CrewAI：通过智能代理的角色分配、目标共享和协同作业，实现高效的团队协作

CrewAI是一款创新的AI协作平台，专为实现智能代理的角色分配、目标共享和协同作业而设计。无论是构建智能助理平台、自动化客服团队，还是多代理研究团队，CrewAI为复杂的多代理互动提供了坚实的支持。

为何选择CrewAI？

🤖 多角色代理设计：为代理定制具体角色、目标和工具，优化协作效率。
📄 自主代理任务委派：代理之间可以自主分配任务，提高解决问题的效率。
🌐 灵活的任务管理：自定义任务的工具和分配方式，动态管理代理任务。
📂 过程驱动：目前支持顺序任务执行，同时正在开发更复杂的共识和层级过程。
🔍 兼容开源模型：既可以使用Open AI，也可接入其他开源模型，实现更广泛的应用。

快速开始

CrewAI的使用流程简单明了。从安装开始，只需几步即可设置您的团队，并开始AI协作。

1. 安装

pip install crewai

如果您还想安装crewai-tools，这是一个包含代理可以使用的工具的软件包，但有更多依赖项，您可以安装它，下面的示例使用它：

pip install 'crewai[tools]'

2. 组建你的团队

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key

# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.

# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'

search_tool = SerperDevTool()

# Define your agents with roles and goals
researcher = Agent(
  role='Senior Research Analyst',
  goal='Uncover cutting-edge developments in AI and data science',
  backstory="""You work at a leading tech think tank.
  Your expertise lies in identifying emerging trends.
  You have a knack for dissecting complex data and presenting actionable insights.""",
  verbose=True,
  allow_delegation=False,
  tools=[search_tool]
  # You can pass an optional llm attribute specifying what mode you wanna use.
  # It can be a local model through Ollama / LM Studio or a remote
  # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
  #
  # import os
  # os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
  #
  # OR
  #
  # from langchain_openai import ChatOpenAI
  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
)
writer = Agent(
  role='Tech Content Strategist',
  goal='Craft compelling content on tech advancements',
  backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
  You transform complex concepts into compelling narratives.""",
  verbose=True,
  allow_delegation=True
)

# Create tasks for your agents
task1 = Task(
  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
  Identify key trends, breakthrough technologies, and potential industry impacts.""",
  expected_output="Full analysis report in bullet points",
  agent=researcher
)

task2 = Task(
  description="""Using the insights provided, develop an engaging blog
  post that highlights the most significant AI advancements.
  Your post should be informative yet accessible, catering to a tech-savvy audience.
  Make it sound cool, avoid complex words so it doesn't sound like AI.""",
  expected_output="Full blog post of at least 4 paragraphs",
  agent=writer
)

# Instantiate your crew with a sequential process
crew = Crew(
  agents=[researcher, writer],
  tasks=[task1, task2],
  verbose=2, # You can set it to 1 or 2 to different logging levels
)

# Get your crew to work!
result = crew.kickoff()

print("######################")
print(result)

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key

# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.

# os.environ["OPENAI_API_BASE"] = 'http://localhost:11434/v1'
# os.environ["OPENAI_MODEL_NAME"] ='openhermes'  # Adjust based on available model
# os.environ["OPENAI_API_KEY"] ='sk-111111111111111111111111111111111111111111111111'

search_tool = SerperDevTool()

# Define your agents with roles and goals
researcher = Agent(
  role='Senior Research Analyst',
  goal='Uncover cutting-edge developments in AI and data science',
  backstory="""You work at a leading tech think tank.
  Your expertise lies in identifying emerging trends.
  You have a knack for dissecting complex data and presenting actionable insights.""",
  verbose=True,
  allow_delegation=False,
  tools=[search_tool]
  # You can pass an optional llm attribute specifying what mode you wanna use.
  # It can be a local model through Ollama / LM Studio or a remote
  # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
  #
  # import os
  # os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
  #
  # OR
  #
  # from langchain_openai import ChatOpenAI
  # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
)
writer = Agent(
  role='Tech Content Strategist',
  goal='Craft compelling content on tech advancements',
  backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
  You transform complex concepts into compelling narratives.""",
  verbose=True,
  allow_delegation=True
)

# Create tasks for your agents
task1 = Task(
  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
  Identify key trends, breakthrough technologies, and potential industry impacts.""",
  expected_output="Full analysis report in bullet points",
  agent=researcher
)

task2 = Task(
  description="""Using the insights provided, develop an engaging blog
  post that highlights the most significant AI advancements.
  Your post should be informative yet accessible, catering to a tech-savvy audience.
  Make it sound cool, avoid complex words so it doesn't sound like AI.""",
  expected_output="Full blog post of at least 4 paragraphs",
  agent=writer
)

# Instantiate your crew with a sequential process
crew = Crew(
  agents=[researcher, writer],
  tasks=[task1, task2],
  verbose=2, # You can set it to 1 or 2 to different logging levels
)

# Get your crew to work!
result = crew.kickoff()

print("######################")
print(result)

关键特性

基于角色的代理设计：为代理定制具体的角色、目标和工具。
自主代理间的任务委派：代理可以自主地委派任务，增强问题解决的效率。
灵活的任务管理：定义可定制工具的任务，并动态分配给代理。
过程驱动：目前仅支持顺序任务执行，但正在研发更复杂的共识和层级过程。
与开源模型兼容：可以运行使用Open AI或其他开源模型的团队。

github_ai_tool_crewai_1

CrewAI与其他平台比较

CrewAI结合了Autogen的灵活性和ChatDev的结构化过程方法，但避免了这些平台的局限性。其过程设计旨在动态适应，无缝融入开发和生产工作流。

CrewAI不仅是一个多代理AI协作平台，更是未来智能协作的领导者。通过创新的技术和灵活的设计，CrewAI为各种规模和类型的团队提供了一个强大的AI协作环境，使得任务执行更加高效、协调和灵活。