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docs/guides/crews/first-crew.mdx
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---
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title: Build Your First Crew
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description: Step-by-step tutorial to create a collaborative AI team that works together to solve complex problems.
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icon: users-gear
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---
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# Build Your First Crew
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In this guide, we'll walk through creating a research crew that will help us research and analyze a topic, then create a comprehensive report. This is a practical example of how AI agents can collaborate to accomplish complex tasks.
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Before starting, make sure you have:
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1. Installed CrewAI following the [installation guide](/installation)
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2. Set up your OpenAI API key in your environment variables
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3. Basic understanding of Python
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## Step 1: Create a New CrewAI Project
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First, let's create a new CrewAI project using the CLI:
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```bash
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crewai create crew research_crew
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cd research_crew
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```
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This will generate a project with the basic structure needed for your crew. The CLI automatically creates:
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- A project directory with the necessary files
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- Configuration files for agents and tasks
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- A basic crew implementation
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- A main script to run the crew
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<Frame caption="CrewAI Framework Overview">
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<img src="../../crews.png" alt="CrewAI Framework Overview" />
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</Frame>
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## Step 2: Explore the Project Structure
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Let's take a moment to understand the project structure created by the CLI:
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```
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research_crew/
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├── .gitignore
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├── pyproject.toml
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├── README.md
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├── .env
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└── src/
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└── research_crew/
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├── __init__.py
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├── main.py
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├── crew.py
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├── tools/
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│ ├── custom_tool.py
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│ └── __init__.py
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└── config/
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├── agents.yaml
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└── tasks.yaml
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```
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This structure follows best practices for Python projects and makes it easy to organize your code.
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## Step 3: Configure Your Agents
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Let's modify the `agents.yaml` file to define two specialized agents: a researcher and an analyst.
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```yaml
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# src/research_crew/config/agents.yaml
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researcher:
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role: >
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Senior Research Specialist for {topic}
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goal: >
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Find comprehensive and accurate information about {topic}
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with a focus on recent developments and key insights
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backstory: >
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You are an experienced research specialist with a talent for
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finding relevant information from various sources. You excel at
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organizing information in a clear and structured manner, making
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complex topics accessible to others.
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llm: openai/gpt-4o-mini
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analyst:
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role: >
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Data Analyst and Report Writer for {topic}
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goal: >
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Analyze research findings and create a comprehensive, well-structured
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report that presents insights in a clear and engaging way
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backstory: >
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You are a skilled analyst with a background in data interpretation
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and technical writing. You have a talent for identifying patterns
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and extracting meaningful insights from research data, then
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communicating those insights effectively through well-crafted reports.
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llm: openai/gpt-4o-mini
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```
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## Step 4: Define Your Tasks
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Now, let's modify the `tasks.yaml` file to define the research and analysis tasks:
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```yaml
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# src/research_crew/config/tasks.yaml
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research_task:
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description: >
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Conduct thorough research on {topic}. Focus on:
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1. Key concepts and definitions
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2. Historical development and recent trends
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3. Major challenges and opportunities
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4. Notable applications or case studies
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5. Future outlook and potential developments
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Make sure to organize your findings in a structured format with clear sections.
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expected_output: >
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A comprehensive research document with well-organized sections covering
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all the requested aspects of {topic}. Include specific facts, figures,
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and examples where relevant.
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agent: researcher
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analysis_task:
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description: >
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Analyze the research findings and create a comprehensive report on {topic}.
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Your report should:
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1. Begin with an executive summary
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2. Include all key information from the research
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3. Provide insightful analysis of trends and patterns
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4. Offer recommendations or future considerations
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5. Be formatted in a professional, easy-to-read style with clear headings
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expected_output: >
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A polished, professional report on {topic} that presents the research
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findings with added analysis and insights. The report should be well-structured
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with an executive summary, main sections, and conclusion.
