Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]

LiteLLM manages

• Translating inputs to the provider's completion and embedding endpoints
• Guarantees consistent 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

pip install litellm 

from litellm import completion import os ## set ENV variables  os.environ["OPENAI_API_KEY"] = "your-openai-key" os.environ["COHERE_API_KEY"] = "your-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=messages) print(response)

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

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'])

LiteLLM supports caching completion() and embedding() calls for all LLMs. Hosted Cache LiteLLM API

import litellm from litellm.caching import Cache import os litellm.cache = Cache() os.environ['OPENAI_API_KEY'] = "" # add to cache response1 = litellm.completion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "why is LiteLLM amazing?"}], caching=True ) # returns cached response response2 = litellm.completion( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "why is LiteLLM amazing?"}], caching=True ) print(f"response1: {response1}") print(f"response2: {response2}")

Spin up a local server to translate openai api calls to any non-openai model (e.g. Huggingface, TogetherAI, Ollama, etc.)

This works for async + streaming as well.

litellm --model <model_name>

Running your model locally or on a custom endpoint ? Set the --api-base parameter see how

Read the Docs

To contribute: Clone the repo locally -> Make a change -> Submit a PR with the change.

Here's how to modify the repo locally: Step 1: Clone the repo

git clone https://github.com/BerriAI/litellm.git 

Step 2: Navigate into the project, and install dependencies:

cd litellm poetry install 

Step 3: Test your change:

cd litellm/tests # pwd: Documents/litellm/litellm/tests pytest . 

Step 4: Submit a PR with your changes! 🚀

• push your fork to your github repo
• submit a PR from there

Learn more on how to make a PR

• Schedule Demo 👋
• Community Discord 💭
• Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
• Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

• Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI, Cohere