a recursive self-improving harness designed to help your agents (and future iterations of those agents) succeed on any task
autocontext is a self-improving harness for AI agents that runs a goal through evaluation, preserves useful lessons, and records artifacts for later runs. Developers use it to help agents and future versions of those agents perform better on recurring tasks. The catalogue add-ons support its agent-improvement workflow.
2 files for Codex, OpenCode and Claude Code: autocontext AGENTS.md, autocontext CLAUDE.md — 4,707 tokens loaded in every session.
AGENTS.md A 536 tok CLAUDE.md A 4,171 tok These files are greyhaven-ai/autocontext's own configuration — they tell Codex, OpenCode and Claude Code how to work on this repository, so they are not mods to install elsewhere. Copy one as a starting point and replace the parts that are about this project.