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Taking Control of AI Workflows with Flows

CrewAI Flows represent the next level in AI orchestration - combining the collaborative power of AI agent crews with the precision and flexibility of procedural programming. While crews excel at agent collaboration, flows give you fine-grained control over exactly how and when different components of your AI system interact. In this guide, we’ll walk through creating a powerful CrewAI Flow that generates a comprehensive learning guide on any topic. This tutorial will demonstrate how Flows provide structured, event-driven control over your AI workflows by combining regular code, direct LLM calls, and crew-based processing.

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What Makes Flows Powerful

Flows enable you to:
  1. Combine different AI interaction patterns - Use crews for complex collaborative tasks, direct LLM calls for simpler operations, and regular code for procedural logic
  2. Build event-driven systems - Define how components respond to specific events and data changes
  3. Maintain state across components - Share and transform data between different parts of your application
  4. Integrate with external systems - Seamlessly connect your AI workflow with databases, APIs, and user interfaces
  5. Create complex execution paths - Design conditional branches, parallel processing, and dynamic workflows

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What You’ll Build and Learn

By the end of this guide, you’ll have:
  1. Created a sophisticated content generation system that combines user input, AI planning, and multi-agent content creation
  2. Orchestrated the flow of information between different components of your system
  3. Implemented event-driven architecture where each step responds to the completion of previous steps
  4. Built a foundation for more complex AI applications that you can expand and customize
This guide creator flow demonstrates fundamental patterns that can be applied to create much more advanced applications, such as:
  • Interactive AI assistants that combine multiple specialized subsystems
  • Complex data processing pipelines with AI-enhanced transformations
  • Autonomous agents that integrate with external services and APIs
  • Multi-stage decision-making systems with human-in-the-loop processes
Let’s dive in and build your first flow!

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Prerequisites

Before starting, make sure you have:
  1. Installed CrewAI following the installation guide
  2. Set up your LLM API key in your environment, following the LLM setup guide
  3. Basic understanding of Python

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Step 1: Create a New CrewAI Flow Project

First, let’s create a new CrewAI Flow project using the CLI. This command sets up a scaffolded project with all the necessary directories and template files for your flow. 
crewai create flow guide_creator_flow
cd guide_creator_flow
This will generate a project with the basic structure needed for your flow.
CrewAI Framework Overview

CrewAI Framework Overview

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Step 2: Understanding the Project Structure

The generated project has the following structure. Take a moment to familiarize yourself with it, as understanding this structure will help you create more complex flows in the future. 
guide_creator_flow/
├── .gitignore
├── pyproject.toml
├── README.md
├── .env
├── main.py
├── crews/
│   └── poem_crew/
│       ├── config/
│       │   ├── agents.yaml
│       │   └── tasks.yaml
│       └── poem_crew.py
└── tools/
    └── custom_tool.py
This structure provides a clear separation between different components of your flow:
  • The main flow logic in the main.py file
  • Specialized crews in the crews directory
  • Custom tools in the tools directory
We’ll modify this structure to create our guide creator flow, which will orchestrate the process of generating comprehensive learning guides.

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Step 3: Add a Content Writer Crew

Our flow will need a specialized crew to handle the content creation process. Let’s use the CrewAI CLI to add a content writer crew: 
crewai flow add-crew content-crew
This command automatically creates the necessary directories and template files for your crew. The content writer crew will be responsible for writing and reviewing sections of our guide, working within the overall flow orchestrated by our main application.

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Step 4: Configure the Content Writer Crew

Now, let’s modify the generated files for the content writer crew. We’ll set up two specialized agents - a writer and a reviewer - that will collaborate to create high-quality content for our guide.
  1. First, update the agents configuration file to define our content creation team: Remember to set llm to the provider you are using. 
# src/guide_creator_flow/crews/content_crew/config/agents.yaml
content_writer:
  role: >
    Educational Content Writer
  goal: >
    Create engaging, informative content that thoroughly explains the assigned topic
    and provides valuable insights to the reader
  backstory: >
    You are a talented educational writer with expertise in creating clear, engaging
    content. You have a gift for explaining complex concepts in accessible language
    and organizing information in a way that helps readers build their understanding.
  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...

content_reviewer:
  role: >
    Educational Content Reviewer and Editor
  goal: >
    Ensure content is accurate, comprehensive, well-structured, and maintains
    consistency with previously written sections
  backstory: >
    You are a meticulous editor with years of experience reviewing educational
    content. You have an eye for detail, clarity, and coherence. You excel at
    improving content while maintaining the original author's voice and ensuring
    consistent quality across multiple sections.
  llm: provider/model-id  # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
These agent definitions establish the specialized roles and perspectives that will shape how our AI agents approach content creation. Notice how each agent has a distinct purpose and expertise.
  1. Next, update the tasks configuration file to define the specific writing and reviewing tasks:
# src/guide_creator_flow/crews/content_crew/config/tasks.yaml
write_section_task:
  description: >
    Write a comprehensive section on the topic: "{section_title}"

