swesmith 0.0.9

The official SWE-smith package - A toolkit for generating software engineering training data at scale.

NeurIPS 2025 Datasets & Benchmarks Track - Spotlight 🔦

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SWE-smith is a toolkit for training SWE-agents. You can:

• Turn any Github repository into a SWE-gym.
• Create unlimited tasks (e.g., file localization, program repair, SWE-bench) for that repo.
• Train an LM to become a better SWE (SWE-agent-LM-32B).

⚒️ Build Environments

If you're interested in turning a GitHub repository into a SWE-gym, install the package from source.

[!TIP] SWE-smith requires Docker to create execution environments. SWE-smith was developed and tested on Ubuntu 22.04.4 LTS. We do not plan on supporting Windows or MacOS.

You can then build a dataset for the repository by...

1. Creating an environment
2. Synthesizing task instances
3. Keep tasks that break 1+ unit tests
4. Generating issue text for your tasks

🏋️ Train SWE-agent's

Training SWE-agent's using the SWE-smith dataset is super simple.

from swesmith.profiles import registry
from datasets import load_dataset
ds = load_dataset("SWE-bench/SWE-smith", split="train") # Loads all 52k task instances
for task in ds:
    rp = registry.get_from_inst(task)  # Get the RepoProfile for the task
    container = rp.get_container(task) # Returns pointer to a Docker container with the task initialized

    """TODO: Train!"""

SWE-smith has been used to

• Fine-tune Qwen 2.5 Coder into SWE-agent-LM-32B (A +32% jump on SWE-bench Verified!) using SWE-agent [Tutorial]
• Perform GRPO style reinforcement learning using SkyRL

💿 Resources

• 52k Task Instances
• SWE-agent-LM-32B; 40.2% pass@1 on SWE-bench Verified!
• 26k SWE-agent Trajectories, including the 5k SWE-agent-LM-32B was trained on.
• 250+ Environments, one Docker image per repo represented in SWE-smith.

And there's more coming!

💫 Contributions

We're actively working on several follow ups! Check out the Contributing Guide for more.

Contact Person: John Yang, Kilian Lieret (Email: [e-mail])

🪪 License

MIT. Check LICENSE for more information.

✍️ Citation

@inproceedings{yang2025swesmith,
  title={SWE-smith: Scaling Data for Software Engineering Agents}, 
  author={John Yang and Kilian Lieret and Carlos E. Jimenez and Alexander Wettig and Kabir Khandpur and Yanzhe Zhang and Binyuan Hui and Ofir Press and Ludwig Schmidt and Diyi Yang},
  booktitle = {Proceedings of the 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025 D&B Spotlight)},
  year={2025},
  eprint={2504.21798},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2504.21798},
  note={arXiv:2504.21798, accepted at NeurIPS 2025 (Spotlight)}
}

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