LiteLLM

LiteLLM gives you a unified interface to access multiple LLMs, track LLM usage, setup guardrails, custom logging and more.

Installation

First, make sure you have the litellm library installed, if not you can install it by running:

pip install litellm

Basic Completion Example

This example shows you how to make a simple completion request to a Featherless AI model.

Completion Example

from litellm import completion
response = completion(
    model="featherless_ai/featherless-ai/Qwerky-72B", # Example model. [1]
    api_key="YOUR FEATHERLESS API KEY",
    messages=[{"role": "user", "content": "Write a short poem about AI."}]
)
print(response.choices[0].message.content)

Streaming Example

LiteLLM also supports streaming responses

Streaming Example

from litellm import completion

response_stream = completion(
    model="featherless_ai/featherless-ai/Qwerky-72B", # Example model. [1]
    api_key="YOUR FEATHERLESS API KEY",
    messages=[{"role": "user", "content": "Tell me a fun fact about space"}],
    stream=True
)

print("Streaming response:")
for chunk in response_stream:
    if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="") # Print content of the chunk
print("\n\nStreaming complete.")

Resources

LiteLLM Featherless docs

The LiteLLM Featherless documentation

LiteLLM Featherless Cookbook

A notebook to guide you on how to setup LiteLLM with Featherless

Model Catalog

Our catalog of models

Last edited: Jun 16, 2025