chatbot-ui vs langgraph
Side-by-side comparison of two AI agent tools
chatbot-uiopen-source
AI chat for any model.
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| chatbot-ui | langgraph | |
|---|---|---|
| Stars | 33.1k | 28.0k |
| Star velocity /mo | -7.5 | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 10 |
| Overall score | 0.24331901430382824 | 0.8081963872278098 |
Pros
- +支持任何 AI 模型，提供极大的灵活性和选择自由
- +提供官方托管版本和自部署选项，满足不同用户需求
- +使用现代技术栈 (Supabase) 确保数据安全和扩展性
- +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
- -本地开发需要 Docker 和 Supabase CLI，增加了环境配置复杂度
- -从 1.0 到 2.0 的重大更新可能导致向后兼容性问题
- -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
- •企业内部 AI 助手：快速为团队部署私有化的 AI 聊天服务
- •AI 产品原型开发：为 AI 应用快速搭建聊天界面进行概念验证
- •多模型对比测试：在同一界面中测试和比较不同 AI 模型的表现
- •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