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Overview

This guide demonstrates how to integrate Maxim SDK tracing with LiteLLM for seamless LLM observability. You’ll learn how to set up Maxim as a logger for LiteLLM, monitor your LLM calls, and attach custom traces for advanced analytics.

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Requirements

litellm>=1.25.0
maxim-py>=3.5.0

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Environment Variables

Set the following environment variables in your environment or .env file: 
MAXIM_API_KEY=
MAXIM_LOG_REPO_ID=
OPENAI_API_KEY=

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Step 1: Initialize Maxim SDK and Add as LiteLLM Logger

import litellm
import os
from maxim import Maxim, Config, LoggerConfig
from maxim.logger.litellm import MaximLiteLLMTracer

logger = Maxim().logger()
# One-line integration: add Maxim tracer to LiteLLM callbacks
litellm.callbacks = [MaximLiteLLMTracer(logger)]

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Step 2: Make LLM Calls with LiteLLM

import os
from litellm import acompletion

response = await acompletion(
  model='openai/gpt-4o',
  api_key=os.getenv('OPENAI_API_KEY'),
  messages=[{'role': 'user', 'content': 'Hello, world!'}],
)

print(response.choices[0].message.content)

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Advanced: Attach a Custom Trace to LiteLLM Generation

You can attach a custom trace to your LiteLLM calls for advanced tracking and correlation across multiple events. 
from maxim.logger.logger import TraceConfig
import uuid

trace = logger.trace(TraceConfig(id=str(uuid.uuid4()), name='litellm-generation'))
trace.event(str(uuid.uuid4()), 'litellm-generation', 'litellm-generation', {})
# Attach trace to LiteLLM call using metadata
response = await acompletion(
  model='openai/gpt-4o',
  api_key=os.getenv('OPENAI_API_KEY'),
  messages=[{'role': 'user', 'content': 'What can you do for me!'}],
  metadata={'maxim': {'trace_id': trace.id, 'span_name': 'litellm-generation'}}
)

print(response.choices[0].message.content)

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Visualizing Traces

LiteLLM Custom Trace

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Resources

LiteLLM integration cookbook (GitHub)

LiteLLM integration cookbook (Colab)

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Notes

  • All LLM calls made with the MaximLiteLLMTracer will be automatically traced and visible in your Maxim dashboard.