LiteLLM

What is LiteLLM?

LiteLLM is an open-source platform designed to streamline the integration and management of large language models (LLMs) by providing a unified API compatible with the OpenAI format, supporting over 100 providers like OpenAI, Azure, Anthropic, and HuggingFace. Its mission is to simplify AI model access for developers and organizations, reducing complexity in handling multiple LLM APIs. The platform offers a Python SDK and a proxy server (LLM Gateway) to manage authentication, load balancing, and cost tracking across various LLM providers. It caters to developers and platform teams seeking efficient, scalable, and provider-agnostic LLM workflows. By abstracting provider-specific complexities, LiteLLM enables seamless model switching, cost optimization, and robust error handling, making it ideal for both experimentation and production-grade applications. Trusted by companies like Rocket Money and Adobe, it empowers users to focus on building AI-driven solutions without managing intricate API differences.

LiteLLM's Core Features

• Unified API format allows calling over 100 LLM APIs using OpenAI-compatible syntax, simplifying integration across providers.
• Load balancing distributes requests across multiple deployments, ensuring high availability and optimized performance.
• Cost tracking automatically monitors and logs LLM usage across providers, enabling accurate budget management.
• Streaming support delivers LLM outputs in manageable chunks, optimizing resource use for memory-intensive tasks.
• Retry and fallback logic automatically handles errors by rerouting requests to alternative models or providers, enhancing reliability.
• Virtual keys and rate limiting allow organizations to manage access and control usage for multiple developers or projects.
• Observability integrations with tools like Langfuse, Helicone, and Datadog provide insights into performance and request tracing.
• Open-source nature ensures transparency, community-driven development, and flexibility for customization.
• Provider-agnostic design mitigates vendor lock-in, allowing users to switch models without code changes.
• Enterprise features like SSO, OIDC/JWT authentication, and Prometheus metrics support large-scale deployments.
• Python SDK simplifies programmatic access to LLMs, enabling developers to build applications with minimal overhead.
• Proxy server deployment option centralizes LLM management, streamlining authentication and request routing.