_names = {n["id"]: n["name"] for n in nodes if n.get("kind") == "folder"}
+            graph = okf.knowledge_graph(pages, cluster_for=lambda p: folder_names.get(p.get("parent_id"), "Ungrouped"))
+            return KnowledgePageGraphResponse(
+                nodes=graph.nodes,
+                edges=graph.edges,
+                total_pages=len(graph.nodes),
+                total_edges=len(graph.edges),
+            )
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in GET /v1/default/banks/{bank_id}/knowledge-base/graph: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.get(
+        "/v1/default/banks/{bank_id}/knowledge-base/export",
+        response_model=KnowledgePageBundleResponse,
+        summary="Export the knowledge base as an OKF bundle",
+        description="Return a portable OKF bundle: a nested index.md, one <id>.md per page, and history logs.",
+        operation_id="export_knowledge_base",
+        tags=["Knowledge Base"],
+    )
+    async def api_export_knowledge_base(
+        bank_id: str,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Export a bank's knowledge base as a flat OKF markdown bundle."""
+        try:
+            nodes = await app.state.memory.list_knowledge_nodes(bank_id=bank_id, request_context=request_context)
+            files = [KnowledgePageBundleFile(path=okf.INDEX_FILENAME, content=okf.render_index(nodes))]
+            for node in nodes:
+                if node.get("kind") != "page":
+                    continue
+                page = await app.state.memory.get_knowledge_page(
+                    bank_id=bank_id, page_id=node["id"], request_context=request_context
+                )
+                if page is None:
+                    continue
+                files.append(
+                    KnowledgePageBundleFile(path=okf.page_filename(node["id"]), content=okf.render_document(page))
+                )
+                if node.get("mental_model_id"):
+                    history = (
+                        await app.state.memory.get_mental_model_history(
+                            bank_id=bank_id,
+                            mental_model_id=node["mental_model_id"],
+                            request_context=request_context,
+                        )
+                        or []
+                    )
+                    if history:
+                        files.append(
+                            KnowledgePageBundleFile(
+                                path=okf.log_filename(node["id"]), content=okf.render_log(page, history)
+                            )
+                        )
+            return KnowledgePageBundleResponse(files=files)
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except (AuthenticationError, HTTPException):
+            raise
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in GET /v1/default/banks/{bank_id}/knowledge-base/export: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.get(
+        "/v1/default/banks/{bank_id}/knowledge-base/pages/{page_id}",
+        response_model=KnowledgePageResponse,
+        summary="Get a knowledge-base page",
+        description="Return a single page as an OKF document (frontmatter + markdown body).",
+        operation_id="get_knowledge_page",
+        tags=["Knowledge Base"],
+    )
+    async def api_get_knowledge_page(
+        bank_id: str,
+        page_id: str,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Get a single page as an OKF document."""
+        try:
+            node = await app.state.memory.get_knowledge_page(
+                bank_id=bank_id, page_id=page_id, request_context=request_context
+            )
+            if node is None:
+                raise HTTPException(status_code=404, detail=f"Knowledge page '{page_id}' not found")
+            return _knowledge_page_response(node)
+        except (AuthenticationError, HTTPException):
+            raise
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in GET /v1/default/banks/{bank_id}/knowledge-base/pages/{page_id}: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.patch(
+        "/v1/default/banks/{bank_id}/knowledge-base/nodes/{node_id}",
+        response_model=KnowledgeNode,
+        summary="Rename or move a knowledge-base node",
+        description="Rename a node (set `name`) and/or move it under another folder (set `parent_id`, "
+        "null for the root).",
+        operation_id="update_knowledge_node",
+        tags=["Knowledge Base"],
+    )
+    async def api_update_knowledge_node(
+        bank_id: str,
+        node_id: str,
+        body: UpdateNodeRequest,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Rename and/or move a node."""
+        try:
+            updated: dict[str, Any] | None = None
+            did_change = False
+            if body.name is not None:
+                did_change = True
+                updated = await app.state.memory.rename_knowledge_node(
+                    bank_id=bank_id, node_id=node_id, name=body.name, request_context=request_context
+                )
+            # parent_id is applied only when present in the body, so passing null
+            # moves the node to the root (distinct from "not provided").
+            if "parent_id" in body.model_fields_set:
+                did_change = True
+                updated = await app.state.memory.move_knowledge_node(
+                    bank_id=bank_id, node_id=node_id, new_parent_id=body.parent_id, request_context=request_context
+                )
+            if not did_change:
+                raise HTTPException(status_code=400, detail="Provide name and/or parent_id to update")
+            if updated is None:
+                raise HTTPException(status_code=404, detail=f"Knowledge node '{node_id}' not found")
+            return _knowledge_node_model(updated)
+        except ValueError as e:
+            raise HTTPException(status_code=400, detail=str(e))
+        except (AuthenticationError, HTTPException):
+            raise
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in PATCH /v1/default/banks/{bank_id}/knowledge-base/nodes/{node_id}: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.delete(
+        "/v1/default/banks/{bank_id}/knowledge-base/nodes/{node_id}",
+        summary="Delete a knowledge-base node",
+        description="Delete a folder or page and its whole subtree (pages' mental models are removed too).",
+        operation_id="delete_knowledge_node",
+        tags=["Knowledge Base"],
+    )
+    async def api_delete_knowledge_node(
+        bank_id: str,
+        node_id: str,
+        request_context: RequestContext = Depends(get_request_context),
+    ):
+        """Delete a node and its subtree."""
+        try:
+            deleted = await app.state.memory.delete_knowledge_node(
+                bank_id=bank_id, node_id=node_id, request_context=request_context
+            )
+            if not deleted:
+                raise HTTPException(status_code=404, detail=f"Knowledge node '{node_id}' not found")
+            return {"status": "deleted"}
+        except (AuthenticationError, HTTPException):
+            raise
+        except OperationValidationError as e:
+            raise HTTPException(status_code=e.status_code, detail=e.reason)
+        except Exception as e:
+            import traceback
+
+            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
+            logger.error(f"Error in DELETE /v1/default/banks/{bank_id}/knowledge-base/nodes/{node_id}: {error_detail}")
+            raise HTTPException(status_code=500, detail=str(e))
+
    # =========================================================================
    # DIRECTIVES ENDPOINTS
    # =========================================================================
@@ -5454,9 +5879,8 @@ def _register_routes(app: FastAPI):
        "/v1/default/banks/{bank_id}/operations/{operation_id}",
        response_model=OperationStatusResponse,
        summary="Get operation status",
-        description="Get the status of a specific async operation. Returns 'pending', 'processing', 'completed', "
-        "'failed', or 'cancelled'. Completed operations remain queryable with their payload for the configured "
-        "retention window and are pruned afterward.",
+        description="Get the status of a specific async operation. Returns 'pending', 'completed', or 'failed'. "
+        "Completed operations are removed from storage, so 'completed' means the operation finished successfully.",
        operation_id="get_operation_status",
        tags=["Operations"],
    )
@@ -5561,42 +5985,6 @@ def _register_routes(app: FastAPI):
            logger.error(f"Error in POST /v1/default/banks/{bank_id}/operations/{operation_id}/retry: {error_detail}")
            raise HTTPException(status_code=500, detail=str(e))

-    @app.delete(
-        "/v1/default/banks/{bank_id}/operations/{operation_id}/delete",
-        response_model=DeleteOperationResponse,
-        summary="Delete a terminal async operation",
-        description="Permanently remove a failed, cancelled, or completed async operation record",
-        operation_id="delete_operation",
-        tags=["Operations"],
-    )
-    @audited("delete_operation", request_param=None)
-    async def api_delete_operation(
-        bank_id: str, operation_id: str, request_context: RequestContext = Depends(get_request_context)
-    ):
-        """Delete a terminal async operation record."""
-        try:
-            try:
-                uuid.UUID(operation_id)
-            except ValueError:
-                raise HTTPException(status_code=400, detail=f"Invalid operation_id format: {operation_id}")
-
-            result = await app.state.memory.delete_operation(bank_id, operation_id, request_context=request_context)
-            return DeleteOperationResponse(**result)
-        except ValueError as e:
-            raise HTTPException(status_code=404, detail=str(e))
-        except OperationValidationError as e:
-            raise HTTPException(status_code=e.status_code, detail=e.reason)
-        except (AuthenticationError, HTTPException):
-            raise
-        except Exception as e:
-            import traceback
-
-            error_detail = f"{str(e)}\n\nTraceback:\n{traceback.format_exc()}"
-            logger.error(
-                f"Error in DELETE /v1/default/banks/{bank_id}/operations/{operation_id}/delete: {error_detail}"
-            )
-            raise HTTPException(status_code=500, detail=str(e))
-
    @app.get(
        "/v1/default/banks/{bank_id}/profile",
        response_model=BankProfileResponse,
@@ -5732,12 +6120,8 @@ def _register_routes(app: FastAPI):
    ):
        """Create or update an agent with disposition and mission."""
        try:
-            # Ensure bank exists, validating create_bank only when this call
-            # actually creates a missing bank.
-            await app.state.memory._ensure_bank_exists(
-                bank_id,
-                request_context,
-            )
+            # Ensure bank exists by getting profile (auto-creates with defaults)
+            await app.state.memory.get_bank_profile(bank_id, request_context=request_context)

            # Update name if provided (stored in DB for display only, deprecated)
            if request.name is not None:
@@ -5929,12 +6313,8 @@ def _register_routes(app: FastAPI):
                    dry_run=True,
                )

-            # Ensure bank exists, validating create_bank only when this import
-            # actually creates a missing target bank.
-            await app.state.memory._ensure_bank_exists(
-                bank_id,
-                request_context,
-            )
+            # Ensure bank exists (auto-creates with defaults if needed)
+            await app.state.memory.get_bank_profile(bank_id, request_context=request_context)

            return await apply_bank_template_manifest(
                memory=app.state.memory,
@@ -0,0 +1,263 @@
+"""Open Knowledge Format (OKF) projection for knowledge pages.
+
+Knowledge pages are a *read-only* OKF view over the existing mental models: each
+mental model is projected into an OKF document — a markdown body with YAML
+frontmatter (``type`` required; ``title``/``description``/``tags``/``timestamp``
+optional) — and pages are linked into a constellation graph via shared tags.
+
+See the Open Knowledge Format spec:
+https://github.com/GoogleCloudPlatform/knowledge-catalog/tree/main/okf
+
+This module is intentionally pure: every function transforms the mental-model
+dicts returned by ``MemoryEngine.list_mental_models`` / ``get_mental_model`` and
+never touches the database. That keeps the OKF contract unit-testable without a
+DB or LLM and lets the HTTP layer stay a thin wrapper.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any
+
+# OKF requires exactly one frontmatter field — ``type``. We default to this when
+# a page does not declare one via a ``type:<x>`` tag.
+DEFAULT_PAGE_TYPE = "knowledge-page"
+
+# A page declares its OKF ``type`` through a tag of the form ``type:runbook``.
+# This keeps the projection schema-free (no new mental_models column): the type
+# is lifted from the existing tags array.
+TYPE_TAG_PREFIX = "type:"
+
+INDEX_FILENAME = "index.md"
+
+# Deterministic, colour-blind-friendly palette. Type → colour is stable across
+# requests so the constellation keeps the same colours between reloads.
+_PALETTE = (
+    "#0074d9",  # blue
+    "#2ecc40",  # green
+    "#b10dc9",  # purple
+    "#ff851b",  # orange
+    "#39cccc",  # teal
+    "#f012be",  # magenta
+    "#3d9970",  # olive
+    "#ff4136",  # red
+)
+
+_EDGE_COLOR = "#9aa5b1"
+
+
+@dataclass(frozen=True)
+class PageType:
+    """A page's OKF ``type`` and the tags that remain after the type tag is split off."""
+
+    type: str
+    display_tags: list[str]
+
+
+@dataclass(frozen=True)
+class KnowledgeGraph:
+    """Cytoscape-style node/edge graph of knowledge pages linked by shared tags."""
+
+    nodes: list[dict[str, Any]] = field(default_factory=list)
+    edges: list[dict[str, Any]] = field(default_factory=list)
+
+
+def _color_for(key: str) -> str:
+    """Stable colour for a string key (FNV-ish hash into the fixed palette)."""
+    h = 0
+    for ch in key:
+        h = (h * 31 + ord(ch)) & 0xFFFFFFFF
+    return _PALETTE[h % len(_PALETTE)]
+
+
+def _scalar(value: Any) -> str:
+    """Emit a YAML-safe double-quoted scalar.
+
+    We always double-quote so arbitrary page names / source queries can't be
+    misread as YAML special forms (``true``, ``2026-01-01``, ``- x``, etc.).
+    """
+    text = str(value)
+    escaped = text.replace("\\", "\\\\").replace('"', '\\"').replace("\n", "\\n").replace("\r", "")
+    return f'"{escaped}"'
+
+
+def page_type(tags: list[str] | None) -> PageType:
+    """Split an OKF ``type`` out of the tag list.
+
+    The first ``type:<x>`` tag wins; all ``type:`` tags are removed from the
+    returned ``display_tags`` so they don't pollute the constellation's
+    shared-tag edges. Falls back to :data:`DEFAULT_PAGE_TYPE`.
+    """
+    resolved = DEFAULT_PAGE_TYPE
+    display: list[str] = []
+    for tag in tags or []:
+        if tag.startswith(TYPE_TAG_PREFIX):
+            suffix = tag[len(TYPE_TAG_PREFIX) :].strip()
+            if suffix and resolved == DEFAULT_PAGE_TYPE:
+                resolved = suffix
+            continue
+        display.append(tag)
+    return PageType(type=resolved, display_tags=display)
+
+
+def _timestamp(mm: dict[str, Any]) -> str | None:
+    return mm.get("last_refreshed_at") or mm.get("created_at")
+
+
+def frontmatter(mm: dict[str, Any]) -> dict[str, Any]:
+    """Build the ordered OKF frontmatter mapping for a mental model.
+
+    ``None``/empty values are dropped by :func:`render_frontmatter`.
+    """
+    pt = page_type(mm.get("tags"))
+    return {
+        "id": mm.get("id"),
+        "type": pt.type,
+        "title": mm.get("name"),
+        "description": mm.get("source_query"),
+        "tags": pt.display_tags,
+        "timestamp": _timestamp(mm),
+    }
+
+
+def render_frontmatter(fm: dict[str, Any]) -> str:
+    """Render a frontmatter mapping into a ``---`` fenced YAML block."""
+    lines = ["---"]
+    for key, value in fm.items():
+        if value is None:
+            continue
+        if isinstance(value, list):
+            if not value:
+                continue
+            lines.append(f"{key}:")
+            lines.extend(f"  - {_scalar(item)}" for item in value)
+        else:
+            lines.append(f"{key}: {_scalar(value)}")
+    lines.append("---")
+    return "\n".join(lines)
+
+
+def render_document(mm: dict[str, Any]) -> str:
+    """Render a full OKF document: frontmatter block + markdown body."""
+    body = (mm.get("content") or "").strip()
+    return f"{render_frontmatter(frontmatter(mm))}\n\n{body}\n" if body else f"{render_frontmatter(frontmatter(mm))}\n"
+
+
+def page_filename(page_id: str) -> str:
+    """OKF bundle filename for a page id."""
+    return f"{page_id}.md"
+
+
+def log_filename(page_id: str) -> str:
+    """OKF reserved per-page history filename."""
+    return f"{page_id}.log.md"
+
+
+def render_index(nodes: list[dict[str, Any]]) -> str:
+    """Render the reserved ``index.md`` — nested OKF navigation over the tree.
+
+    ``nodes`` is the flat folder/page list (each with ``id``, ``kind``, ``name``,
+    ``parent_id``); folders nest their children, pages link to their ``.md``.
+    """
+    fm = render_frontmatter({"type": "index", "title": "Knowledge base"})
+    lines = [fm, "", "# Knowledge base", ""]
+
+    children: dict[Any, list[dict[str, Any]]] = {}
+    for node in nodes:
+        children.setdefault(node.get("parent_id"), []).append(node)
+
+    def walk(parent: Any, depth: int) -> None:
+        ordered = sorted(children.get(parent, []), key=lambda n: (n.get("sort_order", 0), n.get("name") or ""))
+        for node in ordered:
+            indent = "  " * depth
+            if node.get("kind") == "folder":
+                lines.append(f"{indent}- **{node['name']}/**")
+                walk(node["id"], depth + 1)
+            else:
+                description = node.get("source_query") or node.get("description")
+                link = f"{indent}- [{node['name']}](./{page_filename(node['id'])})"
+                lines.append(f"{link} — {description}" if description else link)
+
+    walk(None, 0)
+    if len(lines) == 4:
+        lines.append("_No knowledge pages yet._")
+    return "\n".join(lines) + "\n"
+
+
+def render_log(mm: dict[str, Any], history: list[dict[str, Any]]) -> str:
+    """Render the reserved per-page ``log.md`` from refresh history.
+
+    Each history entry is ``{previous_content, previous_reflect_response,
+    changed_at}`` (newest first), capturing the content *before* a refresh.
+    """
+    name = mm.get("name") or mm.get("id")
+    fm = render_frontmatter({"type": "log", "title": f"{name} — history"})
+    lines = [fm, "", f"# {name} — history", ""]
+    if not history:
+        lines.append("_No refresh history._")
+        return "\n".join(lines) + "\n"
+    for entry in history:
+        changed_at = entry.get("changed_at") or "unknown"
+        previous = (entry.get("previous_content") or "").strip()
+        lines.append(f"## {changed_at}")
+        lines.append("")
+        lines.append(previous if previous else "_(empty)_")
+        lines.append("")
+    return "\n".join(lines).rstrip() + "\n"
+
+
+def knowledge_graph(
+    pages: list[dict[str, Any]],
+    cluster_for: "Callable[[dict[str, Any]], str] | None" = None,
+) -> KnowledgeGraph:
+    """Derive the constellation graph: pages as nodes, shared tags as edges.
+
+    Two pages are linked when they share at least one (non-``type:``) tag; the
+    edge weight is the number of shared tags. Each node's cluster (``type`` field
+    + colour) comes from ``cluster_for(page)`` — the knowledge base groups by
+    parent folder; the default groups by OKF ``type``.
+    """
+    nodes: list[dict[str, Any]] = []
+    tag_sets: list[tuple[str, frozenset[str]]] = []
+    for mm in pages:
+        page_id = mm["id"]
+        pt = page_type(mm.get("tags"))
+        cluster = cluster_for(mm) if cluster_for else pt.type
+        tag_sets.append((page_id, frozenset(pt.display_tags)))
+        nodes.append(
+            {
+                "data": {
+                    "id": page_id,
+                    "label": mm.get("name") or page_id,
+                    "type": cluster,
+                    "tagCount": len(pt.display_tags),
+                    "color": _color_for(cluster),
+                }
+            }
+        )
+
+    edges: list[dict[str, Any]] = []
+    for i in range(len(tag_sets)):
+        source_id, source_tags = tag_sets[i]
+        if not source_tags:
+            continue
+        for j in range(i + 1, len(tag_sets)):
+            target_id, target_tags = tag_sets[j]
+            shared = source_tags & target_tags
+            if not shared:
+                continue
+            edges.append(
+                {
+                    "data": {
+                        "id": f"{source_id}--{target_id}",
+                        "source": source_id,
+                        "target": target_id,
+                        "sharedTags": sorted(shared),
+                        "weight": len(shared),
+                        "color": _EDGE_COLOR,
+                    }
+                }
+            )
+
+    return KnowledgeGraph(nodes=nodes, edges=edges)
@@ -145,17 +145,8 @@ ENV_LLM_BEDROCK_SERVICE_TIER = "HINDSIGHT_API_LLM_BEDROCK_SERVICE_TIER"
 ENV_LLM_GEMINI_SERVICE_TIER = "HINDSIGHT_API_LLM_GEMINI_SERVICE_TIER"
 ENV_LLM_EXTRA_BODY = "HINDSIGHT_API_LLM_EXTRA_BODY"
 ENV_LLM_DEFAULT_HEADERS = "HINDSIGHT_API_LLM_DEFAULT_HEADERS"
-# Grammar-enforced structured output. The global flag applies to every internal
-# LLM call; the per-operation variants override it for a single operation, so an
-# operator can enable strict schema where it fixes malformed/truncated JSON
-# without paying the retry cost on operations whose model can't satisfy it.
-# Resolution per operation: per-operation env -> global env -> built-in default.
 ENV_LLM_STRICT_SCHEMA = "HINDSIGHT_API_LLM_STRICT_SCHEMA"
-ENV_LLM_STRICT_SCHEMA_RETAIN = "HINDSIGHT_API_LLM_STRICT_SCHEMA_RETAIN"
-ENV_LLM_STRICT_SCHEMA_REFLECT = "HINDSIGHT_API_LLM_STRICT_SCHEMA_REFLECT"
-ENV_LLM_STRICT_SCHEMA_CONSOLIDATION = "HINDSIGHT_API_LLM_STRICT_SCHEMA_CONSOLIDATION"
 ENV_LLM_SEND_BANK_AS_USER = "HINDSIGHT_API_LLM_SEND_BANK_AS_USER"
-ENV_LLM_OLLAMA_NUM_CTX = "HINDSIGHT_API_LLM_OLLAMA_NUM_CTX"

 # Per-operation sampling temperature. Each internal LLM call uses a temperature
 # tuned for its task (deterministic extraction vs. creative reflection). These
@@ -257,21 +248,6 @@ def _resolve_operation_temperature(operation_env: str, default: float) -> float
    return _parse_temperature(raw)


-def _resolve_operation_strict_schema(operation_env: str) -> bool:
-    """Resolve a per-operation strict-schema flag: per-op env -> global env -> default.
-
-    Resolved to a concrete bool here rather than left as None, so the call site
-    passes an explicit value and a per-operation "false" can override a global
-    "true" (the wrapper honours an explicit False -- see LLMConfig.call).
-    """
-    raw = os.getenv(operation_env)
-    if raw is None:
-        raw = os.getenv(ENV_LLM_STRICT_SCHEMA)
-    if raw is None:
-        return DEFAULT_LLM_STRICT_SCHEMA
-    return raw.strip().lower() in ("true", "1")
-
-
 # Per-operation LLM configuration (optional, falls back to global LLM config)
 ENV_RETAIN_LLM_PROVIDER = "HINDSIGHT_API_RETAIN_LLM_PROVIDER"
 ENV_RETAIN_LLM_API_KEY = "HINDSIGHT_API_RETAIN_LLM_API_KEY"
@@ -397,7 +373,6 @@ ENV_EMBEDDINGS_LITELLM_SDK_MODEL = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_MODEL"
 ENV_EMBEDDINGS_LITELLM_SDK_API_BASE = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_API_BASE"
 ENV_EMBEDDINGS_LITELLM_SDK_OUTPUT_DIMENSIONS = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_OUTPUT_DIMENSIONS"
 ENV_EMBEDDINGS_LITELLM_SDK_ENCODING_FORMAT = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_ENCODING_FORMAT"
-ENV_EMBEDDINGS_LITELLM_SDK_MAX_INPUT_TOKENS = "HINDSIGHT_API_EMBEDDINGS_LITELLM_SDK_MAX_INPUT_TOKENS"
 ENV_RERANKER_LITELLM_SDK_API_KEY = "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_KEY"
 ENV_RERANKER_LITELLM_SDK_MODEL = "HINDSIGHT_API_RERANKER_LITELLM_SDK_MODEL"
 ENV_RERANKER_LITELLM_SDK_API_BASE = "HINDSIGHT_API_RERANKER_LITELLM_SDK_API_BASE"
@@ -407,7 +382,6 @@ ENV_LITELLM_API_BASE = "HINDSIGHT_API_LITELLM_API_BASE"
 ENV_LITELLM_API_KEY = "HINDSIGHT_API_LITELLM_API_KEY"

 ENV_RERANKER_PROVIDER = "HINDSIGHT_API_RERANKER_PROVIDER"
-ENV_RERANKER_SEND_BANK_AS_HEADER = "HINDSIGHT_API_RERANKER_SEND_BANK_AS_HEADER"
 ENV_RERANKER_LOCAL_MODEL = "HINDSIGHT_API_RERANKER_LOCAL_MODEL"
 ENV_RERANKER_LOCAL_FORCE_CPU = "HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU"
 ENV_RERANKER_LOCAL_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT"
@@ -511,11 +485,6 @@ ENV_LLM_GEMINI_SAFETY_SETTINGS = "HINDSIGHT_API_LLM_GEMINI_SAFETY_SETTINGS"
 # banks, and creation soft-fails to an uncached call, so it never breaks a request.
 ENV_LLM_PROMPT_CACHE_ENABLED = "HINDSIGHT_API_LLM_PROMPT_CACHE_ENABLED"

-# Opt-in diagnostic: when truthy, log the exact request behind any LLM 4xx (the
-# serialized request config with message bodies stripped + length-capped per-message
-# previews). Off by default; server-level only. See engine/providers/llm_debug.py.
-ENV_LLM_DEBUG_DUMP_4XX = "HINDSIGHT_API_LLM_DEBUG_DUMP_4XX"
-
 # Retain settings
 ENV_RETAIN_MAX_COMPLETION_TOKENS = "HINDSIGHT_API_RETAIN_MAX_COMPLETION_TOKENS"
 ENV_RETAIN_CHUNK_SIZE = "HINDSIGHT_API_RETAIN_CHUNK_SIZE"
@@ -615,7 +584,6 @@ ENV_DB_POOL_MAX_SIZE = "HINDSIGHT_API_DB_POOL_MAX_SIZE"
 ENV_DB_COMMAND_TIMEOUT = "HINDSIGHT_API_DB_COMMAND_TIMEOUT"
 ENV_DB_ACQUIRE_TIMEOUT = "HINDSIGHT_API_DB_ACQUIRE_TIMEOUT"
 ENV_DB_STATEMENT_TIMEOUT = "HINDSIGHT_API_DB_STATEMENT_TIMEOUT"
-ENV_DB_MAX_PARALLEL_WORKERS_PER_GATHER = "HINDSIGHT_API_DB_MAX_PARALLEL_WORKERS_PER_GATHER"

 # Wall-clock cap on model/connection initialization at startup. If embeddings,
 # cross-encoder, or LLM verification hang (e.g. an offline HuggingFace download
@@ -630,8 +598,6 @@ ENV_WORKER_MAX_RETRIES = "HINDSIGHT_API_WORKER_MAX_RETRIES"
 ENV_WORKER_TASK_RETRY_BACKOFF_SECONDS = "HINDSIGHT_API_WORKER_TASK_RETRY_BACKOFF_SECONDS"
 ENV_WORKER_HTTP_PORT = "HINDSIGHT_API_WORKER_HTTP_PORT"
 ENV_WORKER_MAX_SLOTS = "HINDSIGHT_API_WORKER_MAX_SLOTS"
-ENV_OPERATION_RETENTION_DAYS = "HINDSIGHT_API_OPERATION_RETENTION_DAYS"
-ENV_OPERATION_CLEANUP_BATCH_SIZE = "HINDSIGHT_API_OPERATION_CLEANUP_BATCH_SIZE"

 # Per-operation-type slot reservations. Each entry maps an operation_type
 # (as stored in async_operations.operation_type) to its env var and default.
@@ -651,7 +617,6 @@ ENV_RETAIN_MAX_CONCURRENT = "HINDSIGHT_API_RETAIN_MAX_CONCURRENT"

 # Reflect agent settings
 ENV_REFLECT_MAX_ITERATIONS = "HINDSIGHT_API_REFLECT_MAX_ITERATIONS"
-ENV_REFLECT_PROMPT_CACHE_ENABLED = "HINDSIGHT_API_REFLECT_PROMPT_CACHE_ENABLED"
 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"
@@ -673,7 +638,6 @@ ENV_RECALL_BUDGET_MAX = "HINDSIGHT_API_RECALL_BUDGET_MAX"

 # Recall candidate gating (per-source cap + BM25 score floor)
 ENV_BM25_MIN_SCORE = "HINDSIGHT_API_BM25_MIN_SCORE"
-ENV_BM25_MAX_QUERY_TERMS = "HINDSIGHT_API_BM25_MAX_QUERY_TERMS"
 ENV_RECALL_MAX_CANDIDATES_PER_SOURCE = "HINDSIGHT_API_RECALL_MAX_CANDIDATES_PER_SOURCE"
 # Per-strategy recall boost. Prioritises specific retrieval arms (semantic,
 # bm25, graph, temporal) on recall via a human priority level — e.g.
@@ -693,16 +657,10 @@ ENV_RECENCY_DECAY_LINEAR_WINDOW_DAYS = "HINDSIGHT_API_RECENCY_DECAY_LINEAR_WINDO
 ENV_RECENCY_DECAY_HALFLIFE_DAYS = "HINDSIGHT_API_RECENCY_DECAY_HALFLIFE_DAYS"

 # Audit log settings
-# AUDIT_LOG_ENABLED is the deployment-wide default and is overridable per bank
-# (and per tenant) through the bank config API, so auditing can be turned on for
-# individual banks without enabling it everywhere.
 ENV_AUDIT_LOG_ENABLED = "HINDSIGHT_API_AUDIT_LOG_ENABLED"
 ENV_AUDIT_LOG_ACTIONS = "HINDSIGHT_API_AUDIT_LOG_ACTIONS"
 ENV_AUDIT_LOG_RETENTION_DAYS = "HINDSIGHT_API_AUDIT_LOG_RETENTION_DAYS"

-# Retain reliability settings
-ENV_FAIL_ON_EXTRACTION_ERRORS = "HINDSIGHT_API_FAIL_ON_EXTRACTION_ERRORS"
-
 # LLM request tracing settings
 ENV_LLM_TRACE_ENABLED = "HINDSIGHT_API_LLM_TRACE_ENABLED"
 ENV_LLM_TRACE_SCOPES = "HINDSIGHT_API_LLM_TRACE_SCOPES"
@@ -803,7 +761,6 @@ DEFAULT_EMBEDDINGS_GEMINI_FORCE_IPV4 = False
 DEFAULT_EMBEDDING_DIMENSION = 384

 DEFAULT_RERANKER_PROVIDER = "local"
-DEFAULT_RERANKER_SEND_BANK_AS_HEADER = False
 DEFAULT_RERANKER_LOCAL_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
 DEFAULT_RERANKER_LOCAL_FORCE_CPU = False  # Force CPU mode for local reranker
 DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4  # Limit concurrent CPU-bound reranking to prevent thrashing
@@ -832,9 +789,6 @@ DEFAULT_SEMANTIC_MIN_SIMILARITY = 0.3
 # zero-score (non-matching) rows on backends — notably VectorChord — whose
 # operator ranks every document rather than pre-filtering to term matches.
 DEFAULT_BM25_MIN_SCORE = 0.0
-# Native tsvector BM25 can optionally cap the OR tsquery built from normalized
-# query tokens. 0 preserves the historical uncapped behavior.
-DEFAULT_BM25_MAX_QUERY_TERMS = 0
 # Per-source candidate cap applied to each retrieval arm (semantic, BM25, graph,
 # temporal) before RRF, so a single over-expanding backend cannot fill the
 # reranker's global candidate budget on its own. 0 disables the cap.
@@ -955,10 +909,6 @@ DEFAULT_RERANKER_LITELLM_MAX_TOKENS_PER_DOC: int | None = None
 # LiteLLM SDK defaults
 DEFAULT_EMBEDDINGS_LITELLM_SDK_MODEL = "cohere/embed-english-v3.0"
 DEFAULT_EMBEDDINGS_LITELLM_SDK_ENCODING_FORMAT = "float"
-# Opt-in per-text input truncation (tiktoken cl100k_base tokens). Off by default;
-# set to the embedding model's real input limit (e.g. 8192 for Bedrock Titan V2)
-# to keep oversized content from permanently failing the embed call. See #2501.
-DEFAULT_EMBEDDINGS_LITELLM_SDK_MAX_INPUT_TOKENS: int | None = None
 DEFAULT_RERANKER_LITELLM_SDK_MODEL = "cohere/rerank-english-v3.0"

 DEFAULT_HOST = "0.0.0.0"
@@ -1009,7 +959,6 @@ DEFAULT_RETAIN_ENTITY_LOOKUP = "trigram"  # "full" or "trigram"
 DEFAULT_RETAIN_ENTITY_RESOLUTION_BATCH_SIZE = 100  # Unique entity names per pg_trgm candidate lookup query
 DEFAULT_RETAIN_BATCH_ENABLED = False  # Use LLM Batch API for fact extraction (only when async=True)
 DEFAULT_LLM_PROMPT_CACHE_ENABLED = True  # Reuse the fixed system prefix via provider prompt caching
-DEFAULT_LLM_DEBUG_DUMP_4XX = False  # Log the exact request behind any LLM 4xx (diagnostic, off by default)
 DEFAULT_RETAIN_BATCH_POLL_INTERVAL_SECONDS = 60  # Batch API polling interval in seconds

 # File storage defaults
@@ -1091,14 +1040,6 @@ DEFAULT_DB_POOL_MAX_SIZE = 100
 DEFAULT_DB_COMMAND_TIMEOUT = 60  # seconds
 DEFAULT_DB_ACQUIRE_TIMEOUT = 30  # seconds
 DEFAULT_DB_STATEMENT_TIMEOUT = 600  # seconds (Postgres statement_timeout applied on every pool connection; 0 disables)
-# Optional cap on Postgres planner parallelism for this process's pool
-# connections (SET max_parallel_workers_per_gather). None leaves the server
-# default untouched. Setting 0 on background-worker processes keeps bulk
-# maintenance queries (consolidation, graph upkeep) from fanning out across
-# cores that latency-sensitive foreground traffic is sharing — parallel
-# workers buy latency, which background work doesn't need, at the cost of
-# concurrent CPU footprint, which multi-tenant primaries do care about.
-DEFAULT_DB_MAX_PARALLEL_WORKERS_PER_GATHER: int | None = None
 DEFAULT_MODEL_INIT_TIMEOUT = 300  # seconds (cap on startup model/connection init; covers first-time downloads)

 # Worker configuration (distributed task processing)
@@ -1109,20 +1050,10 @@ DEFAULT_WORKER_MAX_RETRIES = 3  # Max retries before marking task failed
 DEFAULT_WORKER_TASK_RETRY_BACKOFF_SECONDS = 60  # Seconds between retries on transient task failure
 DEFAULT_WORKER_HTTP_PORT = 8889  # HTTP port for worker metrics/health
 DEFAULT_WORKER_MAX_SLOTS = 10  # Total concurrent tasks per worker
-# Terminal rows keep their payload and metadata for one coherent debug/retry TTL.
-# Zero retention days disables automatic pruning entirely, and is the default:
-# operation history is a user-visible audit trail, so bounding it is an opt-in
-# policy decision rather than something an upgrade silently applies.
-DEFAULT_OPERATION_RETENTION_DAYS = 0
-DEFAULT_OPERATION_CLEANUP_BATCH_SIZE = 1000
 DEFAULT_RETAIN_MAX_CONCURRENT = 4  # Max concurrent retain DB phases (HNSW reads + writes). Limits I/O contention.

 # Reflect agent settings
 DEFAULT_REFLECT_MAX_ITERATIONS = 10  # Max tool call iterations before forcing response
-# Step-by-step context caching for the reflect tool loop (Gemini). On by default;
-# requires the global prompt cache (HINDSIGHT_API_LLM_PROMPT_CACHE_ENABLED) to also
-# be on. Set false to force reflect to run uncached even when prompt caching is on.
-DEFAULT_REFLECT_PROMPT_CACHE_ENABLED = True
 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)
@@ -1163,11 +1094,6 @@ DEFAULT_AUDIT_LOG_ENABLED = False  # Disabled by default
 DEFAULT_AUDIT_LOG_ACTIONS = ""  # Empty = audit all eligible actions
 DEFAULT_AUDIT_LOG_RETENTION_DAYS = -1  # -1 = keep forever

-# Retain reliability defaults
-DEFAULT_FAIL_ON_EXTRACTION_ERRORS = (
-    False  # Preserve existing behavior: retain completes even if some chunks fail extraction
-)
-
 # LLM request tracing defaults
 DEFAULT_LLM_TRACE_ENABLED = True  # Enabled by default
 DEFAULT_LLM_TRACE_SCOPES = ""  # Empty = trace all call scopes
@@ -1283,19 +1209,6 @@ def _parse_positive_int(name: str, raw: str | None, default: int) -> int:
    return parsed


-def _parse_non_negative_int(name: str, raw: str | None, default: int) -> int:
-    """Parse an env var that must be an integer >= 0."""
-    if raw is None or raw == "":
-        return default
-    try:
-        parsed = int(raw)
-    except ValueError as e:
-        raise ValueError(f"{name} must be an integer, got {raw!r}") from e
-    if parsed < 0:
-        raise ValueError(f"{name} must be >= 0, got {parsed}")
-    return parsed
-
-
 def _parse_optional_positive_int(name: str, raw: str | None) -> int | None:
    """Parse an optional env var that must be a positive integer when set."""
    if raw is None or raw == "":
@@ -1303,25 +1216,6 @@ def _parse_optional_positive_int(name: str, raw: str | None) -> int | None:
    return _parse_positive_int(name, raw, 1)


-def _parse_optional_non_negative_int(name: str, raw: str | None) -> int | None:
-    """
-    Parse an optional env var that must be a non-negative integer when set.
-
-    Unlike ``_parse_optional_positive_int``, 0 is a meaningful value here —
-    e.g. ``max_parallel_workers_per_gather = 0`` disables planner parallelism
-    entirely. Unset/empty means "no opinion" (None).
-    """
-    if raw is None or raw == "":
-        return None
-    try:
-        parsed = int(raw)
-    except ValueError as e:
-        raise ValueError(f"{name} must be an integer, got {raw!r}") from e
-    if parsed < 0:
-        raise ValueError(f"{name} must be >= 0, got {parsed}")
-    return parsed
-
-
 def _validate_retain_chunking_int(name: str, value: Any) -> int:
    if isinstance(value, bool) or not isinstance(value, int):
        raise ValueError(f"{name} must be an integer, got {value!r}")
@@ -1678,20 +1572,11 @@ class HindsightConfig:
        dict | None
    )  # Custom headers passed as default_headers to provider SDK clients (e.g. {"X-Component-Id": "hindsight"} for proxies / request tracing)
    llm_strict_schema: bool  # Grammar-enforce structured output via the provider's strongest schema mode (see DEFAULT_LLM_STRICT_SCHEMA)
-    # Per-operation strict-schema overrides. Resolved from the per-operation env
-    # var, falling back to llm_strict_schema's global env var. See
-    # ENV_LLM_STRICT_SCHEMA and _resolve_operation_strict_schema.
-    llm_strict_schema_retain: bool
-    llm_strict_schema_reflect: bool
-    llm_strict_schema_consolidation: bool
    # Tags outbound OpenAI-compatible LLM + embedding calls with `user=<bank_id>` for
    # per-bank cost attribution. Downstream cost gateways (OpenRouter usage accounting,
    # LiteLLM, Helicone) key attribution on the OpenAI `user` field. Opt-in; never
    # overrides a `user` the caller already set.
    llm_send_bank_as_user: bool
-    # Optional native Ollama context window override. Unset lets Ollama use the
-    # model/server default instead of forcing a Hindsight-wide value.
-    llm_ollama_num_ctx: int | None = field(default=None, kw_only=True)

    # Per-operation sampling temperature. None means the temperature parameter is
    # omitted from the call (for models that reject explicit temperatures). See
@@ -1719,10 +1604,6 @@ class HindsightConfig:
    # CachedContent prefix for its system prompt + response schema.
    llm_prompt_cache_enabled: bool

-    # Opt-in diagnostic: log the exact request behind any LLM 4xx. Off by default;
-    # server-level only (not per-bank overridable). See engine/providers/llm_debug.py.
-    llm_debug_dump_4xx: bool
-
    # Built-in llama.cpp configuration (for provider=llamacpp)
    llamacpp_model_path: str | None  # Path to GGUF file (None = auto-download default)
    llamacpp_gpu_layers: int  # -1 = all layers on GPU, 0 = CPU only
@@ -1804,7 +1685,6 @@ class HindsightConfig:
    embeddings_litellm_sdk_api_base: str | None
    embeddings_litellm_sdk_output_dimensions: int | None
    embeddings_litellm_sdk_encoding_format: str | None
-    embeddings_litellm_sdk_max_input_tokens: int | None
    # Gemini/Vertex AI embeddings
    embeddings_gemini_api_key: str | None
    embeddings_gemini_model: str
@@ -1816,7 +1696,6 @@ class HindsightConfig:

    # Reranker
    reranker_provider: str
-    reranker_send_bank_as_header: bool
    reranker_local_model: str
    reranker_local_force_cpu: bool
    reranker_local_max_concurrent: int
@@ -2017,7 +1896,6 @@ class HindsightConfig:
    db_command_timeout: int
    db_acquire_timeout: int
    db_statement_timeout: int
-    db_max_parallel_workers_per_gather: int | None
    model_init_timeout: float

    # Worker configuration (distributed task processing)
@@ -2030,15 +1908,12 @@ class HindsightConfig:
    worker_max_slots: int
    worker_slot_reservations: dict[str, int]
    worker_consolidation_bank_priority: dict[str, int]
-    operation_retention_days: int
-    operation_cleanup_batch_size: int
    retain_max_concurrent: int

    # Reflect agent settings
    reflect_max_iterations: int
    reflect_max_context_tokens: int
    reflect_wall_timeout: int
-    reflect_prompt_cache_enabled: bool

    # OpenTelemetry tracing configuration
    otel_traces_enabled: bool
@@ -2049,19 +1924,11 @@ class HindsightConfig:
    metrics_include_bank_id: bool
    metrics_backlog_enabled: bool

-    # Audit log configuration
-    # audit_log_enabled is hierarchical (env -> tenant -> bank): a deployment can
-    # audit some banks and not others. The actions allowlist and retention window
-    # stay static (server-level): retention is a global sweep with no bank scope.
-    audit_log_enabled: bool  # Whether audit logging is on (overridable per bank)
+    # Audit log configuration (static - server-level only)
+    audit_log_enabled: bool  # Master switch for audit logging
    audit_log_actions: list[str]  # Allowlist of action types (empty = all)
    audit_log_retention_days: int  # -1 = keep forever, >0 = delete after N days

-    # Retain reliability configuration (static - server-level only)
-    # When True, a retain operation that accumulated any fact-extraction errors is
-    # marked 'failed' instead of 'completed', surfacing silent fact loss to clients.
-    fail_on_extraction_errors: bool
-
    # LLM request tracing configuration (static - server-level only)
    llm_trace_enabled: bool  # Master switch for per-bank LLM request tracing
    llm_trace_scopes: list[str]  # Allowlist of call scopes to trace (empty = all)
@@ -2112,7 +1979,6 @@ class HindsightConfig:
    reflect_llm_strategy: LLMStrategyConfig | None = None
    consolidation_llm_members: list[LLMMemberConfig] = field(default_factory=list)
    consolidation_llm_strategy: LLMStrategyConfig | None = None
-    bm25_max_query_terms: int = DEFAULT_BM25_MAX_QUERY_TERMS

    # Class-level sets for configuration categorization

@@ -2170,9 +2036,6 @@ class HindsightConfig:
    _CONFIGURABLE_FIELDS = {
        # MCP tool access control
        "mcp_enabled_tools",
-        # Audit logging on/off, per bank. The actions allowlist and retention
-        # window remain server-level and are deliberately not configurable.
-        "audit_log_enabled",
        # Retention settings (behavioral)
        "retain_chunk_size",
        "retain_structured_chunk_size",
@@ -2316,9 +2179,6 @@ class HindsightConfig:
                f"Invalid semantic_min_similarity: {self.semantic_min_similarity}. Must be between 0.0 and 1.0"
            )

-        if self.bm25_max_query_terms < 0:
-            raise ValueError(f"Invalid bm25_max_query_terms: {self.bm25_max_query_terms}. Must be >= 0")
-
        # Validate bedrock_service_tier
        valid_bedrock_tiers = (None, "flex", "priority", "reserved")
        if self.llm_bedrock_service_tier not in valid_bedrock_tiers:
@@ -2408,13 +2268,6 @@ class HindsightConfig:
                f"Reduce reservations or increase HINDSIGHT_API_WORKER_MAX_SLOTS."
            )

-        if self.operation_retention_days < 0:
-            raise ValueError(f"{ENV_OPERATION_RETENTION_DAYS} must be >= 0, got {self.operation_retention_days}")
-        if self.operation_cleanup_batch_size < 1:
-            raise ValueError(
-                f"{ENV_OPERATION_CLEANUP_BATCH_SIZE} must be >= 1, got {self.operation_cleanup_batch_size}"
-            )
-
    @classmethod
    def from_env(cls) -> "HindsightConfig":
        """Create configuration from environment variables."""
@@ -2464,15 +2317,8 @@ class HindsightConfig:
            llm_extra_body=json.loads(os.getenv(ENV_LLM_EXTRA_BODY, "null")),
            llm_default_headers=json.loads(os.getenv(ENV_LLM_DEFAULT_HEADERS, "null")),
            llm_strict_schema=os.getenv(ENV_LLM_STRICT_SCHEMA, str(DEFAULT_LLM_STRICT_SCHEMA)).lower() in ("true", "1"),
-            llm_strict_schema_retain=_resolve_operation_strict_schema(ENV_LLM_STRICT_SCHEMA_RETAIN),
-            llm_strict_schema_reflect=_resolve_operation_strict_schema(ENV_LLM_STRICT_SCHEMA_REFLECT),
-            llm_strict_schema_consolidation=_resolve_operation_strict_schema(ENV_LLM_STRICT_SCHEMA_CONSOLIDATION),
            llm_send_bank_as_user=os.getenv(ENV_LLM_SEND_BANK_AS_USER, str(DEFAULT_LLM_SEND_BANK_AS_USER)).lower()
            in ("true", "1"),
-            llm_ollama_num_ctx=_parse_optional_positive_int(
-                ENV_LLM_OLLAMA_NUM_CTX,
-                os.getenv(ENV_LLM_OLLAMA_NUM_CTX),
-            ),
            llm_temperature_verification=_resolve_operation_temperature(
                ENV_LLM_TEMPERATURE_VERIFICATION, DEFAULT_LLM_TEMPERATURE_VERIFICATION
            ),
@@ -2497,8 +2343,6 @@ class HindsightConfig:
                ENV_LLM_PROMPT_CACHE_ENABLED, str(DEFAULT_LLM_PROMPT_CACHE_ENABLED)
            ).lower()
            in ("1", "true", "yes", "on"),
-            llm_debug_dump_4xx=os.getenv(ENV_LLM_DEBUG_DUMP_4XX, str(DEFAULT_LLM_DEBUG_DUMP_4XX)).lower()
-            in ("1", "true", "yes", "on"),
            # Built-in llama.cpp configuration
            llamacpp_model_path=os.getenv(ENV_LLAMACPP_MODEL_PATH) or None,
            llamacpp_gpu_layers=int(os.getenv(ENV_LLAMACPP_GPU_LAYERS, str(DEFAULT_LLAMACPP_GPU_LAYERS))),
@@ -2711,9 +2555,6 @@ class HindsightConfig:
            embeddings_litellm_sdk_encoding_format=os.getenv(
                ENV_EMBEDDINGS_LITELLM_SDK_ENCODING_FORMAT, DEFAULT_EMBEDDINGS_LITELLM_SDK_ENCODING_FORMAT
            ),
-            embeddings_litellm_sdk_max_input_tokens=int(v)
-            if (v := os.getenv(ENV_EMBEDDINGS_LITELLM_SDK_MAX_INPUT_TOKENS))
-            else DEFAULT_EMBEDDINGS_LITELLM_SDK_MAX_INPUT_TOKENS,
            # Gemini/Vertex AI embeddings (with fallback to LLM keys)
            embeddings_gemini_api_key=os.getenv(ENV_EMBEDDINGS_GEMINI_API_KEY) or os.getenv(ENV_LLM_API_KEY),
            embeddings_gemini_model=os.getenv(ENV_EMBEDDINGS_GEMINI_MODEL, DEFAULT_EMBEDDINGS_GEMINI_MODEL),
@@ -2735,11 +2576,6 @@ class HindsightConfig:
            or os.getenv(ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY),
            # Reranker
            reranker_provider=os.getenv(ENV_RERANKER_PROVIDER, DEFAULT_RERANKER_PROVIDER),
-            reranker_send_bank_as_header=os.getenv(
-                ENV_RERANKER_SEND_BANK_AS_HEADER,
-                str(DEFAULT_RERANKER_SEND_BANK_AS_HEADER),
-            ).lower()
-            in ("true", "1"),
            reranker_local_model=os.getenv(ENV_RERANKER_LOCAL_MODEL, DEFAULT_RERANKER_LOCAL_MODEL),
            reranker_local_force_cpu=os.getenv(
                ENV_RERANKER_LOCAL_FORCE_CPU, str(DEFAULT_RERANKER_LOCAL_FORCE_CPU)
@@ -2772,11 +2608,6 @@ class HindsightConfig:
            reranker_max_candidates=int(os.getenv(ENV_RERANKER_MAX_CANDIDATES, str(DEFAULT_RERANKER_MAX_CANDIDATES))),
            semantic_min_similarity=float(os.getenv(ENV_SEMANTIC_MIN_SIMILARITY, str(DEFAULT_SEMANTIC_MIN_SIMILARITY))),
            bm25_min_score=float(os.getenv(ENV_BM25_MIN_SCORE, str(DEFAULT_BM25_MIN_SCORE))),
-            bm25_max_query_terms=_parse_non_negative_int(
-                ENV_BM25_MAX_QUERY_TERMS,
-                os.getenv(ENV_BM25_MAX_QUERY_TERMS),
-                DEFAULT_BM25_MAX_QUERY_TERMS,
-            ),
            recall_max_candidates_per_source=int(
                os.getenv(ENV_RECALL_MAX_CANDIDATES_PER_SOURCE, str(DEFAULT_RECALL_MAX_CANDIDATES_PER_SOURCE))
            ),
@@ -3071,10 +2902,6 @@ class HindsightConfig:
            db_command_timeout=int(os.getenv(ENV_DB_COMMAND_TIMEOUT, str(DEFAULT_DB_COMMAND_TIMEOUT))),
            db_acquire_timeout=int(os.getenv(ENV_DB_ACQUIRE_TIMEOUT, str(DEFAULT_DB_ACQUIRE_TIMEOUT))),
            db_statement_timeout=int(os.getenv(ENV_DB_STATEMENT_TIMEOUT, str(DEFAULT_DB_STATEMENT_TIMEOUT))),
-            db_max_parallel_workers_per_gather=_parse_optional_non_negative_int(
-                ENV_DB_MAX_PARALLEL_WORKERS_PER_GATHER,
-                os.getenv(ENV_DB_MAX_PARALLEL_WORKERS_PER_GATHER),
-            ),
            model_init_timeout=float(os.getenv(ENV_MODEL_INIT_TIMEOUT, str(DEFAULT_MODEL_INIT_TIMEOUT))),
            # Worker configuration
            worker_enabled=os.getenv(ENV_WORKER_ENABLED, str(DEFAULT_WORKER_ENABLED)).lower() == "true",
@@ -3097,23 +2924,9 @@ class HindsightConfig:
            worker_consolidation_bank_priority=_parse_bank_priority(
                os.getenv(ENV_WORKER_CONSOLIDATION_BANK_PRIORITY, "")
            ),
-            operation_retention_days=_parse_non_negative_int(
-                ENV_OPERATION_RETENTION_DAYS,
-                os.getenv(ENV_OPERATION_RETENTION_DAYS),
-                DEFAULT_OPERATION_RETENTION_DAYS,
-            ),
-            operation_cleanup_batch_size=_parse_positive_int(
-                ENV_OPERATION_CLEANUP_BATCH_SIZE,
-                os.getenv(ENV_OPERATION_CLEANUP_BATCH_SIZE),
-                DEFAULT_OPERATION_CLEANUP_BATCH_SIZE,
-            ),
            retain_max_concurrent=int(os.getenv(ENV_RETAIN_MAX_CONCURRENT, str(DEFAULT_RETAIN_MAX_CONCURRENT))),
            # Reflect agent settings
            reflect_max_iterations=int(os.getenv(ENV_REFLECT_MAX_ITERATIONS, str(DEFAULT_REFLECT_MAX_ITERATIONS))),
-            reflect_prompt_cache_enabled=os.getenv(
-                ENV_REFLECT_PROMPT_CACHE_ENABLED, str(DEFAULT_REFLECT_PROMPT_CACHE_ENABLED)
-            ).lower()
-            in ("1", "true", "yes", "on"),
            reflect_max_context_tokens=int(
                os.getenv(ENV_REFLECT_MAX_CONTEXT_TOKENS, str(DEFAULT_REFLECT_MAX_CONTEXT_TOKENS))
            ),
@@ -3176,11 +2989,6 @@ class HindsightConfig:
            audit_log_retention_days=int(
                os.getenv(ENV_AUDIT_LOG_RETENTION_DAYS, str(DEFAULT_AUDIT_LOG_RETENTION_DAYS))
            ),
-            # Retain reliability configuration (static, server-level only)
-            fail_on_extraction_errors=os.getenv(
-                ENV_FAIL_ON_EXTRACTION_ERRORS, str(DEFAULT_FAIL_ON_EXTRACTION_ERRORS)
-            ).lower()
-            == "true",
            # LLM request tracing configuration (static, server-level only)
            llm_trace_enabled=os.getenv(ENV_LLM_TRACE_ENABLED, str(DEFAULT_LLM_TRACE_ENABLED)).lower() == "true",
            llm_trace_scopes=[
@@ -10,7 +10,7 @@ import asyncio
 import json
 import logging
 import uuid
-from collections.abc import Awaitable, Callable
+from collections.abc import Callable
 from contextlib import asynccontextmanager
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
@@ -19,7 +19,6 @@ from typing import Any
 from pydantic import BaseModel, Field

 from ..engine.db_utils import acquire_with_retry
-from ..models import RequestContext

 logger = logging.getLogger(__name__)

@@ -120,60 +119,23 @@ class AuditLogger:
        schema_getter: Callable[[], str],
        enabled: bool,
        allowed_actions: list[str],
-        bank_enabled_resolver: Callable[[str, RequestContext | None], Awaitable[bool]] | None = None,
    ) -> None:
        self._pool_getter = pool_getter
        self._schema_getter = schema_getter
        self._enabled = enabled
        self._allowed_actions: frozenset[str] | None = frozenset(allowed_actions) if allowed_actions else None
-        # Resolves the hierarchical ``audit_log_enabled`` for one bank
-        # (env -> tenant -> bank). None means "no per-bank resolution wired",
-        # in which case the global value alone decides.
-        self._bank_enabled_resolver = bank_enabled_resolver

-    def action_allowed(self, action: str) -> bool:
-        """Global action-allowlist check. Cheap, synchronous, bank-independent.
-
-        The allowlist is deployment-wide, so this is a valid pre-filter to skip
-        work for actions that can never be audited. It deliberately does NOT
-        consult the enabled flag: that is per-bank overridable, so a bank may
-        turn auditing ON even when the deployment default is off.
-        """
-        if self._allowed_actions is None:
-            return True
-        return action in self._allowed_actions
-
-    async def should_log(self, action: str, bank_id: str | None, context: RequestContext | None = None) -> bool:
-        """Full audit decision: action allowlist AND the bank's resolved switch.
-
-        ``audit_log_enabled`` is hierarchical (env -> tenant -> bank), so the
-        effective value depends on which bank the action targets. Falls back to
-        the global value when there is no bank in scope or no resolver wired.
-        """
-        if not self.action_allowed(action):
+    def is_enabled(self, action: str) -> bool:
+        """Check if audit logging is enabled for this action."""
+        if not self._enabled:
            return False
-        if bank_id is None or self._bank_enabled_resolver is None:
-            return self._enabled
-        try:
-            return await self._bank_enabled_resolver(bank_id, context)
-        except Exception as e:
-            # Never let a config-resolution failure break the request. Fall back
-            # to the deployment default: a transient DB blip must not silently
-            # create an audit gap for a bank meant to be audited. The tradeoff is
-            # the opt-out direction — a bank that overrode to false under a
-            # default-on deployment will be audited during the outage. We accept
-            # that: a few extra audit rows during a DB blip is the safer failure
-            # than dropping records that compliance may require.
-            logger.warning(f"Audit config resolution failed for bank={bank_id}: {e}; using global default")
-            return self._enabled
+        if self._allowed_actions is not None:
+            return action in self._allowed_actions
+        return True

    def log_fire_and_forget(self, entry: AuditEntry) -> None:
-        """Schedule an audit write as a background task.
-
-        Assumes the caller already made the audit decision via ``should_log``;
-        only the bank-independent allowlist is re-checked here.
-        """
-        if not self.action_allowed(entry.action):
+        """Schedule an audit write as a background task."""
+        if not self.is_enabled(entry.action):
            return
        try:
            asyncio.create_task(self._safe_log(entry))
@@ -220,7 +182,6 @@ async def audit_context(
    bank_id: str | None = None,
    request: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
-    context: RequestContext | None = None,
 ):
    """Async context manager that times the operation and writes audit on exit.

