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Sentry - Log LLM Exceptions

Sentry provides error monitoring for production. LiteLLM can add breadcrumbs and send exceptions to Sentry with this integration

Track exceptions for:

  • litellm.completion() - completion()for 100+ LLMs
  • litellm.acompletion() - async completion()
  • Streaming completion() & acompletion() calls

Usage​

Set SENTRY_DSN & callback​

import litellm, os
os.environ["SENTRY_DSN"] = "your-sentry-url"
litellm.failure_callback=["sentry"]

Sentry callback with completion​

import litellm
from litellm import completion 

litellm.input_callback=["sentry"] # adds sentry breadcrumbing
litellm.failure_callback=["sentry"] # [OPTIONAL] if you want litellm to capture -> send exception to sentry

import os 
os.environ["SENTRY_DSN"] = "your-sentry-url"
os.environ["OPENAI_API_KEY"] = "your-openai-key"

# set bad key to trigger error 
api_key="bad-key"
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey!"}], stream=True, api_key=api_key)

print(response)

Redacting Messages, Response Content from Sentry Logging​

Set litellm.turn_off_message_logging=True This will prevent the messages and responses from being logged to sentry, but request metadata will still be logged.

Let us know if you need any additional options from Sentry.