Kickoff Crew Asynchronously

Introduction

CrewAI provides the ability to kickoff a crew asynchronously, allowing you to start the crew execution in a non-blocking manner. This feature is particularly useful when you want to run multiple crews concurrently or when you need to perform other tasks while the crew is executing. CrewAI offers two approaches for async execution:

Method	Type	Description
`akickoff()`	Native async	True async/await throughout the entire execution chain
`kickoff_async()`	Thread-based	Wraps synchronous execution in `asyncio.to_thread`

For high-concurrency workloads, akickoff() is recommended as it uses native async for task execution, memory operations, and knowledge retrieval.

Native Async Execution with `akickoff()`

The akickoff() method provides true native async execution, using async/await throughout the entire execution chain including task execution, memory operations, and knowledge queries.

Method Signature

Code

async def akickoff(self, inputs: dict) -> CrewOutput:

Parameters

inputs (dict): A dictionary containing the input data required for the tasks.

Returns

CrewOutput: An object representing the result of the crew execution.

Example: Native Async Crew Execution

Code

import asyncio
from crewai import Crew, Agent, Task

# Create an agent
coding_agent = Agent(
    role="Python Data Analyst",
    goal="Analyze data and provide insights using Python",
    backstory="You are an experienced data analyst with strong Python skills.",
    allow_code_execution=True
)

# Create a task
data_analysis_task = Task(
    description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
    agent=coding_agent,
    expected_output="The average age of the participants."
)

# Create a crew
analysis_crew = Crew(
    agents=[coding_agent],
    tasks=[data_analysis_task]
)

# Native async execution
async def main():
    result = await analysis_crew.akickoff(inputs={"ages": [25, 30, 35, 40, 45]})
    print("Crew Result:", result)

asyncio.run(main())

Example: Multiple Native Async Crews

Run multiple crews concurrently using asyncio.gather() with native async:

Code

import asyncio
from crewai import Crew, Agent, Task

coding_agent = Agent(
    role="Python Data Analyst",
    goal="Analyze data and provide insights using Python",
    backstory="You are an experienced data analyst with strong Python skills.",
    allow_code_execution=True
)

task_1 = Task(
    description="Analyze the first dataset and calculate the average age. Ages: {ages}",
    agent=coding_agent,
    expected_output="The average age of the participants."
)

task_2 = Task(
    description="Analyze the second dataset and calculate the average age. Ages: {ages}",
    agent=coding_agent,
    expected_output="The average age of the participants."
)

crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
crew_2 = Crew(agents=[coding_agent], tasks=[task_2])

async def main():
    results = await asyncio.gather(
        crew_1.akickoff(inputs={"ages": [25, 30, 35, 40, 45]}),
        crew_2.akickoff(inputs={"ages": [20, 22, 24, 28, 30]})
    )

    for i, result in enumerate(results, 1):
        print(f"Crew {i} Result:", result)

asyncio.run(main())

Example: Native Async for Multiple Inputs

Use akickoff_for_each() to execute your crew against multiple inputs concurrently with native async:

Code

import asyncio
from crewai import Crew, Agent, Task

coding_agent = Agent(
    role="Python Data Analyst",
    goal="Analyze data and provide insights using Python",
    backstory="You are an experienced data analyst with strong Python skills.",
    allow_code_execution=True
)

data_analysis_task = Task(
    description="Analyze the dataset and calculate the average age. Ages: {ages}",
    agent=coding_agent,
    expected_output="The average age of the participants."
)

analysis_crew = Crew(
    agents=[coding_agent],
    tasks=[data_analysis_task]
)

async def main():
    datasets = [
        {"ages": [25, 30, 35, 40, 45]},
        {"ages": [20, 22, 24, 28, 30]},
        {"ages": [30, 35, 40, 45, 50]}
    ]

    results = await analysis_crew.akickoff_for_each(datasets)

    for i, result in enumerate(results, 1):
        print(f"Dataset {i} Result:", result)

asyncio.run(main())

Thread-Based Async with `kickoff_async()`

The kickoff_async() method provides async execution by wrapping the synchronous kickoff() in a thread. This is useful for simpler async integration or backward compatibility.

Method Signature

Code

async def kickoff_async(self, inputs: dict) -> CrewOutput:

Parameters

inputs (dict): A dictionary containing the input data required for the tasks.

Returns

CrewOutput: An object representing the result of the crew execution.

