Anto01 86637f8eee feat: universal LLM consumption (Phase 1 complete)
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>
2026-04-16 20:14:25 -04:00

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.py provides a live API client for project refresh, project-state inspection, and retrieval-quality audits.
  • docs/operations.md captures 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 model
  • docs/architecture/engineering-ontology-v1.md — V1 object and relationship inventory
  • docs/architecture/engineering-query-catalog.md — 20 v1-required queries
  • docs/architecture/memory-vs-entities.md — canonical home split
  • docs/architecture/promotion-rules.md — Layer 0 to Layer 2 pipeline
  • docs/architecture/conflict-model.md — contradictory facts detection and resolution
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