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Opik Overview

With Comet Opik, debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
Opik agent monitoring example with CrewAI

Opik Agent Dashboard

Opik provides comprehensive support for every stage of your CrewAI application development:
  • Log Traces and Spans: Automatically track LLM calls and application logic to debug and analyze development and production systems. Manually or programmatically annotate, view, and compare responses across projects.
  • Evaluate Your LLM Application’s Performance: Evaluate against a custom test set and run built-in evaluation metrics or define your own metrics in the SDK or UI.
  • Test Within Your CI/CD Pipeline: Establish reliable performance baselines with Opik’s LLM unit tests, built on PyTest. Run online evaluations for continuous monitoring in production.
  • Monitor & Analyze Production Data: Understand your models’ performance on unseen data in production and generate datasets for new dev iterations.

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Setup

Comet provides a hosted version of the Opik platform, or you can run the platform locally. To use the hosted version, simply create a free Comet account and grab you API Key. To run the Opik platform locally, see our installation guide for more information. For this guide we will use CrewAI’s quickstart example.
1

Install required packages

pip install crewai crewai-tools opik --upgrade
2

Configure Opik

import opik
opik.configure(use_local=False)
3

Prepare environment

First, we set up our API keys for our LLM-provider as environment variables:
import os
import getpass

if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")
4

Using CrewAI

The first step is to create our project. We will use an example from CrewAI’s documentation:
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:
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)

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Resources

Opik agent monitoring example with CrewAI