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_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
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)
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 π - i'm openai"}])
OpenAI Proxy - (Docs)
Set Budgets & Rate limits across multiple projects
The proxy provides:
π Proxy Endpoints - Swagger Docs
pip install 'litellm[proxy]'
$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http:https://0.0.0.0:8000
import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http:https://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)
UI on /ui
on your proxy server
Set budgets and rate limits across multiple projects
POST /key/generate
curl 'http:https://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": "[email protected]", "team": "core-infra"}}'
{
"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 | β | β | β | β | β | β |
azure | β | β | β | β | β | β |
aws - sagemaker | β | β | β | β | β | |
aws - bedrock | β | β | β | β | β | |
google - vertex_ai [Gemini] | β | β | β | β | ||
google - palm | β | β | β | β | ||
google AI Studio - gemini | β | β | ||||
mistral ai api | β | β | β | β | β | |
cloudflare AI Workers | β | β | β | β | ||
cohere | β | β | β | β | β | |
anthropic | β | β | β | β | ||
huggingface | β | β | β | β | β | |
replicate | β | β | β | β | ||
together_ai | β | β | β | β | ||
openrouter | β | β | β | β | ||
ai21 | β | β | β | β | ||
baseten | β | β | β | β | ||
vllm | β | β | β | β | ||
nlp_cloud | β | β | β | β | ||
aleph alpha | β | β | β | β | ||
petals | β | β | β | β | ||
ollama | β | β | β | β | ||
deepinfra | β | β | β | β | ||
perplexity-ai | β | β | β | β | ||
Groq AI | β | β | β | β | ||
anyscale | β | β | β | β | ||
voyage ai | β | |||||
xinference [Xorbits Inference] | β |
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! π
- push your fork to your GitHub repo
- submit a PR from there
- Schedule Demo π
- Community Discord π
- Our numbers π +1 (770) 8783-106 / β+1 (412) 618-6238β¬
- Our emails βοΈ [email protected] / [email protected]
- Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI and Cohere.