R2R vs RAGFlow
A side-by-side comparison of two self-hosted self-hosted ai options — licensing, setup difficulty, resource needs, and what each one replaces.
Not the right match-up?
R2R
Production-ready RAG engine with a RESTful API
VS
RAGFlow
RAG engine with deep document understanding
| Feature | R2R | RAGFlow |
|---|---|---|
| Category | Self-Hosted AI | Self-Hosted AI |
| License | MIT | Apache-2.0 |
| Language | Python | Go |
| Setup difficulty | Medium | Medium |
| Min. RAM | 4,096 MB | 8,192 MB |
| Deployment | docker, source | docker, kubernetes |
| GitHub stars | ★ 7,950 | ★ 87,057 |
| First released | 2024 | 2024 |
| Replaces | OpenAI Assistants | NotebookLM |
Why pick each one
Choose R2R if…
- Released under the MIT license
- First-class Docker support for quick deployment
- Mature project with 8k GitHub stars
- Written in Python
Choose RAGFlow if…
- Released under the Apache-2.0 license
- First-class Docker support for quick deployment
- Kubernetes-ready with Helm charts available
- Mature project with 87.1k GitHub stars
Frequently asked questions
Is R2R or RAGFlow better?
Neither is universally better. RAGFlow has the larger community; both share a medium setup difficulty, so the decision comes down to features and licensing.
Are R2R and RAGFlow free and open-source?
Yes. R2R is licensed under MIT and RAGFlow under Apache-2.0. Both can be self-hosted at no software cost.
Can I run R2R and RAGFlow with Docker?
R2R: yes. RAGFlow: yes.