Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]
LiteLLM manages:
- Translate inputs to provider's
completionandembeddingendpoints - Consistent output, text responses will always be available at
['choices'][0]['message']['content'] - Load-balance multiple deployments (e.g. Azure/OpenAI) -
Router1k+ requests/second
Usage (Docs)
Important
LiteLLM v1.0.0 now requires openai>=1.0.0. Migration guide here
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)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, LLMonitor, Helicone, Promptlayer, Traceloop, 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["OPENAI_API_KEY"]
# set callbacks
litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langfuse, llmonitor, supabase
#openai call
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])OpenAI Proxy - (Docs)
Track spend across multiple projects/people
📖 Proxy Endpoints - Swagger Docs
pip install litellm[proxy]$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http://0.0.0.0:8000import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # 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)
Track Spend, Set budgets and create virtual keys for the proxy
POST /key/generate
curl 'http://0.0.0.0:8000/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"}}'{
"key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
"expires": "2023-11-19T01:38:25.838000+00:00" # datetime object
}A simple UI to add new models and let your users create keys.
Live here: https://litellm-jyqbcbn44mcxqq6ufopuha.streamlit.app/
Code: https://github.com/BerriAI/litellm/tree/main/ui
Supported Provider (Docs)
| Provider | Completion | Streaming | Async Completion | Async Streaming | Async Embedding |
|---|---|---|---|---|---|
| openai | ✅ | ✅ | ✅ | ✅ | ✅ |
| azure | ✅ | ✅ | ✅ | ✅ | ✅ |
| aws - sagemaker | ✅ | ✅ | ✅ | ✅ | ✅ |
| aws - bedrock | ✅ | ✅ | ✅ | ✅ | ✅ |
| google - vertex_ai [Gemini] | ✅ | ✅ | ✅ | ✅ | |
| google - palm | ✅ | ✅ | ✅ | ✅ | |
| 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 | ✅ | ✅ | ✅ | ✅ | |
| anyscale | ✅ | ✅ | ✅ | ✅ | |
| voyage ai | ✅ |
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
- Schedule Demo 👋
- Community Discord 💭
- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
- Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI and Cohere.