Event Listeners

Overview

CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI’s internal events.

How It Works

CrewAI uses an event bus architecture to emit events throughout the execution lifecycle. The event system is built on the following components:

CrewAIEventsBus: A singleton event bus that manages event registration and emission
BaseEvent: Base class for all events in the system
BaseEventListener: Abstract base class for creating custom event listeners

When specific actions occur in CrewAI (like a Crew starting execution, an Agent completing a task, or a tool being used), the system emits corresponding events. You can register handlers for these events to execute custom code when they occur.

CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.

With Prompt Tracing you can:

View the complete history of all prompts sent to your LLM
Track token usage and costs
Debug agent reasoning failures
Share prompt sequences with your team
Compare different prompt strategies
Export traces for compliance and auditing

Creating a Custom Event Listener

To create a custom event listener, you need to:

Create a class that inherits from BaseEventListener
Implement the setup_listeners method
Register handlers for the events you’re interested in
Create an instance of your listener in the appropriate file

Here’s a simple example of a custom event listener class:

from crewai.utilities.events import (
    CrewKickoffStartedEvent,
    CrewKickoffCompletedEvent,
    AgentExecutionCompletedEvent,
)
from crewai.utilities.events.base_event_listener import BaseEventListener

class MyCustomListener(BaseEventListener):
    def __init__(self):
        super().__init__()

    def setup_listeners(self, crewai_event_bus):
        @crewai_event_bus.on(CrewKickoffStartedEvent)
        def on_crew_started(source, event):
            print(f"Crew '{event.crew_name}' has started execution!")

        @crewai_event_bus.on(CrewKickoffCompletedEvent)
        def on_crew_completed(source, event):
            print(f"Crew '{event.crew_name}' has completed execution!")
            print(f"Output: {event.output}")

        @crewai_event_bus.on(AgentExecutionCompletedEvent)
        def on_agent_execution_completed(source, event):
            print(f"Agent '{event.agent.role}' completed task")
            print(f"Output: {event.output}")

Properly Registering Your Listener

Simply defining your listener class isn’t enough. You need to create an instance of it and ensure it’s imported in your application. This ensures that:

The event handlers are registered with the event bus
The listener instance remains in memory (not garbage collected)
The listener is active when events are emitted

Option 1: Import and Instantiate in Your Crew or Flow Implementation

The most important thing is to create an instance of your listener in the file where your Crew or Flow is defined and executed:

For Crew-based Applications

Create and import your listener at the top of your Crew implementation file:

# In your crew.py file
from crewai import Agent, Crew, Task
from my_listeners import MyCustomListener

# Create an instance of your listener
my_listener = MyCustomListener()

class MyCustomCrew:
    # Your crew implementation...

    def crew(self):
        return Crew(
            agents=[...],
            tasks=[...],
            # ...
        )

For Flow-based Applications

Create and import your listener at the top of your Flow implementation file:

# In your main.py or flow.py file
from crewai.flow import Flow, listen, start
from my_listeners import MyCustomListener

# Create an instance of your listener
my_listener = MyCustomListener()

class MyCustomFlow(Flow):
    # Your flow implementation...

    @start()
    def first_step(self):
        # ...

This ensures that your listener is loaded and active when your Crew or Flow is executed.

Option 2: Create a Package for Your Listeners

For a more structured approach, especially if you have multiple listeners:

Create a package for your listeners:

my_project/
  ├── listeners/
  │   ├── __init__.py
  │   ├── my_custom_listener.py
  │   └── another_listener.py

In my_custom_listener.py, define your listener class and create an instance:

# my_custom_listener.py
from crewai.utilities.events.base_event_listener import BaseEventListener
# ... import events ...

class MyCustomListener(BaseEventListener):
    # ... implementation ...

# Create an instance of your listener
my_custom_listener = MyCustomListener()

In __init__.py, import the listener instances to ensure they’re loaded:

# __init__.py
from .my_custom_listener import my_custom_listener
from .another_listener import another_listener

# Optionally export them if you need to access them elsewhere
__all__ = ['my_custom_listener', 'another_listener']

Import your listeners package in your Crew or Flow file:

# In your crew.py or flow.py file
import my_project.listeners  # This loads all your listeners

class MyCustomCrew:
    # Your crew implementation...

