---
title: Maxim Integration
description: Start Agent monitoring, evaluation, and observability
icon: bars-staggered
---

# Maxim Integration

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.


## Features: One Line Integration

- **End-to-End Agent Tracing**: Monitor the complete lifecycle of your agents
- **Performance Analytics**: Track latency, tokens consumed, and costs
- **Hyperparameter Monitoring**: View the configuration details of your agent runs
- **Tool Call Tracking**: Observe when and how agents use their tools
- **Advanced Visualisation**: Understand agent trajectories through intuitive dashboards

## Getting Started

### Prerequisites

- Python version >=3.10
- A Maxim account ([sign up here](https://getmaxim.ai/))
- A CrewAI project

### Installation

Install the Maxim SDK via pip:

```python
pip install maxim-py>=3.6.2
```

Or add it to your `requirements.txt`:

```
maxim-py>=3.6.2
```


### Basic Setup

### 1. Set up environment variables

```python
### 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
```

### 2. Import the required packages

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

### 3. Initialise Maxim with your API key

```python
# Initialize Maxim logger
logger = Maxim().logger()

# Instrument CrewAI with just one line
instrument_crewai(logger)
```

### 4. Create and run your CrewAI application as usual

```python

# 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](https://colab.research.google.com/drive/1ZKIZWsmgQQ46n8TH9zLsT1negKkJA6K8?usp=sharing)

## Viewing Your Traces

After running your CrewAI application:

![Example trace in Maxim showing agent interactions](https://raw.githubusercontent.com/maximhq/maxim-docs/master/images/Screenshot2025-05-14at12.10.58PM.png)

1. Log in to your [Maxim Dashboard](https://getmaxim.ai/dashboard)
2. Navigate to your repository
3. View detailed agent traces, including:
    - Agent conversations
    - Tool usage patterns
    - Performance metrics
    - Cost analytics

## Troubleshooting

### Common Issues

- **No traces appearing**: Ensure your API key and repository ID are correc
- 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:
    
    ```python
    instrument_crewai(logger, debug=True)
    ```
    
- Configure your agents with `verbose=True` to capture detailed logs:
    
    ```python
    
    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.

### Support

If you encounter any issues:

- Check the [Maxim Documentation](https://getmaxim.ai/docs)
- Maxim Github [Link](https://github.com/maximhq)