SWE-agent: Automating Software Engineering with Language Models

Summary

SWE-agent is an innovative GitHub repository that empowers language models to autonomously fix issues in real-world software projects. This powerful tool can also be employed for offensive cybersecurity and competitive coding challenges, representing a significant advancement in automated software engineering. Developed by researchers from Princeton and Stanford, it was featured at NeurIPS 2024.

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Introduction

SWE-agent is a groundbreaking project that enables your language model of choice, such as GPT-4o or Claude Sonnet 4, to autonomously interact with tools and resolve issues within real GitHub repositories. Beyond automated bug fixing, it extends its capabilities to finding cybersecurity vulnerabilities and performing various custom coding tasks. Recognized at NeurIPS 2024, SWE-agent is a testament to the potential of AI in transforming software development and security.

With over 17,600 stars and 1,800 forks, this Python-based project has garnered significant attention from the developer community.

Installation

Getting started with SWE-agent is straightforward. For the quickest way to try it out, you can launch it directly in your browser using GitHub Codespaces:

For a more detailed setup, including installation from source, refer to the official documentation:

Installation Guide

Examples

SWE-agent offers diverse applications, showcasing its versatility:

Automated Issue Fixing: Learn how to fix issues in real GitHub repositories by following the Hello world from the command line tutorial.
Cybersecurity: Explore its capabilities in offensive cybersecurity with SWE-agent: EnIGMA, a mode designed for solving capture-the-flag challenges and achieving state-of-the-art results on multiple benchmarks.
Custom Tasks: The agent can be adapted to perform any custom coding task, making it a flexible tool for various development needs.
Benchmarking: Understand how to use SWE-agent for benchmarking on SWE-bench.

Why Use SWE-agent?

SWE-agent stands out for several compelling reasons:

State of the Art: It achieves state-of-the-art performance on SWE-bench among open-source projects.
Free-flowing & Generalizable: The system provides maximum agency to the language model, allowing for flexible and generalizable problem-solving.
Configurable & Fully Documented: Governed by a single yaml file, it is highly configurable and comes with comprehensive documentation.
Made for Research: Designed with research in mind, it is simple and hackable, encouraging further innovation and experimentation.