claude-mem vs deja-vu
Cross-harness session memory: hooks capture what the agent does, an LLM compresses it into observations, and the next session gets the relevant ones back via progressive-disclosure MCP tools. — versus — Indexes the session histories your coding agents already wrote — 17 harnesses, months retroactive — and serves recall over MCP. 84.9% hit@1 on LongMemEval-S, no LLM, no embeddings. One Go binary.
Both give a coding agent cross-session recall. Deja-vu is retroactive and free — it indexes the session logs 17 harnesses already wrote, no LLM, no embeddings, one Go binary. Claude-mem only knows sessions it captured, but pays an LLM per session to produce curated observations instead of raw history.
| claude-mem | deja-vu | |
|---|---|---|
| Stars | 93k | 731 |
| Forks | 8.1k | 58 |
| Language | JavaScript | Go |
| License | Apache-2.0 | MIT |
| Last activity | 2 days ago | yesterday |
| Topics | memory, coding | memory, coding |
| Curated connections | 7 | 6 |
claude-mem — the curator's take
The most widely installed of the capture-and-reinject memory plugins, and the one with the most product around it: 5 lifecycle hooks, a Bun worker with an HTTP API and live web viewer, SQLite plus FTS5 plus Chroma for hybrid search, and a 3-layer MCP search pattern (index → timeline → full detail) that keeps recall at ~50-100 tokens per hit until you actually want the body. Installs into Claude Code, OpenCode, Antigravity, OpenClaw and more, with `<private>` tags to keep things out of the store and optional cloud sync. The tradeoffs: it spends model tokens summarizing every session, it drags in Bun and uv, and what you get back is a generated observation rather than the raw transcript — if you want auditable, greppable memory you can edit by hand, look at the Markdown-first options instead.
deja-vu — the curator's take
The inversion is the insight: every memory tool starts empty and records forward; deja starts FULL from history already on disk, and the no-LLM/no-embedding design means ~1.5ms search, zero keys, zero cost. When NOT: it remembers what your agents did, not curated knowledge — there's no write path for distilled lessons; and its benchmark numbers are self-published, so the standing caution on this shelf applies: benchmark recall on your own corpus.