LiteLLM

LiteLLM is an open-source Python library AImed at developers who work with more than one large language model provIDEr. Rather than learning a separate SDK, authentication scheme, and response fORMat for each vendor, teams can route requests through a single, consistent interface. It is commonly used to streamline prototyping, multi-provider APPlications, and backend services that need flexibility in which model they call.

What it does

The library wraps the APIs of major providers such as OpenAI, Azure openai, anthropic, and Cohere, along with many others in the broader LLM ecosystem. Developers call a small set of functions and specify the model name; LiteLLM maps that request to the correct provider endpoint and normalizes the response. This reduces boilerplate and makes it eASIer to switch models without rewriting application logic.

Key capabilities

• Unified interface: One calling convention across many providers and model families.
• Provider flexibility: Supports hosted commercial APIs as well as some self-hosted or open model backends, though exACT coveRAGe may vary by release.
• streaming and async support: Built for real-time and concurrent workloads typical of production apps.
• Retries and fallbacks: Configurable behavior for handling failures or routing to alternate models.
• Cost and usage tracking: Helpers for monitoring token usage and spend across providers.
• Proxy server option: A companion gateway can expose an OpenAI-compatible endpoint for teams that prefer centralized routing.

Who it is for

LiteLLM suits backend and ML engineers building applications that depend on LLM APIs, especially teams comparing models, manAGIng costs, or avoiding vendor lock-in. It is also useful for platform teams that want a single access layer across many internal projects. It is less relevant for non-technical users seeking a no-code tool, since it requires Python development experience.

How to get started

1. Install the package with pip in a Python environment.
2. Set the relevant provider API keys as environment variables.
3. Import the library and issue a completion call, specifying the target model.
4. Optionally configure retries, fallbacks, or run the proxy server for centralized access.
5. Review usage logs or cost-tracking utilities to monitor spending.

Pricing and access caveat

The core library is distributed as open-source software and is Generally free to use, while the underlying model providers bill for their own API usage. Feature availability, supported providers, and any optional Enterprise offerings may change over time, so check the project documentation for current details before relying on specific functionality.