@@ -229,7 +190,7 @@ async def audit_context(
            result = await do_work()
            entry.response = result_dict
    """
-    if audit_logger is None or not await audit_logger.should_log(action, bank_id, context):
+    if audit_logger is None or not audit_logger.is_enabled(action):
        entry = AuditEntry(action=action, transport=transport, bank_id=bank_id)
        yield entry
        return
@@ -13,8 +13,6 @@ but operators should opt in with that in mind.

 from typing import Any

-RERANKER_BANK_ID_HEADER = "X-Hindsight-Bank-Id"
-

 def apply_bank_attribution(request: dict[str, Any]) -> None:
    """Tag ``request`` with ``user=<bank_id>`` for per-bank cost attribution.
@@ -34,14 +32,3 @@ def apply_bank_attribution(request: dict[str, Any]) -> None:
    bank_id = get_current_bank_id()
    if bank_id:
        request["user"] = bank_id
-
-
-def reranker_bank_attribution_headers() -> dict[str, str]:
-    """Return the fixed per-bank header for trusted remote reranker endpoints."""
-    from ..config import get_config
-    from .memory_engine import get_current_bank_id
-
-    if not get_config().reranker_send_bank_as_header:
-        return {}
-    bank_id = get_current_bank_id()
-    return {RERANKER_BANK_ID_HEADER: bank_id} if bank_id else {}
@@ -1,13 +0,0 @@
-"""Shared causal-link taxonomy.
-
-Retain writes only the canonical relationship.  Transfer import/export also
-preserves historical relationship types so existing banks keep their graph
-semantics without allowing new retain output to create those types.
-"""
-
-CANONICAL_CAUSAL_LINK_TYPE = "caused_by"
-LEGACY_CAUSAL_LINK_TYPE_NAMES = ("causes", "enables", "prevents")
-
-CANONICAL_CAUSAL_LINK_TYPES = frozenset({CANONICAL_CAUSAL_LINK_TYPE})
-LEGACY_CAUSAL_LINK_TYPES = frozenset(LEGACY_CAUSAL_LINK_TYPE_NAMES)
-CAUSAL_LINK_TYPES = (CANONICAL_CAUSAL_LINK_TYPE, *LEGACY_CAUSAL_LINK_TYPE_NAMES)
@@ -109,11 +109,6 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
            end.replace(hour=23, minute=59, second=59, microsecond=999999),
        )

-    def safe_constraint(start: datetime | None, end: datetime | None) -> DateRange | NoTemporalConstraintSentinel:
-        if start is None or end is None:
-            return NO_TEMPORAL_CONSTRAINT
-        return constraint(start, end)
-
    def subtract_months(months: int) -> datetime:
        month_index = reference_date.month - months - 1
        year = reference_date.year + month_index // 12
@@ -131,21 +126,11 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
        day = min(base_date.day, calendar.monthrange(year, month)[1])
        return base_date.replace(year=year, month=month, day=day)

-    def add_years(base_date: datetime, years: int) -> datetime | None:
+    def add_years(base_date: datetime, years: int) -> datetime:
        year = base_date.year + years
-        if year < datetime.min.year or year > datetime.max.year:
-            return None
        day = min(base_date.day, calendar.monthrange(year, base_date.month)[1])
        return base_date.replace(year=year, day=day)

-    def add_days(base_date: datetime | None, days: int) -> datetime | None:
-        if base_date is None:
-            return None
-        try:
-            return base_date + timedelta(days=days)
-        except OverflowError:
-            return None
-
    def has_chinese_temporal_context(match: re.Match[str]) -> bool:
        if match.end() >= len(query):
            return True
@@ -453,11 +438,6 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
            return NO_TEMPORAL_CONSTRAINT
        return constraint(start, reference_date)

-    def safe_since_constraint(start: datetime | None) -> DateRange | NoTemporalConstraintSentinel:
-        if start is None:
-            return NO_TEMPORAL_CONSTRAINT
-        return since_constraint(start)
-
    def since_from_period(
        period: DateRange | None,
    ) -> DateRange | NoTemporalConstraintSentinel | None:
@@ -470,7 +450,7 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
            return None
        return since_constraint(day)

-    def relative_offset_datetime(amount: int, unit: str, direction: int) -> datetime | None:
+    def relative_offset_datetime(amount: int, unit: str, direction: int) -> datetime:
        if unit in ("天", "日"):
            return reference_date + timedelta(days=direction * amount)
        if unit in ("周", "星期", "礼拜"):
@@ -479,15 +459,15 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
            return add_months(reference_date, direction * amount)
        return add_years(reference_date, direction * amount)

-    def point_constraint_at_offset(amount: int, unit: str, direction: int) -> DateRange | NoTemporalConstraintSentinel:
+    def point_constraint_at_offset(amount: int, unit: str, direction: int) -> DateRange:
        d = relative_offset_datetime(amount, unit, direction)
-        return safe_constraint(d, d)
+        return constraint(d, d)

-    def window_to_reference(amount: int, unit: str) -> DateRange | NoTemporalConstraintSentinel:
-        return safe_constraint(relative_offset_datetime(amount, unit, -1), reference_date)
+    def window_to_reference(amount: int, unit: str) -> DateRange:
+        return constraint(relative_offset_datetime(amount, unit, -1), reference_date)

-    def window_from_reference(amount: int, unit: str) -> DateRange | NoTemporalConstraintSentinel:
-        return safe_constraint(reference_date, relative_offset_datetime(amount, unit, 1))
+    def window_from_reference(amount: int, unit: str) -> DateRange:
+        return constraint(reference_date, relative_offset_datetime(amount, unit, 1))

    # Chinese rule guide
    #
@@ -801,8 +781,8 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
    if relative_year_fixed_day_since_match:
        year = relative_year_number(relative_year_fixed_day_since_match.group(1))
        base = add_years(reference_date, year - reference_date.year)
-        d = add_days(base, fixed_day_offset(relative_year_fixed_day_since_match.group(2)))
-        return safe_since_constraint(d)
+        d = base + timedelta(days=fixed_day_offset(relative_year_fixed_day_since_match.group(2)))
+        return since_constraint(d)

    fixed_day_since_match = chinese_search(
        rf"(大大后天|大后天|后天|明天|明日|今天|今日|本日|当日|当天|昨天|昨日|大大前天|大前天|前天)"
@@ -819,7 +799,7 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
        amount = parse_chinese_number(exact_relative_since_match.group(1))
        unit = exact_relative_since_match.group(2)
        if amount is not None:
-            return safe_since_constraint(relative_offset_datetime(amount, unit, -1))
+            return since_constraint(relative_offset_datetime(amount, unit, -1))

    weekend_since_match = chinese_search(
        rf"(?<![上下大小每个各隔])"
@@ -919,8 +899,8 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
    if relative_year_daypart_since_match:
        year = relative_year_number(relative_year_daypart_since_match.group(1))
        base = add_years(reference_date, year - reference_date.year)
-        d = add_days(base, daypart_day_offset(relative_year_daypart_since_match.group(2)))
-        return safe_since_constraint(d)
+        d = base + timedelta(days=daypart_day_offset(relative_year_daypart_since_match.group(2)))
+        return since_constraint(d)

    daypart_since_match = chinese_search(
        rf"(昨晚|昨夜|前晚|前夜|今晚|今早|今晨|明早|明晚|明夜){chinese_since_suffix_pattern}"
@@ -935,17 +915,17 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
    if relative_year_daypart_match:
        year = relative_year_number(relative_year_daypart_match.group(1))
        base = add_years(reference_date, year - reference_date.year)
-        d = add_days(base, daypart_day_offset(relative_year_daypart_match.group(2)))
-        return safe_constraint(d, d)
+        d = base + timedelta(days=daypart_day_offset(relative_year_daypart_match.group(2)))
+        return constraint(d, d)

    # Day-part abbreviations still resolve only to date granularity.
    if chinese_search(r"昨晚|昨夜"):
-        d = add_days(reference_date, daypart_day_offset("昨晚"))
-        return safe_constraint(d, d)
+        d = reference_date + timedelta(days=daypart_day_offset("昨晚"))
+        return constraint(d, d)

    if chinese_search(r"前晚|前夜"):
-        d = add_days(reference_date, daypart_day_offset("前晚"))
-        return safe_constraint(d, d)
+        d = reference_date + timedelta(days=daypart_day_offset("前晚"))
+        return constraint(d, d)

    if chinese_search(r"今晚|今早|今晨"):
        return constraint(reference_date, reference_date)
@@ -961,8 +941,8 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
    if relative_year_fixed_day_match:
        year = relative_year_number(relative_year_fixed_day_match.group(1))
        base = add_years(reference_date, year - reference_date.year)
-        d = add_days(base, fixed_day_offset(relative_year_fixed_day_match.group(2)))
-        return safe_constraint(d, d)
+        d = base + timedelta(days=fixed_day_offset(relative_year_fixed_day_match.group(2)))
+        return constraint(d, d)

    if chinese_search(r"昨天|昨日"):
        d = reference_date - timedelta(days=1)
@@ -1105,7 +1085,7 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
            end_amount = parse_chinese_number(amount_text[-1])
        unit = adjacent_fuzzy_future_match.group(2)
        if start_amount is not None and end_amount is not None:
-            return safe_constraint(
+            return constraint(
                relative_offset_datetime(start_amount, unit, 1),
                relative_offset_datetime(end_amount, unit, 1),
            )
@@ -1113,7 +1093,7 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
    few_future_match = chinese_search(rf"[几数]个?(天|日|周|星期|礼拜|月|年){chinese_relative_future_suffix_pattern}")
    if few_future_match:
        unit = few_future_match.group(1)
-        return safe_constraint(relative_offset_datetime(2, unit, 1), relative_offset_datetime(5, unit, 1))
+        return constraint(relative_offset_datetime(2, unit, 1), relative_offset_datetime(5, unit, 1))

    exact_future_match = chinese_search(
        rf"(?<![{_CHINESE_NUMERAL_PREFIX_CHARS}])([0-9]+|[{_CHINESE_NUMERAL_CHARS}]+)个?(天|日|周|星期|礼拜|月|年){chinese_relative_future_suffix_pattern}"
@@ -1133,7 +1113,7 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
        second_amount = parse_chinese_number(adjacent_fuzzy_past_match.group(2))
        unit = adjacent_fuzzy_past_match.group(3)
        if first_amount is not None and second_amount is not None and second_amount == first_amount + 1:
-            return safe_constraint(
+            return constraint(
                relative_offset_datetime(second_amount, unit, -1),
                relative_offset_datetime(first_amount, unit, -1),
            )
@@ -1164,7 +1144,7 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
        return constraint(reference_date - timedelta(days=150), reference_date - timedelta(days=60))

    if chinese_search(r"一两年前|[两二]三年前|三两年前"):
-        return safe_constraint(add_years(reference_date, -3), add_years(reference_date, -1))
+        return constraint(add_years(reference_date, -3), add_years(reference_date, -1))

    rolling_this_adjacent_match = chinese_search(
        r"这(一两|[两二]三|三两|三四|四五|五六|六七|七八|八九|九十)个?(天|日|周|星期|礼拜|月|年)"
@@ -1174,7 +1154,7 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
        end_amount = 3 if amount_text in ("一两", "三两") else parse_chinese_number(amount_text[-1])
        unit = rolling_this_adjacent_match.group(2)
        if end_amount is not None:
-            return safe_constraint(relative_offset_datetime(end_amount, unit, -1), reference_date)
+            return constraint(relative_offset_datetime(end_amount, unit, -1), reference_date)

    rolling_this_count_match = chinese_search(rf"这([0-9]+|[{_CHINESE_NUMERAL_CHARS}]+)个?(天|日|周|星期|礼拜|月|年)")
    if rolling_this_count_match:
@@ -1211,7 +1191,7 @@ def extract_chinese_period(query: str, reference_date: datetime) -> DateRange |
        end_amount = 3 if amount_text in ("一两", "三两") else parse_chinese_number(amount_text[-1])
        unit = rolling_past_adjacent_match.group(3)
        if end_amount is not None:
-            return safe_constraint(relative_offset_datetime(end_amount, unit, -1), reference_date)
+            return constraint(relative_offset_datetime(end_amount, unit, -1), reference_date)

    rolling_past_few_match = chinese_search(r"(过去|近|最近)几个?(天|日|周|星期|礼拜|月|年)")
    if rolling_past_few_match:
@@ -28,7 +28,6 @@ from fnmatch import fnmatchcase
 from itertools import combinations
 from typing import TYPE_CHECKING, Any, Literal

-import asyncpg
 from pydantic import BaseModel, field_validator

 from ...config import get_config
@@ -103,29 +102,6 @@ class _DedupDecision(BaseModel):
    text: str = ""  # the synthesized merged observation (when action == "merge")
    reason: str = ""

-    @field_validator("action", mode="before")
-    @classmethod
-    def _normalize_action(cls, value: object) -> str:
-        if isinstance(value, str):
-            normalized = value.strip().lower()
-            if normalized in {"merge", "keep"}:
-                return normalized
-
-        logger.warning("Invalid consolidation dedup action %r; defaulting to keep", value)
-        return "keep"
-
-
-def _dedup_decision_from_response(raw: Any) -> _DedupDecision:
-    try:
-        if isinstance(raw, _DedupDecision):
-            return raw
-        if isinstance(raw, str):
-            return _DedupDecision.model_validate_json(raw)
-        return _DedupDecision.model_validate(raw)
-    except ValueError as exc:
-        logger.warning("Invalid consolidation dedup response %r; defaulting to keep: %s", raw, exc)
-        return _DedupDecision(action="keep", reason="invalid structured response")
-

 _DEDUP_PROMPT = """You reconcile long-term memory observations. A NEW observation is about to be \
 stored, and it is highly similar to an EXISTING one:
@@ -133,20 +109,9 @@ stored, and it is highly similar to an EXISTING one:
 [NEW] {new}
 [EXISTING] {existing}

-Respond with ONLY one valid JSON object matching one of these shapes:
-
-For duplicate facts:
-{{"action": "merge", "text": "...", "reason": "..."}}
-
-For distinct facts:
-{{"action": "keep", "text": "", "reason": "..."}}
-
-Do NOT use key=value lines, markdown fences, or any text outside the JSON object.
-
-If they assert the SAME fact (wording aside), set "action" to "merge" and provide "text": a \
-single observation that preserves EVERY detail from both. If they differ in ANY important detail \
-— a number/quantity, a named entity or language, a negation, or a condition — set "action" to \
-"keep" and "text" to an empty string."""
+If they assert the SAME fact (wording aside), respond action="merge" and provide `text`: a single \
+observation that preserves EVERY detail from both. If they differ in ANY important detail — a \
+number/quantity, a named entity or language, a negation, or a condition — respond action="keep"."""


 def _dedup_active(config: Any) -> bool:
@@ -224,13 +189,10 @@ async def _dedup_adjudicate(
    if best_id is None:
        return _DedupOutcome(best_id=None, merged_text="", should_merge=False)

-    decision = _dedup_decision_from_response(
-        await dedup_llm_config.call(
-            messages=[{"role": "user", "content": _DEDUP_PROMPT.format(new=anchor_text, existing=best_text)}],
-            response_format=_DedupDecision,
-            scope="consolidation_dedup",
-            strict_schema=get_config().llm_strict_schema_consolidation,
-        )
+    decision: _DedupDecision = await dedup_llm_config.call(
+        messages=[{"role": "user", "content": _DEDUP_PROMPT.format(new=anchor_text, existing=best_text)}],
+        response_format=_DedupDecision,
+        scope="consolidation_dedup",
    )
    if decision.action != "merge":
        return _DedupOutcome(best_id=best_id, merged_text="", should_merge=False)
@@ -1804,22 +1766,15 @@ async def _append_observation_history(
    history from growing without bound.
    """
    obs_uuid = uuid.UUID(observation_id)
-    try:
-        await conn.execute(
-            f"""
+    await conn.execute(
+        f"""
        INSERT INTO {fq_table("observation_history")} (observation_id, bank_id, content, changed_at)
        VALUES ($1, $2, $3::jsonb, now())
        """,
-            obs_uuid,
-            bank_id,
-            json.dumps(asdict(snapshot)),
-        )
-    except asyncpg.exceptions.ForeignKeyViolationError:
-        logger.warning(
-            f"FK violation writing observation_history for {observation_id}: "
-            "observation was removed before history could be written (race with parallel consolidation). Skipping."
-        )
-        return
+        obs_uuid,
+        bank_id,
+        json.dumps(asdict(snapshot)),
+    )
    if max_entries and max_entries > 0:
        await conn.execute(
            f"""
@@ -2299,11 +2254,6 @@ async def _consolidate_batch_with_llm(
                ],
                "response_format": response_model,
                "scope": "consolidation",
-                # Resolved per operation (HINDSIGHT_API_LLM_STRICT_SCHEMA_CONSOLIDATION, falling
-                # back to the global flag) so an operator can grammar-enforce consolidation's
-                # structured output -- which narrows the raw-JSON failure mode behind #2668 --
-                # without forcing strict schema on operations whose model can't satisfy it.
-                "strict_schema": config.llm_strict_schema_consolidation,
            }
            # Only request an explicit output budget when configured. Left unset by default the key is
            # omitted, so each provider keeps its implicit default (backwards compatible). Operators on
@@ -7,13 +7,11 @@ Configuration via environment variables - see hindsight_api.config for all env v
 """

 import asyncio
-import gc
 import logging
 import os
 import warnings
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any

 import httpx

@@ -47,7 +45,6 @@ from ..config import (
    ENV_RERANKER_TEI_URL,
    ENV_RERANKER_ZEROENTROPY_API_KEY,
 )
-from .bank_attribution import reranker_bank_attribution_headers

 logger = logging.getLogger(__name__)

@@ -89,12 +86,6 @@ def _resolve_malloc_trim():
 _malloc_trim = _resolve_malloc_trim()


-def _release_rerank_heap() -> None:
-    """Release transient Python and native heap memory after local reranking."""
-    gc.collect()
-    _malloc_trim()
-
-
 class CrossEncoderModel(ABC):
    """
    Abstract base class for cross-encoder reranking.
@@ -324,7 +315,7 @@ class LocalSTCrossEncoder(CrossEncoderModel):
            scores = self._model.predict(pairs, batch_size=self.batch_size, show_progress_bar=False)
            return scores.tolist() if hasattr(scores, "tolist") else list(scores)
        finally:
-            _release_rerank_heap()
+            _malloc_trim()

    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
        """
@@ -493,7 +484,6 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
                semaphore,
                "POST",
                f"{self.base_url}/rerank",
-                headers=reranker_bank_attribution_headers(),
                json={
                    "query": query,
                    "texts": texts,
@@ -634,11 +624,7 @@ class _CohereCompatibleRerankClient:
            if self.include_top_n:
                body["top_n"] = len(texts)

-            response = await self._async_client.post(
-                self.rerank_url,
-                headers=reranker_bank_attribution_headers(),
-                json=body,
-            )
+            response = await self._async_client.post(self.rerank_url, json=body)
            response.raise_for_status()
            result = response.json()

@@ -1004,11 +990,11 @@ class FlashRankCrossEncoder(CrossEncoderModel):

    def _predict_sync(self, pairs: list[tuple[str, str]]) -> list[float]:
        """Synchronous predict - processes each query group."""
+        from flashrank import RerankRequest
+
        if not pairs:
            return []

-        from flashrank import RerankRequest
-
        try:
            # Group pairs by query
            query_groups: dict[str, list[tuple[int, str]]] = {}
@@ -1037,7 +1023,7 @@ class FlashRankCrossEncoder(CrossEncoderModel):

            return all_scores
        finally:
-            _release_rerank_heap()
+            _malloc_trim()

    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
        """
@@ -1165,7 +1151,6 @@ class LiteLLMCrossEncoder(CrossEncoderModel):
            # LiteLLM /rerank follows Cohere API format
            response = await self._async_client.post(
                f"{self.api_base}/rerank",
-                headers=reranker_bank_attribution_headers(),
                json={
                    "model": self.model,
                    "query": query,
@@ -1284,11 +1269,10 @@ class LiteLLMSDKCrossEncoder(CrossEncoderModel):
            indices = [idx for idx, _ in indexed_texts]

            # Build kwargs for rerank call
-            rerank_kwargs: dict[str, Any] = {
+            rerank_kwargs = {
                "model": self.model,
                "query": query,
                "documents": texts,
-                "headers": reranker_bank_attribution_headers(),
            }
            if self.api_key:
                rerank_kwargs["api_key"] = self.api_key
@@ -1297,9 +1281,21 @@ class LiteLLMSDKCrossEncoder(CrossEncoderModel):

            response = await self._litellm.arerank(**rerank_kwargs)

-            for result in response.results:
-                original_idx = result["index"]
-                all_scores[indices[original_idx]] = result["relevance_score"]
+            # Map scores back to original positions
+            # Response format: RerankResponse with results list
+            # Each result is a TypedDict with "index" and "relevance_score"
+            if hasattr(response, "results") and response.results:
+                for result in response.results:
+                    # Results are TypedDicts, use dict-style access
+                    original_idx = result["index"]
+                    score = result.get("relevance_score", result.get("score", 0.0))
+                    all_scores[indices[original_idx]] = score
+            elif isinstance(response, list):
+                # Direct list of scores (unlikely but defensive)
+                for i, score in enumerate(response):
+                    all_scores[indices[i]] = score
+            else:
+                logger.warning(f"Unexpected response format from LiteLLM rerank: {type(response)}")

        return all_scores

@@ -307,17 +307,6 @@ class DatabaseBackend(ABC):
        """Close the connection pool and release all resources."""
        ...

-    @property
-    @abstractmethod
-    def is_ready(self) -> bool:
-        """Whether the pool exists and can serve connections.
-
-        False before :meth:`initialize` and after :meth:`shutdown`. Best-effort
-        callers (tracing, auditing) check this to skip work during those windows
-        instead of acquiring and interpreting the resulting error.
-        """
-        ...
-
    @abstractmethod
    @asynccontextmanager
    async def acquire(self) -> AsyncIterator[DatabaseConnection]:
@@ -18,7 +18,6 @@ and mirrors Django's ``DatabaseOperations`` architecture.

 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from datetime import datetime
 from typing import Any

 from .base import DatabaseConnection
@@ -173,25 +172,6 @@ class DataAccessOps(ABC):
        """
        ...

-    @abstractmethod
-    async def bulk_reassert_entities(
-        self,
-        conn: DatabaseConnection,
-        table: str,
-        bank_id: str,
-        entity_ids: list[str],
-        canonical_names: list[str],
-    ) -> None:
-        """Lock resolved parents and re-create any pruned since Phase-1 resolution.
-
-        Closes the retain Phase-1/prune race (#2662): existing rows are locked
-        (PG ``FOR KEY SHARE`` / Oracle ``FOR UPDATE``) so a concurrent
-        ``prune_orphan_entities`` blocks until the caller's transaction commits,
-        while rows already deleted are re-inserted idempotently. ``entity_ids``
-        must be sorted by the caller for a stable lock order.
-        """
-        ...
-
    @abstractmethod
    async def bulk_insert_unit_entities(
        self,
@@ -504,23 +484,6 @@ class DataAccessOps(ABC):

    # -- Task claiming operations ------------------------------------------

-    @abstractmethod
-    async def prune_terminal_operations(
-        self,
-        conn: DatabaseConnection,
-        table: str,
-        cutoff: datetime,
-        *,
-        batch_size: int,
-    ) -> int:
-        """Delete one deterministic batch of terminal operations older than ``cutoff``.
-
-        Implementations must lock candidates without waiting on rows another
-        worker is pruning, never select pending/processing rows, and return the
-        number deleted. The caller provides a transaction around this method.
-        """
-        ...
-
    @abstractmethod
    async def claim_tasks(
        self,
@@ -13,8 +13,6 @@ from .base import DatabaseConnection
 from .ops import DataAccessOps, TagListingParts
 from .result import DictResultRow as ResultRow

-ORACLE_IN_LIST_LIMIT = 1000
-

 class OracleOps(DataAccessOps):
    """Oracle-specific data access operations."""
@@ -218,7 +216,7 @@ class OracleOps(DataAccessOps):
        for orig_name in missing_names:
            row = await conn.fetchrow(
                f"""
-                SELECT id, canonical_name, LOWER(canonical_name) AS name_lower
+                SELECT id, LOWER(canonical_name) AS name_lower
                FROM {table}
                WHERE bank_id = $1 AND LOWER(canonical_name) = LOWER($2)
                """,
@@ -226,37 +224,10 @@ class OracleOps(DataAccessOps):
                orig_name,
            )
            if row:
+                # Wrap in a dict-like to include input_name for downstream compat
                results.append(row)
        return results

-    async def bulk_reassert_entities(
-        self,
-        conn: DatabaseConnection,
-        table: str,
-        bank_id: str,
-        entity_ids: list[str],
-        canonical_names: list[str],
-    ) -> None:
-        # Oracle has no FOR KEY SHARE; FOR UPDATE is the row-lock equivalent that
-        # blocks a concurrent prune DELETE until this transaction commits. Lock
-        # each surviving parent in the caller's stable id order (pruned ids are
-        # simply absent here), then re-insert any that vanished. The translation
-        # layer rewrites ON CONFLICT DO NOTHING to strip-and-catch ORA-00001, so
-        # a name recreated under a new id is suppressed rather than raising.
-        for entity_id in entity_ids:
-            await conn.fetchrow(
-                f"SELECT id FROM {table} WHERE id = $1 FOR UPDATE",
-                entity_id,
-            )
-        await conn.executemany(
-            f"""
-            INSERT INTO {table} (id, bank_id, canonical_name)
-            VALUES ($1, $2, $3)
-            ON CONFLICT DO NOTHING
-            """,
-            [(entity_id, bank_id, canonical_name) for entity_id, canonical_name in zip(entity_ids, canonical_names)],
-        )
-
    async def bulk_insert_unit_entities(
        self,
        conn: DatabaseConnection,
@@ -358,12 +329,6 @@ class OracleOps(DataAccessOps):
        entities_table: str,
        bank_id: str,
    ) -> int:
-        # NB: the Postgres path additionally selects victims FOR UPDATE in sorted
-        # (entity_id_1, entity_id_2) order to prevent the #2529 deadlock against
-        # retain's sorted cooccurrence upsert. Oracle's DELETE can't carry that
-        # ordered-lock CTE the same way, so here we rely on the Pass 2/3 retry
-        # wrap in run_graph_maintenance_job (retry_with_backoff is ORA-00060
-        # deadlock-aware) to recover instead. Deliberate dialect asymmetry.
        deleted = await conn.execute(
            f"""
            DELETE FROM {ec_table}
@@ -482,14 +447,6 @@ class OracleOps(DataAccessOps):
                    FROM {ue_table} ue_target
                    WHERE ue_target.entity_id = se.entity_id
                      AND ue_target.unit_id != ALL($1::uuid[])
-                      -- Filter before applying the cap: candidates from other fact
-                      -- types must not consume this entity's bounded fan-out.
-                      AND EXISTS (
-                          SELECT 1
-                          FROM {mu_table} mu_target
-                          WHERE mu_target.id = ue_target.unit_id
-                            AND mu_target.fact_type = $2
-                      )
                    ORDER BY ue_target.unit_id DESC
                    FETCH FIRST {per_entity_limit} ROWS ONLY
                ) t
@@ -502,6 +459,7 @@ class OracleOps(DataAccessOps):
                       es.score, 'entity' AS source
                FROM entity_scores es
                JOIN {mu_table} mu ON mu.id = es.unit_id
+                WHERE mu.fact_type = $2
                ORDER BY es.score DESC
                FETCH FIRST $3 ROWS ONLY
            )"""
@@ -866,157 +824,6 @@ class OracleOps(DataAccessOps):

    # -- Task claiming operations ------------------------------------------

-    async def prune_terminal_operations(
-        self,
-        conn: DatabaseConnection,
-        table: str,
-        cutoff: datetime,
-        *,
-        batch_size: int,
-    ) -> int:
-        # Oracle rejects a row-limited SELECT ... FOR UPDATE (ORA-02014). Pick
-        # the deterministic bounded IDs first, then lock only that candidate
-        # set and re-check eligibility before deleting in the same transaction.
-        # Clamp to Oracle's 1000-expression IN-list limit because the adapter
-        # expands the candidate UUID list into individual bind variables.
-        # Cancelled children cannot complete parent aggregation, so retain the
-        # parent guard only for completed/failed children. Before removing a
-        # cancelled child, preserve its signal by cancelling a pending parent
-        # in this transaction and refreshing the parent's retention window.
-        # Validate metadata before HEXTORAW: CASE makes malformed UUIDs yield
-        # NULL while keeping the indexed RAW parent.operation_id key unwrapped.
-        effective_batch_size = min(batch_size, ORACLE_IN_LIST_LIMIT)
-        candidates = await conn.fetch(
-            f"""
-            SELECT candidate_operation.operation_id
-            FROM {table} candidate_operation
-            WHERE candidate_operation.status IN ('completed', 'failed', 'cancelled')
-              AND candidate_operation.updated_at < $1
-              AND (
-                  candidate_operation.status = 'cancelled'
-                  OR NOT EXISTS (
-                      SELECT 1
-                      FROM {table} parent
-                      WHERE parent.operation_id = CASE
-                          WHEN REGEXP_LIKE(
-                              JSON_VALUE(
-                                  candidate_operation.result_metadata,
-                                  '$.parent_operation_id' RETURNING VARCHAR2(36) NULL ON ERROR
-                              ),
-                              '^[0-9A-Fa-f]{{8}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{12}}$'
-                          )
-                          THEN HEXTORAW(REPLACE(
-                              JSON_VALUE(
-                                  candidate_operation.result_metadata,
-                                  '$.parent_operation_id' RETURNING VARCHAR2(36) NULL ON ERROR
-                              ),
-                              '-',
-                              ''
-                          ))
-                          ELSE NULL
-                      END
-                        AND parent.bank_id = candidate_operation.bank_id
-                  )
-              )
-            ORDER BY candidate_operation.updated_at, candidate_operation.operation_id
-            LIMIT $2
-            """,
-            cutoff,
-            effective_batch_size,
-        )
-        if not candidates:
-            return 0
-
-        candidate_ids = [row["operation_id"] for row in candidates]
-        locked = await conn.fetch(
-            f"""
-            SELECT candidate_operation.operation_id
-            FROM {table} candidate_operation
-            WHERE candidate_operation.operation_id = ANY($1)
-              AND candidate_operation.status IN ('completed', 'failed', 'cancelled')
-              AND candidate_operation.updated_at < $2
-              AND (
-                  candidate_operation.status = 'cancelled'
-                  OR NOT EXISTS (
-                      SELECT 1
-                      FROM {table} parent
-                      WHERE parent.operation_id = CASE
-                          WHEN REGEXP_LIKE(
-                              JSON_VALUE(
-                                  candidate_operation.result_metadata,
-                                  '$.parent_operation_id' RETURNING VARCHAR2(36) NULL ON ERROR
-                              ),
-                              '^[0-9A-Fa-f]{{8}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{12}}$'
-                          )
-                          THEN HEXTORAW(REPLACE(
-                              JSON_VALUE(
-                                  candidate_operation.result_metadata,
-                                  '$.parent_operation_id' RETURNING VARCHAR2(36) NULL ON ERROR
-                              ),
-                              '-',
-                              ''
-                          ))
-                          ELSE NULL
-                      END
-                        AND parent.bank_id = candidate_operation.bank_id
-                  )
-              )
-            ORDER BY candidate_operation.updated_at, candidate_operation.operation_id
-            FOR UPDATE OF candidate_operation.operation_id SKIP LOCKED
-            """,
-            candidate_ids,
-            cutoff,
-        )
-        if not locked:
-            return 0
-        operation_ids = [row["operation_id"] for row in locked]
-        await conn.execute(
-            f"""
-            UPDATE {table} parent
-            SET status = 'cancelled',
-                updated_at = now(),
-                completed_at = COALESCE(parent.completed_at, now()),
-                error_message = COALESCE(
-                    parent.error_message,
-                    'Cancelled because a child operation was cancelled'
-                )
-            WHERE parent.status = 'pending'
-              AND EXISTS (
-                  SELECT 1
-                  FROM {table} candidate_operation
-                  WHERE candidate_operation.operation_id = ANY($1)
-                    AND candidate_operation.status = 'cancelled'
-                    AND candidate_operation.updated_at < $2
-                    AND candidate_operation.bank_id = parent.bank_id
-                    AND parent.operation_id = CASE
-                        WHEN REGEXP_LIKE(
-                            JSON_VALUE(
-                                candidate_operation.result_metadata,
-                                '$.parent_operation_id' RETURNING VARCHAR2(36) NULL ON ERROR
-                            ),
-                            '^[0-9A-Fa-f]{{8}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{4}}-[0-9A-Fa-f]{{12}}$'
-                        )
-                        THEN HEXTORAW(REPLACE(
-                            JSON_VALUE(
-                                candidate_operation.result_metadata,
-                                '$.parent_operation_id' RETURNING VARCHAR2(36) NULL ON ERROR
-                            ),
-                            '-',
-                            ''
-                        ))
-                        ELSE NULL
-                    END
-              )
-            """,
-            operation_ids,
-            cutoff,
-        )
-        await conn.execute(
-            f"DELETE FROM {table} WHERE operation_id = ANY($1)",
-            operation_ids,
-        )
-        return len(operation_ids)
-
    async def _claim_consolidation_tasks(
        self,
        conn,
@@ -4,40 +4,11 @@ Uses unnest(), LATERAL, DISTINCT ON, and native array operations for
 efficient batch operations.
 """

-from datetime import datetime
-
 from .base import DatabaseConnection
 from .ops import DataAccessOps, TagListingParts
 from .result import ResultRow


-def pg_search_vector_expr(
-    config,
-    *,
-    text_col: str = "text",
-    context_col: str = "context",
-    signals_col: str = "text_signals",
-) -> str | None:
-    """SQL expression that builds ``search_vector`` for the configured PG text-search backend.
-
-    Single source of truth shared by the batch insert (over the ``input_data``
-    CTE columns) and the curation revert recompute (over a ``memory_units`` row),
-    so the two can never drift. Returns ``None`` for backends that leave
-    ``search_vector`` unpopulated — pgroonga / pg_textsearch / pg_search index the
-    base text columns directly and keep only a dummy column, so there is nothing
-    to build.
-
-    ``text_search_extension_native_language`` is validated as a PG identifier in
-    ``HindsightConfig.validate()``, so embedding it as a SQL literal is safe.
-    """
-    combined = f"COALESCE({text_col}, '') || ' ' || COALESCE({context_col}, '') || ' ' || COALESCE({signals_col}, '')"
-    if config.text_search_extension == "vchord":
-        return f"tokenize({combined}, 'llmlingua2')::bm25_catalog.bm25vector"
-    if config.text_search_extension == "native":
-        return f"to_tsvector('{config.text_search_extension_native_language}'::regconfig, {combined})"
-    return None
-
-
 class PostgreSQLOps(DataAccessOps):
    """PostgreSQL-specific data access operations using unnest and LATERAL."""

@@ -122,39 +93,101 @@ class PostgreSQLOps(DataAccessOps):
        config = get_config()
        table = self._get_mu_table()

-        # search_vector is populated inline for backends that store a real vector
-        # (native tsvector, vchord bm25vector). pgroonga / pg_textsearch / pg_search
-        # index the base text columns directly and keep only a dummy column, so the
-        # expression is None and the column is left out of the insert entirely.
-        # Same expression is reused by curation revert (see pg_search_vector_expr).
-        sv_expr = pg_search_vector_expr(config)
-        sv_insert_col = ", search_vector" if sv_expr else ""
-        sv_select_val = f",\n                    {sv_expr}" if sv_expr else ""
-        query = f"""
-            WITH input_data AS (
-                SELECT * FROM unnest(
-                    $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
-                    $8::text[], $9::text[], $10::jsonb[], $11::text[], $12::text[], $13::jsonb[], $14::jsonb[], $15::text[]
-                ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                       context, fact_type, metadata, chunk_id, document_id, tags_json,
-                       observation_scopes_json, text_signals)
-            )
-            INSERT INTO {table} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                                 context, fact_type, metadata, chunk_id, document_id, tags,
-                                 observation_scopes, text_signals{sv_insert_col})
-            SELECT
-                $1,
-                text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
-                context, fact_type, metadata, chunk_id, document_id,
-                COALESCE(
-                    (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
-                    '{{}}'::varchar[]
-                ),
-                observation_scopes_json,
-                text_signals{sv_select_val}
-            FROM input_data
-            RETURNING id
-        """
+        if config.text_search_extension == "vchord":
+            query = f"""
+                WITH input_data AS (
+                    SELECT * FROM unnest(
+                        $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                        $8::text[], $9::text[], $10::jsonb[], $11::text[], $12::text[], $13::jsonb[], $14::jsonb[], $15::text[]
+                    ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                           context, fact_type, metadata, chunk_id, document_id, tags_json,
+                           observation_scopes_json, text_signals)
+                )
+                INSERT INTO {table} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                     context, fact_type, metadata, chunk_id, document_id, tags,
+                                     observation_scopes, text_signals, search_vector)
+                SELECT
+                    $1,
+                    text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                    context, fact_type, metadata, chunk_id, document_id,
+                    COALESCE(
+                        (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
+                        '{{}}'::varchar[]
+                    ),
+                    observation_scopes_json,
+                    text_signals,
+                    tokenize(
+                        COALESCE(text, '') || ' ' || COALESCE(context, '') || ' ' || COALESCE(text_signals, ''),
+                        'llmlingua2'
+                    )::bm25_catalog.bm25vector
+                FROM input_data
+                RETURNING id
+            """
+        elif config.text_search_extension == "native":
+            # search_vector is a regular tsvector column populated here using the
+            # configured native dictionary. It used to be GENERATED ALWAYS with
+            # a hardcoded 'english', which prevented per-deployment language
+            # configuration. text_search_extension_native_language is validated
+            # in HindsightConfig.validate() as a PG identifier, so embedding it
+            # as a SQL literal is safe.
+            query = f"""
+                WITH input_data AS (
+                    SELECT * FROM unnest(
+                        $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                        $8::text[], $9::text[], $10::jsonb[], $11::text[], $12::text[], $13::jsonb[], $14::jsonb[], $15::text[]
+                    ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                           context, fact_type, metadata, chunk_id, document_id, tags_json,
+                           observation_scopes_json, text_signals)
+                )
+                INSERT INTO {table} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                     context, fact_type, metadata, chunk_id, document_id, tags,
+                                     observation_scopes, text_signals, search_vector)
+                SELECT
+                    $1,
+                    text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                    context, fact_type, metadata, chunk_id, document_id,
+                    COALESCE(
+                        (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
+                        '{{}}'::varchar[]
+                    ),
+                    observation_scopes_json,
+                    text_signals,
+                    to_tsvector(
+                        '{config.text_search_extension_native_language}'::regconfig,
+                        COALESCE(text, '') || ' ' || COALESCE(context, '') || ' ' || COALESCE(text_signals, '')
+                    )
+                FROM input_data
+                RETURNING id
+            """
+        else:
+            # pg_textsearch, pgroonga, and pg_search: search_vector is a dummy
+            # TEXT column; the actual full-text index operates on the base text
+            # columns directly, so we don't populate search_vector at insert time.
+            query = f"""
+                WITH input_data AS (
+                    SELECT * FROM unnest(
+                        $2::text[], $3::vector[], $4::timestamptz[], $5::timestamptz[], $6::timestamptz[], $7::timestamptz[],
+                        $8::text[], $9::text[], $10::jsonb[], $11::text[], $12::text[], $13::jsonb[], $14::jsonb[], $15::text[]
+                    ) AS t(text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                           context, fact_type, metadata, chunk_id, document_id, tags_json,
+                           observation_scopes_json, text_signals)
+                )
+                INSERT INTO {table} (bank_id, text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                                     context, fact_type, metadata, chunk_id, document_id, tags,
+                                     observation_scopes, text_signals)
+                SELECT
+                    $1,
+                    text, embedding, event_date, occurred_start, occurred_end, mentioned_at,
+                    context, fact_type, metadata, chunk_id, document_id,
+                    COALESCE(
+                        (SELECT array_agg(elem) FROM jsonb_array_elements_text(tags_json) AS elem),
+                        '{{}}'::varchar[]
+                    ),
+                    observation_scopes_json,
+                    text_signals
+                FROM input_data
+                RETURNING id
+            """

        results = await conn.fetch(
            query,
@@ -277,7 +310,7 @@ class PostgreSQLOps(DataAccessOps):
    ) -> list[ResultRow]:
        return await conn.fetch(
            f"""
-            SELECT e.id, e.canonical_name, LOWER(e.canonical_name) AS name_lower, inputs.input_name
+            SELECT e.id, LOWER(e.canonical_name) AS name_lower, inputs.input_name
            FROM {table} e
            JOIN (
                SELECT LOWER(n) AS input_name_lower, n AS input_name
@@ -289,42 +322,6 @@ class PostgreSQLOps(DataAccessOps):
            missing_names,
        )

-    async def bulk_reassert_entities(
-        self,
-        conn: DatabaseConnection,
-        table: str,
-        bank_id: str,
-        entity_ids: list[str],
-        canonical_names: list[str],
-    ) -> None:
-        # One statement, one round-trip (same shape as bulk_insert_links):
-        #   * the CTE takes FOR KEY SHARE on every parent that still exists,
-        #     held to COMMIT, so a concurrent prune_orphan_entities DELETE blocks
-        #     until the caller's unit_entities insert has committed;
-        #   * the INSERT re-creates only the parents that were already pruned
-        #     (NOT IN locked), carrying the canonical_name resolved in Phase 1.
-        # ON CONFLICT DO NOTHING (no target) keeps the rare case where another
-        # worker recreated the name under a new id from raising — that row stays
-        # absent and its unit link is the sole casualty, never the whole batch.
-        await conn.execute(
-            f"""
-            WITH locked AS (
-                SELECT id FROM {table}
-                WHERE id = ANY($2::uuid[])
-                ORDER BY id
-                FOR KEY SHARE
-            )
-            INSERT INTO {table} (id, bank_id, canonical_name)
-            SELECT t.entity_id, $1, t.canonical_name
-            FROM unnest($2::uuid[], $3::text[]) AS t(entity_id, canonical_name)
-            WHERE t.entity_id NOT IN (SELECT id FROM locked)
-            ON CONFLICT DO NOTHING
-            """,
-            bank_id,
-            entity_ids,
-            canonical_names,
-        )
-
    async def bulk_insert_unit_entities(
        self,
        conn: DatabaseConnection,
@@ -428,48 +425,19 @@ class PostgreSQLOps(DataAccessOps):
        # Scope by joining through entities.bank_id (entity_cooccurrences itself
        # has no bank_id column — entities don't span banks, so scoping via
        # entity_id_1 is sufficient).
-        #
-        # Ordered locking (deadlock avoidance, #2529): retain's concurrent
-        # cooccurrence upsert (entity_resolver._flush_pending) locks rows in
-        # sorted (entity_id_1, entity_id_2) order — sorted specifically to give
-        # every writer one consistent lock-acquisition order. A plain
-        # `DELETE ... USING` scans/locks in whatever order the join plan picks,
-        # so it could lock the same rows in the opposite order and cycle. We
-        # instead select the victims in that same sorted order `FOR UPDATE`
-        # first — the locking clause materialises the CTE and places LockRows
-        # above the Sort, so locks are acquired ascending, matching the upsert —
-        # then delete the already-locked rows. Same order on both sides ⇒ no
-        # cycle (the deadlock is prevented, not merely retried). The Pass 2/3
-        # retry wrap in run_graph_maintenance_job stays as a backstop for the
-        # residual paths (FK cascade from prune_orphan_entities, Oracle).
-        #
-        # The staleness predicate is an INTERSECT of the two entities' unit sets
-        # rather than the equivalent `unit_entities u1 JOIN u2 ON u1.unit_id =
-        # u2.unit_id` self-join (#2473): both INTERSECT branches resolve as Index
-        # Only Scans on idx_unit_entities_entity_unit (entity_id, unit_id), so the
-        # per-pair cost is bounded by the two entities' degrees. The self-join let
-        # the planner pick an anti-join that rescanned a high-degree hub entity's
-        # membership set for every pair — 28-30min on a bank with a ~100K-membership
-        # hub, even when zero rows were stale. Don't "simplify" it back.
        result = await conn.execute(
            f"""
-            WITH victims AS (
-                SELECT c.entity_id_1, c.entity_id_2
-                FROM {ec_table} c
-                JOIN {entities_table} e ON e.id = c.entity_id_1
-                WHERE e.bank_id = $1
-                  AND NOT EXISTS (
-                      SELECT unit_id FROM {ue_table} WHERE entity_id = c.entity_id_1
-                      INTERSECT
-                      SELECT unit_id FROM {ue_table} WHERE entity_id = c.entity_id_2
-                  )
-                ORDER BY c.entity_id_1, c.entity_id_2
-                FOR UPDATE OF c
-            )
            DELETE FROM {ec_table} c
-            USING victims v
-            WHERE c.entity_id_1 = v.entity_id_1
-              AND c.entity_id_2 = v.entity_id_2
+            USING {entities_table} e
+            WHERE e.id = c.entity_id_1
+              AND e.bank_id = $1
+              AND NOT EXISTS (
+                  SELECT 1
+                  FROM {ue_table} u1
+                  JOIN {ue_table} u2 ON u1.unit_id = u2.unit_id
+                  WHERE u1.entity_id = c.entity_id_1
+                    AND u2.entity_id = c.entity_id_2
+              )
            """,
            bank_id,
        )
@@ -573,18 +541,11 @@ class PostgreSQLOps(DataAccessOps):
                    FROM {ue_table} ue_target
                    WHERE ue_target.entity_id = se.entity_id
                      AND ue_target.unit_id != ALL($1::uuid[])
-                      -- Filter before applying the cap: candidates from other fact
-                      -- types must not consume this entity's bounded fan-out.
-                      AND EXISTS (
-                          SELECT 1
-                          FROM {mu_table} mu_target
-                          WHERE mu_target.id = ue_target.unit_id
-                            AND mu_target.fact_type = $2
-                      )
                    ORDER BY ue_target.unit_id DESC
                    LIMIT {per_entity_limit}
                ) t
                JOIN {mu_table} mu ON mu.id = t.unit_id
+                WHERE mu.fact_type = $2
                GROUP BY mu.id
                ORDER BY score DESC
                LIMIT $3
@@ -933,93 +894,6 @@ class PostgreSQLOps(DataAccessOps):

    # -- Task claiming operations ------------------------------------------

-    async def prune_terminal_operations(
-        self,
-        conn: DatabaseConnection,
-        table: str,
-        cutoff: datetime,
-        *,
-        batch_size: int,
-    ) -> int:
-        # Lock only the bounded candidate set. SKIP LOCKED lets multiple
-        # workers prune disjoint batches without waiting or double-deleting.
-        # Cancelled children cannot complete parent aggregation, so retain the
-        # parent guard only for completed/failed children. Before removing a
-        # cancelled child, preserve its signal by cancelling a pending parent
-        # in this transaction and refreshing the parent's retention window.
-        candidates = await conn.fetch(
-            f"""
-            SELECT candidate_operation.operation_id
-            FROM {table} candidate_operation
-            WHERE candidate_operation.status IN ('completed', 'failed', 'cancelled')
-              AND candidate_operation.updated_at < $1
-              AND (
-                  candidate_operation.status = 'cancelled'
-                  OR NOT EXISTS (
-                      SELECT 1
-                      FROM {table} parent
-                      WHERE parent.operation_id = CASE
-                          WHEN candidate_operation.result_metadata->>'parent_operation_id'
-                              ~* '^[0-9a-f]{{8}}-[0-9a-f]{{4}}-[0-9a-f]{{4}}-[0-9a-f]{{4}}-[0-9a-f]{{12}}$'
-                          THEN (candidate_operation.result_metadata->>'parent_operation_id')::uuid
-                          ELSE NULL
-                      END
-                        AND parent.bank_id = candidate_operation.bank_id
-                  )
-              )
-            ORDER BY candidate_operation.updated_at, candidate_operation.operation_id
-            LIMIT $2
-            FOR UPDATE OF candidate_operation SKIP LOCKED
-            """,
-            cutoff,
-            batch_size,
-        )
-        if not candidates:
-            return 0
-
-        candidate_ids = [row["operation_id"] for row in candidates]
-        await conn.execute(
-            f"""
-            UPDATE {table} parent
-            SET status = 'cancelled',
-                updated_at = now(),
-                completed_at = COALESCE(parent.completed_at, now()),
-                error_message = COALESCE(
-                    parent.error_message,
-                    'Cancelled because a child operation was cancelled'
-                )
-            WHERE parent.status = 'pending'
-              AND EXISTS (
-                  SELECT 1
-                  FROM {table} candidate_operation
-                  WHERE candidate_operation.operation_id = ANY($1)
-                    AND candidate_operation.status = 'cancelled'
-                    AND candidate_operation.updated_at < $2
-                    AND candidate_operation.bank_id = parent.bank_id
-                    AND parent.operation_id = CASE
-                        WHEN candidate_operation.result_metadata->>'parent_operation_id'
-                            ~* '^[0-9a-f]{{8}}-[0-9a-f]{{4}}-[0-9a-f]{{4}}-[0-9a-f]{{4}}-[0-9a-f]{{12}}$'
-                        THEN (candidate_operation.result_metadata->>'parent_operation_id')::uuid
-                        ELSE NULL
-                    END
-              )
-            """,
-            candidate_ids,
-            cutoff,
-        )
-        rows = await conn.fetch(
-            f"""
-            DELETE FROM {table}
-            WHERE operation_id = ANY($1)
-              AND status IN ('completed', 'failed', 'cancelled')
-              AND updated_at < $2
-            RETURNING operation_id
-            """,
-            candidate_ids,
-            cutoff,
-        )
-        return len(rows)
-
    async def _claim_consolidation_tasks(
        self,
        conn,
@@ -1242,10 +1242,6 @@ class OracleBackend(DatabaseBackend):
    def __init__(self) -> None:
        self._pool: Any = None
        self._oracledb: Any = None
-        # Oracle pooled sessions retain CURRENT_SCHEMA across checkouts. Cache
-        # SESSION_USER so default-schema acquisitions can explicitly reset a
-        # connection that was previously used for a tenant schema.
-        self._default_schema: str | None = None

    async def initialize(
        self,
@@ -1281,17 +1277,11 @@ class OracleBackend(DatabaseBackend):
        logger.info(f"Oracle pool created (min={min_size}, max={max_size})")

    async def shutdown(self) -> None:
-        # Drop the reference before awaiting close() so is_ready flips False for
-        # the whole teardown, not just after it completes (see PostgreSQLBackend).
-        pool, self._pool = self._pool, None
-        if pool is not None:
-            await pool.close(force=True)
+        if self._pool is not None:
+            await self._pool.close(force=True)
+            self._pool = None
            logger.info("Oracle pool closed")

-    @property
-    def is_ready(self) -> bool:
-        return self._pool is not None
-
    async def _set_session_schema(self, conn: Any) -> None:
        """Set the session schema on an Oracle connection.

@@ -1304,23 +1294,10 @@ class OracleBackend(DatabaseBackend):
        from ..memory_engine import get_current_schema

        schema = get_current_schema()
-        cursor = conn.cursor()
-        try:
-            if self._default_schema is None:
-                await cursor.execute("SELECT SYS_CONTEXT('USERENV', 'SESSION_USER') FROM DUAL")
-                row = await cursor.fetchone()
-                if not row or not row[0]:
-                    raise RuntimeError("Oracle did not return SESSION_USER while resetting CURRENT_SCHEMA")
-                self._default_schema = str(row[0])
-
-            target_schema = self._default_schema if not schema or schema == "public" else schema
-            safe_schema = target_schema.replace('"', '""')
-            await cursor.execute(f'ALTER SESSION SET CURRENT_SCHEMA = "{safe_schema}"')
-        finally:
-            # oracledb's AsyncCursor.close() is synchronous (not a coroutine);
-            # awaiting it raises "object NoneType can't be used in 'await'
-            # expression" and aborts every acquire().
-            cursor.close()
+        if schema and schema != "public":
+            cursor = conn.cursor()
+            await cursor.execute(f'ALTER SESSION SET CURRENT_SCHEMA = "{schema}"')
+            await cursor.close()

    @asynccontextmanager
    async def acquire(self) -> AsyncIterator[OracleConnection]:
@@ -95,12 +95,7 @@ class PostgreSQLBackend(DatabaseBackend):
            command_timeout=command_timeout,
            statement_cache_size=statement_cache_size,
            timeout=acquire_timeout,
-            # init runs once per new connection; setup runs on every acquire,
-            # after asyncpg's release-time RESET ALL. Passing init_callback as
-            # both keeps the per-connection session GUCs (hnsw.ef_search, etc.)
-            # applied after a connection is reused, not just on first creation.
            init=init_callback,
-            setup=init_callback,
        )
        logger.info(
            f"PostgreSQL pool created (min={min_size}, max={max_size}, "
@@ -108,19 +103,11 @@ class PostgreSQLBackend(DatabaseBackend):
        )

    async def shutdown(self) -> None:
-        # Drop the reference *before* awaiting close(): closing is not
-        # instantaneous, and anything acquiring during that window would
-        # otherwise get an asyncpg "pool is closing" error rather than seeing
-        # is_ready False.
-        pool, self._pool = self._pool, None
-        if pool is not None:
-            await pool.close()
+        if self._pool is not None:
+            await self._pool.close()
+            self._pool = None
            logger.info("PostgreSQL pool closed")

-    @property
-    def is_ready(self) -> bool:
-        return self._pool is not None
-
    @asynccontextmanager
    async def acquire(self) -> AsyncIterator[PostgresConnection]:
        pool = self._ensure_pool()
@@ -4,7 +4,6 @@ Database utility functions for connection management with retry logic.

 import asyncio
 import logging
-import random
 import time
 from collections.abc import AsyncIterator
 from contextlib import AsyncExitStack, asynccontextmanager
@@ -17,20 +16,6 @@ DEFAULT_MAX_RETRIES = 3
 DEFAULT_BASE_DELAY = 0.5  # seconds
 DEFAULT_MAX_DELAY = 5.0  # seconds

-
-def _backoff_delay(attempt: int, base_delay: float, max_delay: float) -> float:
-    """Exponential backoff with equal jitter.
-
-    Deterministic backoff makes concurrent retriers wake in lock-step and
-    re-collide on the very same rows, re-triggering the deadlock they just
-    backed off from. "Equal jitter" — half the window fixed, half random —
-    keeps a floor (so we don't hot-spin) while decorrelating the wake-ups, so
-    two contenders that deadlocked together are very unlikely to retry in sync.
-    """
-    ceil = min(base_delay * (2**attempt), max_delay)
-    return ceil / 2 + random.uniform(0, ceil / 2)
-
-
 # Retryable exception types (checked by class name to avoid hard imports)
 _RETRYABLE_EXCEPTION_NAMES = frozenset(
    {
@@ -93,7 +78,7 @@ async def retry_with_backoff(
                raise
            last_exception = e
            if attempt < max_retries:
-                delay = _backoff_delay(attempt, base_delay, max_delay)
+                delay = min(base_delay * (2**attempt), max_delay)
                if type(e).__name__ == "DeadlockDetectedError" or _is_oracle_deadlock(e):
                    logger.warning(
                        "Deadlock detected during parallel document processing — "
@@ -151,7 +136,7 @@ async def acquire_with_retry(backend_or_pool: Any, max_retries: int = DEFAULT_MA
                    if not _is_retryable(e):
                        raise
                    if attempt < max_retries:
-                        delay = _backoff_delay(attempt, DEFAULT_BASE_DELAY, DEFAULT_MAX_DELAY)
+                        delay = min(DEFAULT_BASE_DELAY * (2**attempt), DEFAULT_MAX_DELAY)
                        logger.warning(
                            f"Database acquire failed (attempt {attempt + 1}/{max_retries + 1}): {e}. "
                            f"Retrying in {delay:.1f}s..."
@@ -76,25 +76,6 @@ class _ZeroEntropyEmbedResponse(BaseModel):
    results: list[_ZeroEntropyEmbedResult]


-def _truncate_to_tokens(text: str, max_tokens: int) -> tuple[str, int]:
-    """Truncate ``text`` to at most ``max_tokens`` cl100k_base tokens.
-
-    tiktoken is an approximation of any given provider's tokenizer, so set
-    ``max_tokens`` with a little headroom below the model's real limit.
-
-    Returns the (possibly truncated) text and the original token count (so the
-    caller can report how much was dropped); the count equals ``len(tokens)``
-    whether or not truncation occurred.
-    """
-    from .token_encoding import get_token_encoding
-
-    enc = get_token_encoding()
-    tokens = enc.encode(text)
-    if len(tokens) <= max_tokens:
-        return text, len(tokens)
-    return enc.decode(tokens[:max_tokens]), len(tokens)
-
-
 class Embeddings(ABC):
    """
    Abstract base class for embedding generation.
@@ -1221,7 +1202,6 @@ class LiteLLMSDKEmbeddings(Embeddings):
        batch_size: int = 100,
        timeout: float = 60.0,
        encoding_format: str | None = "float",
-        max_input_tokens: int | None = None,
    ):
        """
        Initialize LiteLLM SDK embeddings client.
@@ -1236,10 +1216,6 @@ class LiteLLMSDKEmbeddings(Embeddings):
            timeout: Request timeout in seconds (default: 60.0)
            encoding_format: Encoding format for embeddings (default: "float").
                Set to None or empty string to omit (needed for Voyage AI, Gemini).
-            max_input_tokens: If set, truncate each input text to this many tokens
-                (tiktoken cl100k_base) before embedding. Needed for models with a
-                fixed input-token limit (e.g. Bedrock Titan V2's hard 8192 cap),
-                where an oversized text would otherwise fail permanently (#2501).
        """
        self.api_key = api_key
        self.model = model
@@ -1248,7 +1224,6 @@ class LiteLLMSDKEmbeddings(Embeddings):
        self.batch_size = batch_size
        self.timeout = timeout
        self.encoding_format = encoding_format or None
-        self.max_input_tokens = max_input_tokens
        self._litellm = None  # Will be set during initialization
        self._dimension: int | None = None

@@ -1325,33 +1300,6 @@ class LiteLLMSDKEmbeddings(Embeddings):
        if not texts:
            return []

-        # Truncate oversized inputs before hitting the provider. Models with a
-        # fixed input-token limit (e.g. Bedrock Titan V2, 8192) reject an
-        # oversized text with a permanent error rather than truncating it
-        # server-side, which strands the caller (e.g. a delta mental model whose
-        # content grew past the cap) with no recovery path. See #2501.
-        if self.max_input_tokens is not None:
-            truncated_texts = []
-            original_token_counts = []
-            for t in texts:
-                new_text, original_tokens = _truncate_to_tokens(t, self.max_input_tokens)
-                truncated_texts.append(new_text)
-                if original_tokens > self.max_input_tokens:
-                    original_token_counts.append(original_tokens)
-            texts = truncated_texts
-            if original_token_counts:
-                logger.warning(
-                    "Embeddings: truncated %d of %d input(s) to %d tokens for model %s "
-                    "(largest was ~%d tokens); embedded content is incomplete. "
-                    "This usually means a mental model's content has grown past the model's "
-                    "input limit — see issue #2501.",
-                    len(original_token_counts),
-                    len(texts),
-                    self.max_input_tokens,
-                    self.model,
-                    max(original_token_counts),
-                )
-
        all_embeddings = []

        # Process in batches
@@ -1743,7 +1691,6 @@ def create_embeddings_from_env() -> Embeddings:
            api_base=config.embeddings_litellm_sdk_api_base,
            output_dimensions=config.embeddings_litellm_sdk_output_dimensions,
            encoding_format=config.embeddings_litellm_sdk_encoding_format,
-            max_input_tokens=config.embeddings_litellm_sdk_max_input_tokens,
        )
    elif provider == "google":
        vertexai_project_id = config.embeddings_vertexai_project_id
@@ -9,11 +9,10 @@ import asyncio
 import json
 import logging
 from collections import defaultdict
-from collections.abc import Iterator
 from dataclasses import dataclass, field
 from datetime import UTC, datetime
 from difflib import SequenceMatcher
-from typing import Any, Final, cast
+from typing import Any, Final

 from .db_utils import acquire_with_retry
 from .memory_engine import fq_table
@@ -26,7 +25,6 @@ from .retain.entity_labels import (
 from .retain.entity_labels import (
    parse_entity_labels as _parse_entity_labels,
 )
-from .retain.types import ResolvedEntity

 logger = logging.getLogger(__name__)

@@ -77,22 +75,6 @@ def _later_date(a: datetime | None, b: datetime | None) -> datetime | None:
    return a if a > b else b


-def _canonical_cooccurrence_pairs(entity_list: list[str]) -> Iterator[tuple[str, str]]:
-    """Yield each distinct pair of ``entity_list`` as ``(a, b)`` with ``a < b``.
-
-    Canonical ordering matches the entity_cooccurrences PK and check constraint.
-    The pair is ordered into fresh locals rather than by swapping the loop
-    variables: ``entity_id_1`` is the outer iterate, so swapping it would leak
-    into the remaining inner iterations and build later pairs off the wrong
-    element.
-    """
-    for i, entity_id_1 in enumerate(entity_list):
-        for entity_id_2 in entity_list[i + 1 :]:
-            if entity_id_1 == entity_id_2:
-                continue
-            yield (entity_id_1, entity_id_2) if entity_id_1 < entity_id_2 else (entity_id_2, entity_id_1)
-
-
@dataclass
 class _CooccurrencePair:
    """A (entity_id_1, entity_id_2) pair observed in a retain batch (for post-txn flush)."""
@@ -248,7 +230,7 @@ class EntityResolver:
        unit_event_date,
        conn=None,
        entity_labels: list | None = None,
-    ) -> list[ResolvedEntity]:
+    ) -> list[str]:
        """
        Resolve multiple entities in batch (MUCH faster than sequential).

@@ -263,8 +245,7 @@ class EntityResolver:
            conn: Optional connection to use (if None, acquires from pool)

        Returns:
-            Resolved entity identities (id + stored canonical name) in the same
-            order as input.
+            List of entity IDs in same order as input
        """
        if not entities_data:
            return []
@@ -290,7 +271,7 @@ class EntityResolver:
        unit_event_date,
        taxonomy_lookup: set[str] | None = None,
        labels_cfg=None,
-    ) -> list[ResolvedEntity]:
+    ) -> list[str]:
        if self.entity_lookup == "trigram":
            # Route to backend-specific fuzzy strategy.
            # Non-PG backends (Oracle) use UTL_MATCH instead of pg_trgm.
@@ -330,7 +311,7 @@ class EntityResolver:
        unit_event_date,
        taxonomy_lookup: set[str] | None = None,
        labels_cfg=None,
-    ) -> list[ResolvedEntity]:
+    ) -> list[str]:
        """Original strategy: load all bank entities then match in Python."""
        # Query ALL candidates for this bank
        all_entities = await conn.fetch(
@@ -414,7 +395,7 @@ class EntityResolver:
        unit_event_date,
        taxonomy_lookup: set[str] | None = None,
        labels_cfg=None,
-    ) -> list[ResolvedEntity]:
+    ) -> list[str]:
        """
        Trigram strategy: fetch only similar candidates per entity name using pg_trgm.

@@ -518,7 +499,7 @@ class EntityResolver:
        unit_event_date: datetime | None,
        taxonomy_lookup: set[str] | None = None,
        labels_cfg=None,
-    ) -> list[ResolvedEntity]:
+    ) -> list[str]:
        """
        Oracle strategy: fetch similar candidates using UTL_MATCH.JARO_WINKLER_SIMILARITY.

