{"id":"3ef561f9-e7a4-4159-85b4-2f5aad5f9929","slug":"machine-machine-openclaw-m2-memory-skill","name":"m2-memory","description":"Vector-based semantic memory using agent.memory.system (Qdrant + BGE-M3). Full PRD v2 system with consolidation, reinforcement scoring, ColBERT reranking, and smart query routing. Use for storing and retrieving memories with semantic search, importance scoring, and entity tagging. Complements existing memory_search/memory_get tools.","canonicalUrl":"https://xpersona.co/skill/machine-machine-openclaw-m2-memory-skill","sourceUrl":"https://github.com/machine-machine/openclaw-m2-memory-skill","homepage":null,"source":"GITHUB_OPENCLEW","vendor":{"slug":"machine-machine","label":"Machine Machine","url":"https://github.com/machine-machine/openclaw-m2-memory-skill"},"protocols":["OPENCLEW"],"capabilities":["sync"],"trustScore":null,"trustConfidence":"unknown","artifactCount":0,"benchmarkCount":0,"lastRelease":null,"freshnessAt":"2026-04-15T00:18:37.821Z","freshnessLabel":"Apr 15, 2026","securityReviewed":true,"openapiReady":false,"stats":[{"label":"Trust score","value":"Unknown"},{"label":"Compatibility","value":"OpenClaw"},{"label":"Freshness","value":"Apr 15, 2026"},{"label":"Vendor","value":"Machine Machine"},{"label":"Artifacts","value":"0"},{"label":"Benchmarks","value":"0"},{"label":"Last release","value":"Unpublished"}],"factsPreview":[{"factKey":"docs_crawl","category":"integration","label":"Crawlable docs","value":"6 indexed pages on the official domain","href":"https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar","sourceUrl":"https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar","sourceType":"search_document","confidence":"medium","observedAt":"2026-04-15T05:03:46.393Z","isPublic":true},{"factKey":"vendor","category":"vendor","label":"Vendor","value":"Machine Machine","href":"https://github.com/machine-machine/openclaw-m2-memory-skill","sourceUrl":"https://github.com/machine-machine/openclaw-m2-memory-skill","sourceType":"profile","confidence":"medium","observedAt":"2026-04-15T00:18:37.821Z","isPublic":true},{"factKey":"protocols","category":"compatibility","label":"Protocol compatibility","value":"OpenClaw","href":"https://xpersona.co/api/v1/agents/machine-machine-openclaw-m2-memory-skill/contract","sourceUrl":"https://xpersona.co/api/v1/agents/machine-machine-openclaw-m2-memory-skill/contract","sourceType":"contract","confidence":"medium","observedAt":"2026-04-15T00:18:37.821Z","isPublic":true},{"factKey":"handshake_status","category":"security","label":"Handshake status","value":"UNKNOWN","href":"https://xpersona.co/api/v1/agents/machine-machine-openclaw-m2-memory-skill/trust","sourceUrl":"https://xpersona.co/api/v1/agents/machine-machine-openclaw-m2-memory-skill/trust","sourceType":"trust","confidence":"medium","observedAt":null,"isPublic":true}],"highlights":["Trust evidence available"],"agentCard":{"name":"m2-memory","description":"Vector-based semantic memory using agent.memory.system (Qdrant + BGE-M3). Full PRD v2 system with consolidation, reinforcement scoring, ColBERT reranking, and smart query routing. Use for storing and retrieving memories with semantic search, importance scoring, and entity tagging. Complements existing memory_search/memory_get tools.","source":"GITHUB_OPENCLEW","sourceId":"github:1146477396","repository":"https://github.com/machine-machine/openclaw-m2-memory-skill","documentation":"https://xpersona.co/skill/machine-machine-openclaw-m2-memory-skill/agent/machine-machine-openclaw-m2-memory-skill","protocols":["OPENCLEW"],"capabilities":["sync"],"languages":["typescript"],"install":{"command":"git clone https://github.com/machine-machine/openclaw-m2-memory-skill.git","ecosystem":"git"},"examples":[{"kind":"example","language":"bash","snippet":"# Store a memory\npython3 scripts/memory_client.py store \"User prefers minimal communication\" --importance 0.8 --entities \"user,preferences\"\n\n# Search memories  \npython3 scripts/memory_client.py search \"what does the user like?\"\n\n# List recent memories\npython3 scripts/memory_client.py recent --hours 24"},{"kind":"example","language":"python","snippet":"from scripts.memory_client import MemoryClient\n\nasync with MemoryClient(agent_id=\"m2\") as mem:\n    # Store\n    await mem.store(\"Important fact\", importance=0.9, entities=[\"topic\"])\n    \n    # Search\n    results = await mem.search(\"related query\", limit=5)\n    \n    # Get by entities\n    results = await mem.get_by_entities([\"topic\"])"}]}}