mnemo
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mnemorecall the decision about using TypeScript"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mnemo
Local‑first memory for AI coding agents — typed, deterministic, project‑scoped.
What it is
A persistent memory layer for AI coding agents (Claude Code, Cursor, Windsurf, any MCP client) that remembers decisions, bugs, progress, and rules across sessions, so you don't re‑explain your project every time.
local‑first — the embedder and the optional
recallmodel run on your machine; external model providers are a possible option, not a requirement;deterministic — a write is a local embedding + insert, with no LLM in the loop. mnemo never runs a model over your memories to extract, merge, or summarize them, and nothing rewrites them in the background; a stored memory changes only on an explicit
supersede/topic_keysignal;on‑demand — nothing runs in the background; the shared service starts under load and exits after an idle grace period;
no Docker, no external DB — the whole store is one process over SQLite +
sqlite-vec+ FTS5;typed & project‑scoped —
decision / progress / rule / learning / research / working-notes, scoped per project, with first‑class cross‑project search on request (scope=all);small MCP surface — one write (
remember) and four reads (searchby meaning,browseby filter,getby id/topic_key,recallfor an LLM‑synthesized answer), plusdeleteand project tools.
Related MCP server: Agent Memory Bridge
How writes work
A write is a local embedding + insert — no LLM on the path. Many memory tools run an LLM on every
write to extract or summarize what was said, and some keep rewriting it in the background; mnemo
doesn't, so what you store is what you get back. The only LLM in the system is the opt‑in recall
read tool: it loads a small model on demand to synthesize an answer over retrieved memories, then
unloads it, and it never changes what's stored (recall is gated behind an explicit force flag).
Retrieval quality
Retrieval is tested in‑repo (tools/eval/) against public benchmarks (LoCoMo, LongMemEval) and a
real project‑fact set, where it compares favorably with other open‑source memory servers on Recall@k
and abstention.
Install & use
See docs/13-usage.md for install, the CLI, and one‑command MCP client setup
(mnemo setup). The full tool surface is in docs/05-mcp-api.md.
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