Example: Thread-Based Async Execution

Code

import asyncio
from crewai import Crew, Agent, Task

coding_agent = Agent(
    role="Python Data Analyst",
    goal="Analyze data and provide insights using Python",
    backstory="You are an experienced data analyst with strong Python skills.",
    allow_code_execution=True
)

data_analysis_task = Task(
    description="Analyze the given dataset and calculate the average age of participants. Ages: {ages}",
    agent=coding_agent,
    expected_output="The average age of the participants."
)

analysis_crew = Crew(
    agents=[coding_agent],
    tasks=[data_analysis_task]
)

async def async_crew_execution():
    result = await analysis_crew.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
    print("Crew Result:", result)

asyncio.run(async_crew_execution())

Example: Multiple Thread-Based Async Crews

Code

import asyncio
from crewai import Crew, Agent, Task

coding_agent = Agent(
    role="Python Data Analyst",
    goal="Analyze data and provide insights using Python",
    backstory="You are an experienced data analyst with strong Python skills.",
    allow_code_execution=True
)

task_1 = Task(
    description="Analyze the first dataset and calculate the average age of participants. Ages: {ages}",
    agent=coding_agent,
    expected_output="The average age of the participants."
)

task_2 = Task(
    description="Analyze the second dataset and calculate the average age of participants. Ages: {ages}",
    agent=coding_agent,
    expected_output="The average age of the participants."
)

crew_1 = Crew(agents=[coding_agent], tasks=[task_1])
crew_2 = Crew(agents=[coding_agent], tasks=[task_2])

async def async_multiple_crews():
    result_1 = crew_1.kickoff_async(inputs={"ages": [25, 30, 35, 40, 45]})
    result_2 = crew_2.kickoff_async(inputs={"ages": [20, 22, 24, 28, 30]})

    results = await asyncio.gather(result_1, result_2)

    for i, result in enumerate(results, 1):
        print(f"Crew {i} Result:", result)

asyncio.run(async_multiple_crews())

Async Streaming

Both async methods support streaming when stream=True is set on the crew:

Code

import asyncio
from crewai import Crew, Agent, Task

agent = Agent(
    role="Researcher",
    goal="Research and summarize topics",
    backstory="You are an expert researcher."
)

task = Task(
    description="Research the topic: {topic}",
    agent=agent,
    expected_output="A comprehensive summary of the topic."
)

crew = Crew(
    agents=[agent],
    tasks=[task],
    stream=True  # Enable streaming
)

async def main():
    streaming_output = await crew.akickoff(inputs={"topic": "AI trends in 2024"})

    # Async iteration over streaming chunks
    async for chunk in streaming_output:
        print(f"Chunk: {chunk.content}")

    # Access final result after streaming completes
    result = streaming_output.result
    print(f"Final result: {result.raw}")

asyncio.run(main())

Potential Use Cases

Parallel Content Generation: Kickoff multiple independent crews asynchronously, each responsible for generating content on different topics. For example, one crew might research and draft an article on AI trends, while another crew generates social media posts about a new product launch.
Concurrent Market Research Tasks: Launch multiple crews asynchronously to conduct market research in parallel. One crew might analyze industry trends, while another examines competitor strategies, and yet another evaluates consumer sentiment.
Independent Travel Planning Modules: Execute separate crews to independently plan different aspects of a trip. One crew might handle flight options, another handles accommodation, and a third plans activities.

Choosing Between `akickoff()` and `kickoff_async()`

Feature	`akickoff()`	`kickoff_async()`
Execution model	Native async/await	Thread-based wrapper
Task execution	Async with `aexecute_sync()`	Sync in thread pool
Memory operations	Async	Sync in thread pool
Knowledge retrieval	Async	Sync in thread pool
Best for	High-concurrency, I/O-bound workloads	Simple async integration
Streaming support	Yes	Yes

​Introduction

​Native Async Execution with akickoff()

​Method Signature

​Parameters

​Returns

​Example: Native Async Crew Execution

​Example: Multiple Native Async Crews

​Example: Native Async for Multiple Inputs

​Thread-Based Async with kickoff_async()

​Method Signature

​Parameters

​Returns

​Example: Thread-Based Async Execution

​Example: Multiple Thread-Based Async Crews

​Async Streaming

​Potential Use Cases

​Choosing Between akickoff() and kickoff_async()

Introduction

Native Async Execution with `akickoff()`

Method Signature

Parameters

Returns

Example: Native Async Crew Execution

Example: Multiple Native Async Crews

Example: Native Async for Multiple Inputs

Thread-Based Async with `kickoff_async()`

Method Signature

Parameters

Returns

Example: Thread-Based Async Execution

Example: Multiple Thread-Based Async Crews

Async Streaming

Potential Use Cases

Choosing Between `akickoff()` and `kickoff_async()`