ClawMem — Context engine for Claude Code, OpenClaw, and Hermes agents
On-device memory for Claude Code, OpenClaw, Hermes, and AI agents. Retrieval-augmented search, hooks, and an MCP server in a single local system. No API keys, no cloud dependencies.
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 keywords, tags, and causal links between entries. Pattern extraction from Engram adds deduplication windows, frequency-based durability scoring, and temporal navigation.
Integrates via Claude Code hooks, an MCP server (works with any MCP-compatible client), a native OpenClaw ContextEngine plugin, or a Hermes Agent MemoryProvider plugin. All paths write to the same local SQLite vault. A decision captured during a Claude Code session shows up immediately when an OpenClaw or Hermes agent picks up the same project.
TypeScript on Bun. MIT License.
What It Does
ClawMem turns your markdown notes, project docs, and research dumps into persistent memory for AI coding agents. It automatically:
- Surfaces relevant context on every prompt (context-surfacing hook)
- Bootstraps sessions with your profile, latest handoff, recent decisions, and stale notes
- Captures decisions, preferences, milestones, and problems from session transcripts using a local GGUF observer model
- Imports conversation exports from Claude Code, ChatGPT, Claude.ai, Slack, and plain text via
clawmem mine
- Generates handoffs at session end so the next session can pick up where you left off
- Learns what matters via a feedback loop that boosts referenced notes and decays unused ones
- Guards against prompt injection in surfaced content
- Classifies query intent (WHY / WHEN / ENTITY / WHAT) to weight search strategies
- Traverses multi-graphs (semantic, temporal, causal) via adaptive beam search
- Evolves memory metadata as new documents create or refine connections
- Infers causal relationships between facts extracted from session observations
- Detects contradictions between new and prior decisions, auto-decaying superseded ones
- Scores document quality using structure, keywords, and metadata richness signals
- Boosts co-accessed documents — notes frequently surfaced together get retrieval reinforcement
- Decomposes complex queries into typed retrieval clauses (BM25/vector/graph) for multi-topic questions
- Cleans stale embeddings automatically before embed runs, removing orphans from deleted/changed documents
- Transaction-safe indexing — crash mid-index leaves zero partial state (atomic commit with rollback)
- Deduplicates hook-generated observations within a 30-minute window using normalized content hashing, preventing memory bloat from repeated hook output
- Navigates temporal neighborhoods around any document via the
timeline tool — progressive disclosure from search to chronological context to full content
- Boosts frequently-revised memories — documents with higher revision counts get a durability signal in composite scoring (capped at 10%)
- Supports pin/snooze lifecycle for persistent boosts and temporary suppression
- Manages document lifecycle — policy-driven archival sweeps with restore
... View full README on GitHub
