Anto01
57b64523fb
scripts/import_openclaw_state.py reads the OpenClaw file continuity layer from clawdbot (T420) via SSH and imports candidate memories into AtoCore. Loose coupling: OpenClaw's internals don't need to change, AtoCore pulls from stable markdown files. Per codex's integration proposal (docs/openclaw-atocore-integration-proposal.md): Classification: - SOUL.md -> identity candidate - USER.md -> identity candidate - MODEL-ROUTING.md -> adaptation candidate (routing rules) - MEMORY.md -> memory candidate (long-term curated) - memory/YYYY-MM-DD.md -> episodic candidate (daily logs, last 7 days) - heartbeat-state.json -> skipped (ops metadata only, not canonical) Delta detection: SHA-256 hash per file stored in project_state under atocore/status/openclaw_import_hashes. Only changed files re-import. Hashes persist across runs so no wasted work. All imports land as status=candidate. Auto-triage filters. Nothing auto-promotes — the importer is a signal producer, the pipeline decides what graduates. Discord: deferred per codex's proposal — no durable local store in current OpenClaw snapshot. Revisit if OpenClaw exposes an export. Wired into cron-backup.sh as Step 3a (before vault refresh + extraction) so OpenClaw signals flow through the same pipeline. Gated on ATOCORE_OPENCLAW_IMPORT=true (default true). 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