MCP App StoreMCP App StoreClawMem provides an on-device hybrid memory system for AI agents, combining BM25 and vector search with LLM-powered query expansion, reranking, and graph-based causal traversal. It exposes hooks for automatic context surfacing and MCP tools for explicit retrieval and lifecycle actions (pin, snooze, forget).
Use ClawMem when you need reliable, local-first memory for agents: surfacing relevant context into prompts, answering cross-session 'why/when' questions, building temporal/semantic graphs after bulk ingestion, or managing memory lifecycle policies for long-lived decisions. Prefer hooks for per-turn context and MCP tools for escalated retrieval, debugging, or maintenance tasks.
ClawMem targets OpenClaw/Hermes and Claude Code environments; it integrates with MCP tools and can be used alongside agent runtimes that support local CLI or REST tool calls.
ClawMem is a comprehensive on-device hybrid memory system for AI agents, offering BM25+vector+rerank retrieval, A-MEM self-evolving notes, lifecycle management, and graph-based causal search. The SKILL.md is exceptionally detailed with clear tool routing, pipeline docs, and troubleshooting. The bundled script is an integration test requiring the full ClawMem project and local servers — not runnable standalone. No security concerns beyond auto-downloading LLM models via node-llama-cpp without checksum verification.
Very well-documented skill with comprehensive operational guidance. The script is clearly a development test artifact, not a functional tool — it imports from ../src/ and needs local infrastructure. Security is solid: no destructive commands, no credential leaks, no exfiltration. Minor deduction for unchecked model downloads. High usefulness given the agent memory problem space.
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