Loading patterns…
LLM-based Routing(LBR)
An intelligent query distribution system that uses a specialized LLM router to analyze incoming requests and dynamically route them to the most appropriate model, API endpoint, or processing pipeline based on query characteristics, ensuring optimal resource utilization and response quality through intent classification and capability matching
In 30 seconds
- What
- Routes incoming requests to different models or endpoints by analyzing query intent and matching it against known capabilities.
- When to use
- Systems with multiple specialized models, APIs, or pipelines where different request types need different processing paths.
- Watch out
- Router misclassification sends requests to wrong endpoints, potentially degrading quality or requiring expensive fallback handling.
Ask the AI expert about this pattern
Opens the assistant with your question prefilled. You review it before sending.
LLM-based Routing: Overview
An intelligent query distribution system that uses a specialized LLM router to analyze incoming requests and dynamically route them to the most appropriate model, API endpoint, or processing pipeline based on query characteristics, ensuring optimal resource utilization and response quality through intent classification and capability matching
- Intent classification and query understanding
- Multi-model orchestration with capability awareness
- Cost-performance optimization through smart routing
- Latency-aware routing for real-time applications
- Fallback mechanisms and graceful degradation
- A/B testing and performance monitoring
- Custom routing rules and domain-specific logic
- Load balancing across model instances
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.
References
The papers, specifications, and repositories this pattern is based on.
- RouteLLM: Learning to Route LLMs with Preference Data (2024)arXiv:2406.18665
- Building Effective Agents - Anthropic (2024)
- A Survey on LLM-based Multi-Agent Systems (2024)
- A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and ApplicationsarXiv:2402.07927
- LangChain Routing Documentation
From the engineer behind this catalog
Get your agent architecture reviewed
This page documents one pattern. Your system runs dozens, and most failures live in how they fit together. Have the whole design reviewed against the 288 patterns in this catalog: architecture, reliability, evaluation and cost, every finding mapped to the pattern that fixes it.
€750 instead of €1,500, one week, written report and walkthrough call, until 30 September