@@ -626,14 +607,11 @@ class EntityResolver:
        cooccurrence_map: dict[str, set[str]],
        taxonomy_lookup: set[str] | None = None,
        labels_cfg=None,
-    ) -> list[ResolvedEntity]:
+    ) -> list[str]:
        """Shared scoring + upsert logic used by both lookup strategies."""

-        # Resolve each entity using pre-fetched candidates. A slot stays None
-        # only if find-or-create fails to produce a row for a mention (a DB
-        # inconsistency); it surfaces as a clear error at the reassert boundary
-        # rather than a silent NOT NULL violation deeper in Phase 2.
-        resolved: list[ResolvedEntity | None] = [None] * len(entities_data)
+        # Resolve each entity using pre-fetched candidates
+        entity_ids = [None] * len(entities_data)
        entities_to_update: list[_EntityStat] = []
        entities_to_create: list[_EntityToCreate] = []

@@ -660,23 +638,21 @@ class EntityResolver:

            if is_label:
                # Exact case-insensitive match only for label entities
-                exact_match: ResolvedEntity | None = None
+                exact_match = None
                entity_text_lower = entity_text.lower()
                for candidate_id, canonical_name, metadata, last_seen, mention_count in candidates:
                    if canonical_name.lower() == entity_text_lower:
-                        exact_match = ResolvedEntity(entity_id=candidate_id, canonical_name=canonical_name)
+                        exact_match = candidate_id
                        break
                if exact_match:
-                    resolved[idx] = exact_match
-                    entities_to_update.append(
-                        _EntityStat(entity_id=exact_match.entity_id, event_date=entity_event_date)
-                    )
+                    entity_ids[idx] = exact_match
+                    entities_to_update.append(_EntityStat(entity_id=exact_match, event_date=entity_event_date))
                else:
                    entities_to_create.append(_EntityToCreate(idx=idx, name=entity_text, event_date=entity_event_date))
                continue

            # Score candidates
-            best_candidate: ResolvedEntity | None = None
+            best_candidate = None
            best_score = 0.0

            nearby_entity_set = {e["text"].lower() for e in nearby_entities if e["text"] != entity_text}
@@ -709,14 +685,14 @@ class EntityResolver:

                if score > best_score:
                    best_score = score
-                    best_candidate = ResolvedEntity(entity_id=candidate_id, canonical_name=canonical_name)
+                    best_candidate = candidate_id

            # Apply unified threshold
            threshold = 0.6

-            if best_score > threshold and best_candidate is not None:
-                resolved[idx] = best_candidate
-                entities_to_update.append(_EntityStat(entity_id=best_candidate.entity_id, event_date=entity_event_date))
+            if best_score > threshold:
+                entity_ids[idx] = best_candidate
+                entities_to_update.append(_EntityStat(entity_id=best_candidate, event_date=entity_event_date))
            else:
                entities_to_create.append(
                    _EntityToCreate(idx=idx, name=entity_data["text"], event_date=entity_event_date)
@@ -749,9 +725,6 @@ class EntityResolver:
            sorted_groups = sorted(groups.items())
            entity_names = [g.name for _, g in sorted_groups]
            entity_dates = [g.event_date for _, g in sorted_groups]
-            # Stored canonical name per lowercase key, so a resurrected parent
-            # keeps the name it was created/matched with rather than a fallback.
-            canonical_by_name = {name_lower: g.name for name_lower, g in sorted_groups}

            # INSERT ... ON CONFLICT DO NOTHING — no row lock on already-existing entities.
            # mention_count starts at 0 here; flush_pending_stats() is the sole source of
@@ -786,14 +759,11 @@ class EntityResolver:
                )
                for row in existing_rows:
                    id_by_name[row["name_lower"]] = row["id"]
-                    canonical_by_name[row["name_lower"]] = row["canonical_name"]
                    # 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 the database produce different lowercase strings.
                    if "input_name" in row:
-                        input_name_lower = row["input_name"].lower()
-                        id_by_name[input_name_lower] = row["id"]
-                        canonical_by_name[input_name_lower] = row["canonical_name"]
+                        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,
@@ -801,64 +771,16 @@ class EntityResolver:
            for name_lower, g in sorted_groups:
                entity_id = id_by_name.get(name_lower)
                if entity_id:
-                    canonical_name = canonical_by_name.get(name_lower, g.name)
                    for original_idx in g.indices:
-                        resolved[original_idx] = ResolvedEntity(entity_id=entity_id, canonical_name=canonical_name)
-                        pending.append(_EntityStat(entity_id=str(entity_id), event_date=g.event_date))
+                        entity_ids[original_idx] = entity_id
+                        pending.append(_EntityStat(entity_id=entity_id, event_date=g.event_date))

        # Accumulate into the resolver's pending list; the orchestrator flushes
        # these with await entity_resolver.flush_pending_stats() after the txn.
        key = self._task_key()
        self._pending_stats.setdefault(key, []).extend(pending)

-        missing = [i for i, entity in enumerate(resolved) if entity is None]
-        if missing:
-            raise RuntimeError(
-                f"Entity resolution produced no row for {len(missing)} mention(s) "
-                f"(indices {missing[:5]}); refusing to link units to a missing parent."
-            )
-        return cast(list[ResolvedEntity], resolved)
-
-    async def reassert_entities_batch(
-        self,
-        bank_id: str,
-        resolved_entities: list[ResolvedEntity],
-        conn,
-    ) -> None:
-        """Lock (and, if pruned, re-create) resolved parents before linking units.
-
-        Phase-1 resolution and the Phase-2 ``unit_entities`` insert run on
-        different transactions. In the gap, ``prune_orphan_entities`` can delete
-        a just-resolved parent — it legitimately has no ``unit_entities`` row
-        yet — and the Phase-2 FK insert then fails, dropping the whole batch as
-        non-retryable (silent memory loss, #2662).
-
-        Called on the Phase-2 connection immediately before
-        ``link_units_to_entities_batch``, this locks the parents that still
-        exist (so the pruner blocks until we commit) and re-inserts any that
-        already vanished, in one round-trip. An entity referenced by a live unit
-        is by definition not an orphan, so resurrecting it is correct.
-        """
-        # Deduplicate by id and lock in a stable order so concurrent reasserts
-        # acquire row locks consistently (same convention as bulk_insert_links).
-        seen: set[str] = set()
-        unique: list[ResolvedEntity] = []
-        for entity in sorted(resolved_entities, key=lambda e: e.entity_id):
-            if entity.entity_id in seen:
-                continue
-            seen.add(entity.entity_id)
-            unique.append(entity)
-
-        if not unique:
-            return
-
-        await self._ops.bulk_reassert_entities(
-            conn,
-            fq_table("entities"),
-            bank_id,
-            [entity.entity_id for entity in unique],
-            [entity.canonical_name for entity in unique],
-        )
+        return entity_ids

    async def link_units_to_entities_batch(
        self,
@@ -931,12 +853,20 @@ class EntityResolver:
        for unit_id, entity_ids in unit_to_entities.items():
            entity_list = list(entity_ids)
            event_date = unit_event_date.get(unit_id)
-            for key in _canonical_cooccurrence_pairs(entity_list):
-                prev = cooccurrence_pairs.get(key, _SENTINEL_MISSING)
-                if prev is _SENTINEL_MISSING:
-                    cooccurrence_pairs[key] = event_date
-                else:
-                    cooccurrence_pairs[key] = _later_date(prev, event_date)
+            for i, entity_id_1 in enumerate(entity_list):
+                for entity_id_2 in entity_list[i + 1 :]:
+                    if entity_id_1 == entity_id_2:
+                        continue
+                    # Canonical ordering (entity_id_1 < entity_id_2) matches the
+                    # entity_cooccurrences PK and check constraint.
+                    if entity_id_1 > entity_id_2:
+                        entity_id_1, entity_id_2 = entity_id_2, entity_id_1
+                    key = (entity_id_1, entity_id_2)
+                    prev = cooccurrence_pairs.get(key, _SENTINEL_MISSING)
+                    if prev is _SENTINEL_MISSING:
+                        cooccurrence_pairs[key] = event_date
+                    else:
+                        cooccurrence_pairs[key] = _later_date(prev, event_date)

        # Accumulate co-occurrence pairs for post-transaction flush.
        # The actual INSERT/UPDATE is deferred to flush_pending_stats() to avoid
@@ -66,20 +66,6 @@ MAX_SEMANTIC_LINKS_PER_UNIT = 50
 # under 1s.
 _DRAIN_BATCH_SIZE = 50

-# Retry budget for the idempotent Pass 2/3 entity/cooccurrence sweep. Higher
-# than db_utils' default (3) because the sweep has no client waiting on it and
-# is safe to rerun, so we'd rather spend a longer jittered-backoff tail than
-# drop a maintenance pass and leak stale graph rows (see run_graph_maintenance_job).
-_SWEEP_MAX_RETRIES = 8
-
-
-@dataclass
-class _SweepCounts:
-    """Prune counts returned by the Pass 2/3 sweep (avoids a bare tuple return)."""
-
-    orphan_entities_pruned: int
-    stale_cooccurrences_pruned: int
-

@dataclass
 class JobResult:
@@ -217,51 +203,27 @@ async def run_graph_maintenance_job(

    # --- Pass 2 & 3: entity / cooccurrence sweeps ---
    # Bank-wide single-statement deletes. Cheap when there's nothing to do.
-    #
-    # Unlike Pass 1's queue claim, these DELETEs aren't protected by any
-    # consistent lock-ordering guarantee: prune_stale_cooccurrences scans
-    # entity_cooccurrences via a join/NOT EXISTS plan, while retain's
-    # concurrent cooccurrence upserts (entity_resolver._flush_pending) lock
-    # the same rows in sorted (entity_id_1, entity_id_2) order. When a sweep
-    # and a concurrent upsert touch overlapping rows in opposite orders,
-    # Postgres detects a genuine circular wait and aborts one side with
-    # DeadlockDetectedError. Both prunes are idempotent bank-wide sweeps —
-    # rerunning only deletes what's still stale — so retrying the whole
-    # transaction on deadlock is safe.
-    from .db_utils import retry_with_backoff
    from .memory_engine import acquire_with_retry

-    async def _run_sweep() -> _SweepCounts:
-        async with acquire_with_retry(backend) as conn:
-            async with conn.transaction():
-                orphan_pruned = await ops.prune_orphan_entities(
-                    conn,
-                    fq_table("entities"),
-                    fq_table("unit_entities"),
-                    bank_id,
-                )
-                # The orphan prune above cascades cooccurrences via FK. The
-                # explicit cooccurrence pass below catches the *stale-count*
-                # case: both entities still exist but no current unit
-                # witnesses them together.
-                stale_pruned = await ops.prune_stale_cooccurrences(
-                    conn,
-                    fq_table("entity_cooccurrences"),
-                    fq_table("unit_entities"),
-                    fq_table("entities"),
-                    bank_id,
-                )
-                return _SweepCounts(orphan_entities_pruned=orphan_pruned, stale_cooccurrences_pruned=stale_pruned)
-
-    # A larger retry budget than the default (3): this is idempotent background
-    # maintenance with no client waiting on it, so a longer retry tail costs
-    # nothing, whereas a dropped sweep silently leaks orphan entities / stale
-    # cooccurrences until the next run. With jittered backoff a single sweep
-    # contending against continuous retain upserts effectively never exhausts
-    # this budget (each retry independently clears with high probability).
-    sweep = await retry_with_backoff(_run_sweep, max_retries=_SWEEP_MAX_RETRIES)
-    result.orphan_entities_pruned = sweep.orphan_entities_pruned
-    result.stale_cooccurrences_pruned = sweep.stale_cooccurrences_pruned
+    async with acquire_with_retry(backend) as conn:
+        async with conn.transaction():
+            result.orphan_entities_pruned = await ops.prune_orphan_entities(
+                conn,
+                fq_table("entities"),
+                fq_table("unit_entities"),
+                bank_id,
+            )
+            # The orphan prune above cascades cooccurrences via FK. The
+            # explicit cooccurrence pass below catches the *stale-count*
+            # case: both entities still exist but no current unit witnesses
+            # them together.
+            result.stale_cooccurrences_pruned = await ops.prune_stale_cooccurrences(
+                conn,
+                fq_table("entity_cooccurrences"),
+                fq_table("unit_entities"),
+                fq_table("entities"),
+                bank_id,
+            )

    elapsed = time.time() - job_start
    logger.info(
@@ -565,30 +565,6 @@ class MemoryEngineInterface(ABC):
        """
        ...

-    @abstractmethod
-    async def delete_operation(
-        self,
-        bank_id: str,
-        operation_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> dict[str, Any]:
-        """
-        Delete a terminal async operation record.
-
-        Args:
-            bank_id: The memory bank ID.
-            operation_id: The operation ID to delete.
-            request_context: Request context for authentication.
-
-        Returns:
-            Dict with success status and message.
-
-        Raises:
-            ValueError: If operation not found.
-        """
-        ...
-
    @abstractmethod
    async def update_bank(
        self,
@@ -6,53 +6,12 @@ enabling support for multiple LLM backends (OpenAI, Anthropic, Gemini, Codex, et
 """

 from abc import ABC, abstractmethod
-from dataclasses import dataclass
 from datetime import datetime
-from enum import StrEnum
-from typing import Any, Self
+from typing import Any

 from .response_models import LLMToolCallResult


-class LLMToolChoiceMode(StrEnum):
-    """Canonical tool-selection modes shared by every LLM provider."""
-
-    AUTO = "auto"
-    NONE = "none"
-    REQUIRED = "required"
-    NAMED = "named"
-
-
-@dataclass(frozen=True, slots=True)
-class LLMToolChoice:
-    """Typed internal tool selection serialized only at provider boundaries."""
-
-    mode: LLMToolChoiceMode
-    function_name: str | None = None
-
-    def __post_init__(self) -> None:
-        if self.mode is LLMToolChoiceMode.NAMED:
-            if self.function_name is None or not self.function_name or self.function_name != self.function_name.strip():
-                raise ValueError("Named tool choice requires a non-empty canonical function name")
-        elif self.function_name is not None:
-            raise ValueError(f"Tool choice mode {self.mode.value!r} cannot include a function name")
-
-    @classmethod
-    def named(cls, function_name: str) -> Self:
-        return cls(mode=LLMToolChoiceMode.NAMED, function_name=function_name)
-
-    @property
-    def selected_function_name(self) -> str:
-        if self.function_name is None:
-            raise ValueError("Tool choice does not select a named function")
-        return self.function_name
-
-
-LLM_TOOL_CHOICE_AUTO = LLMToolChoice(mode=LLMToolChoiceMode.AUTO)
-LLM_TOOL_CHOICE_NONE = LLMToolChoice(mode=LLMToolChoiceMode.NONE)
-LLM_TOOL_CHOICE_REQUIRED = LLMToolChoice(mode=LLMToolChoiceMode.REQUIRED)
-
-
 class LLMInterface(ABC):
    """
    Abstract interface for LLM providers.
@@ -155,9 +114,8 @@ class LLMInterface(ABC):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
        cached_prefix: str | None = None,
-        cached_prefix_message_count: int = 0,
    ) -> LLMToolCallResult:
        """
        Make an LLM API call with tool/function calling support.
@@ -171,7 +129,7 @@ class LLMInterface(ABC):
            max_retries: Maximum retry attempts.
            initial_backoff: Initial backoff time in seconds.
            max_backoff: Maximum backoff time in seconds.
-            tool_choice: Canonical tool-selection policy.
+            tool_choice: How to choose tools - "auto", "none", "required", or specific function.

        Returns:
            LLMToolCallResult with content and/or tool_calls.
@@ -227,45 +185,6 @@ class LLMInterface(ABC):
        """
        return None

-    # ── Step-by-step incremental prompt caching (optional) ─────────────────────
-    #
-    # For agentic loops (reflect) the dominant cost is the conversation prefix
-    # re-sent every turn, not the static system prefix. Providers that can cache
-    # a *growing* prefix implement these: the caller rolls one cache per step
-    # (each covering the previous step's full input), passes its handle plus the
-    # message count it covers to ``call_with_tools`` so only the new turns are
-    # sent fresh, and tears the caches down when the loop ends. Default no-ops so
-    # non-supporting providers transparently run uncached.
-
-    def supports_incremental_prompt_cache(self) -> bool:
-        """Whether this provider can cache a growing multi-turn conversation prefix."""
-        return False
-
-    async def create_incremental_cache(
-        self,
-        *,
-        session_id: str,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]] | None = None,
-    ) -> str | None:
-        """Cache ``system + tools + messages`` and return an opaque handle, or None.
-
-        The handle is passed back to ``call_with_tools(cached_prefix=...,
-        cached_prefix_message_count=len(messages))``. Caches are grouped under
-        ``session_id`` for teardown via ``delete_cache_session``. Returns None
-        when caching is unavailable or the prefix is too small — caller falls
-        back to an uncached call.
-        """
-        return None
-
-    async def delete_cached_prefix(self, name: str) -> None:
-        """Best-effort delete of a single cache handle (a superseded step)."""
-        return None
-
-    async def delete_cache_session(self, session_id: str) -> None:
-        """Best-effort teardown of every cache created under ``session_id``."""
-        return None
-
    async def submit_batch(
        self,
        requests: list[dict[str, Any]],
@@ -376,26 +376,6 @@ class LLMTraceRecorder:
        # INSERTs it patches — but it must not block on unrelated operations).
        self._pending: dict[str | None, set[asyncio.Task]] = {}

-    def _writable(self) -> Any | None:
-        """Return the pool to write through, or None if writing isn't possible.
-
-        Covers the two lifecycle windows in which best-effort trace writes must
-        be skipped rather than attempted: before the backend pool is created
-        (``initialize()`` verifies the LLM before the DB is up) and during/after
-        shutdown. Writes already in flight need no handling — the pools close
-        gracefully, waiting for their connections to be released.
-        """
-        pool = self._pool_getter()
-        if pool is None:
-            return None
-        # Backends declare readiness explicitly; a raw pool (some callers pass
-        # one directly) has no lifecycle flag and is assumed usable.
-        from .db.base import DatabaseBackend
-
-        if isinstance(pool, DatabaseBackend) and not pool.is_ready:
-            return None
-        return pool
-
    def is_enabled(self, scope: str) -> bool:
        """Whether tracing is active for the given call scope."""
        if not self._enabled:
@@ -493,7 +473,7 @@ class LLMTraceRecorder:

    async def _safe_write(self, record: LLMRequestRecord) -> None:
        """Write a trace row. Errors are logged, never raised."""
-        pool = self._writable()
+        pool = self._pool_getter()
        if pool is None:
            logger.debug("LLM trace skipped: pool not available")
            return
@@ -588,9 +568,8 @@ class LLMTraceRecorder:
        # so the UPDATE patches rows that already exist rather than racing ahead
        # of them (without blocking on unrelated operations' pending writes).
        await self._flush_pending(trace_id)
-        pool = self._writable()
+        pool = self._pool_getter()
        if pool is None:
-            logger.debug("LLM trace memory_id attach skipped: pool not available")
            return
        try:
            schema = self._schema_getter()
@@ -12,8 +12,6 @@ import uuid
 from contextlib import AsyncExitStack
 from typing import TYPE_CHECKING, Any

-from json_repair import repair_json
-
 # Vertex AI imports (conditional - for LLMProvider to pass credentials to GeminiLLM)
 try:
    from google.oauth2 import service_account
@@ -29,11 +27,13 @@ from ..config import (
    ENV_REFLECT_LLM_MAX_CONCURRENT,
    ENV_RETAIN_LLM_MAX_CONCURRENT,
 )
-from .llm_interface import LLM_TOOL_CHOICE_AUTO, LLMToolChoice, LLMToolChoiceMode

 if TYPE_CHECKING:
    from .response_models import LLMToolCallResult

+# Seed applied to every Groq request for deterministic behavior.
+DEFAULT_LLM_SEED = 4242
+
 logger = logging.getLogger(__name__)

 # Disable httpx logging
@@ -113,7 +113,7 @@ def _request_params(
    temperature: float | None = None,
    scope: str | None = None,
    response_format: Any | None = None,
-    tool_choice: LLMToolChoice | None = None,
+    tool_choice: str | dict[str, Any] | None = None,
 ) -> dict[str, Any] | None:
    """Build the requested-params bag for tracing — only values the caller set.

@@ -128,8 +128,8 @@ def _request_params(
        params["temperature"] = temperature
    if response_format is not None:
        params["response_schema"] = getattr(response_format, "__name__", None) or "structured"
-    if tool_choice is not None and tool_choice.mode is not LLMToolChoiceMode.AUTO:
-        params["tool_choice"] = tool_choice.function_name or tool_choice.mode.value
+    if tool_choice is not None and tool_choice != "auto":
+        params["tool_choice"] = tool_choice if isinstance(tool_choice, str) else "named"
    return params or None


@@ -184,14 +184,6 @@ def parse_llm_json(raw: str) -> Any:
    1. Markdown code fences (```json ... ```) — strip them before parsing.
    2. Embedded control characters (\\x00-\\x1f, \\x7f) — replace with space
       and retry if the initial parse fails.
-    3. Structural malformation (trailing commas, unterminated strings, single
-       quotes, invalid ``\\escape`` sequences) — repaired as a last resort via
-       ``json_repair`` (#2547/#2544).
-
-    The repair pass is purely *structural*: it fixes JSON that ``json.loads``
-    cannot parse at all. It deliberately does NOT touch content semantics —
-    degenerate-but-valid JSON (repetition loops or leaked scaffolding inside
-    string values) parses fine here and is out of scope for this helper.

    Args:
        raw: Raw text returned by the LLM.
@@ -200,8 +192,7 @@ def parse_llm_json(raw: str) -> Any:
        Parsed Python object (dict, list, etc.).

    Raises:
-        json.JSONDecodeError: If the text cannot be parsed even after cleanup
-            and structural repair (e.g. repair yields an empty result).
+        json.JSONDecodeError: If the text cannot be parsed even after cleanup.
    """
    text = raw.strip()

@@ -218,19 +209,7 @@ def parse_llm_json(raw: str) -> Any:
        # Some models (e.g. Gemini) embed raw control characters inside JSON
        # string values. Replacing them with a space usually produces valid JSON.
        cleaned = re.sub(r"[\x00-\x1f\x7f]", " ", text)
-
-    try:
        return json.loads(cleaned)
-    except json.JSONDecodeError:
-        # Last resort: structural repair of malformed JSON. ``repair_json`` never
-        # raises — unrecoverable input yields an empty result ("" / {} / []). Keep
-        # failing loudly in that case rather than let an empty object masquerade
-        # as a successful parse: callers (retry ladders, the #1833 fail-loud path)
-        # rely on JSONDecodeError to retry or surface the failure.
-        repaired = repair_json(cleaned, return_objects=True)
-        if not repaired:
-            raise
-        return repaired


 _PROVIDERS_WITHOUT_API_KEY = frozenset(
@@ -256,17 +235,6 @@ def requires_api_key(provider: str) -> bool:
    return provider.lower() not in _PROVIDERS_WITHOUT_API_KEY


-def _validate_ollama_num_ctx(value: Any) -> int | None:
-    """Validate a native Ollama context-window override."""
-    if value is None:
-        return None
-    if isinstance(value, bool) or not isinstance(value, int):
-        raise ValueError(f"ollama_num_ctx must be a positive integer, got {value!r}")
-    if value < 1:
-        raise ValueError(f"ollama_num_ctx must be >= 1, got {value}")
-    return value
-
-
 def create_llm_provider(
    provider: str,
    api_key: str,
@@ -286,7 +254,6 @@ def create_llm_provider(
    litellmrouter_config: dict[str, Any] | None = None,
    gemini_service_tier: str | None = None,
    timeout: float | None = None,
-    ollama_num_ctx: int | None = None,
 ) -> Any:  # Returns LLMInterface
    """
    Factory function to create the appropriate LLM provider implementation.
@@ -301,8 +268,6 @@ def create_llm_provider(
        openai_service_tier: OpenAI service tier (for OpenAI provider) - None (default) or "flex" (50% cheaper).
        bedrock_service_tier: Bedrock service tier (for Bedrock provider) - None (default), "flex", "priority", or "reserved".
        gemini_service_tier: Gemini service tier (for Gemini provider) - None (default) or "flex" (50% cheaper).
-        ollama_num_ctx: Native Ollama context window override. None lets Ollama use the
-            model/server default.
        extra_body: Extra request-body params merged into the provider's native
            call. Threaded into OpenAI-compatible, Fireworks, Anthropic, Gemini/
            VertexAI and LiteLLM providers (each merges them in its own parameter
@@ -326,8 +291,6 @@ def create_llm_provider(
    Returns:
        LLMInterface implementation for the specified provider.
    """
-    ollama_num_ctx = _validate_ollama_num_ctx(ollama_num_ctx)
-
    from .providers import (
        AnthropicLLM,
        ClaudeCodeLLM,
@@ -531,7 +494,6 @@ def create_llm_provider(
            groq_service_tier=groq_service_tier,
            openai_service_tier=openai_service_tier,
            extra_body=extra_body,
-            ollama_num_ctx=ollama_num_ctx,
            timeout=timeout,
        )

@@ -569,7 +531,6 @@ class LLMProvider:
        max_retries: int | None = None,
        initial_backoff: float | None = None,
        max_backoff: float | None = None,
-        ollama_num_ctx: int | None = None,
    ):
        """
        Initialize LLM provider.
@@ -584,8 +545,6 @@ class LLMProvider:
            openai_service_tier: OpenAI service tier (None or "flex") - from config.
            bedrock_service_tier: Bedrock service tier (None, "flex", "priority", "reserved") - from config.
            gemini_service_tier: Gemini service tier (None or "flex") - from config.
-            ollama_num_ctx: Native Ollama context window override. ``None`` lets Ollama
-                use the model/server default.
            gemini_safety_settings: Safety settings for Gemini/VertexAI providers.
            extra_body: Extra request-body params merged into the provider's native call
                (OpenAI-compatible, Fireworks, Anthropic, Gemini/VertexAI, LiteLLM).
@@ -639,7 +598,6 @@ class LLMProvider:
        self.openai_service_tier = openai_service_tier
        self.bedrock_service_tier = bedrock_service_tier
        self.gemini_service_tier = gemini_service_tier
-        self.ollama_num_ctx = _validate_ollama_num_ctx(ollama_num_ctx)
        # Gemini safety settings (instance default; can be overridden per-request via context var)
        self.gemini_safety_settings = gemini_safety_settings
        # Gemini prompt caching: when True, retain extraction (and any future
@@ -784,7 +742,6 @@ class LLMProvider:
            gemini_safety_settings=self.gemini_safety_settings,
            prompt_cache_enabled=self.prompt_cache_enabled,
            litellmrouter_config=router_config,
-            ollama_num_ctx=self.ollama_num_ctx,
            timeout=self.timeout,
        )

@@ -846,7 +803,7 @@ class LLMProvider:
        initial_backoff: float | None = None,
        max_backoff: float | None = None,
        skip_validation: bool = False,
-        strict_schema: bool | None = None,
+        strict_schema: bool = False,
        return_usage: bool = False,
        cached_prefix: str | None = None,
    ) -> Any:
@@ -867,10 +824,9 @@ class LLMProvider:
                configured default (``llm_max_backoff``), else 60.0.
            skip_validation: Return raw JSON without Pydantic validation.
            strict_schema: Per-call override requesting grammar-enforced (json_schema strict)
-                structured output instead of the soft json_object path. None (the default)
-                inherits the server-level HINDSIGHT_API_LLM_STRICT_SCHEMA flag; an explicit
-                True or False wins over it, so a caller can force strict output on -- or off --
-                for its own scope. Providers without a strict mode ignore it.
+                structured output instead of the soft json_object path. The server-level
+                HINDSIGHT_API_LLM_STRICT_SCHEMA flag is OR-ed in here so it applies to every call;
+                providers without a strict mode ignore it.
            return_usage: If True, return tuple (result, TokenUsage) instead of just result.

        Returns:
@@ -905,18 +861,14 @@ class LLMProvider:
        )

        # Resolve strict-schema once, here, rather than in each provider: the
-        # per-call argument, falling back to the server-level
-        # HINDSIGHT_API_LLM_STRICT_SCHEMA flag when the caller expressed no
-        # preference. Providers with a json_schema response_format (OpenAI-compatible,
+        # per-call argument OR the server-level HINDSIGHT_API_LLM_STRICT_SCHEMA
+        # flag. Providers with a json_schema response_format (OpenAI-compatible,
        # LiteLLM) then grammar-enforce structured output instead of the fragile
        # soft json_object path; Gemini already enforces its native response_schema,
        # and providers without a strict mode simply ignore the flag.
        from ..config import get_config

-        # An explicit per-call value wins in BOTH directions -- `or` would have made a
-        # per-call False indistinguishable from "unset", silently ignoring any caller
-        # that opts out while the global flag is on.
-        strict_schema = strict_schema if strict_schema is not None else get_config().llm_strict_schema
+        strict_schema = strict_schema or get_config().llm_strict_schema

        # LLM call observability flows through the OTel GenAI recorder
        # (tracing.get_span_recorder().record_llm_call). Provider implementations
@@ -1013,9 +965,8 @@ class LLMProvider:
        max_retries: int | None = None,
        initial_backoff: float | None = None,
        max_backoff: float | None = None,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
        cached_prefix: str | None = None,
-        cached_prefix_message_count: int = 0,
    ) -> "LLMToolCallResult":
        """
        Make an LLM API call with tool/function calling support.
@@ -1032,7 +983,7 @@ class LLMProvider:
                configured default (``llm_initial_backoff``), else 1.0.
            max_backoff: Maximum backoff time in seconds. ``None`` uses the provider's
                configured default (``llm_max_backoff``), else 30.0.
-            tool_choice: Canonical tool-selection policy.
+            tool_choice: How to choose tools - "auto", "none", "required", or {"type": "function", "function": {"name": "..."}}

        Returns:
            LLMToolCallResult with content and/or tool_calls.
@@ -1083,14 +1034,9 @@ class LLMProvider:
                    await stack.enter_async_context(sem)

                # cached_prefix is only set for providers that returned a handle
-                # from get_or_create_cached_prefix() / create_incremental_cache();
-                # forward it (plus how many leading messages it covers) only when
-                # present so non-caching providers keep their signature.
-                cache_kwarg = (
-                    {"cached_prefix": cached_prefix, "cached_prefix_message_count": cached_prefix_message_count}
-                    if cached_prefix is not None
-                    else {}
-                )
+                # from get_or_create_cached_prefix(); forward it only when present
+                # so non-caching providers keep their signature (same as call()).
+                cache_kwarg = {"cached_prefix": cached_prefix} if cached_prefix is not None else {}
                try:
                    # Delegate to provider implementation
                    result = await self._provider_impl.call_with_tools(
@@ -1314,7 +1260,6 @@ class LLMProvider:
            ENV_LLM_GROQ_SERVICE_TIER,
            ENV_LLM_LITELLMROUTER_CONFIG,
            ENV_LLM_MODEL,
-            ENV_LLM_OLLAMA_NUM_CTX,
            ENV_LLM_OPENAI_SERVICE_TIER,
            ENV_LLM_PROMPT_CACHE_ENABLED,
            ENV_LLM_PROVIDER,
@@ -1325,7 +1270,6 @@ class LLMProvider:
            ENV_LLM_VERTEXAI_SERVICE_ACCOUNT_KEY,
            _get_default_model_for_provider,
            _parse_llm_router_config,
-            _parse_optional_positive_int,
            parse_gemini_service_tier,
        )

@@ -1370,7 +1314,6 @@ class LLMProvider:
            ),
            gemini_safety_settings=json.loads(os.getenv(ENV_LLM_GEMINI_SAFETY_SETTINGS, "null")),
            prompt_cache_enabled=prompt_cache_enabled,
-            ollama_num_ctx=_parse_optional_positive_int(ENV_LLM_OLLAMA_NUM_CTX, os.getenv(ENV_LLM_OLLAMA_NUM_CTX)),
            litellmrouter_config=_parse_llm_router_config(ENV_LLM_LITELLMROUTER_CONFIG),
            vertexai_project_id=os.getenv(ENV_LLM_VERTEXAI_PROJECT_ID) or None,
            vertexai_region=os.getenv(ENV_LLM_VERTEXAI_REGION) or None,
@@ -21,10 +21,9 @@ from one place, so we don't spawn a separate ``asyncio`` task per concern:
 The loop wakes on a short fixed tick and runs each job when its own
 ``last_run + interval`` is due (run-at-start, then on interval), so adding jobs
 with different cadences doesn't burst CPU. Cross-tenant discovery goes through
-server-side PL/pgSQL routines (``schemas_with_expired_rows`` and
-``banks_needing_consolidation``, in the configured schema — see ``fq_routine``) —
-one round-trip each — instead of a per-schema query storm, which matters at
-thousands of tenants.
+server-side PL/pgSQL routines (``public.schemas_with_expired_rows`` and
+``public.banks_needing_consolidation``) — one round-trip each — instead of a
+per-schema query storm, which matters at thousands of tenants.
 """

 from __future__ import annotations
@@ -33,13 +32,13 @@ import asyncio
 import logging
 import time
 from collections.abc import Coroutine
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any

 from ..config import HindsightConfig, get_config
 from ..models import RequestContext
 from .db_utils import acquire_with_retry
-from .schema import _is_oracle, fq_routine, fq_table, fq_table_explicit
+from .schema import _is_oracle, fq_table

 if TYPE_CHECKING:
    from .memory_engine import MemoryEngine
@@ -50,10 +49,6 @@ logger = logging.getLogger(__name__)
 _TICK_SECONDS = 60
 # Retention sweeps are not time-sensitive; hourly matches the previous per-sweep cadence.
 _RETENTION_INTERVAL_SECONDS = 3600
-# Operation cleanup deletes one bounded batch per schema per run, so its cadence
-# sets the drain rate for a backlog. Kept at one-per-tick (the value it used while
-# it rode the worker's poll loop) so throughput is unchanged by the move.
-_OPERATION_CLEANUP_INTERVAL_SECONDS = 60


 class MaintenanceLoop:
@@ -102,14 +97,10 @@ class MaintenanceLoop:
    def _any_job_enabled() -> bool:
        cfg = get_config()
        reconcile_on = cfg.consolidation_reconcile_interval_seconds > 0
-        # Not gated on audit_log_enabled: that is per-bank overridable, so rows
-        # can exist even when the deployment default is off. Retention is driven
-        # purely by the (server-level) window.
-        audit_on = cfg.audit_log_retention_days > 0
+        audit_on = cfg.audit_log_enabled and cfg.audit_log_retention_days > 0
        llm_on = cfg.llm_trace_enabled and cfg.llm_trace_retention_days > 0
        mm_refresh_on = cfg.mental_model_refresh_tick_seconds > 0
-        op_cleanup_on = cfg.operation_retention_days > 0
-        return reconcile_on or audit_on or llm_on or mm_refresh_on or op_cleanup_on
+        return reconcile_on or audit_on or llm_on or mm_refresh_on

    # ── loop ───────────────────────────────────────────────────────────────

@@ -143,8 +134,6 @@ class MaintenanceLoop:
        mm_interval = cfg.mental_model_refresh_tick_seconds
        if mm_interval > 0 and self._is_due("mm_refresh", mm_interval):
            await self._run_timed("scheduled mental model refresh", self._run_scheduled_mm_refresh())
-        if cfg.operation_retention_days > 0 and self._is_due("operation_cleanup", _OPERATION_CLEANUP_INTERVAL_SECONDS):
-            await self._run_timed("operation cleanup", self._run_operation_cleanup(cfg))

    async def _run_timed(self, name: str, coro: Coroutine[Any, Any, None]) -> None:
        """Run a maintenance job and emit one timing line for it.
@@ -163,10 +152,7 @@ class MaintenanceLoop:
    async def _run_retention(self, cfg: HindsightConfig) -> None:
        # Retention days are static server-level config, so one global cutoff
        # applies to every tenant schema (the routine sweeps them all).
-        # Not gated on audit_log_enabled: it is per-bank overridable, so a bank
-        # may be writing audit rows while the deployment default is off. Gating
-        # the purge on the global flag would let those rows accumulate forever.
-        if cfg.audit_log_retention_days > 0:
+        if cfg.audit_log_enabled and cfg.audit_log_retention_days > 0:
            await self._purge_expired("audit_log", "started_at", cfg.audit_log_retention_days)
        if cfg.llm_trace_enabled and cfg.llm_trace_retention_days > 0:
            await self._purge_expired("llm_requests", "started_at", cfg.llm_trace_retention_days)
@@ -177,7 +163,7 @@ class MaintenanceLoop:
        try:
            async with acquire_with_retry(backend, max_retries=1) as conn:
                rows = await conn.fetch(
-                    f"SELECT * FROM {fq_routine('schemas_with_expired_rows')}($1, $2, $3)", table, ts_col, days
+                    "SELECT * FROM public.schemas_with_expired_rows($1, $2, $3)", table, ts_col, days
                )
                for row in rows:
                    schema = row[0]
@@ -192,73 +178,6 @@ class MaintenanceLoop:
        except Exception as e:
            logger.warning(f"Retention sweep failed for {table}: {e}")

-    # ── terminal operation cleanup ─────────────────────────────────────────
-
-    async def _run_operation_cleanup(self, cfg: HindsightConfig) -> None:
-        """Prune one bounded batch of expired terminal operations per tenant schema.
-
-        Previously this rode the worker's task-claiming loop, so it only fired
-        when that loop happened to iterate and was interleaved with claiming. It
-        is a periodic housekeeping sweep like the retention jobs above, so it
-        belongs on the same schedule.
-
-        Discovery is one cross-tenant round-trip (``schemas_with_expired_operations``)
-        rather than a connection + prune transaction per tenant; pending and
-        processing rows are never prunable, so a schema holding only in-flight
-        work is correctly reported as having nothing to do.
-        """
-        engine = self._engine
-        backend = engine._backend
-        try:
-            async with acquire_with_retry(backend, max_retries=1) as conn:
-                rows = await conn.fetch(
-                    f"SELECT * FROM {fq_routine('schemas_with_expired_operations')}($1)",
-                    cfg.operation_retention_days,
-                )
-        except Exception as e:
-            logger.warning(f"Operation cleanup discovery failed: {e}")
-            return
-        if not rows:
-            return
-
-        # Prune only schemas the deployment actually serves. The routine reports
-        # every schema owning an async_operations table, including ones tenant
-        # discovery doesn't claim.
-        try:
-            tenants = await engine._tenant_extension.list_tenants()
-        except Exception as e:
-            logger.warning(f"Operation cleanup tenant discovery failed: {e}")
-            return
-        known = {t.schema for t in tenants} | {get_config().database_schema}
-
-        from .memory_engine import _current_schema
-
-        cutoff = datetime.now(timezone.utc) - timedelta(days=cfg.operation_retention_days)
-        pruned = 0
-        for row in rows:
-            schema = row[0]
-            if schema not in known:
-                continue
-            # Oracle resolves unqualified names from a context-bound session
-            # schema; on PostgreSQL this is harmless and fq_table stays explicit.
-            token = _current_schema.set(schema)
-            try:
-                table = fq_table_explicit("async_operations", schema)
-                async with acquire_with_retry(backend, max_retries=1) as conn:
-                    async with conn.transaction():
-                        deleted = await backend.ops.prune_terminal_operations(
-                            conn, table, cutoff, batch_size=cfg.operation_cleanup_batch_size
-                        )
-                if deleted:
-                    pruned += deleted
-                    logger.info(f"Operation cleanup pruned {deleted} expired terminal operations from {schema}")
-            except Exception as e:
-                logger.warning(f"Operation cleanup failed for schema {schema}: {e}")
-            finally:
-                _current_schema.reset(token)
-        if pruned:
-            logger.info(f"Operation cleanup: pruned {pruned} operation(s) total")
-
    # ── consolidation reconcile ──────────────────────────────────────────────

    async def _run_reconcile(self) -> None:
@@ -266,9 +185,7 @@ class MaintenanceLoop:
        engine = self._engine
        try:
            async with acquire_with_retry(engine._backend, max_retries=1) as conn:
-                rows = await conn.fetch(
-                    f"SELECT schema_name, bank_id FROM {fq_routine('banks_needing_consolidation')}()"
-                )
+                rows = await conn.fetch("SELECT schema_name, bank_id FROM public.banks_needing_consolidation()")
        except Exception as e:
            logger.warning(f"Consolidation reconcile discovery failed: {e}")
            return
@@ -327,7 +244,7 @@ class MaintenanceLoop:

        Discovery (the set of cron-scheduled models, minus any with an in-flight
        refresh) is one cross-tenant round-trip via
-        ``mental_models_with_cron()``. Cron *due-ness* is evaluated here in
+        ``public.mental_models_with_cron()``. Cron *due-ness* is evaluated here in
        Python — a scheduled fire has elapsed when the most recent cron boundary at
        or before now is later than ``last_refreshed_at`` — because cron arithmetic
        isn't expressible in plain SQL. Each due model is refreshed only when it is
@@ -339,7 +256,7 @@ class MaintenanceLoop:
            async with acquire_with_retry(engine._backend, max_retries=1) as conn:
                rows = await conn.fetch(
                    "SELECT schema_name, bank_id, mental_model_id, refresh_cron, last_refreshed_at "
-                    f"FROM {fq_routine('mental_models_with_cron')}()"
+                    "FROM public.mental_models_with_cron()"
                )
        except Exception as e:
            logger.warning(f"Scheduled mental model refresh discovery failed: {e}")
@@ -142,21 +142,3 @@ class RefreshMentalModelMetadata:
    def to_dict(self) -> dict[str, Any]:
        """Convert to dict for JSON serialization."""
        return asdict(self)
-
-
-@dataclass
-class RefreshMentalModelOutcomeMetadata:
-    """Machine-readable outcome metadata for a completed refresh_mental_model operation.
-
-    Refresh parity with RetainOutcomeMetadata (#2605): lets a monitoring layer
-    distinguish "refreshed with real content" from "refreshed empty" by reading
-    result_metadata alone, without a follow-up content fetch.
-    """
-
-    content_len: int
-    populated_content: bool
-    based_on_counts: dict[str, int] = field(default_factory=dict)
-
-    def to_dict(self) -> dict[str, Any]:
-        """Convert to dict for JSON serialization."""
-        return asdict(self)
@@ -186,11 +186,7 @@ class MarkitdownParser(FileParser):
        if Path(filename).suffix.lower() not in _TEXT_EXTENSIONS:
            return None
        try:
-            # file_data may arrive as a non-``bytes`` buffer (e.g. a memoryview or
-            # a native/Rust-backed buffer object) that has no ``.decode``; coerce
-            # through the buffer protocol before the UTF-8 probe. The ``tmp.write``
-            # in the caller already relies only on the same buffer protocol.
-            bytes(file_data).decode("utf-8")
+            file_data.decode("utf-8")
        except UnicodeDecodeError:
            return None
        from markitdown import StreamInfo
@@ -14,9 +14,8 @@ import logging
 import time
 from typing import Any

-from hindsight_api.engine.llm_interface import LLM_TOOL_CHOICE_AUTO, LLMInterface, LLMToolChoice
+from hindsight_api.engine.llm_interface import LLMInterface
 from hindsight_api.engine.llm_trace import LLMResponseUsage, stash_response_usage
-from hindsight_api.engine.providers.llm_debug import dump_request_on_4xx
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
 from hindsight_api.metrics import get_metrics_collector

@@ -35,43 +34,6 @@ def _usage_from_anthropic_response(response: Any) -> LLMResponseUsage:
    )


-_EPHEMERAL_CACHE = {"type": "ephemeral"}
-
-
-def _cached_system_blocks(system_prompt: str) -> list[dict[str, Any]]:
-    """Render the system prompt as a block list with a cache_control marker.
-
-    Anthropic prompt caching is a prefix match: marking the (single) system
-    block caches tools + system together. The system prompt is stable per
-    scope — fact extraction reuses it across every chunk, reflect and
-    consolidation keep their stable instructions there — so repeat calls read
-    it at ~10% of the base input price. Markers below the model's minimum
-    cacheable prefix are silently ignored (no write premium), so marking is
-    safe unconditionally. This is the "inline-marker provider" strategy that
-    ``LLMInterface.get_or_create_cached_prefix`` documents for Anthropic.
-    """
-    return [{"type": "text", "text": system_prompt, "cache_control": _EPHEMERAL_CACHE}]
-
-
-def _mark_last_message_for_caching(messages: list[dict[str, Any]]) -> None:
-    """Add a cache_control marker to the final content block, in place.
-
-    Used on the multi-turn (tool-calling) path: the reflect agent loop resends
-    the entire growing conversation each iteration, so this request's
-    end-marker becomes the next iteration's cache read point. Together with
-    the system marker this uses 2 of the 4 allowed breakpoints.
-    """
-    if not messages:
-        return
-    last = messages[-1]
-    content = last.get("content")
-    if isinstance(content, str):
-        if content.strip():  # the API rejects empty text blocks
-            last["content"] = [{"type": "text", "text": content, "cache_control": _EPHEMERAL_CACHE}]
-    elif isinstance(content, list) and content and isinstance(content[-1], dict):
-        content[-1]["cache_control"] = _EPHEMERAL_CACHE
-
-
 class AnthropicLLM(LLMInterface):
    """
    LLM provider using Anthropic's Claude models.
@@ -244,9 +206,7 @@ class AnthropicLLM(LLMInterface):
        }

        if system_prompt:
-            # One-shot calls share only the system prompt with each other, so
-            # that is the sole cache breakpoint on this path.
-            call_params["system"] = _cached_system_blocks(system_prompt)
+            call_params["system"] = system_prompt

        if use_forced_tool:
            # Single tool whose input_schema IS the response schema; force the model to
@@ -386,9 +346,6 @@ class AnthropicLLM(LLMInterface):
                    logger.error(f"Anthropic auth error (HTTP {e.status_code}), not retrying: {str(e)}")
                    raise

-                # Diagnostic dump (opt-in) of the exact request behind any 4xx.
-                dump_request_on_4xx(scope=scope, provider=self.provider, model=self.model, err=e, request=call_params)
-
                last_exception = e
                if attempt < max_retries:
                    # Check if it's a rate limit or server error
@@ -423,7 +380,7 @@ class AnthropicLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
    ) -> LLMToolCallResult:
        """
        Make an LLM API call with tool/function calling support.
@@ -493,11 +450,6 @@ class AnthropicLLM(LLMInterface):
            else:
                anthropic_messages.append({"role": role, "content": content})

-        # Multi-turn tool loop: cache the stable prefix (tools + system) via
-        # the system marker, and the growing conversation via an end-marker
-        # that the next iteration reads back.
-        _mark_last_message_for_caching(anthropic_messages)
-
        call_params: dict[str, Any] = {
            "model": self.model,
            "messages": anthropic_messages,
@@ -505,7 +457,7 @@ class AnthropicLLM(LLMInterface):
            "max_tokens": max_completion_tokens or 4096,
        }
        if system_prompt:
-            call_params["system"] = _cached_system_blocks(system_prompt)
+            call_params["system"] = system_prompt

        if self._extra_body:
            call_params["extra_body"] = self._extra_body
@@ -581,8 +533,6 @@ class AnthropicLLM(LLMInterface):
            except (APIConnectionError, APIStatusError) as e:
                if isinstance(e, APIStatusError) and e.status_code in (401, 403):
                    raise
-                # Diagnostic dump (opt-in) of the exact request behind any 4xx.
-                dump_request_on_4xx(scope=scope, provider=self.provider, model=self.model, err=e, request=call_params)
                last_exception = e
                if attempt < max_retries:
                    await asyncio.sleep(min(initial_backoff * (2**attempt), max_backoff))
@@ -593,217 +543,6 @@ class AnthropicLLM(LLMInterface):
            raise last_exception
        raise RuntimeError("Anthropic tool call failed")

-    # ── Message Batches API (50% token discount) ─────────────────────────────
-
-    _BATCH_TOOL_NAME = "structured_response"
-
-    async def supports_batch_api(self) -> bool:
-        """Anthropic supports batch operations via the Message Batches API."""
-        return True
-
-    @staticmethod
-    def _map_batch_status(processing_status: str) -> str:
-        """Map Anthropic ``processing_status`` onto the OpenAI vocabulary.
-
-        The engine's poll loop breaks on "completed" and hard-fails on
-        "failed"/"expired"/"cancelled"; anything else keeps polling. Anthropic
-        batches only end as "ended" (per-request failures surface in the
-        results, mirroring OpenAI's "completed"-with-errors semantics), so
-        "ended" maps to "completed" and the non-terminal states pass through.
-        """
-        return "completed" if processing_status == "ended" else processing_status
-
-    def _translate_batch_body(self, body: dict[str, Any]) -> dict[str, Any]:
-        """Translate one OpenAI-shaped request body into Messages API params.
-
-        Mirrors the conversion rules of ``call()``: system messages fold into
-        the ``system`` param; ``max_completion_tokens`` becomes ``max_tokens``
-        (default 4096); ``temperature`` is dropped (the sync path never sends
-        it either — current Claude models reject non-default sampling params);
-        an OpenAI ``response_format`` json_schema becomes a single forced
-        tool_use tool when strict (native constrained decoding, issue #1002),
-        else the schema is injected into the system prompt.
-
-        The system prompt carries the same cache_control marker as the sync
-        one-shot path (its sole breakpoint): every request in a retain batch
-        shares the fact-extraction system prompt, so the first item's cache
-        write serves the remaining items as best-effort reads — and the
-        cache-read discount stacks with the 50% batch discount.
-        """
-        system_prompt: str | None = None
-        messages: list[dict[str, Any]] = []
-        for msg in body.get("messages", []):
-            role = msg.get("role", "user")
-            content = msg.get("content", "")
-            if role == "system":
-                system_prompt = (system_prompt + "\n\n" + content) if system_prompt else content
-            else:
-                messages.append({"role": role, "content": content})
-
-        params: dict[str, Any] = {
-            "model": body.get("model") or self.model,
-            "messages": messages,
-            "max_tokens": body.get("max_completion_tokens") or 4096,
-        }
-
-        json_schema = (body.get("response_format") or {}).get("json_schema") or {}
-        schema = json_schema.get("schema")
-        if schema is not None:
-            if json_schema.get("strict"):
-                params["tools"] = [
-                    {
-                        "name": self._BATCH_TOOL_NAME,
-                        "description": "Return the structured response.",
-                        "input_schema": schema,
-                    }
-                ]
-                params["tool_choice"] = {"type": "tool", "name": self._BATCH_TOOL_NAME}
-            else:
-                schema_msg = "\n\nYou must respond with valid JSON matching this schema:\n" + json.dumps(
-                    schema, indent=2, ensure_ascii=False
-                )
-                system_prompt = (system_prompt + schema_msg) if system_prompt else schema_msg
-
-        if system_prompt:
-            params["system"] = _cached_system_blocks(system_prompt)
-
-        # Batch params ARE the raw Messages body, so operator-configured extra
-        # body params merge directly (the sync path routes them through the
-        # SDK's extra_body, which does the same merge server-side).
-        if self._extra_body:
-            params.update(self._extra_body)
-
-        return params
-
-    def _translate_batch_message(self, message: Any) -> dict[str, Any]:
-        """Render an Anthropic Message as the OpenAI response body the engine parses.
-
-        The engine reads ``choices[0].message.content`` (json.loads'ing it when
-        a schema was requested) and sums ``usage`` under the OpenAI key names.
-        Forced-tool responses carry their JSON in the tool_use block's input,
-        so that is re-serialized as the content string.
-        """
-        content = ""
-        tool_input = None
-        for block in message.content:
-            if block.type == "tool_use" and block.name == self._BATCH_TOOL_NAME:
-                tool_input = block.input or {}
-            elif block.type == "text":
-                content += block.text
-        if tool_input is not None:
-            content = json.dumps(tool_input, ensure_ascii=False)
-
-        usage = getattr(message, "usage", None)
-        input_tokens = (usage.input_tokens or 0) if usage else 0
-        output_tokens = (usage.output_tokens or 0) if usage else 0
-
-        return {
-            "choices": [
-                {
-                    "message": {"role": "assistant", "content": content},
-                    "finish_reason": getattr(message, "stop_reason", None),
-                }
-            ],
-            "usage": {
-                "prompt_tokens": input_tokens,
-                "completion_tokens": output_tokens,
-                "total_tokens": input_tokens + output_tokens,
-            },
-        }
-
-    async def submit_batch(
-        self,
-        requests: list[dict[str, Any]],
-        endpoint: str = "/v1/chat/completions",
-        completion_window: str = "24h",
-    ) -> dict[str, Any]:
-        """Submit a batch of requests to the Message Batches API.
-
-        Accepts the engine's OpenAI-JSONL-shaped entries. ``endpoint`` and
-        ``completion_window`` belong to that shared shape and have no Anthropic
-        equivalent (batches always resolve within 24 hours); both are ignored.
-        """
-        batch_requests = [
-            {
-                "custom_id": req["custom_id"],
-                "params": self._translate_batch_body(req.get("body") or {}),
-            }
-            for req in requests
-        ]
-
-        logger.info(f"Submitting Anthropic message batch with {len(batch_requests)} requests")
-        batch = await self._client.messages.batches.create(requests=batch_requests)
-        logger.info(f"Anthropic batch submitted: {batch.id}, status={batch.processing_status}")
-
-        return {
-            "batch_id": batch.id,
-            "status": self._map_batch_status(batch.processing_status),
-            "created_at": batch.created_at,
-            "request_count": len(batch_requests),
-        }
-
-    async def get_batch_status(self, batch_id: str) -> dict[str, Any]:
-        """Get batch status in the shape the engine's poll loop expects."""
-        batch = await self._client.messages.batches.retrieve(batch_id)
-
-        counts = batch.request_counts
-        processing = getattr(counts, "processing", 0) or 0
-        succeeded = getattr(counts, "succeeded", 0) or 0
-        errored = getattr(counts, "errored", 0) or 0
-        canceled = getattr(counts, "canceled", 0) or 0
-        expired = getattr(counts, "expired", 0) or 0
-        resolved = succeeded + errored + canceled + expired
-
-        result: dict[str, Any] = {
-            "batch_id": batch.id,
-            "status": self._map_batch_status(batch.processing_status),
-            "created_at": batch.created_at,
-            "request_counts": {
-                "total": processing + resolved,
-                "completed": resolved,
-                "failed": errored,
-            },
-        }
-
-        ended_at = getattr(batch, "ended_at", None)
-        if ended_at:
-            result["completed_at"] = ended_at
-
-        return result
-
-    async def retrieve_batch_results(self, batch_id: str) -> list[dict[str, Any]]:
-        """Retrieve completed batch results, translated to the OpenAI shape.
-
-        Succeeded entries become ``{"custom_id", "response": {"body": ...}}``;
-        errored/canceled/expired entries become ``{"custom_id", "error": ...}``
-        so the engine's per-result error handling applies unchanged.
-        """
-        batch = await self._client.messages.batches.retrieve(batch_id)
-        if batch.processing_status != "ended":
-            raise ValueError(f"Batch {batch_id} is not completed yet (status: {batch.processing_status})")
-
-        decoder = await self._client.messages.batches.results(batch_id)
-        results: list[dict[str, Any]] = []
-        async for entry in decoder:
-            outcome = entry.result
-            if outcome.type == "succeeded":
-                results.append(
-                    {
-                        "custom_id": entry.custom_id,
-                        "response": {"body": self._translate_batch_message(outcome.message)},
-                    }
-                )
-            else:
-                error = getattr(outcome, "error", None)
-                if error is not None:
-                    detail = f"{getattr(error, 'type', 'error')}: {getattr(error, 'message', error)}"
-                else:
-                    detail = f"batch request {outcome.type}"
-                results.append({"custom_id": entry.custom_id, "error": detail})
-
-        logger.info(f"Retrieved {len(results)} results for Anthropic batch {batch_id}")
-        return results
-
    async def cleanup(self) -> None:
        """Clean up resources (close Anthropic client connections)."""
        if hasattr(self, "_client") and self._client:
@@ -15,7 +15,7 @@ from typing import Any

 from pydantic import ValidationError

-from hindsight_api.engine.llm_interface import LLM_TOOL_CHOICE_AUTO, LLMInterface, LLMToolChoice, LLMToolChoiceMode
+from hindsight_api.engine.llm_interface import LLMInterface
 from hindsight_api.engine.llm_trace import LLMResponseUsage, stash_response_usage
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
 from hindsight_api.metrics import get_metrics_collector
@@ -49,20 +49,6 @@ def _get_isolated_claude_env() -> dict[str, str]:
    return _isolated_claude_env


-def _result_error_detail(message: Any) -> str:
-    """Build an actionable error string from an ``is_error`` ResultMessage.
-
-    The CLI can report a failure with ``is_error=True`` while ``subtype``
-    still reads ``"success"``, putting the real detail in ``result`` (e.g.
-    quota exhaustion: ``You've hit your weekly limit · resets ...`` with
-    ``api_error_status: 429``). The SDK's own fallback exception surfaces
-    only the subtype, producing the misleading "Claude Code returned an
-    error result: success" (issue #2702) — so prefer ``result``.
-    """
-    detail = (message.result or "").strip() or message.subtype or "unknown error"
-    return f"Claude Code reported an error: {detail}"
-
-
 class ClaudeCodeLLM(LLMInterface):
    """
    LLM provider using Claude Code authentication.
@@ -190,7 +176,6 @@ class ClaudeCodeLLM(LLMInterface):
        from claude_agent_sdk import (  # type: ignore[unresolved-import]
            AssistantMessage,
            ClaudeAgentOptions,
-            ResultMessage,
            TextBlock,
            query,
        )
@@ -224,19 +209,9 @@ class ClaudeCodeLLM(LLMInterface):
            user_content += schema_instruction

        # Configure SDK options
-        #
-        # tools=[] is required here for the same reason call_with_tools() below
-        # already sets it: with `tools` left at its default (None -> full
-        # "claude_code" built-in preset), allowed_tools=[] alone does not stop
-        # the CLI from loading the full built-in toolset and deferring into
-        # ToolSearch before answering, which burns the single max_turns=1
-        # budget on a tool-deferral step instead of a text response. Without
-        # this, single-turn calls intermittently fail with "Reached maximum
-        # number of turns (1)" even though the prompt itself needs no tools.
        options = ClaudeAgentOptions(
            system_prompt=system_prompt if system_prompt else None,
            max_turns=1,  # Single-turn for API-style interactions
-            tools=[],  # Disable built-in tools so nothing forces a ToolSearch deferral
            allowed_tools=[],  # Disable tools for standard LLM calls
            env=_get_isolated_claude_env(),
        )
@@ -253,11 +228,6 @@ class ClaudeCodeLLM(LLMInterface):
                        for block in message.content:
                            if isinstance(block, TextBlock):
                                full_text += block.text
-                    elif isinstance(message, ResultMessage) and message.is_error:
-                        # Surface the CLI's actual error text (e.g. quota
-                        # exhaustion) instead of the SDK's subtype-based
-                        # fallback exception (issue #2702).
-                        raise RuntimeError(_result_error_detail(message))

                # The Claude Agent SDK doesn't report exact counts; stash the same
                # char/4 estimate the success path traces so a later parse/validate
@@ -392,7 +362,7 @@ class ClaudeCodeLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
    ) -> LLMToolCallResult:
        """
        Make an LLM API call with tool/function calling support using Claude Agent SDK.
@@ -410,7 +380,7 @@ class ClaudeCodeLLM(LLMInterface):
            max_retries: Maximum retry attempts.
            initial_backoff: Initial backoff time in seconds.
            max_backoff: Maximum backoff time in seconds.
-            tool_choice: Canonical tool-selection policy.
+            tool_choice: How to choose tools - "auto", "none", "required", or specific function dict.
                - "auto": Model decides whether to call tools (default)
                - "required": Model must call at least one tool
                - "none": Model must not call any tools
@@ -423,7 +393,6 @@ class ClaudeCodeLLM(LLMInterface):
            AssistantMessage,
            ClaudeAgentOptions,
            ClaudeSDKClient,
-            ResultMessage,
            SdkMcpTool,
            TextBlock,
            ToolUseBlock,
@@ -504,27 +473,30 @@ class ClaudeCodeLLM(LLMInterface):
        mcp_servers_config = {"hindsight_tools": mcp_server} if sdk_tools else {}

        # Process tool_choice
-        if tool_choice.mode is LLMToolChoiceMode.NAMED:
+        if isinstance(tool_choice, dict) and tool_choice.get("type") == "function":
            # Force a specific tool: filter allowed_tools to only that tool and add instruction
-            forced_name = tool_choice.selected_function_name
-            forced_tool_mcp_name = f"mcp__hindsight_tools__{forced_name}"
-            if forced_tool_mcp_name in allowed_tool_names:
-                allowed_tool_names = [forced_tool_mcp_name]
-                force_instruction = (
-                    f"\n\nIMPORTANT: You MUST call the '{forced_name}' tool. Do not respond with text only."
-                )
-                system_prompt += force_instruction
-                logger.debug(f"Claude Code: Forcing tool call to '{forced_name}'")
-            else:
-                logger.warning(f"Claude Code: Forced tool '{forced_name}' not found in available tools")
-        elif tool_choice.mode is LLMToolChoiceMode.REQUIRED:
+            forced_name = tool_choice.get("function", {}).get("name")
+            if forced_name:
+                # Filter to only the forced tool (with MCP prefix)
+                forced_tool_mcp_name = f"mcp__hindsight_tools__{forced_name}"
+                if forced_tool_mcp_name in allowed_tool_names:
+                    allowed_tool_names = [forced_tool_mcp_name]
+                    # Add strong instruction to system prompt
+                    force_instruction = (
+                        f"\n\nIMPORTANT: You MUST call the '{forced_name}' tool. Do not respond with text only."
+                    )
+                    system_prompt += force_instruction
+                    logger.debug(f"Claude Code: Forcing tool call to '{forced_name}'")
+                else:
+                    logger.warning(f"Claude Code: Forced tool '{forced_name}' not found in available tools")
+        elif tool_choice == "required":
            # Must call at least one tool
            tool_instruction = (
                "\n\nIMPORTANT: You MUST call at least one of the available tools. Do not respond with text only."
            )
            system_prompt += tool_instruction
            logger.debug("Claude Code: Tool call required")
-        elif tool_choice.mode is LLMToolChoiceMode.NONE:
+        elif tool_choice == "none":
            # No tools should be called - disable all tools
            allowed_tool_names = []
            mcp_servers_config = {}
@@ -560,9 +532,6 @@ class ClaudeCodeLLM(LLMInterface):

                    # Receive response
                    async for message in client.receive_response():
-                        if isinstance(message, ResultMessage) and message.is_error:
-                            # Surface the CLI's actual error text (issue #2702).
-                            raise RuntimeError(_result_error_detail(message))
                        if isinstance(message, AssistantMessage):
                            for block in message.content:
                                if isinstance(block, TextBlock):
@@ -19,7 +19,6 @@ from __future__ import annotations

 import base64
 import binascii
-import contextlib
 import json
 import logging
 import os
@@ -32,11 +31,6 @@ from typing import Any

 import httpx

-try:
-    import fcntl
-except ImportError:  # pragma: no cover - Windows
-    fcntl = None  # type: ignore[assignment]
-
 logger = logging.getLogger(__name__)


@@ -64,9 +58,6 @@ _CODEX_TOKEN_REFRESH_SKEW_SECONDS = 60
 _CODEX_TERMINAL_REFRESH_ERROR_CODES = frozenset(
    {"refresh_token_expired", "refresh_token_reused", "refresh_token_invalidated"}
 )
-_CODEX_AUTH_LOCK_TIMEOUT_SECONDS = 20.0
-_CODEX_AUTH_LOCKS_GUARD = threading.Lock()
-_CODEX_AUTH_LOCKS: dict[Path, threading.Lock] = {}


 def default_codex_auth_file() -> Path:
@@ -85,44 +76,6 @@ def default_codex_auth_file() -> Path:
    return Path.home() / ".codex" / "auth.json"


-def _path_scoped_lock(auth_file: Path) -> threading.Lock:
-    key = auth_file.expanduser().resolve(strict=False)
-    with _CODEX_AUTH_LOCKS_GUARD:
-        lock = _CODEX_AUTH_LOCKS.get(key)
-        if lock is None:
-            lock = threading.Lock()
-            _CODEX_AUTH_LOCKS[key] = lock
-        return lock
-
-
-@contextlib.contextmanager
-def _codex_auth_lock(auth_file: Path, timeout_seconds: float = _CODEX_AUTH_LOCK_TIMEOUT_SECONDS):
-    """Cross-process advisory lock for one Codex auth store."""
-    with _path_scoped_lock(auth_file):
-        if fcntl is None:  # pragma: no cover - Windows
-            logger.debug("fcntl unavailable; Codex refresh proceeds without a cross-process lock.")
-            yield
-            return
-
-        lock_path = auth_file.with_suffix(".lock")
-        lock_path.parent.mkdir(parents=True, exist_ok=True)
-        with open(lock_path, "a+") as lock_file:
-            deadline = time.monotonic() + max(1.0, timeout_seconds)
-            while True:
-                try:
-                    fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
-                    break
-                except (BlockingIOError, OSError):
-                    if time.monotonic() >= deadline:
-                        raise TimeoutError("Timed out waiting for the Codex auth store lock") from None
-                    time.sleep(0.05)
-            try:
-                yield
-            finally:
-                with contextlib.suppress(OSError):
-                    fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
-
-
 class CodexRefreshExpiredError(RuntimeError):
    """Raised when the Codex refresh_token itself is no longer valid.

