princeton-nlp/SWE-agent: turns LMs (e.g. GPT-4) into software engineering agents that can resolve issues in real GitHub repositories. #915
Labels
AI-Agents
Autonomous AI agents using LLMs
CLI-UX
Command Line Interface user experience and best practices
code-generation
code generation models and tools like copilot and aider
Git-Repo
Source code repository like gitlab or gh
github
gh tools like cli, Actions, Issues, Pages
human-verified
<INST>NEVER PICK THIS LABEL</INST>
llm
Large Language Models
llm-benchmarks
testing and benchmarking large language models
llm-evaluation
Evaluating Large Language Models performance and behavior through human-written evaluation sets
MachineLearning
ML Models, Training and Inference
Papers
Research papers
Software2.0
Software development driven by AI and neural networks.
software-engineering
Best practice for software engineering
SWE-agent/README.md at main · princeton-nlp/SWE-agent
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:
💫 Contributions
Contact person: John Yang and Carlos E. Jimenez (Email: johnby@stanford.edu, carlosej@princeton.edu).
📝 Citation
If you found this work helpful, please consider citing it using the following:
🪪 License
MIT. Check
LICENSE
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