Completes the Phase 1 master brain keystone: every LLM interaction
across the ecosystem now pulls context from AtoCore automatically.
Three adapters, one HTTP backend:
1. OpenClaw plugin pull (handler.js):
- Added before_prompt_build hook that calls /context/build and
injects the pack via prependContext
- Existing capture hooks (before_agent_start + llm_output)
unchanged
- 6s context timeout, fail-open on AtoCore unreachable
- Deployed to T420, gateway restarted, "7 plugins loaded"
2. atocore-proxy (scripts/atocore_proxy.py):
- Stdlib-only OpenAI-compatible HTTP middleware
- Drop-in layer for Codex, Ollama, LiteLLM, any OpenAI-compat client
- Intercepts /chat/completions: extracts query, pulls context,
injects as system message, forwards to upstream, captures back
- Fail-open: AtoCore down = passthrough without injection
- Configurable via env: UPSTREAM, PORT, CLIENT_LABEL, INJECT, CAPTURE
3. (from prior commit c49363f) atocore-mcp:
- stdio MCP server, stdlib Python, 7 tools exposed
- Registered in Claude Code: "✓ Connected"
Plus quick win:
- Project synthesis moved from Sunday-only to daily cron so wiki /
mirror pages stay fresh (Step C in batch-extract.sh). Lint stays
weekly.
Plus docs:
- docs/universal-consumption.md: configuration guide for all 3 adapters
with registration/env-var tables and verification checklist
Plus housekeeping:
- .gitignore: add .mypy_cache/
Tests: 303/303 passing.
This closes the consumption gap: the reinforcement feedback loop
can now actually work (memories get injected → get referenced →
reinforcement fires → auto-promotion). Every Claude, OpenClaw,
Codex, or Ollama session is automatically AtoCore-grounded.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Three additive upgrades borrowed from Karpathy's LLM Wiki pattern:
1. CONTRADICTION DETECTION: auto-triage now has a fourth verdict —
"contradicts". When a candidate conflicts with an existing memory
(not duplicates, genuine disagreement like "Option A selected"
vs "Option B selected"), the triage model flags it and leaves
it in the queue for human review instead of silently rejecting
or double-storing. Preserves source tension rather than
suppressing it.
2. WEEKLY LINT PASS: scripts/lint_knowledge_base.py checks for:
- Orphan memories (active but zero references after 14 days)
- Stale candidates (>7 days unreviewed)
- Unused entities (no relationships)
- Empty-state projects
- Unregistered projects auto-detected in memories
Runs Sundays via the cron. Outputs a report.
3. WEEKLY SYNTHESIS: scripts/synthesize_projects.py uses sonnet to
generate a 3-5 sentence "current state" paragraph per project
from state + memories + entities. Cached in project_state under
status/synthesis_cache. Wiki project pages now show this at the
top under "Current State (auto-synthesis)". Falls back to a
deterministic summary if no cache exists.
deploy/dalidou/batch-extract.sh: added Step C (synthesis) and
Step D (lint) gated to Sundays via date check.
All additive — nothing existing changes behavior. The database
remains the source of truth; these operations just produce better
synthesized views and catch rot.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
scripts/auto_triage.py: fetches candidate memories, asks a triage
model (claude -p, default sonnet) to classify each as promote /
reject / needs_human, and executes the verdict via the API.
Trust model:
- Auto-promote: model says promote AND confidence >= 0.8 AND
dedup-checked against existing active memories for the project
- Auto-reject: model says reject
- needs_human: everything else stays in queue for manual review
The triage model receives both the candidate content AND a summary
of existing active memories for the same project, so it can detect
duplicates and near-duplicates. The system prompt explicitly lists
the rejection categories learned from the first two manual triage
passes (stale snapshots, impl details, planned-not-implemented,
process rules that belong in ledger not memory).
deploy/dalidou/batch-extract.sh now runs extraction (Step A) then
auto-triage (Step B) in sequence. The nightly cron at 03:00 UTC
will run the full pipeline: backup → cleanup → rsync → extract →
triage. Only needs_human candidates reach the human.
Supports --dry-run for preview without executing.
Supports --model override for multi-model triage (e.g. opus for
higher-quality review, or a future Gemini/Ollama backend).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The claude CLI is installed on the Dalidou HOST but not inside
the Docker container. The /admin/extract-batch API endpoint with
mode=llm silently returned 0 candidates because
shutil.which('claude') was None inside the container.
Fix: extraction runs host-side via deploy/dalidou/batch-extract.sh
which calls scripts/batch_llm_extract_live.py with the host's
PYTHONPATH pointing at the repo's src/. The script:
- Fetches interactions from the API (GET /interactions?since=...)
- Runs extract_candidates_llm() locally (host has claude CLI)
- POSTs candidates back to the API (POST /memory, status=candidate)
- Tracks last-run timestamp via project state
The cron now calls the host-side script instead of the container
API endpoint for LLM mode. Rule-mode extraction in the container
still works via /admin/extract-batch.
The API endpoint retains the mode=llm option for environments
where claude IS inside the container (future Docker image with
claude CLI, or a different deployment model).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
