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
1250 changed files with 32871 additions and 75482 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.
@@ -159,18 +154,6 @@ If any files in `hindsight-api-slim/hindsight_api/api/` were changed:
 - Were the client SDKs regenerated? (`./scripts/generate-clients.sh`)
 - Were the control plane proxy routes updated? (`hindsight-control-plane/src/app/api/`)

-### 7a. Check TS/Python wrapper-client parity
-
-Two of the generated SDKs ship a **hand-written, maintained convenience wrapper** on top of the auto-generated low-level client — and *only* these two:
- **TypeScript**: `hindsight-clients/typescript/src/index.ts` (`HindsightClient`)
- **Python**: `hindsight-clients/python/hindsight_client/hindsight_client.py` (`Hindsight`)
-
-(The Rust/Go/etc. clients are generated-only — no wrapper to keep in sync.)
-
-These wrappers are what most third-party consumers actually call, and they must expose the same surface. **If a change touches one wrapper's method — adds/removes a parameter, changes a default, forwards a new query/body field — the equivalent method in the *other* wrapper must get the same change in the same (or an immediately-following) PR.** A parameter that exists in the generated SDK but is dropped by one wrapper silently strips it for every consumer of that language (this is exactly what #2975 / #3042 fixed for `detail`/`tags_match`/`limit`/`offset` on `listMentalModels`/`getMentalModel`). **Should fix** — flag any wrapper method that gains capabilities in one language but not the other, and add a matching mapping regression test on both sides.
-
-Note: the `client-coverage-check` CI tool only validates **request-body** fields, not GET **query** parameters — so query-param parity gaps are *not* caught automatically and must be checked by hand here.
-
 ### 7b. Check API-layer data-access boundary

 For each changed handler in `hindsight-api-slim/hindsight_api/api/` (e.g. `http.py`, `mcp.py`):
@@ -221,14 +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:
@@ -21,25 +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
-
-# Some backends, including Bedrock Converse, reject JSON Schema maxItems.
-# Disable it only for those backends; consolidation still enforces the cap.
-# HINDSIGHT_API_LLM_SUPPORTS_MAX_ITEMS=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
@@ -78,15 +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
@@ -108,16 +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
-
-# Wall-clock ceiling (seconds) for one retain task in the worker. A retain that
-# blocks indefinitely is cancelled and marked 'failed' — and so becomes
-# retryable — instead of holding its worker slot until the process restarts.
-# Set well above your slowest healthy retain; 0 disables. Default 3600.
-# HINDSIGHT_API_RETAIN_WALL_TIMEOUT=3600
-
 # 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
@@ -133,10 +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)
@@ -156,11 +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
@@ -179,11 +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
-# Opt in to the Apple Silicon MPS GPU (off by default: MPS leaks memory under
-# variable-length workloads). CUDA/XPU still auto-select regardless:
-# HINDSIGHT_API_EMBEDDINGS_LOCAL_ALLOW_MPS=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
@@ -220,28 +169,11 @@ HINDSIGHT_API_LOG_LEVEL=info
 # DeepSeek note: DeepSeek is supported for LLM calls, but not for embeddings.
 # If using DeepSeek as LLM provider, keep embeddings on local/openai/cohere/google/etc.

-# Embedding similarity thresholds. These defaults preserve the behavior calibrated
-# for BAAI/bge-small-en-v1.5. Recalibrate each threshold independently when changing
-# embedding models because cosine-similarity distributions are model-dependent.
-# HINDSIGHT_API_SEMANTIC_MIN_SIMILARITY=0.3
-# HINDSIGHT_API_GRAPH_SEED_MIN_SIMILARITY=0.3
-# HINDSIGHT_API_TEMPORAL_SEMANTIC_MIN_SIMILARITY=0.1
-# HINDSIGHT_API_SEMANTIC_LINK_MIN_SIMILARITY=0.7
-# HINDSIGHT_API_CONSOLIDATION_DEDUP_THRESHOLD=0.97
-
 # 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
-# Opt in to the Apple Silicon MPS GPU (off by default: MPS leaks memory under
-# variable-length workloads). CUDA/XPU still auto-select regardless:
-# HINDSIGHT_API_RERANKER_LOCAL_ALLOW_MPS=false
 # For TEI provider:
 # HINDSIGHT_API_RERANKER_TEI_URL=http://localhost:8081

@@ -263,16 +195,6 @@ HINDSIGHT_API_LOG_LEVEL=info
 # Expose async-operation queue + consolidation-backlog gauges on /metrics.
 # Runs periodic per-schema COUNT queries on a background task (disabled by default).
 # HINDSIGHT_API_METRICS_BACKLOG_ENABLED=true
-#
-# Runtime-stall observability (enabled by default). When a liveness probe fails,
-# these tell you WHY: a blocked event loop vs DB connection-pool exhaustion.
-# The loop watchdog logs the offending stack when the loop is unresponsive; the
-# DB-pool acquire timing logs (and exposes hindsight.db.pool.waiting) when
-# callers queue for a connection. Both are cheap; tune or disable if needed.
-# HINDSIGHT_API_LOOP_WATCHDOG_ENABLED=false
-# HINDSIGHT_API_LOOP_WATCHDOG_STALL_THRESHOLD_MS=1000
-# HINDSIGHT_API_LOOP_WATCHDOG_POLL_INTERVAL_MS=250
-# HINDSIGHT_API_DB_ACQUIRE_WARN_THRESHOLD_MS=1000

 # -----------------------------------------------------------------------------
 # Control Plane (Optional)
@@ -41,8 +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-copilot-cli: ${{ steps.filter.outputs.integrations-copilot-cli }}
      integrations-crewai: ${{ steps.filter.outputs.integrations-crewai }}
      integrations-litellm: ${{ steps.filter.outputs.integrations-litellm }}
      integrations-pydantic-ai: ${{ steps.filter.outputs.integrations-pydantic-ai }}
@@ -154,8 +152,6 @@ jobs:
            - 'hindsight-integrations/continue/**'
          integrations-cursor-cli:
            - 'hindsight-integrations/cursor-cli/**'
-          integrations-copilot-cli:
-            - 'hindsight-integrations/copilot-cli/**'
          integrations-crewai:
            - 'hindsight-integrations/crewai/**'
          integrations-litellm:
@@ -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:
@@ -289,18 +283,6 @@ jobs:
      working-directory: ./hindsight-api-slim
      run: uv build

-    # `uv build` only packages the source; it does not prove the dependency set
-    # resolves or that the code imports on this interpreter. Install into a fresh
-    # env and run a byte-compile + import smoke test so the matrix actually
-    # exercises each Python version (notably 3.14).
-    - name: Install and smoke-test on Python ${{ matrix.python-version }}
-      working-directory: ./hindsight-api-slim
-      run: |
-        uv venv --python ${{ matrix.python-version }} .venv-smoke
-        VIRTUAL_ENV=.venv-smoke uv pip install .
-        .venv-smoke/bin/python -m compileall -q hindsight_api
-        .venv-smoke/bin/python -c "import hindsight_api, hindsight_api.main, hindsight_api.config; from hindsight_api.engine import memory_engine, llm_wrapper; print('import OK')"
-
  build-typescript-client:
    needs: [detect-changes]
    if: >-
@@ -538,17 +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]
@@ -715,43 +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: >-
@@ -1279,10 +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

@@ -1886,27 +1836,6 @@ jobs:
      with:
        ref: ${{ github.event.pull_request.head.sha || '' }}

-    # The Oracle 23ai `free` service image is large; combined with the Python ML
-    # deps (torch) it exhausts the runner's ~14 GB root disk, so uv fails to
-    # extract a wheel with "No space left on device (os error 28)".
-    # Only the cheap, high-yield reclaims are enabled — the Android/.NET/Haskell
-    # dirs plus swap are a few `rm -rf`s worth ~16-21 GB, which is ample headroom:
-    #   - large-packages runs apt-get remove and costs minutes for little gain;
-    #   - tool-cache would delete the preinstalled Python that actions/setup-python
-    #     then has to re-download, making the job slower, not faster;
-    #   - docker-images is pointless here (the Oracle service container is already
-    #     running, so its image is in use and cannot be pruned anyway).
-    - name: Free Disk Space
-      uses: jlumbroso/free-disk-space@main
-      with:
-        tool-cache: false
-        android: true
-        dotnet: true
-        haskell: true
-        large-packages: false
-        docker-images: false
-        swap-storage: true
-
    - name: Setup Oracle test user
      # The SYSTEM tablespace uses manual segment space management which
      # doesn't support VECTOR types. Create an ASSM tablespace and a
@@ -2267,27 +2196,6 @@ jobs:
      with:
        ref: ${{ github.event.pull_request.head.sha || '' }}

-    # The Oracle 23ai `free` service image is large; combined with the Python ML
-    # deps (torch) it exhausts the runner's ~14 GB root disk, so uv fails to
-    # extract a wheel with "No space left on device (os error 28)".
-    # Only the cheap, high-yield reclaims are enabled — the Android/.NET/Haskell
-    # dirs plus swap are a few `rm -rf`s worth ~16-21 GB, which is ample headroom:
-    #   - large-packages runs apt-get remove and costs minutes for little gain;
-    #   - tool-cache would delete the preinstalled Python that actions/setup-python
-    #     then has to re-download, making the job slower, not faster;
-    #   - docker-images is pointless here (the Oracle service container is already
-    #     running, so its image is in use and cannot be pruned anyway).
-    - name: Free Disk Space
-      uses: jlumbroso/free-disk-space@main
-      with:
-        tool-cache: false
-        android: true
-        dotnet: true
-        haskell: true
-        large-packages: false
-        docker-images: false
-        swap-storage: true
-
    - name: Setup Oracle test user
      run: |
        pip install oracledb
@@ -2448,27 +2356,6 @@ jobs:
      with:
        ref: ${{ github.event.pull_request.head.sha || '' }}

-    # The Oracle 23ai `free` service image is large; combined with the Python ML
-    # deps (torch) it exhausts the runner's ~14 GB root disk, so uv fails to
-    # extract a wheel with "No space left on device (os error 28)".
-    # Only the cheap, high-yield reclaims are enabled — the Android/.NET/Haskell
-    # dirs plus swap are a few `rm -rf`s worth ~16-21 GB, which is ample headroom:
-    #   - large-packages runs apt-get remove and costs minutes for little gain;
-    #   - tool-cache would delete the preinstalled Python that actions/setup-python
-    #     then has to re-download, making the job slower, not faster;
-    #   - docker-images is pointless here (the Oracle service container is already
-    #     running, so its image is in use and cannot be pruned anyway).
-    - name: Free Disk Space
-      uses: jlumbroso/free-disk-space@main
-      with:
-        tool-cache: false
-        android: true
-        dotnet: true
-        haskell: true
-        large-packages: false
-        docker-images: false
-        swap-storage: true
-
    - name: Setup Oracle test user
      run: |
        pip install oracledb
@@ -3594,43 +3481,6 @@ jobs:
      # (requires_real_llm) needs a live Hindsight server and runs separately.
      run: uv run pytest tests -v -m "not requires_real_llm"

-  test-copilot-cli-integration:
-    needs: [detect-changes]
-    if: >-
-      (github.event_name == 'workflow_dispatch' ||
-      needs.detect-changes.outputs.integrations-copilot-cli == '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 copilot-cli integration
-      working-directory: ./hindsight-integrations/copilot-cli
-      run: uv build
-
-    - name: Install dependencies
-      working-directory: ./hindsight-integrations/copilot-cli
-      run: uv sync --frozen
-
-    - name: Run tests
-      working-directory: ./hindsight-integrations/copilot-cli
-      run: uv run pytest tests -v
-
  test-crewai-integration:
    needs: [detect-changes]
    if: >-
@@ -4921,8 +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
@@ -5060,7 +4909,6 @@ jobs:
      - test-github-copilot-integration
      - test-codex-integration
      - test-cursor-cli-integration
-      - test-zcode-integration
      - build-ai-sdk-integration
      - test-ai-sdk-integration-deno
      - test-opencode-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

@@ -2,8 +2,8 @@ apiVersion: v2
 name: hindsight
 description: Hindsight helm chart
 type: application
-version: 0.8.6
-appVersion: "0.8.6"
+version: 0.8.4
+appVersion: "0.8.4"
 keywords:
  - ai
  - memory
@@ -60,13 +60,13 @@ spec:
          valueFrom:
            fieldRef:
              fieldPath: metadata.name
-        {{- /* Explicitly set port to override K8s service discovery env var (HINDSIGHT_API_PORT) */}}
-        - name: HINDSIGHT_API_PORT
-          value: {{ .Values.worker.service.targetPort | quote }}
-        {{- /* Inherit LLM config from api.env, then apply worker-specific env.
-               Merge (worker.env wins) so a key set in both does not emit a
-               duplicate env entry, which server-side apply rejects. */}}
-        {{- range $key, $value := merge (deepCopy (.Values.worker.env | default dict)) (.Values.api.env | default dict) }}
+        {{- /* Inherit LLM config from api.env */}}
+        {{- range $key, $value := .Values.api.env }}
+        - name: {{ $key }}
+          value: {{ $value | quote }}
+        {{- end }}
+        {{- /* Worker-specific env vars */}}
+        {{- range $key, $value := .Values.worker.env }}
        - name: {{ $key }}
          value: {{ $value | quote }}
        {{- end }}
@@ -1,6 +1,6 @@
 {
  "name": "@vectorize-io/hindsight-all",
-  "version": "0.8.6",
+  "version": "0.8.4",
  "description": "Node.js programmatic lifecycle manager for Hindsight — embeds a local hindsight daemon in a Node application. Pair with @vectorize-io/hindsight-client for memory operations.",
  "main": "dist/index.js",
  "types": "dist/index.d.ts",
@@ -4,12 +4,12 @@ build-backend = "setuptools.build_meta"

 [project]
 name = "hindsight-all-slim"
-version = "0.8.6"
+version = "0.8.4"
 description = "Hindsight: Agent Memory That Works Like Human Memory - Slim All-in-One Bundle"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
-    "hindsight-api-slim==0.8.6",
+    "hindsight-api-slim==0.8.4",
    "hindsight-client>=0.0.7",
    "hindsight-embed>=0.1.0",
 ]
@@ -4,12 +4,12 @@ build-backend = "hatchling.build"

 [project]
 name = "hindsight-all"
-version = "0.8.6"
+version = "0.8.4"
 description = "Hindsight: Agent Memory That Works Like Human Memory - All-in-One Bundle"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
-    "hindsight-api-slim[all]==0.8.6",
+    "hindsight-api-slim[all]==0.8.4",
    "hindsight-client>=0.0.7",
    "hindsight-embed>=0.1.0",
 ]
@@ -21,7 +21,7 @@ hindsight-embed = { workspace = true }

 [project.optional-dependencies]
 local-llm = [
-    "hindsight-api-slim[local-llm]==0.8.6",
+    "hindsight-api-slim[local-llm]==0.8.4",
 ]
 test = [
    "pytest>=7.0.0",
@@ -53,4 +53,4 @@ __all__ = [
    "RemoteTEICrossEncoder",
    "LLMConfig",
 ]
-__version__ = "0.8.6"
+__version__ = "0.8.4"
@@ -9,7 +9,6 @@ import io
 import json
 import logging
 import zipfile
-from dataclasses import dataclass
 from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any
@@ -17,12 +16,10 @@ from typing import Any
 import asyncpg
 import typer

-from ..config import DEFAULT_DATABASE_SCHEMA, HindsightConfig, load_dotenv_for_entrypoint
+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

@@ -59,6 +56,7 @@ BACKUP_TABLES = [
    "observation_history",
    "mental_models",
    "mental_model_history",
+    "knowledge_pages",
    "directives",
    "async_operations",
    "webhooks",
@@ -68,88 +66,7 @@ BACKUP_TABLES = [
    "graph_maintenance_queue",
 ]

-MANIFEST_VERSION = "2"
-
-
-@dataclass(frozen=True)
-class BackupColumn:
-    """A PostgreSQL column shape required to decode a binary COPY stream."""
-
-    name: str
-    type_name: str
-
-
-async def _table_columns(conn: asyncpg.Connection, schema: str, table: str) -> list[BackupColumn]:
-    rows = await conn.fetch(
-        """
-        SELECT a.attname AS name, pg_catalog.format_type(a.atttypid, a.atttypmod) AS type_name
-        FROM pg_catalog.pg_attribute AS a
-        JOIN pg_catalog.pg_class AS c ON c.oid = a.attrelid
-        JOIN pg_catalog.pg_namespace AS n ON n.oid = c.relnamespace
-        WHERE n.nspname = $1 AND c.relname = $2 AND a.attnum > 0 AND NOT a.attisdropped
-          AND a.attgenerated = ''
-        ORDER BY a.attnum
-        """,
-        schema,
-        table,
-    )
-    return [BackupColumn(name=row["name"], type_name=row["type_name"]) for row in rows]
-
-
-async def _validate_restore_schema(
-    conn: asyncpg.Connection, manifest: dict[str, Any], schema: str
-) -> dict[str, list[str]]:
-    """Validate every COPY stream against the target before destructive work starts.
-
-    Type equality is an exact ``format_type`` string match. This is deliberately
-    stricter than binary-COPY wire compatibility (e.g. ``varchar`` and ``text``
-    share a binary format yet compare unequal here): we would rather fail a
-    genuinely-restorable backup with a clear, actionable error than silently risk
-    a subtle binary mismatch. Restores blocked this way can be recovered by
-    aligning the target schema.
-    """
-    restore_columns: dict[str, list[str]] = {}
-    errors: list[str] = []
-    for table, table_manifest in manifest["tables"].items():
-        source_columns = [BackupColumn(**column) for column in table_manifest["columns"]]
-        target_by_name = {column.name: column for column in await _table_columns(conn, schema, table)}
-        missing = [column.name for column in source_columns if column.name not in target_by_name]
-        mismatched = [
-            f"{column.name} ({column.type_name} in backup, {target_by_name[column.name].type_name} in target)"
-            for column in source_columns
-            if column.name in target_by_name and target_by_name[column.name].type_name != column.type_name
-        ]
-        if missing:
-            errors.append(f"{table}: target is missing backup columns {', '.join(missing)}")
-        if mismatched:
-            errors.append(f"{table}: incompatible column types: {', '.join(mismatched)}")
-        restore_columns[table] = [column.name for column in source_columns]
-
-    if errors:
-        details = "; ".join(errors)
-        raise ValueError(f"Backup schema is incompatible with target schema '{schema}': {details}")
-    return restore_columns
-
-
-def _effective_backup_tables() -> list[str]:
-    """Core backup tables plus any bank-scoped tables a loaded extension declares.
-
-    ``BACKUP_TABLES`` covers only the tables core owns. An extension that
-    provisions its own bank-scoped tables (via ``TenantExtension``) declares
-    them through ``extra_bank_tables()`` so they aren't dropped on restore.
-    Extension tables are appended *after* the core set so restore's forward
-    COPY inserts them after their FK parents (e.g. ``banks``) and the reversed
-    TRUNCATE clears them before those parents.
-    """
-    tables = list(BACKUP_TABLES)
-    tenant_extension = load_extension("TENANT", TenantExtension)
-    if tenant_extension is not None:
-        seen = set(tables)
-        for spec in tenant_extension.extra_bank_tables():
-            if spec.include_in_backup and spec.name not in seen:
-                tables.append(spec.name)
-                seen.add(spec.name)
-    return tables
+MANIFEST_VERSION = "1"


 async def _admin_connect(db_url: str) -> asyncpg.Connection:
@@ -160,8 +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))
@@ -170,18 +86,8 @@ async def _admin_connect(db_url: str) -> asyncpg.Connection:
    return conn


-async def _backup(
-    database_url: str,
-    output_path: Path,
-    schema: str = "public",
-    backup_tables: list[str] | None = None,
-) -> dict[str, Any]:
-    """Backup all tables to a zip file using binary COPY protocol.
-
-    ``backup_tables`` defaults to the core ``BACKUP_TABLES``; callers pass the
-    extension-augmented list from ``_effective_backup_tables()``.
-    """
-    backup_tables = backup_tables if backup_tables is not None else BACKUP_TABLES
+async def _backup(database_url: str, output_path: Path, schema: str = "public") -> dict[str, Any]:
+    """Backup all tables to a zip file using binary COPY protocol."""
    conn = await asyncpg.connect(database_url)
    try:
        tables: dict[str, Any] = {}
@@ -198,24 +104,14 @@ async def _backup(
        # entities table was backed up.
        async with conn.transaction(isolation="repeatable_read"):
            with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zf:
-                for i, table in enumerate(backup_tables, 1):
-                    typer.echo(f"  [{i}/{len(backup_tables)}] Backing up {table}...", nl=False)
+                for i, table in enumerate(BACKUP_TABLES, 1):
+                    typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] Backing up {table}...", nl=False)

                    buffer = io.BytesIO()

-                    columns = await _table_columns(conn, schema, table)
-
-                    # Pin the ordered columns into both the stream and manifest.
-                    # PostgreSQL binary COPY does not encode column identities, so
-                    # restore must validate this shape before truncating any data.
+                    # Use binary COPY for exact type preservation
                    # asyncpg requires schema_name as separate parameter
-                    await conn.copy_from_table(
-                        table,
-                        schema_name=schema,
-                        columns=[column.name for column in columns],
-                        output=buffer,
-                        format="binary",
-                    )
+                    await conn.copy_from_table(table, schema_name=schema, output=buffer, format="binary")

                    data = buffer.getvalue()
                    zf.writestr(f"{table}.bin", data)
@@ -226,7 +122,6 @@ async def _backup(
                    tables[table] = {
                        "rows": row_count,
                        "size_bytes": len(data),
-                        "columns": [{"name": column.name, "type_name": column.type_name} for column in columns],
                    }

                    typer.echo(f" {row_count} rows")
@@ -238,20 +133,8 @@ async def _backup(
        await conn.close()


-async def _restore(
-    database_url: str,
-    input_path: Path,
-    schema: str = "public",
-    backup_tables: list[str] | None = None,
-) -> dict[str, Any]:
-    """Restore all tables from a zip file using binary COPY protocol.
-
-    ``backup_tables`` defaults to the core ``BACKUP_TABLES``; callers pass the
-    extension-augmented list from ``_effective_backup_tables()``. Tables named
-    here but absent from the archive are truncated then skipped for restore, so
-    a stale extension registration never leaves pre-restore rows behind.
-    """
-    backup_tables = backup_tables if backup_tables is not None else BACKUP_TABLES
+async def _restore(database_url: str, input_path: Path, schema: str = "public") -> dict[str, Any]:
+    """Restore all tables from a zip file using binary COPY protocol."""
    conn = await asyncpg.connect(database_url)
    try:
        with zipfile.ZipFile(input_path, "r") as zf:
@@ -260,40 +143,29 @@ async def _restore(
            if manifest.get("version") != MANIFEST_VERSION:
                raise ValueError(f"Unsupported backup version: {manifest.get('version')}")

-            # Complete the compatibility check before entering the transaction
-            # that truncates tables. This turns historical schema drift into an
-            # actionable error without risking the target's existing data.
-            restore_columns = await _validate_restore_schema(conn, manifest, schema)
-
            # Use a transaction for atomic restore - either all tables are
            # restored or none are, preventing partial/inconsistent state.
            async with conn.transaction():
                typer.echo("  Clearing existing data...")
                # Truncate tables in reverse order (respects FK constraints)
-                for table in reversed(backup_tables):
+                for table in reversed(BACKUP_TABLES):
                    qualified_table = _fq_table(table, schema)
                    await conn.execute(f"TRUNCATE TABLE {qualified_table} CASCADE")

                # Restore tables in forward order
-                for i, table in enumerate(backup_tables, 1):
+                for i, table in enumerate(BACKUP_TABLES, 1):
                    filename = f"{table}.bin"
                    if filename not in zf.namelist():
-                        typer.echo(f"  [{i}/{len(backup_tables)}] {table}: skipped (not in backup)")
+                        typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] {table}: skipped (not in backup)")
                        continue

                    expected_rows = manifest["tables"].get(table, {}).get("rows", "?")
-                    typer.echo(f"  [{i}/{len(backup_tables)}] Restoring {table}... {expected_rows} rows")
+                    typer.echo(f"  [{i}/{len(BACKUP_TABLES)}] Restoring {table}... {expected_rows} rows")

                    data = zf.read(filename)
                    buffer = io.BytesIO(data)
                    # asyncpg requires schema_name as separate parameter
-                    await conn.copy_to_table(
-                        table,
-                        schema_name=schema,
-                        columns=restore_columns[table],
-                        source=buffer,
-                        format="binary",
-                    )
+                    await conn.copy_to_table(table, schema_name=schema, source=buffer, format="binary")

                # Refresh materialized view
                typer.echo("  Refreshing materialized views...")
@@ -306,22 +178,20 @@ async def _restore(

 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)
-    return await _backup(resolved_url, output, schema, backup_tables=_effective_backup_tables())
+    return await _backup(resolved_url, output, schema)


 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)
-    return await _restore(resolved_url, input_file, schema, backup_tables=_effective_backup_tables())
+    return await _restore(resolved_url, input_file, schema)


@app.command()
@@ -345,7 +215,7 @@ def backup(
    manifest = asyncio.run(_run_backup(config.database_url, output, schema))

    total_rows = sum(t["rows"] for t in manifest["tables"].values())
-    typer.echo(f"Backed up {total_rows} rows across {len(manifest['tables'])} tables")
+    typer.echo(f"Backed up {total_rows} rows across {len(BACKUP_TABLES)} tables")
    typer.echo(f"Backup saved to {output}")


@@ -378,7 +248,7 @@ def restore(
    manifest = asyncio.run(_run_restore(config.database_url, input_file, schema))

    total_rows = sum(t["rows"] for t in manifest["tables"].values())
-    typer.echo(f"Restored {total_rows} rows across {len(manifest['tables'])} tables")
+    typer.echo(f"Restored {total_rows} rows across {len(BACKUP_TABLES)} tables")
    typer.echo("Restore complete")


@@ -392,17 +262,17 @@ 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)

    config = HindsightConfig.from_env()
-    tenant_extension = load_extension("TENANT", TenantExtension)
    if schema:
        schemas = [schema]
    else:
+        tenant_extension = load_extension("TENANT", TenantExtension)
+
        schemas = [base_schema or DEFAULT_DATABASE_SCHEMA]
        if tenant_extension:
            tenants = await tenant_extension.list_tenants()
@@ -427,36 +297,9 @@ async def _run_migration(
        ensure_extensions=ensure_extensions,
    )

-    # After core migrations, provision any extension-owned bank-scoped tables
-    # per schema so extension schema evolves on the same lifecycle as core
-    # schema (rather than via a lazy first-request path).
-    if tenant_extension is not None:
-        await _provision_extra_bank_tables(resolved_url, schemas, tenant_extension)
-
    return schemas


-async def _provision_extra_bank_tables(
-    resolved_url: str, schemas: list[str], tenant_extension: TenantExtension
-) -> None:
-    """Run the tenant extension's table provisioner for each migrated schema.
-
-    Fires after core migrations complete so extension-owned bank tables are
-    created/evolved on the same lifecycle as core schema. A failure aborts the
-    migration command (and names the offending schema) rather than being
-    swallowed — provisioning is idempotent, so the operator can fix and re-run.
-    """
-    for schema in schemas:
-        conn = await asyncpg.connect(resolved_url)
-        try:
-            await tenant_extension.provision_bank_tables(conn, schema)
-        except Exception as e:
-            typer.echo(f"  Failed to provision extension tables for schema '{schema}': {e}", err=True)
-            raise
-        finally:
-            await conn.close()
-
-
@app.command(name="run-db-migration")
 def run_db_migration(
    schema: str | None = typer.Option(
@@ -511,134 +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)
@@ -646,14 +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()

@@ -765,8 +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)
@@ -825,8 +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)
@@ -891,8 +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)
@@ -960,7 +665,6 @@ def worker_status(


 def main():
-    load_dotenv_for_entrypoint()
    app()


@@ -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)
@@ -1,90 +0,0 @@
-"""Add ``causal_links`` to the curation archive (invalidated_memory_units).
-
-Causal edges (``caused_by`` and the historical ``causes``/``enables``/
-``prevents``) are retain-time extraction output: unlike temporal and semantic
-links they cannot be recomputed from dates or embeddings, and graph maintenance
-never rebuilds them. Invalidation MOVES a fact out of ``memory_units``, so the
-``memory_links → memory_units`` FK cascade deletes every incident edge — and
-revert had no way to bring the causal ones back (#2864).
-
-This column parks the descriptors of the causal edges incident to an archived
-fact — ``[{"from_unit_id", "to_unit_id", "link_type", "weight"}, ...]`` — so
-revert can rematerialize them. It is deliberately unindexed and lives only on
-the archive: live facts keep their causal edges in ``memory_links`` (curation
-edits no longer delete them), and the archive is small, cold, and only read by
-low-frequency curation operations.
-
-Revision ID: c7d1e9a4b3f2
-Revises: d7b2f8a1c934
-Create Date: 2026-07-24
-"""
-
-from collections.abc import Sequence
-
-from alembic import context, op
-
-from hindsight_api.alembic._dialect import run_for_dialect
-
-revision: str = "c7d1e9a4b3f2"
-down_revision: str | Sequence[str] | None = "d7b2f8a1c934"
-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()
-    # NOT NULL DEFAULT is metadata-only on PG 11+, so this is cheap even on a
-    # large archive. Existing rows read as "no causal edges captured" — edges
-    # lost before this migration cannot be reconstructed and are not guessed.
-    op.execute(
-        f"ALTER TABLE {schema}invalidated_memory_units "
-        f"ADD COLUMN IF NOT EXISTS causal_links JSONB NOT NULL DEFAULT '[]'::jsonb"
-    )
-
-
-def _pg_downgrade() -> None:
-    schema = _pg_schema_prefix()
-    op.execute(f"ALTER TABLE {schema}invalidated_memory_units DROP COLUMN IF EXISTS causal_links")
-
-
-def _oracle_upgrade() -> None:
-    # Kept in sync with PG for schema parity (curation itself is PostgreSQL-only
-    # today — it introspects pg_attribute to move rows between the two tables).
-    # Swallow ORA-01430 (column already exists) so the migration is idempotent.
-    op.execute(
-        """
-        BEGIN
-            EXECUTE IMMEDIATE 'ALTER TABLE invalidated_memory_units ADD (causal_links CLOB DEFAULT ''[]''
-                CONSTRAINT imu_causal_links_json CHECK (causal_links IS JSON))';
-        EXCEPTION WHEN OTHERS THEN
-            IF SQLCODE != -1430 THEN RAISE; END IF;
-        END;
-        """
-    )
-
-
-def _oracle_downgrade() -> None:
-    # Swallow ORA-00904 (column does not exist).
-    op.execute(
-        """
-        BEGIN
-            EXECUTE IMMEDIATE 'ALTER TABLE invalidated_memory_units DROP COLUMN causal_links';
-        EXCEPTION WHEN OTHERS THEN
-            IF SQLCODE != -904 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)
@@ -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)
@@ -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)
@@ -66,6 +66,11 @@ def color_end(text: str) -> str:
    return color(text, 1.0)


+def color_mid(text: str) -> str:
+    """Color text with gradient middle color."""
+    return color(text, 0.5)
+
+
 def dim(text: str) -> str:
    """Dim/gray text."""
    return f"\033[38;2;128;128;128m{text}\033[0m"
@@ -19,28 +19,12 @@ from ._pg_search import normalize_pg_search_tokenizer
 from ._vector_index import validate_extension
 from .utils import mask_network_location

+# Load .env file, searching current and parent directories (overrides existing env vars)
+load_dotenv(find_dotenv(usecwd=True), override=True)
+
 logger = logging.getLogger(__name__)


-def load_dotenv_for_entrypoint() -> None:
-    """Load a discovered ``.env`` file for Hindsight's own entry points.
-
-    Importing ``hindsight_api`` (or anything that pulls it in) must NOT mutate
-    the host application's ``os.environ``. See issue #2961: doing so at module
-    scope let an upward ``.env`` walk from the process cwd silently overwrite an
-    embedding application's own configuration.
-
-    This helper is therefore called explicitly from Hindsight's standalone entry
-    points only — the API server (CLI and ``hindsight_api.server:app``), the
-    worker, and the admin CLI. ``override=True`` is deliberate: it preserves the
-    exact precedence those entry points have always had (a discovered ``.env``
-    is authoritative over the ambient process environment). Because a library
-    import never reaches this code path, that precedence no longer leaks into
-    embedders.
-    """
-    load_dotenv(find_dotenv(usecwd=True), override=True)
-
-
 class ConfigFieldAccessError(AttributeError):
    """Raised when trying to access a bank-configurable field from global config."""