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agent: analyst
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context:
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- research_task
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output_file: output/report.md
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```
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## Step 5: Configure Your Crew
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Now, let's modify the `crew.py` file to set up our research crew:
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```python
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# src/research_crew/crew.py
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from crewai import Agent, Crew, Process, Task
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from crewai.project import CrewBase, agent, crew, task
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from crewai_tools import SerperDevTool
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@CrewBase
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class ResearchCrew():
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"""Research crew for comprehensive topic analysis and reporting"""
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config['researcher'],
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verbose=True,
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tools=[SerperDevTool()]
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)
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@agent
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def analyst(self) -> Agent:
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return Agent(
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config=self.agents_config['analyst'],
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verbose=True
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)
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@task
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def research_task(self) -> Task:
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return Task(
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config=self.tasks_config['research_task']
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)
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@task
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def analysis_task(self) -> Task:
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return Task(
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config=self.tasks_config['analysis_task'],
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output_file='output/report.md'
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)
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@crew
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def crew(self) -> Crew:
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"""Creates the research crew"""
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return Crew(
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agents=self.agents,
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tasks=self.tasks,
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process=Process.sequential,
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verbose=True,
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)
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```
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## Step 6: Set Up Your Main Script
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Let's modify the `main.py` file to run our crew:
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```python
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#!/usr/bin/env python
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# src/research_crew/main.py
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import os
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from research_crew.crew import ResearchCrew
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# Create output directory if it doesn't exist
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os.makedirs('output', exist_ok=True)
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def run():
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"""
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Run the research crew.
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"""
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inputs = {
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'topic': 'Artificial Intelligence in Healthcare'
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}
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# Create and run the crew
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result = ResearchCrew().crew().kickoff(inputs=inputs)
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# Print the result
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print("\n\n=== FINAL REPORT ===\n\n")
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print(result.raw)
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print("\n\nReport has been saved to output/report.md")
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if __name__ == "__main__":
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run()
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```
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## Step 7: Set Up Your Environment Variables
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Create a `.env` file in your project root with your API keys:
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```
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OPENAI_API_KEY=your_openai_api_key
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SERPER_API_KEY=your_serper_api_key
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```
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You can get a Serper API key from [Serper.dev](https://serper.dev/).
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## Step 8: Install Dependencies
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Install the required dependencies using the CrewAI CLI:
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```bash
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crewai install
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```
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This command will:
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1. Read the dependencies from your project configuration
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2. Create a virtual environment if needed
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3. Install all required packages
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## Step 9: Run Your Crew
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Now, run your crew using the CrewAI CLI:
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```bash
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crewai run
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```
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Your crew will start working! The researcher will gather information about the specified topic, and the analyst will create a comprehensive report based on that research.
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## Step 10: Review the Output
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Once the crew completes its work, you'll find the final report in the `output/report.md` file. The report will include:
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1. An executive summary
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2. Detailed information about the topic
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3. Analysis and insights
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4. Recommendations or future considerations
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## Exploring Other CLI Commands
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CrewAI offers several other useful CLI commands for working with crews:
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```bash
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# View all available commands
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crewai --help
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# Run the crew
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crewai run
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# Test the crew
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crewai test
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# Reset crew memories
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crewai reset-memories
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# Replay from a specific task
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crewai replay -t <task_id>
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# View the latest task outputs
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crewai log-tasks-outputs
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```
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## Customizing Your Crew
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You can customize your crew in several ways:
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1. **Add more agents**: Create additional specialized roles like a fact-checker or editor
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2. **Modify the process**: Change from `Process.sequential` to `Process.hierarchical` for more complex workflows
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3. **Add custom tools**: Create and add specialized tools for your agents
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4. **Change the topic**: Update the `topic` parameter in the `inputs` dictionary to research different subjects
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## Next Steps
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Now that you've built your first crew, you can:
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1. Experiment with different agent configurations
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2. Try more complex task structures
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3. Implement custom tools for your agents
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4. Explore [CrewAI Flows](/guides/flows/first-flow) for more advanced workflows
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<Check>
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Congratulations! You've successfully built your first CrewAI crew that can research and analyze any topic you provide.
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</Check>
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