> ## Documentation Index
> Fetch the complete documentation index at: https://traceroot.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# CrewAI

> Auto-instrument CrewAI multi-agent collaborative workflows

Automatically capture agent roles, tasks, crew executions, and tool invocations within the CrewAI framework.

## Setup

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    import traceroot
    from traceroot import Integration

    traceroot.initialize(integrations=[Integration.CREWAI, Integration.OPENAI])
    ```
  </Tab>
</Tabs>

## Usage

Once initialized, crew and agent executions are captured automatically:

```python theme={null}
from crewai import Agent, Task, Crew

researcher = Agent(
    role="Research Analyst",
    goal="Uncover the latest developments in AI agent frameworks",
    backstory="You are an expert at identifying emerging trends in AI.",
)

writer = Agent(
    role="Tech Writer",
    goal="Craft a compelling summary of AI agent framework trends",
    backstory="You turn complex technical insights into engaging narratives.",
)

research_task = Task(
    description="Analyze the latest AI agent frameworks and summarize key trends.",
    expected_output="A concise research summary",
    agent=researcher,
)

write_task = Task(
    description="Write a short article based on the research summary.",
    expected_output="A short article on AI agent framework trends",
    agent=writer,
    context=[research_task],
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task])

# The entire crew execution is automatically traced
result = crew.kickoff()
```

## What Gets Captured

| Attribute            | Description                                             |
| -------------------- | ------------------------------------------------------- |
| Crew execution       | The overarching `kickoff()` session                     |
| Agent roles          | Each agent's role, goal, and backstory as span metadata |
| Task steps           | Individual task executions with inputs and outputs      |
| Tool calls           | Tool invocations with input arguments and results       |
| LLM calls            | Raw completion requests to the provider                 |
| Delegation hierarchy | Manager-agent relationships and task assignments        |
| Token usage          | Aggregated across all LLM calls                         |
| Cost                 | Total cost for the full crew execution                  |

## Run the example

Clone the repo and run a complete agent end-to-end.

<Card title="Python" icon="python" href="https://github.com/traceroot-ai/traceroot/tree/main/examples/python/crewai-agent">
  Run the Python example
</Card>