lm-sys/RouteLLM
RouteLLM
RouteLLM is a routing framework and traffic manager designed to direct prompts between high-capability and low-cost large language models. It functions as an API gateway that mimics the OpenAI specification to route requests across different model providers.
The system optimizes operational costs by splitting traffic between model tiers based on predicted win rates and prompt complexity. It includes a calibration tool to analyze sample queries and determine the optimal cost-quality tradeoff for traffic distribution.
The framework provides a tool for measuring the accuracy and cost efficiency of routing strategies using benchmark datasets. It supports extensibility through interface-based plugins and custom routing logic.
Features
- LLM Gateways - Functions as a centralized LLM gateway that aggregates multiple providers into a single OpenAI-compatible API.
- OpenAI-Compatible APIs - Provides a server implementation that adheres to the OpenAI API specification for seamless integration with existing clients.
- Model Dispatchers - Directs prompts to different LLM providers in real time based on calculated complexity and cost.
- LLM Operational Cost Optimization - Optimizes operational expenses by directing prompts to the cheapest model capable of maintaining required quality.
- LLM Routing Frameworks - Provides a comprehensive system for directing prompts between high-capability and low-cost models to optimize spend.
- LLM Traffic Managers - Acts as a traffic manager that calibrates cost-quality tradeoffs by splitting traffic based on predicted win rates.
- Cost-Quality Model Routing - Implements routing logic that selects the most suitable model based on a balance of operational cost and response quality.
- Model Routing - Implements real-time routing logic to decide which specific AI model should handle an incoming query.
- OpenAI-Compatible Model Servers - Implements an API server that mimics the OpenAI specification for seamless drop-in integration with various model providers.
- Tiered Model Workload Splitting - Splits workloads between low-cost and high-capability model tiers using a calculated quality cutoff.
- Routing Threshold Calibration - Ships a tool to analyze query samples and determine the optimal cost-quality threshold for traffic splitting.
- LLM Evaluation Frameworks - Provides a framework for measuring the accuracy and cost efficiency of LLM routing strategies using systematic experiments.
- Model Performance Evaluators - Provides tools to quantify the accuracy and cost-efficiency of routing strategies against ground truth benchmarks.
- LLM Performance Evaluators - Measures the performance and accuracy of routing decisions by evaluating outputs against standardized datasets.
- API Proxies - Ships an API proxy that translates standard AI API formats to backend services without requiring client-side changes.
- LLM Traffic Calibration - Includes a calibration tool to analyze sample queries and find the ideal balance between cost and quality.
- Stateless Request Processing - Implements a stateless request processing pattern to ensure scalability across distributed serving environments.
- Model Evaluation Benchmarks - Includes a pipeline to quantify the cost and quality tradeoffs of routing strategies using standardized benchmark datasets.
- API And Routing - Framework for routing queries to cost-effective models.
- Inference and Serving - Framework for intelligent model routing to save costs.
- Inference Engines - Framework for intelligent model routing to save costs.
