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SWE-agent

Agent

SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed fo...

Copy the install, test the workflow, then decide if it earns a permanent slot.

18,814
Why nowMoving now

Fresh repo activity plus visible builder pull. This is the kind of tool people test before it turns obvious.

DecisionHigh-conviction move

Copy the install, test the workflow, then decide if it earns a permanent slot.

Trial costMedium lift

Reasonable to try, but it will take more than a quick skim to get real signal.

Risk21/100

GitHub health 87/100. no security policy. Fresh enough repo health and manageable issue load keep the risk controlled.

What You Are Adopting

AI Agent

Universal

Model

Multiple

Build Time

Hours

Test This In Your Stack

One command inClean rollbackLow commitment
shieldSandboxedInstalls to ~/.claude — isolated from your projects. One command to remove.

Fastest way to find out if SWE-agent belongs in your setup.

Copy the install command, run a real test, and back it out cleanly if it slows you down.

Try now
git clone https://github.com/SWE-agent/SWE-agent ~/.claude/agents/swe-agent

Run this first. You will know quickly if the workflow earns a permanent slot.

Back out
rm -rf ~/.claude/agents/swe-agent

No messy cleanup loop. If it misses, remove it and keep moving.

Install Location

~/  └─ .claude/      ├─ commands/      ├─ agents/      │   └─ swe-agent/ ← installs here      └─ settings.json

About

SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024]. An open-source agent for the AI coding ecosystem.

README

swe-agent.com

Docs Slack arxiv 2405.15793

mini-swe-agent.com

Warning

Most of our current development effort is on mini-swe-agent, which has superseded SWE-agent. It matches the performance performance of SWE-agent, while being much simpler. See the FAQ for more details about the differences. Our general recommendation is to use mini-SWE-agent instead of SWE-agent going forward.

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.

  • ✅ State of the art on SWE-bench among open-source projects
  • ✅ Free-flowing & generalizable: Leaves maximal agency to the LM
  • ✅ Configurable & fully documented: Governed by a single yaml file
  • ✅ Made for research: Simple & hackable by design

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

📣 News

  • July 24: Mini-SWE-Agent achieves 65% on SWE-bench verified in 100 lines of python!
  • May 2: SWE-agent-LM-32b achieves open-weights SOTA on SWE-bench
  • Feb 28: SWE-agent 1.0 + Claude 3.7 is SoTA on SWE-Bench full
  • Feb 25: SWE-agent 1.0 + Claude 3.7 is SoTA on SWE-bench verified
  • Feb 13: Releasing SWE-agent 1.0: SoTA on SWE-bench light & tons of new features
  • Dec 7: An interview with the SWE-agent & SWE-bench team

🚀 Get started!

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

Read our documentation to learn more:

  • Installation
  • Hello world from the command line
  • Benchmarking on SWE-bench
  • Frequently Asked Questions

SWE-agent for offensive cybersecurity (EnIGMA)

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:

Mini-SWE-Agent    SWE-ReX    SWE-bench    SWE-smith    sb-cli

Contributions

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.

Citation & contact

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:

SWE-agent citation
@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:

EnIGMA citation
@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},
}

🪪 License

MIT. Check LICENSE.

Pytest build-docs codecov pre-commit.ci status Markdown links

Tech Stack

GoPythonClaudeGPT
Open Live ProjectAudit Repo

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ActiveLast commit today
bug_report49open issues
Submitted April 2, 2024

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