GitHub

What is the project about?

SWE-agent is a system that allows language models (like GPT-4o or Claude Sonnet 3.5) to autonomously use tools to interact with computer environments and solve various tasks. It leverages configurable agent-computer interfaces (ACIs). It also has a mode called EnIGMA, specifically designed for offensive cybersecurity challenges.

What problem does it solve?

It automates tasks that typically require human interaction with a computer, such as:

  • Fixing software bugs in GitHub repositories.
  • Performing web-based tasks.
  • Finding cybersecurity vulnerabilities (Capture The Flag challenges).
  • Other custom coding tasks.

It aims to bridge the gap between the capabilities of large language models and their ability to interact with real-world computing environments.

What are the features of the project?

  • Autonomous Tool Use: Language models can use tools to interact with the environment.
  • Configurable Agent-Computer Interfaces (ACIs): Provides a flexible way to define how the agent interacts with the computer.
  • GitHub Repository Interaction: Can directly work with and modify code in GitHub repositories.
  • Web Interaction: Can perform tasks on the web.
  • Cybersecurity Challenge Solving (EnIGMA): Specialized mode for solving Capture The Flag (CTF) challenges, including features like a debugger, server connection tools, and a summarizer for long outputs.
  • Benchmarking: Supports benchmarking on SWE-bench.
  • Customizable Tasks: Can be adapted to various custom tasks beyond the predefined ones.
  • Interactive commands and summarizer: To handle long outputs.

What are the technologies used in the project?

  • Language Models: GPT-4o, Claude Sonnet 3.5, and potentially others.
  • Python: Based on the badges and license, it's likely primarily implemented in Python.
  • Agent-computer interfaces (ACIs)

What are the benefits of the project?

  • Automation: Automates complex tasks, saving time and effort.
  • Research Platform: Provides a platform for research in AI, software engineering, and cybersecurity.
  • State-of-the-Art Performance: Achieves strong results on software engineering and cybersecurity benchmarks.
  • Extensibility: Designed to be adaptable to new tasks and environments.
  • Open Source: MIT licensed, encouraging community contributions.

What are the use cases of the project?

  • Automated Software Bug Fixing: Developers can use it to automatically identify and fix bugs in their code.
  • Web Automation: Automating tasks that involve interacting with websites.
  • Cybersecurity Training and Research: Used for training and research in offensive cybersecurity.
  • General Task Automation: Potentially adaptable to a wide range of tasks that require interaction with a computer.
  • Software Development Assistance: Helping developers with various coding tasks.
SWE-agent screenshot