# *🚅 litellm* [![PyPI Version](https://img.shields.io/pypi/v/litellm.svg)](https://pypi.org/project/litellm/) [![PyPI Version](https://img.shields.io/badge/stable%20version-v0.1.424-blue?color=green&link=https://pypi.org/project/litellm/0.1.1/)](https://pypi.org/project/litellm/0.1.1/) [![CircleCI](https://dl.circleci.com/status-badge/img/gh/BerriAI/litellm/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/gh/BerriAI/litellm/tree/main) ![Downloads](https://img.shields.io/pypi/dm/litellm) [![](https://dcbadge.vercel.app/api/server/wuPM9dRgDw)](https://discord.gg/wuPM9dRgDw) a light package to simplify calling OpenAI, Azure, Cohere, Anthropic, Huggingface API Endpoints. It manages: - translating inputs to the provider's completion and embedding endpoints - guarantees [consistent output](https://litellm.readthedocs.io/en/latest/output/), text responses will always be available at `['choices'][0]['message']['content']` - exception mapping - common exceptions across providers are mapped to the [OpenAI exception types](https://help.openai.com/en/articles/6897213-openai-library-error-types-guidance) # usage None Demo - https://litellm.ai/playground Docs - https://docs.litellm.ai/docs/ **Free** Dashboard - https://docs.litellm.ai/docs/debugging/hosted_debugging ## quick start ``` pip install litellm ``` ```python from litellm import completion ## set ENV variables os.environ["OPENAI_API_KEY"] = "openai key" os.environ["COHERE_API_KEY"] = "cohere key" messages = [{ "content": "Hello, how are you?","role": "user"}] # openai call response = completion(model="gpt-3.5-turbo", messages=messages) # cohere call response = completion(model="command-nightly", messages) ``` Code Sample: [Getting Started Notebook](https://colab.research.google.com/drive/1gR3pY-JzDZahzpVdbGBtrNGDBmzUNJaJ?usp=sharing) Stable version ``` pip install litellm==0.1.424 ``` ## Streaming Queries liteLLM supports streaming the model response back, pass `stream=True` to get a streaming iterator in response. Streaming is supported for OpenAI, Azure, Anthropic, Huggingface models ```python response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) for chunk in response: print(chunk['choices'][0]['delta']) # claude 2 result = completion('claude-2', messages, stream=True) for chunk in result: print(chunk['choices'][0]['delta']) ``` # support / talk with founders - [Our calendar 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) - Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai # why did we build this - **Need for simplicity**: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI, Cohere