head to head · open source
vllm vs RAGLight
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. RAGLight has 673 stars, 102 forks, 22 open issues and last shipped 18 days ago. vllm leads on adoption by 14,188% (96,158 vs 673 stars). vllm is written in Python under Apache-2.0; RAGLight is written in Python under MIT. vllm has attracted 25% 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.
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Side by side
| vllm | RAGLight | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 673 |
| License | Apache-2.0 | MIT |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-10-16 |
| Forks | ⑂ 24K | ⑂ 102 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick vllm if
- You weight community size — 96K 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
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
About vllm
Easy, fast, and cheap LLM serving for everyone 🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us. About vLLM is a fast and easy to use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. vLLM is fast with: State of the art serving throughput Efficient management of…
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…
read the full RAGLight overview →
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Frequently asked questions
Is vllm or RAGLight more popular?
vllm has 96,158 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 (96,158 stars) or its Apache-2.0 licence terms. Choose RAGLight if its feature set, stack or MIT licence fits better. Both are self-hostable.