yoloshii/ClawMem

**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 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, with optional post-import LLM fact extraction (--synthesize) that pulls structured decisions / preferences / milestones / problems and cross-fact links out of otherwise full-text conversation dumps (v0.7.2). Imports preserve **authorship time** (v0.27.0): ranking recency and temporal queries run on when content was actually written, so historical conversations mined today don't rank as fresh — with a metadata-only --backfill-dates lane for vaults mined earlier
- **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 — when a contradiction **judge** is configured (CLAWMEM_JUDGE_*, v0.29.0; disabled otherwise) (with an additional merge-time contradiction gate in the consolidation worker that blocks cross-observation contradictions before they land, v0.7.1)
- **Guards against cross-entity merges** during consolidation — name-aware dual-threshold merge safety compares entity anchors before merging similar observations, preventing "Alice decided X" from merging into "Bob decided X" (v0.7.1)
- **Prevents context bleed in derived insights** — the Phase 3 deductive synthesis pipeline validates every draft against an anti-contamination wrapper (deterministic entity contamination check + LLM validator + dedupe) before writing cross-session deductive observations (v0.7.1)
- **Frames surfaced facts as background knowledge** — context-surfacing wraps injected content in + + blocks, telling the model to treat facts as already-known and exposing memory-graph edges between surfaced docs directly in-prompt (v0.7.1)
- **Injects knowledge-graph facts as structured triples** — when the user's prompt mentions entities already known to the vault, context-surfacing resolves them via a three-path prompt-only extractor (canonical IDs, proper nouns, lowercased n-grams), queries the SPO graph for current-state triples, and appends a block of raw subject predicate object lines to — off for speed, 200 tokens on balanced, 250 on deep, token-truncated at the triple boundary (v0.9.0)
- **Session-scoped focus topic** — clawmem focus set " " --session-id writes a per-session focus file used as a snippet-selection intent: surfaced docs prefer sentences matching the topic. Presentation only since v0.38.0 — a focus never changes expansion, reranking, scoring, ordering, or the surfaced set — session-isolated, fail-open, never writes to SQLite or lifecycle columns (v0.9.0, narrowed v0.38.0)