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memo
Local-first memory for AI agents — offline, no keys
Give any MCP agent (Claude Code, Cursor, Cline) a long-term memory that runs 100% on your machine. Time-machine, contradiction radar, auto-synthesis. MLX on Mac, CPU/Docker on Linux. Markdown is the source of truth. No cloud, no keys. MIT.
Maker here 👋
I built memo because I was tired of re-explaining the same decisions to my AI agents every morning. Claude Code would forget "we use Postgres, not Mongo" the moment the session closed — so I gave it a memory that survives sessions.
Everything runs 100% on your machine: MLX embeddings on Apple Silicon (or a CPU backend on Linux/Docker), hybrid semantic + keyword search, and plain Markdown files as the source of truth — your agent's memory is just notes you can read, edit and graph in Obsidian.
The parts I'm most proud of: a time-machine that rewinds the whole corpus to any past date ("what did we know about this bug last month?"), a contradiction radar that flags stale decisions so agents don't reintroduce them, and a nightly synthesis pass that infers new insights from clusters of related memories.
It's my first MCP server, MIT licensed. Demo GIF + docs in the repo: https://github.com/jagoff/memo
Try it in one command (no install): docker run --rm ghcr.io/jagoff/memo:latest memo doctor
Happy to answer anything!
ran it locally with mlx on my m2 and the contradiction radar caught a stale preference i forgot i had, which was both creepy and useful. local-only memory feels like it should have been table stakes ages ago.
How does the contradiction radar actually detect conflicts between new notes and existing memories? Is it doing real semantic comparison locally or is it more of a keyword/tag diff? Curious how well it catches subtle contradictions vs obvious ones.
About memo on Product Hunt
“Local-first memory for AI agents — offline, no keys”
memo was submitted on Product Hunt and earned 6 upvotes and 5 comments, placing #92 on the daily leaderboard. Give any MCP agent (Claude Code, Cursor, Cline) a long-term memory that runs 100% on your machine. Time-machine, contradiction radar, auto-synthesis. MLX on Mac, CPU/Docker on Linux. Markdown is the source of truth. No cloud, no keys. MIT.
memo was featured in Open Source (68.6k followers), Developer Tools (515.4k followers), Artificial Intelligence (473.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 218k products, making this a competitive space to launch in.
Who hunted memo?
memo was hunted by Fer F. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Fer F