SWE-Agent: AI Agent for Automatic Coding

In this video, Matthew Berman introduces SWE-Agent, an open-source, fully autonomous coding agent developed by a team at Princeton. SWE-Agent specializes in solving GitHub issues by replicating, diagnosing, and fixing bugs, and then submitting the fixes as pull requests. It performs nearly as well as Devin on coding benchmarks, making it a notable tool in the field of AI coding assistants.

The video begins with an overview of SWE-Agent’s capabilities and its impressive performance metrics. Matthew explains how SWE-Agent works by taking a GitHub issue URL, understanding the problem, finding the relevant code, making necessary changes, and submitting a fix. The agent uses a combination of GPT-4 and a custom-built file viewer to navigate and edit large codebases effectively.

Matthew provides a step-by-step tutorial on how to install and set up SWE-Agent. This involves installing Docker and Miniconda, cloning the SWE-Agent GitHub repository, and creating a Conda environment. He demonstrates how to activate the environment and run the setup script to build the Docker image. Despite encountering an error due to his Mac OS setup, Matthew switches to using Lightning.AI, which simplifies the process.

The video includes a demonstration of SWE-Agent in action, where it attempts to fix an issue from its own repository. The agent successfully identifies the problem, navigates the code, and makes the necessary changes before encountering a cost limit on GPT-4 usage, which can be adjusted.

Matthew concludes with a full demo by one of the authors of SWE-Agent, showcasing the agent’s ability to reproduce a bug, identify and fix the issue, and submit a pull request. He highlights the potential of SWE-Agent to revolutionize AI coding assistants and encourages viewers to try it out.

Overall, the video provides a comprehensive review and tutorial of SWE-Agent, making it accessible for users to understand and utilize this powerful coding tool.