AutoGen vs Langflow
Side-by-side comparison of two AI agent tools
Short answer
- Langflow is growing faster: +1,446 GitHub stars in the last 30 days vs +782 for AutoGen.
- Pick AutoGen for: a programming framework for agentic AI. Pick Langflow for: langflow is a powerful tool for building and deploying AI-powered agents and workflows.
From GitHub data refreshed daily.
AutoGenfree
A programming framework for agentic AI
Langflowopen-source
Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
Metrics
| AutoGen | Langflow | |
|---|---|---|
| Stars | 61.2k | 155.5k |
| Star velocity /mo | 781.8947368421053 | 1.4k |
| Commits (90d) | 0 | 842 |
| Releases (6m) | 0 | 10 |
| Downloads (30d, npm + PyPI) | — | 40.1K |
| Overall score | 0.3844257679182666 | 0.8648447396988407 |
Pros
- +支持多代理协作，可以创建复杂的 AI 交互系统
- +提供 AutoGen Studio 无代码界面，降低使用门槛
- +强大的模型集成能力，支持多种主流大语言模型和 MCP 服务器
- +可视化拖拽界面让非技术用户也能快速构建AI工作流
- +支持多种部署方式包括API、MCP服务器和桌面应用，集成灵活性极高
- +内置对所有主流LLM和向量数据库的支持，生态系统完整
Cons
- -需要 Python 3.10 或更高版本，对环境有一定要求
- -项目处于维护模式，新用户被建议使用 Microsoft Agent Framework
- -从 v0.2 升级需要遵循迁移指南，存在向后兼容性问题
- -需要Python 3.10-3.13环境，对非Python用户有技术门槛
- -复杂的企业级功能可能对简单用例过于繁重
- -学习曲线较陡，充分利用所有功能需要时间投入
Use Cases
- •构建多代理对话系统，让不同角色的 AI 代理协作解决复杂问题
- •创建自动化工作流程，通过代理协作完成数据分析、内容生成等任务
- •开发具有网络浏览能力的智能助手，结合 MCP 服务器实现外部工具集成
- •构建多代理协作系统处理复杂业务流程和决策
- •将AI工作流部署为API服务供其他应用程序调用
- •快速原型制作和可视化测试AI工作流的效果和逻辑
FAQ
- Which is more popular, AutoGen or Langflow?
- Langflow has more GitHub stars (155,471 vs 61,248).
- Which is more actively developed, AutoGen or Langflow?
- Langflow had more commits in the last 90 days (842 vs 0).
- Should I use AutoGen or Langflow?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.