head to head · open source
vllm vs knowhere
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. knowhere has 3,882 stars, 337 forks, 40 open issues and last shipped yesterday. vllm leads on adoption by 2,377% (96,158 vs 3,882 stars). vllm is written in Python under Apache-2.0; knowhere is written in Python under Apache-2.0. vllm has attracted 25% as many forks as stars, knowhere 9%. 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 | knowhere | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 3.9K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 24K | ⑂ 337 |
| 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 knowhere if
- You want the knowhere feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and knowhere 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 knowhere
Prepare unstructured data for AI Agents 🔗 Website 📄 Docs 🏠 Self Host 🖥️ Dashboard Overview Knowhere is a document parsing and retrieval system that turns complex, dirty files into persistent, navigable memory for AI agents—especially across local and offline document collections. It ingests unstructured documents and produces persistent, navigable memory: parsing, hierarchy reconstruction, multi modal structuring, and graph construction in a single pipeline. Every result stays connected to its document, section, source pages, and related assets, making the output a natural fit for Agentic RAG , vector based…
read the full knowhere overview →
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Frequently asked questions
Is vllm or knowhere more popular?
vllm has 96,158 GitHub stars and knowhere has 3,882. vllm has the larger community by that measure.
Are vllm and knowhere free?
Both are open source. vllm is licensed under Apache-2.0 and knowhere under Apache-2.0. Neither carries a licence fee.
What is the difference between vllm and knowhere?
vllm is written in Python and knowhere 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 knowhere?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose knowhere if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.