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This integration is only available in the Python SDK.

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Overview

This guide shows you how to integrate Latitude Telemetry into an existing application that uses CrewAI for building multi-agent systems. After completing these steps:
  • Every CrewAI crew execution can be captured as a log in Latitude.
  • Logs are grouped under a prompt, identified by a path, inside a Latitude project.
  • You can inspect agent interactions, task execution, and debug CrewAI-powered features from the Latitude dashboard.
You’ll keep using CrewAI exactly as you do today — Telemetry simply observes and enriches those calls.

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Requirements

Before you start, make sure you have:
  • A Latitude account and API key
  • A Latitude project ID
  • A Python-based project that uses CrewAI
That’s it — prompts do not need to be created ahead of time.

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Steps

1

Install requirements

Add the Latitude Telemetry package to your project:
pip install latitude-telemetry
2

Wrap your CrewAI-powered feature

Initialize Latitude Telemetry and wrap the code that runs CrewAI crews using telemetry.capture.You can use the capture method as a decorator (recommended) or as a context manager:
Using decorator (recommended)
import os
from crewai import Agent, Task, Crew
from latitude_telemetry import Telemetry, Instrumentors, TelemetryOptions

telemetry = Telemetry(
    os.environ["LATITUDE_API_KEY"],
    TelemetryOptions(instrumentors=[Instrumentors.CrewAI]),
)

# Define your agents
researcher = Agent(
    role="Researcher",
    goal="Research and summarize topics concisely",
    backstory="You are a skilled researcher who provides accurate summaries.",
)

writer = Agent(
    role="Writer",
    goal="Write clear and engaging content",
    backstory="You are an experienced writer who creates compelling content.",
)

@telemetry.capture(
    project_id=123,  # The ID of your project in Latitude
    path="research-and-write",  # Add a path to identify this prompt in Latitude
)
def research_and_write(topic: str) -> str:
    # Define tasks for your crew
    research_task = Task(
        description=f"Research the following topic: {topic}",
        expected_output="A comprehensive summary of the topic.",
        agent=researcher,
    )

    write_task = Task(
        description="Write an article based on the research",
        expected_output="A well-written article.",
        agent=writer,
    )

    # Create and run the crew
    crew = Crew(
        agents=[researcher, writer],
        tasks=[research_task, write_task],
    )

    result = crew.kickoff()

    # You can return anything you want — the value is passed through unchanged
    return result.raw
Using context manager
import os
from crewai import Agent, Task, Crew
from latitude_telemetry import Telemetry, Instrumentors, TelemetryOptions

telemetry = Telemetry(
    os.environ["LATITUDE_API_KEY"],
    TelemetryOptions(instrumentors=[Instrumentors.CrewAI]),
)

# Define your agents
researcher = Agent(
    role="Researcher",
    goal="Research and summarize topics concisely",
    backstory="You are a skilled researcher who provides accurate summaries.",
)

def research_topic(topic: str) -> str:
    with telemetry.capture(
        project_id=123,  # The ID of your project in Latitude
        path="research-topic",  # Add a path to identify this prompt in Latitude
    ):
        task = Task(
            description=f"Research the following topic: {topic}",
            expected_output="A comprehensive summary.",
            agent=researcher,
        )

        crew = Crew(agents=[researcher], tasks=[task])
        result = crew.kickoff()

        return result.raw
The path:
  • Identifies the prompt in Latitude
  • Can be new or existing
  • Should not contain spaces or special characters (use letters, numbers, - _ / .)

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Seeing your logs in Latitude

Once your feature is wrapped, logs will appear automatically.
  1. Open the prompt in your Latitude dashboard (identified by path)
  2. Go to the Traces section
  3. Each execution will show:
    • Input and output messages
    • Agent interactions and task completions
    • Model and token usage from underlying LLM calls
    • Latency and errors
    • One trace per crew execution
Each CrewAI agent execution appears as a child span under the captured prompt execution, giving you a full, end-to-end view of what happened.

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That’s it

No changes to your CrewAI agents or crews, no special return values, and no extra plumbing — just wrap the feature you want to observe.