A guide to building long-term compounding knowledge infrastructure. See details on GitHub .

MLE-bench

A benchmark suite and toolkit to evaluate AI agents on machine learning engineering tasks.

Introduction

MLE-bench is a benchmark suite from OpenAI for measuring how well AI agents perform on machine learning engineering tasks. It includes dataset preparation scripts, grading tools, example agents and baselines, and a leaderboard summarizing evaluation results across a diverse set of competitions.

Key features

  • Covers 75 Kaggle competitions with unified prepare and grading scripts.
  • Offers a “lite” evaluation split to reduce compute and dataset size for faster experimentation.
  • Provides grading tools that accept JSONL/CSV submissions for automated scoring.
  • Ships example agents, a reproducible environment image, and experiment code to reproduce leaderboard results.

Use cases

  • Benchmarking and comparing AI agents’ end-to-end ML engineering capabilities across tasks like image classification, tabular prediction and seq2seq.
  • Reproducing experimental setups and leaderboard results from research papers.
  • Building automated evaluation pipelines for continuous benchmarking and CI.

Technical details

  • Implemented primarily in Python; uses the Kaggle API for dataset downloads and Git-LFS for large files.
  • Provides Docker and Conda-based environments for reproducible evaluation runs.
  • Includes experiment scripts, competition splits, and graders to facilitate reproducible and extensible evaluations.

Comments

MLE-bench
Resource Info
🌱 Open Source 📊 Benchmark