Overview:
LiteLLM is an open source AI Gateway that provides a unified interface for calling over 100 large language model (LLM) providers, including OpenAI, Anthropic, Gemini, Bedrock, and Azure. It is designed to simplify the management of multiple LLM API calls by offering a single OpenAI-compatible format, eliminating the need for provider-specific SDKs. You can use it either as a Python SDK for direct integration into your codebase, or deploy the AI Gateway (Proxy Server) as a centralized service for an organization. It is built for teams and developers who need a production-ready gateway with features like virtual keys, spend tracking, and load balancing.
Core Features:
Unified API for 100+ LLMs: Access a wide range of providers through a single, consistent interface using the OpenAI format, supporting endpoints like
/chat/completions,/embeddings,/images, and more.AI Gateway (Proxy Server): A centralized service with authentication (virtual keys), multi-tenant cost tracking, spend management, per-project logging and guardrails, caching, and an admin dashboard UI.
Python SDK: Direct library integration for developers, including a Router with retry/fallback logic, application-level load balancing, cost tracking, and observability callbacks (e.g., Lunary, MLflow, Langfuse).
A2A Agent Gateway: Invoke and manage A2A Agents (e.g., LangGraph, Vertex AI Agent Engine) through the AI Gateway, supporting a client-to-agent protocol.
MCP Gateway: Connect MCP servers to any LLM, allowing you to call MCP tools via the standard
/chat/completionsendpoint.Enterprise Features: Support for Single Sign-On (SSO), custom SLAs, professional support, and feature prioritization under a commercial license.
Use Cases:
Gen AI Enablement / ML Platform Teams: Deploy a centralized LLM gateway for a team or organization to manage access, track costs, and apply guardrails across multiple projects and users.
Developers building LLM projects: Integrate LiteLLM directly into Python code to streamline calls to various providers, handle retries and fallbacks, and monitor usage.
System administrators managing multi-provider LLM access: Set up the Proxy Server with virtual keys for secure access control and an admin dashboard for monitoring and configuration.
Teams needing to connect external tools to LLMs: Use the MCP Gateway to extend LLM capabilities by integrating with Model Context Protocol (MCP) servers, making their tools callable via chat completions.
Why It Matters:
LiteLLM addresses a practical friction point in LLM development: managing different SDKs, authentication patterns, and request formats across multiple providers. By providing a single, OpenAI-compatible interface, it reduces code complexity when switching or testing models. The project’s value lies in its dual-mode flexibility—whether used as a lightweight Python SDK or a full-fledged self-hosted proxy with admin features like virtual keys, cost tracking, and load balancing. Its benchmarked 8ms P95 latency at 1k RPS makes it a pragmatic choice for teams needing a maintainable, scalable AI gateway without vendor lock-in.
