ClawMem

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On-device memory layer for AI agents. Claude Code, Hermes and OpenClaw. Hooks + MCP server + hybrid RAG search.

GitHub ↗

Stars

186

Δ stars 7d

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Δ stars 30d

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Forks

Contributors

npm DL / wk

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PyPI DL / wk

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Language

TypeScript

Last push

2026-05-20

About ClawMem

ClawMem fuses recent research into a retrieval-augmented memory layer that agents actually use. The hybrid architecture combines QMD-derived multi-signal retrieval (BM25 + vector search + reciprocal rank fusion + query expansion + cross-encoder reranking), SAME-inspired composite scoring (recency decay, confidence, content-type half-lives, co-activation reinforcement), MAGMA-style intent classification with multi-graph traversal (semantic, temporal, and causal beam search), and A-MEM self-evolving memory notes that enrich documents with…

Read the full README on GitHub →

Newly tracked — a growth score appears after the first full scoring week. Signals are deliberately suppressed during cold start to avoid false positives.

ClawMem alternatives

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Frequently asked questions

Is ClawMem still maintained?: Yes — actively maintained. The last push was on 2026-05-20, with 5 contributors.
What are the best ClawMem alternatives?: Closest by category and size in our data: ac.tandem/docs-mcp, Instrukt, DataEval/dingo — full list with live signals above.

Topics

Agent Memory RAG (Retrieval-Augmented Generation)Vector Databases mcp-server claude-code openclaw mcp-tools model-context-protocol memory local-first rag ai-agents sqlite embeddings vector-search retrieval-augmented-generation hybrid-search bun plugin ai-agent-memory llama-cpp on-device-ai

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Tracked since 2026-06-17 · data as of 2026-06-18 · 5 open issues ·