LiteLLM

Features

• Unified API Format: LiteLLM allows developers to call a diversity of LLM APIs, including OpenAI, Cohere, and Azure, using a unified OpenAI-styled request format.
  
• Multi-Provider Support: Supports a wide range of language models from different providers, facilitating integration with more than 100 different LLMs.
  
• Consistent Output Structure: Ensures consistent response structures across different providers, easing integration and parsing efforts.
  
• Retry/Fallback Logic: Implements logic to retry or fallback to other deployments, enhancing reliability of language model API calls.
  
• Logging and Observability: Offers in-built callback features for logging to tools like Langfuse, DynamoDB, and Slack for better monitoring.
  
• Proxy Server Capabilities: Includes a proxy server to manage hooks for authentication and logging, cost tracking, and rate limiting.
  

Use Cases:

• Multi-Platform AI Applications: Ideal for developers who require access to multiple language models across different platforms without changing the interfacing code.
  
• Cost Tracking and Budget Management: Organizations can track spending across LLMs and set budgets using proxy key management features.
  
• Enhanced Reliability and Redundancy: Applications that require high uptime and reliability can use LiteLLM's retry/fallback features for consistent performance.
  
• AI Research and Development: Researchers can easily compare and contrast different language models by interfacing with multiple LLM APIs through a single interface.
  

LiteLLM streamlines the integration of various large language models by providing a unified interface and a suite of features that enhance functionality and developer experience.