MLX LM 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?
MLX LM
Run and fine-tune LLMs locally on Apple Silicon with MLX
VS
RAGFlow
RAG engine with deep document understanding
| Feature | MLX LM | RAGFlow |
|---|---|---|
| Category | Self-Hosted AI | Self-Hosted AI |
| License | MIT | Apache-2.0 |
| Language | Python | Go |
| Setup difficulty | Medium | Medium |
| Min. RAM | 16,384 MB | 8,192 MB |
| Deployment | source, binary | docker, kubernetes |
| GitHub stars | ★ 6,543 | ★ 87,057 |
| First released | 2024 | 2024 |
| Replaces | OpenAI API | NotebookLM |
Why pick each one
Choose MLX LM if…
- Released under the MIT license
- Mature project with 6.5k 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 MLX LM 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 MLX LM and RAGFlow free and open-source?
Yes. MLX LM is licensed under MIT and RAGFlow under Apache-2.0. Both can be self-hosted at no software cost.
Can I run MLX LM and RAGFlow with Docker?
MLX LM: check the project docs for container support. RAGFlow: yes.