@@ -161,18 +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_SUPPORTS_MAX_ITEMS = "HINDSIGHT_API_LLM_SUPPORTS_MAX_ITEMS"
 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
@@ -274,35 +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")
-
-
-def _parse_boolean_env(env_name: str, default: bool) -> bool:
-    """Parse a boolean environment variable, rejecting ambiguous values."""
-    raw = os.getenv(env_name)
-    if raw is None:
-        return default
-
-    normalized = raw.strip().lower()
-    if normalized in ("true", "1"):
-        return True
-    if normalized in ("false", "0"):
-        return False
-    raise ValueError(f"Invalid {env_name} value {raw!r}: expected true, false, 1, or 0")
-
-
 # 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"
@@ -314,7 +259,6 @@ ENV_RETAIN_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_RETAIN_LLM_INITIAL_BACKOFF"
 ENV_RETAIN_LLM_MAX_BACKOFF = "HINDSIGHT_API_RETAIN_LLM_MAX_BACKOFF"
 ENV_RETAIN_LLM_TIMEOUT = "HINDSIGHT_API_RETAIN_LLM_TIMEOUT"
 ENV_RETAIN_LLM_LITELLMROUTER_CONFIG = "HINDSIGHT_API_RETAIN_LLM_LITELLMROUTER_CONFIG"
-ENV_RETAIN_LLM_REASONING_EFFORT = "HINDSIGHT_API_RETAIN_LLM_REASONING_EFFORT"

 # Fireworks AI batch inference. Fireworks' batch API is a proprietary
 # account-scoped dataset/job REST API on a control-plane host, distinct from the
@@ -337,7 +281,6 @@ ENV_REFLECT_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_REFLECT_LLM_INITIAL_BACKOFF"
 ENV_REFLECT_LLM_MAX_BACKOFF = "HINDSIGHT_API_REFLECT_LLM_MAX_BACKOFF"
 ENV_REFLECT_LLM_TIMEOUT = "HINDSIGHT_API_REFLECT_LLM_TIMEOUT"
 ENV_REFLECT_LLM_LITELLMROUTER_CONFIG = "HINDSIGHT_API_REFLECT_LLM_LITELLMROUTER_CONFIG"
-ENV_REFLECT_LLM_REASONING_EFFORT = "HINDSIGHT_API_REFLECT_LLM_REASONING_EFFORT"

 ENV_CONSOLIDATION_LLM_PROVIDER = "HINDSIGHT_API_CONSOLIDATION_LLM_PROVIDER"
 ENV_CONSOLIDATION_LLM_API_KEY = "HINDSIGHT_API_CONSOLIDATION_LLM_API_KEY"
@@ -349,12 +292,10 @@ ENV_CONSOLIDATION_LLM_INITIAL_BACKOFF = "HINDSIGHT_API_CONSOLIDATION_LLM_INITIAL
 ENV_CONSOLIDATION_LLM_MAX_BACKOFF = "HINDSIGHT_API_CONSOLIDATION_LLM_MAX_BACKOFF"
 ENV_CONSOLIDATION_LLM_TIMEOUT = "HINDSIGHT_API_CONSOLIDATION_LLM_TIMEOUT"
 ENV_CONSOLIDATION_LLM_LITELLMROUTER_CONFIG = "HINDSIGHT_API_CONSOLIDATION_LLM_LITELLMROUTER_CONFIG"
-ENV_CONSOLIDATION_LLM_REASONING_EFFORT = "HINDSIGHT_API_CONSOLIDATION_LLM_REASONING_EFFORT"

 ENV_EMBEDDINGS_PROVIDER = "HINDSIGHT_API_EMBEDDINGS_PROVIDER"
 ENV_EMBEDDINGS_LOCAL_MODEL = "HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL"
 ENV_EMBEDDINGS_LOCAL_FORCE_CPU = "HINDSIGHT_API_EMBEDDINGS_LOCAL_FORCE_CPU"
-ENV_EMBEDDINGS_LOCAL_ALLOW_MPS = "HINDSIGHT_API_EMBEDDINGS_LOCAL_ALLOW_MPS"
 ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE = "HINDSIGHT_API_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE"
 ENV_EMBEDDINGS_ONNX_MODEL_ID = "HINDSIGHT_API_EMBEDDINGS_ONNX_MODEL_ID"
 ENV_EMBEDDINGS_ONNX_MODEL_PATH = "HINDSIGHT_API_EMBEDDINGS_ONNX_MODEL_PATH"
@@ -432,7 +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"
@@ -442,10 +382,8 @@ 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_ALLOW_MPS = "HINDSIGHT_API_RERANKER_LOCAL_ALLOW_MPS"
 ENV_RERANKER_LOCAL_MAX_CONCURRENT = "HINDSIGHT_API_RERANKER_LOCAL_MAX_CONCURRENT"
 ENV_RERANKER_LOCAL_TRUST_REMOTE_CODE = "HINDSIGHT_API_RERANKER_LOCAL_TRUST_REMOTE_CODE"
 ENV_RERANKER_LOCAL_FP16 = "HINDSIGHT_API_RERANKER_LOCAL_FP16"
@@ -465,9 +403,6 @@ ENV_RERANKER_LITELLM_SDK_TIMEOUT = "HINDSIGHT_API_RERANKER_LITELLM_SDK_TIMEOUT"
 ENV_RERANKER_GOOGLE_TIMEOUT = "HINDSIGHT_API_RERANKER_GOOGLE_TIMEOUT"
 ENV_RERANKER_MAX_CANDIDATES = "HINDSIGHT_API_RERANKER_MAX_CANDIDATES"
 ENV_SEMANTIC_MIN_SIMILARITY = "HINDSIGHT_API_SEMANTIC_MIN_SIMILARITY"
-ENV_GRAPH_SEED_MIN_SIMILARITY = "HINDSIGHT_API_GRAPH_SEED_MIN_SIMILARITY"
-ENV_TEMPORAL_SEMANTIC_MIN_SIMILARITY = "HINDSIGHT_API_TEMPORAL_SEMANTIC_MIN_SIMILARITY"
-ENV_SEMANTIC_LINK_MIN_SIMILARITY = "HINDSIGHT_API_SEMANTIC_LINK_MIN_SIMILARITY"
 ENV_RERANKER_FLASHRANK_MODEL = "HINDSIGHT_API_RERANKER_FLASHRANK_MODEL"
 ENV_RERANKER_FLASHRANK_CACHE_DIR = "HINDSIGHT_API_RERANKER_FLASHRANK_CACHE_DIR"
 ENV_RERANKER_FLASHRANK_CPU_MEM_ARENA = "HINDSIGHT_API_RERANKER_FLASHRANK_CPU_MEM_ARENA"
@@ -532,12 +467,6 @@ ENV_OTEL_DEPLOYMENT_ENVIRONMENT = "HINDSIGHT_API_OTEL_DEPLOYMENT_ENVIRONMENT"
 ENV_METRICS_INCLUDE_BANK_ID = "HINDSIGHT_API_METRICS_INCLUDE_BANK_ID"
 ENV_METRICS_BACKLOG_ENABLED = "HINDSIGHT_API_METRICS_BACKLOG_ENABLED"

-# Runtime-stall observability (loop watchdog + DB pool acquire instrumentation)
-ENV_LOOP_WATCHDOG_ENABLED = "HINDSIGHT_API_LOOP_WATCHDOG_ENABLED"
-ENV_LOOP_WATCHDOG_STALL_THRESHOLD_MS = "HINDSIGHT_API_LOOP_WATCHDOG_STALL_THRESHOLD_MS"
-ENV_LOOP_WATCHDOG_POLL_INTERVAL_MS = "HINDSIGHT_API_LOOP_WATCHDOG_POLL_INTERVAL_MS"
-ENV_DB_ACQUIRE_WARN_THRESHOLD_MS = "HINDSIGHT_API_DB_ACQUIRE_WARN_THRESHOLD_MS"
-
 # Vertex AI configuration
 ENV_LLM_VERTEXAI_PROJECT_ID = "HINDSIGHT_API_LLM_VERTEXAI_PROJECT_ID"
 ENV_LLM_VERTEXAI_REGION = "HINDSIGHT_API_LLM_VERTEXAI_REGION"
@@ -556,11 +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"
@@ -660,7 +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
@@ -675,71 +598,25 @@ 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 worker slot reservations: op_type -> default reserved count.
-# Each entry reserves a guaranteed *minimum* number of slots (a floor) for that
-# operation type within the global WORKER_MAX_SLOTS pool, so a saturated pool can't
-# starve it. Remaining capacity (WORKER_MAX_SLOTS - sum of reservations) is a shared
-# pool usable by any type, so a reservation does NOT cap the type — it may overflow
-# the shared pool. Adding a type here is the only change needed to make it reservable.
-#
-# op_type matches the value stored in async_operations.operation_type; the env var
-# names are derived from it (see _parse_worker_slot_reservations).
-WORKER_SLOT_TYPE_DEFAULTS: dict[str, int] = {
-    "consolidation": 2,
-    "retain": 0,
-    "file_convert_retain": 0,
-    "refresh_mental_model": 0,
-    "graph_maintenance": 0,
-    "import_documents": 0,
+# Per-operation-type slot reservations. Each entry maps an operation_type
+# (as stored in async_operations.operation_type) to its env var and default.
+# Adding a new operation type here is the ONLY change needed to make it
+# reservable via env var — config fields, from_env(), and the
+# worker_slot_reservations property all derive from this dict.
+WORKER_SLOT_RESERVATION_TYPES: dict[str, tuple[str, int]] = {
+    "consolidation": ("HINDSIGHT_API_WORKER_CONSOLIDATION_MAX_SLOTS", 2),
+    "retain": ("HINDSIGHT_API_WORKER_RETAIN_MAX_SLOTS", 0),
+    "file_convert_retain": ("HINDSIGHT_API_WORKER_FILE_CONVERT_RETAIN_MAX_SLOTS", 0),
+    "refresh_mental_model": ("HINDSIGHT_API_WORKER_REFRESH_MENTAL_MODEL_MAX_SLOTS", 0),
+    "graph_maintenance": ("HINDSIGHT_API_WORKER_GRAPH_MAINTENANCE_MAX_SLOTS", 0),
+    "import_documents": ("HINDSIGHT_API_WORKER_IMPORT_DOCUMENTS_MAX_SLOTS", 0),
 }
-
-
-def _parse_worker_slot_reservations() -> dict[str, int]:
-    """Parse per-type RESERVED_SLOTS (and the deprecated _MAX_SLOTS alias).
-
-    ``HINDSIGHT_API_WORKER_<TYPE>_MAX_SLOTS`` is a deprecated alias for
-    ``..._RESERVED_SLOTS`` — despite its name it always set the reservation floor,
-    never a ceiling. It still works but logs a warning. Returns op_type -> reserved
-    floor for entries with a reservation > 0.
-    """
-    reservations: dict[str, int] = {}
-    for op_type, default in WORKER_SLOT_TYPE_DEFAULTS.items():
-        reserved_env = f"HINDSIGHT_API_WORKER_{op_type.upper()}_RESERVED_SLOTS"
-        legacy_env = f"HINDSIGHT_API_WORKER_{op_type.upper()}_MAX_SLOTS"
-        raw_reserved = os.getenv(reserved_env)
-        raw_legacy = os.getenv(legacy_env)
-        if raw_reserved is not None and raw_legacy is not None:
-            raise ValueError(
-                f"Both {reserved_env} and the deprecated {legacy_env} are set; "
-                f"they configure the same value. Keep only {reserved_env}."
-            )
-        if raw_legacy is not None:
-            logger.warning(
-                "%s is deprecated and will be removed in a future release. Despite its name it "
-                "reserves a *minimum* (floor), not a maximum. Rename it to %s.",
-                legacy_env,
-                reserved_env,
-            )
-        reserved_source = raw_reserved if raw_reserved is not None else raw_legacy
-        reserved = int(reserved_source) if reserved_source is not None else default
-        if reserved < 0:
-            raise ValueError(f"{reserved_env} must be >= 0, got {reserved}")
-        if reserved > 0:
-            reservations[op_type] = reserved
-    return reservations
-
-
 ENV_WORKER_CONSOLIDATION_BANK_PRIORITY = "HINDSIGHT_API_WORKER_CONSOLIDATION_BANK_PRIORITY"
 ENV_RETAIN_MAX_CONCURRENT = "HINDSIGHT_API_RETAIN_MAX_CONCURRENT"
-ENV_RETAIN_WALL_TIMEOUT = "HINDSIGHT_API_RETAIN_WALL_TIMEOUT"

 # 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"
@@ -761,7 +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.
@@ -781,16 +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"
@@ -855,7 +725,6 @@ DEFAULT_LLAMACPP_EXTRA_ARGS = None  # Space-separated extra CLI args for llama.c
 # (prose preambles, markdown fences, invalid JSON) — wedging retain/consolidation
 # on parse retries.
 DEFAULT_LLM_STRICT_SCHEMA = False
-DEFAULT_LLM_SUPPORTS_MAX_ITEMS = True

 DEFAULT_LLM_MAX_CONCURRENT = 32
 DEFAULT_LLM_MAX_RETRIES = 3  # Max retry attempts for LLM API calls
@@ -876,9 +745,6 @@ DEFAULT_LLM_GEMINI_SAFETY_SETTINGS = None  # None = use Gemini default safety se
 DEFAULT_EMBEDDINGS_PROVIDER = "local"
 DEFAULT_EMBEDDINGS_LOCAL_MODEL = "BAAI/bge-small-en-v1.5"
 DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU = False  # Force CPU mode for local embeddings
-# Apple Silicon MPS is opt-in: it leaks memory under variable-length workloads
-# (unbounded per-shape kernel/allocator cache). CUDA/XPU still auto-select.
-DEFAULT_EMBEDDINGS_LOCAL_ALLOW_MPS = False
 DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE = False  # Security: disabled by default, required for some models
 DEFAULT_EMBEDDINGS_ONNX_MODEL_ID = "intfloat/multilingual-e5-small"
 DEFAULT_EMBEDDINGS_ONNX_FILE = "onnx/model.onnx"
@@ -895,12 +761,8 @@ 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
-# Apple Silicon MPS is opt-in: it leaks memory under variable-length workloads
-# (unbounded per-shape kernel/allocator cache). CUDA/XPU still auto-select.
-DEFAULT_RERANKER_LOCAL_ALLOW_MPS = False
 DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT = 4  # Limit concurrent CPU-bound reranking to prevent thrashing
 DEFAULT_RERANKER_LOCAL_TRUST_REMOTE_CODE = (
    False  # Security: disabled by default, required for some models like jina-reranker-v2
@@ -923,16 +785,10 @@ DEFAULT_RERANKER_LITELLM_SDK_TIMEOUT = 60.0
 DEFAULT_RERANKER_GOOGLE_TIMEOUT = 60.0
 DEFAULT_RERANKER_MAX_CANDIDATES = 300
 DEFAULT_SEMANTIC_MIN_SIMILARITY = 0.3
-DEFAULT_GRAPH_SEED_MIN_SIMILARITY = 0.3
-DEFAULT_TEMPORAL_SEMANTIC_MIN_SIMILARITY = 0.1
-DEFAULT_SEMANTIC_LINK_MIN_SIMILARITY = 0.7
 # Minimum BM25 score a row must exceed to enter fusion. 0.0 gates out
 # 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.
@@ -1053,10 +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"
@@ -1107,7 +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
@@ -1189,14 +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)
@@ -1207,28 +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.
-# Wall-clock ceiling for one retain task in the worker (0 disables). This is a
-# deadlock/wedge backstop, not a latency target: a retain that blocks forever on
-# a lock, an LLM permit or a queue put would otherwise hold its worker slot until
-# the process restarts, and 'processing' is neither retryable nor cancellable
-# through the API. Set well above any healthy retain so it only ever fires on a
-# genuine wedge — the per-attempt LLM timeout and the retry budget already bound
-# the normal slow path.
-DEFAULT_RETAIN_WALL_TIMEOUT = 3600  # seconds (1 hour)

 # 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)
@@ -1264,26 +1089,11 @@ DEFAULT_OTEL_DEPLOYMENT_ENVIRONMENT = "development"
 DEFAULT_METRICS_INCLUDE_BANK_ID = False  # Disabled by default to avoid high-cardinality OTel metric growth
 DEFAULT_METRICS_BACKLOG_ENABLED = False  # Disabled by default: runs periodic per-schema COUNT queries

-# Runtime-stall observability defaults. Both are cheap and on by default: the
-# watchdog is a single background thread pinging the loop; the DB-pool acquire
-# timing is a monotonic() delta per acquire. They turn a failing liveness probe
-# from "pod restarted, cause unknown" into a logged root cause (blocked loop vs
-# pool exhaustion).
-DEFAULT_LOOP_WATCHDOG_ENABLED = True
-DEFAULT_LOOP_WATCHDOG_STALL_THRESHOLD_MS = 1000  # log a stall once the loop is unresponsive this long
-DEFAULT_LOOP_WATCHDOG_POLL_INTERVAL_MS = 250  # how often the watchdog thread pings the loop
-DEFAULT_DB_ACQUIRE_WARN_THRESHOLD_MS = 1000  # log a warning when a pool acquire waits this long
-
 # Audit log defaults
 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
@@ -1399,19 +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 == "":
@@ -1419,25 +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}")
@@ -1794,24 +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
-    llm_supports_max_items: bool = field(
-        default=DEFAULT_LLM_SUPPORTS_MAX_ITEMS,
-        kw_only=True,
-    )  # Whether structured-output schemas accept JSON Schema maxItems
    # 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
@@ -1839,10 +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
@@ -1862,7 +1623,6 @@ class HindsightConfig:
    retain_llm_max_backoff: float | None
    retain_llm_timeout: float | None
    retain_llm_litellmrouter_config: dict | None
-    retain_llm_reasoning_effort: str | None

    # Fireworks AI batch inference (static, server-level)
    fireworks_account_id: str | None
@@ -1879,7 +1639,6 @@ class HindsightConfig:
    reflect_llm_max_backoff: float | None
    reflect_llm_timeout: float | None
    reflect_llm_litellmrouter_config: dict | None
-    reflect_llm_reasoning_effort: str | None

    consolidation_llm_provider: str | None
    consolidation_llm_api_key: str | None
@@ -1891,13 +1650,11 @@ class HindsightConfig:
    consolidation_llm_max_backoff: float | None
    consolidation_llm_timeout: float | None
    consolidation_llm_litellmrouter_config: dict | None
-    consolidation_llm_reasoning_effort: str | None

    # Embeddings
    embeddings_provider: str
    embeddings_local_model: str
    embeddings_local_force_cpu: bool
-    embeddings_local_allow_mps: bool
    embeddings_local_trust_remote_code: bool
    embeddings_onnx_model_id: str
    embeddings_onnx_model_path: str | None
@@ -1928,7 +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
@@ -1940,10 +1696,8 @@ class HindsightConfig:

    # Reranker
    reranker_provider: str
-    reranker_send_bank_as_header: bool
    reranker_local_model: str
    reranker_local_force_cpu: bool
-    reranker_local_allow_mps: bool
    reranker_local_max_concurrent: int
    reranker_local_trust_remote_code: bool
    reranker_local_fp16: bool
@@ -1955,9 +1709,6 @@ class HindsightConfig:
    reranker_tei_http_timeout: float
    reranker_max_candidates: int
    semantic_min_similarity: float
-    graph_seed_min_similarity: float
-    temporal_semantic_min_similarity: float
-    semantic_link_min_similarity: float
    bm25_min_score: float
    recall_max_candidates_per_source: int
    recall_strategy_boosts: dict[str, str]
@@ -2145,7 +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)
@@ -2158,16 +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
-    retain_wall_timeout: 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
@@ -2178,25 +1924,11 @@ class HindsightConfig:
    metrics_include_bank_id: bool
    metrics_backlog_enabled: bool

-    # Runtime-stall observability (static, server-level only)
-    loop_watchdog_enabled: bool
-    loop_watchdog_stall_threshold_ms: int
-    loop_watchdog_poll_interval_ms: int
-    db_acquire_warn_threshold_ms: int
-
-    # 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)
@@ -2247,7 +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

@@ -2305,13 +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",
-        # Persist raw source text (documents.original_text / chunks.chunk_text).
-        # Per-bank so a data-minimizing bank can keep only derived facts while
-        # others retain the raw source for expansion/re-extraction.
-        "store_document_text",
        # Retention settings (behavioral)
        "retain_chunk_size",
        "retain_structured_chunk_size",
@@ -2450,18 +2174,10 @@ class HindsightConfig:
            self.text_search_extension_pg_search_tokenizer
        )

-        for field_name in (
-            "semantic_min_similarity",
-            "graph_seed_min_similarity",
-            "temporal_semantic_min_similarity",
-            "semantic_link_min_similarity",
-        ):
-            value = getattr(self, field_name)
-            if not 0.0 <= value <= 1.0:
-                raise ValueError(f"Invalid {field_name}: {value}. 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")
+        if not 0.0 <= self.semantic_min_similarity <= 1.0:
+            raise ValueError(
+                f"Invalid semantic_min_similarity: {self.semantic_min_similarity}. Must be between 0.0 and 1.0"
+            )

        # Validate bedrock_service_tier
        valid_bedrock_tiers = (None, "flex", "priority", "reserved")
@@ -2552,13 +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."""
@@ -2566,9 +2275,6 @@ class HindsightConfig:
        llm_provider = os.getenv(ENV_LLM_PROVIDER, DEFAULT_LLM_PROVIDER)
        llm_model = os.getenv(ENV_LLM_MODEL) or _get_default_model_for_provider(llm_provider)

-        # Parse per-type worker slot reservations (floors) once.
-        worker_slot_reservations = _parse_worker_slot_reservations()
-
        config = cls(
            # Database
            database_backend=os.getenv(ENV_DATABASE_BACKEND, DEFAULT_DATABASE_BACKEND).lower(),
@@ -2611,19 +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_supports_max_items=_parse_boolean_env(
-                ENV_LLM_SUPPORTS_MAX_ITEMS,
-                DEFAULT_LLM_SUPPORTS_MAX_ITEMS,
-            ),
            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
            ),
@@ -2648,8 +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))),
@@ -2687,7 +2380,6 @@ class HindsightConfig:
            else None,
            retain_llm_timeout=float(os.getenv(ENV_RETAIN_LLM_TIMEOUT)) if os.getenv(ENV_RETAIN_LLM_TIMEOUT) else None,
            retain_llm_litellmrouter_config=_parse_llm_router_config(ENV_RETAIN_LLM_LITELLMROUTER_CONFIG),
-            retain_llm_reasoning_effort=os.getenv(ENV_RETAIN_LLM_REASONING_EFFORT) or None,
            reflect_llm_provider=os.getenv(ENV_REFLECT_LLM_PROVIDER) or None,
            reflect_llm_api_key=os.getenv(ENV_REFLECT_LLM_API_KEY) or None,
            reflect_llm_model=os.getenv(ENV_REFLECT_LLM_MODEL)
@@ -2713,7 +2405,6 @@ class HindsightConfig:
            if os.getenv(ENV_REFLECT_LLM_TIMEOUT)
            else None,
            reflect_llm_litellmrouter_config=_parse_llm_router_config(ENV_REFLECT_LLM_LITELLMROUTER_CONFIG),
-            reflect_llm_reasoning_effort=os.getenv(ENV_REFLECT_LLM_REASONING_EFFORT) or None,
            consolidation_llm_provider=os.getenv(ENV_CONSOLIDATION_LLM_PROVIDER) or None,
            consolidation_llm_api_key=os.getenv(ENV_CONSOLIDATION_LLM_API_KEY) or None,
            consolidation_llm_model=os.getenv(ENV_CONSOLIDATION_LLM_MODEL)
@@ -2739,7 +2430,6 @@ class HindsightConfig:
            if os.getenv(ENV_CONSOLIDATION_LLM_TIMEOUT)
            else None,
            consolidation_llm_litellmrouter_config=_parse_llm_router_config(ENV_CONSOLIDATION_LLM_LITELLMROUTER_CONFIG),
-            consolidation_llm_reasoning_effort=os.getenv(ENV_CONSOLIDATION_LLM_REASONING_EFFORT) or None,
            # Multi-LLM chains (indexed members + routing strategy)
            llm_members=_parse_llm_members(""),
            llm_strategy=_parse_llm_strategy(os.getenv(ENV_LLM_STRATEGY)),
@@ -2756,10 +2446,6 @@ class HindsightConfig:
                ENV_EMBEDDINGS_LOCAL_FORCE_CPU, str(DEFAULT_EMBEDDINGS_LOCAL_FORCE_CPU)
            ).lower()
            in ("true", "1"),
-            embeddings_local_allow_mps=os.getenv(
-                ENV_EMBEDDINGS_LOCAL_ALLOW_MPS, str(DEFAULT_EMBEDDINGS_LOCAL_ALLOW_MPS)
-            ).lower()
-            in ("true", "1"),
            embeddings_local_trust_remote_code=os.getenv(
                ENV_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE, str(DEFAULT_EMBEDDINGS_LOCAL_TRUST_REMOTE_CODE)
            ).lower()
@@ -2869,9 +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),
@@ -2893,20 +2576,11 @@ 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)
            ).lower()
            in ("true", "1"),
-            reranker_local_allow_mps=os.getenv(
-                ENV_RERANKER_LOCAL_ALLOW_MPS, str(DEFAULT_RERANKER_LOCAL_ALLOW_MPS)
-            ).lower()
-            in ("true", "1"),
            reranker_local_max_concurrent=int(
                os.getenv(ENV_RERANKER_LOCAL_MAX_CONCURRENT, str(DEFAULT_RERANKER_LOCAL_MAX_CONCURRENT))
            ),
@@ -2933,21 +2607,7 @@ 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))),
-            graph_seed_min_similarity=float(
-                os.getenv(ENV_GRAPH_SEED_MIN_SIMILARITY, str(DEFAULT_GRAPH_SEED_MIN_SIMILARITY))
-            ),
-            temporal_semantic_min_similarity=float(
-                os.getenv(ENV_TEMPORAL_SEMANTIC_MIN_SIMILARITY, str(DEFAULT_TEMPORAL_SEMANTIC_MIN_SIMILARITY))
-            ),
-            semantic_link_min_similarity=float(
-                os.getenv(ENV_SEMANTIC_LINK_MIN_SIMILARITY, str(DEFAULT_SEMANTIC_LINK_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))
            ),
@@ -3242,10 +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",
@@ -3260,28 +2916,17 @@ class HindsightConfig:
            ),
            worker_http_port=int(os.getenv(ENV_WORKER_HTTP_PORT, str(DEFAULT_WORKER_HTTP_PORT))),
            worker_max_slots=int(os.getenv(ENV_WORKER_MAX_SLOTS, str(DEFAULT_WORKER_MAX_SLOTS))),
-            worker_slot_reservations=worker_slot_reservations,
+            worker_slot_reservations={
+                op_type: int(os.getenv(env_var, str(default)))
+                for op_type, (env_var, default) in WORKER_SLOT_RESERVATION_TYPES.items()
+                if int(os.getenv(env_var, str(default))) > 0
+            },
            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))),
-            retain_wall_timeout=int(os.getenv(ENV_RETAIN_WALL_TIMEOUT, str(DEFAULT_RETAIN_WALL_TIMEOUT))),
            # 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))
            ),
@@ -3336,18 +2981,6 @@ class HindsightConfig:
            in ("true", "1", "yes"),
            metrics_backlog_enabled=os.getenv(ENV_METRICS_BACKLOG_ENABLED, str(DEFAULT_METRICS_BACKLOG_ENABLED)).lower()
            in ("true", "1", "yes"),
-            # Runtime-stall observability (static, server-level only)
-            loop_watchdog_enabled=os.getenv(ENV_LOOP_WATCHDOG_ENABLED, str(DEFAULT_LOOP_WATCHDOG_ENABLED)).lower()
-            in ("true", "1", "yes"),
-            loop_watchdog_stall_threshold_ms=int(
-                os.getenv(ENV_LOOP_WATCHDOG_STALL_THRESHOLD_MS, str(DEFAULT_LOOP_WATCHDOG_STALL_THRESHOLD_MS))
-            ),
-            loop_watchdog_poll_interval_ms=int(
-                os.getenv(ENV_LOOP_WATCHDOG_POLL_INTERVAL_MS, str(DEFAULT_LOOP_WATCHDOG_POLL_INTERVAL_MS))
-            ),
-            db_acquire_warn_threshold_ms=int(
-                os.getenv(ENV_DB_ACQUIRE_WARN_THRESHOLD_MS, str(DEFAULT_DB_ACQUIRE_WARN_THRESHOLD_MS))
-            ),
            # Audit log configuration (static, server-level only)
            audit_log_enabled=os.getenv(ENV_AUDIT_LOG_ENABLED, str(DEFAULT_AUDIT_LOG_ENABLED)).lower() == "true",
            audit_log_actions=[
@@ -3356,11 +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=[
@@ -331,17 +331,11 @@ class ConfigResolver:
            logger.error(f"Failed to bulk-load bank configs: {e}")
        return result

-    async def validate_bank_config_updates(
-        self,
-        bank_id: str,
-        updates: dict[str, Any],
-        context: RequestContext | None = None,
-        *,
-        projected_bank_overrides: dict[str, Any] | None = None,
-        check_permissions: bool = True,
-    ) -> dict[str, Any]:
+    async def update_bank_config(
+        self, bank_id: str, updates: dict[str, Any], context: RequestContext | None = None
+    ) -> None:
        """
-        Normalize and validate bank configuration overrides.
+        Update bank configuration overrides (with permission checking).

