异步启动 Crew

简介

CrewAI 支持异步启动 crew，使你能够以非阻塞方式开始 crew 执行。这个功能在你想同时运行多个 crew，或者在 crew 执行期间还需要做其他事情时尤其有用。

CrewAI 提供两种异步执行方式：

方法	类型	说明
`akickoff()`	原生 async	在整个执行链中使用真正的 async/await
`kickoff_async()`	基于线程	使用 `asyncio.to_thread` 包装同步执行

使用 `akickoff()` 的原生异步执行

akickoff() 方法提供真正的原生异步执行，在整个执行链中使用 async/await，包括任务执行、内存操作和知识查询。

方法签名

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

参数

inputs（dict）：包含任务所需输入数据的字典。

返回值

CrewOutput：表示 crew 执行结果的对象。

示例：原生异步 Crew 执行

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())

示例：多个原生异步 Crew

使用 asyncio.gather() 并行运行多个 crew：

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())

示例：多个输入的原生异步

使用 akickoff_for_each() 以原生异步并发执行你的 crew，处理多个输入：

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())

基于线程的异步：`kickoff_async()`

kickoff_async() 通过把同步的 kickoff() 包装进线程来提供异步执行。这适合更简单的异步集成或向后兼容。

方法签名

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

参数

inputs（dict）：包含任务所需输入数据的字典。

返回值

CrewOutput：表示 crew 执行结果的对象。

示例：基于线程的异步执行

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())

示例：多个基于线程的异步 Crew

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())

异步流式输出

当 stream=True 设置在 crew 上时，两种异步方法都支持流式输出：

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())

潜在用例

并行内容生成：异步启动多个彼此独立的 crew，分别负责不同主题的内容生成。例如，一个 crew 研究并撰写 AI 趋势文章，另一个 crew 为新品发布生成社交媒体文案。
并发市场调研任务：并行启动多个 crew，分别执行市场调研。一个 crew 分析行业趋势，另一个分析竞争对手策略，第三个评估消费者情绪。
独立的旅行规划模块：把旅行的不同部分分给独立 crew 分别规划。一个 crew 处理机票方案，一个处理住宿，一个规划活动。

选择 `akickoff()` 还是 `kickoff_async()`

特性	`akickoff()`	`kickoff_async()`
执行模型	原生 async/await	基于线程的包装
任务执行	使用 `aexecute_sync()` 的异步执行	在线程池中同步执行
内存操作	异步	在线程池中同步
知识检索	异步	在线程池中同步
最适合	高并发、I/O 密集型工作负载	简单的异步集成
流式支持	是	是

异步启动 Crew

简介

使用 akickoff() 的原生异步执行

方法签名

参数

返回值

示例：原生异步 Crew 执行

示例：多个原生异步 Crew

示例：多个输入的原生异步

基于线程的异步：kickoff_async()

方法签名

参数

返回值

示例：基于线程的异步执行

示例：多个基于线程的异步 Crew

异步流式输出

潜在用例

选择 akickoff() 还是 kickoff_async()

使用 `akickoff()` 的原生异步执行

基于线程的异步：`kickoff_async()`

选择 `akickoff()` 还是 `kickoff_async()`