    Section description: {section_description}
    Target audience: {audience_level} level learners

    Your content should:
    1. Begin with a brief introduction to the section topic
    2. Explain all key concepts clearly with examples
    3. Include practical applications or exercises where appropriate
    4. End with a summary of key points
    5. Be approximately 500-800 words in length

    Format your content in Markdown with appropriate headings, lists, and emphasis.

    Previously written sections:
    {previous_sections}

    Make sure your content maintains consistency with previously written sections
    and builds upon concepts that have already been explained.
  expected_output: >
    A well-structured, comprehensive section in Markdown format that thoroughly
    explains the topic and is appropriate for the target audience.
  agent: content_writer

review_section_task:
  description: >
    Review and improve the following section on "{section_title}":

    {draft_content}

    Target audience: {audience_level} level learners

    Previously written sections:
    {previous_sections}

    Your review should:
    1. Fix any grammatical or spelling errors
    2. Improve clarity and readability
    3. Ensure content is comprehensive and accurate
    4. Verify consistency with previously written sections
    5. Enhance the structure and flow
    6. Add any missing key information

    Provide the improved version of the section in Markdown format.
  expected_output: >
    An improved, polished version of the section that maintains the original
    structure but enhances clarity, accuracy, and consistency.
  agent: content_reviewer
  context:
    - write_section_task
These task definitions provide detailed instructions to our agents, ensuring they produce content that meets our quality standards. Note how the context parameter in the review task creates a workflow where the reviewer has access to the writer’s output.
  1. Now, update the crew implementation file to define how our agents and tasks work together:
# src/guide_creator_flow/crews/content_crew/content_crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai.agents.agent_builder.base_agent import BaseAgent
from typing import List

@CrewBase
class ContentCrew():
    """Content writing crew"""

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

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

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

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

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

    @crew
    def crew(self) -> Crew:
        """Creates the content writing crew"""
        return Crew(
            agents=self.agents,
            tasks=self.tasks,
            process=Process.sequential,
            verbose=True,
        )
This crew definition establishes the relationship between our agents and tasks, setting up a sequential process where the content writer creates a draft and then the reviewer improves it. While this crew can function independently, in our flow it will be orchestrated as part of a larger system.

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Step 5: Create the Flow

Now comes the exciting part - creating the flow that will orchestrate the entire guide creation process. This is where we’ll combine regular Python code, direct LLM calls, and our content creation crew into a cohesive system. Our flow will:
  1. Get user input for a topic and audience level
  2. Make a direct LLM call to create a structured guide outline
  3. Process each section sequentially using the content writer crew
  4. Combine everything into a final comprehensive document
Let’s create our flow in the main.py file: 
#!/usr/bin/env python
import json
import os
from typing import List, Dict
from pydantic import BaseModel, Field
from crewai import LLM
from crewai.flow.flow import Flow, listen, start
from guide_creator_flow.crews.content_crew.content_crew import ContentCrew

# Define our models for structured data
class Section(BaseModel):
    title: str = Field(description="Title of the section")
    description: str = Field(description="Brief description of what the section should cover")

class GuideOutline(BaseModel):
    title: str = Field(description="Title of the guide")
    introduction: str = Field(description="Introduction to the topic")
    target_audience: str = Field(description="Description of the target audience")
    sections: List[Section] = Field(description="List of sections in the guide")
    conclusion: str = Field(description="Conclusion or summary of the guide")

# Define our flow state
class GuideCreatorState(BaseModel):
    topic: str = ""
    audience_level: str = ""
    guide_outline: GuideOutline = None
    sections_content: Dict[str, str] = {}

class GuideCreatorFlow(Flow[GuideCreatorState]):
    """Flow for creating a comprehensive guide on any topic"""

    @start()
    def get_user_input(self):
        """Get input from the user about the guide topic and audience"""
        print("\n=== Create Your Comprehensive Guide ===\n")

        # Get user input
        self.state.topic = input("What topic would you like to create a guide for? ")

        # Get audience level with validation
        while True:
            audience = input("Who is your target audience? (beginner/intermediate/advanced) ").lower()
            if audience in ["beginner", "intermediate", "advanced"]:
                self.state.audience_level = audience
                break
            print("Please enter 'beginner', 'intermediate', or 'advanced'")

        print(f"\nCreating a guide on {self.state.topic} for {self.state.audience_level} audience...\n")
        return self.state

    @listen(get_user_input)
    def create_guide_outline(self, state):
        """Create a structured outline for the guide using a direct LLM call"""
        print("Creating guide outline...")

