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Maxim Overview

Maxim AI provides comprehensive agent monitoring, evaluation, and observability for your CrewAI applications. With Maxim’s one-line integration, you can easily trace and analyse agent interactions, performance metrics, and more.

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Features

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Prompt Management

Maxim’s Prompt Management capabilities enable you to create, organize, and optimize prompts for your CrewAI agents. Rather than hardcoding instructions, leverage Maxim’s SDK to dynamically retrieve and apply version-controlled prompts.

Create, refine, experiment and deploy your prompts via the playground. Organize of your prompts using folders and versions, experimenting with the real world cases by linking tools and context, and deploying based on custom logic.

Easily experiment across models by configuring models and selecting the relevant model from the dropdown at the top of the prompt playground.

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Observability & Evals

Maxim AI provides comprehensive observability & evaluation for your CrewAI agents, helping you understand exactly what’s happening during each execution.

Track your agent’s complete lifecycle, including tool calls, agent trajectories, and decision flows effortlessly.

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Getting Started

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Prerequisites

  • Python version >=3.10
  • A Maxim account (sign up here)
  • Generate Maxim API Key
  • A CrewAI project

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Installation

Install the Maxim SDK via pip:

pip install maxim-py

Or add it to your requirements.txt:

maxim-py

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Basic Setup

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1. Set up environment variables

### Environment Variables Setup

# Create a `.env` file in your project root:

# Maxim API Configuration
MAXIM_API_KEY=your_api_key_here
MAXIM_LOG_REPO_ID=your_repo_id_here

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2. Import the required packages

from crewai import Agent, Task, Crew, Process
from maxim import Maxim
from maxim.logger.crewai import instrument_crewai

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3. Initialise Maxim with your API key

# Instrument CrewAI with just one line
instrument_crewai(Maxim().logger())

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4. Create and run your CrewAI application as usual

# Create your agent
researcher = Agent(
    role='Senior Research Analyst',
    goal='Uncover cutting-edge developments in AI',
    backstory="You are an expert researcher at a tech think tank...",
    verbose=True,
    llm=llm
)

# Define the task
research_task = Task(
    description="Research the latest AI advancements...",
    expected_output="",
    agent=researcher
)

# Configure and run the crew
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    verbose=True
)

try:
    result = crew.kickoff()
finally:
    maxim.cleanup()  # Ensure cleanup happens even if errors occur

That’s it! All your CrewAI agent interactions will now be logged and available in your Maxim dashboard.

Check this Google Colab Notebook for a quick reference - Notebook

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Viewing Your Traces

After running your CrewAI application:

  1. Log in to your Maxim Dashboard

  2. Navigate to your repository

  3. View detailed agent traces, including:

    • Agent conversations
    • Tool usage patterns
    • Performance metrics
    • Cost analytics

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Troubleshooting

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Common Issues

  • No traces appearing: Ensure your API key and repository ID are correct

  • Ensure you’ve called instrument_crewai() before running your crew. This initializes logging hooks correctly.

  • Set debug=True in your instrument_crewai() call to surface any internal errors:

    instrument_crewai(logger, debug=True)

  • Configure your agents with verbose=True to capture detailed logs:

    agent = CrewAgent(..., verbose=True)

  • Double-check that instrument_crewai() is called before creating or executing agents. This might be obvious, but it’s a common oversight.

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Resources

CrewAI Docs

Official CrewAI documentation

Maxim Docs

Official Maxim documentation

Maxim Github

Maxim Github