autogen vs langgraph
Side-by-side comparison of two AI agent tools
autogenfree
A programming framework for agentic AI
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| autogen | langgraph | |
|---|---|---|
| Stars | 56.5k | 28.0k |
| Star velocity /mo | 1.5k | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 10 |
| Overall score | 0.6608497776161022 | 0.8081963872278098 |
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
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
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
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
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
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions