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SWE-agent enables your language model of choice (e.g. GPT-4o or Claude Sonnet 4) to autonomously use tools to fix issues in real GitHub repositories, find cybersecurity vulnerabilities, or perform any custom task.
yamlSWE-agent is built and maintained by researchers from Princeton University and Stanford University.
[!note] Most of our current development effort is on mini-swe-agent, which will eventually supersede SWE-agent. It already matches the performance performance of SWE-agent, while being much simpler. See the FAQ about whether you should switch already.
👉 Try SWE-agent in your browser: 
Read our documentation to learn more:
SWE-agent: EnIGMA is a mode for solving offensive cybersecurity (capture the flag) challenges. EnIGMA achieves state-of-the-art results on multiple cybersecurity benchmarks (see leaderboard). Please use SWE-agent 0.7 while we update EnIGMA for 1.0.
In addition, you might be interested in our other projects:
If you'd like to contribute to the codebase, we welcome issues and pull requests! For larger code changes, we always encourage discussion in issues first.
SWE-agent is an academic project started at Princeton University by John Yang*, Carlos E. Jimenez*, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. Contact person: John Yang, Carlos E. Jimenez, and Kilian Lieret (Email: [email protected], [email protected], [email protected]).
If you found this work helpful, please consider citing it using the following:
@inproceedings{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 R Narasimhan and Ofir Press}, booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}, year={2024}, url={https://arxiv.org/abs/2405.15793} }
If you used the summarizer, interactive commands or the offensive cybersecurity capabilities in SWE-agent, please also consider citing:
@misc{abramovich2024enigmaenhancedinteractivegenerative, title={EnIGMA: Enhanced Interactive Generative Model Agent for CTF Challenges}, author={Talor Abramovich and Meet Udeshi and Minghao Shao and Kilian Lieret and Haoran Xi and Kimberly Milner and Sofija Jancheska and John Yang and Carlos E. Jimenez and Farshad Khorrami and Prashanth Krishnamurthy and Brendan Dolan-Gavitt and Muhammad Shafique and Karthik Narasimhan and Ramesh Karri and Ofir Press}, year={2024}, eprint={2409.16165}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2409.16165}, }
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