RAGFlow vs Hugging Face Transformers

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?
FeatureRAGFlowHugging Face Transformers
CategorySelf-Hosted AISelf-Hosted AI
LicenseApache-2.0Apache-2.0
LanguageGoPython
Setup difficultyMediumHard
Min. RAM8,192 MB8,192 MB
Deploymentdocker, kubernetesbare-metal, source
GitHub stars★ 87,057★ 163,456
First released20242018
ReplacesNotebookLMOpenAI API

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
RAGFlow details

Choose Hugging Face Transformers if…

  • Released under the Apache-2.0 license
  • Mature project with 163.5k GitHub stars
  • Written in Python
Hugging Face Transformers details

Frequently asked questions

Is RAGFlow or Hugging Face Transformers better?

Neither is universally better. Hugging Face Transformers has the larger community, while RAGFlow is simpler to set up (medium difficulty). Choose based on the comparison table above and your own setup.

Are RAGFlow and Hugging Face Transformers free and open-source?

Yes. RAGFlow is licensed under Apache-2.0 and Hugging Face Transformers under Apache-2.0. Both can be self-hosted at no software cost.

Can I run RAGFlow and Hugging Face Transformers with Docker?

RAGFlow: yes. Hugging Face Transformers: check the project docs for container support.

Related comparisons