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Documentation Index

Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt

Use this file to discover all available pages before exploring further.

This integration method is maintained but no longer actively developed. For the best experience and latest features, use our new AI Gateway with unified API access to 100+ models.
LiteLLM is a model I/O library to standardize API calls to Azure, Anthropic, OpenAI, etc. Here’s how you can log your LLM API calls to Helicone from LiteLLM using callbacks.
Note: Custom Properties are available in metadata starting with LiteLLM version 1.41.23.
System Instructions Limitation: When using LiteLLM callbacks, system instructions for Gemini and Claude may not appear as "role": "system" messages in Helicone logs. This is because LiteLLM processes the request before sending it to Helicone.For full system instruction support, consider using proxy-based integration instead.

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1 line integration

Add HELICONE_API_KEY to your environment variables. 
export HELICONE_API_KEY=sk-<your-api-key>
# You can also set it in your code (See below)
Tell LiteLLM you want to log your data to Helicone 
litellm.success_callback=["helicone"]

​
Complete code

from litellm import completion
import os

## set env variables
os.environ["HELICONE_API_KEY"] = "your-helicone-key"
os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", ""

# set callbacks
litellm.success_callback=["helicone"]

#openai call
response = completion(
  model="gpt-4o-mini",
  messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}],
  metadata={
    "Helicone-Property-Hello": "World"
  }
)

#cohere call
response = completion(
  model="command-r",
  messages=[{"role": "user", "content": "Hi 👋 - i'm cohere"}],
  metadata={
    "Helicone-Property-Hello": "World"
  }
)

print(response)
Feel free to check it out and tell us what you think 👋 For proxy-based integration with LiteLLM, see our LiteLLM Proxy Integration guide.