greyhaven-ai/autocontext

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1286 stars · 109 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Autocontext is a recursive self-improving harness that runs AI agents against evaluations, retains useful lessons, and produces traces, reports, playbooks, datasets, and optional local-model training artifacts for subsequent runs. It ships as a Python CLI (autoctx) with a TypeScript/Node CLI variant and a Pi editor extension.

Use cases

• iteratively improve agent performance on a task across runs
• run an agent against an evaluation and keep the lessons learned
• generate playbooks and datasets from agent runs for future iterations
• optimize prompts and strategies automatically over multiple iterations
• train local models from accumulated agent run artifacts
• improve customer-support reply quality with an automated agent loop

When to choose

• you want an automated evaluate-learn-retry loop for LLM agents
• you use Claude Code, Codex, Pi, or OpenAI-compatible agents and want cross-run improvement
• you want traces, reports, and datasets persisted between agent iterations

When to avoid

• you need a simple one-shot agent runner without iterative optimization
• you want a GUI-driven agent builder rather than a CLI harness
• your workflow cannot share state or artifacts between runs

Facets

cli-tool · maturity active

agent-framework llm-training benchmarking cli workflow-automation large-language-models artificial-intelligence developer-tools python cli cross-platform self-improving-agents agent-harness evaluation-loop iterative-optimization claude-code codex agent-traces playbooks ai-agents automation nodejs

Member repositories

For agents

markdown · JSON · MCP: product_card(name="greyhaven-ai/autocontext")