Latest News 🔥
- [2025/12/16] Collaboration: AMD × vLLM Semantic Router: Building the System Intelligence Together
- [2025/12/15] New Blog: Token-Level Truth: Real-Time Hallucination Detection for Production LLMs
- [2025/11/19] New Blog: Signal-Decision Driven Architecture: Reshaping Semantic Routing at Scale
- [2025/11/03] Our paper Category-Aware Semantic Caching for Heterogeneous LLM Workloads published
- [2025/10/27] New Blog: Scaling Semantic Routing with Extensible LoRA
- [2025/10/12] Our paper When to Reason: Semantic Router for vLLM accepted by NeurIPS 2025 MLForSys.
- [2025/10/08] Collaboration: vLLM Semantic Router with vLLM Production Stack Team.
- [2025/09/01] Released the project: vLLM Semantic Router: Next Phase in LLM inference.
We are building the System Level Intelligence for Mixture-of-Models (MoM), bringing the Collective Intelligence into LLM systems, answering the following questions:
- How to capture the missing signals in request, response and context?
- How to combine the signals to make better decisions?
- How to collaborate more efficiently between different models?
- How to secure the real world and LLM system from jailbreaks, pii leaks, hallucinations?
- How to collect the valuable signals and build a self-learning system?
It lives between the real world and models:
A quick overview of the current architecture:
Tip
We recommend that you setup a Python virtual environment to manage dependencies.
$ python -m venv vsr
$ source vsr/bin/activate
$ pip install vllm-srInstalled successfully if you see the following help message:
$ vllm-sr
_ _ __ __ ____ ____
__ _| | |_ _| \/ | / ___|| _ \
\ \ / / | | | | |\/| |_____\___ \| |_) |
\ V /| | | |_| | | |_____|___) | _ <
\_/ |_|_|\__,_|_| | |____/|_| \_\
vLLM Semantic Router - Intelligent routing for vLLM
Usage: vllm-sr [OPTIONS] COMMAND [ARGS]...
vLLM Semantic Router CLI - Intelligent routing and caching for vLLM
endpoints.
Options:
--version Show version and exit.
--help Show this message and exit.
Commands:
config Print generated configuration.
init Initialize vLLM Semantic Router configuration.
logs Show logs from vLLM Semantic Router service.
serve Start vLLM Semantic Router.
status Show status of vLLM Semantic Router services.
stop Stop vLLM Semantic Router.Tip
You can specify the HF_ENDPOINT, HF_TOKEN, and HF_HOME environment variables to configure the Hugging Face credentials.
# Set environment variables (optional)
export HF_ENDPOINT=https://huggingface.co # Or use mirror: https://hf-mirror.com
export HF_TOKEN=your_token_here # Only for gated models
export HF_HOME=/path/to/cache # Optional: custom cache directory
# Start the service - models download automatically
# Environment variables are automatically passed to the container
vllm-sr serveFor comprehensive documentation including detailed setup instructions, architecture guides, and API references, visit:
Complete Documentation at Read the Docs
The documentation includes:
- Installation Guide - Complete setup instructions
- System Architecture - Technical deep dive
- Model Training - How classification models work
- API Reference - Complete API documentation
- Dashboard - vLLM Semantic Router Dashboard
For questions, feedback, or to contribute, please join #semantic-router channel in vLLM Slack.
We host bi-weekly community meetings to sync up with contributors across different time zones:
- First Tuesday of the month: 9:00-10:00 AM EST (accommodates US EST, EU, and Asia Pacific contributors)
- Third Tuesday of the month: 1:00-2:00 PM EST (accommodates US EST and California contributors)
- Meeting Recordings: YouTube
Join us to discuss the latest developments, share ideas, and collaborate on the project!
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}},
}
We opened the project at Aug 31, 2025. We love open source and collaboration ❤️
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.