        # Initialize the LLM
        llm = LLM(model="openai/gpt-4o-mini", response_format=GuideOutline)

        # Create the messages for the outline
        messages = [
            {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
            {"role": "user", "content": f"""
            Create a detailed outline for a comprehensive guide on "{state.topic}" for {state.audience_level} level learners.

            The outline should include:
            1. A compelling title for the guide
            2. An introduction to the topic
            3. 4-6 main sections that cover the most important aspects of the topic
            4. A conclusion or summary

            For each section, provide a clear title and a brief description of what it should cover.
            """}
        ]

        # Make the LLM call with JSON response format
        response = llm.call(messages=messages)

        # Parse the JSON response
        outline_dict = json.loads(response)
        self.state.guide_outline = GuideOutline(**outline_dict)

        # Ensure output directory exists before saving
        os.makedirs("output", exist_ok=True)

        # Save the outline to a file
        with open("output/guide_outline.json", "w") as f:
            json.dump(outline_dict, f, indent=2)

        print(f"Guide outline created with {len(self.state.guide_outline.sections)} sections")
        return self.state.guide_outline

    @listen(create_guide_outline)
    def write_and_compile_guide(self, outline):
        """Write all sections and compile the guide"""
        print("Writing guide sections and compiling...")
        completed_sections = []

        # Process sections one by one to maintain context flow
        for section in outline.sections:
            print(f"Processing section: {section.title}")

            # Build context from previous sections
            previous_sections_text = ""
            if completed_sections:
                previous_sections_text = "# Previously Written Sections\n\n"
                for title in completed_sections:
                    previous_sections_text += f"## {title}\n\n"
                    previous_sections_text += self.state.sections_content.get(title, "") + "\n\n"
            else:
                previous_sections_text = "No previous sections written yet."

            # Run the content crew for this section
            result = ContentCrew().crew().kickoff(inputs={
                "section_title": section.title,
                "section_description": section.description,
                "audience_level": self.state.audience_level,
                "previous_sections": previous_sections_text,
                "draft_content": ""
            })

            # Store the content
            self.state.sections_content[section.title] = result.raw
            completed_sections.append(section.title)
            print(f"Section completed: {section.title}")

        # Compile the final guide
        guide_content = f"# {outline.title}\n\n"
        guide_content += f"## Introduction\n\n{outline.introduction}\n\n"

        # Add each section in order
        for section in outline.sections:
            section_content = self.state.sections_content.get(section.title, "")
            guide_content += f"\n\n{section_content}\n\n"

        # Add conclusion
        guide_content += f"## Conclusion\n\n{outline.conclusion}\n\n"

        # Save the guide
        with open("output/complete_guide.md", "w") as f:
            f.write(guide_content)

        print("\nComplete guide compiled and saved to output/complete_guide.md")
        return "Guide creation completed successfully"

def kickoff():
    """Run the guide creator flow"""
    GuideCreatorFlow().kickoff()
    print("\n=== Flow Complete ===")
    print("Your comprehensive guide is ready in the output directory.")
    print("Open output/complete_guide.md to view it.")

def plot():
    """Generate a visualization of the flow"""
    flow = GuideCreatorFlow()
    flow.plot("guide_creator_flow")
    print("Flow visualization saved to guide_creator_flow.html")

if __name__ == "__main__":
    kickoff()
Let’s analyze what’s happening in this flow:
  1. We define Pydantic models for structured data, ensuring type safety and clear data representation
  2. We create a state class to maintain data across different steps of the flow
  3. We implement three main flow steps:
    • Getting user input with the @start() decorator
    • Creating a guide outline with a direct LLM call
    • Processing sections with our content crew
  4. We use the @listen() decorator to establish event-driven relationships between steps
This is the power of flows - combining different types of processing (user interaction, direct LLM calls, crew-based tasks) into a coherent, event-driven system.

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Step 6: Set Up Your Environment Variables

Create a .env file in your project root with your API keys. See the LLM setup guide for details on configuring a provider.
.env
OPENAI_API_KEY=your_openai_api_key
# or
GEMINI_API_KEY=your_gemini_api_key
# or
ANTHROPIC_API_KEY=your_anthropic_api_key

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Step 7: Install Dependencies

Install the required dependencies: 
crewai install

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Step 8: Run Your Flow

Now it’s time to see your flow in action! Run it using the CrewAI CLI: 
crewai flow kickoff
When you run this command, you’ll see your flow spring to life:
  1. It will prompt you for a topic and audience level
  2. It will create a structured outline for your guide
  3. It will process each section, with the content writer and reviewer collaborating on each
  4. Finally, it will compile everything into a comprehensive guide
This demonstrates the power of flows to orchestrate complex processes involving multiple components, both AI and non-AI.

