litellm

Managing multiple LLM providers with differing APIs, authentication, and request formats quickly becomes complex for engineering teams. This project offers a unified Python SDK and a proxy server that standardises calls to over 100 LLM APIs using the OpenAI API format, reducing integration overhead. It includes features like cost tracking, guardrails, load balancing, and an admin dashboard, which are typically missing in simple SDK wrappers. The project is actively maintained with a recent commit in May 2026 and has gained a significant following with 46,000+ stars, indicating strong community interest. It is particularly useful for ML engineers and tech leads who want to avoid vendor lock-in and simplify multi-provider LLM orchestration. However, teams looking for a lightweight client library without the complexity of a proxy or advanced management features might prefer simpler alternatives like OpenAI's official SDK or LangChain for chaining models rather than this gateway approach.

Choose this when your project needs to integrate multiple LLM providers behind a single, OpenAI-compatible interface with centralized usage control and monitoring. If you only need to interact with one or two providers without the need for proxy-based management, consider using the OpenAI SDK or LangChain, which are simpler and more established for direct model calls and prompt chaining.

Frequently deployed in organisations that require a centralised LLM gateway to manage API keys, cost tracking, and failover across providers. Common use cases include internal AI platforms that support various LLM backends for chatbots, embeddings, or multimodal tasks like image and audio generation, where switching providers without code changes is critical.

Latest commit: 2026-05-10. Activity data is based on daily RepoPi snapshots of the GitHub repository.