chat-ui vs langgraph
Side-by-side comparison of two AI agent tools
chat-uiopen-source
The open source codebase powering HuggingChat
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| chat-ui | langgraph | |
|---|---|---|
| Stars | 10.6k | 28.0k |
| Star velocity /mo | 22.5 | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 1 | 10 |
| Overall score | 0.5836288241572403 | 0.8081963872278098 |
Pros
- +OpenAI协议兼容性强，支持众多LLM提供商，包括本地和云端服务
- +经过实战验证，为HuggingChat等生产环境提供技术支持，稳定性高
- +完全开源且可自部署，提供完整的数据控制权和定制能力
- +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
- -仅支持OpenAI兼容的API，不支持其他协议格式的LLM服务
- -需要配置MongoDB数据库，增加了部署的复杂性
- -移除了提供商特定的集成功能，可能限制某些高级特性的使用
- -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
- •企业内部部署私有化AI聊天服务，确保数据安全和合规性
- •开发者构建基于LLM的聊天应用原型或产品
- •为本地部署的LLM模型（如llama.cpp、Ollama）提供Web界面
- •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