lm-sys/RouteLLM

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.

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