RAGFlow vs Transformer Lab
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?
RAGFlow
RAG engine with deep document understanding
VS
Transformer Lab
Workspace for training and evaluating local LLMs
| Feature | RAGFlow | Transformer Lab |
|---|---|---|
| Category | Self-Hosted AI | Self-Hosted AI |
| License | Apache-2.0 | AGPL-3.0 |
| Language | Go | Python |
| Setup difficulty | Medium | Medium |
| Min. RAM | 8,192 MB | 8,192 MB |
| Deployment | docker, kubernetes | binary, source |
| GitHub stars | ★ 87,057 | ★ 5,170 |
| First released | 2024 | 2024 |
| Replaces | NotebookLM | OpenAI Playground |
Why pick each one
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
Choose Transformer Lab if…
- Released under the AGPL-3.0 license
- Mature project with 5.2k GitHub stars
- Written in Python
Frequently asked questions
Is RAGFlow or Transformer Lab 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 RAGFlow and Transformer Lab free and open-source?
Yes. RAGFlow is licensed under Apache-2.0 and Transformer Lab under AGPL-3.0. Both can be self-hosted at no software cost.
Can I run RAGFlow and Transformer Lab with Docker?
RAGFlow: yes. Transformer Lab: check the project docs for container support.