- LiteLLM - Getting Started
LiteLLM maps exceptions across all supported providers to the OpenAI exceptions All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM
- LiteLLM - GitHub
Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format - [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, Groq] - BerriAI litellm
- LiteLLM
LiteLLM streamlines the complexities of managing multiple LLM models The LiteLLM proxy has streamlined our management of LLMs by standardizing logging, the OpenAI API, and authentication for all models, significantly reducing operational complexities This enables us to quickly adapt to changing demands and swiftly adopt new models
- litellm·PyPI
liteLLM supports streaming the model response back, pass stream=True to get a streaming iterator in response Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc )
- LiteLLM: A Unified Interface for LLM APIs - Medium
LiteLLM is an open-source library that provides a unified interface to call various Large Language Model (LLM) APIs using the same format It’s designed to be a lightweight,
- LiteLLM: An open-source gateway for unified LLM access
LiteLLM is designed as a universal adapter for LLM APIs, allowing developers to interact with various providers through a standardized interface The project supports leading LLM
- Centralizing Multiple AI Services with LiteLLM Proxy
In this article I cover how to setup and configure LiteLLM to access multiple language models from commercial service providers, including OpenAI (via Azure), Anthropic, Meta, Cohere and Mistral
- How to Deploy Lightweight Language Models on Embedded Linux with LiteLLM
Deploying LiteLLM, an open source LLM gateway, on embedded Linux unlocks the ability to run lightweight AI models in resource-constrained environments Acting as a flexible proxy server, LiteLLM provides a unified API interface that accepts OpenAI-style requests — allowing you to interact with local or remote models using a consistent
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