Overmind

Shipping an AI agent is the easy part. The hard part is that six months later it is still making the same mistakes it made on day one, because nothing in the usual stack actually teaches it anything. Overmind is an open-source platform built for what happens after launch: it points at your agent's codebase, maps what the agent can do, ingests the traces your users generate, scores them against the quality metrics you define, and turns the good and bad outcomes into versioned datasets, evaluations, prompt experiments, and eventually fine-tuned models.

The loop runs where you already work. An MCP server plugs into Cursor, Claude Code, OpenCode, and Codex, so building a dataset or launching a fine-tune is a chat command rather than a new dashboard workflow, and the optimizer's winning prompt, tool, and control-flow changes land as plain git diffs you review like any pull request. If you already collect traces in Langfuse, LangSmith, Braintrust, or Galileo, connectors sync them in. Trained models are served next to frontier models on one OpenAI-compatible API, and the weights are yours: download them, retrain them, or roll back.

On cost and licensing: the SDK and CLI are MIT-licensed, the platform is AGPL-3.0, and the full stack is on GitHub to read, self-host, or contribute to. Self-hosting with docker compose is free and runs the same code as the hosted console, but judging, training compute, and storage rely on outside services that bill their own rates, and the managed console has paid plans for heavier usage. Overmind pays off if you are actively shipping and iterating on an agent; otherwise it is one to admire rather than adopt. Read our full discovery: Overmind: Train Your AI Agent on Its Own Mistakes.