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构建您的第一个 CrewAI 智能体

让我们创建一个简单的团队，帮助我们针对特定主题进行研究并报告最新的 AI 发展。

在继续之前，请确保您已完成 CrewAI 的安装。如果您尚未安装，请按照安装指南进行操作。

按照以下步骤开始组建您的团队吧！🚣‍♂️

1

创建您的团队

通过在终端中运行以下命令来创建一个新的团队项目。这将在名为latest-ai-development的目录中创建您团队的基本结构。

crewai create crew latest-ai-development
2

导航到您的新团队项目

cd latest-ai-development
3

修改您的 `agents.yaml` 文件

您可以根据需要修改智能体以适应您的用例，或直接复制粘贴到您的项目中。在您的agents.yaml和tasks.yaml文件中插入的任何变量（如{topic}）都将被main.py文件中该变量的值替换。

agents.yaml
# src/latest_ai_development/config/agents.yaml
researcher:
  role: >
    {topic} Senior Data Researcher
  goal: >
    Uncover cutting-edge developments in {topic}
  backstory: >
    You're a seasoned researcher with a knack for uncovering the latest
    developments in {topic}. Known for your ability to find the most relevant
    information and present it in a clear and concise manner.

reporting_analyst:
  role: >
    {topic} Reporting Analyst
  goal: >
    Create detailed reports based on {topic} data analysis and research findings
  backstory: >
    You're a meticulous analyst with a keen eye for detail. You're known for
    your ability to turn complex data into clear and concise reports, making
    it easy for others to understand and act on the information you provide.
4

修改您的 `tasks.yaml` 文件

tasks.yaml
# src/latest_ai_development/config/tasks.yaml
research_task:
  description: >
    Conduct a thorough research about {topic}
    Make sure you find any interesting and relevant information given
    the current year is 2025.
  expected_output: >
    A list with 10 bullet points of the most relevant information about {topic}
  agent: researcher

reporting_task:
  description: >
    Review the context you got and expand each topic into a full section for a report.
    Make sure the report is detailed and contains any and all relevant information.
  expected_output: >
    A fully fledge reports with the mains topics, each with a full section of information.
    Formatted as markdown without '```'
  agent: reporting_analyst
  output_file: report.md
5

修改您的 `crew.py` 文件

crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
from crewai.agents.agent_builder.base_agent import BaseAgent
from typing import List

@CrewBase
class LatestAiDevelopmentCrew():
  """LatestAiDevelopment crew"""

  agents: List[BaseAgent]
  tasks: List[Task]

  @agent
  def researcher(self) -> Agent:
    return Agent(
      config=self.agents_config['researcher'], # type: ignore[index]
      verbose=True,
      tools=[SerperDevTool()]
    )

  @agent
  def reporting_analyst(self) -> Agent:
    return Agent(
      config=self.agents_config['reporting_analyst'], # type: ignore[index]
      verbose=True
    )

  @task
  def research_task(self) -> Task:
    return Task(
      config=self.tasks_config['research_task'], # type: ignore[index]
    )

  @task
  def reporting_task(self) -> Task:
    return Task(
      config=self.tasks_config['reporting_task'], # type: ignore[index]
      output_file='output/report.md' # This is the file that will be contain the final report.
    )

  @crew
  def crew(self) -> Crew:
    """Creates the LatestAiDevelopment crew"""
    return Crew(
      agents=self.agents, # Automatically created by the @agent decorator
      tasks=self.tasks, # Automatically created by the @task decorator
      process=Process.sequential,
      verbose=True,
    )
6

[可选] 添加团队前置和后置函数

crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
from crewai_tools import SerperDevTool

@CrewBase
class LatestAiDevelopmentCrew():
  """LatestAiDevelopment crew"""

  @before_kickoff
  def before_kickoff_function(self, inputs):
    print(f"Before kickoff function with inputs: {inputs}")
    return inputs # You can return the inputs or modify them as needed

  @after_kickoff
  def after_kickoff_function(self, result):
    print(f"After kickoff function with result: {result}")
    return result # You can return the result or modify it as needed

  # ... remaining code
7

您可以随意向您的团队传递自定义输入

例如，您可以将topic输入传递给您的团队，以定制研究和报告内容。

main.py
#!/usr/bin/env python
# src/latest_ai_development/main.py
import sys
from latest_ai_development.crew import LatestAiDevelopmentCrew

def run():
  """
  Run the crew.
  """
  inputs = {
    'topic': 'AI Agents'
  }
  LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
8

设置您的环境变量

在运行您的团队之前，请确保您在.env文件中将以下键设置为环境变量

  • Serper.dev API 密钥：SERPER_API_KEY=YOUR_KEY_HERE
  • 您选择的模型的配置，例如 API 密钥。请参阅LLM 设置指南，了解如何配置来自任何提供商的模型。
9

锁定并安装依赖项

  • 使用 CLI 命令锁定并安装依赖项 
    crewai install
  • 如果您想安装其他包，可以运行以下命令 
    uv add <package-name>
10

运行您的团队

  • 要运行您的团队，请在项目根目录中执行以下命令 
    crewai run
11

企业版替代方案：在 Crew Studio 中创建

对于 CrewAI 企业版用户，您无需编写代码即可创建相同的团队

  1. 登录您的 CrewAI 企业版账户（在 app.crewai.com 创建一个免费账户）
  2. 打开 Crew Studio
  3. 输入您尝试构建的自动化是什么
  4. 可视化地创建任务并按顺序连接它们
  5. 配置您的输入并点击“下载代码”或“部署”

试用 CrewAI 企业版

在 CrewAI 企业版开始您的免费账户

12

查看您的最终报告

您应该在控制台中看到输出，并且项目根目录中应该生成report.md文件，其中包含最终报告。

以下是报告示例

# Comprehensive Report on the Rise and Impact of AI Agents in 2025

## 1. Introduction to AI Agents
In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.

## 2. Benefits of AI Agents
AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:

- **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
- **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
- **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.

## 3. Popular AI Agent Frameworks
Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:

- **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
- **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
- **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
- **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
- **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
- **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.

These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.

## 4. AI Agents in Human Resources
AI agents are revolutionizing HR practices by automating and optimizing key functions:

- **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
- **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
- **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.

As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.

## 5. AI Agents in Finance
The finance sector is seeing extensive integration of AI agents that enhance financial practices:

- **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
- **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
- **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.

The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.

## 6. Market Trends and Investments
The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.

Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.

## 7. Future Predictions and Implications
Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:

- Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
- Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
- Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.

To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.

## 8. Conclusion
The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.

恭喜！

您已成功设置了您的团队项目，并已准备好开始构建您自己的智能体工作流！

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关于命名一致性的注意事项

您在 YAML 文件（agents.yaml和tasks.yaml）中使用的名称应与您 Python 代码中的方法名称匹配。例如，您可以从tasks.yaml文件中引用特定任务的智能体。这种命名一致性使得 CrewAI 可以自动将您的配置与您的代码关联起来；否则，您的任务将无法正确识别引用。

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示例引用

请注意，我们在agents.yaml（email_summarizer）文件中使用的智能体名称与crew.py（email_summarizer）文件中的方法名称相同。

agents.yaml
email_summarizer:
    role: >
      Email Summarizer
    goal: >
      Summarize emails into a concise and clear summary
    backstory: >
      You will create a 5 bullet point summary of the report
    llm: provider/model-id  # Add your choice of model here

请注意，我们在tasks.yaml（email_summarizer_task）文件中使用的任务名称与crew.py（email_summarizer_task）文件中的方法名称相同。

tasks.yaml
email_summarizer_task:
    description: >
      Summarize the email into a 5 bullet point summary
    expected_output: >
      A 5 bullet point summary of the email
    agent: email_summarizer
    context:
      - reporting_task
      - research_task

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部署您的团队

将您的团队部署到生产环境的最简单方法是通过CrewAI 企业版。

观看此视频教程，了解如何使用 CLI 将您的团队部署到CrewAI 企业版的详细步骤演示。

在企业版上部署

开始使用 CrewAI 企业版，只需点击几下即可将您的团队部署到生产环境。

加入社区

加入我们的开源社区，讨论想法、分享您的项目并与其他 CrewAI 开发者联系。