Getting Started

import QuickStart from '../src/components/QuickStart.js'

LiteLLM simplifies LLM API calls by mapping them all to the OpenAI ChatCompletion format.

basic usage

By default we provide a free $10 community-key to try all providers supported on LiteLLM.

from litellm import completion

## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["COHERE_API_KEY"] = "your-api-key"

messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion(model="gpt-3.5-turbo", messages=messages)

# cohere call
response = completion("command-nightly", messages)

Need a dedicated key? Email us @ krrish@berri.ai

Next Steps 👉 Call all supported models - e.g. Claude-2, Llama2-70b, etc.

More details 👉

streaming

Same example from before. Just pass in stream=True in the completion args.

from litellm import completion

## set ENV variables
os.environ["OPENAI_API_KEY"] = "openai key"
os.environ["COHERE_API_KEY"] = "cohere key"

messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)

# cohere call
response = completion("command-nightly", messages, stream=True)

print(response)

More details 👉

exception handling

LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM.

from openai.error import OpenAIError
from litellm import completion

os.environ["ANTHROPIC_API_KEY"] = "bad-key"
try: 
    # some code 
    completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}])
except OpenAIError as e:
    print(e)

Logging Observability - Log LLM Input/Output (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"}])

More details 👉