Semantic Router is a decision-making layer for large language models and AI agents that uses semantic vector space to route requests without waiting for slow LLM generations.
The tool solves the problem of inefficient tool-use decisions in LLM pipelines by replacing generation-based routing with semantic similarity matching. Rather than having an LLM generate text to decide which action to take, Semantic Router embeds user queries and compares them against pre-defined route embeddings in vector space, making routing decisions nearly instantaneously. Users define Route objects representing decision paths, initialize an encoder model, and create a RouteLayer that performs the semantic matching. When a query arrives, the layer returns the matching route or None if no suitable match exists.
Adoption suits teams building LLM applications and agents where decision latency matters and routing logic can be expressed through semantic examples. The tool works well for systems that need to classify user intent or direct requests to specific handlers before invoking expensive LLM calls. It supports multiple embedding providers including Cohere, OpenAI, Hugging Face, and FastEmbed, with options for fully local execution using HuggingFaceEncoder and LlamaCppLLM. Multi-modal routing is supported for applications processing images alongside text. A HybridRouteLayer variant is available for more complex routing scenarios.
The project maintains active development with regular commits and ongoing issue resolution. The codebase shows consistent expansion of encoder integrations and documentation coverage. Pull requests are regularly processed, indicating responsive maintenance of contributed changes.
