SWE-Agent

About SWE-Agent

SWE-Agent is an open-source AI agent designed to automate software engineering tasks, primarily fixing bugs and resolving issues in GitHub repositories. Built by researchers at Princeton University, it connects large language models to a custom agent-computer interface that allows the model to browse code, edit files, run tests, and execute shell commands. Rather than simply generating code snippets, SWE-Agent operates as an autonomous agent that navigates a codebase, identifies the root cause of a reported issue, and attempts to produce a working fix end-to-end. The primary use case is automated issue resolution. A user can point SWE-Agent at a GitHub issue, and the agent will attempt to reproduce the problem, locate the relevant code, apply a patch, and verify the fix through existing tests. It was benchmarked on SWE-bench, a dataset of real GitHub issues from popular Python repositories, where it demonstrated competitive performance against other automated approaches. Beyond bug fixing, it can be adapted for tasks like writing new features, refactoring code, or running arbitrary command-line workflows, making it flexible for developers who want to experiment with agentic coding pipelines. The tool supports multiple underlying language models, including those from OpenAI and Anthropic, so users can swap in different backends depending on their needs or budget. SWE-Agent is aimed at software developers, AI researchers, and teams exploring LLM-based automation in software development workflows. Because it is open-source and available on GitHub, it is particularly accessible to those who want to self-host, customize the agent behavior, or contribute to the project. There is no proprietary pricing model for SWE-Agent itself, though users bear the cost of API calls to whichever language model they choose to run underneath it. It is best suited for technically proficient users comfortable with Python environments and command-line tooling.

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