An Mixture-of-Models (MoM) router that intelligently directs OpenAI API requests to the most suitable models from a defined pool based on Semantic Understanding of the request's intent (Complexity, Task, Tools).
This is achieved using BERT classification. Conceptually similar to Mixture-of-Experts (MoE) which lives within a model, this system selects the best entire model for the nature of the task.
As such, the overall inference accuracy is improved by using a pool of models that are better suited for different types of tasks:
The screenshot below shows the LLM Router dashboard in Grafana.
The router is implemented in two ways:
- Golang (with Rust FFI based on the candle rust ML framework)
- Python Benchmarking will be conducted to determine the best implementation.
Select the tools to use based on the prompt, avoiding the use of tools that are not relevant to the prompt so as to reduce the number of prompt tokens and improve tool selection accuracy by the LLM.
Automatically inject specialized system prompts based on query classification, ensuring optimal model behavior for different domains (math, coding, business, etc.) without manual prompt engineering.
Detect PII in the prompt, avoiding sending PII to the LLM so as to protect the privacy of the user.
Detect if the prompt is a jailbreak prompt, avoiding sending jailbreak prompts to the LLM so as to prevent the LLM from misbehaving.
Cache the semantic representation of the prompt so as to reduce the number of prompt tokens and improve the overall inference latency.
Comprehensive observability with OpenTelemetry distributed tracing provides fine-grained visibility into the request processing pipeline:
- Request Flow Tracing: Track requests through classification, security checks, caching, and routing
- Performance Analysis: Identify bottlenecks with detailed timing for each operation
- Security Monitoring: Trace PII detection and jailbreak prevention operations
- Routing Decisions: Understand why specific models were selected
- OpenTelemetry Standard: Industry-standard tracing with support for Jaeger, Tempo, and other OTLP backends
See Distributed Tracing Guide for complete setup instructions.
For 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
- Distributed Tracing - Observability and debugging guide
For questions, feedback, or to contribute, please join #semantic-router channel in vLLM Slack.
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 ❤️