@@ -239,34 +192,6 @@ class CodexAuthManager:
            return None
        return data.get("tokens", {}).get("refresh_token")

-    @staticmethod
-    def _load_tokens_from_file(auth_file: Path) -> dict[str, Any] | None:
-        try:
-            with open(auth_file) as f:
-                data = json.load(f)
-        except (OSError, json.JSONDecodeError):
-            return None
-        tokens = data.get("tokens")
-        return tokens if isinstance(tokens, dict) else None
-
-    def _adopt_tokens(self, tokens: dict[str, Any]) -> bool:
-        """Adopt a newer on-disk Codex token set if present."""
-        access_token = tokens.get("access_token")
-        refresh_token = tokens.get("refresh_token")
-        account_id = tokens.get("account_id")
-
-        changed = False
-        if isinstance(access_token, str) and access_token and access_token != self.access_token:
-            self.access_token = access_token
-            changed = True
-        if isinstance(refresh_token, str) and refresh_token and refresh_token != self.refresh_token:
-            self.refresh_token = refresh_token
-            changed = True
-        if isinstance(account_id, str) and account_id and account_id != self.account_id:
-            self.account_id = account_id
-            changed = True
-        return changed
-
    @staticmethod
    def _decode_jwt_exp_unixtime(token: str) -> int | None:
        """Return the JWT ``exp`` claim as a unix timestamp, or None on parse failure.
@@ -300,11 +225,6 @@ class CodexAuthManager:
            return False
        return exp <= int(time.time()) + skew_seconds

-    def _token_is_fresh_with_known_expiry(self, skew_seconds: int = _CODEX_TOKEN_REFRESH_SKEW_SECONDS) -> bool:
-        """True only when the cached token has a known expiry outside the skew window."""
-        exp = self._decode_jwt_exp_unixtime(self.access_token)
-        return exp is not None and exp > int(time.time()) + skew_seconds
-
    # ------------------------------------------------------------------
    # Persistence
    # ------------------------------------------------------------------
@@ -419,93 +339,78 @@ class CodexAuthManager:
                if not self._token_is_stale():
                    return

-            with _codex_auth_lock(self._auth_file):
-                disk_tokens = self._load_tokens_from_file(self._auth_file)
-                if disk_tokens and self._adopt_tokens(disk_tokens):
-                    if force or self._token_is_fresh_with_known_expiry():
-                        return
+            if not self.refresh_token:
+                raise RuntimeError(
+                    "Codex access_token is expired but no refresh_token is available. "
+                    "Run 'codex auth login' to re-authenticate."
+                )

-                if not self.refresh_token:
-                    raise RuntimeError(
-                        "Codex access_token is expired but no refresh_token is available. "
+            log_reason = f" ({reason})" if reason else ""
+            logger.info(f"Refreshing Codex OAuth access_token{log_reason}")
+
+            request_body = {
+                "client_id": _CODEX_CLIENT_ID,
+                "grant_type": "refresh_token",
+                "refresh_token": self.refresh_token,
+            }
+            try:
+                response = self._http_client.post(
+                    _CODEX_REFRESH_TOKEN_URL,
+                    json=request_body,
+                    headers={"Content-Type": "application/json"},
+                    timeout=30.0,
+                )
+            except httpx.RequestError as e:
+                raise RuntimeError(f"Codex OAuth refresh network error: {type(e).__name__}") from e
+
+            if response.status_code == 401:
+                error_code = self._extract_oauth_error_code(response)
+                if error_code in _CODEX_TERMINAL_REFRESH_ERROR_CODES:
+                    raise CodexRefreshExpiredError(
+                        f"Codex refresh_token is permanently invalid (error.code={error_code}). "
                        "Run 'codex auth login' to re-authenticate."
                    )
+                raise CodexRefreshExpiredError(
+                    f"Codex OAuth refresh returned 401 with unrecognized error code "
+                    f"({error_code or 'none'}). Run 'codex auth login' to re-authenticate."
+                )

-                log_reason = f" ({reason})" if reason else ""
-                logger.info(f"Refreshing Codex OAuth access_token{log_reason}")
+            if response.status_code >= 400:
+                raise RuntimeError(f"Codex OAuth refresh failed with HTTP {response.status_code}")

-                request_access_token = self.access_token
-                request_refresh_token = self.refresh_token
-                request_body = {
-                    "client_id": _CODEX_CLIENT_ID,
-                    "grant_type": "refresh_token",
-                    "refresh_token": request_refresh_token,
-                }
-                try:
-                    response = self._http_client.post(
-                        _CODEX_REFRESH_TOKEN_URL,
-                        json=request_body,
-                        headers={"Content-Type": "application/json"},
-                        timeout=30.0,
-                    )
-                except httpx.RequestError as e:
-                    raise RuntimeError(f"Codex OAuth refresh network error: {type(e).__name__}") from e
+            try:
+                body = response.json()
+            except json.JSONDecodeError as e:
+                raise RuntimeError(f"Codex OAuth refresh returned non-JSON body: {e}") from e

-                if response.status_code == 401:
-                    error_code = self._extract_oauth_error_code(response)
-                    disk_tokens = self._load_tokens_from_file(self._auth_file)
-                    if disk_tokens and (
-                        disk_tokens.get("access_token") != request_access_token
-                        or disk_tokens.get("refresh_token") != request_refresh_token
-                    ):
-                        self._adopt_tokens(disk_tokens)
-                        return
-                    if error_code in _CODEX_TERMINAL_REFRESH_ERROR_CODES:
-                        raise CodexRefreshExpiredError(
-                            f"Codex refresh_token is permanently invalid (error.code={error_code}). "
-                            "Run 'codex auth login' to re-authenticate."
-                        )
-                    raise CodexRefreshExpiredError(
-                        f"Codex OAuth refresh returned 401 with unrecognized error code "
-                        f"({error_code or 'none'}). Run 'codex auth login' to re-authenticate."
-                    )
+            new_access = body.get("access_token")
+            if not new_access:
+                raise RuntimeError("Codex OAuth refresh returned no access_token")

-                if response.status_code >= 400:
-                    raise RuntimeError(f"Codex OAuth refresh failed with HTTP {response.status_code}")
+            new_refresh = body.get("refresh_token") or self.refresh_token
+            new_id_token = body.get("id_token")

-                try:
-                    body = response.json()
-                except json.JSONDecodeError as e:
-                    raise RuntimeError(f"Codex OAuth refresh returned non-JSON body: {e}") from e
+            # Update in-memory state first so waiters see fresh credentials
+            # immediately, even if disk write fails.
+            self.access_token = new_access
+            self.refresh_token = new_refresh

-                new_access = body.get("access_token")
-                if not new_access:
-                    raise RuntimeError("Codex OAuth refresh returned no access_token")
+            persisted: dict[str, Any] = {
+                "access_token": new_access,
+                "refresh_token": new_refresh,
+            }
+            if new_id_token:
+                persisted["id_token"] = new_id_token

-                new_refresh = body.get("refresh_token") or self.refresh_token
-                new_id_token = body.get("id_token")
+            try:
+                self._persist_auth_atomic(persisted)
+            except OSError as e:
+                logger.warning(
+                    f"Codex OAuth refresh succeeded but persisting auth.json failed: {type(e).__name__}. "
+                    "In-memory credentials are up to date; on-disk file is stale."
+                )

-                # Update in-memory state first so waiters see fresh credentials
-                # immediately, even if disk write fails.
-                self.access_token = new_access
-                self.refresh_token = new_refresh
-
-                persisted: dict[str, Any] = {
-                    "access_token": new_access,
-                    "refresh_token": new_refresh,
-                }
-                if new_id_token:
-                    persisted["id_token"] = new_id_token
-
-                try:
-                    self._persist_auth_atomic(persisted)
-                except OSError as e:
-                    logger.warning(
-                        f"Codex OAuth refresh succeeded but persisting auth.json failed: {type(e).__name__}. "
-                        "In-memory credentials are up to date; on-disk file is stale."
-                    )
-
-                logger.info("Codex OAuth access_token refreshed successfully")
+            logger.info("Codex OAuth access_token refreshed successfully")

    def ensure_fresh_token(self) -> None:
        """Proactively refresh the access_token if it is near or past expiry.
@@ -25,11 +25,9 @@ from typing import Any

 import httpx

-from hindsight_api.engine.llm_interface import LLM_TOOL_CHOICE_AUTO, LLMInterface, LLMToolChoice, LLMToolChoiceMode
+from hindsight_api.engine.llm_interface import LLMInterface
 from hindsight_api.engine.llm_trace import LLMResponseUsage, stash_response_usage
-from hindsight_api.engine.providers.llm_debug import dump_request_on_4xx
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.engine.structured_output import strict_json_schema
 from hindsight_api.metrics import get_metrics_collector

 from .codex_auth import (
@@ -55,59 +53,6 @@ __all__ = [

 logger = logging.getLogger(__name__)

-# Newer Codex models are gated on the first-party client identity; the previous
-# browser-shaped User-Agent returned "Model not found" for Luna (#2643).
-# Use a neutral version because Hindsight must not claim a specific Codex release.
-_CODEX_ORIGINATOR = "codex_cli_rs"
-_CODEX_USER_AGENT = "codex_cli_rs/0.0.0 (Hindsight)"
-
-# Name of the single forced function tool used to carry structured output when
-# strict_schema is on. The Codex backend speaks the OpenAI Responses API, so a
-# forced function call gives us constrained decoding straight into the response
-# schema — no prompt-injected schema, no raw json.loads on free-form model text,
-# no invalid-\escape retry storm (issue #2504, same class as #1002 / #2339).
-_STRUCTURED_TOOL_NAME = "structured_response"
-
-# Valid JSON string escape characters (the char that may follow a backslash).
-_VALID_JSON_ESCAPE_CHARS = set('"\\/bfnrtu')
-
-
-def _repair_invalid_json_escapes(text: str) -> str:
-    """Best-effort repair of invalid ``\\escape`` sequences in a JSON string.
-
-    Escape-heavy content (code, serial/CLI commands, Windows paths, regexes)
-    makes weaker models emit backslashes that aren't valid JSON escapes (e.g.
-    ``\\d``, ``\\s``, ``C:\\Users``), so ``json.loads`` fails deterministically
-    and every retry re-fails the same way (issue #2504). This doubles any
-    backslash that isn't part of a valid escape so the payload parses. It is a
-    lenient fallback only — the strict_schema forced-tool path is the real fix.
-    """
-    result: list[str] = []
-    i = 0
-    n = len(text)
-    while i < n:
-        ch = text[i]
-        if ch == "\\" and i + 1 < n:
-            nxt = text[i + 1]
-            if nxt in _VALID_JSON_ESCAPE_CHARS:
-                # Preserve the valid escape (both chars) verbatim.
-                result.append(ch)
-                result.append(nxt)
-                i += 2
-                continue
-            # Invalid escape: escape the lone backslash so JSON parses.
-            result.append("\\\\")
-            i += 1
-            continue
-        if ch == "\\" and i + 1 == n:
-            # Trailing lone backslash — escape it.
-            result.append("\\\\")
-            i += 1
-            continue
-        result.append(ch)
-        i += 1
-    return "".join(result)
-

 class CodexLLM(LLMInterface):
    """
@@ -195,18 +140,6 @@ class CodexLLM(LLMInterface):
    def account_id(self) -> str:
        return self._auth_manager.account_id

-    def _build_request_headers(self) -> httpx.Headers:
-        return httpx.Headers(
-            {
-                "Authorization": f"Bearer {self.access_token}",
-                "Content-Type": "application/json",
-                "OpenAI-Account-ID": self.account_id,
-                "User-Agent": _CODEX_USER_AGENT,
-                "Origin": "https://chatgpt.com",
-                "originator": _CODEX_ORIGINATOR,
-            }
-        )
-
    @property
    def refresh_token(self) -> str | None:
        return self._auth_manager.refresh_token
@@ -343,6 +276,32 @@ class CodexLLM(LLMInterface):
        }
        return mapping.get(effort.lower(), "auto")

+    def _normalize_tool_choice(self, tool_choice: str | dict[str, Any]) -> str | dict[str, Any]:
+        """Normalize forced function tool choice for the Codex Responses API.
+
+        Older agent paths may still pass OpenAI chat-completions style named
+        tool choice payloads such as:
+
+            {"type": "function", "function": {"name": "recall"}}
+
+        Codex Responses expects the named function at the top level instead:
+
+            {"type": "function", "name": "recall"}
+        """
+        if not isinstance(tool_choice, dict):
+            return tool_choice
+        if str(tool_choice.get("type") or "").strip() != "function":
+            return tool_choice
+        function_payload = tool_choice.get("function")
+        if isinstance(function_payload, dict):
+            function_name = str(function_payload.get("name") or "").strip()
+            if function_name:
+                return {"type": "function", "name": function_name}
+        function_name = str(tool_choice.get("name") or "").strip()
+        if function_name:
+            return {"type": "function", "name": function_name}
+        return tool_choice
+
    async def verify_connection(self) -> None:
        """Verify Codex connection by making a simple test call."""
        try:
@@ -377,18 +336,7 @@ class CodexLLM(LLMInterface):
        strict_schema: bool = False,
        return_usage: bool = False,
    ) -> Any:
-        """Make API call to Codex backend with SSE streaming.
-
-        Args:
-            strict_schema: Route structured output through a single forced
-                function tool (constrained decoding) instead of prompt-injecting
-                the schema and parsing free-form text. The Codex backend speaks
-                the OpenAI Responses API, so the forced function call emits the
-                response schema directly as tool arguments — eliminating the
-                invalid-``\\escape`` retry storm (issue #2504). When False, falls
-                back to schema-in-prompt + JSON parse, now hardened with a lenient
-                invalid-escape repair before giving up.
-        """
+        """Make API call to Codex backend with SSE streaming."""
        start_time = time.time()

        # Proactively refresh the OAuth access_token if it's near expiry.
@@ -413,22 +361,11 @@ class CodexLLM(LLMInterface):
            else:
                user_messages.append(msg)

-        # Structured output: prefer a single forced function tool (constrained
-        # decoding) over text-injecting the schema and parsing the reply. The
-        # forced tool guarantees schema-shaped JSON in the tool arguments,
-        # eliminating the invalid-\escape retry storm (issue #2504). When
-        # strict_schema is off we keep the schema-in-prompt + json.loads
-        # fallback (now hardened with a lenient escape repair) for callers that
-        # can't force tools.
-        schema = None
-        use_forced_tool = False
+        # Add JSON schema instruction if response_format is provided
        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = strict_json_schema(response_format) if strict_schema else response_format.model_json_schema()
-            if strict_schema:
-                use_forced_tool = True
-            else:
-                schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2, ensure_ascii=False)}"
-                system_instruction += schema_msg
+            schema = response_format.model_json_schema()
+            schema_msg = f"\n\nYou must respond with valid JSON matching this schema:\n{json.dumps(schema, indent=2, ensure_ascii=False)}"
+            system_instruction += schema_msg

        # gpt-5.2-codex only supports "detailed" reasoning summary
        reasoning_summary = "detailed" if "5.2" in self.model else self.reasoning_summary
@@ -455,21 +392,13 @@ class CodexLLM(LLMInterface):
            "prompt_cache_key": str(uuid.uuid4()),
        }

-        if use_forced_tool and schema is not None:
-            # Single function tool whose parameters ARE the response schema;
-            # force it via tool_choice so the backend does constrained decoding.
-            payload["tools"] = [
-                {
-                    "type": "function",
-                    "name": _STRUCTURED_TOOL_NAME,
-                    "description": "Return the structured response.",
-                    "parameters": schema,
-                }
-            ]
-            payload["tool_choice"] = {"type": "function", "name": _STRUCTURED_TOOL_NAME}
-            payload["parallel_tool_calls"] = False
-
-        headers = self._build_request_headers()
+        headers = {
+            "Authorization": f"Bearer {self.access_token}",
+            "Content-Type": "application/json",
+            "OpenAI-Account-ID": self.account_id,
+            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
+            "Origin": "https://chatgpt.com",
+        }

        url = f"{self.base_url}/codex/responses"

@@ -483,15 +412,8 @@ class CodexLLM(LLMInterface):
                response = await self._client.post(url, json=payload, headers=headers, timeout=120.0)
                response.raise_for_status()

-                # Forced-tool path: read structured output from the function-call
-                # arguments (already a JSON string in a dedicated channel) rather
-                # than from free-form assistant text.
-                if use_forced_tool:
-                    text_content, tool_calls = await self._parse_sse_tool_stream(response)
-                    content = text_content or ""
-                else:
-                    tool_calls = []
-                    content = await self._parse_sse_stream(response)
+                # Parse SSE stream
+                content = await self._parse_sse_stream(response)

                # Codex SSE carries no usage block; stash the same char/4 estimate
                # the success path traces so a later parse/validate failure records
@@ -504,28 +426,7 @@ class CodexLLM(LLMInterface):
                )

                # Handle structured output
-                if use_forced_tool:
-                    tool_input = None
-                    for tc in tool_calls:
-                        if tc.name == _STRUCTURED_TOOL_NAME:
-                            tool_input = tc.arguments if isinstance(tc.arguments, dict) else None
-                            break
-                    if tool_input is None:
-                        # Model ignored the forced tool (rare — e.g. a gateway that
-                        # drops tool_choice). Retry so we don't hard-fail.
-                        logger.warning(
-                            f"Codex forced structured tool missing from response "
-                            f"(attempt {attempt + 1}/{max_retries + 1})"
-                        )
-                        if attempt < max_retries:
-                            backoff = min(initial_backoff * (2**attempt), max_backoff)
-                            await asyncio.sleep(backoff)
-                            attempt += 1
-                            continue
-                        raise RuntimeError("Codex did not return the forced structured_response tool call")
-                    content = json.dumps(tool_input)
-                    result = tool_input if skip_validation else response_format.model_validate(tool_input)
-                elif response_format is not None:
+                if response_format is not None:
                    # Models may wrap JSON in markdown
                    clean_content = content
                    if "```json" in content:
@@ -536,20 +437,13 @@ class CodexLLM(LLMInterface):
                    try:
                        json_data = json.loads(clean_content)
                    except json.JSONDecodeError as e:
-                        # Escape-heavy content deterministically re-fails every
-                        # retry (issue #2504). Try a lenient invalid-escape repair
-                        # before burning a retry / re-raising.
-                        try:
-                            json_data = json.loads(_repair_invalid_json_escapes(clean_content))
-                            logger.info("Codex JSON parsed after repairing invalid escape sequences")
-                        except json.JSONDecodeError:
-                            logger.warning(f"Codex JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {e}")
-                            if attempt < max_retries:
-                                backoff = min(initial_backoff * (2**attempt), max_backoff)
-                                await asyncio.sleep(backoff)
-                                attempt += 1
-                                continue
-                            raise
+                        logger.warning(f"Codex JSON parse error (attempt {attempt + 1}/{max_retries + 1}): {e}")
+                        if attempt < max_retries:
+                            backoff = min(initial_backoff * (2**attempt), max_backoff)
+                            await asyncio.sleep(backoff)
+                            attempt += 1
+                            continue
+                        raise

                    if skip_validation:
                        result = json_data
@@ -648,9 +542,6 @@ class CodexLLM(LLMInterface):
                        "Run 'codex auth login' to re-authenticate."
                    ) from e

-                # Diagnostic dump (opt-in) of the exact request behind any 4xx.
-                dump_request_on_4xx(scope=scope, provider=self.provider, model=self.model, err=e, request=payload)
-
                # Log the actual error message from the API
                error_detail = e.response.text[:500] if hasattr(e.response, "text") else str(e)

@@ -746,7 +637,7 @@ class CodexLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
    ) -> LLMToolCallResult:
        """
        Make API call with tool calling support.
@@ -763,7 +654,7 @@ class CodexLLM(LLMInterface):
            max_retries: Maximum retry attempts.
            initial_backoff: Initial backoff time in seconds.
            max_backoff: Maximum backoff time in seconds.
-            tool_choice: Canonical tool-selection policy.
+            tool_choice: How to choose tools - "auto", "none", "required", or a specific function.

        Returns:
            LLMToolCallResult with content and/or tool_calls.
@@ -825,11 +716,7 @@ class CodexLLM(LLMInterface):
            "instructions": system_instruction,
            "input": user_messages,
            "tools": codex_tools,
-            "tool_choice": (
-                {"type": "function", "name": tool_choice.selected_function_name}
-                if tool_choice.mode is LLMToolChoiceMode.NAMED
-                else tool_choice.mode.value
-            ),
+            "tool_choice": self._normalize_tool_choice(tool_choice),
            "parallel_tool_calls": True,
            "reasoning": {"summary": reasoning_summary},
            "store": False,
@@ -838,7 +725,13 @@ class CodexLLM(LLMInterface):
            "prompt_cache_key": str(uuid.uuid4()),
        }

-        headers = self._build_request_headers()
+        headers = {
+            "Authorization": f"Bearer {self.access_token}",
+            "Content-Type": "application/json",
+            "OpenAI-Account-ID": self.account_id,
+            "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
+            "Origin": "https://chatgpt.com",
+        }

        url = f"{self.base_url}/codex/responses"

@@ -935,8 +828,6 @@ class CodexLLM(LLMInterface):
            )

        except Exception as e:
-            # Diagnostic dump (opt-in) of the exact request behind any 4xx.
-            dump_request_on_4xx(scope=scope, provider=self.provider, model=self.model, err=e, request=payload)
            logger.error(f"Codex tool call error: {e}")
            raise

@@ -981,13 +872,8 @@ class CodexLLM(LLMInterface):
                            try:
                                arguments = json.loads(arguments_str)
                            except json.JSONDecodeError:
-                                # Escape-heavy content can emit invalid \escape
-                                # sequences (issue #2504); repair before giving up.
-                                try:
-                                    arguments = json.loads(_repair_invalid_json_escapes(arguments_str))
-                                except json.JSONDecodeError:
-                                    logger.warning(f"Failed to parse tool arguments: {arguments_str}")
-                                    arguments = {}
+                                logger.warning(f"Failed to parse tool arguments: {arguments_str}")
+                                arguments = {}

                            tool_calls.append(
                                LLMToolCall(
@@ -56,14 +56,6 @@ _DEFAULT_REFRESH_MARGIN_SECONDS = 5 * 60
 # to None and callers proceed uncached, rather than stalling the whole batch.
 _DEFAULT_CREATE_TIMEOUT_SECONDS = 30.0

-# TTL for the per-step reflect caches created by ``create_incremental``. These
-# live only for the duration of one reflect (seconds), so the TTL is just a
-# storage backstop in case the explicit ``delete_session`` at reflect end is
-# missed (crash / event-loop teardown). Short so orphaned caches age out fast —
-# storage is billed per token-hour, so a 5-minute cap keeps the cost of a leaked
-# cache negligible.
-_DEFAULT_INCREMENTAL_TTL_SECONDS = 5 * 60
-

@dataclass
 class _CacheEntry:
@@ -100,10 +92,6 @@ class GeminiCacheManager:
        self._create_timeout_seconds = create_timeout_seconds
        self._entries: dict[str, _CacheEntry] = {}
        self._lock = asyncio.Lock()
-        # session_id -> CachedContent names created via ``create_incremental``.
-        # A reflect creates a fresh rolling cache per step under one session id;
-        # ``delete_session`` tears them all down when the reflect finishes.
-        self._sessions: dict[str, list[str]] = {}

    @staticmethod
    def fingerprint(
@@ -241,99 +229,18 @@ class GeminiCacheManager:
            if entry.name == name:
                self._entries.pop(key, None)

-    async def create_incremental(
-        self,
-        *,
-        session_id: str,
-        model: str,
-        system_instruction: str,
-        contents: list[Any],
-        tools: list[dict[str, Any]] | None = None,
-    ) -> str | None:
-        """Create a fresh CachedContent holding ``system + tools + contents`` and
-        track it under ``session_id`` for later teardown.
-
-        Unlike ``get_or_create``, this does NOT deduplicate by fingerprint: each
-        step of a reflect grows the conversation prefix, so every call is a
-        distinct, single-use cache. The reflect loop creates one per step (each
-        covering the previous step's full input) and reuses it for exactly the
-        next model turn, then supersedes it. All caches for the session are
-        deleted by ``delete_session`` when the reflect ends; the short TTL is
-        only a backstop.
-
-        Returns the cache resource name, or ``None`` when caching is disabled,
-        the prefix is below the model minimum, or the create otherwise fails —
-        callers MUST fall back to an uncached call in that case.
-        """
-        try:
-            name = await self._create_cache(
-                model=model,
-                system_instruction=system_instruction,
-                tools=tools,
-                contents=contents,
-                ttl_seconds=_DEFAULT_INCREMENTAL_TTL_SECONDS,
-            )
-        except _CacheNotEligible as e:
-            logger.debug(
-                "GeminiCacheManager: incremental prefix not eligible (model=%s, reason=%s) — caller falls back",
-                model,
-                e,
-            )
-            return None
-        except Exception:
-            logger.exception(
-                "GeminiCacheManager: failed to create incremental cache (model=%s); caller falls back",
-                model,
-            )
-            return None
-        if name is not None:
-            self._sessions.setdefault(session_id, []).append(name)
-        return name
-
-    async def delete(self, name: str) -> None:
-        """Best-effort server-side delete of a single CachedContent.
-
-        Swallows all errors: a failed delete just means the cache ages out on
-        its TTL. Also drops any matching in-process entry.
-        """
-        self.invalidate(name)
-        try:
-            await self._client.aio.caches.delete(name=name)
-        except Exception:
-            logger.debug("GeminiCacheManager: delete of cache %s failed (will age out on TTL)", name, exc_info=True)
-
-    async def delete_session(self, session_id: str) -> None:
-        """Delete every CachedContent created for ``session_id`` (reflect teardown).
-
-        Deletes concurrently and best-effort — a reflect must never fail because
-        a cache couldn't be torn down; the short TTL is the backstop.
-        """
-        names = self._sessions.pop(session_id, [])
-        if not names:
-            return
-        await asyncio.gather(*(self.delete(n) for n in names), return_exceptions=True)
-
    async def _create_cache(
        self,
        *,
        model: str,
        system_instruction: str,
        tools: list[dict[str, Any]] | None = None,
-        contents: list[Any] | None = None,
-        ttl_seconds: int | None = None,
    ) -> str | None:
        """Wrap ``client.aio.caches.create`` with the config we want.

        The SDK surface differs slightly across google-genai versions;
        this implementation targets the >=1.0.0 line where caches live
        under ``client.aio.caches``.
-
-        ``contents`` (already-converted ``genai_types.Content`` turns) is
-        appended after the system_instruction/tools so the cache can hold a
-        growing multi-turn conversation prefix, not just the static prefix —
-        this is what the step-by-step reflect cache relies on. ``ttl_seconds``
-        overrides the manager default (used to give per-step reflect caches a
-        short backstop TTL).
        """
        # Lazy import so this module doesn't require the SDK at import time.
        from google.genai import types as genai_types
@@ -347,10 +254,8 @@ class GeminiCacheManager:
        # still part of the fingerprint so a schema change keys a fresh cache.
        config_kwargs: dict[str, Any] = {
            "system_instruction": system_instruction,
-            "ttl": f"{ttl_seconds if ttl_seconds is not None else self._ttl_seconds}s",
+            "ttl": f"{self._ttl_seconds}s",
        }
-        if contents:
-            config_kwargs["contents"] = contents
        if tools:
            # OpenAI-style {"function": {...}} entries must be converted to
            # Gemini's Tool/FunctionDeclaration shape before caching.
@@ -13,17 +13,15 @@ import json
 import logging
 import time
 from contextvars import ContextVar
-from dataclasses import dataclass
 from typing import Any

 from google import genai
 from google.genai import errors as genai_errors
 from google.genai import types as genai_types

-from hindsight_api.engine.llm_interface import LLM_TOOL_CHOICE_AUTO, LLMInterface, LLMToolChoice, LLMToolChoiceMode
+from hindsight_api.engine.llm_interface import LLMInterface
 from hindsight_api.engine.llm_trace import LLMResponseUsage, stash_response_usage
 from hindsight_api.engine.llm_wrapper import parse_llm_json
-from hindsight_api.engine.providers.llm_debug import dump_request_on_4xx
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
 from hindsight_api.metrics import get_metrics_collector
 from hindsight_api.worker.stage import set_stage
@@ -65,99 +63,6 @@ def _usage_from_gemini_response(response: Any) -> LLMResponseUsage:
    )


-@dataclass(frozen=True)
-class _GeminiConversation:
-    """A message list converted to Gemini's request shape."""
-
-    system_instruction: str | None
-    contents: list["genai_types.Content"]
-
-
-def _convert_messages_to_gemini(msg_list: list[dict[str, Any]]) -> _GeminiConversation:
-    """Convert OpenAI-style messages to a Gemini (system_instruction, contents) pair.
-
-    Shared by ``call_with_tools`` (request body) and the incremental cache
-    builder so a cached prefix and the live request serialise turns identically —
-    any drift would fingerprint differently and defeat the cache. Consecutive
-    ``role="tool"`` messages are grouped into a single ``user`` Content with
-    multiple FunctionResponse parts, matching Gemini's multi-turn requirement.
-    """
-    system_instruction: str | None = None
-    gemini_contents: list[genai_types.Content] = []
-    pending_tool_names_by_call_id: dict[str, str] = {}
-    i = 0
-    while i < len(msg_list):
-        msg = msg_list[i]
-        role = msg.get("role", "user")
-        content = msg.get("content", "")
-
-        if role != "tool" and pending_tool_names_by_call_id:
-            missing_ids = ", ".join(sorted(pending_tool_names_by_call_id))
-            raise ValueError(f"Gemini assistant tool calls require results before the next message: {missing_ids}")
-
-        if role == "system":
-            system_instruction = (system_instruction + "\n\n" + content) if system_instruction else content
-            i += 1
-        elif role == "tool":
-            parts = []
-            while i < len(msg_list) and msg_list[i].get("role") == "tool":
-                tool_msg = msg_list[i]
-                tool_content = tool_msg.get("content", "")
-                tool_call_id = tool_msg["tool_call_id"]
-                tool_name = pending_tool_names_by_call_id.pop(tool_call_id, None)
-                if tool_name is None:
-                    raise ValueError(f"Gemini tool result references unknown tool_call_id {tool_call_id!r}")
-                parts.append(
-                    genai_types.Part(
-                        function_response=genai_types.FunctionResponse(
-                            name=tool_name,
-                            response={"result": tool_content},
-                        )
-                    )
-                )
-                i += 1
-            if pending_tool_names_by_call_id:
-                missing_ids = ", ".join(sorted(pending_tool_names_by_call_id))
-                raise ValueError(f"Gemini assistant tool calls are missing results: {missing_ids}")
-            gemini_contents.append(genai_types.Content(role="user", parts=parts))
-        elif role == "assistant":
-            tool_calls_in_msg = msg.get("tool_calls", [])
-            if tool_calls_in_msg:
-                parts = []
-                if content:
-                    parts.append(genai_types.Part(text=content))
-                for tc in tool_calls_in_msg:
-                    tool_call_id = tc["id"]
-                    fn = tc["function"]
-                    fn_name = fn["name"]
-                    if tool_call_id in pending_tool_names_by_call_id:
-                        raise ValueError(
-                            f"Gemini assistant tool call id {tool_call_id!r} must be unique within its turn"
-                        )
-                    pending_tool_names_by_call_id[tool_call_id] = fn_name
-                    fn_args_str = fn.get("arguments", "{}")
-                    fn_args = parse_llm_json(fn_args_str)
-                    thought_signature = tc.get("thought_signature")
-                    fc_kwargs: dict[str, Any] = {"name": fn_name, "args": fn_args}
-                    part_kwargs: dict[str, Any] = {"function_call": genai_types.FunctionCall(**fc_kwargs)}
-                    if thought_signature:
-                        part_kwargs["thought_signature"] = base64.b64decode(thought_signature)
-                    parts.append(genai_types.Part(**part_kwargs))
-                gemini_contents.append(genai_types.Content(role="model", parts=parts))
-            else:
-                gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
-            i += 1
-        else:
-            gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
-            i += 1
-
-    if pending_tool_names_by_call_id:
-        missing_ids = ", ".join(sorted(pending_tool_names_by_call_id))
-        raise ValueError(f"Gemini assistant tool calls are missing results: {missing_ids}")
-
-    return _GeminiConversation(system_instruction=system_instruction, contents=gemini_contents)
-
-
 class GeminiLLM(LLMInterface):
    """
    LLM provider for Google Gemini and Vertex AI.
@@ -574,17 +479,6 @@ class GeminiLLM(LLMInterface):
                    logger.error(f"Gemini auth error (HTTP {e.code}), not retrying: {str(e)}")
                    raise

-                # Diagnostic dump (opt-in) of the exact request behind any 4xx, captured
-                # before the cache-drop retry below rebuilds the config so we see what failed.
-                dump_request_on_4xx(
-                    scope=scope,
-                    provider=self.provider,
-                    model=self.model,
-                    err=e,
-                    request=generation_config,
-                    messages=gemini_contents,
-                )
-
                # Cached-request safety net: a stale/invalid/expired CachedContent
                # (or an incompatibility like cache + tool_config) surfaces as a 400.
                # Retrying the same cached request can't recover, so on the first
@@ -631,9 +525,8 @@ class GeminiLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
        cached_prefix: str | None = None,
-        cached_prefix_message_count: int = 0,
    ) -> LLMToolCallResult:
        """
        Make a Gemini/VertexAI API call with tool/function calling support.
@@ -647,22 +540,15 @@ class GeminiLLM(LLMInterface):
            max_retries: Maximum retry attempts.
            initial_backoff: Initial backoff time in seconds.
            max_backoff: Maximum backoff time in seconds.
-            tool_choice: Canonical tool-selection policy.
+            tool_choice: How to choose tools (Gemini uses "auto" only).
            cached_prefix: Optional CachedContent resource name (from
-                ``GeminiCacheManager.get_or_create`` or ``create_incremental``).
-                When set, the system_instruction and tool definitions are assumed
+                ``GeminiCacheManager.get_or_create`` with ``tools=...``). When
+                set, the system_instruction and tool definitions are assumed
                to live in the cache; this call will skip resending them and
                the cached prefix is billed at the cached-input rate. The
                ``tools`` argument is still required (the caller may pass
                an empty list when the cache holds them) so existing call
                sites don't break.
-            cached_prefix_message_count: Number of leading ``messages`` already
-                baked into ``cached_prefix`` (the step-by-step reflect cache holds
-                a growing conversation prefix, not just system+tools). Only the
-                messages AFTER this index are sent as request contents — the rest
-                come from the cache and bill at the cached rate. 0 means the cache
-                holds only the static prefix (system+tools), so the full
-                conversation is still sent (legacy behaviour).

        Returns:
            LLMToolCallResult with content and/or tool_calls.
@@ -670,45 +556,86 @@ class GeminiLLM(LLMInterface):
        start_time = time.time()
        using_cache = cached_prefix is not None

-        # Convert tools to Gemini format. While the cache is in use the tool
-        # definitions live in the CachedContent and the SDK rejects re-sending
-        # them alongside ``cached_content`` (see ``_build_tools_config``), but we
-        # still build them unconditionally so the cached-call-failed fallback —
-        # which drops the cache and re-sends prefix + tools inline — has real
-        # tools to send rather than an empty list.
+        # Convert tools to Gemini format. When the cache is in use, the
+        # tool definitions are baked into the CachedContent at create time
+        # and the SDK rejects re-sending them alongside ``cached_content``.
        gemini_tools = []
-        for tool in tools:
-            func = tool.get("function", {})
-            gemini_tools.append(
-                genai_types.Tool(
-                    function_declarations=[
-                        genai_types.FunctionDeclaration(
-                            name=func.get("name", ""),
-                            description=func.get("description", ""),
-                            parameters=func.get("parameters"),
-                        )
-                    ]
+        if not using_cache:
+            for tool in tools:
+                func = tool.get("function", {})
+                gemini_tools.append(
+                    genai_types.Tool(
+                        function_declarations=[
+                            genai_types.FunctionDeclaration(
+                                name=func.get("name", ""),
+                                description=func.get("description", ""),
+                                parameters=func.get("parameters"),
+                            )
+                        ]
+                    )
                )
-            )

-        # Convert messages. ``system_instruction`` and the FULL contents are always
-        # computed: _build_tools_config omits system/tools from the request while
-        # the cache carries the prefix, but the cached-call-failed safety net must
-        # be able to re-send the whole prefix + tools inline.
-        converted = _convert_messages_to_gemini(list(messages))
-        system_instruction = converted.system_instruction
-        full_contents = converted.contents
+        # Convert messages
+        system_instruction = None
+        gemini_contents = []
+        msg_list = list(messages)
+        i = 0
+        while i < len(msg_list):
+            msg = msg_list[i]
+            role = msg.get("role", "user")
+            content = msg.get("content", "")

-        # Step-by-step reflect cache: when the cache already holds the first
-        # ``cached_prefix_message_count`` messages, send ONLY the newer turns as
-        # request contents — the cached prefix supplies the rest at the cached
-        # rate. The split is always at a whole-turn boundary (the reflect loop
-        # advances the cache one completed turn at a time), so slicing the raw
-        # messages before conversion never splits a grouped tool turn.
-        if using_cache and cached_prefix_message_count > 0:
-            delta_contents = _convert_messages_to_gemini(list(messages)[cached_prefix_message_count:]).contents
-        else:
-            delta_contents = full_contents
+            if role == "system":
+                # Always capture system_instruction. _build_tools_config omits it
+                # (and tools) from the request while the cache carries the prefix,
+                # but it must be available so the cached-call-failed safety net can
+                # re-send the prefix + tools inline.
+                system_instruction = (system_instruction + "\n\n" + content) if system_instruction else content
+                i += 1
+            elif role == "tool":
+                # Gemini requires ALL tool responses for a given model turn to be grouped
+                # into a single Content with multiple FunctionResponse parts.
+                # Consecutive role="tool" messages correspond to one model turn's tool calls.
+                parts = []
+                while i < len(msg_list) and msg_list[i].get("role") == "tool":
+                    tool_msg = msg_list[i]
+                    tool_content = tool_msg.get("content", "")
+                    parts.append(
+                        genai_types.Part(
+                            function_response=genai_types.FunctionResponse(
+                                name=tool_msg.get("name", ""),
+                                response={"result": tool_content},
+                            )
+                        )
+                    )
+                    i += 1
+                gemini_contents.append(genai_types.Content(role="user", parts=parts))
+            elif role == "assistant":
+                tool_calls_in_msg = msg.get("tool_calls", [])
+                if tool_calls_in_msg:
+                    # Convert OpenAI-style tool_calls to Gemini function_call parts
+                    # This is required for proper multi-turn conversation history
+                    parts = []
+                    if content:
+                        parts.append(genai_types.Part(text=content))
+                    for tc in tool_calls_in_msg:
+                        fn = tc.get("function", {})
+                        fn_name = fn.get("name", "")
+                        fn_args_str = fn.get("arguments", "{}")
+                        fn_args = parse_llm_json(fn_args_str)
+                        thought_signature = tc.get("thought_signature")
+                        fc_kwargs: dict[str, Any] = {"name": fn_name, "args": fn_args}
+                        part_kwargs: dict[str, Any] = {"function_call": genai_types.FunctionCall(**fc_kwargs)}
+                        if thought_signature:
+                            part_kwargs["thought_signature"] = base64.b64decode(thought_signature)
+                        parts.append(genai_types.Part(**part_kwargs))
+                    gemini_contents.append(genai_types.Content(role="model", parts=parts))
+                else:
+                    gemini_contents.append(genai_types.Content(role="model", parts=[genai_types.Part(text=content)]))
+                i += 1
+            else:
+                gemini_contents.append(genai_types.Content(role="user", parts=[genai_types.Part(text=content)]))
+                i += 1

        # Apply safety settings: context var (per-request bank override) takes precedence over instance default
        effective_safety_settings = _safety_settings_ctx.get()
@@ -738,20 +665,22 @@ class GeminiLLM(LLMInterface):
                config_kwargs["max_output_tokens"] = max_completion_tokens

            # Map OpenAI-style tool_choice to Gemini FunctionCallingConfig
-            if tool_choice.mode is LLMToolChoiceMode.REQUIRED:
+            if tool_choice == "required":
                config_kwargs["tool_config"] = genai_types.ToolConfig(
                    function_calling_config=genai_types.FunctionCallingConfig(
                        mode="ANY",
                    )
                )
-            elif tool_choice.mode is LLMToolChoiceMode.NAMED:
-                config_kwargs["tool_config"] = genai_types.ToolConfig(
-                    function_calling_config=genai_types.FunctionCallingConfig(
-                        mode="ANY",
-                        allowed_function_names=[tool_choice.selected_function_name],
+            elif isinstance(tool_choice, dict) and tool_choice.get("type") == "function":
+                fn_name = tool_choice.get("function", {}).get("name")
+                if fn_name:
+                    config_kwargs["tool_config"] = genai_types.ToolConfig(
+                        function_calling_config=genai_types.FunctionCallingConfig(
+                            mode="ANY",
+                            allowed_function_names=[fn_name],
+                        )
                    )
-                )
-            elif tool_choice.mode is LLMToolChoiceMode.NONE:
+            elif tool_choice == "none":
                config_kwargs["tool_config"] = genai_types.ToolConfig(
                    function_calling_config=genai_types.FunctionCallingConfig(mode="NONE")
                )
@@ -772,14 +701,10 @@ class GeminiLLM(LLMInterface):
            if attempt > 0:
                set_stage(f"llm.gemini.tools.attempt={attempt + 1}/{max_retries + 1}")
            try:
-                # With the cache active, send only the un-cached tail (delta);
-                # on the uncached fallback path send the full conversation so the
-                # re-inlined system+tools prefix has its whole context.
-                active_contents = delta_contents if cache_active else full_contents
                response = await asyncio.wait_for(
                    self._client.aio.models.generate_content(
                        model=self.model,
-                        contents=active_contents,
+                        contents=gemini_contents,
                        config=config,
                    ),
                    timeout=90.0,  # Safety net for network hangs; valid slow responses are <90s
@@ -882,17 +807,6 @@ class GeminiLLM(LLMInterface):
                    logger.error(f"Gemini auth error (HTTP {e.code}), not retrying: {str(e)}")
                    raise

-                # Diagnostic dump (opt-in) of the exact request behind any 4xx, captured
-                # before the cache-drop retry below rebuilds the config so we see what failed.
-                dump_request_on_4xx(
-                    scope=scope,
-                    provider=self.provider,
-                    model=self.model,
-                    err=e,
-                    request=config,
-                    messages=active_contents,
-                )
-
                # Cached-request safety net (see ``call``): a stale/invalid cache or
                # a cache+tool_config conflict surfaces as a 400. Drop the cache,
                # invalidate it for later operations, and retry THIS call inline
@@ -969,56 +883,6 @@ class GeminiLLM(LLMInterface):
            tools=tools,
        )

-    # ── Step-by-step incremental prompt caching (reflect tool loop) ──────────
-
-    def supports_incremental_prompt_cache(self) -> bool:
-        """True when explicit caching is on — the reflect loop can then roll a
-        per-step CachedContent that grows with the conversation."""
-        return self._prompt_cache_enabled
-
-    def _ensure_cache_manager(self) -> Any:
-        if self._cache_manager is None:
-            from hindsight_api.engine.providers.gemini_cache import GeminiCacheManager
-
-            self._cache_manager = GeminiCacheManager(self._client)
-        return self._cache_manager
-
-    async def create_incremental_cache(
-        self,
-        *,
-        session_id: str,
-        messages: list[dict[str, Any]],
-        tools: list[dict[str, Any]] | None = None,
-    ) -> str | None:
-        """Cache ``system + tools + messages`` as a conversation prefix and return
-        its resource name (or ``None`` — caller falls back to an uncached call).
-
-        The reflect loop calls this once per step with the growing message list so
-        each step's cache entirely contains the previous step's input; the next
-        model turn then references it and re-sends only its own delta. Caches are
-        tracked under ``session_id`` and torn down by ``delete_cache_session``.
-        """
-        if not self._prompt_cache_enabled or self._client is None:
-            return None
-        converted = _convert_messages_to_gemini(list(messages))
-        return await self._ensure_cache_manager().create_incremental(
-            session_id=session_id,
-            model=self.model,
-            system_instruction=converted.system_instruction or "",
-            contents=converted.contents,
-            tools=tools,
-        )
-
-    async def delete_cached_prefix(self, name: str) -> None:
-        """Best-effort delete of a single CachedContent (superseded reflect step)."""
-        if self._cache_manager is not None:
-            await self._cache_manager.delete(name)
-
-    async def delete_cache_session(self, session_id: str) -> None:
-        """Tear down every CachedContent created for a reflect session."""
-        if self._cache_manager is not None:
-            await self._cache_manager.delete_session(session_id)
-
    # ── Batch API (Gemini API only — not Vertex AI) ─────────────────────────
    #
    # Google's Gemini Batch API gives a flat 50% discount on input + output
@@ -1166,7 +1030,7 @@ class GeminiLLM(LLMInterface):
        Mirrors the synchronous ``call`` path: system messages become
        ``systemInstruction``; a ``response_format`` json_schema forces JSON
        output (``responseMimeType``), appends the schema as a textual hint, and
-        grammar-enforces via ``responseJsonSchema`` whenever a schema is present.
+        grammar-enforces via ``responseJsonSchema`` when ``strict`` is set.
        """
        system_texts: list[str] = []
        contents: list[dict[str, Any]] = []
@@ -1195,13 +1059,8 @@ class GeminiLLM(LLMInterface):
                system_texts.append(
                    "You must respond with valid JSON matching this schema:\n" + json.dumps(schema, ensure_ascii=False)
                )
-                # #2699: Gemini always grammar-enforces structured output via its native
-                # response_schema (``strict`` is an OpenAI concept, meaningless here). Set
-                # the native schema whenever one is present so the batch path mirrors the
-                # interactive path; otherwise batch requests at default config
-                # (HINDSIGHT_API_LLM_STRICT_SCHEMA=False) get only a textual hint and
-                # intermittently emit malformed JSON, losing every fact in the chunk.
-                generation_config["responseJsonSchema"] = schema
+                if json_schema.get("strict"):
+                    generation_config["responseJsonSchema"] = schema

        request: dict[str, Any] = {"contents": contents}
        if system_texts:
@@ -22,18 +22,9 @@ from typing import Any
 from litellm.exceptions import Timeout as LiteLLMTimeout

 from hindsight_api.config import DEFAULT_LLM_TIMEOUT, ENV_LLM_TIMEOUT
-from hindsight_api.engine.llm_interface import (
-    LLM_TOOL_CHOICE_AUTO,
-    LLMInterface,
-    LLMToolChoice,
-    LLMToolChoiceMode,
-    OutputTooLongError,
-)
+from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError
 from hindsight_api.engine.llm_trace import LLMResponseUsage, stash_response_usage
-from hindsight_api.engine.llm_wrapper import parse_llm_json
-from hindsight_api.engine.providers.llm_debug import dump_request_on_4xx
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.engine.structured_output import strict_json_schema
 from hindsight_api.metrics import get_metrics_collector
 from hindsight_api.worker.stage import set_stage

@@ -242,7 +233,7 @@ class LiteLLMLLM(LLMInterface):

        # Add JSON schema response format if provided
        if response_format is not None and hasattr(response_format, "model_json_schema"):
-            schema = strict_json_schema(response_format) if strict_schema else response_format.model_json_schema()
+            schema = response_format.model_json_schema()
            call_kwargs["response_format"] = {
                "type": "json_schema",
                "json_schema": {
@@ -286,17 +277,7 @@ class LiteLLMLLM(LLMInterface):
                    try:
                        json_data = json.loads(clean_content)
                    except json.JSONDecodeError:
-                        try:
-                            json_data = json.loads(content)
-                        except json.JSONDecodeError:
-                            if attempt < max_retries:
-                                # Prefer a clean re-roll first — a fresh generation
-                                # usually beats repairing a malformed one.
-                                raise
-                            # Retry budget spent: structural repair as a last
-                            # resort (#2547/#2544). Raises again if unrecoverable,
-                            # which the outer handler surfaces loudly.
-                            json_data = parse_llm_json(content)
+                        json_data = json.loads(content)

                    if skip_validation:
                        result = json_data
@@ -397,9 +378,6 @@ class LiteLLMLLM(LLMInterface):
                    logger.error(f"LiteLLM auth error, not retrying: {e}")
                    raise

-                # Diagnostic dump (opt-in) of the exact request behind any 4xx.
-                dump_request_on_4xx(scope=scope, provider=self.provider, model=self.model, err=e, request=call_kwargs)
-
                last_exception = e
                if attempt < max_retries:
                    # Retry on rate limits, connection errors, server errors
@@ -430,20 +408,13 @@ class LiteLLMLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        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"] = (
-            {
-                "type": "function",
-                "function": {"name": tool_choice.selected_function_name},
-            }
-            if tool_choice.mode is LLMToolChoiceMode.NAMED
-            else tool_choice.mode.value
-        )
+        call_kwargs["tool_choice"] = tool_choice

        last_exception = None
        for attempt in range(max_retries + 1):
@@ -553,9 +524,6 @@ class LiteLLMLLM(LLMInterface):
                if "401" in error_str or "403" in error_str or "unauthorized" in error_str:
                    raise

-                # Diagnostic dump (opt-in) of the exact request behind any 4xx.
-                dump_request_on_4xx(scope=scope, provider=self.provider, model=self.model, err=e, request=call_kwargs)
-
                last_exception = e
                if attempt < max_retries:
                    is_retryable = any(
@@ -22,7 +22,7 @@ import time
 from pathlib import Path
 from typing import Any

-from hindsight_api.engine.llm_interface import LLM_TOOL_CHOICE_AUTO, LLMInterface, LLMToolChoice
+from hindsight_api.engine.llm_interface import LLMInterface
 from hindsight_api.engine.response_models import LLMToolCallResult

 logger = logging.getLogger(__name__)
@@ -394,7 +394,7 @@ class LlamaCppLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
    ) -> LLMToolCallResult:
        """Delegate tool calls to the OpenAI-compatible API."""
        await self._ensure_initialized()
@@ -1,168 +0,0 @@
-"""Opt-in diagnostic: dump the exact request behind an LLM 4xx rejection.
-
-Some ``400 INVALID_ARGUMENT`` / ``400 Bad Request`` rejections of structured-output
-calls are not reproducible by reconstructing the request after the fact — the failing
-factor lives in the request as it was actually assembled at runtime. Reconstructed
-replays of the same inputs return ``200``, so the only reliable way to see what the
-model rejected is to capture the real request at the moment it fails.
-
-This helper is provider-agnostic. Every provider's error handler calls
-``dump_request_on_4xx`` with whatever it assembled — a Pydantic config
-(google-genai ``GenerateContentConfig``), a kwargs dict (OpenAI / Anthropic /
-LiteLLM ``**call_params``), etc. — plus the raised error. The helper self-gates:
-it is a no-op unless the ``llm_debug_dump_4xx`` config flag
-(``HINDSIGHT_API_LLM_DEBUG_DUMP_4XX``) is enabled AND the error carries a 4xx
-status, so callers can drop one unconditional call into each ``except`` block.
-
-Safety / scope:
- Off by default — the config flag is unset in normal operation.
- The serialized config omits message bodies (the ``messages``/``contents``/``input``
-  keys are stripped); message previews are length-capped, so an enabled dump can't
-  flood logs or spill large bodies.
- Never raises — diagnostics must not break the request path (falls back to ``repr``).
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from dataclasses import dataclass
-from typing import Any
-
-logger = logging.getLogger(__name__)
-
-# Top-level request keys whose values are message bodies. Stripped from the config
-# view so the dump never spills large user content — previews are logged separately.
-_CONTENT_KEYS = ("messages", "contents", "input")
-
-_PREVIEW_CHARS = 1500
-_CONFIG_REPR_CAP = 8000
-_ERR_CAP = 200
-
-
-def _enabled() -> bool:
-    from hindsight_api.config import get_config
-
-    return bool(get_config().llm_debug_dump_4xx)
-
-
-def status_code_of(err: Any) -> int | None:
-    """Best-effort HTTP status of a provider error, across SDK error shapes.
-
-    OpenAI/Anthropic expose ``status_code``; google-genai uses ``code``; some wrap the
-    status on a ``response``. Returns None when no integer status is discoverable.
-    """
-    for attr in ("status_code", "code", "http_status"):
-        value = getattr(err, attr, None)
-        if isinstance(value, int):
-            return value
-    response = getattr(err, "response", None)
-    if response is not None:
-        value = getattr(response, "status_code", None)
-        if isinstance(value, int):
-            return value
-    return None
-
-
-def _serialize_config(request: Any) -> str:
-    """Render the request config to a string without message bodies, never raising."""
-    try:
-        if request is None:
-            return "null"
-        # Pydantic models (google-genai GenerateContentConfig, SDK params objects).
-        dump = getattr(request, "model_dump_json", None)
-        if callable(dump):
-            return dump(exclude_none=True)
-        if isinstance(request, dict):
-            view = {k: v for k, v in request.items() if k not in _CONTENT_KEYS}
-            return json.dumps(view, ensure_ascii=False, default=str)
-        return repr(request)[:_CONFIG_REPR_CAP]
-    except Exception:
-        return repr(request)[:_CONFIG_REPR_CAP]
-
-
-@dataclass
-class _MessagePreview:
-    """A message rendered for the dump: role + extracted text (not yet length-capped)."""
-
-    role: str
-    text: str
-
-
-def _message_preview(msg: Any) -> _MessagePreview:
-    """Extract role + text from a message across dict and provider-object shapes."""
-    # OpenAI / Anthropic dict: {"role": ..., "content": str | list[block]}
-    if isinstance(msg, dict):
-        role = str(msg.get("role", "?"))
-        content = msg.get("content")
-        if isinstance(content, str):
-            return _MessagePreview(role, content)
-        if isinstance(content, list):
-            text = ""
-            for block in content:
-                if isinstance(block, dict):
-                    text += block.get("text") or ""
-                else:
-                    text += getattr(block, "text", "") or ""
-            return _MessagePreview(role, text)
-        return _MessagePreview(role, "" if content is None else str(content))
-    # google-genai Content: role + parts[].text
-    role = str(getattr(msg, "role", "?"))
-    text = ""
-    for part in getattr(msg, "parts", None) or []:
-        text += getattr(part, "text", None) or ""
-    if not text:
-        text = getattr(msg, "content", "") or ""
-    return _MessagePreview(role, text)
-
-
-def _resolve_messages(request: Any, messages: Any) -> Any:
-    """Where per-message previews come from: explicit ``messages``, else inside ``request``."""
-    if messages is not None:
-        return messages
-    if isinstance(request, dict):
-        for key in _CONTENT_KEYS:
-            if key in request:
-                return request[key]
-    return []
-
-
-def dump_request_on_4xx(
-    *,
-    scope: str,
-    provider: str,
-    model: str,
-    err: Any,
-    request: Any = None,
-    messages: Any = None,
-) -> None:
-    """Log the exact request behind an LLM 4xx when the diagnostic is enabled.
-
-    No-op unless ``HINDSIGHT_API_LLM_DEBUG_DUMP_4XX`` is truthy and ``err`` carries a
-    4xx status. ``request`` is whatever the provider assembled (a Pydantic config, a
-    kwargs dict, ...); ``messages`` overrides where the per-message previews come from
-    (defaults to the message list found inside ``request``).
-    """
-    if not _enabled():
-        return
-    code = status_code_of(err)
-    if code is None or not (400 <= code < 500):
-        return
-    try:
-        cfg_repr = _serialize_config(request)
-        summary = []
-        for msg in _resolve_messages(request, messages) or []:
-            m = _message_preview(msg)
-            summary.append({"role": m.role, "chars": len(m.text), "preview": m.text[:_PREVIEW_CHARS]})
-        logger.error(
-            "[LLM_4XX_DUMP] provider=%s model=%s scope=%s code=%s err=%s config=%s contents=%s",
-            provider,
-            model,
-            scope,
-            code,
-            str(err)[:_ERR_CAP],
-            cfg_repr,
-            json.dumps(summary, ensure_ascii=False),
-        )
-    except Exception as dump_exc:  # never let diagnostics break the request path
-        logger.warning("[LLM_4XX_DUMP] failed to serialize rejected request: %s", dump_exc)
@@ -9,7 +9,7 @@ import logging
 from collections.abc import Callable
 from typing import Any

