š
LiteLLM
Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
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 LiteLLM Proxy Server (LLM Gateway)
Jump to LiteLLM Proxy (LLM Gateway) Docs
Jump to Supported LLM Providers
šØ 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.
[!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 litellm
from litellm import completion
import os
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["COHERE_API_KEY"] = "your-cohere-key"
messages = [{ "content": "Hello, how are you?","role": "user"}]
response = completion(model="gpt-3.5-turbo", messages=messages)
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
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 "")
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
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
os.environ["HELICONE_API_KEY"] = "your-helicone-auth-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"]
litellm.success_callback = ["lunary", "langfuse", "athina", "helicone"]
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi š - i'm openai"}])
LiteLLM Proxy Server (LLM Gateway) - (Docs)
Track spend + Load Balance across multiple projects
Hosted Proxy (Preview)
The proxy provides:
- Hooks for auth
- Hooks for logging
- Cost tracking
- Rate Limiting
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
[!IMPORTANT]
š” Use LiteLLM Proxy with Langchain (Python, JS), OpenAI SDK (Python, JS) Anthropic SDK, Mistral SDK, LlamaIndex, Instructor, Curl
import openai
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000")
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
git clone https://github.com/BerriAI/litellm
cd litellm
echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
echo 'LITELLM_SALT_KEY="sk-1234"' > .env
source .env
docker-compose up
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": "ishaan@berri.ai", "team": "core-infra"}}'
Expected Response
{
"key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
"expires": "2023-11-19T01:38:25.838000+00:00" # datetime object
}
Supported Providers (Docs)
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 -E extra_proxy -E proxy
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! š
- 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:
- ā
Features under the LiteLLM Commercial License:
- ā
Feature Prioritization
- ā
Custom Integrations
- ā
Professional Support - Dedicated discord + slack
- ā
Custom SLAs
- ā
Secure access with Single Sign-On
Support / talk with founders
Why did we build this
- Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI and Cohere.
Contributors