        Args:
            bank_id: Bank identifier
@@ -350,16 +344,9 @@ class ConfigResolver:
                    or Python field format (llm_provider).
                    Only configurable fields are allowed.
            context: Request context for permission checking
-            projected_bank_overrides: Bank overrides to use as the validation
-                base instead of loading the current bank row.
-            check_permissions: Whether client field permissions apply to these
-                updates. Server-owned projected values set this to false.
-
-        Returns:
-            Normalized updates ready to persist.

        Raises:
-            ValueError: If attempting to override invalid/disallowed fields.
+            ValueError: If attempting to override invalid/disallowed fields
        """
        # Normalize keys
        normalized_updates = normalize_config_dict(updates)
@@ -391,7 +378,7 @@ class ConfigResolver:
                )

        # PERMISSIONS: Check tenant/bank permissions
-        if check_permissions and self.tenant_extension and context:
+        if self.tenant_extension and context:
            try:
                allowed_fields = await self.tenant_extension.get_allowed_config_fields(context, bank_id)
                if allowed_fields is not None:  # None means "allow all"
@@ -440,11 +427,7 @@ class ConfigResolver:
        )
        if chunking_fields_updated:
            config_dict = await self._resolve_parent_config_dict(bank_id, context)
-            active_bank_overrides = (
-                await self._load_bank_config(bank_id)
-                if projected_bank_overrides is None
-                else dict(projected_bank_overrides)
-            )
+            active_bank_overrides = await self._load_bank_config(bank_id)
            for key, value in normalized_updates.items():
                if key not in self._configurable_fields:
                    continue
@@ -460,26 +443,17 @@ class ConfigResolver:
            )
            _validate_retain_strategy_chunking(base_config, base_config.retain_strategies)

-        return normalized_updates
+        # Persist the override. Banks are created lazily (on first retain), so a
+        # PATCH that precedes any ingestion would otherwise UPDATE zero rows and
+        # silently no-op while returning 200. Ensure the bank row exists first
+        # (this also creates its per-bank vector indexes), then merge defensively:
+        # COALESCE guards against a NULL config column (NULL || jsonb is NULL),
+        # which would drop the override even when a row is updated.
+        from .engine.retain.fact_storage import ensure_bank_exists

-    async def update_bank_config(
-        self, bank_id: str, updates: dict[str, Any], context: RequestContext | None = None
-    ) -> None:
-        """Validate and persist bank configuration overrides for an existing bank.
-
-        Bank creation belongs to ``MemoryEngine``; this raises ``ValueError`` if
-        the bank does not exist rather than silently discarding the overrides.
-        """
-        normalized_updates = await self.validate_bank_config_updates(bank_id, updates, context)
-        await self._persist_bank_config(bank_id, normalized_updates)
-
-    async def _persist_bank_config(self, bank_id: str, normalized_updates: dict[str, Any]) -> None:
-        """Persist already-validated overrides without changing bank lifecycle state."""
-        # Bank lifecycle belongs to MemoryEngine. Callers must create the row
-        # before reaching this persistence step. COALESCE guards against a NULL
-        # config column (NULL || jsonb is NULL), which would drop the override.
        async with self._backend.acquire() as conn:
-            result = await conn.execute(
+            await ensure_bank_exists(conn, bank_id, ops=self._backend.ops)
+            await conn.execute(
                f"""
                UPDATE {fq_table("banks")}
                SET config = COALESCE(config, '{{}}'::jsonb) || $1::jsonb,
@@ -490,14 +464,6 @@ class ConfigResolver:
                bank_id,
            )

-        # A missing bank row matches zero rows, which would otherwise persist
-        # nothing while reporting success. Fail loudly instead: reaching here
-        # without the row means a caller skipped the engine's provisioning step.
-        # (The Oracle wrapper reshapes rowcount into the same "UPDATE <n>" form.)
-        updated = int(result.split()[-1]) if isinstance(result, str) and result.startswith("UPDATE") else 0
-        if updated == 0:
-            raise ValueError(f"Cannot update config for bank '{bank_id}': the bank does not exist")
-
        logger.info(f"Updated bank config for {bank_id}: {list(normalized_updates.keys())}")

    async def reset_bank_config(self, bank_id: str) -> None:
@@ -14,7 +14,10 @@ import subprocess
 import sys
 import time
 from pathlib import Path
-from typing import IO
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import IO

 logger = logging.getLogger(__name__)

@@ -39,28 +42,37 @@ class IdleTimeoutMiddleware:
        self.app = app
        self.idle_timeout = idle_timeout
        self.last_activity = time.time()
+        self._checker_task = None

    async def __call__(self, scope, receive, send):
+        # Update activity timestamp on each request
        self.last_activity = time.time()
        await self.app(scope, receive, send)

+    def start_idle_checker(self):
+        """Start the background task that checks for idle timeout."""
+        self._checker_task = asyncio.create_task(self._check_idle())
+
    async def _check_idle(self):
-        """Exit the daemon after the configured period without requests."""
+        """Background task that exits the process after idle timeout."""
+        # If idle_timeout is 0, don't auto-exit
        if self.idle_timeout <= 0:
            return

        while True:
-            await asyncio.sleep(30)
+            await asyncio.sleep(30)  # Check every 30 seconds
            idle_time = time.time() - self.last_activity
            if idle_time > self.idle_timeout:
                logger.info(f"Idle timeout reached ({self.idle_timeout}s), shutting down daemon")
+                # Give a moment for any in-flight requests
                await asyncio.sleep(1)
+                # Send SIGTERM to ourselves to trigger graceful shutdown
                import signal

                os.kill(os.getpid(), signal.SIGTERM)


-def _detach_popen_kwargs(log_handle: IO[bytes]) -> dict:
+def _detach_popen_kwargs(log_handle: "IO[bytes]") -> dict:
    """Cross-platform kwargs to spawn a subprocess detached from the caller.

    On POSIX, ``start_new_session=True`` calls ``setsid(2)`` so the child
@@ -157,3 +169,17 @@ def daemonize():
        subprocess.Popen(cmd, env=env, **_detach_popen_kwargs(log_handle))

    sys.exit(0)
+
+
+def check_daemon_running(port: int = DEFAULT_DAEMON_PORT) -> bool:
+    """Check if a daemon is running and responsive on the given port."""
+    import socket
+
+    try:
+        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        sock.settimeout(1)
+        result = sock.connect_ex(("127.0.0.1", port))
+        sock.close()
+        return result == 0
+    except Exception:
+        return False
@@ -3,7 +3,7 @@ Memory Engine - Core implementation of the memory system.

 This package contains all the implementation details of the memory engine:
 - MemoryEngine: Main class for memory operations
- Utility modules: embedding_utils, link_utils, bank_utils
+- Utility modules: embedding_utils, link_utils, think_utils, bank_utils
 - Supporting modules: embeddings, cross_encoder, entity_resolver, etc.
 """

@@ -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
@@ -19,8 +19,6 @@ from typing import Any
 from pydantic import BaseModel, Field

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

 logger = logging.getLogger(__name__)

@@ -121,60 +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))
@@ -189,12 +150,8 @@ class AuditLogger:
            logger.debug("Audit log skipped: pool not available")
            return
        try:
-            # fq_table_explicit qualifies per dialect: "schema".audit_log on
-            # PostgreSQL, bare audit_log on Oracle (where the schema is set at the
-            # session level). A raw f"{schema}.audit_log" produced public.audit_log
-            # on Oracle, where "public" is a reserved word — every write failed
-            # with ORA-00903 even though the table exists.
-            table = fq_table_explicit("audit_log", self._schema_getter())
+            schema = self._schema_getter()
+            table = f"{schema}.audit_log"
            async with acquire_with_retry(pool, max_retries=1) as conn:
                await conn.execute(
                    f"""
@@ -225,7 +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.

@@ -234,7 +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 {}
@@ -1,70 +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.
-"""
-
-from dataclasses import dataclass
-from typing import Any
-
-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)
-
-DEFAULT_CAUSAL_LINK_WEIGHT = 1.0
-
-
-@dataclass(frozen=True)
-class CausalLinkDescriptor:
-    """One causal edge, parked on the curation archive while an endpoint is invalidated.
-
-    Invalidation moves a fact out of ``memory_units``, so the FK cascade deletes
-    its ``memory_links`` rows — and nothing could recreate a causal edge, which
-    is extraction output rather than derived data. The descriptor is what the
-    archive row stores so revert can rematerialize the edge (#2864).
-    """
-
-    from_unit_id: str
-    to_unit_id: str
-    link_type: str
-    weight: float = DEFAULT_CAUSAL_LINK_WEIGHT
-
-    def as_json_dict(self) -> dict[str, Any]:
-        """Serializable form written to ``invalidated_memory_units.causal_links``.
-
-        The key names double as the column list of the ``jsonb_to_recordset``
-        read in ``snapshot_causal_links`` — keep them in sync.
-        """
-        return {
-            "from_unit_id": self.from_unit_id,
-            "to_unit_id": self.to_unit_id,
-            "link_type": self.link_type,
-            "weight": self.weight,
-        }
-
-    @classmethod
-    def from_json_dict(cls, raw: Any) -> "CausalLinkDescriptor | None":
-        """Parse one stored descriptor, or None when it isn't a usable causal edge.
-
-        The archive column is plain JSON with no schema enforcement (a restore
-        from an older backup, or a hand-edited row, can put anything there), and
-        ``memory_links`` has a ``link_type`` CHECK constraint — so an unusable
-        entry is skipped rather than allowed to abort the whole revert.
-        """
-        if not isinstance(raw, dict):
-            return None
-        from_unit_id = raw.get("from_unit_id")
-        to_unit_id = raw.get("to_unit_id")
-        link_type = raw.get("link_type")
-        if not from_unit_id or not to_unit_id or link_type not in CAUSAL_LINK_TYPES:
-            return None
-        return cls(
-            from_unit_id=str(from_unit_id),
-            to_unit_id=str(to_unit_id),
-            link_type=str(link_type),
-            weight=float(raw.get("weight") or DEFAULT_CAUSAL_LINK_WEIGHT),
-        )
@@ -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
@@ -59,25 +58,6 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)


-def _native_search_vector_update(config, param: str) -> str:
-    """UPDATE-clause fragment that repopulates ``search_vector`` inline, or ''
-    when the backend does not maintain a native tsvector column that way.
-
-    ``to_tsvector(...)::regconfig`` is PostgreSQL-only. On Oracle ``search_vector``
-    is a CLOB maintained by Oracle's own text index rather than an inline
-    tsvector, so emit nothing there (mirrors the insert path, which gates
-    ``search_vector`` on the PG-only ``pg_search_vector_expr``). Without this
-    guard the PG expression reaches Oracle and fails with DPY-4010 (the
-    ``::regconfig`` cast becomes an unbound ``:REGCONFIG`` placeholder).
-    """
-    from ..schema import _is_oracle  # noqa: PLC0415
-
-    if config.text_search_extension != "native" or _is_oracle():
-        return ""
-    lang = config.text_search_extension_native_language
-    return f",\n            search_vector = to_tsvector('{lang}'::regconfig, COALESCE({param}, ''))"
-
-
 def _norm_obs_text(text: str) -> str:
    """Whitespace-normalised observation text for exact-duplicate matching.

@@ -122,29 +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:
@@ -152,20 +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:
@@ -228,7 +174,7 @@ async def _dedup_adjudicate(
    grouped = await retrieve_semantic_bm25_combined(
        conn, anchor_emb_str, anchor_text, bank_id, ["observation"], _DEDUP_TOP_K, tags=tags, tags_match=tags_match
    )
-    results = grouped["observation"].semantic
+    results = grouped.get("observation", ([], []))[0]
    best_id: str | None = None
    best_text = ""
    best_sim = threshold  # only candidates at/above the threshold are considered
@@ -243,13 +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)
@@ -281,7 +224,11 @@ async def _dedup_reconcile_create(
    # Fold the new source facts into the twin and persist the merged text. We keep the twin's
    # existing embedding: the merged text is >= threshold similar, so the stored vector stays
    # representative and we avoid a re-embed + a dialect-specific vector UPDATE.
-    search_vector_clause = _native_search_vector_update(config, "$1")
+    search_vector_clause = (
+        f",\n            search_vector = to_tsvector('{config.text_search_extension_native_language}'::regconfig, COALESCE($1, ''))"
+        if config.text_search_extension == "native"
+        else ""
+    )
    await conn.execute(
        f"""
        UPDATE {fq_table("memory_units")}
@@ -337,7 +284,11 @@ async def _dedup_reconcile_update(
    # the create path) then delete the now-redundant updated row. The all_strict/any tag match
    # guarantees twin and updated share scope, so dropping the updated row's tags loses no
    # visibility. Temporal fields follow the surviving twin (minimal scope; matches create).
-    search_vector_clause = _native_search_vector_update(config, "$1")
+    search_vector_clause = (
+        f",\n            search_vector = to_tsvector('{config.text_search_extension_native_language}'::regconfig, COALESCE($1, ''))"
+        if config.text_search_extension == "native"
+        else ""
+    )
    await conn.execute(
        f"""
        UPDATE {fq_table("memory_units")} t
@@ -690,19 +641,9 @@ def _effective_scope_limit(config: Any, fact_tags: list[str]) -> int:
    return config.max_observations_per_scope


-def _build_response_model(
-    max_creates: int | None = None,
-    *,
-    supports_max_items: bool = True,
-) -> type[_ConsolidationBatchResponse]:
-    """Build a response model, optionally constraining creates via JSON schema.
-
-    Some structured-output backends (notably Bedrock Converse) reject the JSON
-    Schema ``maxItems`` keyword emitted by Pydantic's list ``max_length``. Operators
-    can disable the schema hint for those backends; the prompt capacity note and
-    post-response truncation still enforce the observation cap.
-    """
-    if not supports_max_items or max_creates is None or max_creates < 0:
+def _build_response_model(max_creates: int | None = None) -> type[_ConsolidationBatchResponse]:
+    """Build a response model, optionally constraining max creates via JSON schema."""
+    if max_creates is None or max_creates < 0:
        return _ConsolidationBatchResponse

    from pydantic import Field as PydanticField
@@ -1825,22 +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"""
@@ -1921,7 +1855,11 @@ async def _execute_update_action(

    config = get_config()

-    search_vector_clause = _native_search_vector_update(config, "$1")
+    search_vector_clause = (
+        f",\n            search_vector = to_tsvector('{config.text_search_extension_native_language}'::regconfig, COALESCE($1, ''))"
+        if config.text_search_extension == "native"
+        else ""
+    )

    t0 = time.time()
    await conn.execute(
@@ -2037,6 +1975,30 @@ async def _execute_delete_action(
    logger.debug(f"Deleted observation {observation_id}")


+async def _create_memory_links(
+    conn: "Connection",
+    memory_id: uuid.UUID,
+    observation_id: uuid.UUID,
+) -> None:
+    """
+    Placeholder for observation link creation.
+
+    Observations do NOT get any memory_links copied from their source facts.
+    Instead, retrieval uses source_memory_ids to traverse:
+    - Entity connections: observation → source_memory_ids → unit_entities
+    - Semantic similarity: observations have their own embeddings
+    - Temporal proximity: observations have their own temporal fields
+
+    This avoids data duplication and ensures observations are always
+    connected via their source facts' relationships.
+
+    The memory_id and observation_id parameters are kept for interface
+    compatibility but no links are created.
+    """
+    # No links are created - observations rely on source_memory_ids for traversal
+    pass
+
+
 async def _find_related_observations(
    memory_engine: "MemoryEngine",
    bank_id: str,
@@ -2268,10 +2230,7 @@ async def _consolidate_batch_with_llm(
            cached_prefix_name = None

    # Use a constrained response model when observation limit is active
-    response_model = _build_response_model(
-        max_creates=remaining_observation_slots,
-        supports_max_items=config.llm_supports_max_items,
-    )
+    response_model = _build_response_model(max_creates=remaining_observation_slots)

    max_attempts = config.consolidation_max_attempts
    inner_max_retries = config.consolidation_llm_max_retries
@@ -2295,11 +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
@@ -2383,8 +2337,6 @@ async def _create_observation_directly(
    observation_id = uuid.uuid4()

    # Query varies based on text search backend
-    from ..schema import _is_oracle  # noqa: PLC0415
-
    config = get_config()
    if config.text_search_extension == "vchord":
        # VectorChord: manually tokenize and insert search_vector
@@ -2397,12 +2349,10 @@ async def _create_observation_directly(
                    tokenize($3, 'llmlingua2')::bm25_catalog.bm25vector)
            RETURNING id
        """
-    elif config.text_search_extension == "native" and not _is_oracle():
-        # Native (PostgreSQL): search_vector is populated with to_tsvector()
-        # using the configured native language dictionary, matching the batch
-        # insert path in ops_postgresql.insert_facts_batch. On Oracle this falls
-        # through to the no-search_vector branch below (Oracle maintains its text
-        # index separately; to_tsvector/::regconfig is PG-only).
+    elif config.text_search_extension == "native":
+        # Native: search_vector is populated with to_tsvector() using the
+        # configured native language dictionary, matching the batch insert
+        # path in ops_postgresql.insert_facts_batch.
        query = f"""
            INSERT INTO {fq_table("memory_units")} (
                id, bank_id, text, fact_type, embedding, proof_count, source_memory_ids,
@@ -37,36 +37,19 @@ _PROCESSING_RULES = """## PROCESSING RULES

 9. KEEP DISTINCT TOPICS DISTINCT: do not merge observations about different people, entities, or unrelated topics. Merging is for the same canonical fact recurring — not for related-but-distinct claims."""

-# Field-by-field definitions of the input shape used by the cached system
-# prefix. The call site runs .format(), so these strings must contain no braces.
-_FACT_FIELDS = """One per line, formatted as `[uuid] fact text (temporal fields)`:
- `[uuid]`: the fact's identifier — copy it verbatim into `source_fact_ids`
- `occurred_start` / `occurred_end`: when the described event happened. This can be long before the fact was stated — a fact recorded today may describe a 2019 event.
- `mentioned_at`: when the source material that states this fact was written. This is the fact's recency: how up to date the statement is, NOT when it was added to memory. A fact taken from an old document keeps its old `mentioned_at` even if it was only just processed."""
-
-_OBSERVATION_FIELDS = """- `id`: unique identifier — copy this exactly when issuing an UPDATE or DELETE
- `text`: the observation content
- `proof_count`: how many source facts this observation has already merged
- `occurred_start` / `occurred_end`: the span of the events behind the observation — earliest start and latest end across its source facts
- `mentioned_at`: the latest of the `mentioned_at` values of its source facts — the most recent point at which this observation was stated
- `source_memories`: the supporting facts behind this observation. May be partial or absent for large observations — the count above remains the true total. Each entry carries the same `text` and temporal fields as a new fact, plus:
-  - `context`: optional surrounding context for that fact"""
-
 # Stable description of the input shape. For the cached split path this lives in
 # the system prefix (build_consolidation_system_prompt) so it is not re-sent on
 # every batch; the per-batch user message then carries only the actual data.
-_INPUT_FORMAT_NOTE = f"""## INPUT FORMAT
+_INPUT_FORMAT_NOTE = """## INPUT FORMAT

-Each request provides new facts and existing observations. Every temporal field is optional and is omitted when unknown.
-
-### New facts
-
-{_FACT_FIELDS}
-
-### Existing observations
-
-A JSON array pooled from recalls across the new facts. Each entry has:
-{_OBSERVATION_FIELDS}"""
+Each request provides new facts and existing observations:
+- New facts: one per line, each prefixed with its `[uuid]`, followed by the fact text and optional temporal fields.
+- Existing observations: a JSON array pooled from recalls across the new facts. Each entry has:
+  - `id`: unique identifier — copy this exactly when issuing an UPDATE or DELETE
+  - `text`: the observation content
+  - `proof_count`: number of supporting memories
+  - `occurred_start` / `occurred_end`: temporal range of source facts
+  - `source_memories`: array of supporting facts with their text and dates"""

 # Per-batch data section for the cached split path — the stable format
 # explanation above is omitted here (it lives in the cached prefix); only the
@@ -81,6 +64,24 @@ _SPLIT_INPUT_SECTION = """## INPUT

 {observations_text}"""

+# Data section — format placeholders {facts_text} and {observations_text} are substituted at call time
+_INPUT_SECTION = """## INPUT
+
+### New facts
+
+{facts_text}
+
+### Existing observations
+
+JSON array, pooled from recalls across all new facts above. Each entry has:
+- `id`: unique identifier — copy this exactly when issuing an UPDATE or DELETE
+- `text`: the observation content
+- `proof_count`: number of supporting memories
+- `occurred_start` / `occurred_end`: temporal range of source facts
+- `source_memories`: array of supporting facts with their text and dates
+
+{observations_text}"""
+
 _DECISION_GUIDE = """## DECISION GUIDE

 - **Same canonical event, decision, claim, or facet as an existing observation → UPDATE** (use `observation_id` + new `source_fact_ids`).
@@ -141,6 +142,39 @@ Expected output (UPDATE for the state change; CREATE for the unrelated work-hour
 - Return `{{"creates": [], "updates": [], "deletes": []}}` if nothing durable is found."""


+def build_batch_consolidation_prompt(
+    observations_mission: str | None = None,
+    observation_capacity_note: str | None = None,
+    llm_output_language: str | None = None,
+) -> str:
+    """
+    Build the consolidation prompt for batch mode (multiple facts per LLM call).
+
+    The mission defines *what* to track (customisable per bank) and takes
+    priority over the built-in processing rules when the two conflict.
+    Processing rules, decision guide, and output format are always present.
+    When ``llm_output_language`` is set, observations are emitted in that
+    language.
+    """
+    mission = escape_for_prompt(observations_mission or _DEFAULT_MISSION)
+
+    capacity_section = ""
+    if observation_capacity_note:
+        capacity_section = f"\n\n## CAPACITY CONSTRAINT\n\n{escape_for_prompt(observation_capacity_note)}"
+
+    return (
+        "You are a memory consolidation system. Synthesize new facts into "
+        "observations, merging with existing observations when appropriate.\n\n"
+        f"## MISSION\n\n{mission}\n\n"
+        f"{_MISSION_PRIORITY_NOTE}"
+        f"{capacity_section}\n\n"
+        f"{_PROCESSING_RULES}\n\n"
+        f"{_INPUT_SECTION}\n\n"
+        f"{_DECISION_GUIDE}\n\n"
+        f"{_OUTPUT_SECTION}" + output_language_directive(llm_output_language)
+    )
+
+
 def build_consolidation_system_prompt(
    llm_output_language: str | None = None,
 ) -> str:
@@ -12,7 +12,6 @@ import os
 import warnings
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
-from typing import Any

 import httpx

@@ -46,17 +45,47 @@ from ..config import (
    ENV_RERANKER_TEI_URL,
    ENV_RERANKER_ZEROENTROPY_API_KEY,
 )
-from .bank_attribution import reranker_bank_attribution_headers
-from .local_device import (
-    release_local_inference_memory,
-    resolve_model_device_type,
-    select_local_device,
-)
-from .tei_retry import tei_retry_delay

 logger = logging.getLogger(__name__)


+def _resolve_malloc_trim():
+    """Return a callable that asks glibc to release freed heap pages to the OS.
+
+    Local CPU rerankers (FlashRank/ONNX, SentenceTransformers/torch) allocate
+    large transient numpy/tensor buffers per call. On Linux glibc, those pages
+    are freed at the Python level but kept by the allocator as a high-water
+    mark — RSS grows monotonically across many recalls (see issue #1717).
+    Calling `malloc_trim(0)` after each batch returns those pages to the OS.
+
+    Resolved once at import; returns a no-op on non-glibc platforms (macOS,
+    musl, Windows) where the call is unavailable or unnecessary.
+    """
+    import sys
+
+    if sys.platform != "linux":
+        return lambda: None
+
+    import ctypes
+    import ctypes.util
+
+    libc_path = ctypes.util.find_library("c")
+    if libc_path is None:
+        return lambda: None
+    try:
+        libc = ctypes.CDLL(libc_path)
+        trim = libc.malloc_trim
+    except (OSError, AttributeError):
+        # Not glibc (musl has no malloc_trim) or libc lookup failed.
+        return lambda: None
+    trim.argtypes = [ctypes.c_size_t]
+    trim.restype = ctypes.c_int
+    return lambda: trim(0)
+
+
+_malloc_trim = _resolve_malloc_trim()
+
+
 class CrossEncoderModel(ABC):
    """
    Abstract base class for cross-encoder reranking.
@@ -121,7 +150,6 @@ class LocalSTCrossEncoder(CrossEncoderModel):
        fp16: bool = False,
        bucket_batching: bool = False,
        batch_size: int = DEFAULT_RERANKER_LOCAL_BATCH_SIZE,
-        allow_mps: bool = False,
    ):
        """
        Initialize local SentenceTransformers cross-encoder.
@@ -143,9 +171,6 @@ class LocalSTCrossEncoder(CrossEncoderModel):
                            Default: False (opt-in via env var).
            batch_size: Batch size for predict() calls. Optimal values vary by
                       hardware and model (MPS: 32, CUDA: 128+). Default: 32.
-            allow_mps: Opt in to the Apple Silicon MPS GPU. Disabled by default
-                      because MPS leaks memory under variable-length workloads
-                      (see engine/local_device.py). Default: False
        """
        self.model_name = model_name or DEFAULT_RERANKER_LOCAL_MODEL
        self.force_cpu = force_cpu
@@ -153,9 +178,7 @@ class LocalSTCrossEncoder(CrossEncoderModel):
        self.fp16 = fp16
        self.bucket_batching = bucket_batching
        self.batch_size = batch_size
-        self.allow_mps = allow_mps
        self._model = None
-        self._device_type: str = "cpu"
        LocalSTCrossEncoder._max_concurrent = max_concurrent

    @property
@@ -177,13 +200,33 @@ class LocalSTCrossEncoder(CrossEncoderModel):

        logger.info(f"Reranker: initializing local provider with model {self.model_name}")

-        # Determine device based on hardware availability. We always set
-        # low_cpu_mem_usage=False to prevent lazy loading (meta tensors) which can
-        # cause issues when accelerate is installed but no GPU is available.
+        # Determine device based on hardware availability.
+        # We always set low_cpu_mem_usage=False to prevent lazy loading (meta tensors)
+        # which can cause issues when accelerate is installed but no GPU is available.
        # Note: We do NOT use device_map because CrossEncoder internally calls .to(device)
        # after loading, which conflicts with accelerate's device_map handling.
-        # MPS is opt-in (allow_mps) — see engine/local_device.py for why.
-        device = select_local_device(self.force_cpu, self.allow_mps)
+        import torch
+
+        # Force CPU mode if configured (used in daemon mode to avoid MPS/XPC issues on macOS)
+        if self.force_cpu:
+            device = "cpu"
+            logger.info("Reranker: forcing CPU mode (HINDSIGHT_API_RERANKER_LOCAL_FORCE_CPU=1)")
+        else:
+            # Check for GPU (CUDA), Apple Silicon (MPS), or Intel XPU
+            # Wrap in try-except to gracefully handle any device detection issues
+            # (e.g., in CI environments or when PyTorch is built without GPU support)
+            device = "cpu"  # Default to CPU
+            try:
+                has_gpu = torch.cuda.is_available() or (
+                    hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
+                )
+                # Intel Arc XPU support — torch.xpu is available when the XPU build is loaded
+                if not has_gpu and hasattr(torch, "xpu"):
+                    has_gpu = torch.xpu.is_available()
+                if has_gpu:
+                    device = None  # Let sentence-transformers auto-detect GPU/MPS/XPU
+            except Exception as e:
+                logger.warning(f"Failed to detect GPU/MPS/XPU, falling back to CPU: {e}")

        # Patch transformers 5.x compatibility for models using XLM-RoBERTa
        # (e.g., jina-reranker-v2-base-multilingual). transformers 5.x removed
@@ -227,11 +270,9 @@ class LocalSTCrossEncoder(CrossEncoderModel):
                # Restore original logging level
                transformers_logger.setLevel(original_level)

-        self._device_type = resolve_model_device_type(self._model)
-
        # FP16 inference: convert model weights to half precision.
        # Empirically validated: 27-36% faster on MPS, quality-identical (20/20 overlap).
-        if self.fp16 and self._device_type != "cpu":
+        if self.fp16 and device != "cpu":
            self._model.model.half()
            logger.info("Reranker: FP16 inference enabled")

@@ -274,7 +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_local_inference_memory(self._device_type)
+            _malloc_trim()

    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
        """
@@ -390,20 +431,14 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
                        await asyncio.sleep(delay)
                        delay *= 2  # Exponential backoff
                except httpx.HTTPStatusError as e:
-                    # TEI uses 429 as normal overload backpressure. Retry it with
-                    # the same bounded budget as transient server errors.
-                    if (e.response.status_code == 429 or e.response.status_code >= 500) and attempt < self.max_retries:
+                    # Retry on 5xx server errors
+                    if e.response.status_code >= 500 and attempt < self.max_retries:
                        last_error = e
-                        sleep_delay = tei_retry_delay(
-                            e.response,
-                            delay,
-                            request_timeout=self.timeout,
-                        )
                        logger.warning(
-                            f"TEI transient error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. "
-                            f"Retrying in {sleep_delay:.2f}s..."
+                            f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. "
+                            f"Retrying in {delay}s..."
                        )
-                        await asyncio.sleep(sleep_delay)
+                        await asyncio.sleep(delay)
                        delay *= 2
                    else:
                        raise
@@ -449,7 +484,6 @@ class RemoteTEICrossEncoder(CrossEncoderModel):
                semaphore,
                "POST",
                f"{self.base_url}/rerank",
-                headers=reranker_bank_attribution_headers(),
                json={
                    "query": query,
                    "texts": texts,
@@ -590,11 +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()

@@ -889,7 +919,6 @@ class FlashRankCrossEncoder(CrossEncoderModel):
        self.max_length = max_length
        self.cpu_mem_arena = cpu_mem_arena
        self._ranker = None
-        self._device_type: str = "cpu"  # FlashRank runs on CPU via ONNX Runtime
        FlashRankCrossEncoder._max_concurrent = max_concurrent

    @property
@@ -961,11 +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]]] = {}
@@ -994,7 +1023,7 @@ class FlashRankCrossEncoder(CrossEncoderModel):

            return all_scores
        finally:
-            release_local_inference_memory(self._device_type)
+            _malloc_trim()

    async def predict(self, pairs: list[tuple[str, str]]) -> list[float]:
        """
@@ -1122,7 +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,
@@ -1241,11 +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
@@ -1254,9 +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

