githuganswered from live GitHub & Hugging Face data
What's the best open-source platform for creating AI workflows — Dify or RAGFlow?
When comparing Dify and RAGFlow, it's clear you're looking at two robust platforms for AI workflow development, each with its strengths.
Main Takeaways
- Dify is a well-rounded platform with a strong emphasis on agentic workflow development and user-friendly deployment options. It's a powerhouse for quickly building AI applications with rich features.
- RAGFlow, on the other hand, specializes in Retrieval-Augmented Generation (RAG) capabilities and excels in creating context layers for LLMs, making it particularly suitable for applications that require efficient context understanding during data retrieval.
Project Overview
| Feature | Dify | RAGFlow |
|---|---|---|
| Stars | 146,552 stars | 83,621 stars |
| Language | TypeScript | Go |
| License | No Assertion | Apache 2.0 |
| Last Push | 0 days ago (June 25, 2026) | 0 days ago (June 25, 2026) |
| Open Issues | 804 open issues | 2,849 open issues |
| Contributors | 1,362 contributors | 648 contributors |
| Maintenance | ✅ Actively maintained | ✅ Actively maintained |
| Best For | General LLM app development and workflow management | RAG tasks and context-driven AI applications |
| Link | Dify GitHub | RAGFlow GitHub |
Maintenance Insights
Both platforms are actively maintained, with the last commits happening very recently, indicating ongoing development and support. However, the open issue count is significantly higher for RAGFlow, which may signal a more complex codebase or a wider breadth of features that need support.
Additional Features
- Dify integrates well with multiple observability platforms and supports a variety of AI models. Its wide array of features makes transitioning from prototype to production a smoother process. It provides a user-friendly interface and options for self-hosting.
- RAGFlow is particularly tailored for complex data handling and retrieval, making it ideal for environments where context is crucial. It is designed to seamlessly work with multiple chat channels and supports integration with various data sources.
Conclusion
For general AI workflow development and ease of use, Dify edges out as the better option (it’s the safe default). However, if your focus is on leveraging RAG technologies or high-context AI capabilities, RAGFlow may serve your needs better despite its steeper learning curve with more open issues.
Recommendation:
- Choose Dify if you’re aiming for an intuitive, versatile AI application platform.
- Choose RAGFlow if your project demands robust retrieval capabilities and context management.