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SWE-agent turns LMs (e.g. GPT-4) into software engineering agents that can resolve issues in real GitHub repositories.

On SWE-bench, SWE-agent resolves 12.47% of issues, achieving the state-of-the-art performance on the full test set.

We accomplish our results by designing simple LM-centric commands and feedback formats to make it easier for the LM to browse the repository, view, edit and execute code files. We call this an Agent-Computer Interface (ACI). Read more about it in our paper!

SWE-agent is built and maintained by researchers from Princeton University.

You can use SWE-agent either through a web interface (shown above) or through the command line.

🚀 Get started!

👉 Try SWE-agent in your browser: (more information)

Read our documentation to learn more:

• Installation
• Command line usage
• Using the web UI
• Benchmarking on SWE-bench
• Frequently Asked Questions

💫 Contributions

• If you'd like to ask questions, learn about upcoming features, and participate in future development, join our Discord community!
• If you'd like to contribute to the codebase, we welcome issues and pull requests!

Contact person: John Yang and Carlos E. Jimenez (Email: {jy1682, carlosej}@princeton.edu).

📝 Citation

If you found this work helpful, please consider citing it using the following:

@misc{yang2024sweagent,
      title={SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering},
      author={John Yang and Carlos E. Jimenez and Alexander Wettig and Kilian Lieret and Shunyu Yao and Karthik Narasimhan and Ofir Press},
      year={2024},
      eprint={2405.15793},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}

🪪 License

MIT. Check LICENSE.

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