@@ -1608,7 +1647,6 @@ def create_cross_encoder_from_env() -> CrossEncoderModel:
            fp16=config.reranker_local_fp16,
            bucket_batching=config.reranker_local_bucket_batching,
            batch_size=config.reranker_local_batch_size,
-            allow_mps=config.reranker_local_allow_mps,
        )
    elif provider == "cohere":
        api_key = config.reranker_cohere_api_key
@@ -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,
@@ -282,29 +253,22 @@ class OracleOps(DataAccessOps):
    ) -> None:
        if not unit_ids:
            return
-        # Locking upsert (#3034), the Oracle analogue of the PG
-        # ``ON CONFLICT DO UPDATE``. The old IGNORE_ROW_ON_DUPKEY_INDEX insert
-        # skipped duplicates WITHOUT locking the existing row, so a mutation
-        # re-enqueueing an already-queued unit could not block a worker from
-        # concurrently claiming (deleting) that row and processing the unit's
-        # pre-mutation state — the re-enqueue signal was silently lost. MERGE
-        # WHEN MATCHED takes an exclusive row lock on the existing queue row
-        # (the SET is a deliberate no-op that preserves enqueued_at); WHEN NOT
-        # MATCHED inserts a fresh row. That serialises the mutation against the
-        # worker's claim for the same (bank_id, unit_id).
+        # Oracle doesn't support ON CONFLICT; rely on the PK and the
+        # IGNORE_ROW_ON_DUPKEY_INDEX hint to skip duplicates server-side.
+        # The hint name must match the PK constraint exactly.
        #
-        # Sort to enforce a global (bank_id, unit_id) lock-acquisition order,
-        # matching claim_graph_maintenance_batch's delete order, so overlapping
-        # mutation/worker sets acquire the shared row locks ascending and cannot
-        # cycle.
+        # Sort to enforce a global lock-acquisition order on the
+        # (bank_id, unit_id) PK. Without this, two concurrent
+        # transactions inserting overlapping unit_id sets in different
+        # orders can deadlock on the unique-check row locks. Sorting
+        # gives every concurrent caller the same lock order, so
+        # conflicting inserts queue cleanly instead of cycling.
        sorted_unit_ids = sorted(unit_ids)
        await conn.executemany(
            f"""
-            MERGE INTO {table} q
-            USING (SELECT $1 AS bank_id, $2 AS unit_id FROM dual) s
-            ON (q.bank_id = s.bank_id AND q.unit_id = s.unit_id)
-            WHEN MATCHED THEN UPDATE SET q.enqueued_at = q.enqueued_at
-            WHEN NOT MATCHED THEN INSERT (bank_id, unit_id) VALUES (s.bank_id, s.unit_id)
+            INSERT /*+ IGNORE_ROW_ON_DUPKEY_INDEX({table}, pk_graph_maintenance_queue) */
+            INTO {table} (bank_id, unit_id)
+            VALUES ($1, $2)
            """,
            [(bank_id, uid) for uid in sorted_unit_ids],
        )
@@ -329,15 +293,7 @@ class OracleOps(DataAccessOps):
            bank_id,
            limit,
        )
-        # Ordered locking (#3034): the per-row DELETE takes the queue rows'
-        # exclusive locks in executemany array order. Sort the claimed keys by
-        # unit_id so those locks are acquired in the same (bank_id, unit_id)
-        # order the enqueue MERGE uses — overlapping mutation/worker sets then
-        # lock the shared rows ascending and cannot cycle. (The batch is still
-        # *chosen* oldest-first by enqueued_at above; only the lock/delete order
-        # is normalised.) The Pass 1 retry wrap in run_graph_maintenance_job is
-        # the ORA-00060 backstop for any residual interleaving.
-        claimed = sorted(str(row["unit_id"]) for row in rows)
+        claimed = [str(row["unit_id"]) for row in rows]
        if claimed:
            await conn.executemany(
                f"DELETE FROM {table} WHERE bank_id = $1 AND unit_id = $2",
@@ -373,12 +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}
@@ -497,14 +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
@@ -517,6 +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
            )"""
@@ -881,157 +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,
@@ -253,20 +286,11 @@ class PostgreSQLOps(DataAccessOps):
        entity_names: list[str],
        entity_dates: list,
    ) -> dict[str, str]:
-        # ORDER BY LOWER(name) so every concurrent batch inserts in the same order
-        # as the conflict target (bank_id, LOWER(canonical_name)). ON CONFLICT DO
-        # NOTHING takes a ShareLock on the inserting transaction of any speculative
-        # row it collides with, so two batches with overlapping names inserting in
-        # different orders deadlock. The caller already sorts by Python's
-        # ``str.lower()``, which agrees with the index for ASCII but not for every
-        # locale (see the Turkish-İ note in entity_resolver) — ordering in SQL makes
-        # the database's own collation the single arbiter for all writers.
        inserted_rows = await conn.fetch(
            f"""
            INSERT INTO {table} (bank_id, canonical_name, first_seen, last_seen, mention_count)
            SELECT $1, name, COALESCE(event_date, now()), COALESCE(event_date, now()), 0
            FROM unnest($2::text[], $3::timestamptz[]) AS t(name, event_date)
-            ORDER BY LOWER(name)
            ON CONFLICT (bank_id, LOWER(canonical_name))
            DO NOTHING
            RETURNING id, LOWER(canonical_name) AS name_lower
@@ -286,7 +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
@@ -298,42 +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,
@@ -369,24 +357,11 @@ class PostgreSQLOps(DataAccessOps):
        # concurrent caller the same lock order, so conflicting inserts
        # queue cleanly instead of cycling.
        sorted_unit_ids = sorted(unit_ids)
-        # DO UPDATE (not DO NOTHING) on a duplicate enqueue — #3034. The SET is a
-        # deliberate no-op that preserves enqueued_at; its only purpose is to take
-        # the existing row's lock. DO NOTHING does NOT lock the conflicting row, so
-        # a mutation that re-enqueues an already-queued unit could not block a
-        # worker from concurrently claiming (deleting) that row and processing the
-        # unit's pre-mutation state; the re-enqueue signal was then silently lost
-        # and the unit's derived links stayed stale with an empty queue. Locking
-        # the row serialises the mutation against the worker's claim for that
-        # (bank_id, unit_id): the worker either waits for the committed post-mutation
-        # state, or (if it claimed first) this INSERT lands a fresh row after the
-        # worker's delete commits. Row locks are acquired in sorted unit_id order,
-        # matching claim_graph_maintenance_batch, so the two never cycle.
        await conn.execute(
            f"""
            INSERT INTO {table} (bank_id, unit_id)
            SELECT $1, v FROM unnest($2::uuid[]) AS t(v)
-            ON CONFLICT (bank_id, unit_id)
-                DO UPDATE SET enqueued_at = {table}.enqueued_at
+            ON CONFLICT (bank_id, unit_id) DO NOTHING
            """,
            bank_id,
            sorted_unit_ids,
@@ -399,35 +374,16 @@ class PostgreSQLOps(DataAccessOps):
        bank_id: str,
        limit: int,
    ) -> list[str]:
-        # Ordered locking (#3034). Choose the oldest batch by enqueued_at, but
-        # acquire the row locks in (bank_id, unit_id) order — the same order the
-        # enqueue upsert takes them — so a foreground mutation re-enqueueing an
-        # overlapping unit set can never cycle against a worker draining it. The
-        # `chosen` CTE is MATERIALIZED so the enqueued_at pick is fenced from the
-        # locking clause; `FOR UPDATE OF q ... ORDER BY q.unit_id` then puts
-        # LockRows above the Sort, so locks are taken ascending by unit_id (same
-        # idiom as prune_stale_cooccurrences' #2529 ordered lock). A concurrent
-        # enqueue holding one of these rows blocks this claim until it commits, at
-        # which point the worker deletes and processes the committed state.
        rows = await conn.fetch(
            f"""
-            WITH chosen AS MATERIALIZED (
+            DELETE FROM {table}
+            WHERE (bank_id, unit_id) IN (
                SELECT bank_id, unit_id FROM {table}
                WHERE bank_id = $1
                ORDER BY enqueued_at
                LIMIT $2
-            ),
-            locked AS (
-                SELECT q.bank_id, q.unit_id
-                FROM {table} q
-                JOIN chosen c ON c.bank_id = q.bank_id AND c.unit_id = q.unit_id
-                ORDER BY q.unit_id
-                FOR UPDATE OF q
            )
-            DELETE FROM {table} q
-            USING locked l
-            WHERE q.bank_id = l.bank_id AND q.unit_id = l.unit_id
-            RETURNING q.unit_id
+            RETURNING unit_id
            """,
            bank_id,
            limit,
@@ -469,48 +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,
        )
@@ -522,21 +449,11 @@ class PostgreSQLOps(DataAccessOps):
        mu_table: str,
        unit_ids: list[str],
    ) -> list[ResultRow]:
-        # Cast only canonical UUID text inputs, never the indexed column. The old
-        # ``id::text`` predicate silently ignored malformed, uppercase, braced,
-        # and unhyphenated inputs; filtering before the cast preserves that
-        # behavior while allowing the primary-key index to serve the lookup.
        return await conn.fetch(
            f"""
            SELECT id, event_date, fact_type
            FROM {mu_table}
-            WHERE id = ANY(
-                ARRAY(
-                    SELECT input.unit_id::uuid
-                    FROM unnest($1::text[]) AS input(unit_id)
-                    WHERE input.unit_id ~ '^[0-9a-f]{{8}}-[0-9a-f]{{4}}-[0-9a-f]{{4}}-[0-9a-f]{{4}}-[0-9a-f]{{12}}$'
-                )
-            )
+            WHERE id::text = ANY($1)
            """,
            unit_ids,
        )
@@ -624,18 +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
@@ -851,16 +761,10 @@ class PostgreSQLOps(DataAccessOps):
        internal_id: str,
        fact_types: dict[str, str],
    ) -> None:
-        # CONCURRENTLY so the drop takes ShareUpdateExclusive, not ACCESS
-        # EXCLUSIVE, on the shared memory_units table. A plain DROP INDEX blocks
-        # (and deadlocks with) every other bank's concurrent reads/writes on the
-        # table; CONCURRENTLY does not conflict with DML. The caller
-        # (delete_bank) runs this on an autocommit connection after its delete
-        # transaction has committed — CONCURRENTLY cannot run inside a tx.
        for ft, suffix in fact_types.items():
            uid = str(internal_id).replace("-", "")[:16]
            idx = f"idx_mu_emb_{suffix}_{uid}"
-            await conn.execute(f"DROP INDEX CONCURRENTLY IF EXISTS {schema}.{idx}")
+            await conn.execute(f"DROP INDEX IF EXISTS {schema}.{idx}")

    def get_entity_resolution_strategy(self) -> str:
        return "trigram"
@@ -990,93 +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,
@@ -23,8 +23,6 @@ from collections.abc import AsyncIterator
 from contextlib import asynccontextmanager
 from typing import Any, NamedTuple

-from .pool_instrumentation import PoolStats, acquire_conn
-

 class _OracleJSONEncoder(json.JSONEncoder):
    """JSON encoder that handles datetime and UUID objects."""
@@ -148,7 +146,6 @@ _JSON_COL_NAMES = {
    "config",
    "observation_scopes",
    "source_memory_ids",
-    "causal_links",
    "trigger",
    "http_config",
    "event_types",
@@ -447,6 +444,9 @@ def _rewrite_pg_to_oracle(query: str) -> RewriteResult:
    if has_for_update:
        # FOR UPDATE path: use ROWNUM instead of FETCH FIRST.
        # Extract and remove LIMIT clause, inject ROWNUM into WHERE.
+        def _limit_to_rownum(m):
+            return ""  # Remove the LIMIT clause; we'll add ROWNUM below
+
        limit_val = None
        limit_match = re.search(r"\bLIMIT\s+(\d+|:\w+)\b", query, re.IGNORECASE)
        if limit_match:
@@ -1242,11 +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
-        self._acquire_warn_threshold_s: float = 1.0

    async def initialize(
        self,
@@ -1262,10 +1257,6 @@ class OracleBackend(DatabaseBackend):
        oracledb = _import_oracledb()
        self._oracledb = oracledb

-        from ...config import get_config
-
-        self._acquire_warn_threshold_s = get_config().db_acquire_warn_threshold_ms / 1000.0
-
        # Parse URL-format DSN (oracle://user:pass@host:port/service)
        from urllib.parse import urlparse

@@ -1286,17 +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.

@@ -1309,41 +1294,15 @@ 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()
-
-    def _pool_stats(self) -> PoolStats | None:
-        """Snapshot for slow-acquire logs, from oracledb pool attributes."""
-        pool = self._pool
-        if pool is None:
-            return None
-        try:
-            busy = pool.busy
-            return PoolStats(in_use=busy, max=pool.max, idle=pool.opened - busy)
-        except Exception:
-            return None
+        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]:
        pool = self._ensure_pool()
-        conn = await acquire_conn(
-            pool.acquire(), pool_stats=self._pool_stats, warn_threshold_s=self._acquire_warn_threshold_s
-        )
+        conn = await pool.acquire()
        try:
            await self._set_session_schema(conn)
            yield OracleConnection(conn)
@@ -1359,9 +1318,7 @@ class OracleBackend(DatabaseBackend):
    @asynccontextmanager
    async def transaction(self) -> AsyncIterator[OracleConnection]:
        pool = self._ensure_pool()
-        conn = await acquire_conn(
-            pool.acquire(), pool_stats=self._pool_stats, warn_threshold_s=self._acquire_warn_threshold_s
-        )
+        conn = await pool.acquire()
        try:
            await self._set_session_schema(conn)
            yield OracleConnection(conn)
@@ -1,137 +0,0 @@
-"""Instrumentation for database connection-pool acquisition.
-
-asyncpg exposes pool *size* and *idle* counts, but not how many callers are
-currently **queued waiting** for a connection — and that queue depth is the
-signal that actually distinguishes a saturated pool from a healthy one. When the
-pool is exhausted, ``/health`` (which itself acquires a connection to run
-``SELECT 1``) blocks in ``pool.acquire()`` until a connection frees or the acquire
-times out, so a liveness probe can fail **with the event loop completely idle**.
-
-This module tracks the process-wide count of in-flight acquisitions that have not
-yet obtained a connection, and times each acquire so a slow one logs with full
-pool stats. It is the DB-side counterpart to ``loop_watchdog`` (which covers loop
-stalls); together, a stuck ``/health`` can be attributed to either a blocked loop
-or pool exhaustion from the logs alone.
-
-The counter is a plain int mutated only from the event-loop thread (asyncpg
-acquisitions are awaited on the loop), so no lock is needed.
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from collections.abc import AsyncIterator, Callable
-from contextlib import asynccontextmanager
-from dataclasses import dataclass
-from typing import Any
-
-logger = logging.getLogger("hindsight.db.pool")
-
-_waiting = 0  # callers currently blocked in pool.acquire(), process-wide
-
-
-@dataclass(frozen=True, slots=True)
-class PoolStats:
-    """Point-in-time connection-pool utilization snapshot."""
-
-    in_use: int
-    max: int
-    idle: int
-
-
-def waiting_count() -> int:
-    """Number of callers currently blocked waiting to acquire a pooled connection."""
-    return _waiting
-
-
-@asynccontextmanager
-async def instrument_acquire(
-    acquire_cm: Any,
-    *,
-    pool_stats: Callable[[], PoolStats | None] | None = None,
-    warn_threshold_s: float,
-) -> AsyncIterator[Any]:
-    """Wrap a pool's ``acquire()`` context manager with wait tracking + slow-acquire logging.
-
-    Args:
-        acquire_cm: an async context manager yielding a connection (e.g. the object
-            returned by ``asyncpg.Pool.acquire()``).
-        pool_stats: optional zero-arg callable returning a ``PoolStats`` snapshot for
-            the slow-acquire log line.
-        warn_threshold_s: log a warning when the acquire itself takes at least this long.
-
-    Yields:
-        The acquired connection.
-    """
-    global _waiting
-    _waiting += 1
-    start = time.monotonic()
-    acquired = False
-    try:
-        async with acquire_cm as conn:
-            acquired = True
-            _waiting -= 1
-            _record_acquire_wait(time.monotonic() - start, pool_stats, warn_threshold_s)
-            yield conn
-    finally:
-        # If __aenter__ raised (acquire timeout / cancellation), we never
-        # decremented above — do it here so the waiter count can't leak.
-        if not acquired:
-            _waiting -= 1
-
-
-async def acquire_conn(
-    acquire_awaitable: Any,
-    *,
-    pool_stats: Callable[[], PoolStats | None] | None = None,
-    warn_threshold_s: float,
-) -> Any:
-    """Await a pool acquire that returns a connection, with wait tracking + slow log.
-
-    For pools whose acquire is ``conn = await pool.acquire()`` (oracledb) rather than
-    an async context manager (asyncpg — use ``instrument_acquire`` for those). The
-    caller is responsible for releasing the returned connection.
-    """
-    global _waiting
-    _waiting += 1
-    start = time.monotonic()
-    try:
-        conn = await acquire_awaitable
-    finally:
-        _waiting -= 1
-    _record_acquire_wait(time.monotonic() - start, pool_stats, warn_threshold_s)
-    return conn
-
-
-def _record_acquire_wait(
-    wait_s: float,
-    pool_stats: Callable[[], PoolStats | None] | None,
-    warn_threshold_s: float,
-) -> None:
-    try:
-        from ...metrics import get_metrics_collector
-
-        get_metrics_collector().record_db_acquire_wait(wait_s)
-    except Exception:
-        pass
-
-    if wait_s < warn_threshold_s:
-        return
-
-    stats: PoolStats | None = None
-    if pool_stats is not None:
-        try:
-            stats = pool_stats()
-        except Exception:
-            stats = None
-    logger.warning(
-        "slow DB pool acquire: waited %.3fs for a connection "
-        "(in_use=%s max=%s idle=%s waiting=%s). The pool is likely saturated; "
-        "/health can stall on connection acquisition while the event loop is free.",
-        wait_s,
-        stats.in_use if stats else None,
-        stats.max if stats else None,
-        stats.idle if stats else None,
-        _waiting,
-    )
@@ -15,7 +15,6 @@ from typing import Any
 import asyncpg  # noqa: F401

 from .base import DatabaseBackend, DatabaseConnection
-from .pool_instrumentation import PoolStats, instrument_acquire

 logger = logging.getLogger(__name__)

@@ -77,8 +76,6 @@ class PostgreSQLBackend(DatabaseBackend):

    def __init__(self) -> None:
        self._pool: asyncpg.Pool | None = None
-        self._acquire_warn_threshold_s: float = 1.0
-        self._acquire_timeout_s: float | None = None

    async def initialize(
        self,
@@ -91,16 +88,6 @@ class PostgreSQLBackend(DatabaseBackend):
        statement_cache_size: int = 0,
        init_callback: Any | None = None,
    ) -> None:
-        from ...config import get_config
-
-        self._acquire_warn_threshold_s = get_config().db_acquire_warn_threshold_ms / 1000.0
-        # Kept for acquire() below: asyncpg's ``timeout`` create_pool kwarg is a
-        # *connect* kwarg (how long establishing a new connection may take), and
-        # ``Pool.acquire()`` defaults to waiting for a free connection forever.
-        # Passing it here alone made HINDSIGHT_API_DB_ACQUIRE_TIMEOUT a no-op for
-        # the wait it names: a pool-exhaustion stall never surfaced as an error,
-        # it just hung (#3002). 0 restores the unbounded behaviour.
-        self._acquire_timeout_s = acquire_timeout if acquire_timeout > 0 else None
        self._pool = await asyncpg.create_pool(
            dsn,
            min_size=min_size,
@@ -108,12 +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}, "
@@ -121,45 +103,21 @@ 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
-
-    def _pool_stats(self) -> PoolStats | None:
-        """Snapshot for slow-acquire logs. in_use = live connections minus idle ones."""
-        pool = self._pool
-        if pool is None:
-            return None
-        idle = pool.get_idle_size()
-        return PoolStats(in_use=pool.get_size() - idle, max=pool.get_max_size(), idle=idle)
-
    @asynccontextmanager
    async def acquire(self) -> AsyncIterator[PostgresConnection]:
        pool = self._ensure_pool()
-        async with instrument_acquire(
-            pool.acquire(timeout=self._acquire_timeout_s),
-            pool_stats=self._pool_stats,
-            warn_threshold_s=self._acquire_warn_threshold_s,
-        ) as conn:
+        async with pool.acquire() as conn:
            yield PostgresConnection(conn)

    @asynccontextmanager
    async def transaction(self) -> AsyncIterator[PostgresConnection]:
        pool = self._ensure_pool()
-        async with instrument_acquire(
-            pool.acquire(timeout=self._acquire_timeout_s),
-            pool_stats=self._pool_stats,
-            warn_threshold_s=self._acquire_warn_threshold_s,
-        ) as conn:
+        async with pool.acquire() as conn:
            async with conn.transaction():
                yield PostgresConnection(conn)

@@ -164,6 +164,35 @@ class BudgetedOperation:
        """
        return BudgetedPool(pool, self)

+    async def acquire_many(
+        self,
+        pool: Any,
+        count: int,
+    ) -> AsyncIterator[list[Any]]:
+        """
+        Acquire multiple connections within the budget.
+
+        Note: This acquires connections sequentially to respect the budget.
+        For parallel acquisition, use multiple acquire() calls with asyncio.gather().
+        This method is intended for use with raw asyncpg pools only, not DatabaseBackend.
+
+        Args:
+            pool: asyncpg connection pool (raw pool only)
+            count: Number of connections to acquire
+
+        Yields:
+            List of database connections
+        """
+        connections = []
+        try:
+            for _ in range(count):
+                conn = await pool.acquire()
+                connections.append(conn)
+            yield connections
+        finally:
+            for conn in connections:
+                await pool.release(conn)
+

 # Global default manager instance
 _default_manager: ConnectionBudgetManager | None = None
@@ -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..."
@@ -48,12 +48,6 @@ from ..config import (
    ENV_LLM_API_KEY,
 )
 from .bank_attribution import apply_bank_attribution
-from .local_device import (
-    release_local_inference_memory,
-    resolve_model_device_type,
-    select_local_device,
-)
-from .tei_retry import tei_retry_delay

 logger = logging.getLogger(__name__)

@@ -82,25 +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.
@@ -161,13 +136,7 @@ class LocalSTEmbeddings(Embeddings):
    The embedding dimension is auto-detected from the model.
    """

-    def __init__(
-        self,
-        model_name: str | None = None,
-        force_cpu: bool = False,
-        trust_remote_code: bool = False,
-        allow_mps: bool = False,
-    ):
+    def __init__(self, model_name: str | None = None, force_cpu: bool = False, trust_remote_code: bool = False):
        """
        Initialize local SentenceTransformers embeddings.

@@ -179,17 +148,12 @@ class LocalSTEmbeddings(Embeddings):
            trust_remote_code: Allow loading models with custom code (security risk).
                              Required for some models with custom architectures.
                              Default: False (disabled for security)
-            allow_mps: Opt in to the Apple Silicon MPS GPU. Disabled by default
-                      because MPS leaks memory under variable-length workloads
-                      (see engine/local_device.py). Default: False
        """
        self.model_name = model_name or DEFAULT_EMBEDDINGS_LOCAL_MODEL
        self.force_cpu = force_cpu
        self.trust_remote_code = trust_remote_code
-        self.allow_mps = allow_mps
        self._model = None
        self._dimension: int | None = None
-        self._device_type: str = "cpu"

    @property
    def provider_name(self) -> str:
@@ -216,11 +180,31 @@ class LocalSTEmbeddings(Embeddings):

        logger.info(f"Embeddings: initializing local provider with model {self.model_name}")

-        # Determine device based on hardware availability. We always set
-        # low_cpu_mem_usage=False to prevent lazy loading (meta tensors) which can
-        # cause issues when accelerate is installed but no GPU is available.
-        # MPS is opt-in (allow_mps) — see engine/local_device.py for why.
-        device = select_local_device(self.force_cpu, self.allow_mps)
+        # Determine device based on hardware availability.
+        # We always set low_cpu_mem_usage=False to prevent lazy loading (meta tensors)
+        # which can cause issues when accelerate is installed but no GPU is available.
+        import torch
+
+        # Force CPU mode if configured (used in daemon mode to avoid MPS/XPC issues on macOS)
+        if self.force_cpu:
+            device = "cpu"
+            logger.info("Embeddings: forcing CPU mode")
+        else:
+            # Check for GPU (CUDA), Apple Silicon (MPS), or Intel XPU
+            # Wrap in try-except to gracefully handle any device detection issues
+            # (e.g., in CI environments or when PyTorch is built without GPU support)
+            device = "cpu"  # Default to CPU
+            try:
+                has_gpu = torch.cuda.is_available() or (
+                    hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
+                )
+                # Intel Arc XPU support — torch.xpu is available when the XPU build is loaded
+                if not has_gpu and hasattr(torch, "xpu"):
+                    has_gpu = torch.xpu.is_available()
+                if has_gpu:
+                    device = None  # Let sentence-transformers auto-detect GPU/MPS/XPU
+            except Exception as e:
+                logger.warning(f"Failed to detect GPU/MPS/XPU, falling back to CPU: {e}")

        # Suppress verbose transformers warnings during model loading
        # This suppresses the "UNEXPECTED" warnings from BertModel which are harmless
@@ -247,8 +231,7 @@ class LocalSTEmbeddings(Embeddings):
                transformers_logger.setLevel(original_level)

        self._dimension = self._model.get_sentence_embedding_dimension()
-        self._device_type = resolve_model_device_type(self._model)
-        logger.info(f"Embeddings: local provider initialized (dim: {self._dimension}, device: {self._device_type})")
+        logger.info(f"Embeddings: local provider initialized (dim: {self._dimension})")

    def encode(self, texts: list[str]) -> list[list[float]]:
        """
@@ -263,19 +246,8 @@ class LocalSTEmbeddings(Embeddings):
        if self._model is None:
            raise RuntimeError("Embeddings not initialized. Call initialize() first.")

-        try:
-            embeddings = self._model.encode(texts, convert_to_numpy=True, show_progress_bar=False)
-            return [emb.tolist() for emb in embeddings]
-        finally:
-            # Only reclaim the GPU allocator pool here, and only when actually on a
-            # GPU (opt-in MPS/CUDA/XPU). encode() runs in tight retain loops, so a
-            # gc.collect()/malloc_trim on every call is too costly on the CPU default
-            # — and unnecessary: refcounting frees the small transient buffers
-            # immediately and the allocator reuses them for the next batch. (The
-            # reranker keeps its per-batch heap trim for the #1717 CPU case; it runs
-            # on the lighter recall path.) See engine/local_device.py.
-            if self._device_type != "cpu":
-                release_local_inference_memory(self._device_type)
+        embeddings = self._model.encode(texts, convert_to_numpy=True, show_progress_bar=False)
+        return [emb.tolist() for emb in embeddings]


 class OnnxEmbeddings(Embeddings):
@@ -515,20 +487,13 @@ class RemoteTEIEmbeddings(Embeddings):
                    time.sleep(delay)
                    delay *= 2  # Exponential backoff
            except httpx.HTTPStatusError as e:
-                # TEI uses 429 as normal overload backpressure. Retry it with
-                # the same bounded budget as transient server errors.
-                if (e.response.status_code == 429 or e.response.status_code >= 500) and attempt < self.max_retries:
+                # Retry on 5xx server errors
+                if e.response.status_code >= 500 and attempt < self.max_retries:
                    last_error = e
-                    sleep_delay = tei_retry_delay(
-                        e.response,
-                        delay,
-                        request_timeout=self.timeout,
-                    )
                    logger.warning(
-                        f"TEI transient error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. "
-                        f"Retrying in {sleep_delay:.2f}s..."
+                        f"TEI server error (attempt {attempt + 1}/{self.max_retries + 1}): {e}. Retrying in {delay}s..."
                    )
-                    time.sleep(sleep_delay)
+                    time.sleep(delay)
                    delay *= 2
                else:
                    raise
@@ -1237,7 +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.
@@ -1252,10 +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
@@ -1264,7 +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

@@ -1341,33 +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
@@ -1653,7 +1585,6 @@ def create_embeddings_from_env() -> Embeddings:
            model_name=config.embeddings_local_model,
            force_cpu=config.embeddings_local_force_cpu,
            trust_remote_code=config.embeddings_local_trust_remote_code,
-            allow_mps=config.embeddings_local_allow_mps,
        )
    elif provider == "onnx":
        return OnnxEmbeddings(
@@ -1760,7 +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
@@ -948,3 +878,58 @@ class EntityResolver:
                _CooccurrencePair(entity_id_1=e1, entity_id_2=e2, event_date=ed)
                for (e1, e2), ed in cooccurrence_pairs.items()
            )
+
+    async def get_units_by_entity(self, entity_id: str, limit: int = 100) -> list[str]:
+        """
+        Get all units that mention an entity.
+
+        Args:
+            entity_id: Entity ID
+            limit: Max results
+
+        Returns:
+            List of unit IDs
+        """
+        async with acquire_with_retry(self.pool) as conn:
+            rows = await conn.fetch(
+                f"""
+                SELECT unit_id
+                FROM {fq_table("unit_entities")}
+                WHERE entity_id = $1
+                ORDER BY unit_id
+                LIMIT $2
+                """,
+                entity_id,
+                limit,
+            )
+            return [row["unit_id"] for row in rows]
+
+    async def get_entity_by_text(
+        self,
+        bank_id: str,
+        entity_text: str,
+    ) -> str | None:
+        """
+        Find an entity by text (for query resolution).
+
+        Args:
+            bank_id: bank ID
+            entity_text: Entity text to search for
+
+        Returns:
+            Entity ID if found, None otherwise
+        """
+        async with acquire_with_retry(self.pool) as conn:
+            row = await conn.fetchrow(
+                f"""
+                SELECT id FROM {fq_table("entities")}
+                WHERE bank_id = $1
+                  AND canonical_name ILIKE $2
+                ORDER BY mention_count DESC
+                LIMIT 1
+                """,
+                bank_id,
+                entity_text,
+            )
+
+            return row["id"] if row else None
@@ -39,7 +39,6 @@ import uuid as uuid_module
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any

-from ..config import get_config
 from ..models import RequestContext
 from .db.base import DatabaseConnection
 from .retain.link_utils import (
@@ -67,34 +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 _BatchOutcome:
-    """Result of one relink claim+top-up batch (avoids a bare tuple return).
-
-    Returned by value from the retried batch helper so the caller only folds it
-    into ``JobResult`` after the batch's transaction has actually committed — a
-    deadlock/timeout retry rolls the batch back, so accumulating inside the
-    retried body would double-count.
-    """
-
-    units_claimed: int
-    links_added: int
-

@dataclass
 class JobResult:
@@ -117,48 +88,35 @@ class JobResult:
 async def enqueue_relink_victims(
    conn: DatabaseConnection,
    bank_id: str,
-    affected_unit_ids: list[str],
+    deleted_unit_ids: list[str],
    ops: Any,
-    include_affected_units: bool = False,
 ) -> int:
    """Enqueue surviving units whose outgoing temporal/semantic links pointed at
-    ``affected_unit_ids`` for later link top-up.
+    ``deleted_unit_ids`` for later link top-up.

