🚅 LiteLLM

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

Create OpenAI Proxy Server

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.

10/05/2023: LiteLLM is adopting Semantic Versioning for all commits. Learn more
10/16/2023: Self-hosted OpenAI-proxy server Learn more

Usage (Docs)

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)

Streaming (Docs)

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.)

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'])

Supported Provider (Docs)

Provider	Completion	Streaming	Async Completion	Async Streaming
openai	✅	✅	✅	✅
cohere	✅	✅	✅	✅
anthropic	✅	✅	✅	✅
replicate	✅	✅	✅	✅
huggingface	✅	✅	✅	✅
together_ai	✅	✅	✅	✅
openrouter	✅	✅	✅	✅
vertex_ai	✅	✅	✅	✅
palm	✅	✅	✅	✅
ai21	✅	✅	✅	✅
baseten	✅	✅	✅	✅
azure	✅	✅	✅	✅
sagemaker	✅	✅	✅	✅
bedrock	✅	✅	✅	✅
vllm	✅	✅	✅	✅
nlp_cloud	✅	✅	✅	✅
aleph alpha	✅	✅	✅	✅
petals	✅	✅	✅	✅
ollama	✅	✅	✅	✅
deepinfra	✅	✅	✅	✅

Read the Docs

Logging Observability - Log LLM Input/Output (Docs)

LiteLLM exposes pre defined callbacks to send data to LLMonitor, Langfuse, Helicone, Promptlayer, Traceloop, Slack

from litellm import completion

## set env variables for logging tools
os.environ["PROMPTLAYER_API_KEY"] = "your-promptlayer-key"
os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"

os.environ["OPENAI_API_KEY"]

# set callbacks
litellm.success_callback = ["promptlayer", "llmonitor"] # log input/output to promptlayer, llmonitor, supabase

#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])

Contributing

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

Support / talk with founders

Schedule Demo 👋
Community Discord 💭
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 and Cohere.

SlapDrone/litellm