head to head · open source
vllm vs rag_api
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. rag_api has 920 stars, 413 forks, 45 open issues and last shipped 11 days ago. vllm leads on adoption by 10,352% (96,158 vs 920 stars). vllm is written in Python under Apache-2.0; rag_api is written in Python under MIT. vllm has attracted 25% as many forks as stars, rag_api 45%. 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 | rag_api | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 920 |
| License | Apache-2.0 | MIT |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-10-23 |
| Forks | ⑂ 24K | ⑂ 413 |
| 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 rag_api if
- You want the rag_api feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and rag_api 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 rag_api
ID based RAG FastAPI Overview This project integrates Langchain with FastAPI in an Asynchronous, Scalable manner, providing a framework for document indexing and retrieval, using PostgreSQL/pgvector. Files are organized into embeddings by file id . The primary use case is for integration with LibreChat, but this simple API can be used for any ID based use case. The main reason to use the ID approach is to work with embeddings on a file level. This makes for targeted queries when combined with file metadata stored in a database, such as is done by LibreChat. The API will evolve over time to employ different…
read the full rag_api overview →
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Frequently asked questions
Is vllm or rag_api more popular?
vllm has 96,158 GitHub stars and rag_api has 920. vllm has the larger community by that measure.
Are vllm and rag_api free?
Both are open source. vllm is licensed under Apache-2.0 and rag_api under MIT. Neither carries a licence fee.
What is the difference between vllm and rag_api?
vllm is written in Python and rag_api 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 rag_api?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose rag_api if its feature set, stack or MIT licence fits better. Both are self-hostable.