In early 2024, most software engineers were using large AI models as chatbot assistants, answering questions or generating small pieces of code on request. In addition, smaller language models were powering code autocompletion tools in code editors, speeding up the writing of new code.
However, software engineers spend most of their time not writing new code, but rather fixing bugs and implementing features in existing codebases, often comprising tens or hundreds of thousands of lines of code. This means that a large amount of time is spent understanding the codebase, finding the correct place to make changes, and making small-scale modifications. The simple AI tools of early 2024 were not well-suited to this task.
To address this gap, we built SWE-agent. Mimicking the workflow of human engineers, SWE-agent uses tools to perform tasks, working incrementally toward complicated goals. In order to autonomously fix bugs and implement complex features in large software repositories, SWE-agent takes time to navigate the codebase, read select files, and finally make modifications, before testing and validating the changes.
SWE-agent was the first open-source system to score significantly on the SWE-bench benchmark that assesses the performance of AI systems on real-world software engineering tasks. Since the initial release in April 2024, SWE-agent has continued to evolve and regularly ranks at the top of the SWE-bench leaderboard while maintaining a lightweight and accessible design.
SWE-agent enables your language model of choice to use tools to fix issues in real GitHub repositories, find cybersecurity vulnerabilities, or perform any custom task.
It was the first open-source system to significantly score on SWE-bench, far outperforming RAG baselines and creating a breakthrough for agentic AI in software engineering.
Released just days after the commercial equivalent project Devin showed its first public demo, we demonstrated that a simple open-source system with optimized agent tooling could perform similarly (if not better) than a well-funded company's demo, democratizing access to AI-powered software engineering capabilities.
The central innovation discussed in our paper is the design and optimization of the agent-computer interface (ACI) that allows the language model to effectively navigate, understand, and modify large codebases. This includes custom shell commands, file editing interfaces, and feedback mechanisms.
Since the initial release in April 2024, development has never stopped, and SWE-agent still regularly ranks at the top of the SWE-bench leaderboard while maintaining a lightweight, modular architecture that makes it easy to extend and customize for different use cases.