head to head · open source

vllm vs RAGLight

vllm has 92,055 GitHub stars, 22,349 forks, 7,953 open issues and last shipped today. RAGLight has 673 stars, 102 forks, 22 open issues and last shipped 16 days ago. vllm leads on adoption by 13,578% (92,055 vs 673 stars). vllm is written in Python under Apache-2.0; RAGLight is written in Python under MIT. vllm has attracted 24% as many forks as stars, RAGLight 15%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

vllm ★ 92K RAGLight ★ 673 category AI & Machine Learning

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Side by side

	vllm	RAGLight
GitHub stars	★ 92K	★ 673
License	Apache-2.0	MIT
Written in	Python	Python
Last push	2026-09-18	2026-09-02
Forks	⑂ 22K	⑂ 102
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick vllm if

You weight community size — 92K stars and counting
You want the Apache-2.0 license terms
Your stack matches Python
You value the larger contributor base for long-term maintenance

full vllm profile →

pick RAGLight if

You want the RAGLight feature set and don't need the biggest community
You prefer the MIT license terms
Your stack matches Python
You evaluated both and RAGLight fits your workflow better

full RAGLight profile →

About vllm

vLLM is a high throughput, memory efficient Python library for LLM inference and serving, built for ML engineers and platform teams who need to run open weight models on their own hardware at production scale.

read the full vllm overview →

About RAGLight

RAGLight RAGLight is a lightweight and modular Python library for implementing Retrieval Augmented Generation (RAG) . It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference. Designed for simplicity and flexibility, RAGLight provides modular components to easily integrate various LLMs, embeddings, and vector stores, making it an ideal tool for building context aware AI solutions. 📚 Table of Contents Requirements Features Import library Chat with Your Documents Instantly With CLI Ignore Folders Feature Ignore Folders in Configuration Classes Dep…

read the full RAGLight overview →

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Frequently asked questions

Is vllm or RAGLight more popular?

vllm has 92,055 GitHub stars and RAGLight has 673. vllm has the larger community by that measure.

Are vllm and RAGLight free?

Both are open source. vllm is licensed under Apache-2.0 and RAGLight under MIT. Neither carries a licence fee.

What is the difference between vllm and RAGLight?

vllm is written in Python and RAGLight in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, vllm or RAGLight?

Choose vllm if you want the larger community (92,055 stars) or its Apache-2.0 licence terms. Choose RAGLight if its feature set, stack or MIT licence fits better. Both are self-hostable.