-    Must run inside the same transaction that drops those links, *before* the
-    delete (or cascade) fires — once the rows are gone, the join that finds the
-    victims returns nothing.
-
-    ``include_affected_units`` covers the case where the affected units are NOT
-    being removed: an edit deletes every link incident to the edited unit but
-    leaves it live, so the unit needs its own outgoing adjacency rebuilt too.
-    Passing it for a unit that will be gone at commit is harmless but pointless
-    — the drain skips queue rows with no live unit — so callers should only set
-    it when the unit survives the transaction.
+    Must run inside the same transaction that deletes the units, *before* the
+    cascade fires — once the rows are gone, the join that finds the victims
+    returns nothing.

    Args:
-        conn: Database connection inside the active transaction.
-        bank_id: Bank owning the affected units.
-        affected_unit_ids: Memory_unit IDs whose incident temporal/semantic
-            links are about to be (or are being) removed.
+        conn: Database connection inside the active delete transaction.
+        bank_id: Bank owning the deleted units.
+        deleted_unit_ids: Memory_unit IDs about to be (or being) deleted.
        ops: ``DataAccessOps`` instance, supplies the dialect-specific
            bulk-insert path.
-        include_affected_units: Also enqueue ``affected_unit_ids`` themselves,
-            for callers that leave them live. One combined insert (rather than a
-            second call) keeps the queue's sorted lock ordering intact: two
-            transactions editing mutually linked units would otherwise take the
-            ``(bank_id, unit_id)`` keys in opposite orders and deadlock.

    Returns:
-        Number of distinct units passed to the queue insert.
+        Number of distinct victim units enqueued (after dedup against rows
+        already in the queue).
    """
-    if not affected_unit_ids:
+    if not deleted_unit_ids:
        return 0

-    affected_uuids = [uuid_module.UUID(uid) if isinstance(uid, str) else uid for uid in affected_unit_ids]
-    affected_str_set = {str(uid) for uid in affected_uuids}
+    deleted_uuids = [uuid_module.UUID(uid) if isinstance(uid, str) else uid for uid in deleted_unit_ids]
+    deleted_str_set = {str(uid) for uid in deleted_uuids}

-    # Find units (other than the affected ones) that have an outgoing
-    # temporal/semantic link pointing at an affected unit. Entity links are
+    # Find units (other than the ones being deleted) that have an outgoing
+    # temporal/semantic link pointing at a doomed unit. Entity links are
    # intentionally excluded — they're scheduled for removal and would only
    # add noise to the recompute job.
    victim_rows = await conn.fetch(
@@ -169,29 +127,27 @@ async def enqueue_relink_victims(
          AND bank_id = $2
          AND link_type IN ('temporal', 'semantic')
        """,
-        affected_uuids,
+        deleted_uuids,
        bank_id,
    )

-    relink_ids = {row["from_unit_id"] for row in victim_rows if str(row["from_unit_id"]) not in affected_str_set}
-    if include_affected_units:
-        relink_ids.update(affected_uuids)
+    victim_ids = [row["from_unit_id"] for row in victim_rows if str(row["from_unit_id"]) not in deleted_str_set]

-    if not relink_ids:
+    if not victim_ids:
        return 0

    await ops.enqueue_graph_maintenance(
        conn,
        fq_table("graph_maintenance_queue"),
        bank_id,
-        list(relink_ids),
+        victim_ids,
    )

    logger.debug(
-        f"[GRAPH_MAINT] Enqueued {len(relink_ids)} units for relinking in "
-        f"bank={bank_id} ({len(affected_unit_ids)} units affected)"
+        f"[GRAPH_MAINT] Enqueued {len(victim_ids)} relink victims in "
+        f"bank={bank_id} (deleted {len(deleted_unit_ids)} units)"
    )
-    return len(relink_ids)
+    return len(victim_ids)


 async def run_graph_maintenance_job(
@@ -212,26 +168,15 @@ async def run_graph_maintenance_job(

    result = JobResult()
    job_start = time.time()
-    semantic_link_min_similarity = get_config().semantic_link_min_similarity

    # --- Pass 1: relink ---
    # Per-iteration loop: claim → top up → commit. We rely on submit-time
    # dedup to keep at most one job per bank running, so no need for
    # SKIP LOCKED.
-    #
-    # The claim now takes the queue rows FOR UPDATE in (bank_id, unit_id) order
-    # (#3034) so it serialises against a concurrent mutation re-enqueueing the
-    # same units instead of racing it. On PG the matching enqueue/claim lock
-    # order prevents a cycle outright; on Oracle the ordered locks are a strong
-    # mitigation but the exact interleaving is harder to guarantee, so each
-    # batch runs inside retry_with_backoff — which already treats ORA-00060 and
-    # Postgres DeadlockDetectedError as retryable. The batch is idempotent: a
-    # rolled-back claim leaves its rows queued, and _relink_batch only tops up
-    # missing links, so re-running re-claims and re-tops-up cleanly.
-    from .db_utils import retry_with_backoff
-    from .memory_engine import acquire_with_retry
+    iterations = 0
+    while True:
+        from .memory_engine import acquire_with_retry

-    async def _drain_one_batch() -> _BatchOutcome:
        async with acquire_with_retry(backend) as conn:
            async with conn.transaction():
                unit_ids = await ops.claim_graph_maintenance_batch(
@@ -241,28 +186,11 @@ async def run_graph_maintenance_job(
                    _DRAIN_BATCH_SIZE,
                )
                if not unit_ids:
-                    return _BatchOutcome(units_claimed=0, links_added=0)
+                    break

-                links_added = await _relink_batch(
-                    conn,
-                    bank_id,
-                    unit_ids,
-                    ops,
-                    backend,
-                    semantic_link_min_similarity,
-                )
-                return _BatchOutcome(units_claimed=len(unit_ids), links_added=links_added)
+                result.relink_links_added += await _relink_batch(conn, bank_id, unit_ids, ops, backend)

-    iterations = 0
-    while True:
-        # Fold counters in only after the batch commits — retry_with_backoff may
-        # roll back and re-run the body, and accumulating inside it would double-count.
-        outcome = await retry_with_backoff(_drain_one_batch)
-        if outcome.units_claimed == 0:
-            break
-
-        result.relink_links_added += outcome.links_added
-        result.relink_units_processed += outcome.units_claimed
+        result.relink_units_processed += len(unit_ids)
        iterations += 1

        if iterations > 10000:
@@ -275,50 +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.
-    # (retry_with_backoff / acquire_with_retry already imported for Pass 1 above.)
+    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(
@@ -333,7 +238,6 @@ async def _relink_batch(
    victim_ids: list[str],
    ops: Any,
    backend: Any,
-    semantic_link_min_similarity: float,
 ) -> int:
    """Top up temporal/semantic links for a batch of victim units. Returns rows inserted."""
    # Load each victim's metadata. Victims whose units were deleted between
@@ -430,7 +334,6 @@ async def _relink_batch(
                    seed_ids,
                    seed_embs,
                    fact_types=seed_ftypes,
-                    threshold=semantic_link_min_similarity,
                )
                # Strip self-links (rare but possible because the ANN probe
                # has no exclude list — see the comment in compute_semantic_links_ann).
@@ -6,7 +6,6 @@ authentication when a TenantExtension is configured.
 """

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

@@ -14,26 +13,9 @@ if TYPE_CHECKING:
    from hindsight_api.engine.memory_engine import BankLlmHealthInfo, Budget
    from hindsight_api.engine.response_models import RecallResult, ReflectResult
    from hindsight_api.engine.search.tags import TagsMatch
-    from hindsight_api.extensions import BankWriteOperation
    from hindsight_api.models import RequestContext


-@dataclass(frozen=True)
-class BankConfigState:
-    """Resolved bank configuration and its bank-level overrides."""
-
-    config: dict[str, Any]
-    overrides: dict[str, Any]
-
-
-@dataclass(frozen=True)
-class BankTemplateImportWrite:
-    """One bank-write decision reserved for a specific imported resource."""
-
-    operation: "BankWriteOperation"
-    target: str | None = None
-
-
 class MemoryEngineInterface(ABC):
    """
    Abstract interface for the Memory Engine.
@@ -198,37 +180,6 @@ class MemoryEngineInterface(ABC):
        """
        ...

-    @abstractmethod
-    async def get_bank_config(
-        self,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> BankConfigState:
-        """Return resolved configuration after authenticating and authorizing the read."""
-        ...
-
-    @abstractmethod
-    async def update_bank_config(
-        self,
-        bank_id: str,
-        updates: dict[str, Any],
-        *,
-        request_context: "RequestContext",
-    ) -> BankConfigState:
-        """Create a bank if needed and persist validated configuration overrides."""
-        ...
-
-    @abstractmethod
-    async def reset_bank_config(
-        self,
-        bank_id: str,
-        *,
-        request_context: "RequestContext",
-    ) -> BankConfigState:
-        """Remove all bank configuration overrides after authorization."""
-        ...
-
    @abstractmethod
    async def update_bank_disposition(
        self,
@@ -324,8 +275,6 @@ class MemoryEngineInterface(ABC):
        *,
        fact_type: str | None = None,
        search_query: str | None = None,
-        entity_id: str | None = None,
-        created_before: datetime | None = None,
        limit: int = 100,
        offset: int = 0,
        request_context: "RequestContext",
@@ -337,8 +286,6 @@ class MemoryEngineInterface(ABC):
            bank_id: The memory bank ID.
            fact_type: Filter by fact type.
            search_query: Full-text search query.
-            entity_id: Filter to memory units linked to this entity ID.
-            created_before: Keep units with ``created_at`` before this instant.
            limit: Maximum results.
            offset: Pagination offset.
            request_context: Request context for authentication.
@@ -618,30 +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,
@@ -649,8 +572,6 @@ class MemoryEngineInterface(ABC):
        *,
        name: str | None = None,
        mission: str | None = None,
-        config_updates: dict[str, Any] | None = None,
-        create_if_missing: bool = True,
        request_context: "RequestContext",
    ) -> dict[str, Any]:
        """
@@ -660,9 +581,6 @@ class MemoryEngineInterface(ABC):
            bank_id: The memory bank ID.
            name: New bank name (optional).
            mission: New mission text (optional, replaces existing).
-            config_updates: Bank configuration overrides to apply with the profile update.
-            create_if_missing: Create a missing bank when True; otherwise raise
-                a 404 operation error.
            request_context: Request context for authentication.

        Returns:
@@ -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]],
@@ -36,21 +36,6 @@ from .db_utils import acquire_with_retry
 logger = logging.getLogger(__name__)


-def _llm_requests_persistable() -> bool:
-    """Whether the ``llm_requests`` table exists on the active backend.
-
-    ``llm_requests`` is PostgreSQL-only: its migration is ``run_for_dialect(pg=...)``
-    with the Oracle slot intentionally absent, and MaintenanceLoop skips its
-    retention sweep on Oracle for the same reason. On Oracle the table does not
-    exist, so best-effort trace writes must be skipped rather than attempted —
-    otherwise every LLM call fires an INSERT that fails with ORA-00903 and spams
-    the error log. Mirrors the ``_is_oracle()`` gate in MaintenanceLoop.start.
-    """
-    from .schema import _is_oracle
-
-    return not _is_oracle()
-
-
 # ── bank/operation attribution (carried across the async call chain) ──────────


@@ -391,32 +376,10 @@ 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:
            return False
-        if not _llm_requests_persistable():
-            return False
        if self._allowed_scopes is not None:
            return scope in self._allowed_scopes
        return True
@@ -510,7 +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
@@ -583,7 +546,7 @@ class LLMTraceRecorder:
        ids are snapshotted synchronously here because the caller may reset the
        context immediately after.
        """
-        if not self._enabled or not _llm_requests_persistable() or trace_ctx is None or not trace_ctx.trace_id:
+        if not self._enabled or trace_ctx is None or not trace_ctx.trace_id:
            return
        created_ids = list(dict.fromkeys([*(created or []), *trace_ctx.created_memory_ids]))
        source_ids = list(dict.fromkeys([*(source or []), *trace_ctx.source_memory_ids]))
@@ -605,9 +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:
@@ -888,13 +844,7 @@ class LLMProvider:
        from ..worker.stage import set_stage

        structured = "+structured" if response_format is not None else ""
-        # `.queued` until the concurrency permits are in hand — see the acquire
-        # below. Without it, a call waiting on a saturated semaphore is
-        # indistinguishable from one the provider is actively running, and the
-        # label points at the provider (#3002: an operator lost an hour to
-        # "llm.bedrock.*" for tasks that had never reached Bedrock).
-        base_stage = f"llm.{self.provider}.{scope}{structured}"
-        set_stage(f"{base_stage}.queued")
+        set_stage(f"llm.{self.provider}.{scope}{structured}")

        # Resolve the retry policy: explicit per-call arg wins, else the provider's
        # configured per-operation/global default, else this method's own fallback.
@@ -911,18 +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
@@ -954,7 +900,6 @@ class LLMProvider:
            async with AsyncExitStack() as stack:
                for sem in _semaphores_for_scope(scope):
                    await stack.enter_async_context(sem)
-                set_stage(base_stage)

                # cached_prefix is only set for providers that returned a handle
                # from get_or_create_cached_prefix() (e.g. Gemini); it's None for
@@ -1020,9 +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.
@@ -1039,16 +983,14 @@ 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.
        """
        from ..worker.stage import set_stage

-        # `.queued` until the permits are held — see the structured path above.
-        base_stage = f"llm.{self.provider}.{scope}+tools"
-        set_stage(f"{base_stage}.queued")
+        set_stage(f"llm.{self.provider}.{scope}+tools")

        # Resolve the retry policy: explicit per-call arg wins, else the provider's
        # configured per-operation/global default, else this method's own fallback.
@@ -1090,17 +1032,11 @@ class LLMProvider:
            async with AsyncExitStack() as stack:
                for sem in _semaphores_for_scope(scope):
                    await stack.enter_async_context(sem)
-                set_stage(base_stage)

                # 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(
@@ -1324,7 +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,
@@ -1335,7 +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,
        )

@@ -1380,7 +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,
@@ -1,172 +0,0 @@
-"""Device selection and post-inference memory release for local (in-process)
-SentenceTransformer / CrossEncoder models.
-
-Two concerns live here, both about keeping a local API instance's memory flat:
-
-**1. Device selection — MPS is opt-in.**
-On Apple Silicon the PyTorch **MPS** (Metal) backend caches a distinct compiled
-kernel graph *and* allocator pool per unique input tensor shape, and never
-releases them. Under the variable-length, high-volume recall/rerank/embed traffic
-the engine generates (documents and candidate sets of every size), that per-shape
-cache grows without bound. A local instance was observed idling at ~20 GB — ~9.4 GB
-of Metal graphics memory plus ~8 GB of native heap, essentially all of it stale
-per-shape MPS cache. CPU inference has no per-shape cache: the same workload holds
-flat at a few hundred MB, with negligible latency cost for the small default
-models (and MPS actually *slows down* over time as it recompiles graphs for new
-shapes). So MPS is excluded from auto-detection and must be opted into explicitly;
-CUDA and Intel XPU still auto-select.
-
-This is a confirmed, still-open PyTorch bug in the MPSGraph compilation cache
-(keyed on tensor shape, no eviction path). We are tracking it upstream:
-  - https://github.com/pytorch/pytorch/issues/181213
-    ([MPS] unbounded RSS growth with varying-shape inference — our exact case)
-  - https://github.com/pytorch/pytorch/issues/164299 (graphCache identified as
-    the primary leak culprit)
-  - https://github.com/pytorch/pytorch/issues/182815 (proposes, but has not yet
-    shipped, a torch.mps.invalidate_graph_cache() API / PYTORCH_MPS_DISABLE_GRAPH_CACHE
-    env var that would let us keep MPS)
-No released mitigation exists today: empty_cache(), synchronize(),
-PYTORCH_MPS_HIGH_WATERMARK_RATIO, and autorelease pools were all confirmed
-ineffective upstream. Revisit MPS-as-default once one of those knobs lands.
-
-**2. Memory release after each batch.**
-Local CPU inference allocates large transient numpy/tensor buffers per call. The
-allocator keeps those freed pages as a high-water mark, so RSS grows monotonically
-across many calls (issue #1717). We return them to the OS after each batch —
-``malloc_trim`` on glibc/Linux, ``malloc_zone_pressure_relief`` on macOS (the
-original #1717 fix covered only Linux). When the model ran on a GPU we also empty
-that backend's allocator pool via ``torch.<backend>.empty_cache()``.
-"""
-
-from __future__ import annotations
-
-import ctypes
-import ctypes.util
-import gc
-import logging
-import sys
-
-logger = logging.getLogger(__name__)
-
-
-def select_local_device(force_cpu: bool, allow_mps: bool) -> str | None:
-    """Choose the device for a local SentenceTransformer / CrossEncoder.
-
-    Returns a value suitable to pass as the model's ``device`` argument:
-
-    - ``"cpu"``  — forced CPU, or the only accelerator is MPS and it is not allowed.
-    - ``None``   — let sentence-transformers auto-detect (picks CUDA / XPU,
-                   handling multi-GPU correctly).
-    - ``"mps"``  — Apple Silicon GPU, only when ``allow_mps`` is set.
-
-    MPS is never auto-selected because its per-shape cache leaks unbounded memory
-    under the engine's variable-length workload (see the module docstring). Set the
-    matching ``*_ALLOW_MPS`` config flag to opt back in.
-    """
-    if force_cpu:
-        return "cpu"
-
-    try:
-        import torch
-
-        if torch.cuda.is_available():
-            return None  # auto-detect CUDA
-        if hasattr(torch, "xpu") and torch.xpu.is_available():
-            return None  # auto-detect Intel XPU
-
-        mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-        if mps_available:
-            if allow_mps:
-                return "mps"
-            logger.info(
-                "Local model: MPS (Apple Silicon GPU) is available but disabled by "
-                "default because its per-shape cache leaks memory under variable-length "
-                "workloads; running on CPU. Set the *_ALLOW_MPS flag to opt in."
-            )
-            return "cpu"
-        return "cpu"
-    except Exception as e:  # pragma: no cover - defensive
-        logger.warning("Local device detection failed, falling back to CPU: %s", e)
-        return "cpu"
-
-
-def resolve_model_device_type(model: object) -> str:
-    """Best-effort device *type* ("cpu" / "cuda" / "mps" / "xpu") of a loaded model.
-
-    Used to decide which GPU allocator pool to empty after inference. Falls back to
-    ``"cpu"`` (the safe no-op choice for release) if the device can't be read.
-    """
-    device = getattr(model, "device", None)
-    if device is None:
-        inner = getattr(model, "model", None)  # CrossEncoder wraps the HF model
-        device = getattr(inner, "device", None)
-    try:
-        return device.type if device is not None else "cpu"
-    except Exception:  # pragma: no cover - defensive
-        return "cpu"
-
-
-def _resolve_heap_trim():
-    """Return a callable that asks the C allocator to release freed pages to the OS.
-
-    glibc (Linux) exposes ``malloc_trim``; macOS exposes
-    ``malloc_zone_pressure_relief``. Resolved once at import; returns a no-op on
-    platforms where neither is available (musl, Windows).
-    """
-    if sys.platform == "linux":
-        libc_path = ctypes.util.find_library("c")
-        if libc_path is None:
-            return lambda: None
-        try:
-            libc = ctypes.CDLL(libc_path)
-            trim = libc.malloc_trim
-        except (OSError, AttributeError):
-            # Not glibc (musl has no malloc_trim) or libc lookup failed.
-            return lambda: None
-        trim.argtypes = [ctypes.c_size_t]
-        trim.restype = ctypes.c_int
-        return lambda: trim(0)
-
-    if sys.platform == "darwin":
-        try:
-            libc = ctypes.CDLL("/usr/lib/libSystem.dylib")
-            default_zone = libc.malloc_default_zone
-            default_zone.restype = ctypes.c_void_p
-            relief = libc.malloc_zone_pressure_relief
-            relief.argtypes = [ctypes.c_void_p, ctypes.c_size_t]
-            relief.restype = ctypes.c_size_t
-        except (OSError, AttributeError):
-            return lambda: None
-        # pressure_relief(zone, goal=0) reclaims as much as possible.
-        return lambda: relief(default_zone(), 0)
-
-    return lambda: None
-
-
-_heap_trim = _resolve_heap_trim()
-
-
-def _empty_gpu_cache(device_type: str | None) -> None:
-    """Empty the allocator pool of the GPU backend the model ran on, if any."""
-    if not device_type or device_type == "cpu":
-        return
-    try:
-        import torch
-
-        backend = getattr(torch, device_type, None)  # torch.cuda / torch.mps / torch.xpu
-        if backend is not None and hasattr(backend, "empty_cache"):
-            backend.empty_cache()
-    except Exception:  # pragma: no cover - defensive
-        pass
-
-
-def release_local_inference_memory(device_type: str | None = None) -> None:
-    """Release transient heap (and GPU allocator) memory after a local inference batch.
-
-    Frees Python objects, returns freed native pages to the OS, and empties the GPU
-    allocator pool when the model ran on a GPU. Safe to call on every platform and
-    device; the pieces that don't apply are cheap no-ops.
-    """
-    gc.collect()
-    _heap_trim()
-    _empty_gpu_cache(device_type)
@@ -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,
        )
@@ -224,27 +209,10 @@ 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
-            # Pin the configured model (issue #2881). Without this the spawned CLI
-            # runs its own default model — an Opus-class model on Pro/Max OAuth —
-            # regardless of HINDSIGHT_API_*_LLM_MODEL, while metrics/logs still print
-            # self.model, so the mismatch is invisible. The isolated CLAUDE_CONFIG_DIR
-            # (fresh temp dir) means a host settings.json can't reach the CLI either,
-            # so passing it through here is the only channel.
-            model=self.model or None,
            env=_get_isolated_claude_env(),
        )

@@ -260,11 +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
@@ -324,7 +287,7 @@ class ClaudeCodeLLM(LLMInterface):

                # Record trace span
                try:
-                    from hindsight_api.tracing import _serialize_for_span, get_span_recorder
+                    from hindsight_api.tracing import get_span_recorder

                    span_recorder = get_span_recorder()
                    span_recorder.record_llm_call(
@@ -332,17 +295,15 @@ class ClaudeCodeLLM(LLMInterface):
                        model=self.model,
                        scope=scope,
                        messages=messages,
-                        response_content=_serialize_for_span(result),
+                        response_content=result if isinstance(result, str) else result.model_dump_json(),
                        input_tokens=estimated_input,
                        output_tokens=estimated_output,
                        duration=duration,
                        finish_reason=None,
                        error=None,
                    )
-                except Exception as span_error:
-                    # Tracing must remain best-effort, but expose instrumentation
-                    # bugs that would otherwise silently erase spans (#3025).
-                    logger.debug("Claude Code span recording failed: %s", span_error, exc_info=True)
+                except Exception:
+                    pass  # logging failure must never affect the operation

                # Log slow calls
                if duration > 10.0:
@@ -401,7 +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.
@@ -419,7 +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
@@ -432,7 +393,6 @@ class ClaudeCodeLLM(LLMInterface):
            AssistantMessage,
            ClaudeAgentOptions,
            ClaudeSDKClient,
-            ResultMessage,
            SdkMcpTool,
            TextBlock,
            ToolUseBlock,
@@ -513,27 +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 = {}
@@ -541,33 +504,16 @@ class ClaudeCodeLLM(LLMInterface):
        # else: tool_choice == "auto" or unspecified - use default behavior (no changes needed)

        # Configure SDK options with MCP server
-        #
        # tools=[] disables built-in CLI tools (Read, Write, Bash, ToolSearch, etc.)
        # Without this, Claude Code CLI defers MCP tools when too many built-in tools
-        # are loaded, forcing Claude to use ToolSearch first — which wastes the turn
+        # are loaded, forcing Claude to use ToolSearch first — which wastes the max_turns
        # budget and prevents direct MCP tool calls.
-        #
-        # max_turns=1 is critical (issue #2966). call_with_tools() is one *round* of
-        # an agentic loop the caller drives: the model proposes tool calls, we return
-        # them, and the orchestrator (reflect/agent.py) executes the REAL tools and
-        # feeds the results back on the next call. The SDK, however, runs its own
-        # in-process loop: it invokes our SDK MCP handlers — which are deliberate
-        # placeholders returning "[Tool <name> called successfully]" (no real data) —
-        # and lets the model react. With max_turns >= 2 the model calls recall, sees
-        # the empty placeholder, re-queries with reworded searches, exhausts the turn
-        # budget, and the run ends in error_max_turns with its tool calls discarded —
-        # exactly the "0 tool calls / no information" failure in #2966. Capping at a
-        # single turn stops the SDK from acting on the placeholder results: the model
-        # emits its first tool call (or a text answer) and we return that to the caller
-        # unchanged, matching how every other provider's call_with_tools() behaves.
        options = ClaudeAgentOptions(
            system_prompt=system_prompt if system_prompt else None,
            tools=[],  # Disable built-in tools so MCP tools load eagerly
-            max_turns=1,  # One round: propose tool calls (or answer); caller drives the loop
+            max_turns=2,  # Allow tool call + tool result round-trip
            mcp_servers=mcp_servers_config,
            allowed_tools=allowed_tool_names,
-            # Pin the configured model (issue #2881) — see the call() options block.
-            model=self.model or None,
            env=_get_isolated_claude_env(),
        )

@@ -604,21 +550,6 @@ class ClaudeCodeLLM(LLMInterface):
                                            arguments=block.input,
                                        )
                                    )
-                            if tool_calls:
-                                # This round proposed tool call(s). Stop consuming the
-                                # stream so the SDK does not run another turn against our
-                                # placeholder handlers (issue #2966) — the caller executes
-                                # the real tools and calls us again with the results.
-                                break
-                        elif isinstance(message, ResultMessage) and message.is_error:
-                            # With max_turns=1 the CLI reports error_max_turns whenever
-                            # the model spent its single turn issuing a tool call (there
-                            # was no follow-up turn to emit final text). That is expected
-                            # here and not a failure: we already captured the tool call
-                            # above and break before reaching this branch. Only a genuine
-                            # error with nothing to return should surface (issue #2702).
-                            if not tool_calls:
-                                raise RuntimeError(_result_error_detail(message))

                # Record metrics
                duration = time.time() - start_time
@@ -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):
    """
@@ -172,8 +117,8 @@ class CodexLLM(LLMInterface):
        if self.model.startswith("openai/"):
            self.model = self.model[len("openai/") :]

-        # Reasoning summary controls presentation separately from the backend's
-        # reasoning effort, which is sent unchanged in each request payload.
+        # Map reasoning effort to Codex reasoning summary format
+        # Codex supports: "auto", "concise", "detailed"
        self.reasoning_summary = self._map_reasoning_effort(reasoning_effort)

        # HTTP client for SSE streaming
@@ -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
@@ -448,28 +385,20 @@ class CodexLLM(LLMInterface):
            "tools": [],
            "tool_choice": "auto",
            "parallel_tool_calls": True,
-            "reasoning": {"effort": self.reasoning_effort, "summary": reasoning_summary},
+            "reasoning": {"summary": reasoning_summary},
            "store": False,  # Codex uses stateless mode
            "stream": True,  # SSE streaming
            "include": ["reasoning.encrypted_content"],
            "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
@@ -573,7 +467,7 @@ class CodexLLM(LLMInterface):

                # Record trace span
                try:
-                    from hindsight_api.tracing import _serialize_for_span, get_span_recorder
+                    from hindsight_api.tracing import get_span_recorder

                    # Estimate tokens for tracing
                    estimated_input = sum(len(m.get("content", "")) for m in messages) // 4
@@ -584,17 +478,15 @@ class CodexLLM(LLMInterface):
                        model=self.model,
                        scope=scope,
                        messages=messages,
-                        response_content=_serialize_for_span(result),
+                        response_content=result if isinstance(result, str) else result.model_dump_json(),
                        input_tokens=estimated_input,
                        output_tokens=estimated_output,
                        duration=duration,
                        finish_reason=None,
                        error=None,
                    )
-                except Exception as span_error:
-                    # Tracing must remain best-effort, but expose instrumentation
-                    # bugs that would otherwise silently erase spans (#3025).
-                    logger.debug("Codex span recording failed: %s", span_error, exc_info=True)
+                except Exception:
+                    pass  # logging failure must never affect the operation

                if return_usage:
                    # Codex doesn't provide token counts, estimate based on content
@@ -650,9 +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)

@@ -748,7 +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.
@@ -765,7 +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.
@@ -827,20 +716,22 @@ 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": {"effort": self.reasoning_effort, "summary": reasoning_summary},
+            "reasoning": {"summary": reasoning_summary},
            "store": False,
            "stream": True,
            "include": ["reasoning.encrypted_content"],
            "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"

@@ -937,8 +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

@@ -983,13 +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.
@@ -424,7 +329,8 @@ class GeminiLLM(LLMInterface):
        last_exception = None

        for attempt in range(max_retries + 1):
-            set_stage(f"llm.gemini.{scope}.attempt={attempt + 1}/{max_retries + 1}")
+            if attempt > 0:
+                set_stage(f"llm.gemini.{scope}.attempt={attempt + 1}/{max_retries + 1}")
            try:
                response = await asyncio.wait_for(
                    self._client.aio.models.generate_content(
@@ -573,17 +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
@@ -630,9 +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.
@@ -646,22 +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.
@@ -669,45 +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()
@@ -737,20 +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")
                )
@@ -768,16 +698,13 @@ class GeminiLLM(LLMInterface):

        last_exception = None
        for attempt in range(max_retries + 1):
-            set_stage(f"llm.gemini.tools.attempt={attempt + 1}/{max_retries + 1}")
+            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
@@ -880,17 +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
@@ -967,56 +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
@@ -1164,7 +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]] = []
@@ -1193,13 +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": {
@@ -255,7 +246,8 @@ class LiteLLMLLM(LLMInterface):
        last_exception = None

        for attempt in range(max_retries + 1):
-            set_stage(f"llm.{self._stage_label}.{scope}.attempt={attempt + 1}/{max_retries + 1}")
+            if attempt > 0:
+                set_stage(f"llm.{self._stage_label}.{scope}.attempt={attempt + 1}/{max_retries + 1}")
            try:
                response = await asyncio.wait_for(
                    self._acompletion(**call_kwargs),
@@ -285,17 +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
@@ -396,9 +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
@@ -429,24 +408,18 @@ 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):
-            set_stage(f"llm.{self._stage_label}.tools.attempt={attempt + 1}/{max_retries + 1}")
+            if attempt > 0:
+                set_stage(f"llm.{self._stage_label}.tools.attempt={attempt + 1}/{max_retries + 1}")
            try:
                response = await asyncio.wait_for(
                    self._acompletion(**call_kwargs),
@@ -551,9 +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, LLMToolChoiceMode
+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.
@@ -266,7 +266,7 @@ class MockLLM(LLMInterface):
            else:
                result = LLMToolCallResult(content="mock response", finish_reason="stop")
        else:
-            result = self._compliant_tool_call(tools, tool_choice, messages)
+            result = LLMToolCallResult(content="mock response", finish_reason="stop")

