A programmable Mixture-of-Models router for heterogeneous LLM inference
5,731
stars
1,824
commits
Go
primary language
Sep 11, 2026
updated
Make Your Mixture-of-Models Programmable.
Documentation | Playground | Blog | Publications | Hugging Face | Slack
vLLM Semantic Router is a programmable routing layer for building Mixture-of-Models systems across heterogeneous LLM infrastructure. It evaluates request signals, user preferences, and application policies to select—or compose—the right model path for each request.
Use it to improve quality, cost, latency, privacy, and safety without hard-coding routing logic into applications.
| Dimension | Fragmented today | With vLLM SR |
|---|---|---|
| Models | Models specialize in different work. | Compose personalized model paths. |
| Compute | GPUs, accelerators, edge, and cloud coexist. | Route across heterogeneous compute. |
| Location | Inference spans edge, private, and cloud. | Keep data within its boundaries. |
| Preference | "Best" changes by user and workload. | Make every preference executable. |
curl -fsSL https://vllm-sr.ai/install.sh | bash
For platform notes, detailed setup options, and troubleshooting, see the Installation Guide.
love@vllm-sr.aivllm-srMore announcements are available on the Blog and Publications pages.
For questions, feedback, or to contribute, please join the #semantic-router channel in vLLM Slack.
We host community meetings on the first and third Tuesday of each month to sync with contributors across different time zones:
If you want to contribute, start with CONTRIBUTING.md.
For repository-native development workflow and validation commands, use AGENTS.md as the entrypoint and tools/agent/docs/README.md as the canonical index.
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 are grateful to our sponsors who support us:
AMD provides us with GPU resources and ROCm™ software for training and researching frontier router models, enhancing E2E testing, and building the online models playground.
(top 30 of 159)
Go
46.3%
Python
20.2%
TypeScript
15.6%
Rust
7.8%
CSS
3.8%
Shell
2.0%
TeX
1.2%
A programmable Mixture-of-Models router for heterogeneous LLM inference
5,731
stars
1,824
commits
Go
primary language
Sep 11, 2026
updated
Make Your Mixture-of-Models Programmable.
Documentation | Playground | Blog | Publications | Hugging Face | Slack
vLLM Semantic Router is a programmable routing layer for building Mixture-of-Models systems across heterogeneous LLM infrastructure. It evaluates request signals, user preferences, and application policies to select—or compose—the right model path for each request.
Use it to improve quality, cost, latency, privacy, and safety without hard-coding routing logic into applications.
| Dimension | Fragmented today | With vLLM SR |
|---|---|---|
| Models | Models specialize in different work. | Compose personalized model paths. |
| Compute | GPUs, accelerators, edge, and cloud coexist. | Route across heterogeneous compute. |
| Location | Inference spans edge, private, and cloud. | Keep data within its boundaries. |
| Preference | "Best" changes by user and workload. | Make every preference executable. |
curl -fsSL https://vllm-sr.ai/install.sh | bash
For platform notes, detailed setup options, and troubleshooting, see the Installation Guide.
love@vllm-sr.aivllm-srMore announcements are available on the Blog and Publications pages.
For questions, feedback, or to contribute, please join the #semantic-router channel in vLLM Slack.
We host community meetings on the first and third Tuesday of each month to sync with contributors across different time zones:
If you want to contribute, start with CONTRIBUTING.md.
For repository-native development workflow and validation commands, use AGENTS.md as the entrypoint and tools/agent/docs/README.md as the canonical index.
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 are grateful to our sponsors who support us:
AMD provides us with GPU resources and ROCm™ software for training and researching frontier router models, enhancing E2E testing, and building the online models playground.
(top 30 of 159)
Go
46.3%
Python
20.2%
TypeScript
15.6%
Rust
7.8%
CSS
3.8%
Shell
2.0%
TeX
1.2%