Persistent memory for AI agents, over MCP. Local-first, negative learning, hybrid recall. Docs and community home (source is maintained privately).
Persistent memory for AI agents, over MCP. Ground truths, recorded failures that block repeats, decision causality, and hybrid recall. A structured SQLite memory on your machine. No cloud, no separate LLM.
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Most AI coding sessions start from zero. Wyrm gives the agent a memory that persists across them. It keeps your project's decisions, conventions, open work, and dead-ends in a structured database on your own machine, and hands them back to the model at the start of the next session. An agent connected to Wyrm recalls what was decided last week instead of re-deriving it, and can be stopped from repeating an approach that already failed.
It speaks the Model Context Protocol, so it drops into Claude, Cursor, Copilot, Windsurf, and Codex without glue code.
Then, from inside your client, ask it to call wyrmcapabilities to confirm the connection. The everyday loop is four steps the agent runs on its own once the habit sets in:
Every number Wyrm publishes comes from a benchmark committed to the source, reproducible on your own data. The negative-learning firewall and the recall lift are the two that matter most, and both are covered below.
It remembers what failed, not just what worked
Most memory tools store successes. Wyrm also records dead-ends and blocks the repeat. You record a failed approach with wyrmfailurerecord, and a later wyrmfailurecheck surfaces it before the agent walks back into it. Across a session that stops re-litigating solved problems; across a fleet of agents, one worker's dead-end warns the rest, once.
From the project README.
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⚡ The open-source persistent cognitive memory layer for AI coding agents. Stop your Cursor, Claude & Copilot from having amnesia.
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