CrewAI

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

Install

Python

pip install traceroot crewai

Set up

Python

import traceroot
from traceroot import Integration

traceroot.initialize(integrations=[Integration.CREWAI, Integration.OPENAI])

Usage

Once initialized, crew and agent executions are captured automatically:

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()

Verify

Run your code and confirm the trace arrives:

In the TraceRoot UI: Navigate to Tracing > Traces in TraceRoot Cloud. You should see a trace with root span Crew_<id>.kickoff; naming the crew with Crew(name=...) uses that name instead.
Via the CLI: Run traceroot traces list to inspect your recent traces from the terminal.

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	Aggregated when the underlying model IDs match TraceRoot’s pricing table

Limitations

CrewAI delegates LLM generation to underlying providers (e.g., OpenAI, Anthropic). Initializing your provider integration (such as Integration.OPENAI) alongside CrewAI is recommended for accurate token usage and cost calculation.

Run the example

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

Python

Run the Python example

Next steps

Python SDK Reference

Explore the full Python SDK reference and instrumentation options.

Cost Tracking

Learn how TraceRoot calculates token costs and tracks model spend.

Overview

Native

Frameworks

Model Providers

Internal Model Providers

Install

Set up

Usage

Verify

What gets captured

Limitations

Run the example

Python

Next steps

Python SDK Reference

Cost Tracking

​Install

​Set up

​Usage

​Verify

​What gets captured

​Limitations

​Run the example

Python

​Next steps

Python SDK Reference

Cost Tracking

Install

Set up

Usage

Verify

What gets captured

Limitations

Run the example

Next steps