Does LiteLLM support local models?

Yes, it provides native support for local providers like llamafile, Ollama, and LocalAI, treating them as OpenAI-compatible endpoints.

Can I use LiteLLM for cost tracking?

Yes, LiteLLM includes built-in features for tracking token usage and calculating costs across different providers automatically.

What is the main benefit of using LiteLLM?

LiteLLM allows you to use a single code pattern to interact with over 100 different LLM providers, making it easy to switch models or providers without rewriting your integration logic.

How does LiteLLM handle errors across different providers?

It maps diverse provider-specific exceptions to standard OpenAI error types, ensuring your error-handling logic remains consistent regardless of the backend.

Does it support asynchronous operations?

Yes, LiteLLM provides both synchronous completion() and asynchronous acompletion() functions, including support for streaming responses.

LiteLLM Integration

Name: LiteLLM Integration
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Simplifies Python-based LLM interactions by providing a unified interface for over 100 cloud and local providers using the OpenAI format.

About

LiteLLM provides a standardized Python wrapper that allows developers to call APIs from multiple LLM providers—including Anthropic, OpenAI, Azure, and local servers like llamafile—using a single, consistent OpenAI-compatible format. It eliminates the complexity of provider-specific SDKs by offering unified exception mapping, automatic retry and fallback logic, and built-in cost tracking. This skill is essential for building resilient, provider-agnostic AI applications and seamless local-to-cloud development workflows within Claude Code.

Key Features

Built-in retry and fallback logic for resilient API integration
3 GitHub stars
Native support for local llamafile and Ollama server routing
Automatic usage monitoring and cost tracking across all providers
Standardized exception mapping to OpenAI-style error types
Unified completion() function for 100+ cloud and local LLM providers

Use Cases

Implementing robust error handling for production AI applications
Routing requests to local LLM servers for privacy-focused development
Switching between cloud providers like Anthropic and OpenAI without code changes

About

Key Features

Built-in retry and fallback logic for resilient API integration
3 GitHub stars
Native support for local llamafile and Ollama server routing
Automatic usage monitoring and cost tracking across all providers
Standardized exception mapping to OpenAI-style error types
Unified completion() function for 100+ cloud and local LLM providers

Use Cases

Implementing robust error handling for production AI applications
Routing requests to local LLM servers for privacy-focused development
Switching between cloud providers like Anthropic and OpenAI without code changes