About
SWE-agent, developed by the talented researchers at Princeton University, elevates the capabilities of large language models such as GPT-4 into effective software engineering agents. With a remarkable ability to resolve issues in real GitHub repositories, SWE-agent demonstrates impressive performance metrics, tackling 12.47% of challenges in comprehensive test sets and achieving an even higher resolution rate of 23% in the SWE-bench lite evaluations. By focusing on simple, language model-centric commands and a unique Agent-Computer Interface (ACI), SWE-agent streamlines the process of navigating through code repositories, allowing users to view, edit, and execute files effortlessly.
Highlights
One of the standout features of SWE-agent is its mode specifically designed for offensive cybersecurity, dubbed EnIGMA. This innovative mode boasts state-of-the-art results on multiple cybersecurity benchmarks, including significant performance improvements over previous models in the NYU CTF challenge. EnIGMA not only enhances SWE-agent's capabilities with tools for debugging and server connections, but it also includes a summarizer feature to succinctly manage lengthy outputs. The collaborative spirit of this project is evident in its community-oriented approach, encouraging contributions through Discord and welcoming codebase improvements via issues and pull requests. Whether you are looking to increase your productivity in software engineering or explore advanced cybersecurity challenges, SWE-agent provides a robust platform to meet your needs. Join the community and discover how SWE-agent can transform your approach to software development!