🚅 LiteLLM

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

OpenAI Proxy Server | Enterprise Tier

LiteLLM manages:

• Translate inputs to provider's completion, embedding, and image_generation endpoints
• Consistent output, text responses will always be available at ['choices'][0]['message']['content']
• Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router
• Set Budgets & Rate limits per project, api key, model OpenAI Proxy Server

Jump to OpenAI Proxy Docs
Jump to Supported LLM Providers

🚨 Stable Release: v1.34.1

Support for more providers. Missing a provider or LLM Platform, raise a feature request.

Usage (Docs)

Important

LiteLLM v1.0.0 now requires openai>=1.0.0. Migration guide here

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)

Call any model supported by a provider, with model=<provider_name>/<model_name>. There might be provider-specific details here, so refer to provider docs for more information

Async (Docs)

from litellm import acompletion
import asyncio

async def test_get_response():
    user_message = "Hello, how are you?"
    messages = [{"content": user_message, "role": "user"}]
    response = await acompletion(model="gpt-3.5-turbo", messages=messages)
    return response

response = asyncio.run(test_get_response())
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.)

from litellm import completion
response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
for part in response:
    print(part.choices[0].delta.content or "")

# claude 2
response = completion('claude-2', messages, stream=True)
for part in response:
    print(part.choices[0].delta.content or "")

Logging Observability (Docs)

LiteLLM exposes pre defined callbacks to send data to Langfuse, DynamoDB, s3 Buckets, LLMonitor, Helicone, Promptlayer, Traceloop, Athina, Slack

from litellm import completion

## set env variables for logging tools
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
os.environ["ATHINA_API_KEY"] = "your-athina-api-key"

os.environ["OPENAI_API_KEY"]

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

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

OpenAI Proxy - (Docs)

Set Budgets & Rate limits across multiple projects

The proxy provides:

1. Hooks for auth
2. Hooks for logging
3. Cost tracking
4. Rate Limiting

📖 Proxy Endpoints - Swagger Docs

Quick Start Proxy - CLI

pip install 'litellm[proxy]'

Step 1: Start litellm proxy

$ litellm --model huggingface/bigcode/starcoder

#INFO: Proxy running on http://0.0.0.0:4000

Step 2: Make ChatCompletions Request to Proxy

import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

Proxy Key Management (Docs)

UI on /ui on your proxy server

Set budgets and rate limits across multiple projects POST /key/generate

Request

curl 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data-raw '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m","metadata": {"user": "[email protected]", "team": "core-infra"}}'

Expected Response

{
    "key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
    "expires": "2023-11-19T01:38:25.838000+00:00" # datetime object
}

Supported Providers (Docs)

Provider	Completion	Streaming	Async Completion	Async Streaming	Async Embedding	Async Image Generation
openai	✅	✅	✅	✅	✅	✅
azure	✅	✅	✅	✅	✅	✅
aws - sagemaker	✅	✅	✅	✅	✅
aws - bedrock	✅	✅	✅	✅	✅
google - vertex_ai [Gemini]	✅	✅	✅	✅
google - palm	✅	✅	✅	✅
google AI Studio - gemini	✅		✅
mistral ai api	✅	✅	✅	✅	✅
cloudflare AI Workers	✅	✅	✅	✅
cohere	✅	✅	✅	✅	✅
anthropic	✅	✅	✅	✅
huggingface	✅	✅	✅	✅	✅
replicate	✅	✅	✅	✅
together_ai	✅	✅	✅	✅
openrouter	✅	✅	✅	✅
ai21	✅	✅	✅	✅
baseten	✅	✅	✅	✅
vllm	✅	✅	✅	✅
nlp_cloud	✅	✅	✅	✅
aleph alpha	✅	✅	✅	✅
petals	✅	✅	✅	✅
ollama	✅	✅	✅	✅
deepinfra	✅	✅	✅	✅
perplexity-ai	✅	✅	✅	✅
Groq AI	✅	✅	✅	✅
anyscale	✅	✅	✅	✅
voyage ai					✅
xinference [Xorbits Inference]					✅

Read the Docs

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
poetry run flake8
poetry run pytest .

Step 4: Submit a PR with your changes! 🚀

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

Enterprise

For companies that need better security, user management and professional support

Talk to founders

This covers:

• ✅ Features under the LiteLLM Commercial License:
• ✅ Feature Prioritization
• ✅ Custom Integrations
• ✅ Professional Support - Dedicated discord + slack
• ✅ Custom SLAs
• ✅ Secure access with Single Sign-On

Support / talk with founders

• Schedule Demo 👋
• Community Discord 💭
• Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
• Our emails ✉️ [email protected] / [email protected]

Why did we build this

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

Contributors