Dify vs ragflow
Side-by-side comparison of two AI agent tools
Short answer
- Dify is growing faster: +3,637 GitHub stars in the last 30 days vs +2,402 for ragflow.
- Pick Dify for: production-ready platform for agentic workflow development. Pick ragflow for: open-source RAG engine combining knowledge retrieval and agent capabilities for LLMs.
From GitHub data refreshed daily.
Difyfree
Production-ready platform for agentic workflow development.
ragflowopen-source
Open-source RAG engine combining knowledge retrieval and agent capabilities for LLMs
Metrics
| Dify | ragflow | |
|---|---|---|
| Stars | 157.8k | 91.6k |
| Star velocity /mo | 3.6k | 2.4k |
| Commits (90d) | 2.4k | 2.7k |
| Releases (6m) | 9 | 10 |
| Overall score | 0.8805791472432994 | 0.9098521001650974 |
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
- +结合了先进的RAG技术和Agent能力，提供比传统RAG更强大的功能
- +开源且拥有活跃社区支持，GitHub星数超过7.6万，可信度高
- +提供云服务和Docker容器化部署，支持多种部署方式
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
- -作为相对复杂的RAG系统，可能需要一定的技术背景才能充分配置和优化
- -大规模部署可能需要相当的计算资源和存储空间
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
- •企业知识库问答系统，基于内部文档为员工提供智能查询服务
- •智能客服系统，结合产品文档和FAQ提供准确的客户支持
- •研究助手应用，帮助研究人员从大量学术文献中检索相关信息
FAQ
- Which is more popular, Dify or ragflow?
- Dify has more GitHub stars (157,757 vs 91,619).
- Which is more actively developed, Dify or ragflow?
- ragflow had more commits in the last 90 days (2,666 vs 2,369).
- Should I use Dify or ragflow?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.