CrewAI

LangWatch does not have a built-in auto-tracking integration for CrewAI. However, you can use community-provided instrumentors to integrate CrewAI with LangWatch.

Community Instrumentors

There are two main community instrumentors available for CrewAI: To use these instrumentors with LangWatch, you would typically configure them to export telemetry data via OpenTelemetry, which LangWatch can then ingest.

Installation

Install LangWatch, CrewAI and one of the two instrumentors:

pip install langwatch crewai openinference-instrumentation-crewai
# or, for OpenLLMetry:
pip install langwatch crewai opentelemetry-instrumentation-crewai

The SDK reads LANGWATCH_API_KEY from the environment.

Integrating Community Instrumentors with LangWatch

Community-provided OpenTelemetry instrumentors for CrewAI, like those from OpenLLMetry or OpenInference, capture trace data from your CrewAI agents and tasks. LangWatch reads that data through OpenTelemetry. There are two main ways to integrate these:

1. Via `langwatch.setup()`

You can pass an instance of the CrewAI instrumentor to the instrumentors list in the langwatch.setup() call. LangWatch will then manage the lifecycle of this instrumentor.

import langwatch
from crewai import Agent, Task, Crew
from openinference.instrumentation.crewai import CrewAIInstrumentor

# Ensure LANGWATCH_API_KEY is set in your environment, or set it in `setup`
langwatch.setup(
    instrumentors=[CrewAIInstrumentor()]
)

# Define your CrewAI agents and tasks
researcher = Agent(
  role='Senior Researcher',
  goal='Discover new insights on AI',
  backstory='A seasoned researcher with a knack for uncovering hidden gems.'
)
writer = Agent(
  role='Expert Writer',
  goal='Craft compelling content on AI discoveries',
  backstory='A wordsmith who can make complex AI topics accessible and engaging.'
)

task1 = Task(
  description='Investigate the latest advancements in LLM prompting techniques.',
  expected_output='A bullet list of the key advancements.',
  agent=researcher
)
task2 = Task(
  description='Write a blog post summarizing the findings.',
  expected_output='A short blog post in markdown.',
  agent=writer
)

# Create and run the crew
crew = Crew(
  agents=[researcher, writer],
  tasks=[task1, task2],
  verbose=True
)

@langwatch.trace(name="CrewAI Execution with OpenInference")
def run_crewai_process_oi():
    result = crew.kickoff()
    return result

if __name__ == "__main__":
    print("Running CrewAI process with OpenInference...")
    output = run_crewai_process_oi()
    print("\n\nCrewAI Process Output:")
    print(output)

import langwatch
from crewai import Agent, Task, Crew
from opentelemetry.instrumentation.crewai import CrewAIInstrumentor

# Ensure LANGWATCH_API_KEY is set in your environment, or set it in `setup`
langwatch.setup(
    instrumentors=[CrewAIInstrumentor()]
)

# Define your CrewAI agents and tasks
researcher = Agent(
  role='Senior Researcher',
  goal='Discover new insights on AI',
  backstory='A seasoned researcher with a knack for uncovering hidden gems.'
)
writer = Agent(
  role='Expert Writer',
  goal='Craft compelling content on AI discoveries',
  backstory='A wordsmith who can make complex AI topics accessible and engaging.'
)

task1 = Task(
  description='Investigate the latest advancements in LLM prompting techniques.',
  expected_output='A bullet list of the key advancements.',
  agent=researcher
)
task2 = Task(
  description='Write a blog post summarizing the findings.',
  expected_output='A short blog post in markdown.',
  agent=writer
)

# Create and run the crew
crew = Crew(
  agents=[researcher, writer],
  tasks=[task1, task2],
  verbose=True
)

@langwatch.trace(name="CrewAI Execution with OpenLLMetry")
def run_crewai_process_ollm():
    result = crew.kickoff()
    return result

if __name__ == "__main__":
    print("Running CrewAI process with OpenLLMetry...")
    output = run_crewai_process_ollm()
    print("\n\nCrewAI Process Output:")
    print(output)

Install the instrumentation library for the instrumentor you use:

OpenLLMetry: pip install opentelemetry-instrumentation-crewai
OpenInference: pip install openinference-instrumentation-crewai