-from ..llm_interface import LLM_TOOL_CHOICE_AUTO, LLMInterface, LLMToolChoice
+from ..llm_interface import LLMInterface
 from ..response_models import LLMToolCall, LLMToolCallResult, TokenUsage

 logger = logging.getLogger(__name__)
@@ -200,7 +200,7 @@ class MockLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
    ) -> LLMToolCallResult:
        """
        Make a mock LLM API call with tool/function calling support.
@@ -10,7 +10,7 @@ it raises a clear error instead of a confusing connection failure.
 import logging
 from typing import Any

-from ..llm_interface import LLM_TOOL_CHOICE_AUTO, LLMInterface, LLMToolChoice
+from ..llm_interface import LLMInterface
 from ..response_models import LLMToolCallResult

 logger = logging.getLogger(__name__)
@@ -65,7 +65,7 @@ class NoneLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
    ) -> LLMToolCallResult:
        """Raise LLMNotAvailableError — no LLM is configured."""
        raise LLMNotAvailableError(
@@ -36,18 +36,9 @@ from openai import APIConnectionError, APIStatusError, AsyncOpenAI, LengthFinish

 from hindsight_api.config import DEFAULT_LLM_TIMEOUT, ENV_LLM_TIMEOUT
 from hindsight_api.engine.bank_attribution import apply_bank_attribution
-from hindsight_api.engine.llm_interface import (
-    LLM_TOOL_CHOICE_AUTO,
-    LLMInterface,
-    LLMToolChoice,
-    LLMToolChoiceMode,
-    OutputTooLongError,
-    ProviderRateLimitResetError,
-)
+from hindsight_api.engine.llm_interface import LLMInterface, OutputTooLongError, ProviderRateLimitResetError
 from hindsight_api.engine.llm_trace import LLMResponseUsage, stash_response_usage
-from hindsight_api.engine.providers.llm_debug import dump_request_on_4xx
 from hindsight_api.engine.response_models import LLMToolCall, LLMToolCallResult, TokenUsage
-from hindsight_api.engine.structured_output import strict_json_schema
 from hindsight_api.metrics import get_metrics_collector
 from hindsight_api.worker.stage import set_stage

@@ -56,31 +47,15 @@ logger = logging.getLogger(__name__)
 # Seed applied to every Groq request for deterministic behavior
 DEFAULT_LLM_SEED = 4242
 JSON_MODE_USER_HINT = "Return valid json only."
-DEFAULT_VERIFICATION_MAX_COMPLETION_TOKENS = 512

-
-def _validate_ollama_num_ctx(value: Any) -> int | None:
-    """Validate a native Ollama context-window override."""
-    if value is None:
-        return None
-    if isinstance(value, bool) or not isinstance(value, int):
-        raise ValueError(f"ollama_num_ctx must be a positive integer, got {value!r}")
-    if value < 1:
-        raise ValueError(f"ollama_num_ctx must be >= 1, got {value}")
-    return value
-
-
-# Provider implementations that advertise tool_choice="required"
+# Self-hosted OpenAI-compatible servers that advertise tool_choice="required"
 # but silently ignore it: instead of forcing a tool call they return
 # finish_reason "stop"/"tool_calls" with an EMPTY tool_calls array and no error.
 # Reflect's agent loop then sees no tool call, runs synthesis with no retrieval,
 # and answers "I don't have information" even when the bank holds the answer.
 # See issues #1563 (LM Studio), #1179 (LM Studio + Qwen), #1877 (vLLM with
-# --enable-auto-tool-choice). The generic OpenAI provider is intentionally not
-# inferred from its URL: custom OpenAI-compatible endpoints can implement the
-# required-tool contract, and silently downgrading them changes request semantics.
-# llama-server (the "llamacpp" provider) honors "required" correctly and is
-# intentionally excluded (#1179).
+# --enable-auto-tool-choice). llama-server (the "llamacpp" provider) honors
+# "required" correctly and is intentionally excluded (#1179).
 _TOOL_CHOICE_REQUIRED_UNSUPPORTED_PROVIDERS = frozenset({"lmstudio", "ollama"})


@@ -92,68 +67,23 @@ class ProviderResponseError(RuntimeError):
        self.retryable = retryable