        # Set mock token usage on result if not already set
        if result.input_tokens == 0:
@@ -297,61 +297,6 @@ class MockLLM(LLMInterface):

        return result

-    @staticmethod
-    def _compliant_tool_call(
-        tools: list[dict[str, Any]],
-        tool_choice: LLMToolChoice,
-        messages: list[dict[str, Any]],
-    ) -> LLMToolCallResult:
-        """Default tool response: simulate a compliant tool-calling model.
-
-        Real providers drive the reflect loop entirely through tool calls -- they
-        honor a forced tool choice, then finish via ``done`` -- and the reflect
-        agent now rejects a turn that yields no tool call at all (a transport that
-        can't tool-call raises ReflectToolCallError). So the mock must behave like a
-        working provider here rather than returning bare "mock response" prose,
-        which used to be salvaged as the answer. Only this default path is affected;
-        tests that script turns via ``_response_callback`` / ``_mock_response`` are not.
-        """
-        tool_names = {t.get("function", {}).get("name") for t in tools}
-
-        def _mock_query() -> str:
-            for message in reversed(messages):
-                content = message.get("content")
-                if message.get("role") == "user" and isinstance(content, str) and content.strip():
-                    return content[:200]
-            return "mock query"
-
-        # Honor a forced retrieval tool so the loop actually runs recall/search and
-        # gathers evidence (populates based_on for tests that assert on it).
-        if tool_choice.mode is LLMToolChoiceMode.NAMED and tool_choice.function_name in {
-            "search_mental_models",
-            "search_observations",
-            "recall",
-        }:
-            return LLMToolCallResult(
-                tool_calls=[
-                    LLMToolCall(
-                        id="mock_forced",
-                        name=tool_choice.function_name,
-                        arguments={"reason": "mock", "query": _mock_query()},
-                    )
-                ],
-                finish_reason="tool_calls",
-            )
-
-        # Auto turn: finish via the done tool, mirroring a model that has gathered
-        # enough. The reflect evidence guardrail handles the empty-bank case (no
-        # evidence -> forced text synthesis on the final iteration).
-        if "done" in tool_names:
-            return LLMToolCallResult(
-                tool_calls=[LLMToolCall(id="mock_done", name="done", arguments={"answer": "mock response"})],
-                finish_reason="tool_calls",
-            )
-
-        # No done tool offered (non-reflect tool call): fall back to plain text.
-        return LLMToolCallResult(content="mock response", finish_reason="stop")
-
    @staticmethod
    def _build_mock_facts(messages: list[dict]) -> dict:
        """Build a canned fact extraction response from the user message text.
@@ -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

@@ -56,57 +47,17 @@ 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"})

-# Local providers whose OpenAI-compatible surface always lives under a `/v1`
-# path (LM Studio: http://localhost:1234/v1, Ollama: http://localhost:11434/v1).
-# For these we know the exact endpoint shape, so a bare host base URL can be
-# normalized safely. Cloud/proxy endpoints are left untouched — their path is
-# provider-specific and must be supplied verbatim.
-_V1_PATH_LOCAL_PROVIDERS = frozenset({"lmstudio", "ollama"})
-
-
-def _ensure_v1_base_url(base_url: str) -> str:
-    """Append the OpenAI-compatible ``/v1`` prefix to a bare local base URL.
-
-    LM Studio's server UI advertises its address as ``http://localhost:1234``,
-    so users commonly set ``HINDSIGHT_API_LLM_BASE_URL`` to that bare host. The
-    OpenAI SDK then POSTs to ``<host>/chat/completions`` and LM Studio rejects it
-    with ``Unexpected endpoint or method`` — its OpenAI-compatible routes live
-    under ``/v1``. Only a base URL with no meaningful path (bare host or a lone
-    trailing slash) is rewritten; anything with an explicit path (e.g. a reverse
-    proxy mount or an already-correct ``/v1``) is returned unchanged. See #2922.
-    """
-    parsed = urlparse(base_url)
-    if parsed.path.strip("/"):
-        return base_url
-    return urlunparse(parsed._replace(path="/v1"))
-

 class ProviderResponseError(RuntimeError):
    """Raised when a provider returns a success response without usable content."""
@@ -116,68 +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
@@ -529,8 +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,
    ):
        """
@@ -541,15 +445,10 @@ class OpenAICompatibleLLM(LLMInterface):
            api_key: API key (optional for ollama/lmstudio).
            base_url: Base URL for the API (uses defaults for groq/ollama/lmstudio if empty).
            model: Model name.
-            reasoning_effort: Reasoning effort level for supported models
-                ("none", "low", "medium", "high"). "none" is required when calling
-                function tools on some reasoning models, which reject every other
-                value — including omitting the parameter entirely.
+            reasoning_effort: Reasoning effort level for supported models ("low", "medium", "high").
            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)
@@ -604,11 +503,6 @@ class OpenAICompatibleLLM(LLMInterface):
                # lives on a separate control-plane host — see FireworksLLM.
                self.base_url = "https://api.fireworks.ai/inference/v1"

-        # Normalize bare local base URLs (e.g. a user pasting the address shown
-        # in the LM Studio UI) so the OpenAI SDK targets the `/v1` routes. See #2922.
-        if self.provider in _V1_PATH_LOCAL_PROVIDERS and self.base_url:
-            self.base_url = _ensure_v1_base_url(self.base_url)
-
        # For ollama/lmstudio, use dummy key if not provided
        if self.provider in ("ollama", "lmstudio") and not self.api_key:
            self.api_key = "local"
@@ -635,7 +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 {}

@@ -666,17 +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:
        """
@@ -689,7 +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,
@@ -751,11 +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]],
@@ -843,7 +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
@@ -859,7 +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
@@ -902,7 +789,8 @@ class OpenAICompatibleLLM(LLMInterface):
            # Surface attempt count in worker stage so JSON-schema retry loops
            # are visible from logs (small models on strict structured output
            # often loop here). Cheap no-op outside worker context.
-            set_stage(f"llm.{self.provider}.{scope}.attempt={attempt + 1}/{max_retries + 1}")
+            if attempt > 0:
+                set_stage(f"llm.{self.provider}.{scope}.attempt={attempt + 1}/{max_retries + 1}")
            try:
                if response_format is not None:
                    response = await self._client.chat.completions.create(**call_params)
@@ -998,9 +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,
@@ -1010,8 +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
@@ -1080,9 +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
                )
@@ -1173,7 +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.
@@ -1187,43 +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.
@@ -1258,16 +1147,8 @@ class OpenAICompatibleLLM(LLMInterface):
                temperature = max(0.01, min(temperature, 1.0))
            call_params["temperature"] = temperature

-        # Set reasoning_effort for reasoning models, matching call(). Omitting it
-        # here is not a neutral default: OpenAI rejects function tools on a
-        # reasoning model unless reasoning_effort is present and set to "none",
-        # so leaving it out fails exactly like sending an unsupported value.
-        if self._supports_reasoning_model():
-            call_params["reasoning_effort"] = self.reasoning_effort
-
        # 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:
@@ -1278,7 +1159,8 @@ class OpenAICompatibleLLM(LLMInterface):
        last_exception = None

        for attempt in range(max_retries + 1):
-            set_stage(f"llm.{self.provider}.tools.attempt={attempt + 1}/{max_retries + 1}")
+            if attempt > 0:
+                set_stage(f"llm.{self.provider}.tools.attempt={attempt + 1}/{max_retries + 1}")
            try:
                response = await self._client.chat.completions.create(**call_params)

@@ -1314,8 +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,
@@ -1325,8 +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
@@ -1388,10 +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
                )
@@ -1463,10 +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:
@@ -1482,7 +1355,8 @@ class OpenAICompatibleLLM(LLMInterface):

        async with httpx.AsyncClient(timeout=300.0) as client:
            for attempt in range(max_retries + 1):
-                set_stage(f"llm.ollama_native.{scope}.attempt={attempt + 1}/{max_retries + 1}")
+                if attempt > 0:
+                    set_stage(f"llm.ollama_native.{scope}.attempt={attempt + 1}/{max_retries + 1}")
                try:
                    response = await client.post(native_url, json=payload, headers=headers)
                    response.raise_for_status()
@@ -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)
@@ -7,13 +7,12 @@ The reflect agent uses an iterative loop with tools to:
 3. Expand memories (get chunk/document context)
 """

-from .agent import ReflectAgentResult, ReflectToolCallError, run_reflect_agent
+from .agent import ReflectAgentResult, run_reflect_agent
 from .models import ReflectAction, ReflectActionBatch

 __all__ = [
    "run_reflect_agent",
    "ReflectAgentResult",
-    "ReflectToolCallError",
    "ReflectAction",
    "ReflectActionBatch",
 ]
@@ -10,11 +10,11 @@ Uses hierarchical retrieval:
 import asyncio
 import json
 import logging
+import re
 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,
@@ -49,25 +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."
-
-
-class ReflectToolCallError(RuntimeError):
-    """The model never produced a tool call reflect could understand.
-
-    Reflect is driven entirely by structured tool calls (``recall``, ``expand``,
-    ``done`` ...). Some provider transports do not actually support function
-    calling and silently drop the tool definitions from the request (e.g. litellm's
-    Vertex AI gpt-oss MaaS path strips ``tools``/``tool_choice`` when the model is
-    flagged as not supporting them). The model then answers in free text that may
-    mimic a ``done`` payload. Rather than salvage that untooled text -- and risk
-    surfacing raw tool-call JSON as the answer -- we fail loudly so the caller can
-    switch to a tool-calling-capable model/transport.
-    """
-

 def _normalize_tool_name(name: str) -> str:
    """Normalize tool name from various LLM output formats.
@@ -101,12 +82,148 @@ def _is_done_tool(name: str) -> bool:
    return _normalize_tool_name(name) == "done"


+# Pattern to match done() call as text - handles done({...}) with nested JSON
+_DONE_CALL_PATTERN = re.compile(r"done\s*\(\s*\{.*$", re.DOTALL)
+
+# Patterns for leaked structured output in the answer field
+_LEAKED_JSON_SUFFIX = re.compile(
+    r'\s*```(?:json)?\s*\{[^}]*(?:"(?:observation_ids|memory_ids|mental_model_ids)"|\})\s*```\s*$',
+    re.DOTALL | re.IGNORECASE,
+)
+_TRAILING_IDS_PATTERN = re.compile(
+    r"\s*(?:observation_ids|memory_ids|mental_model_ids)\s*[=:]\s*\[.*?\]\s*$", re.DOTALL | re.IGNORECASE
+)
+_JSON_CODE_FENCE_PATTERN = re.compile(r"^\s*```(?:json)?\s*(\{.*\})\s*```\s*$", re.DOTALL | re.IGNORECASE)
+
+_DONE_ARGUMENT_KEYS = frozenset(
+    {
+        "answer",
+        "directive_compliance",
+        "memory_ids",
+        "mental_model_ids",
+        "observation_ids",
+        "model_ids",
+    }
+)
+_DONE_ARGUMENT_MARKER_KEYS = _DONE_ARGUMENT_KEYS - {"answer"}
+_LEAKED_JSON_ID_KEYS = frozenset({"memory_ids", "mental_model_ids", "observation_ids", "model_ids"})
+
+
+def _unwrap_leaked_done_arguments(text: str) -> str | None:
+    """Return the answer when a done tool call was rendered as JSON text.
+
+    Some providers leak the done tool's argument object instead of surfacing it
+    as a native tool call, e.g. {"answer": "...", "memory_ids": [...]}. Only
+    unwrap objects that match the done argument shape so normal JSON answers
+    stay intact.
+    """
+    candidate = text.strip()
+    if not candidate:
+        return None
+
+    fenced = _JSON_CODE_FENCE_PATTERN.match(candidate)
+    if fenced:
+        candidate = fenced.group(1).strip()
+
+    try:
+        payload = json.loads(candidate)
+    except json.JSONDecodeError:
+        return None
+
+    if not isinstance(payload, dict):
+        return None
+    answer = payload.get("answer")
+    if not isinstance(answer, str) or not answer.strip():
+        return None
+
+    keys = set(payload)
+    if not keys.intersection(_DONE_ARGUMENT_MARKER_KEYS):
+        return None
+    if not keys.issubset(_DONE_ARGUMENT_KEYS):
+        return None
+
+    for key in ("memory_ids", "mental_model_ids", "observation_ids", "model_ids"):
+        value = payload.get(key)
+        if value is not None and not isinstance(value, list):
+            return None
+
+    return answer.strip()
+
+
+def _strip_trailing_id_json_object(text: str) -> str:
+    stripped = text.rstrip()
+    if not stripped.endswith("}"):
+        return text.strip()
+
+    start = stripped.rfind("{")
+    if start < 0:
+        return text.strip()
+
+    try:
+        payload = json.loads(stripped[start:])
+    except json.JSONDecodeError:
+        return text.strip()
+
+    if not isinstance(payload, dict) or not payload:
+        return text.strip()
+    keys = set(payload)
+    if not keys.issubset(_LEAKED_JSON_ID_KEYS):
+        return text.strip()
+
+    return stripped[:start].strip()
+
+
+def _clean_answer_text(text: str) -> str:
+    """Clean up answer text by removing any done() tool call syntax.
+
+    Some LLMs output the done() call as text instead of a proper tool call.
+    This strips out patterns like: done({"answer": "...", ...})
+    """
+    unwrapped = _unwrap_leaked_done_arguments(text)
+    if unwrapped is not None:
+        return unwrapped
+
+    # Remove done() call pattern from the end of the text
+    cleaned = _DONE_CALL_PATTERN.sub("", text).strip()
+    return cleaned if cleaned else text
+
+
+def _clean_done_answer(text: str) -> str:
+    """Clean up the answer field from a done() tool call.
+
+    Some LLMs leak structured output patterns into the answer text, such as:
+    - JSON code blocks with observation_ids/memory_ids at the end
+    - Raw JSON objects with these fields
+    - Plain text like "observation_ids: [...]"
+
+    This cleans those patterns while preserving the actual answer content.
+    """
+    if not text:
+        return text
+
+    unwrapped = _unwrap_leaked_done_arguments(text)
+    if unwrapped is not None:
+        return unwrapped
+
+    cleaned = text
+
+    # Remove leaked JSON in code blocks at the end
+    cleaned = _LEAKED_JSON_SUFFIX.sub("", cleaned).strip()
+
+    # Remove leaked raw JSON objects at the end
+    cleaned = _strip_trailing_id_json_object(cleaned)
+
+    # Remove trailing ID patterns
+    cleaned = _TRAILING_IDS_PATTERN.sub("", cleaned).strip()
+
+    return cleaned if cleaned else text
+
+
 async def _generate_structured_output(
    answer: str,
    response_schema: dict,
    llm_config: "LLMProvider",
    reflect_id: str,
-    max_tokens: int | None = None,
 ) -> StructuredOutputResult:
    """Generate structured output from an answer using the provided JSON schema.

@@ -115,10 +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
@@ -209,8 +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,
@@ -302,104 +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,
@@ -420,13 +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,
-    store_document_text: bool = True,
-    *,
-    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.
@@ -454,6 +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
@@ -464,8 +472,8 @@ async def _run_reflect_agent_inner(

    # Get tools for this agent (with directive compliance field if directives exist).
    # The expand tool only reads back raw source text (chunks/documents), so it is
-    # useless and excluded when document text storage is disabled (per bank).
-    include_expand = store_document_text
+    # useless and excluded when document text storage is disabled.
+    include_expand = get_config().store_document_text
    tools = get_reflect_tools(
        directive_rules=directive_rules,
        include_mental_models=has_mental_models,
@@ -490,75 +498,30 @@ 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
-    # Whether the model has ever produced a tool call reflect could understand.
-    # Stays False when a transport silently strips tool support (the model then
-    # only ever returns free text) -- that case fails via ReflectToolCallError.
-    saw_tool_call = False
    tool_trace: list[ToolCall] = []
    tool_trace_summary: list[dict[str, Any]] = []
    llm_trace: list[dict[str, Any]] = []
@@ -672,12 +635,12 @@ async def _run_reflect_agent_inner(
                    "output_tokens": usage.output_tokens,
                }
            )
-            answer = response.strip()
+            answer = _clean_answer_text(response.strip())

            # 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
@@ -737,11 +700,11 @@ async def _run_reflect_agent_inner(
                    "output_tokens": usage.output_tokens,
                }
            )
-            answer = response.strip()
+            answer = _clean_answer_text(response.strip())

            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
@@ -776,35 +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,
@@ -812,9 +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
@@ -880,12 +826,12 @@ async def _run_reflect_agent_inner(
                    "output_tokens": usage.output_tokens,
                }
            )
-            answer = response.strip()
+            answer = _clean_answer_text(response.strip())

            # 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
@@ -904,29 +850,83 @@ async def _run_reflect_agent_inner(
                directives_applied=directives_applied,
            )

-        # No tool calls this turn.
+        # No tool calls - LLM wants to respond with text
        if not result.tool_calls:
-            # Reflect is driven by structured tool calls. A turn with no tool call
-            # means one of two things:
-            #   * the model already gathered evidence via earlier tool calls and is
-            #     now stopping -- fine, synthesize a clean final answer below;
-            #   * the transport can't produce tool calls at all, so it only ever
-            #     returns free text (e.g. litellm strips tools on the Vertex gpt-oss
-            #     MaaS path). In that case ``saw_tool_call`` is still False.
-            # We no longer salvage that free text as the answer -- it can be a raw
-            # done()-payload with sibling id fields leaking into user-visible text.
-            # Fail loudly instead so the caller picks a tool-calling-capable model.
-            if not saw_tool_call:
-                snippet = (result.content or "").strip()
-                if len(snippet) > 500:
-                    snippet = snippet[:500] + "..."
-                detail = f" Response: {snippet!r}" if snippet else " The model returned no content."
-                raise ReflectToolCallError(
-                    f"Reflect requires a tool-calling model, but {llm_config.provider}/{llm_config.model} "
-                    f"produced no usable tool call (the transport may not support function calling)." + detail
+            # When directives are present but no evidence has been gathered,
+            # the LLM tends to echo directive content verbatim as its answer.
+            # Fall through to the final-prompt path which doesn't include
+            # directives and handles "no data" gracefully.
+            has_gathered_evidence = (
+                bool(available_memory_ids) or bool(available_mental_model_ids) or bool(available_observation_ids)
+            )
+            directive_leak_risk = directives and not has_gathered_evidence
+            if result.content and not directive_leak_risk:
+                answer = _clean_answer_text(result.content.strip())
+
+                # The call_with_tools call above is intentionally uncapped so the
+                # LLM has headroom to emit tool-call JSON plus any intermediate
+                # reasoning. But when the LLM short-circuits and returns text
+                # directly, that text becomes the user-visible final answer and
+                # must respect max_tokens like the forced-final paths do. If it
+                # overshoots, run one extra capped call to rewrite it within
+                # the cap.
+                if 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,
+                    )
+                    total_input_tokens += rewrite_usage.input_tokens
+                    total_output_tokens += rewrite_usage.output_tokens
+                    total_cached_tokens += getattr(rewrite_usage, "cached_tokens", 0) or 0
+                    total_thoughts_tokens += getattr(rewrite_usage, "thoughts_tokens", 0) or 0
+                    llm_trace.append(
+                        {
+                            "scope": "final_rewrite",
+                            "duration_ms": int((time.time() - rewrite_start) * 1000),
+                            "input_tokens": rewrite_usage.input_tokens,
+                            "output_tokens": rewrite_usage.output_tokens,
+                        }
+                    )
+                    answer = _clean_answer_text(rewritten.strip())
+
+                # 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)
+                    structured_output = struct.structured_output
+                    total_input_tokens += struct.input_tokens
+                    total_output_tokens += struct.output_tokens
+                    total_cached_tokens += struct.cached_tokens
+                    total_thoughts_tokens += struct.thoughts_tokens
+
+                _log_completion(answer, iteration + 1)
+                return ReflectAgentResult(
+                    text=answer,
+                    structured_output=structured_output,
+                    iterations=iteration + 1,
+                    tools_called=total_tools_called,
+                    tool_trace=tool_trace,
+                    llm_trace=_get_llm_trace(),
+                    usage=_get_usage(),
+                    directives_applied=directives_applied,
                )
-            # Model tool-called earlier and is now stopping: fall through to a clean
-            # forced final synthesis (tools disabled, prose expected).
+            # Empty response, force final
            prompt = build_final_prompt(
                query, context_history, bank_profile, context, max_context_tokens=max_context_tokens
            )
@@ -958,12 +958,12 @@ async def _run_reflect_agent_inner(
                    "output_tokens": usage.output_tokens,
                }
            )
-            answer = response.strip()
+            answer = _clean_answer_text(response.strip())

            # 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
@@ -982,11 +982,6 @@ async def _run_reflect_agent_inner(
                directives_applied=directives_applied,
            )

-        # The model produced at least one tool call reflect could parse: it can
-        # drive the loop, so a later text-only turn is a legitimate stop, not a
-        # broken transport.
-        saw_tool_call = True
-
        # Check for done tool call (handle various LLM output formats)
        done_call = next((tc for tc in result.tool_calls if _is_done_tool(tc.name)), None)
        if done_call:
@@ -1006,6 +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."
@@ -1039,7 +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)
@@ -1071,6 +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."
@@ -1082,16 +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(
@@ -1174,6 +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),
                    }
                )
@@ -1257,56 +1244,15 @@ 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

-    # ``done`` is a structured tool call: trust its ``answer`` field verbatim.
-    # Sibling id fields (memory_ids, ...) live in their own arguments and are
-    # validated separately below -- they can't bleed into a parsed answer string.
-    answer = args.get("answer", "").strip()
+    # Extract and clean the answer - some LLMs leak structured output into the answer text
+    raw_answer = args.get("answer", "").strip()
+    answer = _clean_done_answer(raw_answer) if raw_answer else ""
    if not answer:
-        answer = NO_ANSWER_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 = 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]
@@ -1315,16 +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)
@@ -1439,35 +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":
@@ -1481,63 +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", [])
@@ -421,6 +421,76 @@ def build_system_prompt_for_tools(
    return "\n".join(parts)


+def build_agent_prompt(
+    query: str,
+    context_history: list[dict],
+    bank_profile: dict,
+    additional_context: str | None = None,
+) -> str:
+    """Build the user prompt for the reflect agent."""
+    parts = []
+
+    # Bank identity
+    name = bank_profile.get("name", "Assistant")
+    mission = bank_profile.get("mission", "")
+
+    parts.append(f"## Memory Bank Context\nName: {name}")
+    if mission:
+        parts.append(f"Mission: {mission}")
+
+    # Disposition traits if present
+    disposition = bank_profile.get("disposition", {})
+    if disposition:
+        traits = []
+        if "skepticism" in disposition:
+            traits.append(f"skepticism={disposition['skepticism']}")
+        if "literalism" in disposition:
+            traits.append(f"literalism={disposition['literalism']}")
+        if "empathy" in disposition:
+            traits.append(f"empathy={disposition['empathy']}")
+        if traits:
+            parts.append(f"Disposition: {', '.join(traits)}")
+
+    # Additional context from caller
+    if additional_context:
+        parts.append(f"\n## Additional Context\n{additional_context}")
+
+    # Tool call history
+    if context_history:
+        parts.append("\n## Tool Results (synthesize and reason from this data)")
+        for i, entry in enumerate(context_history, 1):
+            tool = entry["tool"]
+            output = entry["output"]
+            # Format as proper JSON for LLM readability
+            try:
+                output_str = json.dumps(output, indent=2, default=str, ensure_ascii=False)
+            except (TypeError, ValueError):
+                output_str = str(output)
+            parts.append(f"\n### Call {i}: {tool}\n```json\n{output_str}\n```")
+
+    # The question
+    parts.append(f"\n## Question\n{query}")
+
+    # Instructions
+    if context_history:
+        parts.append(
+            "\n## Instructions\n"
+            "Based on the tool results above, either call more tools or provide your final answer. "
+            "Synthesize and reason from the data - make reasonable inferences when helpful. "
+            "If you have related information, use it to give the best possible answer."
+        )
+    else:
+        parts.append(
+            "\n## Instructions\n"
+            "Start by searching for relevant information using the hierarchical retrieval strategy:\n"
+            "1. Try search_mental_models() first for curated summaries\n"
+            "2. Try search_observations() for consolidated knowledge\n"
+            "3. Use recall() for specific details or to verify stale data"
+        )
+
+    return "\n".join(parts)
+
+
 def build_final_prompt(
    query: str,
    context_history: list[dict],
@@ -820,3 +890,38 @@ ABSOLUTE RULES:
 OUTPUT FORMAT:
 - Output ONLY the updated markdown document. No preamble, no explanation, no diff markers, no commentary.
 - Do not wrap the output in code fences unless the CURRENT DOCUMENT itself was entirely a code fence."""
+
+
+def build_delta_prompt(
+    *,
+    current_content: str,
+    candidate_content: str,
+    supporting_facts: list[dict[str, Any]],
+    source_query: str,
+) -> str:
+    """Build the user prompt for a delta-mode mental model refresh.
+
+    Args:
+        current_content: The existing mental model content (to preserve as much as possible).
+        candidate_content: Fresh synthesis from the reflect agent reflecting new reality.
+        supporting_facts: Flat list of fact dicts (id, text, type) supporting the candidate.
+        source_query: The mental model's source query, for topical framing.
+    """
+    fact_lines: list[str] = []
+    for f in supporting_facts:
+        fid = f.get("id", "")
+        text = (f.get("text") or "").strip().replace("\n", " ")
+        ftype = f.get("type", "")
+        fact_lines.append(f"- [{ftype}:{fid}] {text}")
+    facts_block = "\n".join(fact_lines) if fact_lines else "(no supporting facts retrieved)"
+
+    return (
+        f"## Topic\n{source_query}\n\n"
+        f"## CURRENT DOCUMENT\n```markdown\n{current_content}\n```\n\n"
+        f"## CANDIDATE UPDATE\n```markdown\n{candidate_content}\n```\n\n"
+        f"## SUPPORTING FACTS\n{facts_block}\n\n"
+        "## Task\n"
+        "Produce the updated mental model document by applying the minimum necessary changes "
+        "to CURRENT DOCUMENT so that it reflects CANDIDATE UPDATE and SUPPORTING FACTS. "
+        "Preserve unchanged content byte-for-byte. Output only the final markdown."
+    )
@@ -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()
@@ -95,10 +95,8 @@ async def tool_search_mental_models(
    params: list[Any] = [bank_id, str(query_embedding), max_results]
    next_param = 4

-    # Exact matching treats absent or empty tags as the global scope. Do not
-    # skip the filter, or mental models would see every scope while the other
-    # reflect retrieval tools correctly see only untagged data.
-    if tags or tags_match == "exact":
+    # Use the centralized tag filtering logic
+    if tags:
        tag_clause, tag_params, next_param = build_tags_where_clause(tags, param_offset=next_param, match=tags_match)
        filters += f" {tag_clause}"
        params.extend(tag_params)
@@ -330,21 +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"""
@@ -400,12 +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(
@@ -12,7 +12,7 @@ from pydantic import BaseModel, Field

 from ..._vector_index import index_using_clause, uses_per_bank_vector_indexes
 from ...config import get_config
-from ..db_utils import acquire_with_retry, retry_with_backoff
+from ..db_utils import acquire_with_retry
 from ..memory_engine import fq_table, get_current_schema
 from ..response_models import DispositionTraits

@@ -188,19 +188,8 @@ async def get_or_create_bank_profile(pool, bank_id: str) -> BankProfileResult:
    or rolls back atomically with the caller's write), use
    ``get_or_create_bank_profile_on_conn`` instead.
    """
-
-    # A fresh bank builds its per-(bank, fact_type) partial vector indexes with
-    # a plain CREATE INDEX (it must — this runs inside the bank-create tx, and
-    # CONCURRENTLY cannot). That CREATE takes a ShareLock on the shared
-    # memory_units table, which can deadlock with concurrent writers. The build
-    # is idempotent (INSERT ... ON CONFLICT + CREATE INDEX IF NOT EXISTS), so a
-    # transient deadlock (40P01 / ORA-00060) is safe to retry as a whole tx.
-    async def _create() -> BankProfileResult:
-        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 retry_with_backoff(_create)
+    async with acquire_with_retry(pool) as conn:
+        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:
@@ -8,7 +8,7 @@ import hashlib
 import logging
 from dataclasses import dataclass

-from ...config import _get_raw_config
+from ...config import get_config
 from ..memory_engine import fq_table
 from .types import ChunkMetadata

@@ -64,64 +64,14 @@ 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,
    )


 async def store_chunks_batch(
-    conn,
-    bank_id: str,
-    document_id: str,
-    chunks: list[ChunkMetadata],
-    ops=None,
-    store_document_text: bool | None = None,
+    conn, bank_id: str, document_id: str, chunks: list[ChunkMetadata], ops=None
 ) -> dict[int, str]:
    """
    Store document chunks in the database.
@@ -132,9 +82,6 @@ async def store_chunks_batch(
        document_id: Document identifier
        chunks: List of ChunkMetadata objects
        ops: DataAccessOps instance (from backend.ops)
-        store_document_text: Whether to persist raw chunk text. When ``None``,
-            falls back to the server-level default; callers on the retain path
-            pass the per-bank resolved value.

    Returns:
        Dictionary mapping global chunk index to chunk_id
@@ -145,9 +92,7 @@ async def store_chunks_batch(
    # When document text storage is disabled, persist empty chunk_text (the
    # column is NOT NULL) while still computing content_hash from the real text
    # so delta-retain dedup is unaffected.
-    # Fallback to the raw global default (not get_config(), which guards
-    # bank-configurable fields); the retain path always passes the resolved value.
-    store_text = store_document_text if store_document_text is not None else _get_raw_config().store_document_text
+    store_text = get_config().store_document_text

    # Prepare chunk data for batch insert
    chunk_ids = []
@@ -179,3 +124,21 @@ async def store_chunks_batch(
    )

    return chunk_id_map
+
+
+def map_facts_to_chunks(facts_chunk_indices: list[int], chunk_id_map: dict[int, str]) -> list[str | None]:
+    """
+    Map fact chunk indices to chunk IDs.
+
+    Args:
+        facts_chunk_indices: List of chunk indices for each fact
+        chunk_id_map: Dictionary mapping chunk index to chunk_id
+
+    Returns:
+        List of chunk_ids (same length as facts_chunk_indices)
+    """
+    chunk_ids = []
+    for chunk_idx in facts_chunk_indices:
+        chunk_id = chunk_id_map.get(chunk_idx)
+        chunk_ids.append(chunk_id)
+    return chunk_ids
@@ -33,6 +33,36 @@ def _validate_embedding_vector(vector: list[float], *, index: int, expected_dime
    return vector


+def generate_embedding(
+    embeddings_backend: EmbeddingsBackend, text: str, input_type: EmbeddingInputType = "document"
+) -> list[float]:
+    """
+    Generate embedding for text using the provided embeddings backend.
+
+    Args:
+        embeddings_backend: Embeddings instance to use for encoding
+        text: Text to embed
+        input_type: Whether text is retained document text or recall/search query text.
+
+    Returns:
+        Embedding vector (dimension depends on embeddings backend)
+    """
+    try:
+        embeddings = _encode_with_input_type(embeddings_backend, [text], input_type)
+    except Exception as e:
+        raise Exception(f"Failed to generate embedding: {str(e)}")
+
+    if len(embeddings) != 1:
+        raise RuntimeError(
+            f"Embeddings backend returned {len(embeddings)} vectors for 1 input text; expected exact 1:1 alignment"
+        )
+    return _validate_embedding_vector(
+        embeddings[0],
+        index=0,
+        expected_dimension=embeddings_backend.dimension,
+    )
+
+
 def _encode_with_input_type(
    embeddings_backend: EmbeddingsBackend, texts: list[str], input_type: EmbeddingInputType
 ) -> list[list[float]]:
@@ -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 +203,27 @@ 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."""
+        parts = [self.what]
+
+        # Add 'who' if not N/A
+        if self.who and self.who.upper() != "N/A":
+            parts.append(f"Involving: {self.who}")
+
+        # Add 'why' if not N/A
+        if self.why and self.why.upper() != "N/A":
+            parts.append(self.why)
+
+        if len(parts) == 1:
+            return parts[0]
+
+        return " | ".join(parts)


 class FactExtractionResponse(BaseModel):
@@ -236,55 +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."""

