LeanDojo: Machine Learning for Theorem Proving in Lean

LeanDojo is a Python library for learning–based theorem provers in Lean, providing two main features:

Extracting data (proof states, tactics, premises, etc.) from Lean repos.
Interacting with Lean programmatically.

LeanDojo's current version is compatible with Lean 4 v4.3.0-rc2 or later. We strongly suggest using the current version. However, you may use the legacy branch if you want to work with earlier versions (including Lean 3).

Requirements

Supported platforms: Linux, Windows WSL, and macOS
Git >= 2.25
3.9 <= Python < 3.11
wget
elan
Recommended: Generate a GitHub personal access token and set the environment variable GITHUB_ACCESS_TOKEN to it

Installation

LeanDojo is available on PyPI and can be installed via pip:

pip install lean-dojo

It can also be installed locally from the Git repo:

pip install .

Documentation

Questions and Bugs

For general questions and discussions, please use GitHub Discussions.
To report a potential bug, please open an issue. In the issue, please include your OS information, the version of LeanDojo, the exact steps to reproduce the error, and complete logs in debug mode (setting the environment variable VERBOSE to 1). The more details you provide, the better we will be able to help you.

Citation

LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Neural Information Processing Systems (NeurIPS), 2023
Kaiyu Yang, Aidan Swope, Alex Gu, Rahul Chalamala,
Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar

@inproceedings{yang2023leandojo,
  title={{LeanDojo}: Theorem Proving with Retrieval-Augmented Language Models},
  author={Yang, Kaiyu and Swope, Aidan and Gu, Alex and Chalamala, Rahul and Song, Peiyang and Yu, Shixing and Godil, Saad and Prenger, Ryan and Anandkumar, Anima},
  booktitle={Neural Information Processing Systems (NeurIPS)},
  year={2023}
}