pip install crewai crewai-tools opik --upgrade
    ```
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Configure Opik"></span>
```python
    import opik
    opik.configure(use_local=False)
    ```
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Prepare environment"></span>
First, we set up our API keys for our LLM-provider as environment variables:

```python
    import os
    import getpass

    if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
    ```
  <span class="step-end"></span>

  <span class="step-marker" data-step-title="Using CrewAI"></span>
The first step is to create our project. We will use an example from CrewAI’s documentation:

```python
    from crewai import Agent, Crew, Task, Process


    class YourCrewName:
        def agent_one(self) -> Agent:
            return Agent(
                role="Data Analyst",
                goal="Analyze data trends in the market",
                backstory="An experienced data analyst with a background in economics",
                verbose=True,
            )

        def agent_two(self) -> Agent:
            return Agent(
                role="Market Researcher",
                goal="Gather information on market dynamics",
                backstory="A diligent researcher with a keen eye for detail",
                verbose=True,
            )

        def task_one(self) -> Task:
            return Task(
                name="Collect Data Task",
                description="Collect recent market data and identify trends.",
                expected_output="A report summarizing key trends in the market.",
                agent=self.agent_one(),
            )

        def task_two(self) -> Task:
            return Task(
                name="Market Research Task",
                description="Research factors affecting market dynamics.",
                expected_output="An analysis of factors influencing the market.",
                agent=self.agent_two(),
            )

        def crew(self) -> Crew:
            return Crew(
                agents=[self.agent_one(), self.agent_two()],
                tasks=[self.task_one(), self.task_two()],
                process=Process.sequential,
                verbose=True,
            )

    ```

Now we can import Opik’s tracker and run our crew:

```python
    from opik.integrations.crewai import track_crewai

    track_crewai(project_name="crewai-integration-demo")

    my_crew = YourCrewName().crew()
    result = my_crew.kickoff()

    print(result)
    ```

After running your CrewAI application, visit the Opik app to view:

* LLM traces, spans, and their metadata
* Agent interactions and task execution flow
* Performance metrics like latency and token usage
* Evaluation metrics (built-in or custom)
  <span class="step-end"></span>
<span class="steps-end"></span>

## Resources

* [🦉 Opik Documentation](https://www.comet.com/docs/opik/)
* [👉 Opik + CrewAI Colab](https://colab.research.google.com/github/comet-ml/opik/blob/main/apps/opik-documentation/documentation/docs/cookbook/crewai.ipynb)
* [🐦 X](https://x.com/cometml)
* [💬 Slack](https://slack.comet.com/)