Skill: jared-goering/openclaw-supermemory (version 0.2.1)

--- name: supermemory version: 0.2.1 description: Long-term agent memory with atomic fact extraction, relational versioning, semantic search, and entity profiles. Extracts facts from conversations, tracks how knowledge changes over time (updates/contradicts/extends), and provides instant recall across sessions and agents. Local-first (SQLite + on-device embeddings). tags: memory, knowledge-graph, semantic-search, multi-agent, local-first --- # Supermemory Long-term memory for AI agents. Extracts atomic facts from text, tracks relations between memories (updates, contradicts, extends), embeds locally for semantic search, and auto-builds entity profiles. ## Setup ```bash pip install openclaw-supermemory[local] supermemory init # creates ~/.supermemory/memory.db supermemory serve # starts API on :8642 ``` Requires an LLM API key for fact extraction (default: Anthropic Haiku). ```bash export ANTHROPIC_API_KEY=sk-... # or configure via ~/.supermemory/config.yaml ``` ## Commands ### Ingest (extract facts from text) ```bash supermemory ingest "The project deadline moved to April 15. Sarah replaced Tom as lead." \ --session meeting-notes --agent kit ``` LLM extracts atomic facts, categorizes them (person, decision, event, insight, preference, project), detects entities, and finds relations to existing memories. When a fact updates an existing one, the old memory is marked `superseded`. ### Search ```bash supermemory search "project deadline" --top-k 10 supermemory search "project deadline" --all # include superseded supermemory search "project deadline" --as-of 2026-03-01 # time travel ``` ### Entity operations ```bash supermemory stats # counts, categories supermemory history Sarah # version timeline supermemory profile Sarah # auto-built entity profile ``` ### API ``` GET /api/health # status + memory count POST /api/search # {"query": "...", "top_k": 10} POST /api/ingest # {"text": "...", "session_id": "..."} GET /api/entities # all known entities GET /api/entity/{name} # entity memories + profile POST /api/search_entities # entity-aware cross-session search POST /api/aggregate # count/sum queries over event clusters ``` Search latency: ~32ms warm, ~8s cold start (embedding model load). ## Agent integration ### Recall at session start Inject relevant context before the agent processes a message: ```bash supermemory search "current projects and priorities" --top-k 5 ``` ### Auto-ingest from responses After meaningful agent turns, extract and store facts: ```bash supermemory ingest "$RESPONSE_TEXT" --session $SESSION --agent $AGENT_ID ``` ### OpenClaw plugin (zero-config) Install the [supermemory-claw plugin](https://github.com/jared-goering/openclaw-supermemory/tree/main/plugin) for automatic memory injection and extraction with no agent code changes. ## Architecture - **Storage:** SQLite with WAL mode (concurrent reads, single writer) - **Embeddings:** Local sentence-transformers (free, on-device) or API (OpenAI/Cohere/Voyage via litellm) - **Extraction:** LLM-based atomic fact extraction with relation detection (default: Haiku) - **Entity system:** Join tables, aliases, auto-merged profiles across sources - **Multi-agent:** Single DB with agent_id tagging, cross-agent semantic search ## Cost ~$0.01-0.02 per ingest (3 LLM calls: extract, relate, profile). Search is free (local embeddings). ## Links - [PyPI](https://pypi.org/project/openclaw-supermemory/) - [GitHub](https://github.com/jared-goering/openclaw-supermemory) - [Plugin](https://github.com/jared-goering/openclaw-supermemory/tree/main/plugin)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)