AI coding assistants forget everything between sessions, and the instruction files teams write for them (AGENTS.md, CLAUDE.md) go stale the moment a library changes. On long jobs like a major-version upgrade, they also drown in their own history and start repeating themselves.
Evergreen is a long-running agent that does the upgrade and learns as it goes. It fixes one file at a time: known errors are fixed instantly by a saved rule; new ones get live official docs from Nimble, and Liquid's LFM2.5-8B-A1B model writes the patch entirely on the laptop, with no cloud AI. A fix is kept only if it passes safety guards and no previously passing test breaks; otherwise it's rolled back and retried, so progress never goes backwards.
Every kept fix becomes a lesson with a confidence score, a source link and the library version it was proven on, and is written into the project's AGENTS.md so the next session with any assistant starts out knowing it. All history (attempts, patches, test results, lookups) lives in a Tinybird RawTree database instead of the prompt, so the prompt stays the same size from the first file to the last. In our run, it took a pandas 1.5 to 2.2 upgrade to all 21 tests passing.
A live Next.js dashboard on Vercel reads RawTree and shows each run as it happens: tests passing over time, every patch and its diff, the rulebook, Nimble lookups, rollbacks, and zero human interventions.
Built with Liquid, Nimble, Tinybird/RawTree, Python and Vercel.
