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SWE-Agent - AI Code Repair Agent

An open-source AI agent that navigates code repositories, edits files, and runs tests to autonomously resolve GitHub issues, achieving state-of-the-art performance on SWE-bench.

  • Autonomous Issue Resolution
  • Agent-Computer Interface
  • Benchmark Dominance
  • Multi-LLM Integration
💰Free
SWE-Agent - AI Code Repair Agent
SWE-Agent - AI Code Repair Agent

What is SWE-Agent?

SWE-Agent is an open-source AI software engineering agent that autonomously resolves GitHub issues from start to finish. It intelligently navigates entire codebases, edits files, and executes tests using a specialized Agent-Computer Interface. Designed for engineering and DevOps teams aiming to automate bug fixes and accelerate development, SWE-Agent’s key differentiator is its state-of-the-art performance on the SWE-bench benchmark, proving its capability to handle complex, real-world software engineering tasks far beyond simple code generation.

Key Features & Benefits

1

Autonomous Issue Resolution

Agents independently navigate codebases, edit files, and execute tests to resolve GitHub issues end-to-end without human intervention.

2

Agent-Computer Interface

Specialized tools provide agents with file navigation, editing capabilities, and shell access for comprehensive repository interaction.

3

Benchmark Dominance

Achieves 12.29% issue resolution rate on SWE-bench, outperforming all existing models in real-world software engineering tasks.

4

Multi-LLM Integration

Supports configuration with various large language models for flexible and optimized performance across coding tasks.

Use Cases

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Autonomous GitHub Issue Resolution

SWE-Agent can autonomously take a GitHub issue, navigate the repository, and generate a pull request to fix the bug without human intervention.

"We pull things back to the management layer, where I just assign a bug report and the bot tries to fix it completely autonomously."
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Assign Tasks via Workplace Integrations

SWE-Agent integrates with issue trackers and messaging platforms (e.g., Asana, Slack) to accept assignments and report back completion status automatically.

"These new agentic coding tools… operate like the manager of an engineering team, assigning issues through workplace systems like Asana or Slack and checking in when a solution has been reached."
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Automated Debugging of Misnamed File Bugs

In a real-world example, SWE-Agent rapidly identified a misnamed file error in a GitHub repository and corrected the code path to resolve the issue.

"It correctly determined that the root cause of the bug was a line that pointed to the wrong location for a file, then navigated through the project… and amended the code so that everything ran properly."
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Deep Technical Walkthrough

Co-author Ofir Press presents a 20-minute deep dive into SWE-Agent’s design decisions, interface architecture, and evaluation on coding benchmarks.


Pros & Cons

  • Achieves state-of-the-art performance on SWE-bench among open-source projects

  • Integrates seamlessly with workplace systems like Asana and Slack

  • Requires careful configuration of the Agent-Computer Interface for best results

  • Depends on high-end LLMs (e.g., GPT-4), increasing API usage costs


FAQs

How does SWE-Agent differ from ChatGPT for coding?
SWE-Agent specializes in repository-level problem-solving with tools for file navigation/editing, while ChatGPT focuses on general code snippets without repository context. SWE-Agent can interact with an entire codebase, including navigating directories, editing files, and running tests, making it suitable for more complex, repository-level tasks. ChatGPT generally works by providing code snippets or answering coding questions without direct interaction with a live development environment or repository.
What types of GitHub issues can SWE-Agent resolve?
It handles bug fixes, feature implementations, and test adjustments in Python repositories, as demonstrated in the SWE-bench benchmark. This includes issues found in popular open-source repositories like Matplotlib, Pytest, and Django.
Can SWE-Agent work with private repositories?
Yes, it operates on locally cloned repositories, making it compatible with both public and private codebases through proper configuration. You can provide a GitHub token to access private repositories.
What infrastructure is needed to run SWE-Agent?
Requires Python 3.9+, Docker for sandboxing, and API access to LLM providers like OpenAI or Anthropic for model inference. It uses SWE-ReX for sandboxed code execution, which supports local Docker, AWS, and Modal deployments. For local models, it supports any model that serves to an endpoint with an OpenAI-compatible API.

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Information

  • github.com
  • Published date6/28/2025
  • Last updated6/28/2025