| name | openclaw-memories |
| description | Agent memory with ALMA meta-learning, LLM fact extraction, and full-text search. Observer calls remote LLM APIs (OpenAI/Anthropic/Gemini). ALMA and Indexer work offline. |
OpenClaw Memory System
Three components for agent memory:
- ALMA — Evolves memory designs through mutation + evaluation (offline)
- Observer — Extracts structured facts from conversations via LLM API (requires API key)
- Indexer — Full-text search over workspace Markdown files (offline)
Environment Variables
Observer requires one of:
OPENAI_API_KEYANTHROPIC_API_KEY- Or pass
apiKey in config
ALMA and Indexer require no keys or network access.
How It Works
ALMA (Algorithm Learning via Meta-learning Agents)
Proposes memory system designs, evaluates them, keeps the best. Uses gaussian mutation and simulated annealing to explore the design space.
alma.propose() → design
alma.evaluate(design.id, metrics) → score
alma.best() → top design
alma.top(5) → leaderboard
Observer
Sends conversation history to an LLM, gets back structured facts:
- Kind: world fact / biographical / opinion / observation
- Priority: high / medium / low
- Entities: mentioned people/places
- Confidence: 0.0–1.0 for opinions
Fails gracefully — returns empty array if LLM is unavailable.
Indexer
Chunks workspace Markdown files and indexes them for search:
MEMORY.md — core factsmemory/YYYY-MM-DD.md — daily logsbank/entities/*.md — entity summariesbank/opinions.md — beliefs with confidence
indexer.index() → count of chunks indexed
indexer.search('query') → ranked results
indexer.rebuild() → re-index from scratch
Install
npm install @artale/openclaw-memory
Limitations
- Indexer uses an in-memory mock database, not real SQLite FTS5. Search works but ranking is simplified.
- Observer calls remote APIs — not offline. Only ALMA and Indexer work without network.
- No dashboard — removed in v2 for simplicity.
Source
5 files, 578 lines, 0 runtime dependencies.
https://github.com/arosstale/openclaw-memory
