{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Routes LLM requests to appropriate models in a mixture-of-models setup based on semantic intent classification, with a CLI for local Docker deployment, Kubernetes support, and a web dashboard for monitoring and testing.","skillfed_tags":["llm-routing","kubernetes-ready","observability-included"],"use_cases":["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"],"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.\n\nThe 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.","worth_installing":"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)."},"id":"vllm-sr","links":{"html":"https://skillfed.io/packages/vllm-sr","md":"https://skillfed.io/packages/vllm-sr.md","pypi":"https://pypi.org/project/vllm-sr/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-05","license_spdx":null,"license_treatment":"permissive","name":"vllm-sr","python_support":"supports_current","summary":"vLLM Semantic Router - Intelligent routing for Mixture-of-Models"},"popularity":{"monthly_downloads":75567,"position":14701,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.0"}