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MLE-bench

Roadmap

75 Kaggle competitions where agents build and submit ML solutions.

On the roadmap

MLE-bench is catalogued but not runnable yet, so there are no usage docs — we do not document what does not run. The fact sheet below is sourced from the paper; the protocols it will implement are stable today.

Paper
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
Citation
Chan et al., 2024, arXiv:2410.07095
License
Custom (OpenAI)
Homepage
https://github.com/openai/mle-bench
How an eval goes live
  1. Implement an EvalRunner against the stable protocols.
  2. Bundle a small real-schema sample so it runs offline.
  3. Point the catalog entry's runner at the class.
  4. Ship its docs in the same change — required to flip live.

pip install agi-evals