@@ -352,9 +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(
@@ -366,7 +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):
@@ -399,15 +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):
@@ -439,14 +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):
@@ -730,11 +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}"""

@@ -1152,8 +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)
@@ -1277,32 +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,
@@ -1371,15 +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
@@ -1395,7 +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,
@@ -1414,11 +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:
@@ -1427,10 +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", [])

@@ -1467,8 +1392,6 @@ async def _extract_facts_from_chunk(
                # Fallback to old format if new fields not present
                if not what:
                    what = get_value("factual_core")
-                if not what:
-                    what = get_value("text")
                if not what:
                    # In verbatim mode, 'what' is intentionally absent — text is backfilled from chunk
                    if extraction_mode != "verbatim":
@@ -1528,9 +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)
@@ -1540,7 +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:
@@ -1565,12 +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")
@@ -1578,7 +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
@@ -1636,9 +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

@@ -1677,7 +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(
@@ -1729,22 +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"
@@ -2186,10 +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)
@@ -2201,19 +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 = []
@@ -2231,8 +2161,6 @@ async def extract_facts_from_contents_batch_api(
            what = get_value("what")
            if not what:
                what = get_value("factual_core")
-            if not what:
-                what = get_value("text")
            if not what:
                continue

@@ -2282,9 +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)
@@ -2294,7 +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:
@@ -2319,18 +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 = []
@@ -2412,18 +2349,15 @@ async def extract_facts_from_contents_batch_api(

    for chunk_meta, chunk_facts in facts_by_chunk:
        content = contents[chunk_meta.content_index]
-        extraction_group_start_idx = global_fact_idx

        for fact_from_llm in chunk_facts:
            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 [], extraction_group_start_idx, len(chunk_facts)
-                ),
+                causal_relations=_convert_causal_relations(fact_from_llm.causal_relations or [], global_fact_idx),
                content_index=chunk_meta.content_index,
                chunk_index=chunk_meta.chunk_index,
                context=content.context,
@@ -2548,7 +2482,8 @@ async def extract_facts_from_contents(
    # Step 1: Create parallel fact extraction tasks
    fact_extraction_tasks = []
    for item in contents:
-        # Call extract_facts_from_text directly (defined earlier in this file).
+        # Call extract_facts_from_text directly (defined earlier in this file)
+        # to avoid circular import with utils.extract_facts
        task = extract_facts_from_text(
            text=item.content,
            event_date=item.event_date,
@@ -2606,37 +2541,40 @@ async def extract_facts_from_contents(
        fact_idx_in_content = 0
        for chunk_idx_in_content, (chunk_text, chunk_fact_count) in enumerate(chunks_from_llm):
            chunk_global_idx = chunk_start_idx + chunk_idx_in_content
-            extraction_group_start_idx = global_fact_idx
-            chunk_facts = facts_from_llm[fact_idx_in_content : fact_idx_in_content + chunk_fact_count]

-            for fact_from_llm in chunk_facts:
-                # Convert Fact model from LLM to ExtractedFactType dataclass
-                # mentioned_at is always the event_date (when the conversation/document occurred)
-                extracted_fact = ExtractedFactType(
-                    fact_text=fact_from_llm.fact,
-                    fact_type=fact_from_llm.fact_type,
-                    entities=list(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
-                    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 [], extraction_group_start_idx, len(chunk_facts)
-                    ),
-                    content_index=content_index,
-                    chunk_index=chunk_global_idx,
-                    context=content.context,
-                    # mentioned_at: always the event_date (when the conversation/document occurred)
-                    mentioned_at=content.event_date,
-                    metadata=content.metadata,
-                    tags=content.tags,
-                    observation_scopes=content.observation_scopes,
-                )
+            for _ in range(chunk_fact_count):
+                if fact_idx_in_content < len(facts_from_llm):
+                    fact_from_llm = facts_from_llm[fact_idx_in_content]

-                extracted_facts.append(extracted_fact)
-                global_fact_idx += 1
-                fact_idx_in_content += 1
+                    # Convert Fact model from LLM to ExtractedFactType dataclass
+                    # mentioned_at is always the event_date (when the conversation/document occurred)
+                    extracted_fact = ExtractedFactType(
+                        fact_text=fact_from_llm.fact,
+                        fact_type=fact_from_llm.fact_type,
+                        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
+                        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
+                        ),
+                        content_index=content_index,
+                        chunk_index=chunk_global_idx,
+                        context=content.context,
+                        # mentioned_at: always the event_date (when the conversation/document occurred)
+                        mentioned_at=content.event_date,
+                        metadata=content.metadata,
+                        tags=content.tags,
+                        observation_scopes=content.observation_scopes,
+                    )
+
+                    extracted_facts.append(extracted_fact)
+                    global_fact_idx += 1
+                    fact_idx_in_content += 1

    # Step 4: For verbatim mode, collapse to one fact per chunk with original text
    if config.retain_extraction_mode == "verbatim":
@@ -2688,9 +2626,7 @@ def _parse_datetime(date_str: str):
        return None


-def _convert_causal_relations(
-    relations_from_llm, extraction_group_start_idx: int, extraction_group_size: int
-) -> list[CausalRelationType]:
+def _convert_causal_relations(relations_from_llm, fact_start_idx: int) -> list[CausalRelationType]:
    """
    Convert causal relations from LLM format to ExtractedFact format.

@@ -2698,16 +2634,9 @@ def _convert_causal_relations(
    """
    causal_relations = []
    for rel in relations_from_llm:
-        target_fact_index = rel.target_fact_index
-        if (
-            not isinstance(target_fact_index, int)
-            or isinstance(target_fact_index, bool)
-            or not 0 <= target_fact_index < extraction_group_size
-        ):
-            continue
        causal_relation = CausalRelationType(
            relation_type=rel.relation_type,
-            target_fact_index=extraction_group_start_idx + target_fact_index,
+            target_fact_index=fact_start_idx + rel.target_fact_index,
        )
        causal_relations.append(causal_relation)
    return causal_relations
@@ -9,7 +9,7 @@ import logging
 import uuid
 from datetime import datetime

-from ...config import _get_raw_config, get_config
+from ...config import get_config
 from ..memory_engine import fq_table
 from .bank_utils import DEFAULT_DISPOSITION, create_bank_vector_indexes
 from .fact_extraction import _sanitize_text
@@ -35,26 +35,6 @@ async def get_document_content(
    return row


-async def count_document_memory_units(
-    conn,
-    bank_id: str,
-    document_id: str,
-) -> int:
-    """Count the memory units a document currently owns.
-
-    This is the number reported as ``memory_unit_count`` by the Documents API and
-    by the ``retain.completed`` webhook. Zero means the document is stored but
-    unreachable through recall/reflect — only memory units carry embeddings, so a
-    document without them cannot be retrieved until it is reprocessed (#3040).
-    """
-    count = await conn.fetchval(
-        f"SELECT COUNT(*) FROM {fq_table('memory_units')} WHERE bank_id = $1 AND document_id = $2",
-        bank_id,
-        document_id,
-    )
-    return int(count or 0)
-
-
 async def insert_facts_batch(
    conn, bank_id: str, facts: list[ProcessedFact], document_id: str | None = None, ops=None
 ) -> list[str]:
@@ -291,7 +271,6 @@ async def handle_document_tracking(
    retain_params: dict | None = None,
    document_tags: list[str] | None = None,
    ops=None,
-    store_document_text: bool | None = None,
 ) -> None:
    """
    Handle document tracking in the database (full-replace mode).
@@ -379,7 +358,6 @@ async def handle_document_tracking(
        retain_params,
        document_tags,
        preserved_created_at=preserved_created_at,
-        store_document_text=store_document_text,
    )


@@ -390,7 +368,6 @@ async def upsert_document_metadata(
    combined_content: str,
    retain_params: dict | None = None,
    document_tags: list[str] | None = None,
-    store_document_text: bool | None = None,
 ) -> None:
    """
    Update document metadata without deleting existing facts/chunks.
@@ -403,16 +380,7 @@ async def upsert_document_metadata(
    combined_content = _sanitize_text(combined_content) or ""
    content_hash = hashlib.sha256(combined_content.encode()).hexdigest()

-    await _upsert_document_row(
-        conn,
-        bank_id,
-        document_id,
-        combined_content,
-        content_hash,
-        retain_params,
-        document_tags,
-        store_document_text=store_document_text,
-    )
+    await _upsert_document_row(conn, bank_id, document_id, combined_content, content_hash, retain_params, document_tags)


 async def _upsert_document_row(
@@ -424,7 +392,6 @@ async def _upsert_document_row(
    retain_params: dict | None = None,
    document_tags: list[str] | None = None,
    preserved_created_at: datetime | None = None,
-    store_document_text: bool | None = None,
 ) -> None:
    """Insert or update a document row.

@@ -436,13 +403,8 @@ async def _upsert_document_row(
    When ``store_document_text`` is disabled, the raw source text
    is dropped and ``original_text`` is stored as NULL. The ``content_hash`` is
    still computed from the real content so delta-retain dedup is unaffected.
-    ``store_document_text`` defaults to the server-level config when ``None``;
-    the retain path passes the per-bank resolved value.
    """
-    # Fallback to the raw global default (not get_config(), which guards
-    # bank-configurable fields); the retain path always passes the resolved value.
-    store_text = store_document_text if store_document_text is not None else _get_raw_config().store_document_text
-    original_text = combined_content if store_text else None
+    original_text = combined_content if get_config().store_document_text else None
    await conn.execute(
        f"""
        INSERT INTO {fq_table("documents")} (id, bank_id, original_text, content_hash, retain_params, tags, created_at, updated_at)
@@ -37,7 +37,6 @@ async def create_semantic_links_batch(
    bank_id: str,
    unit_ids: list[str],
    embeddings: list[list[float]],
-    threshold: float,
    pre_computed_ann_links: list[tuple] | None = None,
    ops=None,
 ) -> int:
@@ -53,7 +52,6 @@ async def create_semantic_links_batch(
        bank_id: Bank identifier
        unit_ids: List of unit IDs to create links for
        embeddings: List of embedding vectors (same length as unit_ids)
-        threshold: Minimum cosine similarity for semantic links
        pre_computed_ann_links: Pre-computed ANN results from Phase 1

    Returns:
@@ -66,14 +64,7 @@ async def create_semantic_links_batch(
        raise ValueError(f"Mismatch between unit_ids ({len(unit_ids)}) and embeddings ({len(embeddings)})")

    return await link_utils.create_semantic_links_batch(
-        conn,
-        bank_id,
-        unit_ids,
-        embeddings,
-        threshold=threshold,
-        log_buffer=[],
-        pre_computed_ann_links=pre_computed_ann_links,
-        ops=ops,
+        conn, bank_id, unit_ids, embeddings, log_buffer=[], pre_computed_ann_links=pre_computed_ann_links, ops=ops
    )


@@ -83,9 +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
@@ -101,7 +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)

@@ -4,20 +4,10 @@ Link creation utilities for temporal, semantic, and entity links.

 import logging
 import time
-from datetime import UTC
+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,
-    CAUSAL_LINK_TYPES,
-    DEFAULT_CAUSAL_LINK_WEIGHT,
-    LEGACY_CAUSAL_LINK_TYPES,
-    CausalLinkDescriptor,
-)
-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__)

@@ -119,6 +109,101 @@ def _normalize_datetime(dt):
    return dt


+def compute_temporal_links(
+    new_units: dict,
+    candidates: list,
+    time_window_hours: int = 24,
+) -> list:
+    """
+    Compute temporal links between new units and candidate neighbors.
+
+    This is a pure function that takes query results and returns link tuples,
+    making it easy to test without database access.
+
+    Args:
+        new_units: Dict mapping unit_id (str) to event_date (datetime)
+        candidates: List of dicts with 'id' and 'event_date' keys (candidate neighbors)
+        time_window_hours: Time window in hours for temporal links
+
+    Returns:
+        List of tuples: (from_unit_id, to_unit_id, 'temporal', weight, None)
+    """
+    if not new_units:
+        return []
+
+    links = []
+    for unit_id, unit_event_date in new_units.items():
+        # Units without event_date can't form temporal links
+        if unit_event_date is None:
+            continue
+        # Normalize unit_event_date for consistent comparison
+        unit_event_date_norm = _normalize_datetime(unit_event_date)
+
+        # Calculate time window bounds with overflow protection
+        try:
+            time_lower = unit_event_date_norm - timedelta(hours=time_window_hours)
+        except OverflowError:
+            time_lower = datetime.min.replace(tzinfo=UTC)
+        try:
+            time_upper = unit_event_date_norm + timedelta(hours=time_window_hours)
+        except OverflowError:
+            time_upper = datetime.max.replace(tzinfo=UTC)
+
+        # Filter candidates within this unit's time window
+        matching_neighbors = [
+            (row["id"], row["event_date"])
+            for row in candidates
+            if time_lower <= _normalize_datetime(row["event_date"]) <= time_upper
+        ][:10]  # Limit to top 10
+
+        for recent_id, recent_event_date in matching_neighbors:
+            # Calculate temporal proximity weight
+            time_diff_hours = abs(
+                (unit_event_date_norm - _normalize_datetime(recent_event_date)).total_seconds() / 3600
+            )
+            weight = max(0.3, 1.0 - (time_diff_hours / time_window_hours))
+            links.append((unit_id, str(recent_id), "temporal", weight, None))
+
+    return _cap_links_per_unit(links)
+
+
+def compute_temporal_query_bounds(
+    new_units: dict,
+    time_window_hours: int = 24,
+) -> tuple:
+    """
+    Compute the min/max date bounds for querying temporal neighbors.
+
+    Args:
+        new_units: Dict mapping unit_id (str) to event_date (datetime)
+        time_window_hours: Time window in hours
+
+    Returns:
+        Tuple of (min_date, max_date) with overflow protection
+    """
+    if not new_units:
+        return None, None
+
+    # Normalize all dates to be timezone-aware to avoid comparison issues
+    # Filter out None values — units without event_date can't form temporal links
+    all_dates = [_normalize_datetime(d) for d in new_units.values() if d is not None]
+
+    if not all_dates:
+        return None, None
+
+    try:
+        min_date = min(all_dates) - timedelta(hours=time_window_hours)
+    except OverflowError:
+        min_date = datetime.min.replace(tzinfo=UTC)
+
+    try:
+        max_date = max(all_dates) + timedelta(hours=time_window_hours)
+    except OverflowError:
+        max_date = datetime.max.replace(tzinfo=UTC)
+
+    return min_date, max_date
+
+
 def _log(log_buffer, message, level="info"):
    """Helper to log to buffer if available, otherwise use logger.

@@ -215,7 +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.

@@ -236,10 +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
@@ -247,10 +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,
@@ -269,7 +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,
@@ -277,11 +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(
@@ -431,8 +512,7 @@ async def compute_semantic_links_ann(
    embeddings: list[list[float]],
    fact_types: list[str] | None = None,
    top_k: int = 50,
-    *,
-    threshold: float,
+    threshold: float = 0.7,
    log_buffer: list[str] = None,
 ) -> list[tuple]:
    """
@@ -573,13 +653,12 @@ def compute_semantic_links_within_batch(
    unit_ids: list[str],
    embeddings: list[list[float]],
    top_k: int = 50,
-    *,
-    threshold: float,
+    threshold: float = 0.7,
 ) -> list[tuple]:
    """
    Compute semantic links between units within the same batch (no DB needed).

-    Uses cosine similarity on embeddings already in memory — instant.
+    Uses numpy dot product on embeddings already in memory — instant.

    Args:
        unit_ids: Unit IDs (real IDs from insert_facts_batch)
@@ -596,25 +675,15 @@ def compute_semantic_links_within_batch(
    import numpy as np

    links = []
-    new_embeddings_matrix = np.asarray(embeddings, dtype=float)
-    norms = np.linalg.norm(new_embeddings_matrix, axis=1)
-    valid_embeddings = np.isfinite(new_embeddings_matrix).all(axis=1) & np.isfinite(norms) & (norms > 0)
-    normalized_embeddings = np.zeros_like(new_embeddings_matrix)
-    normalized_embeddings[valid_embeddings] = (
-        new_embeddings_matrix[valid_embeddings] / norms[valid_embeddings, np.newaxis]
-    )
+    new_embeddings_matrix = np.array(embeddings)

    for i, unit_id in enumerate(unit_ids):
-        if not valid_embeddings[i]:
-            continue
-
        other_indices = [j for j in range(len(unit_ids)) if j != i]
        if not other_indices:
            continue

-        other_embeddings = normalized_embeddings[other_indices]
-        similarities = np.dot(other_embeddings, normalized_embeddings[i])
-        similarities[~valid_embeddings[other_indices]] = -np.inf
+        other_embeddings = new_embeddings_matrix[other_indices]
+        similarities = np.dot(other_embeddings, new_embeddings_matrix[i])

        above_threshold = np.where(similarities >= threshold)[0]
        if len(above_threshold) > 0:
@@ -634,8 +703,7 @@ async def create_semantic_links_batch(
    unit_ids: list[str],
    embeddings: list[list[float]],
    top_k: int = 50,
-    *,
-    threshold: float,
+    threshold: float = 0.7,
    log_buffer: list[str] = None,
    pre_computed_ann_links: list[tuple] | None = None,
    ops=None,
@@ -670,12 +738,7 @@ async def create_semantic_links_batch(

        # Within-batch similarities (numpy, no DB)
        batch_start = time_mod.time()
-        within_batch_links = compute_semantic_links_within_batch(
-            unit_ids,
-            embeddings,
-            top_k,
-            threshold=threshold,
-        )
+        within_batch_links = compute_semantic_links_within_batch(unit_ids, embeddings, top_k, threshold)
        all_links.extend(within_batch_links)
        _log(
            log_buffer,
@@ -708,61 +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
@@ -779,13 +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
@@ -816,108 +848,3 @@ async def _write_causal_links_batch(

        traceback.print_exc()
        raise
-
-
-async def snapshot_causal_links(conn: DatabaseConnection, bank_id: str, unit_id: str) -> list[CausalLinkDescriptor]:
-    """Collect the causal edges that must survive a unit's move to the archive.
-
-    Causal edges are retain-time extraction output: unlike temporal/semantic
-    links they can't be recomputed from dates or embeddings, and nothing
-    rebuilds them (graph maintenance only relinks temporal/semantic, and
-    consolidation regenerates observations, not raw-fact edges). Invalidation
-    removes the live row, so the FK cascade takes every incident edge with it —
-    hence this snapshot, parked on the archive row (#2864).
-
-    The snapshot merges two sources:
-
-    * the unit's currently materialized causal edges, and
-    * descriptors already parked on *archived* peers that name this unit — an
-      edge whose other endpoint was invalidated first is no longer in
-      ``memory_links``, so the peer's snapshot is the only copy left.
-
-    Keeping a copy on every archived endpoint makes revert order irrelevant:
-    whichever endpoint comes back last sees both sides live and rematerializes.
-
-    Returns:
-        The descriptors to store on the archive row (deduplicated across both
-        sources by the UNION).
-    """
-    rows = await conn.fetch(
-        f"""
-        SELECT from_unit_id, to_unit_id, link_type, weight
-        FROM {fq_table("memory_links")}
-        WHERE (from_unit_id = $1 OR to_unit_id = $1)
-          AND bank_id = $2
-          AND link_type = ANY($3::text[])
-        UNION
-        SELECT d.from_unit_id, d.to_unit_id, d.link_type, d.weight
-        FROM {fq_table("invalidated_memory_units")} a
-        CROSS JOIN LATERAL jsonb_to_recordset(a.causal_links)
-            AS d(from_unit_id uuid, to_unit_id uuid, link_type text, weight float8)
-        WHERE a.bank_id = $2
-          AND a.causal_links <> '[]'::jsonb
-          AND (d.from_unit_id = $1 OR d.to_unit_id = $1)
-          -- Same guard as CausalLinkDescriptor.from_json_dict: the column is
-          -- schemaless JSON, and a malformed entry would otherwise be copied
-          -- forward as a NULL-endpoint descriptor.
-          AND d.from_unit_id IS NOT NULL
-          AND d.to_unit_id IS NOT NULL
-          AND d.link_type = ANY($3::text[])
-        """,
-        unit_id,
-        bank_id,
-        list(CAUSAL_LINK_TYPES),
-    )
-    return [
-        CausalLinkDescriptor(
-            from_unit_id=str(row["from_unit_id"]),
-            to_unit_id=str(row["to_unit_id"]),
-            link_type=row["link_type"],
-            weight=float(row["weight"]) if row["weight"] is not None else DEFAULT_CAUSAL_LINK_WEIGHT,
-        )
-        for row in rows
-    ]
-
-
-async def rematerialize_causal_links(
-    conn: DatabaseConnection,
-    bank_id: str,
-    stored_descriptors: list,
-    ops: DataAccessOps | None = None,
-) -> int:
-    """Recreate archived causal edges whose endpoints are both live again.
-
-    Counterpart of :func:`snapshot_causal_links`, called when a fact reverts to
-    ``valid``. Descriptors whose peer is still archived (or was permanently
-    deleted) are silently dropped from this insert: the bulk writer only takes
-    links whose endpoints exist in ``memory_units``. That is the point — a
-    still-archived peer keeps its own copy of the descriptor and materializes
-    the edge when *it* reverts.
-
-    Insertion is ``ON CONFLICT DO NOTHING``, so repeated invalidate/revert
-    cycles never duplicate an edge.
-
-    Args:
-        stored_descriptors: The archive row's ``causal_links`` payload, already
-            decoded from JSON. Entries that don't parse as a causal edge are
-            skipped (see :meth:`CausalLinkDescriptor.from_json_dict`).
-
-    Returns:
-        Number of descriptors submitted (not all of which may materialize).
-    """
-    parsed = [CausalLinkDescriptor.from_json_dict(raw) for raw in stored_descriptors]
-    links = [
-        (
-            descriptor.from_unit_id,
-            descriptor.to_unit_id,
-            descriptor.link_type,
-            descriptor.weight,
-            None,
-        )
-        for descriptor in parsed
-        if descriptor is not None
-    ]
-    if not links:
-        return 0
-    await _bulk_insert_links(conn, links, bank_id=bank_id, ops=ops)
-    return len(links)
@@ -22,7 +22,6 @@ from ...extensions.memory_defense import (
    apply_redaction,
    parse_policy,
 )
-from ...metrics import get_metrics_collector
 from ...worker.stage import set_stage
 from ..db_utils import acquire_with_retry
 from ..memory_engine import count_tokens, fq_table
@@ -72,25 +71,6 @@ def _redact_document_body(body: str, config: Any) -> str:
    return apply_redaction(body).content


-def _is_strict_append_of_stored_document(
-    stored_original_text: str | None,
-    document_body_override: str | None,
-    config: Any,
-) -> bool:
-    """Return whether an oversized document body strictly appends stored text.
-
-    ``documents.original_text`` is sanitized and may also be Memory Defense
-    redacted before persistence. Apply those same transformations to the
-    complete incoming body before comparing it with the stored prefix.
-    """
-    if stored_original_text is None or document_body_override is None:
-        return False
-
-    redacted_body = _redact_document_body(document_body_override, config)
-    sanitized_body = fact_extraction._sanitize_text(redacted_body) or ""
-    return len(sanitized_body) > len(stored_original_text) and sanitized_body.startswith(stored_original_text)
-
-
 async def _fire_memory_defense_webhook(
    webhook_manager: Any,
    *,
@@ -163,7 +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,
@@ -172,15 +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(
@@ -270,12 +246,10 @@ from . import (
    link_creation,
 )
 from .types import (
-    CausalRelation,
    ChunkMetadata,
-    ExtractedFact,
+    EntityResolutionResult,
    Phase1Result,
    ProcessedFact,
-    ResolvedEntity,
    RetainContent,
    RetainContentDict,
 )
@@ -286,39 +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]
-
-
-async def _record_retain_document_outcome(pool: Any, bank_id: str, document_id: str, units_created: int) -> None:
-    """Emit the per-document retain outcome metric.
-
-    The metric reports the document's unit count *after* this retain, not what
-    this call created: a delta retain that touches one chunk can legitimately
-    create zero units while the document keeps the units of its unchanged chunks,
-    and an oversized document is retained as several sequential sub-batches. Only
-    a document left with zero units is unreachable through recall/reflect and
-    worth alerting on (#3040).
-
-    ``units_created > 0`` already settles the outcome, so the count query only
-    runs on the zero case — which is exactly the cheap path (nothing was written).
-    Best-effort: telemetry must never fail a retain.
-    """
-    try:
-        total = units_created
-        if total == 0:
-            async with acquire_with_retry(pool) as conn:
-                total = await fact_storage.count_document_memory_units(conn, bank_id, document_id)
-        get_metrics_collector().record_retain_document(bank_id=bank_id, memory_unit_count=total)
-    except Exception:
-        logger.debug("Failed to record retain document outcome metric", exc_info=True)
-
-
 def _resolve_narrator(profile_name: str, bank_id: str) -> str | None:
    """Resolve the narrator (memory owner) used to prime fact extraction.

@@ -403,7 +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,
@@ -421,17 +362,15 @@ async def _pre_resolve_phase1(
        if not skip_semantic_ann:
            fact_types = [fact.fact_type for fact in processed_facts]
            semantic_ann_links = await compute_semantic_links_ann(
-                resolve_conn,
-                bank_id,
-                placeholder_unit_ids,
-                embeddings,
-                fact_types=fact_types,
-                threshold=config.semantic_link_min_similarity,
-                log_buffer=log_buffer,
+                resolve_conn, bank_id, placeholder_unit_ids, embeddings, fact_types=fact_types, log_buffer=log_buffer
            )

    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,
    )

@@ -482,7 +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],
@@ -509,7 +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
        )
@@ -522,10 +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")

@@ -546,7 +480,6 @@ async def _insert_facts_and_links(
                bank_id,
                unit_ids,
                embeddings_for_links,
-                threshold=config.semantic_link_min_similarity,
                pre_computed_ann_links=semantic_ann_links,
                ops=ops,
            )
@@ -616,90 +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(
@@ -880,7 +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,
@@ -979,12 +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]
@@ -1006,12 +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):
@@ -1149,8 +989,6 @@ async def _run_final_semantic_ann(
    pool: Any,
    bank_id: str,
    unit_ids: list[str],
-    *,
-    threshold: float,
    log_buffer: list[str],
 ) -> None:
    """
@@ -1229,7 +1067,6 @@ async def _run_final_semantic_ann(
                    chunk_embs,
                    fact_types=chunk_ftypes,
                    top_k=20,  # Recall uses at most 20 neighbors
-                    threshold=threshold,
                    log_buffer=log_buffer,
                )
                if ann_links:
@@ -1367,14 +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
@@ -1438,38 +1267,26 @@ async def _streaming_retain_batch(

        tasks: list[asyncio.Task] = []
        skipped_total = 0
-        try:
-            for i, chunk_text in enumerate(all_pre_chunks):
-                chunk_hash = chunk_storage.compute_chunk_hash(chunk_text)
-                if chunk_hash in existing_chunk_hashes:
-                    # Memory: skipped chunks aren't needed either.
-                    all_pre_chunks[i] = ""
-                    skipped_total += 1
-                    continue
-                tasks.append(asyncio.create_task(_extract_one(i, chunk_text)))
+        for i, chunk_text in enumerate(all_pre_chunks):
+            chunk_hash = chunk_storage.compute_chunk_hash(chunk_text)
+            if chunk_hash in existing_chunk_hashes:
+                # Memory: skipped chunks aren't needed either.
+                all_pre_chunks[i] = ""
+                skipped_total += 1
+                continue
+            tasks.append(asyncio.create_task(_extract_one(i, chunk_text)))

-            if skipped_total > 0:
-                log_buffer.append(
-                    f"[streaming] Producer: skipped {skipped_total}/{total_chunks} already-committed chunks"
-                )
+        if skipped_total > 0:
+            log_buffer.append(f"[streaming] Producer: skipped {skipped_total}/{total_chunks} already-committed chunks")

-            # Wait for all extractions; collect exceptions
-            results = await asyncio.gather(*tasks, return_exceptions=True)
-            for r in results:
-                if isinstance(r, BaseException):
-                    producer_error.append(r)
+        # Wait for all extractions; collect exceptions
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        for r in results:
+            if isinstance(r, BaseException):
+                producer_error.append(r)

-            # Signal the consumer that production is done
-            await chunk_queue.put(None)
-        finally:
-            # Cancellation arriving mid-fan-out (the consumer failed, or the worker's
-            # wall-clock ceiling fired) must not strand extraction tasks. Cancelling
-            # the gather above already propagates to them, but tasks created before
-            # we reach it would otherwise survive and park on `chunk_queue.put()`
-            # for the life of the process.
-            for extraction in tasks:
-                if not extraction.done():
-                    extraction.cancel()
+        # Signal the consumer that production is done
+        await chunk_queue.put(None)

    # ---- DB Consumer ----
    # Drains enriched chunks from the queue in batches and runs
@@ -1559,26 +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

