{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Semantic Router routes LLM requests to predefined decision paths using vector embeddings instead of waiting for slow LLM generations, enabling fast semantic-based intent classification.","skillfed_tags":["llm-routing","semantic-search","intent-classification"],"use_cases":["Route chatbot conversations to different prompt templates or handlers based on detected topic (politics, chitchat, support requests).","Classify incoming support tickets or healthcare administrative requests into workflow categories for downstream processing.","Implement intent-based branching in LLM agents to decide which tool or API to call without waiting for LLM generation.","Filter out-of-scope queries in production chatbots by detecting when user input doesn't match any known route.","Optimize LLM pipeline latency by replacing slow LLM-based routing with fast semantic vector matching."],"what_it_does":"Semantic Router is a decision-making layer for LLM applications that classifies user intents using semantic vector embeddings rather than generating LLM responses for routing. It defines a set of named routes, each associated with example utterances; incoming queries are embedded and matched against route embeddings to determine which decision path to take. If no route matches above a threshold, it returns None.\n\nThe package integrates with embedding providers (OpenAI, Cohere, Hugging Face, FastEmbed) and vector stores (Pinecone, Qdrant), and supports dynamic routes that can call functions or generate parameters. It's designed for chatbots, agents, and LLM applications where you need to branch logic based on user intent without the latency of full LLM inference for every routing decision.","worth_installing":"Yes, with conditions. Install if you need fast semantic intent classification in an LLM application and can manage an API key for embeddings (or use local models via optional dependencies). The low install friction, active maintenance, MIT license, and zero known vulnerabilities support adoption. The substantial dependency footprint is typical for LLM packages and not a blocker. Verify that your embedding provider and vector store choices align with your deployment model (cloud vs. local)."},"id":"semantic-router","links":{"html":"https://skillfed.io/packages/semantic-router","md":"https://skillfed.io/packages/semantic-router.md","pypi":"https://pypi.org/project/semantic-router/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-26","license_spdx":"MIT","license_treatment":"permissive","name":"semantic-router","python_support":"supports_current","summary":"Super fast semantic router for AI decision making"},"popularity":{"monthly_downloads":453597,"position":6574,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.16"}