2. Direct Instrumentation

If you have an existing OpenTelemetry TracerProvider configured in your application (or if LangWatch is configured to use the global provider), you can use the community instrumentor’s instrument() method directly. LangWatch will automatically pick up the spans generated by these instrumentors as long as its exporter is part of the active TracerProvider.

import langwatch
from crewai import Agent, Task, Crew
from openinference.instrumentation.crewai import CrewAIInstrumentor
# from opentelemetry.sdk.trace import TracerProvider # If managing your own provider
# from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter # If managing your own provider

langwatch.setup()

# Instrument CrewAI directly using OpenInference
CrewAIInstrumentor().instrument()

planner = Agent(
  role='Event Planner',
  goal='Plan an engaging tech conference',
  backstory='An experienced planner with a passion for technology events.'
)
task_planner = Task(
  description='Outline the agenda for a 3-day AI conference.',
  expected_output='A day-by-day agenda.',
  agent=planner
)
conference_crew = Crew(agents=[planner], tasks=[task_planner])

@langwatch.trace(name="CrewAI Direct Instrumentation with OpenInference")
def plan_conference_oi():
    agenda = conference_crew.kickoff()
    return agenda

if __name__ == "__main__":
    print("Planning conference with OpenInference (direct)...")
    conference_agenda = plan_conference_oi()
    print("\n\nConference Agenda:")
    print(conference_agenda)

import langwatch
from crewai import Agent, Task, Crew
from opentelemetry.instrumentation.crewai import CrewAIInstrumentor
# from opentelemetry.sdk.trace import TracerProvider # If managing your own provider
# from opentelemetry.sdk.trace.export import SimpleSpanProcessor, ConsoleSpanExporter # If managing your own provider

langwatch.setup()

# Instrument CrewAI directly using OpenLLMetry
CrewAIInstrumentor().instrument()

planner = Agent(
  role='Event Planner',
  goal='Plan an engaging tech conference',
  backstory='An experienced planner with a passion for technology events.'
)
task_planner = Task(
  description='Outline the agenda for a 3-day AI conference.',
  expected_output='A day-by-day agenda.',
  agent=planner
)
conference_crew = Crew(agents=[planner], tasks=[task_planner])

@langwatch.trace(name="CrewAI Direct Instrumentation with OpenLLMetry")
def plan_conference_ollm():
    agenda = conference_crew.kickoff()
    return agenda

if __name__ == "__main__":
    print("Planning conference with OpenLLMetry (direct)...")
    conference_agenda = plan_conference_ollm()
    print("\n\nConference Agenda:")
    print(conference_agenda)

Key points for community instrumentors:

These instrumentors typically patch CrewAI at a global level or integrate deeply with its execution flow, meaning all CrewAI operations (agents, tasks, tools) should be captured once instrumented.
If using langwatch.setup(instrumentors=[...]), LangWatch handles the setup and lifecycle of the instrumentor.
If instrumenting directly (e.g., CrewAIInstrumentor().instrument()), ensure that the TracerProvider used by the instrumentor is the same one LangWatch is exporting from. This usually means LangWatch is configured to use an existing global provider or one you explicitly pass to langwatch.setup().
Always refer to the specific documentation of the community instrumentor (OpenLLMetry or OpenInference) for the most accurate and up-to-date installation and usage instructions, including the correct class names for instrumentors and any specific setup requirements.

Check it worked

Run the example. Then open your project in LangWatch: a new trace appears in the traces list within a few seconds. Open it to see the crew’s agents, tasks and model calls nested inside the trace named in the decorator, for example “CrewAI Execution with OpenInference”. If no trace appears, check that LANGWATCH_API_KEY is set in the environment the script runs in.

Overview

Tools

Community Instrumentors

Installation

Integrating Community Instrumentors with LangWatch

1. Via `langwatch.setup()`

2. Direct Instrumentation

Key points for community instrumentors:

Check it worked

​Community Instrumentors

​Installation

​Integrating Community Instrumentors with LangWatch

​1. Via langwatch.setup()

​2. Direct Instrumentation

​Key points for community instrumentors:

​Check it worked

Community Instrumentors

Installation

Integrating Community Instrumentors with LangWatch

1. Via `langwatch.setup()`

2. Direct Instrumentation

Key points for community instrumentors:

Check it worked