Anto01
7863ab3825
User observation that triggered this: 'AtoCore was meant to remember
and triage by its own, not me specifically asking to remember things'.
Correct — the system IS capturing autonomously (Stop hook + OpenClaw
plugin), but extraction was nightly-only. So 'I talked about APM
today' didn't show up in memories until the next 03:00 UTC cron run.
Fix: split the lightweight extraction + triage into a new hourly cron.
The heavy nightly (backup, rsync, OpenClaw import, synthesis, harness,
integrity, emerging detector) stays at 03:00 UTC — no reason to run
those hourly.
hourly-extract.sh does ONLY:
- Step A: batch_llm_extract_live.py (limit 50, ~1h window)
- Step B: auto_triage.py (3-tier, max_batches=3)
Lock file prevents overlap on rate-limit retries.
After this lands: latency from 'you told me X' to 'X is an active
memory' drops from ~24h to ~1h (plus the ~5min it takes for
extraction + triage to complete on a typical <20 interactions/hour).
The 'atocore_remember' MCP tool stays as an escape hatch for
conversations that happen outside captured channels (Claude Desktop
web, phone), NOT as the primary capture path. The primary path is
automatic: Claude Code / OpenClaw captures → hourly extract → 3-tier
triage → active memory.
Install cron entry manually:
0 * * * * /srv/storage/atocore/app/deploy/dalidou/hourly-extract.sh \
>> /home/papa/atocore-logs/hourly-extract.log 2>&1
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
AtoCore
Personal context engine that enriches LLM interactions with durable memory, structured context, and project knowledge.
Quick Start
pip install -e .
uvicorn src.atocore.main:app --port 8100
Usage
# Ingest markdown files
curl -X POST http://localhost:8100/ingest \
-H "Content-Type: application/json" \
-d '{"path": "/path/to/notes"}'
# Build enriched context for a prompt
curl -X POST http://localhost:8100/context/build \
-H "Content-Type: application/json" \
-d '{"prompt": "What is the project status?", "project": "myproject"}'
# CLI ingestion
python scripts/ingest_folder.py --path /path/to/notes
# Live operator client
python scripts/atocore_client.py health
python scripts/atocore_client.py audit-query "gigabit" 5
API Endpoints
| Method | Path | Description |
|---|---|---|
| POST | /ingest | Ingest markdown file or folder |
| POST | /query | Retrieve relevant chunks |
| POST | /context/build | Build full context pack |
| GET | /health | Health check |
| GET | /debug/context | Inspect last context pack |
Architecture
FastAPI (port 8100)
|- Ingestion: markdown -> parse -> chunk -> embed -> store
|- Retrieval: query -> embed -> vector search -> rank
|- Context Builder: retrieve -> boost -> budget -> format
|- SQLite (documents, chunks, memories, projects, interactions)
'- ChromaDB (vector embeddings)
Configuration
Set via environment variables (prefix ATOCORE_):
| Variable | Default | Description |
|---|---|---|
| ATOCORE_DEBUG | false | Enable debug logging |
| ATOCORE_PORT | 8100 | Server port |
| ATOCORE_CHUNK_MAX_SIZE | 800 | Max chunk size (chars) |
| ATOCORE_CONTEXT_BUDGET | 3000 | Context pack budget (chars) |
| ATOCORE_EMBEDDING_MODEL | paraphrase-multilingual-MiniLM-L12-v2 | Embedding model |
Testing
pip install -e ".[dev]"
pytest
Operations
scripts/atocore_client.pyprovides a live API client for project refresh, project-state inspection, and retrieval-quality audits.docs/operations.mdcaptures the current operational priority order: retrieval quality, Wave 2 trusted-operational ingestion, AtoDrive scoping, and restore validation.
Architecture Notes
Implementation-facing architecture notes live under docs/architecture/.
Current additions:
docs/architecture/engineering-knowledge-hybrid-architecture.md— 5-layer hybrid modeldocs/architecture/engineering-ontology-v1.md— V1 object and relationship inventorydocs/architecture/engineering-query-catalog.md— 20 v1-required queriesdocs/architecture/memory-vs-entities.md— canonical home splitdocs/architecture/promotion-rules.md— Layer 0 to Layer 2 pipelinedocs/architecture/conflict-model.md— contradictory facts detection and resolution