What is the LiteLLM skill for Claude Code?

The LiteLLM skill provides domain-specific guidance and code patterns for using the LiteLLM library within Claude Code to interact with over 100 different LLM providers using a single interface.

Does LiteLLM handle errors consistently across different providers?

Yes, one of the core features of LiteLLM is mapping provider-specific errors to standard OpenAI exception types like RateLimitError or AuthenticationError.

Does LiteLLM support cost tracking?

Yes, LiteLLM includes built-in functionality to track token usage and calculate costs across various providers automatically.

Can I use LiteLLM for asynchronous programming?

Yes, LiteLLM provides an 'acompletion()' function specifically for asynchronous LLM calls and streaming responses.

How do I connect to a local llamafile server using this skill?

When using LiteLLM, ensure you use the 'llamafile/' prefix for the model name and set the 'api_base' to your local endpoint (usually http://localhost:8080/v1).

LiteLLM Interface

Name: LiteLLM Interface
Author: Jamie-BitFlight

byJamie-BitFlight

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Data Science & ML

Simplifies LLM interactions by providing a unified Python interface for 100+ AI providers with consistent OpenAI-format syntax.

About

LiteLLM acts as a universal adapter for Large Language Model APIs, allowing developers to switch between providers like OpenAI, Anthropic, and local llamafile servers without changing their core code structure. It streamlines development by offering standardized exception mapping, built-in retry logic, and cost tracking across all supported models. This skill is essential for building resilient AI applications that require provider flexibility, hybrid cloud-local workflows, or centralized LLM proxy management.

Key Features

Seamless integration with local llamafile and Ollama servers
Built-in retry/fallback logic and request timeout management
10 GitHub stars
Unified completion() function for 100+ cloud and local providers
Standardized OpenAI-compatible exception mapping and error handling
Automatic usage tracking and cost calculation across different APIs

Use Cases

Switching between cloud providers and local models for development and production
Building a centralized LLM proxy gateway for unified configuration and cost management
Implementing robust error handling and automatic retries for resilient AI features

About

Key Features

Seamless integration with local llamafile and Ollama servers
Built-in retry/fallback logic and request timeout management
10 GitHub stars
Unified completion() function for 100+ cloud and local providers
Standardized OpenAI-compatible exception mapping and error handling
Automatic usage tracking and cost calculation across different APIs

Use Cases

Switching between cloud providers and local models for development and production
Building a centralized LLM proxy gateway for unified configuration and cost management
Implementing robust error handling and automatic retries for resilient AI features