AutoResearch (Karpathy)
Built by Andrej Karpathy (OpenAI founding team, former Tesla AI director) — a strong credibility signal.
Autonomous ML research loop from Andrej Karpathy: give an AI agent a small but real LLM training setup (a simplified single-GPU version of nanochat) and let it experiment overnight. The agent edits train.py — architecture, optimizer, hyperparameters — trains for a fixed 5-minute budget, keeps changes that improve validation bits-per-byte, and repeats. You steer the research org by editing program.md, not the Python files.
- Run overnight autonomous experiments where an agent iterates on a small GPT training loop
- Compare agent-driven architecture and hyperparameter changes fairly under a fixed 5-minute training budget (val_bpb metric)
- Design your own autonomous research org by iterating on program.md agent instructions
Built by Andrej Karpathy (OpenAI founding team, former Tesla AI director) — a strong credibility signal. It is a minimal working template for agent-driven ML experimentation: the human programs the research org in Markdown while the agent iterates the training code against an objective metric.
ML researchers and practitioners with an NVIDIA GPU who want to experiment with agent-driven training-loop optimization and autonomous overnight experimentation.
https://github.com/karpathy/autoresearch
By Andrej Karpathy
How to Get It
This is a methodology or approach. Paste the instructions below into a Claude conversation to get started.
git clone https://github.com/karpathy/autoresearch && cd autoresearch && uv sync && uv run prepare.py # one-time data prep (~2 min); test with: uv run train.py; then point your agent at program.md
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
After installing, paste this into Claude:
Help me run overnight autonomous experiments where an agent iterates on a small GPT training loop
Trust Signals Reviewed
Community Pulse Active
Discussed on Hacker News, Reddit
- Andrew Karpathy’s “autoresearch”: An autonomous loop where AI edits PyTorch, run — Reddit · 717 pts
- Autoresearch on an old research idea — Hacker News · 428 pts
- Karpathy just open-sourced autoresearch. One GPU. 100 ML experiments. Overnight — Reddit · 385 pts
3 mentions across 2 sources
Reviewer notes
Reviewed review. These are observations, not a security certification.
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 90,169 GitHub stars; 9 contributors; last commit 108d ago; license no license.
2026-05-10: Strong credibility signal from the author, but 2 production mentions against 71k stars tells you this is more reference architecture than battle-tested tooling—the community is watching, not shipping. In practice it's useful for prototyping multi-step research pipelines: web search, paper pull, synthesis in sequence without manual handoffs. The gap to close before team deployment is observability and failure handling; those patterns aren't mature here yet. If you need something in production now, LangChain's research agent templates or AutoGPT variants have more operational mileage. Worth a close read if you're designing your own agent workflow from scratch.
Things to check
- Recently discovered. Looks promising but has limited community feedback so far. We added it early so you can evaluate it before the crowd finds it.
How to evaluate tools before deploying →
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Evaluation
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 90,169 GitHub stars; 9 contributors; last commit 108d ago; license no license.