-def _is_json(text: str) -> bool:
-    """True if ``text`` parses as a JSON value."""
-    try:
-        json.loads(text)
-    except (json.JSONDecodeError, ValueError):
-        return False
-    return True
-
-
-def _outer_json_span(content: str) -> str | None:
-    """Return the outermost ``{...}`` / ``[...]`` span if it parses as JSON, else None.
-
-    Fallback for responses where fences are partial/absent or the model wrapped
-    the JSON in surrounding prose. Only returned when it is valid JSON so callers
-    never receive a worse candidate than the raw content.
-    """
-    starts = [i for i in (content.find("{"), content.find("[")) if i >= 0]
-    ends = [i for i in (content.rfind("}"), content.rfind("]")) if i >= 0]
-    if not starts or not ends:
-        return None
-    start, end = min(starts), max(ends)
-    if end <= start:
-        return None
-    candidate = content[start : end + 1].strip()
-    return candidate if _is_json(candidate) else None
-
-
 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. Fences are detected by line (a closing
-    ``` must sit alone on its line) so triple-backticks *inside* JSON string
-    values do not truncate the payload. When the stripped candidate is not
-    valid JSON (partial fence, prose-wrapped output, truncated response), fall
-    back to the outermost parseable JSON span. Returns the original content
-    unchanged if no better candidate is found.
+    response format is requested. This strips the fences while preserving
+    the JSON content inside. Returns the original content unchanged if
+    no fences are detected.
    """
-    candidate = content
-    if "```" in content:
-        lines = content.split("\n")
-        # Find first line that starts a code fence (``` optionally followed by language)
-        fence_start = next((i for i, line in enumerate(lines) if line.startswith("```")), None)
-        if fence_start is not None:
-            # Find matching closing fence (``` alone or with trailing whitespace)
-            fence_end = next(
-                (j for j in range(fence_start + 1, len(lines)) if lines[j].strip() == "```"),
-                None,
-            )
-            if fence_end is not None:
-                candidate = "\n".join(lines[fence_start + 1 : fence_end]).strip()
-
-    if _is_json(candidate):
-        return candidate
-
-    # Fence stripping did not yield valid JSON — try to recover the outer JSON span.
-    span = _outer_json_span(content)
-    if span is not None:
-        return span
-
-    return candidate
+    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


 # Reasoning/thinking tags emitted by extended-thinking models. Some providers
@@ -505,8 +435,6 @@ class OpenAICompatibleLLM(LLMInterface):
        timeout: float | None = None,
        groq_service_tier: str | None = None,
        extra_body: dict[str, Any] | None = None,
-        *,
-        ollama_num_ctx: int | None = None,
        **kwargs: Any,
    ):
        """
@@ -521,8 +449,6 @@ class OpenAICompatibleLLM(LLMInterface):
            timeout: Request timeout in seconds (uses env var or 120s default).
            groq_service_tier: Groq service tier ("on_demand", "flex", "auto").
            extra_body: Extra body params merged into every API call.
-            ollama_num_ctx: Native Ollama context window override. None lets Ollama use
-                the model/server default.
            **kwargs: Additional provider-specific parameters.
        """
        super().__init__(provider, api_key, base_url, model, reasoning_effort, **kwargs)
@@ -603,7 +529,6 @@ class OpenAICompatibleLLM(LLMInterface):
        # Service tier configuration (from config, not env vars)
        self.groq_service_tier = groq_service_tier
        self.openai_service_tier = kwargs.get("openai_service_tier")
-        self.ollama_num_ctx = _validate_ollama_num_ctx(ollama_num_ctx)
        # User-configured extra body params (merged into every API call)
        self._config_extra_body = extra_body or {}

@@ -634,17 +559,17 @@ class OpenAICompatibleLLM(LLMInterface):
    def _drops_tool_choice_required(self) -> bool:
        """Whether this endpoint silently ignores ``tool_choice="required"``.

-        Only explicitly identified provider implementations are classified as
-        unsupported. A custom base URL does not identify endpoint capabilities:
-        an OpenAI-compatible endpoint may correctly enforce required tool calls,
-        and replacing ``required`` with ``auto`` would violate the caller's named
-        tool choice after the tools list has been narrowed.
+        True for self-hosted OpenAI-compatible servers known to return an empty
+        tool_calls array for "required" instead of forcing a call (#1563/#1179/
+        #1877). Covers LM Studio / Ollama directly, plus any server reached via
+        the generic "openai" provider with a custom ``base_url`` (e.g. a local
+        vLLM endpoint). The real OpenAI API (no base_url override) honors
+        "required", and cloud providers keep their own default base_urls, so both
+        are left untouched.
        """
-        return self.provider in _TOOL_CHOICE_REQUIRED_UNSUPPORTED_PROVIDERS
-
-    def _verification_max_completion_tokens(self) -> int:
-        """Return the startup verification budget for OpenAI-compatible gateways."""
-        return DEFAULT_VERIFICATION_MAX_COMPLETION_TOKENS
+        if self.provider in _TOOL_CHOICE_REQUIRED_UNSUPPORTED_PROVIDERS:
+            return True
+        return self.provider == "openai" and bool(self.base_url)

    async def verify_connection(self) -> None:
        """
@@ -657,7 +582,7 @@ class OpenAICompatibleLLM(LLMInterface):
            logger.info(f"Verifying connection: {self.provider}/{self.model}")
            await self.call(
                messages=[{"role": "user", "content": "Say 'ok'"}],
-                max_completion_tokens=self._verification_max_completion_tokens(),
+                max_completion_tokens=100,
                max_retries=2,
                initial_backoff=0.5,
                max_backoff=2.0,
@@ -719,11 +644,6 @@ class OpenAICompatibleLLM(LLMInterface):
        # use the widely-supported max_tokens
        return "max_tokens"

-    def _apply_provider_extra_body_defaults(self, extra_body: dict[str, Any]) -> None:
-        """Apply provider-specific extra_body defaults while preserving user overrides."""
-        if self.provider == "minimax":
-            extra_body.setdefault("thinking", {"type": "disabled"})
-
    async def call(
        self,
        messages: list[dict[str, str]],
@@ -811,7 +731,6 @@ class OpenAICompatibleLLM(LLMInterface):

        # Provider-specific parameters
        extra_body: dict[str, Any] = {**self._config_extra_body}
-        self._apply_provider_extra_body_defaults(extra_body)
        if self.provider == "groq":
            call_params["seed"] = DEFAULT_LLM_SEED
            # Add service_tier if configured
@@ -827,7 +746,7 @@ class OpenAICompatibleLLM(LLMInterface):
        if response_format is not None:
            schema = None
            if hasattr(response_format, "model_json_schema"):
-                schema = strict_json_schema(response_format) if strict_schema else response_format.model_json_schema()
+                schema = response_format.model_json_schema()

            if strict_schema and schema is not None:
                # Use OpenAI's strict JSON schema enforcement
@@ -967,9 +886,7 @@ class OpenAICompatibleLLM(LLMInterface):
                    output_tokens = max(0, output_tokens - thoughts_tokens)
                    total_tokens = max(0, total_tokens - thoughts_tokens)

-                # Record LLM metrics. ``output_tokens`` is visible-only by now, so
-                # ``thoughts_tokens`` has to be recorded alongside it or the reasoning
-                # half of the billed output reaches no counter at all.
+                # Record LLM metrics
                metrics = get_metrics_collector()
                metrics.record_llm_call(
                    provider=self.provider,
@@ -979,8 +896,6 @@ class OpenAICompatibleLLM(LLMInterface):
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    success=True,
-                    cached_input_tokens=cached_tokens,
-                    thoughts_tokens=thoughts_tokens,
                )

                # Record trace span
@@ -1049,9 +964,6 @@ class OpenAICompatibleLLM(LLMInterface):
                    logger.error(f"Auth error (HTTP {e.status_code}), not retrying: {str(e)}")
                    raise

-                # Diagnostic dump (opt-in) of the exact request behind any 4xx.
-                dump_request_on_4xx(scope=scope, provider=self.provider, model=self.model, err=e, request=call_params)
-
                _raise_provider_quota_defer(
                    e, provider=self.provider, model=self.model, scope=scope, max_backoff=max_backoff
                )
@@ -1142,7 +1054,7 @@ class OpenAICompatibleLLM(LLMInterface):
        max_retries: int = 5,
        initial_backoff: float = 1.0,
        max_backoff: float = 30.0,
-        tool_choice: LLMToolChoice = LLM_TOOL_CHOICE_AUTO,
+        tool_choice: str | dict[str, Any] = "auto",
    ) -> LLMToolCallResult:
        """
        Make an LLM API call with tool/function calling support.
@@ -1156,43 +1068,51 @@ class OpenAICompatibleLLM(LLMInterface):
            max_retries: Maximum retry attempts.
            initial_backoff: Initial backoff time in seconds.
            max_backoff: Maximum backoff time in seconds.
-            tool_choice: Canonical tool-selection policy.
+            tool_choice: How to choose tools - "auto", "none", "required", or specific function.

        Returns:
            LLMToolCallResult with content and/or tool_calls.
        """
        start_time = time.time()

-        request_tool_choice: str | None
-        if tool_choice.mode is LLMToolChoiceMode.NAMED:
-            forced_name = tool_choice.selected_function_name
-            filtered = [tool for tool in tools if tool.get("function", {}).get("name") == forced_name]
-            if len(filtered) != 1:
-                raise ValueError(
-                    f"Named tool_choice must reference exactly one declared tool; "
-                    f"found {len(filtered)} definitions for {forced_name!r}"
-                )
-            tools = filtered
-            request_tool_choice = LLMToolChoiceMode.REQUIRED.value
-        elif tool_choice.mode is LLMToolChoiceMode.AUTO:
-            request_tool_choice = None
-        else:
-            request_tool_choice = tool_choice.mode.value
+        request_tool_choice: str | dict[str, Any] | None = tool_choice
+
+        # Normalize named tool_choice dicts to "required" + filter tools.
+        # Some providers (e.g. LM Studio, Ollama) reject the OpenAI named format
+        # {"type": "function", "function": {"name": "..."}}.  The semantics are
+        # identical to tool_choice="required" with the tools list restricted to
+        # just the requested tool, so we apply that transformation where supported.
+        if isinstance(request_tool_choice, dict) and request_tool_choice.get("type") == "function":
+            forced_name = request_tool_choice.get("function", {}).get("name")
+            if forced_name:
+                filtered = [t for t in tools if t.get("function", {}).get("name") == forced_name]
+                if filtered:
+                    tools = filtered
+                request_tool_choice = "required"

        # DeepSeek accepts tool calls but rejects explicit required/named
        # tool_choice values. The tools list has already been narrowed for
        # forced calls, so omitting tool_choice preserves the practical behavior.
-        if "deepseek" in self.model.lower() and tool_choice.mode is not LLMToolChoiceMode.AUTO:
+        if "deepseek" in self.model.lower() and request_tool_choice != "auto":
            request_tool_choice = None

-        # LM Studio and Ollama silently drop tool_choice="required", returning an
-        # empty tool_calls array instead of forcing a call (#1563/#1179).
+        # "auto" is the OpenAI API default — omitting tool_choice is semantically
+        # identical. Some providers (e.g. DeepSeek's reasoner pathway, which
+        # deepseek-v4-flash falls into when thinking mode is enabled) reject the
+        # parameter outright, returning HTTP 400 even for value "auto". Sending it
+        # only when the caller asks for a non-default behaviour avoids those 400s
+        # without changing semantics for compliant providers.
+        if request_tool_choice == "auto":
+            request_tool_choice = None
+
+        # vLLM (--enable-auto-tool-choice), LM Studio, Ollama and similar
+        # self-hosted servers silently drop tool_choice="required", returning an
+        # empty tool_calls array instead of forcing a call (#1563/#1179/#1877).
        # Downgrade to auto (None) so the model still gets to call a tool. Named
        # tool_choice dicts were already normalized to "required" + a single
        # filtered tool above, so the call stays practically forced even under
-        # auto. Generic OpenAI-compatible endpoints retain the canonical
-        # ``required`` contract regardless of whether they use a custom base URL.
-        if request_tool_choice == LLMToolChoiceMode.REQUIRED.value and self._drops_tool_choice_required():
+        # auto. The real OpenAI API honors "required" and is left untouched.
+        if request_tool_choice == "required" and self._drops_tool_choice_required():
            request_tool_choice = None

        # DeepSeek tool-call replies can carry provider-specific reasoning_content.
@@ -1229,7 +1149,6 @@ class OpenAICompatibleLLM(LLMInterface):

        # Provider-specific parameters
        extra_body: dict[str, Any] = {**self._config_extra_body}
-        self._apply_provider_extra_body_defaults(extra_body)
        if self.provider == "groq":
            call_params["seed"] = DEFAULT_LLM_SEED
        if extra_body:
@@ -1277,8 +1196,6 @@ class OpenAICompatibleLLM(LLMInterface):
                if thoughts_tokens:
                    output_tokens = max(0, output_tokens - thoughts_tokens)

-                # See ``call()``: record the reasoning and cached counts too, so no
-                # billed token is dropped from the metrics counters.
                metrics = get_metrics_collector()
                metrics.record_llm_call(
                    provider=self.provider,
@@ -1288,8 +1205,6 @@ class OpenAICompatibleLLM(LLMInterface):
                    input_tokens=input_tokens,
                    output_tokens=output_tokens,
                    success=True,
-                    cached_input_tokens=cached_tokens,
-                    thoughts_tokens=thoughts_tokens,
                )

                # Record OpenTelemetry span
@@ -1351,10 +1266,6 @@ class OpenAICompatibleLLM(LLMInterface):
                        f"not retrying: {_summarize_status_error(e)}"
                    )
                    raise
-
-                # Diagnostic dump (opt-in) of the exact request behind any 4xx.
-                dump_request_on_4xx(scope=scope, provider=self.provider, model=self.model, err=e, request=call_params)
-
                _raise_provider_quota_defer(
                    e, provider=self.provider, model=self.model, scope=scope, max_backoff=max_backoff
                )
@@ -1426,10 +1337,9 @@ class OpenAICompatibleLLM(LLMInterface):

        # Add optional parameters with optimized defaults for Ollama
        options: dict[str, Any] = {
+            "num_ctx": 16384,  # 16k context window for larger prompts
            "num_batch": 512,  # Optimal batch size for prompt processing
        }
-        if self.ollama_num_ctx is not None:
-            options["num_ctx"] = self.ollama_num_ctx
        if max_completion_tokens:
            options["num_predict"] = max_completion_tokens
        if temperature is not None:
@@ -6,7 +6,6 @@ structured information like temporal constraints.
 """

 import logging
-import re
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta

@@ -20,103 +19,6 @@ from hindsight_api.engine.temporal_periods import (

 logger = logging.getLogger(__name__)

-# dateparser.search_dates over-matches: short common words that happen to be
-# weekday/month abbreviations in *some* language ("we"/"me"/"did" -> a weekday,
-# "do" -> Sunday) come back as bogus dates. When such a false positive appears
-# *before* the real date in the query, taking the first match (or a hard-coded
-# blacklist of such words) silently produces a wrong temporal window — worse
-# than none, because the constraint is non-null so nothing downstream can tell
-# extraction failed. See issue #2768.
-#
-# Instead of blacklisting words one at a time (a moving target — every short
-# word dateparser resolves is a new instance of the same bug), we score each
-# match by the date signal it actually carries and keep only matches with a
-# real signal, preferring the strongest. A bare weekday abbreviation carries no
-# day/month/year and scores zero, so it is rejected regardless of language or
-# dateparser version.
-_TOKEN_RE = re.compile(r"[a-z0-9]+")
-_MONTH_WORDS = {
-    "january",
-    "february",
-    "march",
-    "april",
-    "may",
-    "june",
-    "july",
-    "august",
-    "september",
-    "october",
-    "november",
-    "december",
-}
-_RELATIVE_WORDS = {"today", "yesterday", "tomorrow", "tonight", "now"}
-_WEEKDAY_WORDS = {
-    "monday",
-    "tuesday",
-    "wednesday",
-    "thursday",
-    "friday",
-    "saturday",
-    "sunday",
-}
-_PERIOD_WORDS = {
-    "last",
-    "next",
-    "this",
-    "past",
-    "coming",
-    "ago",
-    "week",
-    "weeks",
-    "month",
-    "months",
-    "year",
-    "years",
-    "day",
-    "days",
-    "hour",
-    "hours",
-    "minute",
-    "minutes",
-    "quarter",
-    "decade",
-    "century",
-    "weekend",
-    "morning",
-    "afternoon",
-    "evening",
-    "night",
-    "noon",
-    "midnight",
-}
-
-
-def _date_match_score(text: str) -> int:
-    """Score how strong a temporal signal a matched span carries.
-
-    A score of 0 means the span is a bare token with no explicit date content
-    (the false-positive class from issue #2768) and should be rejected. Higher
-    scores mean a stronger, less ambiguous date reference. A digit is the
-    strongest signal (day/year/ISO date); an explicit English month/relative
-    word next; weekday names and period words weakest but still explicit.
-    """
-    tokens = _TOKEN_RE.findall(text.lower())
-    if not tokens:
-        return 0
-    score = 0
-    if any(any(ch.isdigit() for ch in tok) for tok in tokens):
-        score += 100
-    token_set = set(tokens)
-    if token_set & _MONTH_WORDS:
-        score += 50
-    if token_set & _RELATIVE_WORDS:
-        score += 50
-    if token_set & _WEEKDAY_WORDS:
-        score += 30
-    if token_set & _PERIOD_WORDS:
-        score += 20
-    return score
-

 class TemporalConstraint(BaseModel):
    """
@@ -262,23 +164,20 @@ class DateparserQueryAnalyzer(QueryAnalyzer):
        if not results:
            return QueryAnalysis(temporal_constraint=None)

-        # Score each match by the date signal it carries and keep only those
-        # with a real signal, rejecting bare weekday/month-abbreviation false
-        # positives ("we"/"me"/"did"). Prefer the strongest match, breaking ties
-        # by longest span, so an explicit date ("in May", "2026-06-10") always
-        # beats an earlier weak word regardless of position. See issue #2768.
-        scored_results = [
-            (_date_match_score(text), len(text), date)
+        # Filter out false positives (common words parsed as dates)
+        false_positives = {"do", "may", "march", "will", "can", "sat", "sun", "mon", "tue", "wed", "thu", "fri"}
+        valid_results = [
+            (text, date)
            for text, date in results
-            if not is_embedded_cjk_dateparser_match(query, text)
+            if (text.lower() not in false_positives or len(text) > 3)
+            and not is_embedded_cjk_dateparser_match(query, text)
        ]
-        scored_results = [entry for entry in scored_results if entry[0] > 0]

-        if not scored_results:
+        if not valid_results:
            return QueryAnalysis(temporal_constraint=None)

-        # Highest signal score wins; ties broken by the longest matched span.
-        _, _, parsed_date = max(scored_results, key=lambda entry: (entry[0], entry[1]))
+        # Use the first valid date found
+        _, parsed_date = valid_results[0]

        # Create constraint for single day
        start_date = parsed_date.replace(hour=0, minute=0, second=0, microsecond=0)
@@ -15,7 +15,6 @@ import time
 from typing import TYPE_CHECKING, Any, Awaitable, Callable

 from ...config import get_config
-from ..llm_interface import LLM_TOOL_CHOICE_AUTO, LLMToolChoice
 from .models import DirectiveInfo, LLMCall, ReflectAgentResult, StructuredOutputResult, TokenUsageSummary, ToolCall
 from .prompts import (
    _extract_directive_rules,
@@ -50,11 +49,6 @@ logger = logging.getLogger(__name__)

 DEFAULT_MAX_ITERATIONS = 10

-# Fallback answer when the LLM returns nothing usable. Consumers that need to
-# tell a real answer from this placeholder (e.g. refresh outcome metadata's
-# populated_content) compare against this constant rather than the literal.
-NO_ANSWER_TEXT = "No answer provided."
-

 def _normalize_tool_name(name: str) -> str:
    """Normalize tool name from various LLM output formats.
@@ -230,7 +224,6 @@ async def _generate_structured_output(
    response_schema: dict,
    llm_config: "LLMProvider",
    reflect_id: str,
-    max_tokens: int | None = None,
 ) -> StructuredOutputResult:
    """Generate structured output from an answer using the provided JSON schema.

@@ -239,10 +232,6 @@ async def _generate_structured_output(
        response_schema: JSON Schema for the expected output structure
        llm_config: LLM provider for making the extraction call
        reflect_id: Reflect ID for logging
-        max_tokens: Output-token budget for the extraction call, mirroring the
-            plain reflect calls (omitted when None); without it, reasoning /
-            preamble models can exhaust the provider default before emitting any
-            JSON (finish_reason=length, empty content -> issue #2431)

    Returns:
        A StructuredOutputResult carrying the structured output (None if
@@ -333,8 +322,6 @@ OUTPUT:"""
            ],
            response_format=DynamicModel,
            scope="reflect_structured",
-            strict_schema=get_config().llm_strict_schema_reflect,
-            max_completion_tokens=max_tokens,
            max_retries=1,
            initial_backoff=0.25,
            max_backoff=1.0,
@@ -426,104 +413,7 @@ def _all_mental_models_are_usable_and_fresh(tool_output: dict[str, Any]) -> bool
    return True


-# Detached cache-teardown tasks. asyncio holds only weak references to tasks, so
-# a fire-and-forget task can be garbage-collected mid-flight — keep a strong
-# reference here until it finishes.
-_cache_cleanup_tasks: set[asyncio.Task] = set()
-
-
-def _spawn_cache_cleanup(
-    provider_impl: Any,
-    session_id: str,
-    cache_tasks: list[asyncio.Task],
-    reflect_id: str,
-) -> None:
-    """Delete a reflect's ephemeral context caches in the background.
-
-    The per-reflect caches are dead the moment the reflect returns — nothing ever
-    reuses them — so the caller must not wait on teardown: draining the in-flight
-    create plus the delete round-trips would add latency to every single answer.
-    Detach it instead. The short cache TTL is the backstop if the process dies
-    before the task runs.
-    """
-
-    async def _cleanup() -> None:
-        try:
-            # Let any overlapped create land first, so its cache is registered in
-            # the session and actually gets deleted rather than lingering to TTL.
-            if cache_tasks:
-                await asyncio.gather(*cache_tasks, return_exceptions=True)
-            await provider_impl.delete_cache_session(session_id)
-        except Exception:
-            logger.debug("[REFLECT %s] cache session teardown failed (will age out on TTL)", reflect_id)
-
-    try:
-        task = asyncio.create_task(_cleanup())
-    except RuntimeError:
-        # No running loop to detach onto (not expected in the server); TTL cleans up.
-        return
-    _cache_cleanup_tasks.add(task)
-    task.add_done_callback(_cache_cleanup_tasks.discard)
-
-
 async def run_reflect_agent(
-    llm_config: "LLMProvider",
-    bank_id: str,
-    query: str,
-    bank_profile: dict[str, Any],
-    search_mental_models_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
-    search_observations_fn: Callable[[str, int], Awaitable[dict[str, Any]]],
-    recall_fn: Callable[[str, int, int], Awaitable[dict[str, Any]]],
-    expand_fn: Callable[[list[str], str], Awaitable[dict[str, Any]]],
-    **kwargs: Any,
-) -> ReflectAgentResult:
-    """Public entrypoint: runs the agent loop and tears down any per-step context
-    caches it created.
-
-    The step-by-step caches (Gemini ``CachedContent``) are ephemeral — scoped to
-    exactly one reflect and never reused after it — so teardown is scheduled on
-    every exit path (answer, error, cancellation) but runs **detached**: the
-    caller gets its answer without waiting on the delete round-trips. The short
-    cache TTL is the backstop if the teardown never runs; the delete is
-    best-effort and never allowed to fail a reflect.
-    """
-    reflect_id = f"{bank_id[:8]}-{int(time.time() * 1000) % 100000}"
-    provider_impl = getattr(llm_config, "_provider_impl", None)
-    # Reflect step-by-step caching needs the provider to support it AND the
-    # dedicated reflect flag (on by default; distinct from the global prompt-cache
-    # switch so it can be turned off for reflect alone).
-    incremental_caching = (
-        provider_impl is not None
-        and provider_impl.supports_incremental_prompt_cache()
-        and get_config().reflect_prompt_cache_enabled
-    )
-    cache_session_id = f"reflect:{reflect_id}"
-    # In-flight cache-create tasks (scheduled to overlap tool execution). Awaited
-    # before teardown so every created cache is tracked and deleted — no orphans.
-    cache_tasks: list[asyncio.Task] = []
-    try:
-        return await _run_reflect_agent_inner(
-            llm_config,
-            bank_id,
-            query,
-            bank_profile,
-            search_mental_models_fn,
-            search_observations_fn,
-            recall_fn,
-            expand_fn,
-            reflect_id=reflect_id,
-            provider_impl=provider_impl,
-            incremental_caching=incremental_caching,
-            cache_session_id=cache_session_id,
-            cache_tasks=cache_tasks,
-            **kwargs,
-        )
-    finally:
-        if incremental_caching and provider_impl is not None:
-            _spawn_cache_cleanup(provider_impl, cache_session_id, cache_tasks, reflect_id)
-
-
-async def _run_reflect_agent_inner(
    llm_config: "LLMProvider",
    bank_id: str,
    query: str,
@@ -544,12 +434,6 @@ async def _run_reflect_agent_inner(
    max_context_tokens: int = 100_000,
    llm_output_language: str | None = None,
    cancel_check: Callable[[], None] | None = None,
-    *,
-    reflect_id: str,
-    provider_impl: Any,
-    incremental_caching: bool,
-    cache_session_id: str,
-    cache_tasks: list[asyncio.Task],
 ) -> ReflectAgentResult:
    """
    Execute the reflect agent loop using native tool calling.
@@ -577,6 +461,7 @@ async def _run_reflect_agent_inner(
    Returns:
        ReflectAgentResult with final answer and metadata
    """
+    reflect_id = f"{bank_id[:8]}-{int(time.time() * 1000) % 100000}"
    start_time = time.time()

    # Build directives_applied for the trace
@@ -613,68 +498,27 @@ async def _run_reflect_agent_inner(
        {"role": "user", "content": query},
    ]

-    # Step-by-step context caching for the agentic tool loop.
-    #
-    # Caching only the static system+tools prefix wins little here: it's dwarfed
-    # by the tool results (recall/observations) that get re-sent on every turn.
-    # Instead we roll a cache forward one step at a time — after each turn the
-    # cache is extended to cover that turn's FULL input, so the next ``auto`` turn
-    # reuses the entire prior conversation at the cached rate and sends only its
-    # own new tool results as the delta. Each new tool payload is therefore billed
-    # at full price exactly once (the turn it's produced), then cached thereafter.
-    #
-    # The cache create for turn N+1 covers turn N's input, which is fully known the
-    # moment turn N's LLM call returns — so we kick it off as a background task that
-    # runs CONCURRENTLY with turn N's tool execution (``_schedule_cache``) and only
-    # await it (``_resolve_pending_cache``) right before the next ``auto`` call,
-    # hiding the create latency behind work we'd do anyway.
-    #
-    # ``rolling_cache_boundary`` is the number of leading ``messages`` baked into
-    # the adopted ``rolling_cache_name``. ``incremental_caching`` is False for
-    # providers/config without explicit caching, so every branch below is a no-op.
-    rolling_cache_name: str | None = None
-    rolling_cache_boundary = 0
-    pending_cache_task: asyncio.Task | None = None
-    pending_cache_boundary = 0
-
-    async def _resolve_pending_cache() -> None:
-        """Adopt the overlapped next-cache once it's ready as the rolling cache.
-
-        Best-effort: a failed/``None`` create just leaves the previous (smaller)
-        cache in place, so the next call sends a larger delta but stays correct.
-        """
-        nonlocal rolling_cache_name, rolling_cache_boundary, pending_cache_task
-        if pending_cache_task is None:
-            return
-        task = pending_cache_task
-        pending_cache_task = None
+    # Opt into context caching for the agentic tool loop. The system
+    # prompt and tool definitions are stable for the duration of this
+    # reflect call (and across reflects against the same bank), so
+    # caching them once and reusing across every iteration of the loop
+    # collapses the dominant input cost — the prefix repeated on every
+    # turn. ``get_or_create_cached_prefix`` returns None when caching is
+    # disabled, unsupported, or the prefix is too small; the
+    # ``call_with_tools`` invocation below transparently falls back to
+    # the uncached path in that case.
+    cached_prefix_name: str | None = None
+    provider_impl = getattr(llm_config, "_provider_impl", None)
+    if provider_impl is not None and provider_impl.supports_prompt_caching():
        try:
-            new_name = await task
-        except Exception:
-            new_name = None
-        if new_name is not None:
-            rolling_cache_name = new_name
-            rolling_cache_boundary = pending_cache_boundary
-
-    def _schedule_cache(upto: int) -> None:
-        """Start building the cache covering ``messages[:upto]`` in the background
-        so it overlaps the tool execution that follows this turn."""
-        nonlocal pending_cache_task, pending_cache_boundary
-        # ``messages[:upto]`` is snapshotted now, so appends during tool execution
-        # can't change what gets cached. ``ensure_future`` raises if the provider
-        # didn't return a coroutine (e.g. a test double) — caching is a soft
-        # optimisation and must never break a reflect, so swallow and skip.
-        try:
-            task = asyncio.ensure_future(
-                provider_impl.create_incremental_cache(
-                    session_id=cache_session_id, messages=messages[:upto], tools=tools
-                )
+            cached_prefix_name = await provider_impl.get_or_create_cached_prefix(
+                system_instruction=system_prompt,
+                tools=tools,
            )
        except Exception:
-            return
-        pending_cache_boundary = upto
-        pending_cache_task = task
-        cache_tasks.append(task)
+            # Caching is a soft optimisation; never let a cache-side
+            # error block a reflect.
+            cached_prefix_name = None

    # Tracking
    total_tools_called = 0
@@ -796,7 +640,7 @@ async def _run_reflect_agent_inner(
            # Generate structured output if schema provided
            structured_output = None
            if response_schema and answer:
-                struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id, max_tokens)
+                struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id)
                structured_output = struct.structured_output
                total_input_tokens += struct.input_tokens
                total_output_tokens += struct.output_tokens
@@ -860,7 +704,7 @@ async def _run_reflect_agent_inner(

            structured_output = None
            if response_schema and answer:
-                struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id, max_tokens)
+                struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id)
                structured_output = struct.structured_output
                total_input_tokens += struct.input_tokens
                total_output_tokens += struct.output_tokens
@@ -895,35 +739,12 @@ async def _run_reflect_agent_inner(

        if stop_forcing_from_iteration is not None and iteration >= stop_forcing_from_iteration:
            # A fresh mental model already short-circuited the forced path.
-            iter_tool_choice = LLM_TOOL_CHOICE_AUTO
+            iter_tool_choice: str | dict = "auto"
        elif iteration < len(forced_sequence):
-            iter_tool_choice = LLMToolChoice.named(forced_sequence[iteration])
+            iter_tool_choice = {"type": "function", "function": {"name": forced_sequence[iteration]}}
        else:
-            iter_tool_choice = LLM_TOOL_CHOICE_AUTO
+            iter_tool_choice = "auto"

-        # Will the NEXT turn be an ``auto`` turn (the only kind that references a
-        # cache)? The cache we schedule this turn covers this turn's input and is
-        # used by the next turn, so we only bother building it when the next turn
-        # can use it — skipping the wasted creates between two forced turns.
-        next_iter = iteration + 1
-        if stop_forcing_from_iteration is not None and next_iter >= stop_forcing_from_iteration:
-            next_is_auto = True
-        elif next_iter < len(forced_sequence):
-            next_is_auto = False
-        else:
-            next_is_auto = True
-
-        # Before an ``auto`` turn, adopt the cache that was being built in the
-        # background during the previous turn's tool execution. It covers that
-        # turn's full input, so THIS call reuses the entire prior conversation at
-        # the cached rate and sends only the turns appended since. Forced turns
-        # can't use a cache (Gemini rejects ``cached_content`` + ``tool_config``),
-        # but the cache still advances underneath them, so the first ``auto`` turn
-        # inherits a cache covering all the forced results.
-        if incremental_caching and iter_tool_choice is LLM_TOOL_CHOICE_AUTO:
-            await _resolve_pending_cache()
-
-        call_msg_count = len(messages)
        try:
            ct_kwargs: dict[str, Any] = dict(
                messages=messages,
@@ -931,9 +752,15 @@ async def _run_reflect_agent_inner(
                scope="reflect_tool_call",
                tool_choice=iter_tool_choice,
            )
-            if incremental_caching and iter_tool_choice is LLM_TOOL_CHOICE_AUTO and rolling_cache_name is not None:
-                ct_kwargs["cached_prefix"] = rolling_cache_name
-                ct_kwargs["cached_prefix_message_count"] = rolling_cache_boundary
+            # Gemini rejects ``cached_content`` alongside a per-request
+            # ``tool_config`` (forced tool choice): "CachedContent can not be used
+            # with GenerateContent request setting system_instruction, tools or
+            # tool_config." The forced-sequence iterations set tool_config, so only
+            # the ``auto`` iterations can reference the cache; forced iterations send
+            # the prefix inline. The cache (tools + system prompt) is identical
+            # either way, so this just limits *which* iterations are billed cached.
+            if cached_prefix_name is not None and iter_tool_choice == "auto":
+                ct_kwargs["cached_prefix"] = cached_prefix_name
            result = await llm_config.call_with_tools(**ct_kwargs)
            llm_duration = int((time.time() - llm_start) * 1000)
            consecutive_errors = 0
@@ -1004,7 +831,7 @@ async def _run_reflect_agent_inner(
            # Generate structured output if schema provided
            structured_output = None
            if response_schema and answer:
-                struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id, max_tokens)
+                struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id)
                structured_output = struct.structured_output
                total_input_tokens += struct.input_tokens
                total_output_tokens += struct.output_tokens
@@ -1081,9 +908,7 @@ async def _run_reflect_agent_inner(
                # Generate structured output if schema provided
                structured_output = None
                if response_schema and answer:
-                    struct = await _generate_structured_output(
-                        answer, response_schema, llm_config, reflect_id, max_tokens
-                    )
+                    struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id)
                    structured_output = struct.structured_output
                    total_input_tokens += struct.input_tokens
                    total_output_tokens += struct.output_tokens
@@ -1138,7 +963,7 @@ async def _run_reflect_agent_inner(
            # Generate structured output if schema provided
            structured_output = None
            if response_schema and answer:
-                struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id, max_tokens)
+                struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id)
                structured_output = struct.structured_output
                total_input_tokens += struct.input_tokens
                total_output_tokens += struct.output_tokens
@@ -1176,6 +1001,7 @@ async def _run_reflect_agent_inner(
                    {
                        "role": "tool",
                        "tool_call_id": done_call.id,
+                        "name": done_call.name,  # Required by Gemini
                        "content": json.dumps(
                            {
                                "error": "You must search for information first. Use search_mental_models(), search_observations(), or recall() before providing your final answer."
@@ -1209,7 +1035,6 @@ async def _run_reflect_agent_inner(
                    directives_applied=directives_applied,
                    llm_config=llm_config,
                    response_schema=response_schema,
-                    max_tokens=max_tokens,
                )

        # Execute other tools in parallel (exclude done tool in all its format variants)
@@ -1241,6 +1066,7 @@ async def _run_reflect_agent_inner(
                    {
                        "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."
@@ -1252,16 +1078,6 @@ async def _run_reflect_agent_inner(

            other_tools = allowed_tools

-            # Kick off the next-turn cache (covering THIS call's input) so it
-            # builds concurrently with the tool execution below — hiding the
-            # create latency. Only schedule when the next turn is ``auto`` (the
-            # only kind that references it); the next turn's pre-call resolve then
-            # adopts it. Resolve any prior in-flight create first so we don't drop
-            # its handle.
-            if incremental_caching and next_is_auto:
-                await _resolve_pending_cache()
-                _schedule_cache(call_msg_count)
-
            # Execute tools in parallel
            tool_tasks = [
                _execute_tool_with_timing(
@@ -1344,6 +1160,7 @@ async def _run_reflect_agent_inner(
                    {
                        "role": "tool",
                        "tool_call_id": tc.id,
+                        "name": tc.name,  # Required by Gemini
                        "content": json.dumps(output, default=str, ensure_ascii=False),
                    }
                )
@@ -1427,7 +1244,6 @@ async def _process_done_tool(
    directives_applied: list[DirectiveInfo],
    llm_config: "LLMProvider | None" = None,
    response_schema: dict | None = None,
-    max_tokens: int | None = None,
 ) -> ReflectAgentResult:
    """Process the done tool call and return the result."""
    args = done_call.arguments
@@ -1436,46 +1252,7 @@ async def _process_done_tool(
    raw_answer = args.get("answer", "").strip()
    answer = _clean_done_answer(raw_answer) if raw_answer else ""
    if not answer:
-        answer = NO_ANSWER_TEXT
-
-    final_usage = usage
-    if llm_config and max_tokens is not None and count_cl100k_tokens(answer) > max_tokens:
-        rewrite_start = time.time()
-        rewritten, rewrite_usage = await llm_config.call(
-            messages=[
-                {
-                    "role": "system",
-                    "content": (
-                        "Rewrite the user's text so it fits within the requested token budget. "
-                        "Preserve the key facts and structure; drop lower-priority detail. "
-                        "Respond with the rewritten text only, no preamble."
-                    ),
-                },
-                {
-                    "role": "user",
-                    "content": f"Target budget: {max_tokens} tokens.\n\nText to rewrite:\n{answer}",
-                },
-            ],
-            scope="reflect",
-            max_completion_tokens=max_tokens,
-            return_usage=True,
-        )
-        answer = _clean_answer_text(rewritten.strip())
-        final_usage = TokenUsageSummary(
-            input_tokens=usage.input_tokens + rewrite_usage.input_tokens,
-            output_tokens=usage.output_tokens + rewrite_usage.output_tokens,
-            total_tokens=usage.total_tokens + rewrite_usage.input_tokens + rewrite_usage.output_tokens,
-            cached_tokens=usage.cached_tokens + (getattr(rewrite_usage, "cached_tokens", 0) or 0),
-            thoughts_tokens=usage.thoughts_tokens + (getattr(rewrite_usage, "thoughts_tokens", 0) or 0),
-        )
-        llm_trace.append(
-            LLMCall(
-                scope="final_rewrite",
-                duration_ms=int((time.time() - rewrite_start) * 1000),
-                input_tokens=rewrite_usage.input_tokens,
-                output_tokens=rewrite_usage.output_tokens,
-            )
-        )
+        answer = "No answer provided."

    # Validate IDs (only include IDs that were actually retrieved)
    used_memory_ids = [mid for mid in (args.get("memory_ids") or []) if mid in available_memory_ids]
@@ -1484,16 +1261,17 @@ async def _process_done_tool(

    # Generate structured output if schema provided
    structured_output = None
+    final_usage = usage
    if response_schema and llm_config and answer:
-        struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id, max_tokens)
+        struct = await _generate_structured_output(answer, response_schema, llm_config, reflect_id)
        structured_output = struct.structured_output
        # Add structured output tokens to usage
        final_usage = TokenUsageSummary(
-            input_tokens=final_usage.input_tokens + struct.input_tokens,
-            output_tokens=final_usage.output_tokens + struct.output_tokens,
-            total_tokens=final_usage.total_tokens + struct.input_tokens + struct.output_tokens,
-            cached_tokens=final_usage.cached_tokens + struct.cached_tokens,
-            thoughts_tokens=final_usage.thoughts_tokens + struct.thoughts_tokens,
+            input_tokens=usage.input_tokens + struct.input_tokens,
+            output_tokens=usage.output_tokens + struct.output_tokens,
+            total_tokens=usage.total_tokens + struct.input_tokens + struct.output_tokens,
+            cached_tokens=usage.cached_tokens + struct.cached_tokens,
+            thoughts_tokens=usage.thoughts_tokens + struct.thoughts_tokens,
        )

    log_completion(answer, iterations)
@@ -1608,35 +1386,22 @@ async def _execute_tool(
        query = args.get("query")
        if not query:
            return {"error": "search_mental_models requires a query parameter"}
-        max_results, error = _parse_tool_int_arg_or_error(args, "max_results", default=5)
-        if error:
-            return {"error": error}
+        max_results = int(args.get("max_results") or 5)
        return await search_mental_models_fn(query, max_results)

    elif tool_name == "search_observations":
        query = args.get("query")
        if not query:
            return {"error": "search_observations requires a query parameter"}
-        max_tokens, error = _parse_tool_int_arg_or_error(args, "max_tokens", default=5000, minimum=1000)
-        if error:
-            return {"error": error}
+        max_tokens = max(int(args.get("max_tokens") or 5000), 1000)  # Default 5000, min 1000
        return await search_observations_fn(query, max_tokens)

    elif tool_name == "recall":
        query = args.get("query")
        if not query:
            return {"error": "recall requires a query parameter"}
-        max_tokens, error = _parse_tool_int_arg_or_error(args, "max_tokens", default=2048, minimum=1000)
-        if error:
-            return {"error": error}
-        max_chunk_tokens, error = _parse_tool_int_arg_or_error(
-            args,
-            "max_chunk_tokens",
-            default=1000,
-            minimum=1000,
-        )
-        if error:
-            return {"error": error}
+        max_tokens = max(int(args.get("max_tokens") or 2048), 1000)  # Default 2048, min 1000
+        max_chunk_tokens = max(int(args.get("max_chunk_tokens") or 1000), 1000)  # Always enabled, min 1000
        return await recall_fn(query, max_tokens, max_chunk_tokens)

    elif tool_name == "expand":
@@ -1650,63 +1415,23 @@ async def _execute_tool(
        return {"error": f"Unknown tool: {tool_name}"}


-_NULLISH_TOOL_INT_STRINGS = {"", "none", "null"}
-
-
-def _parse_tool_int_arg(args: dict[str, Any], key: str, *, default: int, minimum: int | None = None) -> int:
-    raw_value = args.get(key)
-    if not raw_value:
-        value = default
-    elif isinstance(raw_value, str) and raw_value.strip().lower() in _NULLISH_TOOL_INT_STRINGS:
-        value = default
-    else:
-        value = int(raw_value)
-    if minimum is None:
-        return value
-    return max(value, minimum)
-
-
-def _parse_tool_int_arg_or_error(
-    args: dict[str, Any],
-    key: str,
-    *,
-    default: int,
-    minimum: int | None = None,
-) -> tuple[int, str | None]:
-    try:
-        return _parse_tool_int_arg(args, key, default=default, minimum=minimum), None
-    except (OverflowError, TypeError, ValueError):
-        return default, f"{key} must be an integer or null-like value"
-
-
-def _summarize_tool_int_arg(args: dict[str, Any], key: str, *, default: int, minimum: int | None = None) -> str:
-    try:
-        return str(_parse_tool_int_arg(args, key, default=default, minimum=minimum))
-    except (OverflowError, TypeError, ValueError):
-        return f"invalid:{args.get(key)!r}"
-
-
-def _summarize_tool_query(args: dict[str, Any]) -> str:
-    query = args.get("query") or ""
-    if not isinstance(query, str):
-        query = str(query)
-    return f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
-
-
 def _summarize_input(tool_name: str, args: dict[str, Any]) -> str:
    """Create a summary of tool input for logging, showing all params."""
    if tool_name == "search_mental_models":
-        query_preview = _summarize_tool_query(args)
-        max_results = _summarize_tool_int_arg(args, "max_results", default=5)
+        query = args.get("query", "")
+        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
+        max_results = int(args.get("max_results") or 5)
        return f"(query={query_preview}, max_results={max_results})"
    elif tool_name == "search_observations":
-        query_preview = _summarize_tool_query(args)
-        max_tokens = _summarize_tool_int_arg(args, "max_tokens", default=5000, minimum=1000)
+        query = args.get("query", "")
+        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
+        max_tokens = max(int(args.get("max_tokens") or 5000), 1000)
        return f"(query={query_preview}, max_tokens={max_tokens})"
    elif tool_name == "recall":
-        query_preview = _summarize_tool_query(args)
-        max_tokens = _summarize_tool_int_arg(args, "max_tokens", default=2048, minimum=1000)
-        max_chunk_tokens = _summarize_tool_int_arg(args, "max_chunk_tokens", default=1000, minimum=1000)
+        query = args.get("query", "")
+        query_preview = f"'{query[:30]}...'" if len(query) > 30 else f"'{query}'"
+        max_tokens = max(int(args.get("max_tokens") or 2048), 1000)
+        max_chunk_tokens = max(int(args.get("max_chunk_tokens") or 1000), 1000)
        return f"(query={query_preview}, max_tokens={max_tokens}, max_chunk_tokens={max_chunk_tokens})"
    elif tool_name == "expand":
        memory_ids = args.get("memory_ids", [])
@@ -177,17 +177,20 @@ _HEADING_RX = re.compile(r"^(#{1,6})\s+(.+?)\s*$")
 _BULLET_RX = re.compile(r"^\s*[-*+]\s+(.*)$")
 _ORDERED_RX = re.compile(r"^\s*\d+[.)]\s+(.*)$")
 _FENCE_RX = re.compile(r"^```([A-Za-z0-9_+-]*)\s*$")
-_SEPARATOR_RX = re.compile(r"\s*([-*_])\1{2,}\s*")
+
+
+def _strip_separators(lines: list[str]) -> list[str]:
+    """Drop horizontal-rule lines (`---`, `***`) used as section separators.
+
+    Our renderer never emits these, but LLM output frequently includes them
+    between sections; treating them as blank lines avoids parsing them as
+    paragraphs.
+    """
+    return ["" if re.fullmatch(r"\s*([-*_])\1{2,}\s*", line) else line for line in lines]


 def _split_blocks(lines: list[str]) -> list[list[str]]:
-    """Group consecutive non-blank lines into block chunks.
-
-    Horizontal-rule lines (`---`, `***`) count as blank.  Our renderer never
-    emits these, but LLM output frequently includes them between sections;
-    treating them as blank avoids parsing them as paragraphs.  Inside a fence
-    they are code, not a separator, so they are kept verbatim.
-    """
+    """Group consecutive non-blank lines into block chunks."""
    chunks: list[list[str]] = []
    current: list[str] = []
    in_fence = False
@@ -199,7 +202,7 @@ def _split_blocks(lines: list[str]) -> list[list[str]]:
        if in_fence:
            current.append(line)
            continue
-        if line.strip() == "" or _SEPARATOR_RX.fullmatch(line):
+        if line.strip() == "":
            if current:
                chunks.append(current)
                current = []
@@ -247,7 +250,8 @@ def parse_markdown(markdown: str) -> StructuredDocument:
    so we never silently drop user content.  Section IDs are unique slugs of
    their headings.
    """
-    lines = (markdown or "").splitlines()
+    raw_lines = (markdown or "").splitlines()
+    lines = _strip_separators(raw_lines)

    sections: list[Section] = []
    used_ids: set[str] = set()
@@ -328,21 +328,18 @@ async def tool_expand(
    if not memory_ids:
        return {"error": "memory_ids is required and must not be empty"}

-    # Validate and convert UUIDs. Each id keeps a handle on its own UUID: a list of
-    # only the valid ones no longer lines up with memory_ids once one id is invalid.
-    uuid_by_id: dict[str, uuid.UUID] = {}
+    # Validate and convert UUIDs
+    valid_uuids: list[uuid.UUID] = []
    errors: dict[str, str] = {}
    for mid in memory_ids:
        try:
-            uuid_by_id[mid] = uuid.UUID(mid)
+            valid_uuids.append(uuid.UUID(mid))
        except ValueError:
            errors[mid] = f"Invalid memory_id format: {mid}"

-    if not uuid_by_id:
+    if not valid_uuids:
        return {"error": "No valid memory IDs provided", "details": errors}

-    valid_uuids = list(uuid_by_id.values())
-
    # Batch fetch all memory units
    memories = await conn.fetch(
        f"""
@@ -398,12 +395,12 @@ async def tool_expand(

    # Build results
    results: list[dict[str, Any]] = []
-    for mid in memory_ids:
+    for mid, mem_uuid in zip(memory_ids, valid_uuids):
        if mid in errors:
            results.append({"memory_id": mid, "error": errors[mid]})
            continue

-        memory = memory_map.get(uuid_by_id[mid])
+        memory = memory_map.get(mem_uuid)
        if not memory:
            results.append({"memory_id": mid, "error": f"Memory not found: {mid}"})
            continue
@@ -255,20 +255,13 @@ class MemoryFact(BaseModel):
    @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).
-
-        Also coerces non-string dict values (e.g., integer IDs stored in JSONB)
-        to strings, preventing ValidationError when consolidation encounters
-        metadata like {"original_id": 348} instead of {"original_id": "348"}.
-        """
+        """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

-            v = json.loads(v)
-        if isinstance(v, dict):
-            return {str(k): str(val) for k, val in v.items()}
+            return json.loads(v)
        return v

    chunk_id: str | None = Field(
@@ -189,8 +189,7 @@ async def get_or_create_bank_profile(pool, bank_id: str) -> BankProfileResult:
    ``get_or_create_bank_profile_on_conn`` instead.
    """
    async with acquire_with_retry(pool) as conn:
-        async with conn.transaction():
-            return await get_or_create_bank_profile_on_conn(conn, bank_id, ops=pool.ops)
+        return await get_or_create_bank_profile_on_conn(conn, bank_id, ops=pool.ops)


 async def get_or_create_bank_profile_on_conn(conn, bank_id: str, *, ops) -> BankProfileResult:
@@ -64,53 +64,8 @@ async def delete_chunks_by_ids(conn, chunk_ids: list[str]) -> None:
    """
    if not chunk_ids:
        return
-
-    # PostgreSQL's FK cascade deletes child memory_links in executor-chosen
-    # order. Concurrent chunk deletes for the same bank can then lock overlapping
-    # memory_links in opposite orders and deadlock. Delete links explicitly in a
-    # total order before deleting chunks so every writer takes row locks the same
-    # way; the FK cascade still handles anything inserted later in this txn.
    await conn.execute(
-        f"""
-        WITH target_units AS MATERIALIZED (
-            SELECT id
-            FROM {fq_table("memory_units")}
-            WHERE chunk_id = ANY($1::text[])
-        ),
-        ordered_links AS MATERIALIZED (
-            SELECT ml.ctid
-            FROM {fq_table("memory_links")} ml
-            WHERE EXISTS (
-                SELECT 1
-                FROM target_units tu
-                WHERE tu.id = ml.from_unit_id OR tu.id = ml.to_unit_id
-            )
-            ORDER BY
-                LEAST(ml.from_unit_id, ml.to_unit_id),
-                GREATEST(ml.from_unit_id, ml.to_unit_id),
-                ml.link_type,
-                COALESCE(ml.entity_id, '00000000-0000-0000-0000-000000000000'::uuid)
-            FOR UPDATE OF ml
-        )
-        DELETE FROM {fq_table("memory_links")} ml
-        USING ordered_links ol
-        WHERE ml.ctid = ol.ctid
-        """,
-        chunk_ids,
-    )
-    await conn.execute(
-        f"""
-        WITH ordered_chunks AS MATERIALIZED (
-            SELECT chunk_id
-            FROM {fq_table("chunks")}
-            WHERE chunk_id = ANY($1::text[])
-            ORDER BY chunk_id
-            FOR UPDATE
-        )
-        DELETE FROM {fq_table("chunks")} c
-        USING ordered_chunks oc
-        WHERE c.chunk_id = oc.chunk_id
-        """,
+        f"DELETE FROM {fq_table('chunks')} WHERE chunk_id = ANY($1::text[])",
        chunk_ids,
    )

@@ -7,7 +7,7 @@ Handles entity extraction and resolution for stored facts.
 import logging

 from . import link_utils
-from .types import EntityResolutionResult, ProcessedFact
+from .types import ProcessedFact

 logger = logging.getLogger(__name__)

@@ -58,7 +58,7 @@ async def resolve_entities(
    log_buffer: list[str] = None,
    user_entities_per_content: dict[int, list[dict]] = None,
    entity_labels: list | None = None,
-) -> EntityResolutionResult:
+) -> tuple[list[str], list[tuple], dict[str, list[str]]]:
    """
    Phase 1: Resolve entity names to canonical IDs (read-heavy).

@@ -76,10 +76,10 @@ async def resolve_entities(
        entity_labels: Optional entity label taxonomy

    Returns:
-        EntityResolutionResult with the resolved identities and unit mappings.
+        Tuple of (resolved_entity_ids, entity_to_unit, unit_to_entity_ids).
    """
    if not unit_ids or not facts:
-        return EntityResolutionResult(resolved_entities=[], entity_to_unit=[], unit_to_entity_ids={})
+        return [], [], {}

    if len(unit_ids) != len(facts):
        raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and facts ({len(facts)})")
@@ -15,10 +15,9 @@ from typing import Any, Literal, cast
 from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator

 from ..llm_interface import ProviderRateLimitResetError
-from ..llm_wrapper import LLMConfig, OutputTooLongError, parse_llm_json, sanitize_llm_output
+from ..llm_wrapper import LLMConfig, OutputTooLongError, sanitize_llm_output
 from ..operation_metadata import RetainExtractionErrors
 from ..response_models import TokenUsage
-from ..structured_output import strict_json_schema
 from .entity_labels import (
    EntityLabelsConfig,
    MapField,
@@ -33,7 +32,7 @@ def _extract_map_entities(
    entity_obj: dict,
    fields: dict[str, MapField],
    prefix: str,
-    validated_entities: list[str],
+    validated_entities: "list[Entity]",
    existing_texts_lower: set[str],
 ) -> None:
    """Recursively extract key:field:value entity strings from a map entity dict."""
@@ -60,7 +59,7 @@ def _extract_map_entities(
                    continue
                label_str = f"{prefix}{field_name}:{v.strip()}"
                if label_str.lower() not in existing_texts_lower:
-                    validated_entities.append(label_str)
+                    validated_entities.append(Entity(text=label_str))
                    existing_texts_lower.add(label_str.lower())
        else:
            # text or value — single string
@@ -68,7 +67,7 @@ def _extract_map_entities(
                continue
            label_str = f"{prefix}{field_name}:{field_val.strip()}"
            if label_str.lower() not in existing_texts_lower:
-                validated_entities.append(label_str)
+                validated_entities.append(Entity(text=label_str))
                existing_texts_lower.add(label_str.lower())


@@ -115,33 +114,12 @@ def _sanitize_text(text: str | None) -> str | None:
    return sanitize_llm_output(text)


-def _coerce_entity_strings(v: Any) -> Any:
-    """
-    Normalize the LLM's `entities` field to a plain list of strings.
+class Entity(BaseModel):
+    """An entity extracted from text."""

-    The schema previously asked for `Entity` objects ({"text": "..."}) while the
-    prompt's few-shot examples taught a flat string array. Models that followed
-    the examples literally returned strings, and the entities were silently
-    dropped — none were ever persisted (#2749). The `Entity` wrapper carried no
-    information beyond the string, so it was removed rather than taught to the
-    prompt; the object form is still unwrapped here for models that learned it
-    and for in-flight batch jobs.
-
-    Returns non-list input untouched so pydantic reports the type error itself.
-    """
-    if v is None:
-        return []
-    if not isinstance(v, list):
-        return v
-    coerced = []
-    for item in v:
-        if isinstance(item, dict):
-            text = item.get("text")
-            if isinstance(text, str):
-                coerced.append(text)
-        else:
-            coerced.append(item)
-    return coerced
+    text: str = Field(
+        description="The specific, named entity as it appears in the fact. Must be a proper noun or specific identifier."
+    )


 class Fact(BaseModel):
@@ -166,7 +144,7 @@ class Fact(BaseModel):
    )

    # Optional structured data
-    entities: list[str] | None = None
+    entities: list[Entity] | None = None
    causal_relations: list["CausalRelation"] | None = None


@@ -217,9 +195,7 @@ class ExtractedFact(BaseModel):
    fact_type: Literal["world", "assistant"] = Field(
        description="'world' = objective/external facts, including user preferences, rules, corrections, and constraints even when stated during a conversation. 'assistant' = actions, experiences, or observations the assistant/agent actually performed."
    )
-    entities: list[str] = Field(
-        default_factory=list, description='People, places, concepts - plain strings, e.g. ["Alice", "Kubernetes"]'
-    )
+    entities: list[Entity] | None = Field(default=None, description="People, places, concepts")
    causal_relations: list[FactCausalRelation] | None = Field(
        default=None, description="Links to previous facts (target_index < this fact's index)"
    )
@@ -227,7 +203,10 @@ class ExtractedFact(BaseModel):
    @field_validator("entities", mode="before")
    @classmethod
    def ensure_entities_list(cls, v):
-        return _coerce_entity_strings(v)
+        """Ensure entities is always a list (convert None to empty list)."""
+        if v is None:
+            return []
+        return v

    def build_fact_text(self) -> str:
        """Combine all dimensions into a single comprehensive fact string."""
@@ -253,55 +232,6 @@ class FactExtractionResponse(BaseModel):
    facts: list[ExtractedFact] = Field(description="List of extracted factual statements")


-def _split_chunk_for_output_retry(chunk: str) -> tuple[str, str] | None:
-    """Split an oversized extraction chunk without corrupting structured input."""
-    stripped = chunk.strip()
-    if len(stripped) <= 1:
-        return None
-
-    try:
-        parsed = json.loads(stripped)
-    except (TypeError, ValueError, json.JSONDecodeError):
-        parsed = None
-
-    if isinstance(parsed, list):
-        if len(parsed) >= 2:
-            mid = len(parsed) // 2
-            return json.dumps(parsed[:mid]), json.dumps(parsed[mid:])
-
-        if len(parsed) == 1 and isinstance(parsed[0], dict):
-            turn = parsed[0]
-            content = turn.get("content")
-            if isinstance(content, str) and len(content) > 1:
-                cut = len(content) // 2
-                first_turn = dict(turn)
-                second_turn = dict(turn)
-                first_turn["content"] = content[:cut]
-                second_turn["content"] = content[cut:]
-                return json.dumps([first_turn]), json.dumps([second_turn])
-
-        return None
-
-    # Split plain text at the midpoint, preferring sentence boundaries nearby.
-    mid_point = len(stripped) // 2
-    search_range = int(len(stripped) * 0.2)
-    search_start = max(0, mid_point - search_range)
-    search_end = min(len(stripped), mid_point + search_range)
-
-    best_split = mid_point
-    for ending in [". ", "! ", "? ", "\n\n"]:
-        pos = stripped.rfind(ending, search_start, search_end)
-        if pos != -1:
-            best_split = pos + len(ending)
-            break
-
-    first_half = stripped[:best_split].strip()
-    second_half = stripped[best_split:].strip()
-    if not first_half or not second_half or first_half == stripped or second_half == stripped:
-        return None
-    return first_half, second_half
-
-
 class ExtractedFactVerbose(BaseModel):
    """A single extracted fact with verbose field descriptions for detailed extraction."""

@@ -369,9 +299,9 @@ class ExtractedFactVerbose(BaseModel):
        description="'world' = objective/external facts about the user, other people, events, general knowledge, preferences, rules, corrections, or constraints. 'assistant' = actions, experiences, or observations the assistant/agent actually performed (e.g., 'I changed X', 'I discovered Y')."
    )

-    entities: list[str] = Field(
-        default_factory=list,
-        description="Named entities, objects, AND abstract concepts from the fact, as plain strings (e.g. [\"Alice\", \"friendship\"]). Include: people names, organizations, places, significant objects (e.g., 'coffee maker', 'car'), AND abstract concepts/themes (e.g., 'friendship', 'career growth', 'loss', 'celebration'). Extract anything that could help link related facts together.",
+    entities: list[Entity] | None = Field(
+        default=None,
+        description="Named entities, objects, AND abstract concepts from the fact. Include: people names, organizations, places, significant objects (e.g., 'coffee maker', 'car'), AND abstract concepts/themes (e.g., 'friendship', 'career growth', 'loss', 'celebration'). Extract anything that could help link related facts together.",
    )

    causal_relations: list[FactCausalRelation] | None = Field(
@@ -383,7 +313,9 @@ class ExtractedFactVerbose(BaseModel):
    @field_validator("entities", mode="before")
    @classmethod
    def ensure_entities_list(cls, v):
-        return _coerce_entity_strings(v)
+        if v is None:
+            return []
+        return v


 class FactExtractionResponseVerbose(BaseModel):
@@ -416,15 +348,17 @@ class ExtractedFactNoCausal(BaseModel):
    fact_type: Literal["world", "assistant"] = Field(
        description="'world' = about the user/others, including user preferences, rules, corrections, and constraints. 'assistant' = actions or experiences the assistant/agent actually performed."
    )
-    entities: list[str] = Field(
-        default_factory=list,
-        description='Named entities, objects, and concepts from the fact, as plain strings (e.g. ["Alice", "Kubernetes"]).',
+    entities: list[Entity] | None = Field(
+        default=None,
+        description="Named entities, objects, and concepts from the fact.",
    )

    @field_validator("entities", mode="before")
    @classmethod
    def ensure_entities_list(cls, v):
-        return _coerce_entity_strings(v)
+        if v is None:
+            return []
+        return v


 class FactExtractionResponseNoCausal(BaseModel):
@@ -456,14 +390,14 @@ class VerbatimExtractedFact(BaseModel):
    fact_type: Literal["world", "assistant"] = Field(
        description="'world' = objective/external facts. 'assistant' = first-person actions, experiences, or observations by the speaker."
    )
-    entities: list[str] = Field(
-        default_factory=list, description='People, places, concepts - plain strings, e.g. ["Alice", "Kubernetes"]'
-    )
+    entities: list[Entity] | None = Field(default=None, description="People, places, concepts")

    @field_validator("entities", mode="before")
    @classmethod
    def ensure_entities_list(cls, v):
-        return _coerce_entity_strings(v)
+        if v is None:
+            return []
+        return v


 class VerbatimFactExtractionResponse(BaseModel):
@@ -747,11 +681,6 @@ Use "Event Date" from input as reference for relative dates.
 ENTITIES
 ══════════════════════════════════════════════════════════════════════════

-ALWAYS return "entities" as an array of plain strings — never objects, never null.
-Correct: entities=["Alice", "Kubernetes", "CKA"]
-Wrong:   entities as an array of objects with a "text" key ← never use this form
-Use an empty array [] only when the fact truly names nothing.
-
 Include: people names, organizations, places, key objects, abstract concepts (career, friendship, etc.)
 Always include "user" when fact is about the user.{examples}"""

@@ -1169,8 +1098,8 @@ def _build_extraction_prompt_and_schema(config) -> tuple[str, type]:
            }
            if not free_form_entities:
                dynamic_fields["entities"] = (
-                    list[str],
-                    Field(default_factory=list, description="Leave empty — labels-only mode"),
+                    list[Entity] | None,
+                    Field(default=None, description="Leave empty — labels-only mode"),
                )
            # Inherit parent's required fields and add 'labels' so it appears in the JSON schema
            # required array (the base class json_schema_extra overrides required entirely)
@@ -1294,32 +1223,19 @@ def _build_request_body(llm_config, config, prompt: str, user_message: str, resp
        request_body["service_tier"] = llm_config._provider_impl.openai_service_tier

    # Add response_format (JSON schema). The batch path builds the request body
-    # directly instead of going through LLMProvider.call(), so resolve the
-    # strict-schema flag here too: strict=True grammar-enforces the output on capable
-    # backends rather than relying on the model to emit clean JSON. Reads the
-    # retain-scoped field, which already folds in the global HINDSIGHT_API_LLM_STRICT_SCHEMA
-    # fallback, so the batch and streaming paths can't disagree.
+    # directly instead of going through LLMProvider.call(), so honour
+    # HINDSIGHT_API_LLM_STRICT_SCHEMA here too: strict=True grammar-enforces the
+    # output on capable backends rather than relying on the model to emit clean JSON.
    if hasattr(response_schema, "model_json_schema"):
-        schema = (
-            strict_json_schema(response_schema) if config.llm_strict_schema else response_schema.model_json_schema()
-        )
+        schema = response_schema.model_json_schema()
        request_body["response_format"] = {
            "type": "json_schema",
-            "json_schema": {"name": "facts", "schema": schema, "strict": config.llm_strict_schema_retain},
+            "json_schema": {"name": "facts", "schema": schema, "strict": config.llm_strict_schema},
        }

    return request_body


-def _coerce_fact_response(response: Any) -> dict[str, Any] | None:
-    """Accept the schema wrapper, or a recoverable top-level facts array."""
-    if isinstance(response, dict):
-        return response
-    if isinstance(response, list) and all(isinstance(item, dict) for item in response):
-        return {"facts": response}
-    return None
-
-
 async def _extract_facts_from_chunk(
    chunk: str,
    chunk_index: int,
@@ -1388,15 +1304,10 @@ async def _extract_facts_from_chunk(
    llm_max_retries = (
        config.retain_llm_max_retries if config.retain_llm_max_retries is not None else config.llm_max_retries
    )
-    # OUTER content-validation attempts (re-prompts on malformed JSON). Follows the
-    # same `N + 1` convention as the providers' transport-retry loops — N retries after
-    # the initial request — so a zero budget still performs one request (#2731). The raw
-    # budget is forwarded unchanged to llm_config.call(), which owns transport retries.
-    outer_attempts = llm_max_retries + 1
    last_error: Exception | None = None

    usage = TokenUsage()  # Track cumulative usage across retries
-    for attempt in range(outer_attempts):
+    for attempt in range(llm_max_retries):
        try:
            initial_backoff = (
                config.retain_llm_initial_backoff
@@ -1412,7 +1323,6 @@ async def _extract_facts_from_chunk(
                response_format=response_schema,
                scope="retain_extract_facts",
                temperature=config.llm_temperature_retain,
-                strict_schema=config.llm_strict_schema_retain,
                max_completion_tokens=config.retain_max_completion_tokens,
                max_retries=llm_max_retries,
                initial_backoff=initial_backoff,
@@ -1431,11 +1341,10 @@ async def _extract_facts_from_chunk(
            has_malformed_facts = False

            # Handle malformed LLM responses
-            coerced_response_json = _coerce_fact_response(extraction_response_json)
-            if coerced_response_json is None:
-                if attempt < outer_attempts - 1:
+            if not isinstance(extraction_response_json, dict):
+                if attempt < llm_max_retries - 1:
                    logger.warning(
-                        f"LLM returned non-dict JSON on attempt {attempt + 1}/{outer_attempts}: {type(extraction_response_json).__name__}. Retrying..."
+                        f"LLM returned non-dict JSON on attempt {attempt + 1}/{llm_max_retries}: {type(extraction_response_json).__name__}. Retrying..."
                    )
                    continue
                else:
@@ -1444,10 +1353,9 @@ async def _extract_facts_from_chunk(
                    # worker's retry machinery and ultimately fails loudly — never
                    # silently commit the document with 0 facts. See issue #1833.
                    raise RuntimeError(
-                        f"Fact extraction failed: LLM returned non-dict JSON after {outer_attempts} attempts "
+                        f"Fact extraction failed: LLM returned non-dict JSON after {llm_max_retries} attempts "
                        f"({type(extraction_response_json).__name__}). Raw: {str(extraction_response_json)[:500]}"
                    )
-            extraction_response_json = coerced_response_json

            raw_facts = extraction_response_json.get("facts", [])

@@ -1543,9 +1451,21 @@ async def _extract_facts_from_chunk(
                    elif fact_data.get("occurred_start"):
                        fact_data["occurred_end"] = fact_data["occurred_start"]

-                # Entities are plain strings. Older prompts taught a {"text": ...}
-                # object form, so keep unwrapping it for models that still emit it.
-                validated_entities = _coerce_entity_strings(get_value("entities"))
+                # Add entities if present (validate as Entity objects)
+                # LLM sometimes returns strings instead of {"text": "..."} format
+                entities = get_value("entities")
+                validated_entities = []
+                if entities:
+                    # Validate and normalize each entity
+                    for ent in entities:
+                        if isinstance(ent, str):
+                            # Normalize string to Entity object
+                            validated_entities.append(Entity(text=ent))
+                        elif isinstance(ent, dict) and "text" in ent:
+                            try:
+                                validated_entities.append(Entity.model_validate(ent))
+                            except Exception as e:
+                                logger.warning(f"Invalid entity {ent}: {e}")

                # Post-process label entities from structured labels object
                entity_labels_raw = getattr(config, "entity_labels", None)
@@ -1555,7 +1475,7 @@ async def _extract_facts_from_chunk(
                    labels_lookup = build_labels_lookup(labels_cfg)
                    labels_data = llm_fact.get("labels") or {}
                    if isinstance(labels_data, dict):
-                        existing_texts_lower = {e.lower() for e in validated_entities}
+                        existing_texts_lower = {e.text.lower() for e in validated_entities}
                        for group in labels_cfg.attributes:
                            value = labels_data.get(group.key)
                            if not value:
@@ -1580,12 +1500,12 @@ async def _extract_facts_from_chunk(
                                label_str = f"{group.key}:{v.strip()}"
                                if group.type == "text":
                                    if label_str.lower() not in existing_texts_lower:
-                                        validated_entities.append(label_str)
+                                        validated_entities.append(Entity(text=label_str))
                                        existing_texts_lower.add(label_str.lower())
                                elif (
                                    label_str.lower() in labels_lookup and label_str.lower() not in existing_texts_lower
                                ):
-                                    validated_entities.append(label_str)
+                                    validated_entities.append(Entity(text=label_str))
                                    existing_texts_lower.add(label_str.lower())
                                else:
                                    logger.warning(f"Label '{label_str}' not in valid label values, skipping")
@@ -1593,7 +1513,7 @@ async def _extract_facts_from_chunk(
                    # In labels-only mode, keep only label entities
                    if not free_form_entities:
                        validated_entities = [
-                            e for e in validated_entities if is_label_entity(e, labels_cfg, labels_lookup)
+                            e for e in validated_entities if is_label_entity(e.text, labels_cfg, labels_lookup)
                        ]
                elif not free_form_entities:
                    # No labels but free_form disabled: clear all entities
@@ -1651,9 +1571,9 @@ async def _extract_facts_from_chunk(
                    continue

            # If we got malformed facts and haven't exhausted retries, try again
-            if has_malformed_facts and len(chunk_facts) < len(raw_facts) * 0.8 and attempt < outer_attempts - 1:
+            if has_malformed_facts and len(chunk_facts) < len(raw_facts) * 0.8 and attempt < llm_max_retries - 1:
                logger.warning(
-                    f"Got {len(raw_facts) - len(chunk_facts)} malformed facts out of {len(raw_facts)} on attempt {attempt + 1}/{outer_attempts}. Retrying..."
+                    f"Got {len(raw_facts) - len(chunk_facts)} malformed facts out of {len(raw_facts)} on attempt {attempt + 1}/{llm_max_retries}. Retrying..."
                )
                continue

@@ -1692,7 +1612,7 @@ async def _extract_facts_from_chunk(
    # If we exhausted all retries, raise the last error or a descriptive fallback
    if last_error is not None:
        raise last_error
-    raise RuntimeError(f"Fact extraction failed after {outer_attempts} attempts: LLM did not return valid JSON")
+    raise RuntimeError(f"Fact extraction failed after {llm_max_retries} attempts: LLM did not return valid JSON")


 async def _extract_facts_with_auto_split(
@@ -1744,22 +1664,33 @@ async def _extract_facts_with_auto_split(
            metadata=metadata,
        )
    except OutputTooLongError:
-        # Output exceeded token limits - split the chunk and retry. Conversation
-        # chunks are JSON arrays, so preserve array/turn boundaries when possible.
+        # Output exceeded token limits - split the chunk in half and retry
        logger.warning(
            f"Output too long for chunk {chunk_index + 1}/{total_chunks} "
-            f"({len(chunk)} chars). Splitting and retrying..."
+            f"({len(chunk)} chars). Splitting in half and retrying..."
        )

-        split_chunks = _split_chunk_for_output_retry(chunk)
-        if split_chunks is None:
-            logger.warning(
-                f"Cannot make progress splitting chunk {chunk_index + 1}/{total_chunks} "
-                f"({len(chunk)} chars); dropping this sub-chunk."
-            )
-            return [], TokenUsage()
+        # Split at the midpoint, preferring sentence boundaries
+        mid_point = len(chunk) // 2

-        first_half, second_half = split_chunks
+        # Try to find a sentence boundary near the midpoint
+        # Look for ". ", "! ", "? " within 20% of midpoint
+        search_range = int(len(chunk) * 0.2)
+        search_start = max(0, mid_point - search_range)
+        search_end = min(len(chunk), mid_point + search_range)
+
+        sentence_endings = [". ", "! ", "? ", "\n\n"]
+        best_split = mid_point
+
+        for ending in sentence_endings:
+            pos = chunk.rfind(ending, search_start, search_end)
+            if pos != -1:
+                best_split = pos + len(ending)
+                break
+
+        # Split the chunk
+        first_half = chunk[:best_split].strip()
+        second_half = chunk[best_split:].strip()

        logger.info(
            f"Split chunk {chunk_index + 1} into two sub-chunks: {len(first_half)} chars and {len(second_half)} chars"
@@ -2201,10 +2132,7 @@ async def extract_facts_from_contents_batch_api(
        content_str = message.get("content", "{}")

        try:
-            # #2701: use the lenient parser (strips markdown fences, scrubs
-            # embedded control chars) so recoverable batch responses — e.g.
-            # transient Gemini quirks — aren't dropped along with all their facts.
-            extraction_response_json = parse_llm_json(content_str)
+            extraction_response_json = json.loads(content_str)
        except json.JSONDecodeError as e:
            message = f"{custom_id}: failed to parse JSON: {e}"
            logger.error(message)
@@ -2216,19 +2144,6 @@ async def extract_facts_from_contents_batch_api(
            )
            continue

-        response_type_name = type(extraction_response_json).__name__
-        extraction_response_json = _coerce_fact_response(extraction_response_json)
-        if extraction_response_json is None:
-            message = f"{custom_id}: LLM returned non-dict JSON ({response_type_name})"
-            logger.error(message)
-            extraction_errors.add(message)
-            chunks_metadata.append(
-                ChunkMetadata(
-                    chunk_text=chunk_content, fact_count=0, content_index=content_index, chunk_index=chunk_idx
-                )
-            )
-            continue
-
        # Parse facts (reuse existing logic from _extract_facts_from_chunk)
        raw_facts = extraction_response_json.get("facts", [])
        chunk_facts = []
@@ -2295,9 +2210,18 @@ async def extract_facts_from_contents_batch_api(
                elif fact_data.get("occurred_start"):
                    fact_data["occurred_end"] = fact_data["occurred_start"]

-            # Entities are plain strings. Older prompts taught a {"text": ...}
-            # object form, so keep unwrapping it for models that still emit it.
-            validated_entities = _coerce_entity_strings(get_value("entities"))
+            # Entities
+            entities = get_value("entities")
+            validated_entities = []
+            if entities:
+                for ent in entities:
+                    if isinstance(ent, str):
+                        validated_entities.append(Entity(text=ent))
+                    elif isinstance(ent, dict) and "text" in ent:
+                        try:
+                            validated_entities.append(Entity.model_validate(ent))
+                        except Exception:
+                            pass

            # Post-process label entities from structured labels object
            entity_labels_raw = getattr(config, "entity_labels", None)
@@ -2307,7 +2231,7 @@ async def extract_facts_from_contents_batch_api(
                labels_lookup_batch = build_labels_lookup(labels_cfg_batch)
                labels_data = llm_fact.get("labels") or {}
                if isinstance(labels_data, dict):
-                    existing_texts_lower = {e.lower() for e in validated_entities}
+                    existing_texts_lower = {e.text.lower() for e in validated_entities}
                    for group in labels_cfg_batch.attributes:
                        value = labels_data.get(group.key)
                        if not value:
@@ -2332,18 +2256,18 @@ async def extract_facts_from_contents_batch_api(
                            label_str = f"{group.key}:{v.strip()}"
                            if group.type == "text":
                                if label_str.lower() not in existing_texts_lower:
-                                    validated_entities.append(label_str)
+                                    validated_entities.append(Entity(text=label_str))
                                    existing_texts_lower.add(label_str.lower())
                            elif (
                                label_str.lower() in labels_lookup_batch
                                and label_str.lower() not in existing_texts_lower
                            ):
-                                validated_entities.append(label_str)
+                                validated_entities.append(Entity(text=label_str))
                                existing_texts_lower.add(label_str.lower())

                if not free_form_entities_batch:
                    validated_entities = [
-                        e for e in validated_entities if is_label_entity(e, labels_cfg_batch, labels_lookup_batch)
+                        e for e in validated_entities if is_label_entity(e.text, labels_cfg_batch, labels_lookup_batch)
                    ]
            elif not free_form_entities_batch:
                validated_entities = []
@@ -2430,7 +2354,7 @@ async def extract_facts_from_contents_batch_api(
            extracted_fact = ExtractedFactType(
                fact_text=fact_from_llm.fact,
                fact_type=fact_from_llm.fact_type,
-                entities=list(fact_from_llm.entities or []),
+                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,
                causal_relations=_convert_causal_relations(fact_from_llm.causal_relations or [], global_fact_idx),
@@ -2627,7 +2551,7 @@ async def extract_facts_from_contents(
                    extracted_fact = ExtractedFactType(
                        fact_text=fact_from_llm.fact,
                        fact_type=fact_from_llm.fact_type,
-                        entities=list(fact_from_llm.entities or []),
+                        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)
                        if fact_from_llm.occurred_start
@@ -74,9 +74,7 @@ async def create_causal_links_batch(
    """
    Create causal links between facts.

-    Retain writes the canonical ``caused_by`` relationship only. The database and
-    retrieval paths also recognize historical causal types so imported and
-    pre-existing memories remain traversable.
+    Links facts that have causal relationships (causes, enables, prevents).

    Args:
        conn: Database connection
@@ -92,7 +90,22 @@ async def create_causal_links_batch(
    if len(unit_ids) != len(facts):
        raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and facts ({len(facts)})")

-    causal_relations_per_fact = [fact.causal_relations or [] for fact in facts]
+    # Extract causal relations in the format expected by link_utils
+    # Format: List of lists, where each inner list is the causal relations for that fact
+    causal_relations_per_fact = []
+    for fact in facts:
+        if fact.causal_relations:
+            # Convert CausalRelation objects to dicts
+            relations_dicts = [
+                {
+                    "relation_type": rel.relation_type,
+                    "target_fact_index": rel.target_fact_index,
+                }
+                for rel in fact.causal_relations
+            ]
+            causal_relations_per_fact.append(relations_dicts)
+        else:
+            causal_relations_per_fact.append([])

    link_count = await link_utils.create_causal_links_batch(conn, bank_id, unit_ids, causal_relations_per_fact, ops=ops)

@@ -7,11 +7,7 @@ import time
 from datetime import UTC, datetime, timedelta

 from ..._vector_index import ann_search_tuning_settings, configured_vector_extension
-from ..causal_links import CANONICAL_CAUSAL_LINK_TYPES, LEGACY_CAUSAL_LINK_TYPES
-from ..db.base import DatabaseConnection
-from ..db.ops import DataAccessOps
 from ..memory_engine import fq_table
-from .types import CausalRelation, EntityResolutionResult

 logger = logging.getLogger(__name__)

@@ -304,7 +300,7 @@ async def resolve_entities_only(
    llm_entities: list[list[dict]],
    log_buffer: list[str] = None,
    entity_labels: list | None = None,
-) -> EntityResolutionResult:
+) -> tuple[list[str], list[tuple], dict[str, list[str]]]:
    """
    Phase 1 of entity processing: resolve entity names to canonical IDs.

@@ -325,10 +321,10 @@ async def resolve_entities_only(
        entity_labels: Optional entity label taxonomy

    Returns:
-        EntityResolutionResult carrying the resolved entity identities (id +
-        stored canonical name, in flattened order), the flat-index → unit map,
-        and the unit → entity-id map used to remap placeholder unit IDs in
-        Phase 2.
+        Tuple of (resolved_entity_ids, entity_to_unit, unit_to_entity_ids) where:
+        - resolved_entity_ids: list of entity IDs in same order as flattened entities
+        - entity_to_unit: maps flat index to (unit_id, local_index, fact_date)
+        - unit_to_entity_ids: maps unit_id to list of resolved entity IDs
    """
    all_entities_flat, _all_entities, entity_to_unit = _prepare_entities_for_resolution(
        unit_ids, sentences, fact_dates, llm_entities, log_buffer
@@ -336,10 +332,10 @@ async def resolve_entities_only(

    if not all_entities_flat:
        _log(log_buffer, "  [6.2] Entity resolution (batched): 0 entities", level="debug")
-        return EntityResolutionResult(resolved_entities=[], entity_to_unit=[], unit_to_entity_ids={})
+        return [], [], {}

    step_start = time.time()
-    resolved_entities = await entity_resolver.resolve_entities_batch(
+    resolved_entity_ids = await entity_resolver.resolve_entities_batch(
        bank_id=bank_id,
        entities_data=all_entities_flat,
        context=context,
@@ -358,7 +354,7 @@ async def resolve_entities_only(
    for idx, (unit_id, _local_idx, _fact_date) in enumerate(entity_to_unit):
        if unit_id not in unit_to_entity_ids:
            unit_to_entity_ids[unit_id] = []
-        unit_to_entity_ids[unit_id].append(resolved_entities[idx].entity_id)
+        unit_to_entity_ids[unit_id].append(resolved_entity_ids[idx])

    _log(
        log_buffer,
@@ -366,11 +362,7 @@ async def resolve_entities_only(
        level="debug",
    )

-    return EntityResolutionResult(
-        resolved_entities=resolved_entities,
-        entity_to_unit=entity_to_unit,
-        unit_to_entity_ids=unit_to_entity_ids,
-    )
+    return resolved_entity_ids, entity_to_unit, unit_to_entity_ids


 async def create_temporal_links_batch_per_fact(
@@ -779,61 +771,28 @@ async def create_semantic_links_batch(


 async def create_causal_links_batch(
-    conn: DatabaseConnection,
+    conn,
    bank_id: str,
    unit_ids: list[str],
-    causal_relations_per_fact: list[list[CausalRelation]],
-    ops: DataAccessOps | None = None,
+    causal_relations_per_fact: list[list[dict]],
+    ops=None,
 ) -> int:
-    """Create canonical causal links for the retain pipeline.
-
-    Retain must only create the backward-looking ``caused_by`` form. Historical
-    types are restored exclusively through ``restore_legacy_causal_links_batch``.
    """
-    return await _write_causal_links_batch(
-        conn,
-        bank_id,
-        unit_ids,
-        causal_relations_per_fact,
-        CANONICAL_CAUSAL_LINK_TYPES,
-        ops=ops,
-    )
+    Create causal links between facts based on LLM-extracted causal relationships.

-
-async def restore_legacy_causal_links_batch(
-    conn: DatabaseConnection,
-    bank_id: str,
-    unit_ids: list[str],
-    causal_relations_per_fact: list[list[CausalRelation]],
-    ops: DataAccessOps | None = None,
-) -> int:
-    """Restore historical causal links while importing a transfer archive.
-
-    This is deliberately separate from the retain writer: retrieval continues
-    reading historical types, but only transfer import may create them.
-    """
-    return await _write_causal_links_batch(
-        conn,
-        bank_id,
-        unit_ids,
-        causal_relations_per_fact,
-        LEGACY_CAUSAL_LINK_TYPES,
-        ops=ops,
-    )
-
-
-async def _write_causal_links_batch(
-    conn: DatabaseConnection,
-    bank_id: str,
-    unit_ids: list[str],
-    causal_relations_per_fact: list[list[CausalRelation]],
-    allowed_relation_types: frozenset[str],
-    ops: DataAccessOps | None = None,
-) -> int:
-    """Write causal links after the caller has selected its allowed taxonomy.
+    Args:
+        conn: Database connection
+        unit_ids: List of unit IDs (in same order as causal_relations_per_fact)
+        causal_relations_per_fact: List of causal relations for each fact.
+            Each element is a list of dicts with:
+            - target_fact_index: Index into unit_ids for the target fact
+            - relation_type: "caused_by"

    Returns:
        Number of causal links created
+
+    Causal link type:
+    - "caused_by": This fact was caused by the target fact
    """
    if not unit_ids or not causal_relations_per_fact:
        return 0
@@ -850,13 +809,15 @@ async def _write_causal_links_batch(
            from_unit_id = unit_ids[fact_idx]

            for relation in causal_relations:
-                target_idx = relation.target_fact_index
-                relation_type = relation.relation_type
+                target_idx = relation["target_fact_index"]
+                relation_type = relation["relation_type"]

-                if relation_type not in allowed_relation_types:
+                # Validate relation_type - only "caused_by" is supported (DB constraint)
+                valid_types = {"caused_by"}
+                if relation_type not in valid_types:
                    logger.error(
                        f"Invalid relation_type '{relation_type}' (type: {type(relation_type).__name__}) "
-                        f"from fact {fact_idx}. Must be one of: {allowed_relation_types}. "
+                        f"from fact {fact_idx}. Must be one of: {valid_types}. "
                        f"Relation data: {relation}"
                    )
                    continue
@@ -143,7 +143,7 @@ async def _fire_memory_defense_webhook(
        logger.warning("memory_defense webhook delivery failed", exc_info=True)


-async def _audit_memory_defense(
+def _audit_memory_defense(
    audit_logger: Any,
    *,
    bank_id: str,
@@ -152,15 +152,11 @@ async def _audit_memory_defense(
 ) -> None:
    """Write a fire-and-forget ``memory_defense`` audit entry for a non-allow decision.

-    No-op when auditing is off for this bank. ``audit_log_enabled`` is per-bank
-    overridable, so the decision must be awaited here rather than relying on the
-    logger's synchronous allowlist check alone.
+    No-op when audit logging is disabled (the logger gates on its own config).
    The action taken (redact/block) and what matched live in the entry metadata.
    """
    if audit_logger is None:
        return
-    if not await audit_logger.should_log("memory_defense", bank_id):
-        return
    from ..audit import AuditEntry

    entry = AuditEntry(
@@ -250,12 +246,10 @@ from . import (
    link_creation,
 )
 from .types import (
-    CausalRelation,
    ChunkMetadata,
-    ExtractedFact,
+    EntityResolutionResult,
    Phase1Result,
    ProcessedFact,
-    ResolvedEntity,
    RetainContent,
    RetainContentDict,
 )
@@ -266,15 +260,6 @@ RetainOutboxCallback = Callable[[asyncpg.Connection], Awaitable[None]]
 RetainOutboxCallbackFactory = Callable[[list[RetainContentDict]], RetainOutboxCallback | None]


-@dataclass
-class _ProcessedFactBatch:
-    """Aligned survivors from converting extracted facts for storage."""
-
-    extracted_facts: list[ExtractedFact]
-    processed_facts: list[ProcessedFact]
-    retained_index_by_original: list[int | None]
-
-
 def _resolve_narrator(profile_name: str, bank_id: str) -> str | None:
    """Resolve the narrator (memory owner) used to prime fact extraction.

@@ -359,7 +344,7 @@ async def _pre_resolve_phase1(
    embeddings = [fact.embedding for fact in processed_facts]

    async with acquire_with_retry(pool) as resolve_conn:
-        entity_resolution = await entity_processing.resolve_entities(
+        resolved_entity_ids, entity_to_unit, unit_to_entity_ids = await entity_processing.resolve_entities(
            entity_resolver,
            resolve_conn,
            bank_id,
@@ -381,7 +366,11 @@ async def _pre_resolve_phase1(
            )

    return Phase1Result(
-        entities=entity_resolution,
+        entities=EntityResolutionResult(
+            resolved_entity_ids=resolved_entity_ids,
+            entity_to_unit=entity_to_unit,
+            unit_to_entity_ids=unit_to_entity_ids,
+        ),
        semantic_ann_links=semantic_ann_links,
    )

@@ -432,7 +421,7 @@ async def _insert_facts_and_links(
    processed_facts: list[ProcessedFact],
    config,
    log_buffer: list[str],
-    resolved_entities: list[ResolvedEntity],
+    resolved_entity_ids: list[str],
    entity_to_unit: list[tuple],
    unit_to_entity_ids: dict[str, list[str]],
    semantic_ann_links: list[tuple],
@@ -459,7 +448,6 @@ async def _insert_facts_and_links(
        # Entity resolution was done in Phase 1 (separate connection).
        # Remap placeholder IDs to actual unit IDs.
        step_start = time.time()
-        resolved_entity_ids = [entity.entity_id for entity in resolved_entities]
        remapped_entity_to_unit, _remapped_unit_to_entity_ids, remapped_semantic = _remap_phase1_results(
            resolved_entity_ids, entity_to_unit, unit_to_entity_ids, semantic_ann_links or [], unit_ids
        )
@@ -472,10 +460,6 @@ async def _insert_facts_and_links(
            (unit_id, resolved_entity_ids[idx], fact_date)
            for idx, (unit_id, _local_idx, fact_date) in enumerate(remapped_entity_to_unit)
        ]
-        # Lock/re-create the resolved parents on THIS transaction before linking,
-        # closing the window where prune_orphan_entities could have deleted one
-        # between Phase-1 resolution and this insert (#2662).
-        await entity_resolver.reassert_entities_batch(bank_id, resolved_entities, conn=conn)
        await entity_resolver.link_units_to_entities_batch(unit_entity_pairs, conn=conn)
        log_buffer.append(f"  Insert unit_entities: {len(unit_entity_pairs)} pairs in {time.time() - step_start:.3f}s")

@@ -565,90 +549,9 @@ async def _extract_and_embed(
    embeddings = await embedding_processing.generate_embeddings_batch(embeddings_model, augmented_texts)
    log_buffer.append(f"  Generate embeddings: {len(embeddings)} embeddings in {time.time() - step_start:.3f}s")

-    fact_batch = _process_extracted_facts(extracted_facts, embeddings)
+    processed_facts = [ProcessedFact.from_extracted_fact(ef, emb) for ef, emb in zip(extracted_facts, embeddings)]

-    return fact_batch.extracted_facts, fact_batch.processed_facts, chunks, usage
-
-
-def _remap_causal_relations(
-    relations_per_fact: list[list[CausalRelation]],
-    retained_index_by_original: list[int | None],
-) -> list[list[CausalRelation]]:
-    """Remap a causal relation matrix after facts have been filtered.
-
-    Both the source row and each ``target_fact_index`` use fact ordinals. A
-    rejected source disappears with its row; a relation to a rejected target
-    must disappear rather than silently pointing at the next surviving fact.
-    """
-    remapped = [[] for retained_index in retained_index_by_original if retained_index is not None]
-    for original_source, retained_source in enumerate(retained_index_by_original):
-        if retained_source is None:
-            continue
-        for relation in relations_per_fact[original_source]:
-            original_target = relation.target_fact_index
-            retained_target = (
-                retained_index_by_original[original_target]
-                if 0 <= original_target < len(retained_index_by_original)
-                else None
-            )
-            if retained_target is None:
-                continue
-            remapped[retained_source].append(
-                CausalRelation(
-                    relation_type=relation.relation_type,
-                    target_fact_index=retained_target,
-                )
-            )
-    return remapped
-
-
-def _process_extracted_facts(
-    extracted_facts: list[ExtractedFact],
-    embeddings: list[list[float]],
-) -> _ProcessedFactBatch:
-    """Process facts while preserving their positional relationships.
-
-    ``ProcessedFact.from_extracted_fact`` may reject a degenerate fact. Keep
-    the surviving extracted and processed facts in lockstep, and translate
-    causal ordinals from the original extraction into that retained sequence.
-    The returned index table is also used by transfer import for archive-only
-    links and observation source references.
-    """
-    if len(extracted_facts) != len(embeddings):
-        raise ValueError(
-            f"Extracted facts/embeddings length mismatch: {len(extracted_facts)} facts, {len(embeddings)} embeddings"
-        )
-
-    retained_extracted: list[ExtractedFact] = []
-    processed_facts: list[ProcessedFact] = []
-    retained_index_by_original: list[int | None] = [None] * len(extracted_facts)
-
-    for original_index, (extracted_fact, embedding) in enumerate(zip(extracted_facts, embeddings, strict=True)):
-        processed_fact = ProcessedFact.from_extracted_fact(extracted_fact, embedding)
-        if processed_fact is None:
-            continue
-        retained_index_by_original[original_index] = len(processed_facts)
-        retained_extracted.append(extracted_fact)
-        processed_facts.append(processed_fact)
-
-    remapped_relations = _remap_causal_relations(
-        [fact.causal_relations for fact in extracted_facts],
-        retained_index_by_original,
-    )
-    for extracted_fact, processed_fact, causal_relations in zip(
-        retained_extracted,
-        processed_facts,
-        remapped_relations,
-        strict=True,
-    ):
-        extracted_fact.causal_relations = causal_relations
-        processed_fact.causal_relations = causal_relations
-
-    return _ProcessedFactBatch(
-        extracted_facts=retained_extracted,
-        processed_facts=processed_facts,
-        retained_index_by_original=retained_index_by_original,
-    )
+    return extracted_facts, processed_facts, chunks, usage


 async def retain_batch(
@@ -829,7 +732,7 @@ async def retain_batch(
                    document_id=_item_doc_id,
                    decision=_decision,
                )
-                await _audit_memory_defense(
+                _audit_memory_defense(
                    audit_logger,
                    bank_id=bank_id,
                    document_id=_item_doc_id,
@@ -928,12 +831,6 @@ async def retain_batch(
            first = contents_dicts[0]
            if first.get("context"):
                existing_content["context"] = first["context"]
-            if first.get("event_date"):
-                existing_content["event_date"] = first["event_date"]
-            if first.get("metadata"):
-                existing_content["metadata"] = first["metadata"]
-            if first.get("observation_scopes") is not None:
-                existing_content["observation_scopes"] = first["observation_scopes"]
            if first.get("tags"):
                existing_content["tags"] = first["tags"]
            contents_dicts = [existing_content, *contents_dicts]
@@ -955,12 +852,6 @@ async def retain_batch(
                    contents_dicts = [{"content": json.dumps(_merged, ensure_ascii=False)}]
                    if first.get("context"):
                        contents_dicts[0]["context"] = first["context"]
-                    if first.get("event_date"):
-                        contents_dicts[0]["event_date"] = first["event_date"]
-                    if first.get("metadata"):
-                        contents_dicts[0]["metadata"] = first["metadata"]
-                    if first.get("observation_scopes") is not None:
-                        contents_dicts[0]["observation_scopes"] = first["observation_scopes"]
                    if first.get("tags"):
                        contents_dicts[0]["tags"] = first["tags"]
            except (json.JSONDecodeError, ValueError, TypeError):
@@ -1313,14 +1204,6 @@ async def _streaming_retain_batch(
    retain_params, merged_tags = _build_retain_params(contents_dicts, document_tags)
    # Track whether document tracking has been done (by the first batch)
    doc_tracking_done = [False]
-    # Track whether the transactional-outbox callback has already fired inside a
-    # batch write TXN. The in-TXN fire only runs on a final facts-bearing batch
-    # (is_last=True); two success paths never reach it — a committed-chunk count
-    # that lands exactly on a chunk_batch_size boundary (the sentinel drains an
-    # empty batch), and a final batch that extracts zero facts (it returns before
-    # the insert). A post-loop fallback fires the callback in those cases, so this
-    # flag exists to guarantee the callback fires exactly once.
-    outbox_fired = [False]

    # ---------------------------------------------------------------------------
    # Producer-consumer pipeline: LLM extraction runs concurrently with DB writes
@@ -1493,26 +1376,12 @@ async def _streaming_retain_batch(
            # from a single oversized item sharing one document_id — without it
            # each sub-batch restarts at 0 and their chunk_ids collide (#1888).
            doc_chunk_index = global_idx + chunk_index_offset
-            fact_index_offset = len(batch_processed)
-            for fact, processed_fact in zip(extracted, processed, strict=True):
+            for fact in extracted:
                fact.content_index = content_idx_in_batch
                if fact.chunk_index is not None:
                    fact.chunk_index = doc_chunk_index
-                processed_fact.content_index = content_idx_in_batch
-
-                # Each producer call extracts one chunk, so its causal ordinals
-                # start at zero. Translate them into the combined consumer-batch
-                # sequence before link creation; otherwise later chunks can point
-                # at equally numbered facts from the first completed chunk.
-                causal_relations = [
-                    CausalRelation(
-                        relation_type=relation.relation_type,
-                        target_fact_index=relation.target_fact_index + fact_index_offset,
-                    )
-                    for relation in processed_fact.causal_relations
-                ]
-                fact.causal_relations = causal_relations
-                processed_fact.causal_relations = causal_relations
+            for pf in processed:
+                pf.content_index = content_idx_in_batch
            for cm in chunk_meta:
                cm.chunk_index = doc_chunk_index

@@ -1525,12 +1394,7 @@ async def _streaming_retain_batch(
        nonlocal total_usage
        total_usage = total_usage + batch_usage

-        # ``batch_extracted`` contains only survivors after the degenerate-text
-        # guard. Chunk metadata still records whether extraction originally
-        # produced facts, so an all-rejected batch follows the normal write path
-        # and preserves chunk/outbox behavior from before filtering was added.
-        had_extracted_facts = bool(batch_extracted) or any(chunk.fact_count for chunk in batch_chunk_meta)
-        if not had_extracted_facts:
+        if not batch_extracted:
            # Even with 0 facts, the first batch must still run document tracking
            # (cascade-delete + insert doc row) to establish ownership and prevent
            # concurrent requests from interleaving. Later batches can safely skip.
@@ -1726,7 +1590,7 @@ async def _streaming_retain_batch(
                        batch_processed,
                        config,
                        log_buffer,
-                        resolved_entities=phase1.entities.resolved_entities,
+                        resolved_entity_ids=phase1.entities.resolved_entity_ids,
                        entity_to_unit=phase1.entities.entity_to_unit,
                        unit_to_entity_ids=phase1.entities.unit_to_entity_ids,
                        semantic_ann_links=[],
@@ -1737,12 +1601,6 @@ async def _streaming_retain_batch(

                logger.info(f"[streaming] Phase 2 (write txn): {time.time() - p2_start:.3f}s")

-                # The write TXN above committed the transactional-outbox row in the
-                # same transaction as this batch's facts. Record it so the post-loop
-                # fallback doesn't queue a duplicate delivery.
-                if is_last and outbox_callback is not None:
-                    outbox_fired[0] = True
-
                # Best-effort: flush entity_cooccurrences and other deferred stats.
                try:
                    await entity_resolver.flush_pending_stats()
@@ -1876,21 +1734,6 @@ async def _streaming_retain_batch(
                    combined_content = ""
                    log_buffer.append(f"[streaming] Document {effective_doc_id} tracked (no facts extracted)")

-        # Transactional-outbox fallback. The in-TXN fire only runs on a final
-        # facts-bearing batch (is_last=True). When the committed-chunk count lands
-        # exactly on a chunk_batch_size boundary the sentinel drains an empty batch
-        # and never marks one last; when the final batch extracts zero facts it
-        # returns before the insert; and when every chunk is skipped as already
-        # committed no batch runs at all. In each of those the retain still
-        # succeeded, so the retain.completed delivery must be queued — exactly once,
-        # in its own transaction (there is no batch TXN left to attach it to). Skip
-        # it on a concurrent takeover: an aborted request must not emit completion.
-        if outbox_callback is not None and not outbox_fired[0] and not pipeline_aborted[0]:
-            async with acquire_with_retry(pool) as conn:
-                async with conn.transaction():
-                    await outbox_callback(conn)
-            outbox_fired[0] = True
-
        # Mark facts as committed in operation metadata (crash recovery checkpoint)
        if operation_id and all_unit_ids:
            try:
@@ -2343,7 +2186,7 @@ async def _try_delta_retain(
                    processed_facts,
                    config,
                    log_buffer,
-                    resolved_entities=phase1.entities.resolved_entities,
+                    resolved_entity_ids=phase1.entities.resolved_entity_ids,
                    entity_to_unit=phase1.entities.entity_to_unit,
                    unit_to_entity_ids=phase1.entities.unit_to_entity_ids,
                    semantic_ann_links=phase1.semantic_ann_links,
@@ -5,14 +5,11 @@ These dataclasses provide type safety throughout the retain operation,
 from content input to fact storage.
 """

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

-logger = logging.getLogger(__name__)
-

 class RetainContentDict(TypedDict, total=False):
    """Type definition for content items in retain_batch_async.
@@ -99,12 +96,10 @@ class CausalRelation:
    """
    Causal relationship between facts.

-    Retain emits only the backward-looking ``caused_by`` form. Transfer import
-    reuses this structure to restore historical causal types without allowing
-    normal retain writes to create them.
+    Represents how one fact was caused by another.
    """

-    relation_type: str  # ``caused_by`` for retain; legacy types for transfer restore
+    relation_type: str  # "caused_by"
    target_fact_index: int  # Index of the target fact in the batch


@@ -190,47 +185,10 @@ class ProcessedFact:
        """Check if this fact was marked as a duplicate."""
        return self.unit_id is None

-    @staticmethod
-    def _is_degenerate_text(text: str) -> bool:
-        """Check if fact text has zero information content.
-
-        Rejects empty strings, whitespace-only, single punctuation marks,
-        and common LLM hallucination patterns that carry no semantic meaning.
-        """
-        stripped = (text or "").strip()
-        if not stripped:
-            return True
-        # Single or repeated punctuation patterns with no semantic content
-        degenerate_patterns = {
-            "...",
-            "…",
-            "-",
-            "--",
-            "---",
-            ".",
-            "..",
-            "•",
-            "·",
-            "*",
-            "**",
-            "***",
-            "_,_",
-            "_, _, _",
-        }
-        if stripped in degenerate_patterns:
-            return True
-        # Strings composed entirely of punctuation and whitespace
-        if all(c in ".,;:!?-–—…\"'`´ \t\n\r" for c in stripped):
-            return True
-        # Very short text (<= 2 chars) that is only punctuation
-        if len(stripped) <= 2 and all(not c.isalnum() for c in stripped):
-            return True
-        return False
-
    @staticmethod
    def from_extracted_fact(
        extracted_fact: "ExtractedFact", embedding: list[float], chunk_id: str | None = None
-    ) -> "ProcessedFact | None":
+    ) -> "ProcessedFact":
        """
        Create ProcessedFact from ExtractedFact.

@@ -240,17 +198,8 @@ class ProcessedFact:
            chunk_id: Optional chunk ID

        Returns:
-            ProcessedFact ready for storage, or None if the fact text is degenerate
-            (zero information content — punctuation-only, empty, etc.)
+            ProcessedFact ready for storage
        """
-        fact_text = extracted_fact.fact_text or ""
-        if ProcessedFact._is_degenerate_text(fact_text):
-            logger.warning(
-                f"Rejected degenerate fact text: type={extracted_fact.fact_type}, "
-                f"text={fact_text[:80]!r}, entities={extracted_fact.entities}"
-            )
-            return None
-
        # Use occurred dates only if explicitly provided by LLM
        occurred_start = extracted_fact.occurred_start
        occurred_end = extracted_fact.occurred_end
@@ -260,7 +209,7 @@ class ProcessedFact:
        entities = [EntityRef(name=name) for name in extracted_fact.entities]

        return ProcessedFact(
-            fact_text=fact_text,
+            fact_text=extracted_fact.fact_text,
            fact_type=extracted_fact.fact_type,
            embedding=embedding,
            occurred_start=occurred_start,
@@ -277,43 +226,19 @@ class ProcessedFact:
        )


-@dataclass
-class ResolvedEntity:
-    """Identity of a resolved entity carried across the retain phase boundary.
-
-    ``canonical_name`` is the value stored on the entity row (NOT the raw input
-    mention), captured during Phase-1 resolution. It is threaded to Phase 2 so a
-    parent pruned between phases can be re-created with its real name — the row
-    is gone by then, so the name is otherwise unrecoverable (#2662).
-    """
-
-    entity_id: str
-    canonical_name: str
-
-    def __post_init__(self) -> None:
-        # Callers pass UUID objects or strings; normalize once so downstream
-        # comparisons, set membership, and SQL binds all see a plain str.
-        self.entity_id = str(self.entity_id)
-
-
@dataclass
 class EntityResolutionResult:
    """
    Result of Phase 1 entity resolution.

-    Contains resolved entity identities and the mapping data needed to remap
+    Contains resolved entity IDs and the mapping data needed to remap
    placeholder unit IDs to real IDs after fact insertion in Phase 2.
    """

-    resolved_entities: list[ResolvedEntity]
+    resolved_entity_ids: list[str]
    entity_to_unit: list[tuple]
    unit_to_entity_ids: dict[str, list[str]]

-    @property
-    def resolved_entity_ids(self) -> list[str]:
-        """Entity IDs in flattened resolution order (used by link remapping)."""
-        return [entity.entity_id for entity in self.resolved_entities]
-

@dataclass
 class Phase1Result:
@@ -27,23 +27,6 @@ def fq_table(table_name: str) -> str:
    return f"{get_current_schema()}.{table_name}"


-def fq_routine(name: str) -> str:
-    """Schema-qualified name of a cross-tenant discovery routine.
-
-    These routines are database-global — each enumerates ``pg_class`` across every
-    schema and dispatches per schema — so exactly one copy exists, installed into
-    the configured schema by ``b6d2f8a4c1e7``. Calling it through the configured
-    schema rather than a hardcoded ``public.`` is what makes a deployment living
-    in a dedicated non-``public`` schema work (#2638).
-
-    Unlike :func:`fq_table` this ignores the per-request schema contextvar: the
-    routines are deliberately cross-tenant, called from background loops that have
-    no request context.
-    """
-    schema = get_config().database_schema or "public"
-    return '"' + schema.replace('"', '""') + '".' + name
-
-
 def fq_table_explicit(table: str, schema: str | None = None) -> str:
    """Get fully-qualified table name with an explicit schema override.

@@ -40,6 +40,8 @@ class GraphRetriever(ABC):
        fact_type: str,
        budget: int,
        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
        adjacency=None,  # TypedAdjacency, optional pre-loaded graph
        tags: list[str] | None = None,  # Visibility scope tags for filtering
        tags_match: TagsMatch = "any",  # How to match tags: 'any' (OR) or 'all' (AND)
@@ -57,6 +59,8 @@ class GraphRetriever(ABC):
            fact_type: Fact type to filter ('world', 'experience', 'observation')
            budget: Maximum number of nodes to explore/return
            query_text: Original query text (optional, for some strategies)
+            semantic_seeds: Pre-computed semantic entry points (from semantic retrieval)
+            temporal_seeds: Pre-computed temporal entry points (from temporal retrieval)
            adjacency: Pre-loaded typed adjacency graph (optional)
            tags: Optional list of tags for visibility filtering (OR matching)

@@ -1,8 +1,8 @@
 """
 Link Expansion graph retrieval.

-Selects bounded semantic seeds, then expands through three parallel,
-first-class signals stored in memory_links:
+Expands from semantic/temporal seeds through three parallel, first-class signals
+stored in memory_links:

 1. Entity links  — query-time self-join through unit_entities. Score = number of distinct
                   shared entities between the seed set and each candidate, computed via
@@ -127,6 +127,8 @@ class LinkExpansionRetriever(GraphRetriever):
        fact_type: str,
        budget: int,
        query_text: str | None = None,
+        semantic_seeds: list[RetrievalResult] | None = None,
+        temporal_seeds: list[RetrievalResult] | None = None,
        adjacency=None,
        tags: list[str] | None = None,
        tags_match: TagsMatch = "any",
@@ -144,6 +146,8 @@ class LinkExpansionRetriever(GraphRetriever):
            fact_type: Fact type to filter
            budget: Maximum results to return
            query_text: Original query text (unused)
+            semantic_seeds: Pre-computed semantic entry points
+            temporal_seeds: Pre-computed temporal entry points
            adjacency: Unused, kept for interface compatibility
            tags: Optional list of tags for visibility filtering

@@ -154,28 +158,32 @@ class LinkExpansionRetriever(GraphRetriever):
        timings = GraphRetrievalTimings(fact_type=fact_type)

        async with acquire_with_retry(pool) as conn:
-            # Graph traversal deliberately chooses its own bounded seeds. The semantic and temporal
-            # retrieval arms have independent candidate limits and thresholds, so reusing their
-            # results would silently change graph-retrieval recall behavior.
-            seeds_start = time.time()
-            all_seeds = await _find_semantic_seeds(
-                conn,
-                query_embedding_str,
-                bank_id,
-                fact_type,
-                limit=20,
-                threshold=0.3,
-                tags=tags,
-                tags_match=tags_match,
-                tag_groups=tag_groups,
-                created_after=created_after,
-                created_before=created_before,
-            )
-            timings.seeds_time = time.time() - seeds_start
-            logger.debug(
-                f"[LinkExpansion] Found {len(all_seeds)} semantic seeds for fact_type={fact_type} "
-                f"(tags={tags}, tags_match={tags_match})"
-            )
+            # Find seeds if not provided
+            if semantic_seeds:
+                all_seeds = list(semantic_seeds)
+            else:
+                seeds_start = time.time()
+                all_seeds = await _find_semantic_seeds(
+                    conn,
+                    query_embedding_str,
+                    bank_id,
+                    fact_type,
+                    limit=20,
+                    threshold=0.3,
+                    tags=tags,
+                    tags_match=tags_match,
+                    tag_groups=tag_groups,
+                    created_after=created_after,
+                    created_before=created_before,
+                )
+                timings.seeds_time = time.time() - seeds_start
+                logger.debug(
+                    f"[LinkExpansion] Found {len(all_seeds)} semantic seeds for fact_type={fact_type} "
+                    f"(tags={tags}, tags_match={tags_match})"
+                )
+
+            if temporal_seeds:
+                all_seeds.extend(temporal_seeds)

            if not all_seeds:
                return [], timings
@@ -235,16 +243,12 @@ class LinkExpansionRetriever(GraphRetriever):
        }

        sorted_ids = sorted(score_map.keys(), key=lambda x: score_map[x], reverse=True)[:budget]
+        rows = [row_map[fact_id] for fact_id in sorted_ids]

        results = []
-        for fact_id in sorted_ids:
-            row = row_map[fact_id]
+        for row in rows:
            result = RetrievalResult.from_db_row(dict(row))
-            # ``activation`` is used to re-sort graph results after fact types are
-            # combined. It must retain the final additive score rather than the
-            # raw score from one signal, which would otherwise discard the other
-            # signals and make the cross-fact-type order disagree with this order.
-            result.activation = score_map[fact_id]
+            result.activation = row["score"]
            results.append(result)

        # filter_results_by_tags is a no-op when no filter applies (tags falsy and not
@@ -15,7 +15,7 @@ from dataclasses import dataclass, field
 from datetime import UTC, datetime
 from typing import TYPE_CHECKING, Any, Optional

-from ...config import DEFAULT_BM25_MAX_QUERY_TERMS, get_config
+from ...config import get_config
 from ..db_utils import acquire_with_retry
 from ..memory_engine import fq_table
 from ..sql import create_sql_dialect
@@ -222,12 +222,7 @@ async def retrieve_semantic_bm25_combined(
    # --- BM25 UNION ALL arms (one per fact_type, only when tokens present) ---
    if _include_bm25:
        text_ext = config.text_search_extension
-        bm25_text_param: str = dialect.prepare_bm25_text(
-            tokens,
-            query_text,
-            text_search_extension=text_ext,
-            max_query_terms=getattr(config, "bm25_max_query_terms", DEFAULT_BM25_MAX_QUERY_TERMS),
-        )
+        bm25_text_param: str = dialect.prepare_bm25_text(tokens, query_text, text_search_extension=text_ext)
        for i, ft in enumerate(fact_types):
            arms.append(
                dialect.build_bm25_arm(
@@ -621,7 +616,7 @@ 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, mu.metadata, mu.proof_count,
+                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)
@@ -798,7 +793,7 @@ async def retrieve_all_fact_types_parallel(
                tc_start,
                tc_end,
                budget=thinking_budget,
-                semantic_threshold=0.1,
+                semantic_threshold=min_semantic if min_semantic is not None else 0.1,
                tags=tags,
                tags_match=tags_match,
                tag_groups=tag_groups,
@@ -822,6 +817,8 @@ async def retrieve_all_fact_types_parallel(
            fact_type=ft,
            budget=thinking_budget,
            query_text=query_text,
+            semantic_seeds=None,
+            temporal_seeds=None,
            tags=tags,
            tags_match=tags_match,
            tag_groups=tag_groups,
@@ -22,7 +22,7 @@ class GraphRetrievalTimings:
    pattern_count: int = 0  # Number of patterns executed
    fusion: float = 0.0  # Time for RRF fusion
    fetch: float = 0.0  # Time to fetch memory unit details
-    seeds_time: float = 0.0  # Time spent selecting semantic graph seeds
+    seeds_time: float = 0.0  # Time to find semantic seeds (if fallback used)
    result_count: int = 0  # Number of results returned
    # Detailed per-hop timing: list of {hop, exec_time, uncached, load_time, edges_loaded, total_time}
    hop_details: list[dict] = field(default_factory=list)
@@ -300,6 +300,19 @@ class SQLDialect(ABC):
        """FOR UPDATE SKIP LOCKED clause (same on both PG and Oracle)."""
        ...

+    @abstractmethod
+    def advisory_lock(self, id_param: str) -> str:
+        """Advisory lock expression.
+
+        Args:
+            id_param: Parameter placeholder for the lock ID.
+
+        Returns:
+            PG: "pg_try_advisory_lock($1)"
+            Oracle: "SELECT ... FOR UPDATE NOWAIT" equivalent.
+        """
+        ...
+
    # -- UUID generation -------------------------------------------------

    @abstractmethod
@@ -436,7 +449,6 @@ class SQLDialect(ABC):
        query_text: str,
        *,
        text_search_extension: str = "native",
-        max_query_terms: int | None = None,
    ) -> str:
        """Prepare the text parameter value for BM25 search.

@@ -447,8 +459,6 @@ class SQLDialect(ABC):
            tokens: Tokenized query words.
            query_text: Original query text.
            text_search_extension: Full-text search backend variant.
-            max_query_terms: Optional backend-specific token cap. 0 or None
-                leaves query terms uncapped.

        Returns:
            Prepared text string to bind as the BM25 text parameter.
@@ -203,6 +203,10 @@ class OracleDialect(SQLDialect):
    def for_update_skip_locked(self) -> str:
        return "FOR UPDATE SKIP LOCKED"

+    def advisory_lock(self, id_param: str) -> str:
+        # Oracle doesn't have advisory locks. Use SELECT FOR UPDATE NOWAIT on a lock row.
+        return "SELECT 1 FROM dual FOR UPDATE NOWAIT"
+
    # -- UUID generation -------------------------------------------------

    def generate_uuid(self) -> str:
@@ -299,7 +303,6 @@ class OracleDialect(SQLDialect):
        query_text: str,
        *,
        text_search_extension: str = "native",
-        max_query_terms: int | None = None,
    ) -> str:
        # Oracle Text: filter tokens with special chars, escape reserved words
        # with curly braces (e.g. "about" → "{about}"), and join with OR.
@@ -118,6 +118,9 @@ class PostgreSQLDialect(SQLDialect):
    def for_update_skip_locked(self) -> str:
        return "FOR UPDATE SKIP LOCKED"

+    def advisory_lock(self, id_param: str) -> str:
+        return f"pg_try_advisory_lock({id_param})"
+
    # -- UUID generation -------------------------------------------------

    def generate_uuid(self) -> str:
@@ -251,11 +254,8 @@ class PostgreSQLDialect(SQLDialect):
        query_text: str,
        *,
        text_search_extension: str = "native",
-        max_query_terms: int | None = None,
    ) -> str:
        if text_search_extension in ("vchord", "pg_textsearch", "pgroonga", "pg_search"):
            return query_text
-        if max_query_terms is not None and max_query_terms > 0:
-            tokens = tokens[:max_query_terms]
        # native tsvector: join tokens with OR operator
        return " | ".join(tokens)
@@ -1,30 +0,0 @@
-"""Canonical JSON Schema serialization for OpenAI strict output."""
-
-from typing import Any
-
-from pydantic import BaseModel
-from pydantic.json_schema import GenerateJsonSchema, JsonSchemaValue
-from pydantic_core import core_schema
-
-
-class OpenAIStrictSchemaGenerator(GenerateJsonSchema):
-    """Emit the strict JSON Schema subset required by OpenAI-compatible APIs."""
-
-    def model_schema(self, schema: core_schema.ModelSchema) -> JsonSchemaValue:
-        json_schema = super().model_schema(schema)
-        properties = json_schema.get("properties")
-        if type(properties) is dict:
-            json_schema["required"] = list(properties)
-            json_schema["additionalProperties"] = False
-        return json_schema
-
-    def default_schema(self, schema: core_schema.WithDefaultSchema) -> JsonSchemaValue:
-        json_schema = super().default_schema(schema)
-        if json_schema.get("default", object()) is None:
-            json_schema.pop("default")
-        return json_schema
-
-
-def strict_json_schema(response_format: type[BaseModel]) -> dict[str, Any]:
-    """Serialize a typed response model directly into OpenAI's strict subset."""
-    return response_format.model_json_schema(schema_generator=OpenAIStrictSchemaGenerator)
@@ -128,11 +128,6 @@ def _extract_non_chinese_period(query: str, reference_date: datetime) -> DateRan
        "december|diciembre|dicembre|d[ée]cembre|dezember": 12,
    }
    for pattern, month_num in month_patterns.items():
-        # Skip when a day number precedes the month ("13 июля 2026", "13 July 2026"):
-        # that is an exact date, and collapsing it to the whole month loses precision.
-        # dateparser resolves those correctly, so let them fall through to it.
-        if re.search(rf"\b\d{{1,2}}\s+({pattern})\b", query, re.IGNORECASE):
-            continue
        match = re.search(rf"\b({pattern})\s+(\d{{4}})\b", query, re.IGNORECASE)
        if match:
            year = int(match.group(2))
@@ -19,14 +19,10 @@ from decimal import Decimal
 from typing import Any
 from uuid import UUID

-from ..causal_links import CAUSAL_LINK_TYPES
 from ..db_utils import acquire_with_retry
 from ..schema import fq_table
 from .schema import (
-    CARRIED_HISTORY_TABLES,
-    HISTORY_TABLES,
    SCHEMA_VERSION,
-    BankRowsJSONEncoding,
    TransferCausalRelation,
    TransferChunk,
    TransferDocument,
@@ -75,7 +71,9 @@ _BANK_ROW_TABLES = ("banks", "mental_models", "directives", "webhooks")
 # keep their (id, bank_id) across export/import, so their refresh history can be
 # re-attached. The surrogate ``id`` is dropped on dump so the target reassigns it
 # (see _dump_history_rows); restored after its parent table (mental_models).
+_CARRIED_HISTORY_TABLES = ("mental_model_history",)
 # Operational history — only carried with include_history=True.
+_HISTORY_TABLES = ("audit_log", "llm_requests")
 # Intentionally never exported.
 _SKIP_TABLES = frozenset(
    {
@@ -127,9 +125,10 @@ class _LoadedExport:
    unit_index: dict[Any, _UnitLocation] = field(default_factory=dict)


-# Retain currently writes only ``caused_by``. The legacy types stay in archives
-# so importing a historical bank preserves its graph; temporal/semantic/entity
-# links are regenerated against the target bank.
+# Causal link types that retain persists between facts. Only these travel in the
+# archive; temporal/semantic/entity links are regenerated against the target bank.
+_CAUSAL_LINK_TYPES = ("caused_by", "causes", "enables", "prevents")
+
 # Facts of these types are exported; observations are derived and excluded.
 _EXPORTED_FACT_TYPES = ("world", "experience")

@@ -139,12 +138,7 @@ def _as_jsonb(value: Any) -> Any:
    if value is None:
        return None
    if isinstance(value, str):
-        try:
-            return json.loads(value)
-        except json.JSONDecodeError:
-            # Admin connections register a JSONB decoder, so a valid scalar such
-            # as `"combined"` arrives here as the already-decoded `combined`.
-            return value
+        return json.loads(value)
    return value


@@ -272,13 +266,7 @@ async def _dump_history_rows(conn: Any, table: str, bank_id: str) -> list[dict]:
    return [{k: v for k, v in dict(row).items() if k not in _DERIVED_COLUMNS and k != "id"} for row in rows]


-async def export_bank(
-    conn: Any,
-    bank_id: str,
-    *,
-    include_history: bool = False,
-    bank_rows_json_encoding: BankRowsJSONEncoding = "serialized",
-) -> bytes:
+async def export_bank(conn: Any, bank_id: str, *, include_history: bool = False) -> bytes:
    """Export an entire bank into a portable ZIP archive (no embeddings).

    Produces a superset of the documents archive: the logical
@@ -299,11 +287,11 @@ async def export_bank(
    observations = await _load_observations(conn, bank_id, loaded.unit_index)

    bank_rows = {table: await _dump_bank_rows(conn, table, bank_id) for table in _BANK_ROW_TABLES}
-    for table in CARRIED_HISTORY_TABLES:
+    for table in _CARRIED_HISTORY_TABLES:
        bank_rows[table] = await _dump_history_rows(conn, table, bank_id)
    history_rows: dict[str, list[dict]] = {}
    if include_history:
-        history_rows = {table: await _dump_bank_rows(conn, table, bank_id) for table in HISTORY_TABLES}
+        history_rows = {table: await _dump_bank_rows(conn, table, bank_id) for table in _HISTORY_TABLES}

    archive = io.BytesIO()
    fact_total = 0
@@ -333,7 +321,6 @@ async def export_bank(
            directive_count=len(bank_rows.get("directives", [])),
            webhook_count=len(bank_rows.get("webhooks", [])),
            includes_history=include_history,
-            bank_rows_json_encoding=bank_rows_json_encoding,
        )
        zf.writestr("manifest.json", manifest.model_dump_json(indent=2))

@@ -556,7 +543,7 @@ async def _attach_causal_relations(conn: Any, loaded: _LoadedFacts) -> None:
          AND from_unit_id = ANY($2)
          AND to_unit_id = ANY($2)
        """,
-        list(CAUSAL_LINK_TYPES),
+        list(_CAUSAL_LINK_TYPES),
        list(loaded.unit_index.keys()),
    )
    for row in rows:
@@ -18,9 +18,8 @@ from dataclasses import dataclass, field
 from datetime import UTC, date, datetime
 from typing import Any, Literal

-from ..causal_links import CANONICAL_CAUSAL_LINK_TYPE, LEGACY_CAUSAL_LINK_TYPES
 from ..db_utils import acquire_with_retry
-from ..retain import bank_utils, chunk_storage, embedding_processing, fact_storage, link_utils, orchestrator
+from ..retain import bank_utils, chunk_storage, embedding_processing, fact_storage, orchestrator
 from ..retain.types import (
    CausalRelation,
    ChunkMetadata,
@@ -30,10 +29,7 @@ from ..retain.types import (
 )
 from ..schema import fq_table
 from .schema import (
-    CARRIED_HISTORY_TABLES,
-    HISTORY_TABLES,
    SCHEMA_VERSION,
-    BankRowsJSONEncoding,
    TransferDocument,
    TransferFact,
    TransferManifest,
@@ -87,14 +83,6 @@ class _ObservationOutcome:
    skipped: int = 0


-@dataclass
-class _ImportedFactBatch:
-    """Inserted fact IDs paired with their ordinals in the source archive."""
-
-    unit_ids: list[str]
-    original_ordinals: list[int]
-
-
@dataclass
 class ParsedArchive:
    """A transfer archive after parsing/validation."""
@@ -177,7 +165,7 @@ async def import_documents(
        if target_id != document.id:
            result.remapped_document_ids[document.id] = target_id

-        imported_facts = await _import_one_document(
+        unit_ids = await _import_one_document(
            backend=backend,
            embeddings_model=embeddings_model,
            entity_resolver=entity_resolver,
@@ -190,16 +178,16 @@ async def import_documents(
            outbox_callback_factory=outbox_callback_factory,
        )
        result.documents_imported += 1
-        result.facts_imported += len(imported_facts.unit_ids)
+        result.facts_imported += len(unit_ids)
        result.imported_documents.append(
            ImportedDocument(
                document_id=target_id,
-                unit_ids=imported_facts.unit_ids,
+                unit_ids=unit_ids,
                content=document.original_text or "",
                tags=list(document.tags),
            )
        )
-        for ordinal, unit_id in zip(imported_facts.original_ordinals, imported_facts.unit_ids, strict=True):
+        for ordinal, unit_id in enumerate(unit_ids):
            ref_map[(document.id, ordinal)] = unit_id

    if parsed.observations:
@@ -233,6 +221,8 @@ _BANK_CHILD_TABLES = ("mental_models", "directives", "webhooks")
 # Child-history carried verbatim; restored after its parent (mental_models) so the
 # foreign key resolves. Surrogate ids were dropped on export (the target reassigns
 # them), so these restore via fresh IDENTITY values.
+_CARRIED_HISTORY_TABLES = ("mental_model_history",)
+_HISTORY_TABLES = ("audit_log", "llm_requests")


@dataclass
@@ -273,29 +263,18 @@ def parse_bank_archive(archive_bytes: bytes) -> ParsedBankArchive:
                f"Not a whole-bank archive (archive_type={manifest.archive_type!r}); use import_documents instead"
            )
        bank_rows: dict[str, list[dict]] = {}
-        for table in ("banks", *_BANK_CHILD_TABLES, *CARRIED_HISTORY_TABLES):
+        for table in ("banks", *_BANK_CHILD_TABLES, *_CARRIED_HISTORY_TABLES):
            fname = f"{table}.json"
            bank_rows[table] = json.loads(zf.read(fname)) if fname in names else []
        history_rows: dict[str, list[dict]] = {}
-        for table in HISTORY_TABLES:
+        for table in _HISTORY_TABLES:
            fname = f"history/{table}.json"
            if fname in names:
                history_rows[table] = json.loads(zf.read(fname))
    return ParsedBankArchive(manifest=manifest, bank_rows=bank_rows, history_rows=history_rows)


-def _resolve_bank_rows_json_encoding(manifest: TransferManifest) -> BankRowsJSONEncoding:
-    """Resolve row JSON provenance, including the released v1 archive contract."""
-    return manifest.bank_rows_json_encoding or "decoded"
-
-
-async def _restore_rows(
-    conn: Any,
-    table: str,
-    rows: list[dict],
-    *,
-    bank_rows_json_encoding: BankRowsJSONEncoding = "decoded",
-) -> int:
+async def _restore_rows(conn: Any, table: str, rows: list[dict]) -> int:
    """Insert verbatim rows into a bank-scoped table, coercing JSON-encoded values
    back to the column's type (timestamps, uuids, jsonb). ``ON CONFLICT DO NOTHING``
    keeps an import idempotent and safe to re-run against a partially-filled target."""
@@ -322,12 +301,9 @@ async def _restore_rows(
            value = row[col]
            if data_type in ("jsonb", "json"):
                # asyncpg has no JSON codec on these raw connections; pass JSON
-                # text and cast. Provenance is required because a decoded JSON
-                # scalar containing JSON text is indistinguishable from a raw
-                # serialized object after the outer archive JSON is parsed.
-                if value is not None and (bank_rows_json_encoding == "decoded" or not isinstance(value, str)):
-                    value = json.dumps(value)
-                values.append(value)
+                # text and cast. Values may already be str (no codec on export) or
+                # a Python object (codec on export) — normalize to text either way.
+                values.append(value if isinstance(value, str) or value is None else json.dumps(value))
                placeholders.append(f"${position}::jsonb")
                continue
            if value is not None and isinstance(value, str):
@@ -376,7 +352,6 @@ async def import_bank(
    if ops is None:
        ops = backend.ops
    parsed = parse_bank_archive(archive_bytes)
-    bank_rows_json_encoding = _resolve_bank_rows_json_encoding(parsed.manifest)
    source_bank_id = parsed.manifest.source_bank_id
    bank_id = target_bank_id or source_bank_id

@@ -399,22 +374,10 @@ async def import_bank(
                f"(it is not a merge). Delete the bank first, or pass a different target bank id."
            )
        # Bank row first — children (documents, mental_models, …) FK to it.
-        await _restore_rows(
-            conn,
-            "banks",
-            parsed.bank_rows.get("banks", []),
-            bank_rows_json_encoding=bank_rows_json_encoding,
-        )
-        # The restored banks row bypasses the fresh-INSERT gate that normally
-        # creates per-bank vector indexes, so create them explicitly here while
-        # the bank is still empty (facts are imported below, so the build is
-        # instant). get_or_create_bank_profile would NOT do this: the row now
-        # exists, so it takes the SELECT branch and skips index creation —
-        # leaving the restored bank falling back to the global index +
-        # post-filter (slower, under-returning recall). See #2645.
-        internal_id = await conn.fetchval(f"SELECT internal_id FROM {fq_table('banks')} WHERE bank_id = $1", bank_id)
-        if internal_id is not None:
-            await bank_utils.create_bank_vector_indexes(conn, bank_id, str(internal_id), ops=ops)
+        await _restore_rows(conn, "banks", parsed.bank_rows.get("banks", []))
+    # Ensure the bank's per-bank vector indexes exist (no-op for global-index
+    # extensions); idempotent and keeps the restored banks row (ON CONFLICT DO NOTHING).
+    await bank_utils.get_or_create_bank_profile(backend, bank_id)

    doc_result = await import_documents(
        backend=backend,
@@ -436,38 +399,17 @@ async def import_bank(
    )
    async with acquire_with_retry(backend) as conn:
        result.mental_models_imported = await _restore_rows(
-            conn,
-            "mental_models",
-            parsed.bank_rows.get("mental_models", []),
-            bank_rows_json_encoding=bank_rows_json_encoding,
+            conn, "mental_models", parsed.bank_rows.get("mental_models", [])
        )
        # Restored after mental_models so the (mental_model_id, bank_id) FK resolves.
        result.mental_model_history_imported = await _restore_rows(
-            conn,
-            "mental_model_history",
-            parsed.bank_rows.get("mental_model_history", []),
-            bank_rows_json_encoding=bank_rows_json_encoding,
-        )
-        result.directives_imported = await _restore_rows(
-            conn,
-            "directives",
-            parsed.bank_rows.get("directives", []),
-            bank_rows_json_encoding=bank_rows_json_encoding,
-        )
-        result.webhooks_imported = await _restore_rows(
-            conn,
-            "webhooks",
-            parsed.bank_rows.get("webhooks", []),
-            bank_rows_json_encoding=bank_rows_json_encoding,
+            conn, "mental_model_history", parsed.bank_rows.get("mental_model_history", [])
        )
+        result.directives_imported = await _restore_rows(conn, "directives", parsed.bank_rows.get("directives", []))
+        result.webhooks_imported = await _restore_rows(conn, "webhooks", parsed.bank_rows.get("webhooks", []))
        if include_history:
-            for table in HISTORY_TABLES:
-                result.history_rows_imported += await _restore_rows(
-                    conn,
-                    table,
-                    parsed.history_rows.get(table, []),
-                    bank_rows_json_encoding=bank_rows_json_encoding,
-                )
+            for table in _HISTORY_TABLES:
+                result.history_rows_imported += await _restore_rows(conn, table, parsed.history_rows.get(table, []))

    logger.info(
        "[transfer] Imported bank %s: %d doc(s), %d fact(s), %d observation(s), "
@@ -519,8 +461,8 @@ async def _import_one_document(
    target_id: str,
    ops: Any,
    outbox_callback_factory: Any = None,
-) -> _ImportedFactBatch:
-    """Re-embed and insert a document; map original fact ordinals to new unit ids."""
+) -> list[str]:
+    """Re-embed and insert a single document; returns the new unit ids in fact order."""
    log_buffer: list[str] = []

    # Fire the same retain.completed webhook retain emits, transactionally inside
@@ -532,21 +474,12 @@ async def _import_one_document(
    )

    extracted_facts = [_to_extracted_fact(fact) for fact in document.facts]
-    legacy_causal_relations = _legacy_causal_relations(document)

    processed_facts: list[ProcessedFact] = []
-    retained_index_by_original: list[int | None] = []
    if extracted_facts:
        augmented = embedding_processing.augment_texts_with_dates(extracted_facts, format_date_fn)
        embeddings = await embedding_processing.generate_embeddings_batch(embeddings_model, augmented)
-        fact_batch = orchestrator._process_extracted_facts(extracted_facts, embeddings)
-        extracted_facts = fact_batch.extracted_facts
-        processed_facts = fact_batch.processed_facts
-        retained_index_by_original = fact_batch.retained_index_by_original
-        legacy_causal_relations = orchestrator._remap_causal_relations(
-            legacy_causal_relations,
-            retained_index_by_original,
-        )
+        processed_facts = [ProcessedFact.from_extracted_fact(ef, emb) for ef, emb in zip(extracted_facts, embeddings)]

    contents = [RetainContent(content=document.original_text or "")]
    chunk_meta = [
@@ -582,15 +515,6 @@ async def _import_one_document(
                document.tags,
                ops=ops,
            )
-            if document.created_at is not None:
-                # Transfer archives carry source provenance. Apply it here,
-                # without changing normal retain/upsert timestamp semantics.
-                await conn.execute(
-                    f"UPDATE {fq_table('documents')} SET created_at = $1 WHERE id = $2 AND bank_id = $3",
-                    document.created_at,
-                    target_id,
-                    bank_id,
-                )

            chunk_id_map: dict[int, str] = {}
            if chunk_meta:
@@ -612,7 +536,7 @@ async def _import_one_document(
                processed_facts,
                config,
                log_buffer,
-                resolved_entities=phase1.entities.resolved_entities,
+                resolved_entity_ids=phase1.entities.resolved_entity_ids,
                entity_to_unit=phase1.entities.entity_to_unit,
                unit_to_entity_ids=phase1.entities.unit_to_entity_ids,
                semantic_ann_links=phase1.semantic_ann_links,
@@ -621,34 +545,14 @@ async def _import_one_document(
                ops=ops,
            )

-            # Retain writes only ``caused_by``. Restore legacy archive edges
-            # separately so their distinct direction and semantics survive a
-            # transfer without broadening the normal retain write contract.
-            if result_unit_ids and legacy_causal_relations:
-                await link_utils.restore_legacy_causal_links_batch(
-                    conn,
-                    bank_id,
-                    result_unit_ids[0],
-                    legacy_causal_relations,
-                    ops=ops,
-                )
-
        try:
            await entity_resolver.flush_pending_stats()
        except Exception:
            logger.warning("[transfer] Entity stats flush failed for document %s", target_id, exc_info=True)

    logger.debug("[transfer] Imported document %s:\n%s", target_id, "\n".join(log_buffer))
-    # Single content item -> result_unit_ids[0] follows the retained fact order.
-    retained_unit_ids = list(result_unit_ids[0]) if result_unit_ids else []
-    return _ImportedFactBatch(
-        unit_ids=retained_unit_ids,
-        original_ordinals=[
-            original_index
-            for original_index, retained_index in enumerate(retained_index_by_original)
-            if retained_index is not None
-        ],
-    )
+    # Single content item -> result_unit_ids[0] holds the new unit ids in fact order.
+    return list(result_unit_ids[0]) if result_unit_ids else []


 async def _import_observations(
@@ -718,19 +622,11 @@ async def _import_observations(

            all_source_ids: set[uuid.UUID] = set()
            for (obs, sources), obs_unit_id in zip(resolved, obs_unit_ids):
-                observation_uuid = uuid.UUID(obs_unit_id)
-                if obs.event_date is not None:
-                    # insert_facts_batch derives event_date for normal writes;
-                    # transfer restores the source value carried by the archive.
-                    await conn.execute(
-                        f"UPDATE {fq_table('memory_units')} SET event_date = $1 WHERE id = $2 AND bank_id = $3",
-                        obs.event_date,
-                        observation_uuid,
-                        bank_id,
-                    )
                source_uuids = [uuid.UUID(s) for s in sources]
                all_source_ids.update(source_uuids)
-                await _link_observation_sources(conn, ops, bank_id, observation_uuid, source_uuids, obs.proof_count)
+                await _link_observation_sources(
+                    conn, ops, bank_id, uuid.UUID(obs_unit_id), source_uuids, obs.proof_count
+                )

            # Mark source facts consolidated so the target consolidator skips them.
            if all_source_ids:
@@ -809,7 +705,6 @@ def _to_extracted_fact(fact: TransferFact) -> ExtractedFact:
        causal_relations=[
            CausalRelation(relation_type=rel.relation_type, target_fact_index=rel.target_fact_index)
            for rel in fact.causal_relations
-            if rel.relation_type == CANONICAL_CAUSAL_LINK_TYPE
        ],
        content_index=0,
        chunk_index=fact.chunk_index,
@@ -819,19 +714,3 @@ def _to_extracted_fact(fact: TransferFact) -> ExtractedFact:
        tags=list(fact.tags),
        observation_scopes=fact.observation_scopes,
    )
-
-
-def _legacy_causal_relations(document: TransferDocument) -> list[list[CausalRelation]]:
-    """Return legacy archive edges for transfer-only restoration.
-
-    Invalid archive values are excluded. The write helper repeats the explicit
-    compatibility allowlist as a persistence boundary.
-    """
-    return [
-        [
-            CausalRelation(relation_type=relation.relation_type, target_fact_index=relation.target_fact_index)
-            for relation in fact.causal_relations
-            if relation.relation_type in LEGACY_CAUSAL_LINK_TYPES
-        ]
-        for fact in document.facts
-    ]
@@ -22,14 +22,7 @@ from pydantic import BaseModel, Field
 # Bump when the archive layout changes in a backward-incompatible way.
 SCHEMA_VERSION = 1

-# Whole-bank transfer table classifications shared by export and import.
-# Child history is always carried after its mental-model parent; operational
-# history is optional and included only when the caller requests it.
-CARRIED_HISTORY_TABLES = ("mental_model_history",)
-HISTORY_TABLES = ("audit_log", "llm_requests")
-
 ObservationScopes = Literal["per_tag", "combined", "all_combinations", "shared"] | list[list[str]]
-BankRowsJSONEncoding = Literal["decoded", "serialized"]


 class TransferCausalRelation(BaseModel):
@@ -143,7 +136,3 @@ class TransferManifest(BaseModel):
    webhook_count: int = 0
    # True when --include-history carried audit_log / llm_requests.
    includes_history: bool = False
-    # How JSON/JSONB values in bank/history row files were represented by the
-    # producing connection. Absent on legacy v1 archives; import treats those as
-    # decoded because the released producer was the codec-enabled admin CLI.
-    bank_rows_json_encoding: BankRowsJSONEncoding | None = None
@@ -1,216 +0,0 @@
-"""Per-bank vector index coverage checks and repair.
-
-Per-(bank, fact_type) partial vector indexes are created only when a bank is
-first created (instant on an empty bank). A bank that becomes *populated*
-outside that fresh-INSERT path — via a logical restore, a cross-version upgrade,
-or a vector-extension switch (e.g. ScaNN→pgvector) — never gets them, so its
-bank-scoped recall silently falls back to the global index + post-filter, which
-is both slower and under-returns results. See issue #2645.
-
-This module is the shared engine for detecting and repairing that gap. It is
-driven by the ``repair-bank`` admin command; the build always uses
-``CREATE INDEX CONCURRENTLY`` on a raw autocommit connection so it never takes
-``ACCESS EXCLUSIVE`` on the shared ``memory_units`` table.
-"""
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass, field
-from typing import Any
-
-from .retain.bank_utils import _BANK_INDEX_FACT_TYPES, _bank_index_name
-
-logger = logging.getLogger(__name__)
-
-# Postgres renders the partial predicate of an indexdef with parenthesized
-# comparison operands and an explicit ::text cast, e.g.
-# `... WHERE ((fact_type = 'world'::text) AND (bank_id = 'b1'::text))`.
-# fact_type is emitted first (it is written first in the CREATE INDEX). Match
-# that exact rendering so a mere name collision never counts as healthy.
-_BANK_INDEX_PARTIAL_SUFFIX = " WHERE ((fact_type = "
-
-# Access methods that legitimately back a per-(bank, fact_type) partial index.
-# An index whose access method drifted after a backend switch does not match,
-# so the health check treats it as unhealthy (rebuild).
-_SUPPORTED_INDEX_AM: tuple[str, ...] = (
-    "btree",
-    "gin",
-    "gist",
-    "hnsw",
-    "ivfflat",
-    "diskann",
-    "vchordrq",
-)
-
-
-@dataclass
-class SchemaVectorIndexResult:
-    """Per-schema outcome of a vector-index repair pass."""
-
-    schema: str
-    banks_scanned: int = 0
-    already_present: int = 0
-    created: int = 0
-    skipped: int = 0  # would-create, reported under --dry-run
-    failed: int = 0
-    failed_indexes: list[str] = field(default_factory=list)
-
-
-def _quote_identifier(value: str) -> str:
-    return '"' + value.replace('"', '""') + '"'
-
-
-async def _index_health(conn: Any, schema: str, index_names: list[str]) -> dict[str, bool]:
-    """Return valid-and-usable state for each requested index in one query.
-
-    Health requires the index to be valid AND ready, defined over the expected
-    ``memory_units`` table, to use a supported access method, and to carry our
-    partial predicate. A name-only match is *not* enough: an INVALID leftover
-    (from an interrupted concurrent build) or an index whose access method
-    drifted after a backend switch must count as unhealthy so it is rebuilt —
-    ``pg_indexes``/``IF NOT EXISTS`` alone would silently treat those as present.
-    """
-    if not index_names:
-        return {}
-    rows = await conn.fetch(
-        """
-        SELECT c.relname AS index_name,
-               (i.indisvalid AND i.indisready
-                AND t.relname = 'memory_units'
-                AND am.amname = ANY($3::text[])
-                AND pg_get_indexdef(i.indexrelid) LIKE $4
-               ) AS healthy
-        FROM pg_class c
-        JOIN pg_namespace n ON n.oid = c.relnamespace
-        JOIN pg_index i ON i.indexrelid = c.oid
-        JOIN pg_class t ON t.oid = i.indrelid
-        JOIN pg_am am ON am.oid = c.relam
-        WHERE n.nspname = $1 AND c.relname = ANY($2::text[])
-        """,
-        schema,
-        index_names,
-        list(_SUPPORTED_INDEX_AM),
-        "%" + _BANK_INDEX_PARTIAL_SUFFIX + "%",
-    )
-    return {row["index_name"]: bool(row["healthy"]) for row in rows}
-
-
-async def _repair_schema(
-    conn: Any,
-    schema: str,
-    index_clause: str,
-    *,
-    dry_run: bool,
-    bank_id: str | None,
-) -> SchemaVectorIndexResult:
-    result = SchemaVectorIndexResult(schema=schema)
-    qschema = _quote_identifier(schema)
-
-    if bank_id is not None:
-        banks = await conn.fetch(
-            f"SELECT bank_id, internal_id FROM {qschema}.banks WHERE bank_id = $1",  # noqa: S608 — schema is a quoted identifier
-            bank_id,
-        )
-    else:
-        banks = await conn.fetch(f"SELECT bank_id, internal_id FROM {qschema}.banks ORDER BY bank_id")  # noqa: S608
-    result.banks_scanned = len(banks)
-
-    # Resolve expected index names for every bank, then check them all in one
-    # catalog query rather than one round-trip per index.
-    expected_by_bank: list[tuple[str, dict[str, str]]] = []
-    all_index_names: list[str] = []
-    for bank in banks:
-        expected = {ft: _bank_index_name(ft, str(bank["internal_id"])) for ft in _BANK_INDEX_FACT_TYPES}
-        expected_by_bank.append((bank["bank_id"], expected))
-        all_index_names.extend(expected.values())
-    health = await _index_health(conn, schema, all_index_names)
-
-    for bid, expected in expected_by_bank:
-        # Render the bank_id literal server-side so escaping does not depend on
-        # standard_conforming_strings (the predicate is inlined into the DDL).
-        bank_id_literal = await conn.fetchval("SELECT quote_literal($1::text)", bid)
-        for ft in _BANK_INDEX_FACT_TYPES:
-            index_name = expected[ft]
-            healthy = health.get(index_name)
-            if healthy is True:
-                result.already_present += 1
-                continue
-            if dry_run:
-                result.skipped += 1
-                continue
-
-            qindex = _quote_identifier(index_name)
-            qualified = f"{qschema}.{qindex}"
-            try:
-                # An unhealthy-but-present index (INVALID leftover, wrong access
-                # method) must be dropped first — IF NOT EXISTS cannot repair it.
-                if healthy is False:
-                    await conn.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {qualified}")
-                await conn.execute(
-                    f"CREATE INDEX CONCURRENTLY IF NOT EXISTS {qindex} "
-                    f"ON {qschema}.memory_units {index_clause} "
-                    f"WHERE fact_type = '{ft}' AND bank_id = {bank_id_literal}"
-                )
-                result.created += 1
-            except Exception as exc:  # noqa: BLE001 — one failed index must not abort the rest
-                result.failed += 1
-                result.failed_indexes.append(qualified)
-                logger.warning(
-                    "Failed to repair vector index %s (bank=%s, fact_type=%s): %s — "
-                    "dropping the invalid leftover so a re-run can retry.",
-                    qualified,
-                    bid,
-                    ft,
-                    exc,
-                )
-                # A failed concurrent build leaves an INVALID index behind that
-                # would shadow the good one; drop it so a re-run retries cleanly.
-                try:
-                    await conn.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {qualified}")
-                except Exception as cleanup_exc:  # noqa: BLE001
-                    logger.warning("Cleanup DROP INDEX for %s also failed: %s", qualified, cleanup_exc)
-
-    return result
-
-
-async def _safe_repair_schema(
-    conn: Any,
-    schema: str,
-    index_clause: str,
-    *,
-    dry_run: bool,
-    bank_id: str | None,
-) -> SchemaVectorIndexResult:
-    try:
-        return await _repair_schema(conn, schema, index_clause, dry_run=dry_run, bank_id=bank_id)
-    except Exception as exc:  # noqa: BLE001 — one bad schema must not abort the whole sweep
-        logger.warning("Vector index repair aborted for schema %s: %s", schema, exc)
-        return SchemaVectorIndexResult(schema=schema, failed=1, failed_indexes=[f"{schema}.<schema-error>"])
-
-
-async def repair_vector_indexes(
-    conn: Any,
-    schemas: list[str],
-    index_clause: str,
-    *,
-    dry_run: bool = False,
-    bank_id: str | None = None,
-) -> list[SchemaVectorIndexResult]:
-    """Rebuild missing or invalid per-bank vector indexes across ``schemas``.
-
-    ``conn`` must be a raw autocommit PostgreSQL connection: ``CREATE INDEX
-    CONCURRENTLY`` cannot run inside a transaction block. When ``bank_id`` is
-    given, only that bank is reconciled (in each schema); otherwise every bank
-    is scanned.
-
-    Concurrency is handled by idempotency, not a lock (project rule: no advisory
-    locks — they are unreliable behind connection poolers). Every build is
-    ``CREATE INDEX CONCURRENTLY IF NOT EXISTS`` guarded by a valid/ready health
-    check, so a second concurrent run is a no-op on already-built indexes; if two
-    runs race the *same* missing index, Postgres rejects one build and the
-    per-index handler drops the leftover so a re-run converges cleanly.
-    """
-    return [
-        await _safe_repair_schema(conn, schema, index_clause, dry_run=dry_run, bank_id=bank_id) for schema in schemas
-    ]
@@ -45,7 +45,6 @@ from hindsight_api.extensions.operation_validator import (
    # Consolidation operation
    ConsolidateContext,
    ConsolidateResult,
-    CreateBankContext,
    # File Conversion
    FileConvertResult,
    # Mental Model operations
@@ -106,7 +105,6 @@ __all__ = [
    "BankReadOperation",
    "BankWriteContext",
    "BankWriteOperation",
-    "CreateBankContext",
    # Operation Validator - Consolidation
    "ConsolidateContext",
    "ConsolidateResult",
@@ -359,7 +359,6 @@ class BankWriteOperation(StrEnum):
    DELETE_DIRECTIVE = "delete_directive"
    DELETE_DOCUMENT = "delete_document"
    DELETE_MENTAL_MODEL = "delete_mental_model"
-    DELETE_OPERATION = "delete_operation"
    DELETE_WEBHOOK = "delete_webhook"
    MERGE_BANK_MISSION = "merge_bank_mission"
    REPROCESS_DOCUMENT = "reprocess_document"
@@ -398,14 +397,6 @@ class BankWriteContext:
    request_context: "RequestContext"


-@dataclass
-class CreateBankContext:
-    """Context for validating creation of a new bank."""
-
-    bank_id: str
-    request_context: "RequestContext"
-
-
@dataclass
 class BankListContext:
    """Context for filtering the bank list (post-query)."""
@@ -890,23 +881,6 @@ class OperationValidatorExtension(Extension, ABC):
        """
        return ValidationResult.accept()

-    async def validate_create_bank(self, ctx: CreateBankContext) -> ValidationResult:
-        """
-        Validate creation of a new bank before the bank row is inserted.
-
-        Override to implement custom validation logic for operations that
-        explicitly or implicitly create a bank.
-
-        Args:
-            ctx: Context containing:
-                - bank_id: Bank identifier
-                - request_context: Request context with auth info
-
-        Returns:
-            ValidationResult indicating whether the bank may be created.
-        """
-        return ValidationResult.accept()
-
    async def filter_bank_list(self, ctx: BankListContext) -> BankListResult:
        """
        Filter the bank list after querying.
@@ -9,7 +9,7 @@ import json
 import logging
 from dataclasses import dataclass
 from datetime import datetime, timezone
-from typing import Any, Callable, get_args
+from typing import Any, Callable

 from fastmcp import FastMCP
 from mcp.types import ToolAnnotations
@@ -23,7 +23,7 @@ from hindsight_api.config import (
 from hindsight_api.engine.audit import AuditEntry, AuditLogger
 from hindsight_api.engine.memory_engine import Budget
 from hindsight_api.engine.response_models import VALID_RECALL_FACT_TYPES, MinScores
-from hindsight_api.engine.search.tags import TagGroup, TagsMatch
+from hindsight_api.engine.search.tags import TagGroup
 from hindsight_api.extensions import OperationValidationError
 from hindsight_api.models import RequestContext

@@ -512,8 +512,7 @@ def _apply_audit_logging(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCon
        """Create an audited wrapper for a tool's run method."""

        async def _audited_run(arguments, _name=tool_name, _orig=original_run):
-            # Cheap bank-independent pre-filter before resolving bank_id.
-            if not audit_logger.action_allowed(_name):
+            if not audit_logger.is_enabled(_name):
                return await _orig(arguments)

            bank_id = None
@@ -522,10 +521,6 @@ def _apply_audit_logging(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCon
            elif hasattr(arguments, "get"):
                bank_id = arguments.get("bank_id")

-            # Per-bank decision, resolved after bank_id is known.
-            if not await audit_logger.should_log(_name, bank_id):
-                return await _orig(arguments)
-
            entry = AuditEntry(
                action=_name,
                transport="mcp",
@@ -563,8 +558,7 @@ def _apply_audit_logging(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCon
        if original_call_tool:

            async def _audited_call_tool(name, arguments=None, **kwargs):
-                # Cheap bank-independent pre-filter before resolving bank_id.
-                if name not in _AUDITABLE_MCP_TOOLS or not audit_logger.action_allowed(name):
+                if name not in _AUDITABLE_MCP_TOOLS or not audit_logger.is_enabled(name):
                    return await original_call_tool(name, arguments, **kwargs)

                bank_id = None
@@ -573,10 +567,6 @@ def _apply_audit_logging(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCon
                        config.bank_id_resolver() if config.bank_id_resolver else None
                    )

-                # Per-bank decision, resolved after bank_id is known.
-                if not await audit_logger.should_log(name, bank_id):
-                    return await original_call_tool(name, arguments, **kwargs)
-
                entry = AuditEntry(
                    action=name,
                    transport="mcp",
@@ -1235,9 +1225,7 @@ def _register_create_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCo
        """
        try:
            request_context = _get_request_context(config)
-            # create_bank may auto-create the bank; validate that explicit
-            # creation permission before reading the resulting profile.
-            await memory._ensure_bank_exists(bank_id, request_context)
+            # get_bank_profile auto-creates bank if it doesn't exist
            profile = await memory.get_bank_profile(bank_id, request_context=request_context)

            # Update name/mission if provided
@@ -1264,10 +1252,7 @@ def _register_create_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsCo


 def _validate_mental_model_inputs(
-    name: str | None = None,
-    source_query: str | None = None,
-    max_tokens: int | None = None,
-    tags_match: str | None = None,
+    name: str | None = None, source_query: str | None = None, max_tokens: int | None = None
 ) -> str | None:
    """Validate mental model inputs, returning an error message or None if valid."""
    if name is not None and not name.strip():
@@ -1276,9 +1261,6 @@ def _validate_mental_model_inputs(
        return "source_query cannot be empty"
    if max_tokens is not None and (max_tokens < 256 or max_tokens > 8192):
        return f"max_tokens must be between 256 and 8192, got {max_tokens}"
-    if tags_match is not None and tags_match not in get_args(TagsMatch):
-        valid = ", ".join(get_args(TagsMatch))
-        return f"tags_match must be one of {valid}, got {tags_match!r}"
    return None


@@ -1460,7 +1442,6 @@ def _register_create_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MC
            source_query: str,
            mental_model_id: str | None = None,
            tags: list[str] | None = None,
-            tags_match: str | None = None,
            max_tokens: int = 2048,
            trigger_refresh_after_consolidation: bool = False,
            bank_id: str | None = None,
@@ -1482,12 +1463,6 @@ def _register_create_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MC
                source_query: The query to run through reflect to generate content
                mental_model_id: Optional custom ID (alphanumeric lowercase with hyphens). Auto-generated if not provided.
                tags: Optional tags for scoped visibility filtering
-                tags_match: How this model's tags are matched against memories when the content
-                    is (re)generated. One of 'any' (match any tag, like recall/reflect), 'all'
-                    (match all tags), 'any_strict', 'all_strict', or 'exact'. If omitted, a tagged
-                    model defaults to 'all_strict' — a memory must carry EVERY one of the model's
-                    tags to be included, which silently filters out memories that only carry a
-                    subset. Pass 'any' when your memories use narrow single-topic tags.
                max_tokens: Maximum tokens for generated content (256-8192, default: 2048)
                trigger_refresh_after_consolidation: If True, automatically refresh this model after memory consolidation. Default: False
                bank_id: Optional bank (defaults to session bank). Use for cross-bank operations.
@@ -1498,15 +1473,13 @@ def _register_create_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MC
                    return '{"error": "No bank_id configured"}'

                validation_error = _validate_mental_model_inputs(
-                    name=name, source_query=source_query, max_tokens=max_tokens, tags_match=tags_match
+                    name=name, source_query=source_query, max_tokens=max_tokens
                )
                if validation_error:
                    return json.dumps({"error": validation_error})

                request_context = _get_request_context(config)
-                trigger: dict[str, Any] = {"refresh_after_consolidation": trigger_refresh_after_consolidation}
-                if tags_match is not None:
-                    trigger["tags_match"] = tags_match
+                trigger = {"refresh_after_consolidation": trigger_refresh_after_consolidation}

                # Create with placeholder content
                model = await memory.create_mental_model(
@@ -1553,7 +1526,6 @@ def _register_create_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MC
            source_query: str,
            mental_model_id: str | None = None,
            tags: list[str] | None = None,
-            tags_match: str | None = None,
            max_tokens: int = 2048,
            trigger_refresh_after_consolidation: bool = False,
        ) -> dict:
@@ -1574,12 +1546,6 @@ def _register_create_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MC
