mnemo

Give your AI agents a git repo as a brain.

🌐 Live site: https://joaquimlegal.github.io/mnemo/

mnemo is persistent, local-first memory for AI agents (OpenCode, Claude Code, Cursor, Codex…). Agents remember by writing Markdown files into a git repo — so you get auditability, branching, snapshots, and rollback for free.

$ mm new "We chose Postgres" --body "over Mongo, because of transactions" --tags decision,db --importance 0.9
wrote main/20260811-221936-2d37

$ mm search "which database did we pick"
20260811-221936-2d37  0.81  [main]  We chose Postgres

$ mm log
4e246fa  mem: add 20260811-221936-2d37 - We chose Postgres

$ mm undo        # revert the last change — history is preserved
$ mm branch exp  # fork an alternative memory timeline

Why

LLM agents are stateless: every session forgets everything. You repeat decisions, preferences, and context over and over — burning tokens and letting the agent re-open settled questions.

mnemo fixes the pain points:

• Cross-session onboarding — a new session reads yesterday's memories and starts knowing.

• Multi-agent shared reality — the planner writes decisions, the implementer reads them.

• Auditability — git log shows exactly what the agent knew, and when. (The requirement the agent community keeps asking for in 2026.)

• Token economy — recall the 3 memories that matter instead of re-reading the whole project.

• 100% local — memory is a folder on your machine. No cloud, no lock-in.

Related MCP server: Engram

How

              ┌─────────────────────┐
  agent ─────▶│  MCP server (mm mcp) │──┐
              └─────────────────────┘  │
              ┌─────────────────────┐  │   .mnemo/          (a git repo)
  OpenCode ──▶│  plugin (auto)       │──┼──▶ memories/<agent>/<id>.md
  plugin      │  capture + seed      │  │        └── frontmatter + markdown
              └─────────────────────┘  │   search: BM25 + recency + importance
  human ─────▶│  mm CLI               │──┘   audit:  git log / diff / revert
              └─────────────────────┘

Every memory is a human-readable Markdown file with metadata, and every mutation is a git commit. Search is classic BM25 plus a recency/importance rank — zero dependencies, ~90% recall@1 on synthetic corpora.

Install & use

npm i -g mnemo-mem

# in your project:
mm init
mm setup-opencode        # installs the OpenCode plugin + MCP config, then restart OpenCode

The OpenCode plugin makes memory automatic: at the end of a session it summarizes what was decided/learned and stores it; at the start of the next session it seeds the agent with the recent highlights. You don't maintain memory — it happens.

For other agents, add the MCP server:

{ "mcpServers": { "mnemo": { "command": "mm", "args": ["mcp"] } } }

And drop prompts/AGENTS.md into your project so agents know to call recall before work and remember after decisions.

CLI reference

mm init / new / ls / search / cat / rm
mm log | undo | revert <commit> | snapshot <tag> | tags | branches | branch | switch
mm mcp | setup-opencode

Documentation

• docs/01-architecture.md — the one idea, module by module

• docs/02-git-substrate.md — why git, not a vector DB

• docs/03-search.md — BM25 + hybrid ranking, with math

• docs/04-mcp.md — the MCP tools and the OpenCode plugin

Roadmap

• Core store (Markdown + frontmatter, per-agent profiles)

• Git substrate (auto-commit, branches, undo, snapshots)

• Search (BM25 + recency/importance ranking, optional embeddings)

• MCP server + OpenCode plugin (auto-capture + auto-recall)

• Consolidation (auto-compress many small memories into an executive summary)

• Semantic search defaults, more embedders

• Benchmarks on real agent sessions

Develop

npm install
npm test              # vitest — 32 tests
npm run build         # tsc
npm run benchmark     # recall@k on 100/1000-memory corpora
bash scripts/demo.sh  # watch it work
vhs -o demo.gif scripts/demo.tape   # regenerate the README GIF (needs vhs + ttyd)

License

MIT — © 2026 JoaquimLegal