Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, etc.]
LiteLLM manages:
- Translate inputs to provider's
completion,embedding, andimage_generationendpoints - 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: Use docker images with the -stable tag. These have undergone 12 hour load tests, before being published.
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
LiteLLM v1.40.14+ now requires pydantic>=2.0.0. No changes required.
pip install litellmfrom 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 Lunary, Langfuse, DynamoDB, s3 Buckets, Helicone, Promptlayer, Traceloop, Athina, Slack
from litellm import completion
## set env variables for logging tools
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
os.environ["OPENAI_API_KEY"]
# set callbacks
litellm.success_callback = ["lunary", "langfuse", "athina"] # log input/output to lunary, langfuse, supabase, athina etc
#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])OpenAI Proxy - (Docs)
Track spend + Load Balance across multiple projects
The proxy provides:
📖 Proxy Endpoints - Swagger Docs
pip install 'litellm[proxy]'$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on https://0.0.0.0:4000import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="https://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)
Connect the proxy with a Postgres DB to create proxy keys
# Get the code
git clone https://github.com/BerriAI/litellm
# Go to folder
cd litellm
# Add the master key
echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
source .env
# Start
docker-compose upUI on /ui on your proxy server
Set budgets and rate limits across multiple projects
POST /key/generate