This is how third-party event listeners are registered in the CrewAI codebase.

Available Event Types

CrewAI provides a wide range of events that you can listen for:

Crew Events

CrewKickoffStartedEvent: Emitted when a Crew starts execution
CrewKickoffCompletedEvent: Emitted when a Crew completes execution
CrewKickoffFailedEvent: Emitted when a Crew fails to complete execution
CrewTestStartedEvent: Emitted when a Crew starts testing
CrewTestCompletedEvent: Emitted when a Crew completes testing
CrewTestFailedEvent: Emitted when a Crew fails to complete testing
CrewTrainStartedEvent: Emitted when a Crew starts training
CrewTrainCompletedEvent: Emitted when a Crew completes training
CrewTrainFailedEvent: Emitted when a Crew fails to complete training

Agent Events

AgentExecutionStartedEvent: Emitted when an Agent starts executing a task
AgentExecutionCompletedEvent: Emitted when an Agent completes executing a task
AgentExecutionErrorEvent: Emitted when an Agent encounters an error during execution

Task Events

TaskStartedEvent: Emitted when a Task starts execution
TaskCompletedEvent: Emitted when a Task completes execution
TaskFailedEvent: Emitted when a Task fails to complete execution
TaskEvaluationEvent: Emitted when a Task is evaluated

Tool Usage Events

ToolUsageStartedEvent: Emitted when a tool execution is started
ToolUsageFinishedEvent: Emitted when a tool execution is completed
ToolUsageErrorEvent: Emitted when a tool execution encounters an error
ToolValidateInputErrorEvent: Emitted when a tool input validation encounters an error
ToolExecutionErrorEvent: Emitted when a tool execution encounters an error
ToolSelectionErrorEvent: Emitted when there’s an error selecting a tool

Knowledge Events

KnowledgeRetrievalStartedEvent: Emitted when a knowledge retrieval is started
KnowledgeRetrievalCompletedEvent: Emitted when a knowledge retrieval is completed
KnowledgeQueryStartedEvent: Emitted when a knowledge query is started
KnowledgeQueryCompletedEvent: Emitted when a knowledge query is completed
KnowledgeQueryFailedEvent: Emitted when a knowledge query fails
KnowledgeSearchQueryFailedEvent: Emitted when a knowledge search query fails

LLM Guardrail Events

LLMGuardrailStartedEvent: Emitted when a guardrail validation starts. Contains details about the guardrail being applied and retry count.
LLMGuardrailCompletedEvent: Emitted when a guardrail validation completes. Contains details about validation success/failure, results, and error messages if any.

Flow Events

FlowCreatedEvent: Emitted when a Flow is created
FlowStartedEvent: Emitted when a Flow starts execution
FlowFinishedEvent: Emitted when a Flow completes execution
FlowPlotEvent: Emitted when a Flow is plotted
MethodExecutionStartedEvent: Emitted when a Flow method starts execution
MethodExecutionFinishedEvent: Emitted when a Flow method completes execution
MethodExecutionFailedEvent: Emitted when a Flow method fails to complete execution

LLM Events

LLMCallStartedEvent: Emitted when an LLM call starts
LLMCallCompletedEvent: Emitted when an LLM call completes
LLMCallFailedEvent: Emitted when an LLM call fails
LLMStreamChunkEvent: Emitted for each chunk received during streaming LLM responses

Memory Events

MemoryQueryStartedEvent: Emitted when a memory query is started. Contains the query, limit, and optional score threshold.
MemoryQueryCompletedEvent: Emitted when a memory query is completed successfully. Contains the query, results, limit, score threshold, and query execution time.
MemoryQueryFailedEvent: Emitted when a memory query fails. Contains the query, limit, score threshold, and error message.
MemorySaveStartedEvent: Emitted when a memory save operation is started. Contains the value to be saved, metadata, and optional agent role.
MemorySaveCompletedEvent: Emitted when a memory save operation is completed successfully. Contains the saved value, metadata, agent role, and save execution time.
MemorySaveFailedEvent: Emitted when a memory save operation fails. Contains the value, metadata, agent role, and error message.
MemoryRetrievalStartedEvent: Emitted when memory retrieval for a task prompt starts. Contains the optional task ID.
MemoryRetrievalCompletedEvent: Emitted when memory retrieval for a task prompt completes successfully. Contains the task ID, memory content, and retrieval execution time.

