Dify vs Langflow
Side-by-side comparison of two AI agent tools
Short answer
- Dify is growing faster: +3,659 GitHub stars in the last 30 days vs +1,454 for Langflow.
- Pick Dify for: production-ready platform for agentic workflow development. Pick Langflow for: langflow is a powerful tool for building and deploying AI-powered agents and workflows.
From GitHub data refreshed daily.
Difyfree
Production-ready platform for agentic workflow development.
Langflowopen-source
Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
Metrics
| Dify | Langflow | |
|---|---|---|
| Stars | 157.7k | 155.4k |
| Star velocity /mo | 3.7k | 1.5k |
| Commits (90d) | 2.3k | 843 |
| Releases (6m) | 9 | 10 |
| Overall score | 0.8915216194065667 | 0.8756373988907742 |
Pros
- +生产级稳定性和企业级功能支持，适合大规模部署应用
- +可视化工作流编辑器，大幅降低 AI 应用开发门槛
- +活跃的开源社区和丰富的生态系统，持续更新迭代
- +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
- -学习曲线存在，需要时间熟悉平台的各种组件和配置
- -复杂工作流的性能优化需要深入了解平台机制
- -自部署版本需要一定的运维能力和资源投入
- -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
- •企业客服机器人和智能助手的快速开发与部署
- •复杂业务流程的自动化处理，如文档分析、数据处理等
- •知识库问答系统和内容生成应用的构建
- •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, Dify or Langflow?
- Dify has more GitHub stars (157,652 vs 155,422).
- Which is more actively developed, Dify or Langflow?
- Dify had more commits in the last 90 days (2,316 vs 843).
- Should I use Dify or Langflow?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.