ai-memory vs deja-vu
Rust long-term memory server for coding CLIs: lifecycle hooks capture sessions into a sanitized shared wiki, then hand a bounded summary to the next agent so Codex can resume what Claude Code started. — 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.
Opposite entry point: deja-vu retro-indexes session logs your harnesses already wrote — no hooks, no embeddings, months of history instantly — while ai-memory installs hooks to capture and summarize from here on.
| ai-memory | deja-vu | |
|---|---|---|
| Stars | 4.3k | 688 |
| Forks | 314 | 55 |
| Language | Rust | Go |
| License | MIT | MIT |
| Last activity | today | today |
| Topics | memory, coding | memory, coding |
| Curated connections | 6 | 4 |
ai-memory — the curator's take
The strongest bet if you genuinely switch harnesses mid-task: hook and MCP installers for roughly fifteen CLIs, a portable event ledger, `ai-memory run <harness>` for managed cross-vendor resume, and one Rust binary you can systemd. You pay for that reach in surface area — per-harness caveats matter (several CLIs discard SessionStart stdout, so handoffs come back through MCP `memory_handoff_accept`; Codex and Kiro need an explicit `finalize-session`), and you configure capture exclusions plus LLM and embedding providers. If you live in exactly one harness, a single-harness plugin gets you 80% of this with a tenth of the setup.
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.