@@ -1591,12 +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.
@@ -1624,7 +1422,6 @@ async def _streaming_retain_batch(
                                combined_content,
                                retain_params,
                                merged_tags,
-                                store_document_text=getattr(config, "store_document_text", True),
                            )
                        else:
                            await fact_storage.handle_document_tracking(
@@ -1636,7 +1433,6 @@ async def _streaming_retain_batch(
                                retain_params,
                                merged_tags,
                                ops=pool.ops,
-                                store_document_text=getattr(config, "store_document_text", True),
                            )
                        doc_tracking_done[0] = True
                        # Memory: combined_content has been persisted; release
@@ -1728,7 +1524,6 @@ async def _streaming_retain_batch(
                                combined_content,
                                retain_params,
                                merged_tags,
-                                store_document_text=getattr(config, "store_document_text", True),
                            )
                            log_buffer.append(
                                f"[streaming] Document {effective_doc_id} updated "
@@ -1744,7 +1539,6 @@ async def _streaming_retain_batch(
                                retain_params,
                                merged_tags,
                                ops=pool.ops,
-                                store_document_text=getattr(config, "store_document_text", True),
                            )
                            log_buffer.append(f"[streaming] Document {effective_doc_id} tracked (full content)")
                        doc_tracking_done[0] = True
@@ -1771,12 +1565,7 @@ async def _streaming_retain_batch(
                    chunk_id_map = {}
                    if batch_chunk_meta:
                        chunk_id_map = await chunk_storage.store_chunks_batch(
-                            conn,
-                            bank_id,
-                            effective_doc_id,
-                            batch_chunk_meta,
-                            ops=pool.ops,
-                            store_document_text=getattr(config, "store_document_text", True),
+                            conn, bank_id, effective_doc_id, batch_chunk_meta, ops=pool.ops
                        )
                        log_buffer.append(
                            f"  Store chunks: {len(batch_chunk_meta)} chunks in {time.time() - step_start:.3f}s"
@@ -1801,7 +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=[],
@@ -1812,27 +1601,13 @@ 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.
-            #
-            # This MUST run after the `acquire_with_retry` block above has exited,
-            # not inside it: flush_pending_stats() acquires its own connection, and
-            # the write above is only committed when the enclosing acquire() block
-            # exits. On Oracle (oracledb does not autocommit — the backend commits
-            # on clean exit of acquire()) doing this inside the block deadlocks
-            # permanently: connection #2 waits on the row locks the still-open
-            # connection #1 holds on `entities`, while connection #1 cannot commit
-            # until this call returns. Oracle never reports ORA-00060 for it,
-            # because session #1 is blocked in Python rather than on the database.
-            try:
-                await entity_resolver.flush_pending_stats()
-            except Exception:
-                logger.warning(f"Entity stats flush (consumer batch {consumer_batch_idx + 1}) failed", exc_info=True)
+                # Best-effort: flush entity_cooccurrences and other deferred stats.
+                try:
+                    await entity_resolver.flush_pending_stats()
+                except Exception:
+                    logger.warning(
+                        f"Entity stats flush (consumer batch {consumer_batch_idx + 1}) failed", exc_info=True
+                    )

            logger.info(
                f"[streaming] Consumer batch {consumer_batch_idx + 1} total "
@@ -1907,27 +1682,8 @@ async def _streaming_retain_batch(
            logger.warning("Failed to check operation recovery state", exc_info=True)

    if not facts_already_committed:
-        # Run producer and consumer concurrently.
-        #
-        # Cancellation is explicit because plain gather() leaks: when the consumer
-        # raises (a deadlock victim, a lock timeout) gather propagates that error
-        # immediately but leaves the producer — and every extraction task under it
-        # — running. Those tasks then block forever on `chunk_queue.put()` into a
-        # queue nobody drains, pinning their chunk payloads and still spending LLM
-        # permits and tokens on an operation that already failed (#3002). The same
-        # applies when the worker's wall-clock ceiling cancels us from above.
-        producer_task = asyncio.create_task(_llm_producer())
-        consumer_task = asyncio.create_task(_db_consumer())
-        try:
-            await asyncio.gather(producer_task, consumer_task)
-        finally:
-            for pipeline_task in (producer_task, consumer_task):
-                if not pipeline_task.done():
-                    pipeline_task.cancel()
-            # Await the cancellations so neither half outlives this call; the
-            # results are already accounted for by the gather above (or by the
-            # exception that is propagating).
-            await asyncio.gather(producer_task, consumer_task, return_exceptions=True)
+        # Run producer and consumer concurrently
+        await asyncio.gather(_llm_producer(), _db_consumer())

        # Propagate producer errors (e.g. LLM failures)
        if producer_error:
@@ -1960,7 +1716,6 @@ async def _streaming_retain_batch(
                            combined_content,
                            retain_params,
                            merged_tags,
-                            store_document_text=getattr(config, "store_document_text", True),
                        )
                    else:
                        await fact_storage.handle_document_tracking(
@@ -1972,7 +1727,6 @@ async def _streaming_retain_batch(
                            retain_params,
                            merged_tags,
                            ops=pool.ops,
-                            store_document_text=getattr(config, "store_document_text", True),
                        )
                    doc_tracking_done[0] = True
                    # Memory: combined_content has been persisted and won't be
@@ -1980,21 +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:
@@ -2048,13 +1787,7 @@ async def _streaming_retain_batch(
    if all_unit_ids and not pipeline_aborted[0]:
        ann_start = time.time()
        try:
-            await _run_final_semantic_ann(
-                pool,
-                bank_id,
-                all_unit_ids,
-                threshold=config.semantic_link_min_similarity,
-                log_buffer=log_buffer,
-            )
+            await _run_final_semantic_ann(pool, bank_id, all_unit_ids, log_buffer)
        except Exception:
            # ANN pass is best-effort. FK violations can occur if a concurrent
            # retain cascade-deleted our units between the batch commit and here.
@@ -2080,9 +1813,6 @@ async def _streaming_retain_batch(
    log_buffer.append(f"{'=' * 60}")
    logger.info("\n" + "\n".join(log_buffer) + "\n")

-    if not pipeline_aborted[0]:
-        await _record_retain_document_outcome(pool, bank_id, effective_doc_id, len(all_unit_ids))
-
    # Map all unit_ids back to the original content items.
    # For streaming mode with a single document, all units belong to content 0.
    result_unit_ids = [all_unit_ids] + [[] for _ in contents[1:]]
@@ -2171,26 +1901,12 @@ async def _try_delta_retain(
    # between this read and the write. The write TXN verifies the hash hasn't
    # changed; if it has, we fall back to streaming (which has full protection).
    async with acquire_with_retry(pool) as conn:
-        if document_body_override is not None:
-            doc_row_at_load = await conn.fetchrow(
-                f"SELECT content_hash, original_text FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2",
-                effective_doc_id,
-                bank_id,
-            )
-            doc_hash_at_load = doc_row_at_load["content_hash"] if doc_row_at_load else None
-            original_text_at_load = doc_row_at_load["original_text"] if doc_row_at_load else None
-        else:
-            doc_hash_at_load = await conn.fetchval(
-                f"SELECT content_hash FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2",
-                effective_doc_id,
-                bank_id,
-            )
-            original_text_at_load = None
-
-        # Load chunks after the document version. If a concurrent writer commits
-        # between these reads, the hash precondition on metadata-only writes (or
-        # the extraction freshness recheck below) forces a streaming fallback.
        existing_chunks = await chunk_storage.load_existing_chunks(conn, bank_id, effective_doc_id)
+        doc_hash_at_load = await conn.fetchval(
+            f"SELECT content_hash FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2",
+            effective_doc_id,
+            bank_id,
+        )

    if not existing_chunks:
        return None
@@ -2222,30 +1938,6 @@ async def _try_delta_retain(
    )

    if not unchanged_indices:
-        if _is_strict_append_of_stored_document(
-            original_text_at_load,
-            document_body_override,
-            config,
-        ):
-            log_buffer.append(
-                "[delta] First oversized slice has no stored chunk match, but "
-                "the complete document strictly appends the stored source — "
-                "preserving historical chunks and advancing document metadata"
-            )
-            return await _delta_metadata_only(
-                pool,
-                bank_id,
-                contents_dicts,
-                contents,
-                effective_doc_id,
-                document_tags,
-                log_buffer,
-                start_time,
-                outbox_callback,
-                document_body_override=document_body_override,
-                config=config,
-                expected_content_hash=doc_hash_at_load,
-            )
        logger.info(f"Delta retain: no unchanged chunks for {effective_doc_id}, falling back to full retain")
        return None

@@ -2266,7 +1958,6 @@ async def _try_delta_retain(
            outbox_callback,
            document_body_override=document_body_override,
            config=config,
-            expected_content_hash=doc_hash_at_load,
        )

    # Build content items for only the changed/new chunks
@@ -2285,7 +1976,6 @@ async def _try_delta_retain(
            outbox_callback,
            document_body_override=document_body_override,
            config=config,
-            expected_content_hash=doc_hash_at_load,
        )

    # Freshness recheck BEFORE the (expensive) LLM extraction.
@@ -2338,7 +2028,6 @@ async def _try_delta_retain(
                outbox_callback,
                document_body_override=document_body_override,
                config=config,
-                expected_content_hash=recheck_hash,
            )
        log_buffer.append(
            f"[delta] Recheck: {len(recheck.changed) + len(recheck.new) + len(recheck.removed)} chunks still differ — "
@@ -2468,12 +2157,7 @@ async def _try_delta_retain(
                        for cm in new_chunk_metadata
                    ]
                    chunk_id_map = await chunk_storage.store_chunks_batch(
-                        conn,
-                        bank_id,
-                        effective_doc_id,
-                        remapped_chunks,
-                        ops=pool.ops,
-                        store_document_text=getattr(config, "store_document_text", True),
+                        conn, bank_id, effective_doc_id, remapped_chunks, ops=pool.ops
                    )
                    for chunk_idx, chunk_id in chunk_id_map.items():
                        chunk_id_map_by_doc[(effective_doc_id, chunk_idx)] = chunk_id
@@ -2502,7 +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,
@@ -2510,6 +2194,12 @@ async def _try_delta_retain(
                    ops=pool.ops,
                )

+            # Flush deferred entity_cooccurrences stats (post-transaction, best-effort).
+            try:
+                await entity_resolver.flush_pending_stats()
+            except Exception:
+                logger.warning("Entity stats flush failed — retrieval unaffected", exc_info=True)
+
            total_time = time.time() - start_time
            log_buffer.append(f"{'=' * 60}")
            log_buffer.append(
@@ -2520,20 +2210,11 @@ async def _try_delta_retain(
            log_buffer.append(f"{'=' * 60}")
            logger.info("\n" + "\n".join(log_buffer) + "\n")

-        # Flush deferred entity_cooccurrences stats (best-effort). Must run after
-        # the acquire() block above has exited — see the streaming path for why
-        # doing this while still holding the connection deadlocks on Oracle.
-        try:
-            await entity_resolver.flush_pending_stats()
-        except Exception:
-            logger.warning("Entity stats flush failed — retrieval unaffected", exc_info=True)
-
    if db_semaphore is not None:
        async with db_semaphore:
            await _run_delta_db_work()
    else:
        await _run_delta_db_work()
-    await _record_retain_document_outcome(pool, bank_id, effective_doc_id, sum(len(ids) for ids in result_unit_ids))
    # Count content + context tokens that actually went through extraction.
    # ``delta_contents`` holds the per-chunk RetainContent items for the
    # changed/new chunks (see ``_build_delta_contents``) — i.e. exactly what
@@ -2555,22 +2236,16 @@ async def _delta_metadata_only(
    *,
    document_body_override: str | None = None,
    config: Any = None,
-    expected_content_hash: str | None = None,
-) -> tuple[list[list[str]], TokenUsage, int] | None:
+):
    """Handle the case where no chunks changed — just update document metadata and tags."""
    async with acquire_with_retry(pool) as conn:
        async with conn.transaction():
            # Lock the document row to serialize with concurrent retains
-            current_content_hash = await conn.fetchval(
+            await conn.fetchval(
                f"SELECT content_hash FROM {fq_table('documents')} WHERE id = $1 AND bank_id = $2 FOR UPDATE",
                document_id,
                bank_id,
            )
-            if expected_content_hash is not None and current_content_hash != expected_content_hash:
-                log_buffer.append(
-                    f"[delta] Document {document_id} changed before metadata update — falling back to full retain"
-                )
-                return None
            # When this sub-batch is a slice of an oversized item, write the
            # full original body (issue #1838) instead of just the slice.
            # Redact the override since it bypassed per-chunk screening.
@@ -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


@@ -185,47 +180,15 @@ class ProcessedFact:
    # Observation scopes for consolidation
    observation_scopes: Literal["per_tag", "combined", "all_combinations", "shared"] | list[list[str]] | None = 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
+    @property
+    def is_duplicate(self) -> bool:
+        """Check if this fact was marked as a duplicate."""
+        return self.unit_id is None

    @staticmethod
    def from_extracted_fact(
        extracted_fact: "ExtractedFact", embedding: list[float], chunk_id: str | None = None
-    ) -> "ProcessedFact | None":
+    ) -> "ProcessedFact":
        """
        Create ProcessedFact from ExtractedFact.

@@ -235,17 +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
@@ -255,7 +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,
@@ -272,43 +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:
@@ -341,3 +271,11 @@ class RetainBatch:

    # Results (populated after storage)
    unit_ids_by_content: list[list[str]] = field(default_factory=list)
+
+    def get_facts_for_content(self, content_index: int) -> list[ExtractedFact]:
+        """Get all extracted facts for a specific content item."""
+        return [f for f in self.extracted_facts if f.content_index == content_index]
+
+    def get_chunks_for_content(self, content_index: int) -> list[ChunkMetadata]:
+        """Get all chunks for a specific content item."""
+        return [c for c in self.chunks if c.content_index == content_index]
@@ -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.

@@ -40,13 +40,14 @@ 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)
        tag_groups: list[TagGroup] | None = None,  # Compound boolean tag filter groups
        created_after: datetime | None = None,  # Only include memory_units created after this time
        created_before: datetime | None = None,  # Only include memory_units created before this time
-        preselected_semantic_seeds: list[RetrievalResult] | None = None,
    ) -> tuple[list[RetrievalResult], GraphRetrievalTimings | None]:
        """
        Retrieve relevant facts via graph traversal.
@@ -58,9 +59,10 @@ 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)
-            preselected_semantic_seeds: Independently thresholded graph entry points already fetched by the caller

        Returns:
            Tuple of (List of RetrievalResult with activation scores, optional timing info)
@@ -1,16 +1,16 @@
 """
 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
                   COUNT(DISTINCT entity_id). Uses a LATERAL per-entity cap
                   (graph_per_entity_limit, default 200) to prevent high-fanout entities
                   from exploding the self-join intermediate rows.
-2. Semantic links — precomputed kNN graph (each new fact linked to its most
-                    similar existing facts at insert time, subject to the configured threshold). Checked
+2. Semantic links — precomputed kNN graph (each new fact linked to its top-5 most
+                    similar existing facts at insert time, similarity >= 0.7). Checked
                    in both directions since the graph is not symmetric. Score = weight.
 3. Causal links  — explicit causal chains (causes/caused_by/enables/prevents).
                   Score = weight + 1.0 (boosted as highest-quality signal).
@@ -31,7 +31,7 @@ import time
 from datetime import datetime
 from typing import Any

-from ...config import DEFAULT_GRAPH_SEED_MIN_SIMILARITY, get_config
+from ...config import get_config
 from ..db_utils import acquire_with_retry
 from ..memory_engine import fq_table
 from .graph_retrieval import GraphRetriever
@@ -40,8 +40,6 @@ from .types import GraphRetrievalTimings, RetrievalResult

 logger = logging.getLogger(__name__)

-GRAPH_SEED_LIMIT = 20
-

 async def _find_semantic_seeds(
    conn,
@@ -49,7 +47,7 @@ async def _find_semantic_seeds(
    bank_id: str,
    fact_type: str,
    limit: int = 20,
-    threshold: float = DEFAULT_GRAPH_SEED_MIN_SIMILARITY,
+    threshold: float = 0.3,
    tags: list[str] | None = None,
    tags_match: TagsMatch = "any",
    tag_groups: list[TagGroup] | None = None,
@@ -129,13 +127,14 @@ 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",
        tag_groups: list[TagGroup] | None = None,
        created_after: "datetime | None" = None,
        created_before: "datetime | None" = None,
-        preselected_semantic_seeds: list[RetrievalResult] | None = None,
    ) -> tuple[list[RetrievalResult], GraphRetrievalTimings | None]:
        """
        Retrieve facts by expanding links from seeds.
@@ -147,9 +146,10 @@ 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
-            preselected_semantic_seeds: Graph-specific entry points derived from a shared semantic candidate pool

        Returns:
            Tuple of (results, timings)
@@ -158,18 +158,18 @@ class LinkExpansionRetriever(GraphRetriever):
        timings = GraphRetrievalTimings(fact_type=fact_type)

        async with acquire_with_retry(pool) as conn:
-            if preselected_semantic_seeds is None:
-                # A shared semantic pool is reusable only when its SQL threshold
-                # covers the independently configured graph threshold. Otherwise
-                # retain graph retrieval's own query and result semantics.
+            # 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=GRAPH_SEED_LIMIT,
-                    threshold=get_config().graph_seed_min_similarity,
+                    limit=20,
+                    threshold=0.3,
                    tags=tags,
                    tags_match=tags_match,
                    tag_groups=tag_groups,
@@ -177,16 +177,13 @@ class LinkExpansionRetriever(GraphRetriever):
                    created_before=created_before,
                )
                timings.seeds_time = time.time() - seeds_start
-            else:
-                all_seeds = preselected_semantic_seeds
+                logger.debug(
+                    f"[LinkExpansion] Found {len(all_seeds)} semantic seeds for fact_type={fact_type} "
+                    f"(tags={tags}, tags_match={tags_match})"
+                )

-            logger.debug(
-                "LinkExpansion found %s semantic seeds for fact_type=%s (tags=%s, tags_match=%s)",
-                len(all_seeds),
-                fact_type,
-                tags,
-                tags_match,
-            )
+            if temporal_seeds:
+                all_seeds.extend(temporal_seeds)

            if not all_seeds:
                return [], timings
@@ -214,7 +211,7 @@ class LinkExpansionRetriever(GraphRetriever):
        #
        # Entity score: tanh(count × 0.5) maps shared-entity count to [0, 1]:
        #   1 entity → 0.46,  2 → 0.76,  3 → 0.91,  4 → 0.96  (saturates naturally)
-        # Semantic score: similarity weight, already above the configured construction floor.
+        # Semantic score: similarity weight, already ∈ [0.7, 1.0].
        # Causal score:   link weight, already ∈ [0, 1].
        #
        # Facts appearing in multiple signals accumulate higher scores, rewarding
@@ -246,16 +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
@@ -15,12 +15,12 @@ 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, DEFAULT_TEMPORAL_SEMANTIC_MIN_SIMILARITY, 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
 from .graph_retrieval import GraphRetriever
-from .link_expansion_retrieval import GRAPH_SEED_LIMIT, LinkExpansionRetriever
+from .link_expansion_retrieval import LinkExpansionRetriever
 from .tags import TagGroup, TagsMatch, build_tag_groups_where_clause, build_tags_where_clause_simple
 from .types import GraphRetrievalTimings, RetrievalResult

@@ -67,15 +67,6 @@ class MultiFactTypeRetrievalResult:
    max_conn_wait: float = 0.0


-@dataclass
-class SemanticBm25Result:
-    """Per-fact-type candidates returned by the shared semantic/BM25 query."""
-
-    semantic: list[RetrievalResult]
-    bm25: list[RetrievalResult]
-    graph_seeds: list[RetrievalResult] | None
-
-
 # Default graph retriever instance (can be overridden)
 _default_graph_retriever: GraphRetriever | None = None

@@ -115,8 +106,7 @@ async def retrieve_semantic_bm25_combined(
    created_before: datetime | None = None,
    min_semantic: float | None = None,
    min_keyword: float | None = None,
-    graph_seed_min_similarity: float | None = None,
-) -> dict[str, SemanticBm25Result]:
+) -> dict[str, tuple[list[RetrievalResult], list[RetrievalResult]]]:
    """
    Combined semantic + BM25 retrieval for multiple fact types in a single query.

@@ -148,10 +138,9 @@ async def retrieve_semantic_bm25_combined(
        tags_match: Tag matching mode

    Returns:
-        Candidate groups for each fact type. ``graph_seeds`` is ``None`` when
-        the semantic query's threshold is too strict to cover graph entry points.
+        Dict mapping fact_type -> (semantic_results, bm25_results)
    """
-    result_dict = {ft: SemanticBm25Result(semantic=[], bm25=[], graph_seeds=None) for ft in fact_types}
+    result_dict: dict[str, tuple[list[RetrievalResult], list[RetrievalResult]]] = {ft: ([], []) for ft in fact_types}

    config = get_config()
    tokens = tokenize_query(query_text)
@@ -233,12 +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(
@@ -318,18 +302,8 @@ async def retrieve_semantic_bm25_combined(
        else:
            raise

-    # Group results. The semantic SQL deliberately over-fetches for HNSW recall;
-    # when that pool also covers the graph threshold, derive graph entry points
-    # from the same ordered rows instead of issuing one duplicate ANN query per
-    # fact type. Convert only the prefix either consumer can observe, not the
-    # entire HNSW over-fetch pool.
-    graph_seed_threshold = (
-        graph_seed_min_similarity
-        if graph_seed_min_similarity is not None and sem_min <= graph_seed_min_similarity
-        else None
-    )
-    semantic_candidate_limit = max(limit, GRAPH_SEED_LIMIT if graph_seed_threshold is not None else 0)
-    semantic_candidates: dict[str, list[RetrievalResult]] = {ft: [] for ft in fact_types}
+    # Group results; trim semantic to limit (over-fetched for HNSW approximation).
+    sem_counts: dict[str, int] = {ft: 0 for ft in fact_types}
    for r in rows:
        row = dict(r)
        source = row.pop("source")
@@ -337,19 +311,11 @@ async def retrieve_semantic_bm25_combined(
        if ft not in result_dict:
            continue
        if source == "semantic":
-            if len(semantic_candidates[ft]) < semantic_candidate_limit:
-                semantic_candidates[ft].append(RetrievalResult.from_db_row(row))
+            if sem_counts[ft] < limit:
+                result_dict[ft][0].append(RetrievalResult.from_db_row(row))
+                sem_counts[ft] += 1
        else:
-            result_dict[ft].bm25.append(RetrievalResult.from_db_row(row))
-
-    for ft, candidates in semantic_candidates.items():
-        result_dict[ft].semantic.extend(candidates[:limit])
-        if graph_seed_threshold is not None:
-            result_dict[ft].graph_seeds = [
-                candidate
-                for candidate in candidates
-                if candidate.similarity is not None and candidate.similarity >= graph_seed_threshold
-            ][:GRAPH_SEED_LIMIT]
+            result_dict[ft][1].append(RetrievalResult.from_db_row(row))

    return result_dict

@@ -422,7 +388,7 @@ async def retrieve_temporal_combined(
    start_date: datetime,
    end_date: datetime,
    budget: int,
-    semantic_threshold: float = DEFAULT_TEMPORAL_SEMANTIC_MIN_SIMILARITY,
+    semantic_threshold: float = 0.1,
    tags: list[str] | None = None,
    tags_match: TagsMatch = "any",
    tag_groups: list[TagGroup] | None = None,
@@ -650,7 +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)
@@ -776,7 +742,6 @@ async def retrieve_all_fact_types_parallel(
    import time

    retriever = graph_retriever or get_default_graph_retriever()
-    config = get_config()
    start_time = time.time()
    timings: dict[str, float] = {}

@@ -813,7 +778,6 @@ async def retrieve_all_fact_types_parallel(
            created_before=created_before,
            min_semantic=min_semantic,
            min_keyword=min_keyword,
-            graph_seed_min_similarity=config.graph_seed_min_similarity,
        )
        semantic_bm25_time = time.time() - semantic_bm25_start

@@ -829,7 +793,7 @@ async def retrieve_all_fact_types_parallel(
                tc_start,
                tc_end,
                budget=thinking_budget,
-                semantic_threshold=config.temporal_semantic_min_similarity,
+                semantic_threshold=min_semantic if min_semantic is not None else 0.1,
                tags=tags,
                tags_match=tags_match,
                tag_groups=tag_groups,
@@ -853,12 +817,13 @@ 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,
            created_after=created_after,
            created_before=created_before,
-            preselected_semantic_seeds=semantic_bm25_results[ft].graph_seeds,
        )
        return ft, results, time.time() - graph_start, graph_timing

@@ -873,8 +838,7 @@ async def retrieve_all_fact_types_parallel(

    for ft in fact_types:
        # Get semantic + bm25 results for this fact type
-        semantic_results = semantic_bm25_results[ft].semantic
-        bm25_results = semantic_bm25_results[ft].bm25
+        semantic_results, bm25_results = semantic_bm25_results.get(ft, ([], []))

        # Find graph results for this fact type
        graph_results = []
@@ -77,6 +77,31 @@ def format_facts_for_prompt(facts: list[MemoryFact]) -> str:
    return json.dumps(formatted, indent=2, ensure_ascii=False)


+def format_entity_summaries_for_prompt(entities: dict) -> str:
+    """Format entity summaries for inclusion in the reflect prompt.
+
+    Args:
+        entities: Dict mapping entity name to EntityState objects
+
+    Returns:
+        Formatted string with entity summaries, or empty string if no summaries
+    """
+    if not entities:
+        return ""
+
+    summaries = []
+    for name, state in entities.items():
+        # Get summary from observations (summary is stored as single observation)
+        if state.observations:
+            summary_text = state.observations[0].text
+            summaries.append(f"## {name}\n{summary_text}")
+
+    if not summaries:
+        return ""
+
+    return "\n\n".join(summaries)
+
+
 def build_think_prompt(
    agent_facts_text: str,
    world_facts_text: str,
@@ -230,3 +230,25 @@ class SearchTrace(BaseModel):
            if visit.node_id == node_id:
                return visit
        return None
+
+    def get_search_path_to_node(self, node_id: str) -> list[NodeVisit]:
+        """Get the path from entry point to a specific node."""
+        path = []
+        current_visit = self.get_visit_by_node_id(node_id)
+
+        while current_visit:
+            path.insert(0, current_visit)
+            if current_visit.parent_node_id:
+                current_visit = self.get_visit_by_node_id(current_visit.parent_node_id)
+            else:
+                break
+
+        return path
+
+    def get_nodes_by_link_type(self, link_type: Literal["temporal", "semantic", "entity"]) -> list[NodeVisit]:
+        """Get all nodes reached via a specific link type."""
+        return [v for v in self.visits if v.link_type == link_type]
+
+    def get_entry_point_nodes(self) -> list[NodeVisit]:
+        """Get all entry point visits."""
+        return [v for v in self.visits if v.is_entry_point]
@@ -11,6 +11,7 @@ from typing import Any, Literal

 from .trace import (
    EntryPoint,
+    LinkInfo,
    NodeVisit,
    PruningDecision,
    QueryInfo,
@@ -210,6 +211,56 @@ class SearchTracer:
        elif link_type == "entity":
            self.entity_links_followed += 1

+    def add_neighbor_link(
+        self,
+        from_node_id: str,
+        to_node_id: str,
+        link_type: Literal["temporal", "semantic", "entity"],
+        link_weight: float,
+        entity_id: str | None,
+        new_activation: float | None,
+        followed: bool,
+        prune_reason: str | None = None,
+        is_supplementary: bool = False,
+    ):
+        """
+        Record a link to a neighbor (whether followed or not).
+
+        Args:
+            from_node_id: Source node
+            to_node_id: Target node
+            link_type: Type of link
+            link_weight: Weight of link
+            entity_id: Entity ID if link is entity-based
+            new_activation: Activation passed to neighbor (None for supplementary links)
+            followed: Whether link was followed
+            prune_reason: Why link was not followed (if not followed)
+            is_supplementary: Whether this is a supplementary link (multiple connections)
+        """
+        # Find the visit for the source node
+        visit = None
+        for v in self.visits:
+            if v.node_id == from_node_id:
+                visit = v
+                break
+
+        if visit is None:
+            # Node not found, skip
+            return
+
+        link_info = LinkInfo(
+            to_node_id=to_node_id,
+            link_type=link_type,
+            link_weight=link_weight,
+            entity_id=entity_id,
+            new_activation=new_activation,
+            followed=followed,
+            prune_reason=prune_reason,
+            is_supplementary=is_supplementary,
+        )
+
+        visit.neighbors_explored.append(link_info)
+
    def prune_node(
        self,
        node_id: str,
@@ -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)
@@ -1,73 +0,0 @@
-"""Shared retry timing for Text Embeddings Inference HTTP clients."""
-
-import math
-import random
-from datetime import datetime, timezone
-from email.utils import parsedate_to_datetime
-
-import httpx
-
-# Ceiling for a single backoff sleep, independent of the client's request
-# timeout. The reranker holds its concurrency semaphore across the sleep, so a
-# large server-supplied Retry-After would stall every queued rerank behind it;
-# capping well below the request timeout keeps a slow proxy from converting a
-# transient overload into a minutes-long recall stall.
-MAX_RETRY_DELAY_SECONDS = 5.0
-
-# Fraction of the delay used as the jitter window, matching the "equal jitter"
-# policy of ``db_utils._backoff_delay``. TEI overload is self-synchronising -- a
-# burst of concurrent requests exhausts the permit pool at the same instant and
-# gets 429ed together -- so the window has to be wide enough to actually break
-# the burst apart. A token few percent would leave the callers retrying in
-# lockstep and re-colliding on the same exhausted pool.
-JITTER_RATIO = 0.5
-
-
-def _retry_after_seconds(value: str | None) -> float | None:
-    if not isinstance(value, str) or not value:
-        return None
-    try:
-        seconds = float(value)
-    except (TypeError, ValueError, OverflowError):
-        try:
-            retry_at = parsedate_to_datetime(value)
-            if retry_at.tzinfo is None:
-                retry_at = retry_at.replace(tzinfo=timezone.utc)
-            seconds = (retry_at - datetime.now(timezone.utc)).total_seconds()
-        except (IndexError, TypeError, ValueError, OverflowError):
-            return None
-    return max(0.0, seconds) if math.isfinite(seconds) else None
-
-
-def _delay_limit(request_timeout: float) -> float:
-    """Longest sleep we are willing to take between attempts."""
-    if not math.isfinite(request_timeout) or request_timeout < 0:
-        return MAX_RETRY_DELAY_SECONDS
-    return min(request_timeout, MAX_RETRY_DELAY_SECONDS)
-
-
-def tei_retry_delay(
-    response: httpx.Response,
-    fallback_delay: float,
-    *,
-    request_timeout: float,
-) -> float:
-    """Seconds to sleep before retrying a transient TEI response.
-
-    A server-supplied ``Retry-After`` wins over the caller's exponential
-    backoff, and both are bounded by :func:`_delay_limit`. The result is spread
-    over a jitter window so concurrent callers do not resume in lockstep.
-    """
-    retry_after = _retry_after_seconds(response.headers.get("Retry-After"))
-    limit = _delay_limit(request_timeout)
-    fallback = fallback_delay if math.isfinite(fallback_delay) else 0.0
-    requested_delay = max(fallback, retry_after or 0.0, 0.0)
-    delay = min(requested_delay, limit)
-    if delay <= 0:
-        return 0.0
-    spread = delay * JITTER_RATIO
-    if requested_delay >= limit:
-        # Already at the ceiling, so the only room to de-synchronise is downward.
-        return delay - random.uniform(0.0, spread)
-    # Retry-After is a minimum, so spread upward -- but never past the ceiling.
-    return delay + random.uniform(0.0, min(spread, limit - delay))
--- 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