Event Handler Structure

Each event handler receives two parameters:

source: The object that emitted the event
event: The event instance, containing event-specific data

The structure of the event object depends on the event type, but all events inherit from BaseEvent and include:

timestamp: The time when the event was emitted
type: A string identifier for the event type

Additional fields vary by event type. For example, CrewKickoffCompletedEvent includes crew_name and output fields.

Advanced Usage: Scoped Handlers

For temporary event handling (useful for testing or specific operations), you can use the scoped_handlers context manager:

from crewai.utilities.events import crewai_event_bus, CrewKickoffStartedEvent

with crewai_event_bus.scoped_handlers():
    @crewai_event_bus.on(CrewKickoffStartedEvent)
    def temp_handler(source, event):
        print("This handler only exists within this context")

    # Do something that emits events

# Outside the context, the temporary handler is removed

Use Cases

Event listeners can be used for a variety of purposes:

Logging and Monitoring: Track the execution of your Crew and log important events
Analytics: Collect data about your Crew’s performance and behavior
Debugging: Set up temporary listeners to debug specific issues
Integration: Connect CrewAI with external systems like monitoring platforms, databases, or notification services
Custom Behavior: Trigger custom actions based on specific events

Best Practices

Keep Handlers Light: Event handlers should be lightweight and avoid blocking operations
Error Handling: Include proper error handling in your event handlers to prevent exceptions from affecting the main execution
Cleanup: If your listener allocates resources, ensure they’re properly cleaned up
Selective Listening: Only listen for events you actually need to handle
Testing: Test your event listeners in isolation to ensure they behave as expected

By leveraging CrewAI’s event system, you can extend its functionality and integrate it seamlessly with your existing infrastructure.

Get Started

Guides

Core Concepts

MCP Integration

Tools

Observability

Learn

Telemetry

Event Listeners

Overview

How It Works

Creating a Custom Event Listener

Properly Registering Your Listener

Option 1: Import and Instantiate in Your Crew or Flow Implementation

For Crew-based Applications

For Flow-based Applications

Option 2: Create a Package for Your Listeners

Available Event Types

Crew Events

Agent Events

Task Events

Tool Usage Events

Knowledge Events

LLM Guardrail Events

Flow Events

LLM Events

Memory Events

Event Handler Structure

Advanced Usage: Scoped Handlers

Use Cases

Best Practices

Get Started

Guides

Core Concepts

MCP Integration

Tools

Observability

Learn

Telemetry

​Overview

​How It Works

​Creating a Custom Event Listener

​Properly Registering Your Listener

​Option 1: Import and Instantiate in Your Crew or Flow Implementation

​For Crew-based Applications

​For Flow-based Applications

​Option 2: Create a Package for Your Listeners

​Available Event Types

​Crew Events

​Agent Events

​Task Events

​Tool Usage Events

​Knowledge Events

​LLM Guardrail Events

​Flow Events

​LLM Events

​Memory Events

​Event Handler Structure

​Advanced Usage: Scoped Handlers

​Use Cases

​Best Practices

Overview

How It Works

Creating a Custom Event Listener

Properly Registering Your Listener

Option 1: Import and Instantiate in Your Crew or Flow Implementation

For Crew-based Applications

For Flow-based Applications

Option 2: Create a Package for Your Listeners

Available Event Types

Crew Events

Agent Events

Task Events

Tool Usage Events

Knowledge Events

LLM Guardrail Events

Flow Events

LLM Events

Memory Events

Event Handler Structure

Advanced Usage: Scoped Handlers

Use Cases

Best Practices