AutoContext — Self-Improving Agent Harness

Main

AutoContext is best read as a harness layer, not a single prompt package. Its job is to loop on scenarios, evaluate outputs, and feed improvements back into the next agent pass.

That matters when teams have moved past hobby usage and need repeatable improvement instead of ad hoc intuition about why a run succeeded.

The README gives two practical integration modes: CLI-first with Pi or provider env vars, and MCP-first inside Claude Code. That flexibility is a strong sign the project was built for real usage, not just demos.

Source-backed notes

• README offers a 30-second path with uv tool install autocontext==0.5.0.
• It documents provider-based operation for Anthropic, OpenAI, Gemini, Mistral, Groq, OpenRouter, Azure, Claude CLI, Codex CLI, and MLX.
• Pi runtime and Claude Code MCP integration are both called out as supported paths.

FAQ

Q: Is AutoContext tied to one model provider? A: No. The README documents several providers plus Claude CLI, Codex CLI, and MLX paths.

Q: What is the fastest install path? A: uv tool install autocontext==0.5.0, then point it at Pi or another supported provider.

Q: Why use it? A: It helps repeated agent runs improve systematically instead of starting from scratch every time.