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Step 9: Visualize Your Flow

One of the powerful features of flows is the ability to visualize their structure: 
crewai flow plot
This will create an HTML file that shows the structure of your flow, including the relationships between different steps and the data that flows between them. This visualization can be invaluable for understanding and debugging complex flows.

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Step 10: Review the Output

Once the flow completes, you’ll find two files in the output directory:
  1. guide_outline.json: Contains the structured outline of the guide
  2. complete_guide.md: The comprehensive guide with all sections
Take a moment to review these files and appreciate what you’ve built - a system that combines user input, direct AI interactions, and collaborative agent work to produce a complex, high-quality output.

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The Art of the Possible: Beyond Your First Flow

What you’ve learned in this guide provides a foundation for creating much more sophisticated AI systems. Here are some ways you could extend this basic flow:

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Enhancing User Interaction

You could create more interactive flows with:
  • Web interfaces for input and output
  • Real-time progress updates
  • Interactive feedback and refinement loops
  • Multi-stage user interactions

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Adding More Processing Steps

You could expand your flow with additional steps for:
  • Research before outline creation
  • Image generation for illustrations
  • Code snippet generation for technical guides
  • Final quality assurance and fact-checking

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Creating More Complex Flows

You could implement more sophisticated flow patterns:
  • Conditional branching based on user preferences or content type
  • Parallel processing of independent sections
  • Iterative refinement loops with feedback
  • Integration with external APIs and services

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Applying to Different Domains

The same patterns can be applied to create flows for:
  • Interactive storytelling: Create personalized stories based on user input
  • Business intelligence: Process data, generate insights, and create reports
  • Product development: Facilitate ideation, design, and planning
  • Educational systems: Create personalized learning experiences

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Key Features Demonstrated

This guide creator flow demonstrates several powerful features of CrewAI:
  1. User interaction: The flow collects input directly from the user
  2. Direct LLM calls: Uses the LLM class for efficient, single-purpose AI interactions
  3. Structured data with Pydantic: Uses Pydantic models to ensure type safety
  4. Sequential processing with context: Writes sections in order, providing previous sections for context
  5. Multi-agent crews: Leverages specialized agents (writer and reviewer) for content creation
  6. State management: Maintains state across different steps of the process
  7. Event-driven architecture: Uses the @listen decorator to respond to events

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Understanding the Flow Structure

Let’s break down the key components of flows to help you understand how to build your own:

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1. Direct LLM Calls

Flows allow you to make direct calls to language models when you need simple, structured responses: 
llm = LLM(
    model="model-id-here",  # gpt-4o, gemini-2.0-flash, anthropic/claude...
    response_format=GuideOutline
)
response = llm.call(messages=messages)
This is more efficient than using a crew when you need a specific, structured output.

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2. Event-Driven Architecture

Flows use decorators to establish relationships between components: 
@start()
def get_user_input(self):
    # First step in the flow
    # ...

@listen(get_user_input)
def create_guide_outline(self, state):
    # This runs when get_user_input completes
    # ...
This creates a clear, declarative structure for your application.

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3. State Management

Flows maintain state across steps, making it easy to share data: 
class GuideCreatorState(BaseModel):
    topic: str = ""
    audience_level: str = ""
    guide_outline: GuideOutline = None
    sections_content: Dict[str, str] = {}
This provides a type-safe way to track and transform data throughout your flow.

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4. Crew Integration

Flows can seamlessly integrate with crews for complex collaborative tasks: 
result = ContentCrew().crew().kickoff(inputs={
    "section_title": section.title,
    # ...
})
This allows you to use the right tool for each part of your application - direct LLM calls for simple tasks and crews for complex collaboration.

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Next Steps

Now that you’ve built your first flow, you can:
  1. Experiment with more complex flow structures and patterns
  2. Try using @router() to create conditional branches in your flows
  3. Explore the and_ and or_ functions for more complex parallel execution
  4. Connect your flow to external APIs, databases, or user interfaces
  5. Combine multiple specialized crews in a single flow
Congratulations! You’ve successfully built your first CrewAI Flow that combines regular code, direct LLM calls, and crew-based processing to create a comprehensive guide. These foundational skills enable you to create increasingly sophisticated AI applications that can tackle complex, multi-stage problems through a combination of procedural control and collaborative intelligence.