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vLLM Semantic Router

Documentation Hugging Face License Crates.io Test And Build Ask DeepWiki

🎯 Playground | 📚 Website | 🚀 Quick Start | 📣 Blog | 📖 Publications


Latest News 🔥


Quick Start

Installation

$ curl -fsSL https://vllm-semantic-router.com/install.sh | bash

For detailed setup options, platform notes, and troubleshooting, see the Docs.

Important

Online playground default credentials:

  • username: love@vllm-sr.ai
  • password: vllm-sr

Goals

We are building the System Level Intelligence for Mixture-of-Models (MoM), bringing the Collective Intelligence into LLM systems, answering the following questions:

  1. How to capture the missing signals in request, response and context?
  2. How to combine the signals to make better decisions?
  3. How to collaborate more efficiently between different models?
  4. How to secure the real world and LLM system from jailbreaks, pii leaks, hallucinations?
  5. How to collect the valuable signals and build a self-learning system?

vLLM Semantic Router Banner

Where it lives

It lives between the real world and models:

level

Documentation 📖

For comprehensive documentation including detailed setup instructions, architecture guides, and API references, visit:

Complete Documentation at Read the Docs

The documentation includes:

Community 👋

For questions, feedback, or to contribute, please join #semantic-router channel in vLLM Slack.

Community Meetings 📅

We host bi-weekly community meetings to sync up with contributors across different time zones:

Join us to discuss the latest developments, share ideas, and collaborate on the project!

Citation

If you find Semantic Router helpful in your research or projects, please consider citing it:

@misc{semanticrouter2025,
  title={vLLM Semantic Router},
  author={vLLM Semantic Router Team},
  year={2025},
  howpublished={\url{https://github.com/vllm-project/semantic-router}},
}

Star History 🔥

We opened the project at Aug 31, 2025. We love open source and collaboration ❤️

Star History Chart

Sponsors 👋

We are grateful to our sponsors who support us:


AMD provides us with GPU resources and ROCm™ Software for training and researching the frontier router models, enhancing e2e testing, and building online models playground.