<!-- mcp-name: io.github.jagoff/memo --> <!-- Banner: keep it, but make sure it loads fast (<200KB, WebP if possible) -->

memo

Your coding agent starts every session with amnesia. memo fixes that — 100% on your own machine.

Persistent, searchable memory for Claude Code, Codex, Cursor, Cline, Devin, and OpenCode. No cloud, no API keys, no Ollama, no vector DB to run. And it spends fewer tokens, not more.

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Install

curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.6.2/install.sh | bash

_{Prefer a package manager? uv tool install mlx-memo · pipx install mlx-memo · brew tap jagoff/memo && brew install mlx-memo}

Then:

memo doctor                                   # self-check
memo save 'we use Postgres, not Mongo'        # save a decision
memo search 'what database did we pick?'      # search by meaning

That's it. Your agents pick it up over MCP automatically — the installer wires every client it finds.

<details> <summary>Installing on another Mac or handing setup to an agent?</summary>

New Mac:

curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.6.2/install.sh | bash
memo sync bootstrap [email protected]:yourname/memo-sync.git

Agent-managed setup:

curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.6.2/install.sh | bash
memo doctor --strict-runtime

</details>

On Linux or just want to look around first?

docker run --rm ghcr.io/jagoff/memo:latest memo doctor

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Why this saves you money

Most memory servers add context. memo is built to remove it.

Profile	Tools	Schema tokens
agent (default)	38	~3.8k
core / slim	55	~5.0k
full / default	159	~18k

The default MCP surface is 38 tools, not 159: about 79% fewer tool schemas. It exposes 38 tools / ~3.8k schema tokens versus 159 tools / ~18k tokens on the full surface — overhead paid every session, in every client.

Ambient recall injects one relevant memory before the model answers. The bundled Claude Code hook caps that injection at ~160 tokens. memo roi reads the real grounding and re-ask ledgers, then estimates accumulated savings with disclosed defaults (350 tokens per grounded recall and 900 per avoided re-ask).

memo roi       # value from grounded recalls and avoided re-asks
memo tokens    # usage-savings ledger

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Three things nothing else does

🕰️ Time-machine — query your knowledge as it was

memo as-of ask "what was the deploy strategy?" --date 2026-02-01
memo diff --from 2026-01-01 --to 2026-03-01

Full historical reconstruction by reverse-replaying history.db. Useful when you need to know why past-you made a call, not just what past-you decided.

⚡ Contradiction radar — memory that notices when you change your mind

memo contradict scan      # find conflicting facts corpus-wide
memo contradict triage    # resolve: fuse / newer-wins / dismiss

Change a decision and memo flags the now-stale version, so the agent stops reintroducing what you already threw out.

🔮 Dream — i