Does LiteLLM support asynchronous operations?

Yes, LiteLLM provides acompletion() for asynchronous calls and supports both sync and async streaming for all compatible providers.

What is the primary benefit of using LiteLLM?

LiteLLM provides a single, unified Python interface to call over 100 LLM APIs, allowing developers to switch between providers or use local models without changing their core implementation.

Does LiteLLM support standardized error handling?

Yes, LiteLLM maps error codes from all supported providers to standard OpenAI exception types, such as RateLimitError, AuthenticationError, and BadRequestError.

How do I connect LiteLLM to a local llamafile server?

You must use the 'llamafile/' prefix in the model name and point the api_base to 'http://localhost:8080/v1', ensuring the /v1 suffix is included for OpenAI compatibility.

Can I use LiteLLM for cost tracking?

LiteLLM includes built-in cost tracking and usage monitoring, providing automatic calculations for token usage across different provider pricing structures.

LiteLLM Integration

Name: LiteLLM Integration
Author: Jamie-BitFlight

byJamie-BitFlight

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Data Science & ML

Simplifies LLM API integration by providing a unified Python interface for over 100 providers using a consistent OpenAI-compatible format.

About

The LiteLLM skill empowers Claude to assist developers in building robust, provider-agnostic AI applications. It standardizes interactions across major cloud providers like OpenAI, Anthropic, and AWS Bedrock, as well as local servers like llamafile, through a single completion function. By providing expert guidance on standardized exception mapping, built-in retry logic, and specific llamafile routing patterns, this skill ensures that developers can switch between models or implement fallback strategies with minimal code changes.

Key Features

Seamless local LLM integration via llamafile and Ollama routing
11 GitHub stars
Unified completion() and acompletion() functions for 100+ LLM providers
Built-in retry and fallback logic for resilient application development
Standardized OpenAI-style exception mapping for consistent error handling
Automatic cost tracking and usage monitoring across multiple providers

Use Cases

Routing requests across multiple cloud and local LLM providers using a single interface
Configuring local development environments with llamafile for privacy or offline testing
Implementing production-grade error handling and retry logic for LLM-powered apps

About

Key Features

Seamless local LLM integration via llamafile and Ollama routing
11 GitHub stars
Unified completion() and acompletion() functions for 100+ LLM providers
Built-in retry and fallback logic for resilient application development
Standardized OpenAI-style exception mapping for consistent error handling
Automatic cost tracking and usage monitoring across multiple providers

Use Cases

Routing requests across multiple cloud and local LLM providers using a single interface
Configuring local development environments with llamafile for privacy or offline testing
Implementing production-grade error handling and retry logic for LLM-powered apps