goose vs SWE-agent
Side-by-side comparison of two AI agent tools
Short answer
- goose is growing faster: +3,352 GitHub stars in the last 30 days vs +254 for SWE-agent.
- Pick goose for: an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test. Pick SWE-agent for: sWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice.
From GitHub data refreshed daily.
gooseopen-source
an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM
SWE-agentopen-source
SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM of choice. It can also be employed for offensive cybersecurity or competitive coding challenges. [NeurIPS 2024]
Metrics
| goose | SWE-agent | |
|---|---|---|
| Stars | 54.9k | 20.5k |
| Star velocity /mo | 3.4k | 254.3684210526316 |
| Commits (90d) | 805 | 6 |
| Releases (6m) | 10 | 0 |
| Overall score | 0.8821825173304099 | 0.4088976488846652 |
Pros
- +支持任何LLM模型且可多模型配置，灵活性极高
- +能够自主完成端到端开发任务，不仅仅是代码建议
- +开源架构支持自定义扩展和MCP服务器集成
- +在SWE-bench基准测试中达到开源项目的最先进性能水平
- +支持多种主流大语言模型（GPT-4o、Claude Sonnet 4等），配置灵活
- +专为研究设计，架构简单且文档完善，易于定制和扩展
Cons
- -需要本地安装和配置，对新手用户可能有一定门槛
- -作为自主代理执行任务时可能需要用户监督和验证结果
- -开发重心已转移到mini-swe-agent项目，原项目维护可能受到影响
- -主要面向研究用途，生产环境的稳定性和可靠性可能不如商业解决方案
Use Cases
- •从零开始构建完整项目原型，包括代码编写和测试
- •对现有代码库进行重构和优化改进
- •管理复杂的工程流水线和自动化开发工作流
- •自动修复GitHub仓库中的代码问题和bug
- •网络安全领域的漏洞发现和渗透测试
- •竞赛编程和算法挑战的自动化解决
FAQ
- Which is more popular, goose or SWE-agent?
- goose has more GitHub stars (54,890 vs 20,475).
- Which is more actively developed, goose or SWE-agent?
- goose had more commits in the last 90 days (805 vs 6).
- Should I use goose or SWE-agent?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.