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SWE-agent – SWE-agent 软件工程体

Agent that autonomously fixes GitHub issues in software repos

View on GitHub ↗ 在 GitHub 查看 ↗
Category分类
AI Agent AI 智能体
agent
GitHub StarsGitHub 星数
14k+
Community adoption社区认可度
License许可证
Open Source
Free to use 免费使用
Tags标签
agent, code, autonomous
4 tags total个标签

What Is SWE-agent? SWE-agent 是什么?

SWE-agent is an open-source autonomous AI agent system with 14k+ GitHub stars. Agent that autonomously fixes GitHub issues in software repos

As a autonomous AI agent system, SWE-agent is designed to help developers and teams automate complex tasks by combining planning, tool use, and iterative execution. Instead of following a fixed script, it dynamically adapts its approach based on intermediate results and feedback.

The project is maintained on GitHub at github.com/princeton-nlp/SWE-agent and is actively developed with a strong open-source community. With 14k+ stars, it is one of the most widely adopted tools in its category.

A well-regarded project with 14k+ stars, SWE-agent has proven itself in production deployments. Best suited for developers who want AI assistance integrated directly into their workflow rather than switching to a chat interface. The context window size limits its usefulness for very large codebase refactoring tasks.

A well-regarded project with 14k+ stars, SWE-agent has proven itself in production deployments. Best suited for developers who want AI assistance integrated directly into their workflow rather than switching to a chat interface. The context window size limits its usefulness for very large codebase refactoring tasks.

— AI Nav Editorial Team

Use Cases 应用场景

SWE-agent is used across a wide range of autonomous task scenarios. Here are the most common workflows teams automate with SWE-agent:

🔍 Research Automation

Gather, analyze, and synthesize information from the web, databases, and documents autonomously.

💻 Code Generation & Debugging

Implement features, fix bugs, write tests, and refactor codebases with minimal human intervention.

📊 Data Processing Pipelines

Build automated workflows that ingest, transform, validate, and analyze data at scale.

🌐 Multi-Step Task Execution

Complete complex goals requiring planning across many tools, APIs, and decision branches.

Key Features 核心功能

  • 🤖
    Agent Capabilities — Autonomous task execution with planning, tool use, self-correction, and iterative goal pursuit.
  • 💻
    Code Intelligence — AI-powered code generation, completion, review, and refactoring across all major programming languages.
  • 🚀
    Autonomous Execution — Self-directed task completion—set a goal and the system plans and executes without step-by-step guidance.
  • 🔓
    Open Source — MIT/Apache licensed—inspect, fork, modify, and self-host with no vendor lock-in.

Getting Started with SWE-agent SWE-agent 快速开始

To get started with SWE-agent, visit the GitHub repository and follow the installation instructions in the README. Agent frameworks typically require an API key for the LLM backend (OpenAI, Anthropic, or a local model via Ollama).

💡 Tip: Check the GitHub repository's Issues and Discussions pages for community support, and the Releases page for the latest stable version.

Similar AI Agents 相似 AI 智能体

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Frequently Asked Questions 常见问题

What can SWE-agent do autonomously?
SWE-agent can browse the web, read and write files, execute code in a sandbox, call external APIs, and chain these actions to complete complex multi-step goals—all without human confirmation at each step.
How much does running SWE-agent cost?
The software itself is MIT-licensed and free. It requires an LLM API (OpenAI, Anthropic, or local Ollama). A typical task costs $0.50–$5 in API usage with GPT-4o. Always set a token budget limit to prevent runaway costs on long tasks.
Is it safe to run SWE-agent without supervision?
For production-critical systems, always run with human-in-the-loop confirmation enabled. SWE-agent includes confirmation prompts for destructive actions by default. Never grant access to credentials or production infrastructure without explicit scope limits.
How does SWE-agent compare to prompt chaining?
SWE-agent goes beyond prompt chaining by adding dynamic planning, real tool execution, and self-correction loops. Unlike a fixed chain of prompts, it adapts its approach based on intermediate results—making it suitable for open-ended tasks where the exact steps aren't known in advance.