                source_query: The query to run through reflect to generate content
                mental_model_id: Optional custom ID (alphanumeric lowercase with hyphens). Auto-generated if not provided.
                tags: Optional tags for scoped visibility filtering
-                tags_match: How this model's tags are matched against memories when the content
-                    is (re)generated. One of 'any' (match any tag, like recall/reflect), 'all'
-                    (match all tags), 'any_strict', 'all_strict', or 'exact'. If omitted, a tagged
-                    model defaults to 'all_strict' — a memory must carry EVERY one of the model's
-                    tags to be included, which silently filters out memories that only carry a
-                    subset. Pass 'any' when your memories use narrow single-topic tags.
                max_tokens: Maximum tokens for generated content (256-8192, default: 2048)
                trigger_refresh_after_consolidation: If True, automatically refresh this model after memory consolidation. Default: False
            """
@@ -1589,15 +1555,13 @@ def _register_create_mental_model(mcp: FastMCP, memory: MemoryEngine, config: MC
                    return {"error": "No bank_id configured"}

                validation_error = _validate_mental_model_inputs(
-                    name=name, source_query=source_query, max_tokens=max_tokens, tags_match=tags_match
+                    name=name, source_query=source_query, max_tokens=max_tokens
                )
                if validation_error:
                    return {"error": validation_error}

                request_context = _get_request_context(config)
-                trigger: dict[str, Any] = {"refresh_after_consolidation": trigger_refresh_after_consolidation}
-                if tags_match is not None:
-                    trigger["tags_match"] = tags_match
+                trigger = {"refresh_after_consolidation": trigger_refresh_after_consolidation}

                model = await memory.create_mental_model(
                    bank_id=target_bank,
@@ -2279,8 +2243,6 @@ def _register_list_memories(mcp: FastMCP, memory: MemoryEngine, config: MCPTools
            limit: int = 100,
            offset: int = 0,
            bank_id: str | None = None,
-            tags: list[str] | None = None,
-            tags_match: TagsMatch = "any",
        ) -> str:
            """
            Browse stored memories with optional filtering.
@@ -2294,10 +2256,6 @@ def _register_list_memories(mcp: FastMCP, memory: MemoryEngine, config: MCPTools
                limit: Maximum number of results (default: 100)
                offset: Pagination offset (default: 0)
                bank_id: Optional bank (defaults to session bank). Use for cross-bank operations.
-                tags: Optional list of tag names to filter by.
-                tags_match: How to combine tags: 'any' (OR, default) or 'all' (AND)
-                    both also include untagged memories; 'any_strict'/'all_strict'
-                    exclude untagged; 'exact' matches the tag set exactly.
            """
            try:
                target_bank = bank_id or config.bank_id_resolver()
@@ -2310,8 +2268,6 @@ def _register_list_memories(mcp: FastMCP, memory: MemoryEngine, config: MCPTools
                    search_query=q,
                    limit=limit,
                    offset=offset,
-                    tags=tags,
-                    tags_match=tags_match,
                    request_context=_get_request_context(config),
                )
                return json.dumps(result, indent=2, default=str)
@@ -2330,8 +2286,6 @@ def _register_list_memories(mcp: FastMCP, memory: MemoryEngine, config: MCPTools
            q: str | None = None,
            limit: int = 100,
            offset: int = 0,
-            tags: list[str] | None = None,
-            tags_match: TagsMatch = "any",
        ) -> dict:
            """
            Browse stored memories with optional filtering.
@@ -2344,10 +2298,6 @@ def _register_list_memories(mcp: FastMCP, memory: MemoryEngine, config: MCPTools
                q: Optional text search query to filter memories
                limit: Maximum number of results (default: 100)
                offset: Pagination offset (default: 0)
-                tags: Optional list of tag names to filter by.
-                tags_match: How to combine tags: 'any' (OR, default) or 'all' (AND)
-                    both also include untagged memories; 'any_strict'/'all_strict'
-                    exclude untagged; 'exact' matches the tag set exactly.
            """
            try:
                target_bank = config.bank_id_resolver()
@@ -2360,8 +2310,6 @@ def _register_list_memories(mcp: FastMCP, memory: MemoryEngine, config: MCPTools
                    search_query=q,
                    limit=limit,
                    offset=offset,
-                    tags=tags,
-                    tags_match=tags_match,
                    request_context=_get_request_context(config),
                )
                return result
@@ -3197,10 +3145,7 @@ def _register_get_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfi
                profile = await memory.get_bank_profile(
                    target_bank,
                    request_context=_get_request_context(config),
-                    create_if_missing=False,
                )
-                if profile is None:
-                    return json.dumps({"error": f"Bank '{target_bank}' not found"})
                if "disposition" in profile and hasattr(profile["disposition"], "model_dump"):
                    profile["disposition"] = profile["disposition"].model_dump()
                return json.dumps(profile, indent=2, default=str)
@@ -3228,10 +3173,7 @@ def _register_get_bank(mcp: FastMCP, memory: MemoryEngine, config: MCPToolsConfi
                profile = await memory.get_bank_profile(
                    target_bank,
                    request_context=_get_request_context(config),
-                    create_if_missing=False,
                )
-                if profile is None:
-                    return {"error": f"Bank '{target_bank}' not found"}
                if "disposition" in profile and hasattr(profile["disposition"], "model_dump"):
                    profile["disposition"] = profile["disposition"].model_dump()
                return profile
@@ -56,11 +56,6 @@ MIGRATION_LOCK_ID = 123456789
 _alembic_lock = threading.Lock()


-def _set_alembic_main_option(config: Config, name: str, value: str) -> None:
-    """Set an Alembic option without treating URL percent escapes as interpolation."""
-    config.set_main_option(name, value.replace("%", "%%"))
-
-
 def _detect_vector_extension(conn, vector_extension: str = "pgvector") -> str:
    """Validate configured vector extension and preserve Azure DiskANN detection."""
    return detect_vector_extension(conn, vector_extension)
@@ -196,22 +191,22 @@ def _run_migrations_internal(database_url: str, script_location: str, schema: st
    alembic_cfg = Config()

    # Set the script location (where alembic versions are stored)
-    _set_alembic_main_option(alembic_cfg, "script_location", script_location)
+    alembic_cfg.set_main_option("script_location", script_location)

    # Set the database URL
-    _set_alembic_main_option(alembic_cfg, "sqlalchemy.url", database_url)
+    alembic_cfg.set_main_option("sqlalchemy.url", database_url)

    # Configure logging (optional, but helps with debugging)
    # Uses Python's logging system instead of alembic.ini
-    _set_alembic_main_option(alembic_cfg, "prepend_sys_path", ".")
+    alembic_cfg.set_main_option("prepend_sys_path", ".")

    # Set path_separator to avoid deprecation warning
-    _set_alembic_main_option(alembic_cfg, "path_separator", "os")
+    alembic_cfg.set_main_option("path_separator", "os")

    # If targeting a specific schema, pass it to env.py via config
    # env.py will handle setting search_path and version_table_schema
    if schema:
-        _set_alembic_main_option(alembic_cfg, "target_schema", schema)
+        alembic_cfg.set_main_option("target_schema", schema)

    # Run migrations under a process-level lock.  Alembic uses module-level
    # global proxies that are not thread-safe, so concurrent command.upgrade()
@@ -436,8 +431,8 @@ def check_migration_status(

        # Create config programmatically
        alembic_cfg = Config()
-        _set_alembic_main_option(alembic_cfg, "script_location", script_location)
-        _set_alembic_main_option(alembic_cfg, "path_separator", "os")
+        alembic_cfg.set_main_option("script_location", script_location)
+        alembic_cfg.set_main_option("path_separator", "os")

        script = ScriptDirectory.from_config(alembic_cfg)
        head_rev = script.get_current_head()
@@ -284,8 +284,6 @@ class MemoryLink(Base):
    entity = relationship("Entity", back_populates="memory_links")

    __table_args__ = (
-        # Retain writes ``caused_by`` only. Keep the historical causal values
-        # valid so existing rows and transfer archives remain queryable.
        CheckConstraint(
            "link_type IN ('temporal', 'semantic', 'entity', 'causes', 'caused_by', 'enables', 'prevents')",
            name="memory_links_link_type_check",
@@ -2,7 +2,6 @@ from __future__ import annotations

 import asyncio
 import logging
-from dataclasses import dataclass
 from typing import TYPE_CHECKING

 if TYPE_CHECKING:
@@ -154,22 +153,7 @@ async def stop_embedded_postgres() -> None:
        await _default_instance.stop()


-@dataclass(frozen=True)
-class Pg0Url:
-    """Parsed representation of a ``pg0`` embedded-database URL.
-
-    ``username``/``password`` are ``None`` when the URL omits credentials, in
-    which case the pg0 defaults (``hindsight``/``hindsight``) apply.
-    """
-
-    is_pg0: bool
-    instance_name: str | None = None
-    port: int | None = None
-    username: str | None = None
-    password: str | None = None
-
-
-def parse_pg0_url(db_url: str) -> Pg0Url:
+def parse_pg0_url(db_url: str) -> tuple[bool, str | None, int | None]:
    """
    Parse a database URL and check if it's a pg0:// embedded database URL.

@@ -177,47 +161,29 @@ def parse_pg0_url(db_url: str) -> Pg0Url:
    - "pg0" -> default instance "hindsight"
    - "pg0://instance-name" -> named instance
    - "pg0://instance-name:port" -> named instance with explicit port
-    - "pg0://user:pwd@instance-name:port" -> named instance with credentials
-      (``user`` or ``user:pwd``; either half may be present)
    - Any other URL (e.g., postgresql://) -> not a pg0 URL

    Args:
        db_url: The database URL to parse

    Returns:
-        A :class:`Pg0Url`. When ``is_pg0`` is False the remaining fields are None.
+        Tuple of (is_pg0, instance_name, port)
+        - is_pg0: True if this is a pg0 URL
+        - instance_name: The instance name (or None if not pg0)
+        - port: The explicit port (or None for auto-assign)
    """
    if db_url == "pg0":
-        return Pg0Url(is_pg0=True, instance_name="hindsight")
+        return True, "hindsight", None

-    if not db_url.startswith("pg0://"):
-        return Pg0Url(is_pg0=False)
+    if db_url.startswith("pg0://"):
+        url_part = db_url[6:]  # Remove "pg0://"
+        if ":" in url_part:
+            instance_name, port_str = url_part.rsplit(":", 1)
+            return True, instance_name or "hindsight", int(port_str)
+        else:
+            return True, url_part or "hindsight", None

-    url_part = db_url[6:]  # Remove "pg0://"
-
-    # Split optional "user:pwd@" credentials from the "instance:port" host part.
-    # rsplit on the last "@" so passwords may contain "@".
-    username: str | None = None
-    password: str | None = None
-    if "@" in url_part:
-        creds, url_part = url_part.rsplit("@", 1)
-        user_part, sep, pwd_part = creds.partition(":")
-        username = user_part or None
-        password = pwd_part if sep else None
-
-    if ":" in url_part:
-        instance_name, port_str = url_part.rsplit(":", 1)
-        port: int | None = int(port_str)
-    else:
-        instance_name, port = url_part, None
-
-    return Pg0Url(
-        is_pg0=True,
-        instance_name=instance_name or "hindsight",
-        port=port,
-        username=username,
-        password=password,
-    )
+    return False, None, None


 async def resolve_database_url(db_url: str) -> str:
@@ -233,13 +199,8 @@ async def resolve_database_url(db_url: str) -> str:
    Returns:
        The resolved postgresql:// connection URL
    """
-    parsed = parse_pg0_url(db_url)
-    if parsed.is_pg0:
-        kwargs: dict[str, object] = {"name": parsed.instance_name, "port": parsed.port}
-        if parsed.username is not None:
-            kwargs["username"] = parsed.username
-        if parsed.password is not None:
-            kwargs["password"] = parsed.password
-        pg0 = EmbeddedPostgres(**kwargs)
+    is_pg0, instance_name, port = parse_pg0_url(db_url)
+    if is_pg0:
+        pg0 = EmbeddedPostgres(name=instance_name, port=port)
        return await pg0.ensure_running()
    return db_url
@@ -197,11 +197,6 @@ def main():
    shared_pool = max(0, config.worker_max_slots - sum(reservations.values()))
    print(f"  Slot reservations: {reservations_str}")
    print(f"  Shared pool: {shared_pool}")
-    if config.operation_retention_days == 0:
-        print("  Operation retention: disabled (terminal rows and payloads are kept)")
-    else:
-        print(f"  Operation retention: {config.operation_retention_days} days (terminal rows, payloads, and metadata)")
-        print(f"  Operation cleanup batch: {config.operation_cleanup_batch_size} rows/schema/cycle")
    print(f"  HTTP server: {args.http_host}:{args.http_port}")
    print()

@@ -269,7 +264,6 @@ def main():
            max_slots=config.worker_max_slots,
            slot_reservations=config.worker_slot_reservations,
            consolidation_bank_priority=config.worker_consolidation_bank_priority or None,
-            max_retries=config.worker_max_retries,
        )

        # Create the HTTP app for metrics/health
@@ -37,19 +37,6 @@ def _metric_operation_label(operation_type: str | None) -> str:
    return operation_type or "unknown"


-def _updated_row_count(result: Any) -> int:
-    """Extract a row count from backend execute() results."""
-    if isinstance(result, int):
-        return result
-    if isinstance(result, str):
-        try:
-            return int(result.rsplit(" ", 1)[-1])
-        except (TypeError, ValueError):
-            return 0
-    rowcount = getattr(result, "rowcount", None)
-    return rowcount if isinstance(rowcount, int) else 0
-
-
 if TYPE_CHECKING:
    from hindsight_api.engine.db.base import DatabaseBackend, DatabaseConnection
    from hindsight_api.extensions.tenant import TenantExtension
@@ -161,7 +148,6 @@ class WorkerPoller:
        max_slots: int = 10,
        slot_reservations: dict[str, int] | None = None,
        consolidation_bank_priority: dict[str, int] | None = None,
-        max_retries: int = 3,
    ):
        """
        Initialize the worker poller.
@@ -184,8 +170,6 @@ class WorkerPoller:
                Patterns support ``*`` as wildcard. A bare ``*`` key is the catch-all default.
                When set, consolidation tasks are claimed in priority tiers rather than
                pure created_at order. None or empty dict preserves current behavior.
-            max_retries: Maximum retry attempts before a task is marked failed.
-                Must be >= 0. Default 3 (matches DEFAULT_WORKER_MAX_RETRIES).
        """
        self._backend = backend
        self._worker_id = worker_id
@@ -207,7 +191,6 @@ class WorkerPoller:
        self._consolidation_bank_priority: dict[str, int] | None = (
            consolidation_bank_priority if consolidation_bank_priority else None
        )
-        self._max_retries = max(0, max_retries)  # Never negative
        # Cache of which optional PG routines are installed on the server
        # (probed once, memoised for the life of the poller).
        from ..engine.db.optional_routines import OptionalRoutines
@@ -226,8 +209,6 @@ class WorkerPoller:
        # Rotation offset for per-tenant fair claiming. Advances past the last
        # schema we serviced so a busy tenant can't monopolize the poll order.
        self._next_schema_idx: int = 0
-        # Retention cleanup runs outside the claim loop. Keep one task per
-        # poller so maintenance cannot overlap with itself or block slot refill.

    @staticmethod
    def _normalize_poll_schema(schema: str | None) -> str | None:
@@ -523,20 +504,17 @@ class WorkerPoller:
                return result

    async def _mark_completed(self, operation_id: str, schema: str | None):
-        """Mark a processing task as completed, then propagate to parent if needed."""
+        """Mark a task as completed."""
        table = fq_table("async_operations", schema)
        async with self._backend.acquire() as conn:
-            async with conn.transaction():
-                result = await conn.execute(
-                    f"""
-                    UPDATE {table}
-                    SET status = 'completed', completed_at = now(), updated_at = now()
-                    WHERE operation_id = $1 AND status = 'processing'
-                    """,
-                    operation_id,
-                )
-                if _updated_row_count(result):
-                    await self._maybe_update_parent_operation(operation_id, schema, conn)
+            await conn.execute(
+                f"""
+                UPDATE {table}
+                SET status = 'completed', completed_at = now(), updated_at = now()
+                WHERE operation_id = $1
+                """,
+                operation_id,
+            )

    async def _mark_failed(self, operation_id: str, error_message: str, schema: str | None):
        """Mark a task as failed with error message, then propagate to parent if applicable."""
@@ -771,7 +749,6 @@ class WorkerPoller:
                task.task_dict["_schema"] = task.schema
            await self._executor(task.task_dict)
            logger.debug(f"Task {task.operation_id} execution finished")
-            await self._mark_completed(task.operation_id, task.schema)
            terminal_success = True
        except DeferOperation as e:
            # Deferral is not a terminal outcome — do not record a completion.
@@ -801,18 +778,14 @@ class WorkerPoller:

        This handles the case where a worker crashes while processing tasks.
        On startup, we reset any tasks stuck in 'processing' for this worker_id
-        back to 'pending' so they can be picked up again — provided their
-        ``retry_count`` has not reached ``max_retries``. Tasks at/over the
-        limit are moved to 'failed' with an explanatory error message,
-        breaking the infinite loop where a task that kills the worker
-        (OOM, infinite loop) is re-claimed forever.
+        back to 'pending' so they can be picked up again.

        Also recovers batch API operations that were in-flight.

        If tenant_extension is configured, recovers across all tenant schemas.

        Returns:
-            Number of tasks recovered (reset to pending, not including failed)
+            Number of tasks recovered
        """
        schemas = await self._get_schemas()
        total_count = 0
@@ -825,71 +798,20 @@ class WorkerPoller:
                batch_count = await self._recover_batch_operations(schema)
                total_count += batch_count

-                # Then reset normal worker tasks. Crash-interrupted tasks count
-                # toward the retry budget (worker_max_retries / HINDSIGHT_API_WORKER_MAX_RETRIES).
-                # Without this, a task that kills the worker (OOM, infinite loop)
-                # is reset forever: claim → grind → crash → recover → re-claim…
-                # Two separate UPDATEs so their row counts are meaningful:
-                #   1. Tasks under limit → increment retry_count, reset to pending
-                #   2. Tasks at/over limit → move to failed with a clear reason
-                max_retries = self._max_retries
+                # Then reset normal worker tasks
                async with self._backend.acquire() as conn:
-                    # Tasks under the limit: increment retry_count and reset to pending
                    result = await conn.execute(
                        f"""
                        UPDATE {table}
-                        SET status = 'pending', worker_id = NULL, claimed_at = NULL,
-                            retry_count = COALESCE(retry_count, 0) + 1, updated_at = now()
-                        WHERE status = 'processing' AND worker_id = $1
-                          AND result_metadata->>'batch_id' IS NULL
-                          AND COALESCE(retry_count, 0) < $2
+                        SET status = 'pending', worker_id = NULL, claimed_at = NULL, updated_at = now()
+                        WHERE status = 'processing' AND worker_id = $1 AND result_metadata->>'batch_id' IS NULL
                        """,
                        self._worker_id,
-                        max_retries,
-                    )
-                    # Tasks that exceeded the limit: move to failed. RETURNING
-                    # gives us the ids so their parent aggregators can be rolled
-                    # up below — a batch_retain child sub-batch carries
-                    # parent_operation_id (not batch_id) in its metadata, so it IS
-                    # eligible to be failed here, and without propagating that
-                    # terminal state the parent is stranded in 'processing' forever
-                    # (the same crash loop this method fixes, one level up).
-                    failed_rows = await conn.fetch(
-                        f"""
-                        UPDATE {table}
-                        SET status = 'failed', worker_id = NULL, claimed_at = NULL,
-                            error_message = 'exceeded max recovery attempts after crash (retry_count >= {max_retries})',
-                            completed_at = now(), updated_at = now()
-                        WHERE status = 'processing' AND worker_id = $1
-                          AND result_metadata->>'batch_id' IS NULL
-                          AND COALESCE(retry_count, 0) >= $2
-                        RETURNING operation_id
-                        """,
-                        self._worker_id,
-                        max_retries,
                    )

-                # Roll each failed child up to its parent aggregator, one
-                # transaction per child so a single problematic parent can't undo
-                # the others — mirrors the per-task transaction the in-process
-                # _mark_failed path uses. The failing UPDATE above already committed,
-                # so the children stay failed regardless; _maybe_update_parent_operation
-                # no-ops for tasks without a parent_operation_id.
-                for failed_row in failed_rows:
-                    async with self._backend.acquire() as conn:
-                        async with conn.transaction():
-                            await self._maybe_update_parent_operation(str(failed_row["operation_id"]), schema, conn)
-
-                pending_count = int(result.split()[-1]) if result else 0
-                failed_count = len(failed_rows)
-                total_count += pending_count
-                if failed_count > 0:
-                    schema_display = f'"{schema}"' if schema else str(schema)
-                    logger.warning(
-                        f"Worker {self._worker_id} moved {failed_count} tasks to 'failed' "
-                        f"(exceeded {max_retries} recovery attempts in schema "
-                        f"{schema_display})"
-                    )
+                # Parse "UPDATE N" to get count
+                count = int(result.split()[-1]) if result else 0
+                total_count += count
            except Exception as e:
                # Format schema for logging: custom schemas in quotes, None as-is
                schema_display = f'"{schema}"' if schema else str(schema)
@@ -1016,7 +938,6 @@ class WorkerPoller:
                    for task in tasks:
                        await self.execute_task(task)

-                if tasks:
                    # Continue immediately to claim more tasks (if slots available)
                    continue

@@ -51,7 +51,7 @@ dependencies = [
    "anthropic>=0.40.0",
    "typer>=0.9.0",
    "cohere>=5.0.0",
-    "litellm>=1.84.0", # 1.82.7/1.82.8 had a supply chain compromise (yanked); 1.83.0+ also fixes GHSA-jjhc-v7c2-5hh6 / GHSA-53mr-6c8q-9789 / GHSA-pq44-5pcq-4r5g / GHSA-8cjq-wjmh-q42r; 1.84.0 fixes GHSA-4xpc-pv4p-pm3w
+    "litellm>=1.83.14", # 1.82.7/1.82.8 had a supply chain compromise (yanked); 1.83.0+ also fixes GHSA-jjhc-v7c2-5hh6 / GHSA-53mr-6c8q-9789 / GHSA-pq44-5pcq-4r5g / GHSA-8cjq-wjmh-q42r
    "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)
    "winloop>=0.1.0; sys_platform == 'win32'",
@@ -62,7 +62,7 @@ dependencies = [
    "langchain-core>=1.2.22", # Path traversal in legacy load_prompt functions fix
    "langsmith>=0.8.18", # GHSA-f4xh-w4cj-qxq8: arbitrary server-side file read in TracingMiddleware fix (supersedes >=0.6.3 SSRF tracing-header-injection floor)
    "protobuf>=6.33.5", # JSON recursion depth bypass fix
-    "pillow>=12.3.0", # Multiple HIGH image parsing vulnerabilities fixed in 12.3.0
+    "pillow>=12.1.1", # Out-of-bounds write in PSD image loading fix
    "cryptography>=48.0.1", # GHSA-537c-gmf6-5ccf: bundled-OpenSSL OOB read fix needs >=48.0.1. Prior <47 cap (47.0.0 SIGILL on ARM64 Docker/Podman, pyca/cryptography#14733) lifted — 47/48/49 verified importing + RSA sign/verify cleanly on linux/arm64 (Docker on Apple Silicon) and native arm64 macOS; upstream issue closed unconfirmed.
    "filelock>=3.20.1", # TOCTOU race condition fix
    "authlib>=1.6.9", # Account takeover/JWS header injection vulnerability fix
@@ -75,18 +75,16 @@ dependencies = [
    "claude-agent-sdk>=0.2.82",
    "boto3>=1.42.74",
    "croniter>=2.0.0", # Cron parsing for scheduled mental model refresh
-    "json-repair>=0.30.0", # Structural repair of malformed LLM JSON (last-resort parse fallback)
 ]

 [project.optional-dependencies]
 local-ml = [
    # Local ML models for embeddings/reranking
    "sentence-transformers>=3.3.0",
-    "transformers>=5.5.0", # ReDoS fixes; 5.5.0 clears GHSA-fgcw-684q-jj6r (LightGlue RCE)
-    # transformers enforces tokenizers<=0.23.0 with a runtime check, but has
-    # shipped metadata declaring a wider range than it actually enforces. Keep
-    # this cap: without it an in-place upgrade can pull tokenizers 0.23.1 and
-    # break local embeddings/reranker startup. See issue #2055.
+    "transformers>=4.53.0", # Security fixes for ReDoS vulnerabilities
+    # transformers (incl. latest 5.x) hard-requires tokenizers<=0.23.0 via a
+    # runtime check; without this cap an in-place upgrade can pull tokenizers
+    # 0.23.1 and break local embeddings/reranker startup. See issue #2055.
    "tokenizers>=0.22.0,<=0.23.0",
    "torch>=2.6.0", # CVE fix for remote code execution
    "einops>=0.8.2",
@@ -106,7 +104,7 @@ local-llm = [
 local-onnx = [
    # In-process ONNX Runtime embeddings without an Ollama/TEI sidecar
    "onnxruntime>=1.17.0",
-    "transformers>=5.5.0", # 5.5.0 clears GHSA-fgcw-684q-jj6r (LightGlue RCE)
+    "transformers>=4.53.0",
    "tokenizers>=0.22.0,<=0.23.0", # See issue #2055 (transformers caps tokenizers<=0.23.0)
    "huggingface-hub>=0.20.0",
    "numpy>=1.26.0",
@@ -24,33 +24,6 @@ from dotenv import load_dotenv
 # per worker process. Guarded so slim/no-torch environments still collect.
 try:
    import torch  # noqa: F401  # eager one-time init; see comment above
-
-    # Same class of problem, different torch module. transformers' lazy loader
-    # imports `torch._inductor.test_operators` while resolving classes such as
-    # AutoModelForSequenceClassification / GenerationMixin (exercised by the
-    # cross-encoder / reranker tests). That module registers an `_inductor_test`
-    # TORCH_LIBRARY namespace at module-body level, and under pytest-xdist its
-    # body can execute twice, raising "Only a single TORCH_LIBRARY can be used
-    # to register the namespace _inductor_test". The failure surfaces on
-    # whichever shard runs the reranker tests, masked by transformers as a
-    # misleading "sentence-transformers is required for LocalSTEmbeddings"
-    # ImportError. Seed it once here so the later lazy import is a sys.modules
-    # cache hit and the body never re-executes.
-    import torch._inductor.test_operators  # noqa: F401  # see comment above
-
-    # Seed the rest of the native embedding/reranker stack the same way, and for
-    # the same reason. transformers and safetensors/tokenizers ship PyO3/Rust
-    # and C extensions whose module bodies are not safe to execute twice
-    # (safetensors raises "PyO3 modules ... may only be initialized once per
-    # interpreter process"). When these are first imported lazily from inside a
-    # fixture's event loop / sentence-transformers' thread pools, or re-executed
-    # by transformers' lazy-loader retry path, the second init aborts and — like
-    # the torch cases above — is re-raised as a misleading
-    # "sentence-transformers is required" ImportError on the reranker shard.
-    # Importing the whole chain here (single-threaded, at collection time) puts
-    # every submodule in sys.modules so later imports are cache hits.
-    import transformers  # noqa: F401  # seeds safetensors/tokenizers once
-    import sentence_transformers  # noqa: F401
 except ImportError:
    pass

@@ -163,7 +136,7 @@ def pg0_db_url(db_url, tmp_path_factory, worker_id):
    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).is_pg0:
+    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

@@ -171,9 +144,9 @@ def pg0_db_url(db_url, tmp_path_factory, worker_id):
        return db_url

    if db_url:
-        _parsed = _parse_pg0_url(db_url)
-        pg0_instance_name = _parsed.instance_name or DEFAULT_PG0_INSTANCE_NAME
-        pg0_instance_port = _parsed.port or DEFAULT_PG0_PORT
+        _, 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
@@ -624,18 +597,3 @@ async def api_client(memory):
    transport = httpx.ASGITransport(app=app)
    async with httpx.AsyncClient(transport=transport, base_url="http://test") as client:
        yield client
-
-
-def enable_audit_default(memory, enabled: bool) -> None:
-    """Set the deployment-wide default for the hierarchical ``audit_log_enabled``.
-
-    ``audit_log_enabled`` resolves through env -> tenant -> bank, and the
-    ConfigResolver snapshots the global layer at construction time. Tests that
-    want "auditing on by default" therefore have to update that snapshot;
-    flipping ``AuditLogger._enabled`` alone only covers actions with no bank in
-    scope. Per-bank overrides are set with ``resolver.update_bank_config``.
-    """
-    from dataclasses import replace
-
-    resolver = memory._config_resolver
-    resolver._global_config = replace(resolver._global_config, audit_log_enabled=enabled)
@@ -1,49 +0,0 @@
-"""Tests for the admin CLI whole-bank transfer boundary."""
-
-from pathlib import Path
-from unittest.mock import AsyncMock
-
-import pytest
-
-from hindsight_api.admin import cli
-
-
-class _FakeConnection:
-    def __init__(self) -> None:
-        self.closed = False
-
-    async def close(self) -> None:
-        self.closed = True
-
-
-@pytest.mark.asyncio
-async def test_run_export_bank_declares_decoded_json_rows(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
-    """The codec-enabled admin producer must identify its rows as decoded."""
-    connection = _FakeConnection()
-    export_bank = AsyncMock(return_value=b"archive")
-
-    async def fake_admin_connect(db_url: str) -> _FakeConnection:
-        assert db_url == "postgresql://example"
-        return connection
-
-    monkeypatch.setattr(cli, "_admin_connect", fake_admin_connect)
-    monkeypatch.setattr(cli, "export_bank", export_bank)
-
-    output = tmp_path / "bank.zip"
-    size = await cli._run_export_bank(
-        "postgresql://example",
-        "source-bank",
-        output,
-        "tenant_schema",
-        include_history=True,
-    )
-
-    export_bank.assert_awaited_once_with(
-        connection,
-        "source-bank",
-        include_history=True,
-        bank_rows_json_encoding="decoded",
-    )
-    assert output.read_bytes() == b"archive"
-    assert size == len(b"archive")
-    assert connection.closed is True
@@ -1,273 +0,0 @@
-"""Anthropic Message Batches support for the provider batch interface.
-
-The engine's batch path (retain fact extraction, gated on
-``retain_batch_enabled``) speaks the OpenAI batch wire shape: JSONL entries
-with ``custom_id``/``method``/``url``/``body`` going in, and
-``response.body.choices[0].message.content`` (+ OpenAI-keyed ``usage``) coming
-out. ``AnthropicLLM`` translates both directions onto the Message Batches API,
-which bills all token usage at 50% of standard price.
-
-Translation rules mirror the provider's synchronous ``call()`` path:
- system messages fold into the ``system`` param;
- ``max_completion_tokens`` becomes ``max_tokens`` (default 4096);
- ``temperature`` is dropped (the sync path never sends it either — current
-  Claude models reject non-default sampling params);
- ``response_format`` with ``strict=True`` becomes a single forced tool_use
-  tool (native constrained decoding, issue #1002); non-strict injects the
-  schema into the system prompt and expects JSON text back.
-"""
-
-import json
-from types import SimpleNamespace
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-pytestmark = pytest.mark.asyncio
-
-
-def _make_provider():
-    with patch("anthropic.AsyncAnthropic") as mock_client_cls:
-        mock_client_cls.return_value = MagicMock()
-        from hindsight_api.engine.providers.anthropic_llm import AnthropicLLM
-
-        provider = AnthropicLLM(
-            provider="anthropic",
-            api_key="fake-key",
-            base_url="",
-            model="claude-sonnet-5",
-        )
-    provider._client = MagicMock()
-    return provider
-
-
-_SCHEMA = {
-    "type": "object",
-    "properties": {"facts": {"type": "array", "items": {"type": "string"}}},
-    "required": ["facts"],
-}
-
-
-def _openai_request(custom_id: str, *, strict: bool = True, temperature: float | None = 0.1) -> dict:
-    body = {
-        "model": "claude-sonnet-5",
-        "messages": [
-            {"role": "system", "content": "Extract facts."},
-            {"role": "user", "content": f"Text for {custom_id}"},
-        ],
-        "max_completion_tokens": 2000,
-        "response_format": {
-            "type": "json_schema",
-            "json_schema": {"name": "facts", "schema": _SCHEMA, "strict": strict},
-        },
-    }
-    if temperature is not None:
-        body["temperature"] = temperature
-    return {"custom_id": custom_id, "method": "POST", "url": "/v1/chat/completions", "body": body}
-
-
-def _batch(status: str = "in_progress", **counts) -> SimpleNamespace:
-    defaults = {"processing": 0, "succeeded": 0, "errored": 0, "canceled": 0, "expired": 0}
-    defaults.update(counts)
-    return SimpleNamespace(
-        id="msgbatch_test1",
-        processing_status=status,
-        created_at="2026-07-08T00:00:00Z",
-        ended_at="2026-07-08T00:30:00Z" if status == "ended" else None,
-        request_counts=SimpleNamespace(**defaults),
-    )
-
-
-class _AsyncIter:
-    def __init__(self, items):
-        self._items = list(items)
-
-    def __aiter__(self):
-        self._iter = iter(self._items)
-        return self
-
-    async def __anext__(self):
-        try:
-            return next(self._iter)
-        except StopIteration:
-            raise StopAsyncIteration from None
-
-
-def _succeeded_entry(custom_id: str, tool_input: dict) -> SimpleNamespace:
-    block = SimpleNamespace(type="tool_use", name="structured_response", input=tool_input, text=None)
-    message = SimpleNamespace(
-        content=[block],
-        usage=SimpleNamespace(input_tokens=100, output_tokens=40, cache_read_input_tokens=0),
-        stop_reason="tool_use",
-    )
-    return SimpleNamespace(custom_id=custom_id, result=SimpleNamespace(type="succeeded", message=message))
-
-
-def _errored_entry(custom_id: str) -> SimpleNamespace:
-    error = SimpleNamespace(type="invalid_request", message="bad request")
-    return SimpleNamespace(custom_id=custom_id, result=SimpleNamespace(type="errored", error=error))
-
-
-async def test_supports_batch_api():
-    provider = _make_provider()
-    assert await provider.supports_batch_api() is True
-
-
-async def test_submit_batch_translates_openai_requests():
-    provider = _make_provider()
-    provider._client.messages.batches.create = AsyncMock(return_value=_batch("in_progress", processing=2))
-
-    requests = [_openai_request("chunk_0"), _openai_request("chunk_1")]
-    metadata = await provider.submit_batch(requests)
-
-    provider._client.messages.batches.create.assert_awaited_once()
-    submitted = provider._client.messages.batches.create.await_args.kwargs["requests"]
-    assert [r["custom_id"] for r in submitted] == ["chunk_0", "chunk_1"]
-
-    params = submitted[0]["params"]
-    assert params["model"] == "claude-sonnet-5"
-    # System message folded into the system param (as the cached block list
-    # the sync call() path sends), not left in messages.
-    assert "Extract facts." in params["system"][0]["text"]
-    assert all(m["role"] != "system" for m in params["messages"])
-    assert params["messages"] == [{"role": "user", "content": "Text for chunk_0"}]
-    assert params["max_tokens"] == 2000
-    # temperature is dropped, mirroring the sync call() path.
-    assert "temperature" not in params
-    # strict=True → forced tool_use (native constrained decoding).
-    assert params["tools"][0]["input_schema"] == _SCHEMA
-    assert params["tool_choice"] == {"type": "tool", "name": "structured_response"}
-
-    assert metadata["batch_id"] == "msgbatch_test1"
-    assert metadata["status"] == "in_progress"
-    assert metadata["request_count"] == 2
-
-
-async def test_submit_batch_non_strict_schema_injects_into_system():
-    provider = _make_provider()
-    provider._client.messages.batches.create = AsyncMock(return_value=_batch("in_progress", processing=1))
-
-    await provider.submit_batch([_openai_request("chunk_0", strict=False)])
-
-    params = provider._client.messages.batches.create.await_args.kwargs["requests"][0]["params"]
-    assert "tools" not in params
-    assert "tool_choice" not in params
-    # Schema is injected into the system prompt for JSON-text output —
-    # inside the cached block, so the injection is part of the cached prefix.
-    assert "facts" in params["system"][0]["text"]
-    assert "valid JSON" in params["system"][0]["text"]
-
-
-async def test_submit_batch_system_carries_cache_control_marker():
-    """Batch items share their system prompt, so it gets the cache marker.
-
-    Mirrors the sync ``call()`` one-shot rule: system is the sole cache
-    breakpoint. Within a Message Batch every request carries the same fact-
-    extraction system prompt, so the first request's cache write serves the
-    rest as best-effort reads (and stacks with the 50% batch discount).
-    """
-    provider = _make_provider()
-    provider._client.messages.batches.create = AsyncMock(return_value=_batch("in_progress", processing=1))
-
-    await provider.submit_batch([_openai_request("chunk_0")])
-
-    params = provider._client.messages.batches.create.await_args.kwargs["requests"][0]["params"]
-    assert params["system"] == [{"type": "text", "text": "Extract facts.", "cache_control": {"type": "ephemeral"}}]
-    # One-shot items: no end-marker on messages (that breakpoint only pays
-    # off on the sync tool loop, where the next iteration reads it back).
-    assert "cache_control" not in json.dumps(params["messages"])
-
-
-async def test_submit_batch_without_system_message_sends_no_system_param():
-    provider = _make_provider()
-    provider._client.messages.batches.create = AsyncMock(return_value=_batch("in_progress", processing=1))
-
-    body = {
-        "model": "claude-sonnet-5",
-        "messages": [{"role": "user", "content": "no system here"}],
-        "max_completion_tokens": 1000,
-    }
-    request = {"custom_id": "chunk_0", "method": "POST", "url": "/v1/chat/completions", "body": body}
-    await provider.submit_batch([request])
-
-    params = provider._client.messages.batches.create.await_args.kwargs["requests"][0]["params"]
-    assert "system" not in params
-    assert "cache_control" not in json.dumps(params["messages"])
-
-
-async def test_get_batch_status_in_progress():
-    provider = _make_provider()
-    provider._client.messages.batches.retrieve = AsyncMock(
-        return_value=_batch("in_progress", processing=3, succeeded=1)
-    )
-
-    status = await provider.get_batch_status("msgbatch_test1")
-
-    assert status["batch_id"] == "msgbatch_test1"
-    assert status["status"] == "in_progress"
-    assert status["request_counts"]["total"] == 4
-    assert status["request_counts"]["completed"] == 1
-
-
-async def test_get_batch_status_ended_maps_to_completed():
-    """The engine's poll loop breaks on the OpenAI-vocabulary status 'completed'."""
-    provider = _make_provider()
-    provider._client.messages.batches.retrieve = AsyncMock(return_value=_batch("ended", succeeded=3, errored=1))
-
-    status = await provider.get_batch_status("msgbatch_test1")
-
-    assert status["status"] == "completed"
-    assert status["request_counts"]["total"] == 4
-    assert status["request_counts"]["completed"] == 4
-    assert status["request_counts"]["failed"] == 1
-    assert status["completed_at"] == "2026-07-08T00:30:00Z"
-
-
-async def test_retrieve_batch_results_translates_to_openai_shape():
-    provider = _make_provider()
-    provider._client.messages.batches.retrieve = AsyncMock(return_value=_batch("ended", succeeded=1, errored=1))
-    entries = [
-        _succeeded_entry("chunk_0", {"facts": ["Alice is an engineer."]}),
-        _errored_entry("chunk_1"),
-    ]
-    provider._client.messages.batches.results = AsyncMock(return_value=_AsyncIter(entries))
-
-    results = await provider.retrieve_batch_results("msgbatch_test1")
-
-    by_id = {r["custom_id"]: r for r in results}
-    ok = by_id["chunk_0"]
-    body = ok["response"]["body"]
-    # The engine reads choices[0].message.content and json.loads() it.
-    assert json.loads(body["choices"][0]["message"]["content"]) == {"facts": ["Alice is an engineer."]}
-    # Usage arrives under the OpenAI key names the engine sums.
-    assert body["usage"] == {"prompt_tokens": 100, "completion_tokens": 40, "total_tokens": 140}
-
-    failed = by_id["chunk_1"]
-    assert failed["error"]
-    assert "response" not in failed
-
-
-async def test_retrieve_batch_results_text_content_passthrough():
-    """Non-strict requests come back as text blocks; concatenate them as content."""
-    provider = _make_provider()
-    provider._client.messages.batches.retrieve = AsyncMock(return_value=_batch("ended", succeeded=1))
-    text_block = SimpleNamespace(type="text", text='{"facts": []}')
-    message = SimpleNamespace(
-        content=[text_block],
-        usage=SimpleNamespace(input_tokens=10, output_tokens=5, cache_read_input_tokens=0),
-        stop_reason="end_turn",
-    )
-    entry = SimpleNamespace(custom_id="chunk_0", result=SimpleNamespace(type="succeeded", message=message))
-    provider._client.messages.batches.results = AsyncMock(return_value=_AsyncIter([entry]))
-
-    results = await provider.retrieve_batch_results("msgbatch_test1")
-
-    assert results[0]["response"]["body"]["choices"][0]["message"]["content"] == '{"facts": []}'
-
-
-async def test_retrieve_batch_results_raises_when_not_ended():
-    provider = _make_provider()
-    provider._client.messages.batches.retrieve = AsyncMock(return_value=_batch("in_progress", processing=2))
-
-    with pytest.raises(ValueError, match="not completed"):
-        await provider.retrieve_batch_results("msgbatch_test1")
@@ -1,181 +0,0 @@
-"""Anthropic prompt caching via inline cache_control markers.
-
-``LLMInterface.get_or_create_cached_prefix`` documents Anthropic as an
-"inline-marker provider": rather than returning an explicit cache handle, the
-provider marks the reusable prefix inside ``call`` / ``call_with_tools`` with
-``cache_control`` breakpoints. Cache reads bill at ~10% of the base input
-price; a marker below the model's minimum cacheable prefix is silently
-ignored by the API (no premium), so marking is safe unconditionally.
-
-Two breakpoints (of the 4 allowed):
- the system prompt, in both entry points — it is stable per scope (fact
-  extraction reuses it across every chunk; reflect/consolidation put their
-  stable instructions there), so tools+system cache across calls;
- the last message content block, in ``call_with_tools`` only — the reflect
-  agent loop resends the whole growing conversation each iteration, so each
-  request's end-marker becomes the next iteration's cache read point.
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from pydantic import BaseModel
-
-pytestmark = pytest.mark.asyncio
-
-EPHEMERAL = {"type": "ephemeral"}
-
-
-def _make_provider():
-    with patch("anthropic.AsyncAnthropic") as mock_client_cls:
-        mock_client_cls.return_value = MagicMock()
-        from hindsight_api.engine.providers.anthropic_llm import AnthropicLLM
-
-        provider = AnthropicLLM(
-            provider="anthropic",
-            api_key="fake-key",
-            base_url="",
-            model="claude-sonnet-5",
-        )
-    provider._client = MagicMock()
-    return provider
-
-
-def _text_response(text: str = "ok"):
-    block = MagicMock()
-    block.type = "text"
-    block.text = text
-    resp = MagicMock()
-    resp.content = [block]
-    resp.usage = MagicMock(input_tokens=10, output_tokens=2, cache_read_input_tokens=0)
-    resp.stop_reason = "end_turn"
-    return resp
-
-
-def _tool_response():
-    resp = MagicMock()
-    resp.content = []
-    resp.usage = MagicMock(input_tokens=10, output_tokens=2, cache_read_input_tokens=0)
-    resp.stop_reason = "end_turn"
-    return resp
-
-
-class _Out(BaseModel):
-    facts: list[str]
-
-
-async def test_call_marks_system_prompt_for_caching():
-    provider = _make_provider()
-    provider._client.messages.create = AsyncMock(return_value=_text_response())
-
-    with patch("hindsight_api.engine.providers.anthropic_llm.get_metrics_collector"):
-        await provider.call(
-            messages=[
-                {"role": "system", "content": "Stable extraction instructions."},
-                {"role": "user", "content": "Chunk text."},
-            ],
-            scope="test",
-            max_retries=0,
-        )
-
-    params = provider._client.messages.create.await_args.kwargs
-    assert params["system"] == [{"type": "text", "text": "Stable extraction instructions.", "cache_control": EPHEMERAL}]
-    # User messages are untouched in call() — one-shot calls share no
-    # conversation prefix with each other, only the system prompt.
-    assert params["messages"] == [{"role": "user", "content": "Chunk text."}]
-
-
-async def test_call_non_strict_schema_lands_inside_cached_system_block():
-    """Schema injection happens before marking, so the marked block includes it."""
-    provider = _make_provider()
-    provider._client.messages.create = AsyncMock(return_value=_text_response('{"facts": []}'))
-
-    with patch("hindsight_api.engine.providers.anthropic_llm.get_metrics_collector"):
-        await provider.call(
-            messages=[
-                {"role": "system", "content": "Extract."},
-                {"role": "user", "content": "Text."},
-            ],
-            response_format=_Out,
-            scope="test",
-            max_retries=0,
-        )
-
-    params = provider._client.messages.create.await_args.kwargs
-    assert len(params["system"]) == 1
-    system_block = params["system"][0]
-    assert system_block["cache_control"] == EPHEMERAL
-    assert "Extract." in system_block["text"]
-    assert "valid JSON" in system_block["text"]
-
-
-async def test_call_without_system_prompt_sends_no_system_param():
-    provider = _make_provider()
-    provider._client.messages.create = AsyncMock(return_value=_text_response())
-
-    with patch("hindsight_api.engine.providers.anthropic_llm.get_metrics_collector"):
-        await provider.call(
-            messages=[{"role": "user", "content": "hi"}],
-            scope="test",
-            max_retries=0,
-        )
-
-    assert "system" not in provider._client.messages.create.await_args.kwargs
-
-
-async def test_call_with_tools_marks_system_and_last_message():
-    provider = _make_provider()
-    provider._client.messages.create = AsyncMock(return_value=_tool_response())
-
-    with patch("hindsight_api.engine.providers.anthropic_llm.get_metrics_collector"):
-        await provider.call_with_tools(
-            messages=[
-                {"role": "system", "content": "Reflect agent instructions."},
-                {"role": "user", "content": "Question?"},
-                {"role": "assistant", "content": "Working on it."},
-                {"role": "user", "content": "Latest turn."},
-            ],
-            tools=[{"function": {"name": "recall", "description": "d", "parameters": {"type": "object"}}}],
-            max_retries=0,
-        )
-
-    params = provider._client.messages.create.await_args.kwargs
-    assert params["system"] == [{"type": "text", "text": "Reflect agent instructions.", "cache_control": EPHEMERAL}]
-
-    messages = params["messages"]
-    # Earlier messages carry no markers — only the final block gets one, so
-    # the next iteration of the agent loop reads the whole prefix from cache.
-    assert messages[0] == {"role": "user", "content": "Question?"}
-    assert messages[1] == {"role": "assistant", "content": "Working on it."}
-    assert messages[2]["content"] == [{"type": "text", "text": "Latest turn.", "cache_control": EPHEMERAL}]
-
-
-async def test_call_with_tools_marks_last_block_of_tool_result_message():
-    """Tool-result turns arrive as block lists; the marker goes on the last block."""
-    provider = _make_provider()
-    provider._client.messages.create = AsyncMock(return_value=_tool_response())
-
-    with patch("hindsight_api.engine.providers.anthropic_llm.get_metrics_collector"):
-        await provider.call_with_tools(
-            messages=[
-                {"role": "user", "content": "Question?"},
-                {
-                    "role": "assistant",
-                    "tool_calls": [
-                        {"id": "t1", "function": {"name": "recall", "arguments": "{}"}},
-                        {"id": "t2", "function": {"name": "recall", "arguments": "{}"}},
-                    ],
-                },
-                {"role": "tool", "tool_call_id": "t1", "content": "result one"},
-                {"role": "tool", "tool_call_id": "t2", "content": "result two"},
-            ],
-            tools=[{"function": {"name": "recall", "description": "d", "parameters": {"type": "object"}}}],
-            max_retries=0,
-        )
-
-    messages = provider._client.messages.create.await_args.kwargs["messages"]
-    last_blocks = messages[-1]["content"]
-    assert last_blocks[-1]["type"] == "tool_result"
-    assert last_blocks[-1]["cache_control"] == EPHEMERAL
-    # The earlier tool-result message is unmarked.
-    assert all("cache_control" not in block for block in messages[-2]["content"])
@@ -107,8 +107,5 @@ async def test_non_strict_keeps_text_injection_fallback():
        )
    kwargs = provider._client.messages.create.call_args.kwargs
    assert "tools" not in kwargs  # no forced tool when not strict
-    # system is a cache_control-marked block list; the schema text-injection
-    # lands inside the (single) block.
-    system_text = "".join(block["text"] for block in (kwargs.get("system") or []))
-    assert "valid JSON matching this schema" in system_text
+    assert "valid JSON matching this schema" in (kwargs.get("system") or "")
    assert isinstance(result, _Decision)
@@ -2,7 +2,6 @@

 import asyncio
 import json
-import os
 import uuid

 import pytest
@@ -516,259 +515,6 @@ async def test_retain_outcome_metadata_records_zero_counts(memory, request_conte
    assert "extraction_errors_sample" not in parent["result_metadata"]


-@pytest.mark.asyncio
-async def test_all_degenerate_facts_still_persist_document_chunks(memory, request_context, monkeypatch):
-    """Filtering every extracted fact must not turn an extracted chunk into the zero-extraction fast path."""
-    from hindsight_api.engine.response_models import TokenUsage
-    from hindsight_api.engine.retain import fact_extraction
-    from hindsight_api.engine.retain.types import ChunkMetadata, ExtractedFact
-
-    async def degenerate_extract_facts_from_contents(contents, *_args, **_kwargs):
-        return (
-            [ExtractedFact(fact_text="...", fact_type="world", content_index=0, chunk_index=0)],
-            [ChunkMetadata(chunk_text=contents[0].content, fact_count=1, content_index=0, chunk_index=0)],
-            TokenUsage(),
-        )
-
-    monkeypatch.setattr(fact_extraction, "extract_facts_from_contents", degenerate_extract_facts_from_contents)
-
-    bank_id = f"test_all_degenerate_{uuid.uuid4().hex[:8]}"
-    document_id = "all-degenerate-document"
-    try:
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="A source chunk whose only extracted fact is rejected.",
-            document_id=document_id,
-            request_context=request_context,
-        )
-
-        chunks = await memory.list_document_chunks(bank_id, document_id, limit=10, request_context=request_context)
-        units = await memory.list_memory_units(bank_id, request_context=request_context)
-        assert [chunk["chunk_index"] for chunk in chunks["items"]] == [0]
-        assert units["total"] == 0
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_streaming_offsets_chunk_local_causal_fact_indices(memory, request_context, monkeypatch):
-    """Causal targets from independently extracted chunks must stay within their source chunk."""
-    from hindsight_api.engine.response_models import TokenUsage
-    from hindsight_api.engine.retain import fact_extraction
-    from hindsight_api.engine.retain.types import CausalRelation, ChunkMetadata, ExtractedFact
-
-    chunks = ["first-streaming-chunk", "second-streaming-chunk"]
-    monkeypatch.setattr(fact_extraction, "chunk_text", lambda *_args, **_kwargs: chunks)
-
-    async def extract_chunk_facts(contents, *_args, **_kwargs):
-        chunk_text = contents[0].content
-        return (
-            [
-                ExtractedFact(fact_text=f"{chunk_text} cause", fact_type="world", chunk_index=0),
-                ExtractedFact(
-                    fact_text=f"{chunk_text} effect",
-                    fact_type="world",
-                    chunk_index=0,
-                    causal_relations=[CausalRelation(relation_type="caused_by", target_fact_index=0)],
-                ),
-            ],
-            [ChunkMetadata(chunk_text=chunk_text, fact_count=2, content_index=0, chunk_index=0)],
-            TokenUsage(),
-        )
-
-    monkeypatch.setattr(fact_extraction, "extract_facts_from_contents", extract_chunk_facts)
-
-    bank_id = f"test_streaming_causal_{uuid.uuid4().hex[:8]}"
-    try:
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Content is replaced by the deterministic chunk_text stub.",
-            document_id="streaming-causal-document",
-            request_context=request_context,
-        )
-
-        pool = await memory._get_pool()
-        rows = await pool.fetch(
-            """
-            SELECT source.text AS source_text, target.text AS target_text
-            FROM memory_links links
-            JOIN memory_units source ON source.id = links.from_unit_id
-            JOIN memory_units target ON target.id = links.to_unit_id
-            WHERE links.bank_id = $1 AND links.link_type = 'caused_by'
-            """,
-            bank_id,
-        )
-        assert {(row["source_text"], row["target_text"]) for row in rows} == {
-            (f"{chunk} effect", f"{chunk} cause") for chunk in chunks
-        }
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-@pytest.mark.asyncio
-async def test_degenerate_fact_preserves_later_chunk_provenance(memory, request_context, monkeypatch):
-    """A rejected degenerate fact must not shift chunk provenance onto a later chunk's survivor.
-
-    Regression for #2794: filtering only ``processed_facts`` left ``extracted_facts``
-    full-length, so the consumer's ``zip(batch_extracted, batch_processed)`` paired
-    every survivor after a rejected fact with the wrong extracted fact — assigning it
-    the wrong chunk_id.
-
-    Each of the two chunks emits [real, degenerate]. After the first (real, degenerate)
-    pair the zip is off-by-one for the rest of the batch, so *both* surviving real facts
-    collapse onto whichever chunk sorted first — regardless of the nondeterministic
-    producer completion order. The fix filters both lists in lockstep per chunk, so each
-    real fact keeps its own chunk_index.
-    """
-    from hindsight_api.engine.response_models import TokenUsage
-    from hindsight_api.engine.retain import fact_extraction
-    from hindsight_api.engine.retain.types import ChunkMetadata, ExtractedFact
-
-    chunks = ["chunk-zero-source", "chunk-one-source"]
-    monkeypatch.setattr(fact_extraction, "chunk_text", lambda *_args, **_kwargs: chunks)
-
-    real_fact_by_chunk = {chunks[0]: "chunk zero real fact", chunks[1]: "chunk one real fact"}
-
-    async def extract_chunk_facts(contents, *_args, **_kwargs):
-        chunk_text = contents[0].content
-        facts = [
-            ExtractedFact(fact_text=real_fact_by_chunk[chunk_text], fact_type="world", chunk_index=0),
-            ExtractedFact(fact_text="...", fact_type="world", chunk_index=0),
-        ]
-        return (
-            facts,
-            [ChunkMetadata(chunk_text=chunk_text, fact_count=len(facts), content_index=0, chunk_index=0)],
-            TokenUsage(),
-        )
-
-    monkeypatch.setattr(fact_extraction, "extract_facts_from_contents", extract_chunk_facts)
-
-    bank_id = f"test_degen_provenance_{uuid.uuid4().hex[:8]}"
-    try:
-        await memory.retain_async(
-            bank_id=bank_id,
-            content="Content is replaced by the deterministic chunk_text stub.",
-            document_id="degen-provenance-document",
-            request_context=request_context,
-        )
-
-        pool = await memory._get_pool()
-        rows = await pool.fetch(
-            """
-            SELECT units.text AS fact_text, chunks.chunk_index AS chunk_index
-            FROM memory_units units
-            JOIN chunks ON chunks.chunk_id = units.chunk_id
-            WHERE units.bank_id = $1
-            """,
-            bank_id,
-        )
-        assert {(row["fact_text"], row["chunk_index"]) for row in rows} == {
-            ("chunk zero real fact", 0),
-            ("chunk one real fact", 1),
-        }
-    finally:
-        await memory.delete_bank(bank_id, request_context=request_context)
-
-
-async def _seed_retain_op_with_errors(pool, bank_id: str, error_count: int) -> uuid.UUID:
-    """Insert a pending retain operation whose outcome metadata records extraction errors."""
-    operation_id = uuid.uuid4()
-    await pool.execute(
-        """
-        INSERT INTO async_operations (operation_id, bank_id, operation_type, result_metadata, status)
-        VALUES ($1, $2, $3, $4, $5)
-        """,
-        operation_id,
-        bank_id,
-        "retain",
-        json.dumps(
-            {
-                "unit_ids_count": 3,
-                "extraction_errors_count": error_count,
-                "extraction_errors_sample": ["chunk 2 failed to parse"],
-            }
-        ),
-        "pending",
-    )
-    return operation_id
-
-
-async def _op_row(pool, operation_id: uuid.UUID):
-    return await pool.fetchrow(
-        "SELECT status, error_message FROM async_operations WHERE operation_id = $1",
-        operation_id,
-    )
-
-
-@pytest.mark.asyncio
-async def test_completion_marks_failed_when_flag_on_and_errors_present(memory):
-    """With the escape hatch on, a retain that dropped facts ends 'failed' (issue #2700)."""
-    from hindsight_api.config import ENV_FAIL_ON_EXTRACTION_ERRORS, clear_config_cache
-
-    bank_id = "test_fail_on_extraction_errors_on"
-    pool = await memory._get_pool()
-    await _ensure_bank(pool, bank_id)
-    operation_id = await _seed_retain_op_with_errors(pool, bank_id, error_count=2)
-
-    os.environ[ENV_FAIL_ON_EXTRACTION_ERRORS] = "true"
-    clear_config_cache()
-    try:
-        await memory._mark_operation_completed(str(operation_id))
-    finally:
-        del os.environ[ENV_FAIL_ON_EXTRACTION_ERRORS]
-        clear_config_cache()
-
-    row = await _op_row(pool, operation_id)
-    assert row["status"] == "failed"
-    assert row["error_message"] is not None
-    assert "2" in row["error_message"]
-    assert "extraction error" in row["error_message"].lower()
-
-
-@pytest.mark.asyncio
-async def test_completion_stays_completed_when_flag_off(memory):
-    """Default behavior is preserved: extraction errors still complete the operation."""
-    from hindsight_api.config import ENV_FAIL_ON_EXTRACTION_ERRORS, clear_config_cache
-
-    bank_id = "test_fail_on_extraction_errors_off"
-    pool = await memory._get_pool()
-    await _ensure_bank(pool, bank_id)
-    operation_id = await _seed_retain_op_with_errors(pool, bank_id, error_count=2)
-
-    os.environ.pop(ENV_FAIL_ON_EXTRACTION_ERRORS, None)
-    clear_config_cache()
-    try:
-        await memory._mark_operation_completed(str(operation_id))
-    finally:
-        clear_config_cache()
-
-    row = await _op_row(pool, operation_id)
-    assert row["status"] == "completed"
-    assert row["error_message"] is None
-
-
-@pytest.mark.asyncio
-async def test_completion_completed_when_flag_on_but_no_errors(memory):
-    """The flag only fails operations that actually accumulated extraction errors."""
-    from hindsight_api.config import ENV_FAIL_ON_EXTRACTION_ERRORS, clear_config_cache
-
-    bank_id = "test_fail_on_extraction_errors_none"
-    pool = await memory._get_pool()
-    await _ensure_bank(pool, bank_id)
-    operation_id = await _seed_retain_op_with_errors(pool, bank_id, error_count=0)
-
-    os.environ[ENV_FAIL_ON_EXTRACTION_ERRORS] = "true"
-    clear_config_cache()
-    try:
-        await memory._mark_operation_completed(str(operation_id))
-    finally:
-        del os.environ[ENV_FAIL_ON_EXTRACTION_ERRORS]
-        clear_config_cache()
-
-    row = await _op_row(pool, operation_id)
-    assert row["status"] == "completed"
-
-
@pytest.mark.asyncio
 async def test_retain_records_user_provided_document_ids(memory, request_context):
    """User-supplied document_ids land in child op result_metadata.document_ids."""
@@ -13,19 +13,14 @@ import pytest_asyncio

 from hindsight_api.api import create_app
 from hindsight_api.config import get_config
-from tests.conftest import enable_audit_default


@pytest_asyncio.fixture
 async def audit_api_client(memory):
-    """Create a test client with audit logging enabled deployment-wide."""
-    # audit_log_enabled is hierarchical, so the effective value for a
-    # bank-scoped action comes from the ConfigResolver, not the logger's own
-    # flag. Set both so this fixture models "enabled by default, no per-bank
-    # override" — the logger flag covers actions with no bank in scope.
+    """Create a test client with audit logging enabled."""
+    # Enable audit logging on the memory engine's audit logger
    memory._audit_logger._enabled = True
    memory._audit_logger._allowed_actions = None  # All actions
-    enable_audit_default(memory, True)

    app = create_app(memory, initialize_memory=False)
    transport = httpx.ASGITransport(app=app)
@@ -402,7 +397,6 @@ async def test_audit_log_action_allowlist(memory):
    """Test that only allowed actions are audited when allowlist is set."""
    memory._audit_logger._enabled = True
    memory._audit_logger._allowed_actions = frozenset({"recall"})  # Only audit recall
-    enable_audit_default(memory, True)

    app = create_app(memory, initialize_memory=False)
    transport = httpx.ASGITransport(app=app)
@@ -1,220 +0,0 @@
-"""Tests for the per-bank ``audit_log_enabled`` override.
-
-``audit_log_enabled`` resolves through env -> tenant -> bank, so a deployment
-can audit some banks and not others. These tests cover both directions of the
-override (a bank opting IN when the default is off, and a bank opting OUT when
-the default is on), plus the fallbacks that decide behaviour when there is no
-bank in scope or config resolution fails.
-"""
-
-import asyncio
-from datetime import datetime
-
-import httpx
-import pytest
-import pytest_asyncio
-
-from hindsight_api.api import create_app
-from hindsight_api.engine.audit import AuditLogger
-from tests.conftest import enable_audit_default
-
-# Audit writes are fire-and-forget; give the background task room to land.
-_AUDIT_SETTLE_SECONDS = 1.0
-
-
-@pytest_asyncio.fixture
-async def client(memory):
-    """HTTP client whose audit allowlist is open (all actions auditable)."""
-    memory._audit_logger._allowed_actions = None
-    app = create_app(memory, initialize_memory=False)
-    transport = httpx.ASGITransport(app=app)
-    async with httpx.AsyncClient(transport=transport, base_url="http://test") as c:
-        yield c
-
-
-async def _recall(client, bank_id: str) -> None:
-    r = await client.post(
-        f"/v1/default/banks/{bank_id}/memories/recall",
-        json={"query": "per-bank audit test"},
-    )
-    assert r.status_code == 200, r.text
-    await asyncio.sleep(_AUDIT_SETTLE_SECONDS)
-
-
-async def _audited_actions(client, bank_id: str) -> list[str]:
-    r = await client.get(f"/v1/default/banks/{bank_id}/audit-logs")
-    assert r.status_code == 200, r.text
-    return [e["action"] for e in r.json()["items"]]
-
-
-def _bank(prefix: str) -> str:
-    return f"{prefix}_{datetime.now().timestamp()}"
-
-
-# ── the override, both directions ──────────────────────────────────────────
-
-
-@pytest.mark.asyncio
-async def test_bank_opts_in_when_default_off(client, memory):
-    """A bank override of True audits even though the deployment default is off."""
-    enable_audit_default(memory, False)
-    memory._audit_logger._enabled = False
-    bank_id = _bank("audit_optin")
-
-    await client.put(f"/v1/default/banks/{bank_id}", json={})
-    await memory._config_resolver.update_bank_config(bank_id, {"audit_log_enabled": True})
-
-    await _recall(client, bank_id)
-    assert "recall" in await _audited_actions(client, bank_id)
-
-
-@pytest.mark.asyncio
-async def test_bank_opts_out_when_default_on(client, memory):
-    """A bank override of False suppresses auditing that is otherwise on."""
-    enable_audit_default(memory, True)
-    memory._audit_logger._enabled = True
-    bank_id = _bank("audit_optout")
-
-    await client.put(f"/v1/default/banks/{bank_id}", json={})
-    await memory._config_resolver.update_bank_config(bank_id, {"audit_log_enabled": False})
-
-    await _recall(client, bank_id)
-    # The create_bank above was audited (it ran before the override existed),
-    # so assert on the action the override was meant to suppress.
-    assert "recall" not in await _audited_actions(client, bank_id)
-
-
-@pytest.mark.asyncio
-async def test_banks_are_independent(client, memory):
-    """One bank's override does not leak into another bank."""
-    enable_audit_default(memory, False)
-    memory._audit_logger._enabled = False
-    audited, quiet = _bank("audit_on"), _bank("audit_off")
-
-    for b in (audited, quiet):
-        await client.put(f"/v1/default/banks/{b}", json={})
-    await memory._config_resolver.update_bank_config(audited, {"audit_log_enabled": True})
-
-    await _recall(client, audited)
-    await _recall(client, quiet)
-
-    assert "recall" in await _audited_actions(client, audited)
-    assert await _audited_actions(client, quiet) == []
-
-
-@pytest.mark.asyncio
-async def test_no_override_uses_deployment_default(client, memory):
-    """A bank with no explicit override follows the deployment default."""
-    enable_audit_default(memory, True)
-    memory._audit_logger._enabled = True
-    bank_id = _bank("audit_inherit")
-
-    await client.put(f"/v1/default/banks/{bank_id}", json={})
-    await _recall(client, bank_id)
-    assert "recall" in await _audited_actions(client, bank_id)
-
-
-# ── AuditLogger decision logic (no DB) ─────────────────────────────────────
-
-
-def _logger(*, enabled: bool, resolver=None, actions: list[str] | None = None) -> AuditLogger:
-    return AuditLogger(
-        pool_getter=lambda: None,
-        schema_getter=lambda: "public",
-        enabled=enabled,
-        allowed_actions=actions or [],
-        bank_enabled_resolver=resolver,
-    )
-
-
-@pytest.mark.asyncio
-async def test_allowlist_short_circuits_before_resolution():
-    """A disallowed action never triggers a per-bank lookup."""
-    calls: list[str] = []
-
-    async def resolver(bank_id, context=None):
-        calls.append(bank_id)
-        return True
-
-    log = _logger(enabled=True, resolver=resolver, actions=["recall"])
-    assert await log.should_log("reflect", "b1") is False
-    assert calls == [], "allowlist must short-circuit before resolving bank config"
-
-
-@pytest.mark.asyncio
-async def test_no_bank_in_scope_uses_global_default():
-    """With no bank_id there is nothing to resolve, so the global value decides."""
-
-    async def resolver(bank_id, context=None):
-        raise AssertionError("must not resolve without a bank_id")
-
-    assert await _logger(enabled=True, resolver=resolver).should_log("recall", None) is True
-    assert await _logger(enabled=False, resolver=resolver).should_log("recall", None) is False
-
-
-@pytest.mark.asyncio
-async def test_resolution_failure_falls_back_to_global_default():
-    """A resolver error must not drop audit rows for an audited deployment.
-
-    Fails OPEN (to the deployment default) rather than closed: a transient DB
-    blip should not silently create an audit gap for a bank meant to be audited.
-    """
-
-    async def broken(bank_id, context=None):
-        raise RuntimeError("config backend down")
-
-    assert await _logger(enabled=True, resolver=broken).should_log("recall", "b1") is True
-    assert await _logger(enabled=False, resolver=broken).should_log("recall", "b1") is False
-
-
-@pytest.mark.asyncio
-async def test_unwired_resolver_uses_global_default():
-    """Without a resolver the logger behaves exactly as before this feature."""
-    assert await _logger(enabled=True).should_log("recall", "b1") is True
-    assert await _logger(enabled=False).should_log("recall", "b1") is False
-
-
-@pytest.mark.asyncio
-async def test_gating_ignores_config_permission_filter(memory, request_context):
-    """A tenant permission filter must not suppress a bank's audit override.
-
-    get_allowed_config_fields controls which fields a user may *modify* (and it
-    filters the API-facing config read). Audit gating is an internal decision
-    and must see the bank's true stored value: a deployment that makes
-    audit_log_enabled read-only for some users must still audit banks that
-    opted in. Regression guard for the resolve_full_config vs get_bank_config
-    distinction in MemoryEngine._resolve_bank_audit_enabled.
-    """
-    from hindsight_api.config_resolver import ConfigResolver
-    from hindsight_api.extensions.tenant import Tenant, TenantContext, TenantExtension
-
-    bank_id = "audit-perm-filter"
-
-    class RestrictiveExtension(TenantExtension):
-        def __init__(self):
-            pass  # skip Extension.__init__(config); nothing here needs config
-
-        async def authenticate(self, context):
-            return TenantContext(schema_name="public")
-
-        async def list_tenants(self):
-            return [Tenant(schema="public")]
-
-        async def get_allowed_config_fields(self, context, bank_id):
-            # audit_log_enabled deliberately absent: read-only for this user.
-            return {"retain_chunk_size"}
-
-    await memory.get_bank_profile(bank_id, request_context=request_context)
-
-    # Store the opt-in with an allow-all resolver (the write is a separate
-    # concern from gating), then swap in the restrictive extension.
-    await memory._config_resolver.update_bank_config(bank_id, {"audit_log_enabled": True}, request_context)
-    restrictive = ConfigResolver(backend=memory._backend, tenant_extension=RestrictiveExtension())
-    memory._config_resolver = restrictive
-
-    # The API-facing read is filtered (proving the filter is actually active)...
-    api_view = await restrictive.get_bank_config(bank_id, request_context)
-    assert "audit_log_enabled" not in api_view
-
-    # ...but gating still sees the real value and audits the bank.
-    assert await memory._resolve_bank_audit_enabled(bank_id, request_context) is True
@@ -17,17 +17,14 @@ import pytest
 from pydantic import BaseModel

 from hindsight_api.engine.bank_attribution import apply_bank_attribution
-from hindsight_api.engine.cross_encoder import LiteLLMCrossEncoder
 from hindsight_api.engine.embeddings import OpenAIEmbeddings
 from hindsight_api.engine.memory_engine import (
-    MemoryEngine,
    _bind_bank_id,
    _current_bank_id,
    get_current_bank_id,
 )
 from hindsight_api.engine.providers.openai_compatible_llm import OpenAICompatibleLLM
 from hindsight_api.engine.retain.embedding_utils import generate_embeddings_batch
-from hindsight_api.models import RequestContext


@pytest.fixture(autouse=True)
@@ -59,9 +56,31 @@ class TestBankContextVar:
    def test_default_is_none(self):
        assert get_current_bank_id() is None

+    def test_set_and_reset(self):
+        token = _current_bank_id.set("user-42")
+        try:
+            assert get_current_bank_id() == "user-42"
+        finally:
+            _current_bank_id.reset(token)
+        assert get_current_bank_id() is None
+
+    def test_reset_runs_even_on_exception(self):
+        """A finally-based reset must unwind the binding even when the body raises."""
+        token = _current_bank_id.set("user-boom")
+        try:
+            with pytest.raises(ValueError):
+                try:
+                    assert get_current_bank_id() == "user-boom"
+                    raise ValueError("boom")
+                finally:
+                    _current_bank_id.reset(token)
+        finally:
+            pass
+        assert get_current_bank_id() is None
+

 class TestBindBankIdDecorator:
-    """The engine binds the bank via @_bind_bank_id on recall/retain/batch/reflect/task methods."""
+    """The engine binds the bank via @_bind_bank_id on recall/retain/batch/task methods."""

    async def test_binds_named_arg_positional_and_keyword(self):
        @_bind_bank_id()
@@ -98,41 +117,6 @@ class TestBindBankIdDecorator:

        assert await op(12345) is None

-    async def test_engine_provider_paths_bind_and_reset_their_bank_arguments(self):
-        engine = object.__new__(MemoryEngine)
-        engine._reflect_llm_config = None
-        engine._operation_validator = None
-        observed_bank_ids: list[str | None] = []
-
-        with patch(
-            "hindsight_api.engine.memory_engine.sanitize_text",
-            side_effect=lambda value: observed_bank_ids.append(get_current_bank_id()) or value,
-        ):
-            with pytest.raises(ValueError, match="Memory LLM API key not set"):
-                await engine.reflect_async("user-reflect", "question", request_context=RequestContext())
-
-        assert observed_bank_ids == ["user-reflect", "user-reflect"]
-        assert get_current_bank_id() is None
-
-        with (
-            patch.object(
-                engine,
-                "_authenticate_tenant",
-                AsyncMock(side_effect=lambda _context: observed_bank_ids.append(get_current_bank_id())),
-            ),
-            patch.object(engine, "_get_backend", AsyncMock(side_effect=RuntimeError("stop after authentication"))),
-        ):
-            with pytest.raises(RuntimeError, match="stop after authentication"):
-                await engine.update_memory_unit(
-                    "user-update",
-                    "54a647e5-0a22-4e5d-8504-b8bfca2a6142",
-                    text="corrected",
-                    request_context=RequestContext(),
-                )
-
-        assert observed_bank_ids[-1] == "user-update"
-        assert get_current_bank_id() is None
-

 # ── LLM provider: user injection ──────────────────────────────────────────────

@@ -274,22 +258,27 @@ def test_embeddings_user_injected_when_flag_on_and_bank_set():
    assert captured[0]["user"] == "user-emb"


-async def test_litellm_proxy_sends_bank_header():
-    encoder = LiteLLMCrossEncoder(api_base="https://rerank.example", model="will-memory-rerank")
-    response = SimpleNamespace(
-        raise_for_status=lambda: None,
-        json=lambda: {"results": [{"index": 0, "relevance_score": 0.91}]},
-    )
-    encoder._async_client = SimpleNamespace(post=AsyncMock(return_value=response))
+def test_embeddings_user_not_injected_when_flag_off():
+    _set_flag(False)
+    emb = _openai_embeddings()
+    captured: list[dict] = []
+    emb._client = _fake_embed_client(captured)
+    token = _current_bank_id.set("user-emb")
+    try:
+        emb.encode(["hello"])
+    finally:
+        _current_bank_id.reset(token)
+    assert "user" not in captured[0]

-    with patch(
-        "hindsight_api.engine.cross_encoder.reranker_bank_attribution_headers",
-        return_value={"X-Hindsight-Bank-Id": "bank-litellm-proxy"},
-    ):
-        scores = await encoder.predict([("query", "document")])

-    assert scores == [0.91]
-    assert encoder._async_client.post.call_args.kwargs["headers"] == {"X-Hindsight-Bank-Id": "bank-litellm-proxy"}
+def test_embeddings_user_not_injected_when_bank_unset():
+    _set_flag(True)
+    emb = _openai_embeddings()
+    captured: list[dict] = []
+    emb._client = _fake_embed_client(captured)
+    assert get_current_bank_id() is None
+    emb.encode(["hello"])
+    assert "user" not in captured[0]


 # ── Executor context propagation ──────────────────────────────────────────────
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Nicolò Boschi	0022d427d3	feat(integrations): add hindsight-opencode-coding plugin Reflect-only long-term memory for coding agents in OpenCode, with a git+chat backfill and (opt-in) live session write-back. - reflect + INJECT: on a task, reflect() the symptom and push the root-cause answer into the system prompt (no tools/recall). - backfill: every commit (full message + full diff, commit timestamp + git metadata) under a 'git' retain strategy; each chat as a JSON user/assistant transcript with custom extraction (<=2 coherent facts) under a 'chat' strategy; observations on; optional codebase knowledge pages. - live write-back (opt-in HINDSIGHT_RETAIN_SESSIONS): every N turns upsert the tool-filtered transcript under a stable conversation:<sessionID> document_id.	2026-07-02 13:55:40 +02:00
Nicolò Boschi	1f9bad0858	feat(knowledge-base): default pages to living-document trigger + 4096 tokens Client-created pages had no server curation applying a trigger, so they fell back to the plain mental-model default (no refresh, full mode, all fact types). Make a knowledge page a living document by default: when the client omits `trigger`, use observation-only + delta + exclude_mental_models + refresh_after_consolidation; when it omits `max_tokens`, default to 4096 (vs the mental-model 2048). Clients can still override either.	2026-07-02 10:30:40 +02:00
Nicolò Boschi	138bf02f29	refactor(knowledge-base): drop server-side curation + folder missions The knowledge base is now purely client-managed (CRUD over folders/pages); the server no longer auto-curates. Removes the folder curator entirely and the folder `mission` concept, and leads the sidebar with Knowledge Base. - Remove engine/knowledge_curator.py, the curate_folder task (handler + dispatch + submit_async_curate_folder / _bank_folders), the post-consolidation curation hook, and the folder-create / mission-update curation triggers. - Remove folder `mission` and `last_curated_at` (columns + engine + API + UI); keep `managed` as a client-set flag. Migration a5b6 now adds `managed` only; the last_curated_at migration is dropped and the unique-index migration repointed. Single alembic head preserved. - API: KnowledgeNode/CreateFolderRequest/UpdateNodeRequest lose `mission`; PATCH node handles name/parent_id only. - Control plane: sidebar leads with Knowledge Base (before Memories); remove the mission field, edit-mission dialog, and mission display from the KB view. - Delete the curator tests; regenerate OpenAPI + SDK clients.	2026-07-02 10:30:39 +02:00
Nicolò Boschi	df178aae8a	refactor(hindsight-fs): mirror the knowledge-base tree, not mental models Re-point hindsight-fs at the knowledge base so it projects a bank's folder/page hierarchy as nested directories + .md files, instead of a flat list of mental models. - client: fetch GET /knowledge-base/tree + /export (two calls, any bank size) and join by page id; replaces the paginated mental-models list. - format: planMirror() walks the tree into folder dirs + page files at nested paths (slug per segment, collision-safe); pages render the page's OKF doc. - sync: create folder dirs, write pages at nested paths, prune removed pages and emptied folders; state keyed by relative path + tracked dirs. - config/cli: drop the mental-model `detail` flag; `list` prints folders+pages; help/README updated. Tests rewritten for the tree/export model. Verified live against a bank's knowledge base: the `people` folder mirrors to people/anna.md + people/marco.md with OKF frontmatter.	2026-07-02 10:30:39 +02:00
Nicolò Boschi	a43026b8f4	feat(hindsight-fs): mirror a bank's mental models as a live local folder Add @vectorize-io/hindsight-fs, a CLI under hindsight-tools/ that mirrors a Hindsight bank's mental models as real markdown files (YAML frontmatter + body) in a local directory, refreshed from the API on an interval. Once mounted, ordinary shell tools (ls, cat, grep, find, ...) work against current memory. - Pull-based sync engine: full list each tick, write changed/new/tampered files, skip unchanged (content-hashed), prune deleted models. Atomic writes; a transient API error never wipes the mirror. - One-way mirror enforced two ways: files are read-only (0444) so agent edits fail with EACCES, plus a tamper-revert backstop that compares on-disk bytes and overwrites drift on the next pass. --writable opts out. - Commands: mount/start/stop/restart/sync/status/list/logs/unmount. Background daemon via detached process + pidfile; per-mount config is remembered. - status doubles as a healthcheck: --json report and a non-zero exit when the mount is dead/failed/stale (--stale-after overrides the threshold). - Tests: unit (sync engine, frontmatter, health) + e2e that spawns the real CLI against a mock API and exercises real bash commands. 26 tests.	2026-07-02 10:30:39 +02:00
Nicolò Boschi	5c425e276e	feat(knowledge-base): self-curating knowledge base (OKF pages + folder missions) Server-side knowledge base: a hierarchy of folders and pages over mental models, projected to the Open Knowledge Format, with a mission-driven curator that maintains pages automatically after each consolidation. - knowledge_pages table (PG + Oracle): parent_id tree, kind folder/page, mission, managed, last_curated_at; partial unique index on (folder, name) for concurrency-safe dedup; added to BACKUP_TABLES. - api/okf.py: OKF serializer (frontmatter + body, index/log, constellation graph). - engine/knowledge_curator.py: folder curator (LLM op plan + safe apply); reads new memories since last curation (delta, not recall); ops create/merge/delete page + spawn sub-folder (bounded depth<=3, <=8). Runs as an async curate_folder task on folder/mission create and after consolidation. Curator pages use an observation-only delta trigger with exclude_mental_models. - MemoryEngine: folder/page CRUD, tree, curate, async submit + worker handler. - /v1/default/banks/{bank}/knowledge-base/* endpoints. - Control plane: knowledge-base tree view + constellation toggle, missions, OKF page panel + bundle export; proxies, client, sidebar, i18n. - Tests: okf unit, knowledge-base HTTP, curator apply + dedup guard, hs_llm_core e2e. - Regenerated OpenAPI + SDK clients + docs-skill.	2026-07-02 10:30:39 +02:00