vllm-sr
vLLM Semantic Router - Intelligent routing for Mixture-of-Models
Decision gist · record as of 2026-08-14
Yes, if you need to route requests across multiple LLM models and want a batteries-included solution with dashboard, observability, and Kubernetes support. The low install friction and active maintenance are positive signals. Verify first whether you need the full Docker/Kubernetes stack or if a lighter routing library would suffice, and confirm that the package supports your deployment model (local Docker, Kubernetes, or programmatic library use).AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Docker is required on Linux, macOS, or WSL2 to run `vllm-sr serve`; native Windows Python environments can use the CLI for configuration and validation only.
- Python >= 3.10 is required.
- Low install friction with a pure-Python wheel and standard dependencies.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and note material changes.
last release 2026-06-05 (70 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 75,567 downloads/mo, #14,701 on PyPI
Alternatives
Verify before relying
# Install
pip install vllm-sr
# Start the router (requires Docker on Linux/macOS/WSL2)
export HF_TOKEN=hf_xxx
vllm-sr serve
# In another terminal, evaluate a prompt
vllm-sr eval --prompt "Explain inflation vs recession in plain English."
# Or send a chat message through the router
vllm-sr chat "hello"- Whether the package works as a library (programmatic API) or is primarily a CLI tool
- Performance characteristics and latency overhead of the routing layer
- Whether Kubernetes deployment requires a running vLLM cluster or if it manages model serving
What it is and what it does
vLLM Semantic Router is a request router for systems that serve multiple large language models. It intercepts incoming requests, classifies them by semantic intent using signals (domains, keywords, embeddings), and forwards them to the appropriate model based on routing rules you define. The package provides a CLI that starts a local Docker-based stack including the router, an Envoy proxy, a web dashboard, and a fleet simulator, plus support for deploying the same configuration to Kubernetes via Helm.
The router exposes a chat completions API and integrates with observability tools (Jaeger, Prometheus, Grafana) for tracing and metrics. Configuration is YAML-first; you define listeners, model providers, routing decisions, and optional plugins (caching, memory, system prompt injection, hallucination detection) in a canonical config file. The dashboard persists state locally and can run multiple isolated stacks on the same machine using environment variables to offset ports and isolate container names.
Use it for
- Route domain-specific queries (e.g., economics questions) to specialized models trained on that domain
- Load-balance requests across multiple models to reduce latency and cost by selecting the smallest model that can handle each request
- Cache semantically similar requests to avoid redundant LLM calls and improve response time
- Deploy a multi-model inference system to Kubernetes with automatic credential handling and observability built in
- Test and validate routing decisions interactively via the CLI (vllm-sr eval) before deploying to production
- Monitor model usage, latency, and routing decisions through embedded Grafana dashboards and Prometheus metrics
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to route requests across multiple LLM models and want a batteries-included solution with dashboard, observability, and Kubernetes support.
The low install friction and active maintenance are positive signals. Verify first whether you need the full Docker/Kubernetes stack or if a lighter routing library would suffice, and confirm that the package supports your deployment model (local Docker, Kubernetes, or programmatic library use).
Install
vllm-sr on PyPI
Before you install
Low install friction with a pure-Python wheel and standard dependencies. Active maintenance as of 70 days ago with support for Python 3.10–3.12. Docker is required for the local serve command on Linux, macOS, or WSL2; native Windows Python environments can use the CLI for configuration only.
Docker is required on Linux, macOS, or WSL2 to run `vllm-sr serve`; native Windows Python environments can use the CLI for configuration and validation only. Python >= 3.10 is required.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and note material changes.
Quickstart
# Install
pip install vllm-sr
# Start the router (requires Docker on Linux/macOS/WSL2)
export HF_TOKEN=hf_xxx
vllm-sr serve
# In another terminal, evaluate a prompt
vllm-sr eval --prompt "Explain inflation vs recession in plain English."
# Or send a chat message through the router
vllm-sr chat "hello"Verify before relying
- Whether the package works as a library (programmatic API) or is primarily a CLI tool
- Performance characteristics and latency overhead of the routing layer
- Whether Kubernetes deployment requires a running vLLM cluster or if it manages model serving
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesclickpyyamljinja2requestspydantichuggingface_hub |
| Maintenance | Actively maintained 70 days since the last release |
| First released | |
| Downloads | 75,567 / month, #14,701 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: vllm_sr-0.3.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “llm request routing”
- vllm-srRoutes LLM requests to appropriate models in a mixture-of-models…
- semantic-routerSemantic Router routes LLM requests to predefined decision paths…
- vllm-routerRoutes and load-balances requests across vLLM worker instances with…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also semantic-router · vllm-router · sglang-router · vllm · vllm-tpu · chatlas · jupyter-ai-router · litellm-enterprise · llama-index-llms-openai