The best libraries for calling LLM APIs from Python, compared honestly.
Calling LLM APIs from Python means choosing between official SDKs (OpenAI, Anthropic), multi-provider abstraction libraries (LiteLLM, aisuite), or building your own thin wrapper. Each approach trades off simplicity, flexibility, and vendor independence.
#1FastAPI AI Kit's Unified LLM Layer
This kitA thin abstraction over OpenAI and Anthropic SDKs built into FastAPI AI Kit. Swap providers with an env var, automatic token tracking, and streaming support.
Pros
- Minimal abstraction — direct SDK calls under the hood, easy to debug
- Token tracking and billing metering built in at the call layer
- Streaming support for both OpenAI and Anthropic with a unified interface
Cons
- Supports OpenAI and Anthropic by default — adding other providers requires extension
- Not a standalone library — part of the FastAPI AI Kit codebase
- Less provider coverage than LiteLLM
Best for: Teams using FastAPI AI Kit who want simple, debuggable LLM calls with built-in billing.
#2Official SDKs (openai, anthropic)
The official Python SDKs from OpenAI and Anthropic. Direct, well-documented, and always up-to-date with the latest API features.
Pros
- First-party support — always current with the latest API features
- Excellent documentation and type hints
- No abstraction overhead — you know exactly what's happening
Cons
- Each provider has a different API — switching requires code changes
- No built-in token tracking or cost metering
- No provider fallback or routing logic
Best for: Teams committed to a single LLM provider who want the simplest, most direct integration.
#3LiteLLM
A multi-provider LLM abstraction library that provides a unified interface for 100+ LLM providers with OpenAI-compatible API format.
Pros
- Supports 100+ providers with a single interface
- OpenAI-compatible format makes migration easy
- Built-in spend tracking and rate limiting
Cons
- Adds a dependency and abstraction layer
- Some provider-specific features are lost in the abstraction
- Can lag behind official SDKs for newest features
Best for: Teams that need to support many LLM providers or want maximum provider flexibility.
#4aisuite (by Andrew Ng)
A lightweight multi-provider library that provides a simple, consistent interface for calling LLMs. More minimal than LiteLLM.
Pros
- Very simple, lightweight API
- Backed by Andrew Ng's team — credible AI/ML pedigree
- Easy to understand — minimal abstraction
Cons
- Newer project with a smaller community
- Fewer features than LiteLLM (no built-in spend tracking)
- Limited streaming support in some providers
Best for: Teams who want multi-provider support with the least possible abstraction.
Our take
For production AI APIs, start with official SDKs or FastAPI AI Kit's built-in abstraction. Add LiteLLM when you genuinely need 10+ providers. Avoid adding an abstraction layer until you have a concrete reason — most products only use 1–2 LLM providers.
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