/crewai-developer

name: crewai-developer
description: Comprehensive CrewAI framework guide for building collaborative AI agent teams and structured workflows. Use when developing multi-agent systems with CrewAI, creating autonomous AI crews, orchestrating flows, implementing agents with roles and tools, or building production-ready AI automation. Essential for developers building intelligent agent systems, task automation, and complex AI workflows.

CrewAI Developer Guide

Overview

CrewAI is a lean, lightning-fast Python framework for building collaborative AI agent teams and structured workflows. It empowers developers to create autonomous AI agents with specific roles, tools, and goals that work together to tackle complex tasks. This skill covers Crews (autonomous collaboration), Flows (structured orchestration), agents, tasks, and enterprise deployment.

Core Concepts

Agents: Specialized Team Members

Agents are autonomous AI units with specific roles, goals, and capabilities.

from crewai import Agent

# Create a research agent
researcher = Agent(
    role='Senior Research Analyst',
    goal='Uncover cutting-edge developments in AI and data science',
    backstory="""You are an expert at a leading tech think tank.
    Your expertise lies in identifying emerging trends and technologies in AI,
    data science, and machine learning.""",
    verbose=True,
    allow_delegation=False,
    tools=[search_tool, scrape_tool]
)

# Create a writer agent
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 on technology and innovation.
    You transform complex concepts into compelling narratives.""",
    verbose=True,
    allow_delegation=True,
    tools=[write_tool]
)

Agent Key Properties

agent = Agent(
    role='Role Name',              # The agent's job title
    goal='Specific objective',     # What the agent aims to achieve
    backstory='Background story',  # Context and expertise
    verbose=True,                  # Enable detailed logging
    allow_delegation=False,        # Can delegate tasks to other agents
    tools=[tool1, tool2],         # Available tools
    llm=custom_llm,               # Custom LLM configuration
    max_iter=15,                  # Maximum iterations for task
    max_rpm=10,                   # Rate limit (requests per minute)
    memory=True,                  # Enable memory
    cache=True,                   # Enable response caching
    system_template="template",   # Custom system prompt template
    prompt_template="template",   # Custom prompt template
    response_template="template"  # Custom response template
)

Tasks: Individual Assignments

Tasks define specific work to be completed by agents.

from crewai import Task

# Research task
research_task = Task(
    description="""Conduct a comprehensive analysis of the latest advancements in AI.
    Identify key trends, breakthrough technologies, and potential industry impacts.
    Compile your findings in a detailed report.""",
    expected_output='A comprehensive 3-paragraph report on AI advancements',
    agent=researcher,
    tools=[search_tool],
    output_file='research_report.md'
)

# Writing task
write_task = Task(
    description="""Using the research analyst's report, develop an engaging blog post
    highlighting the most significant AI advancements.
    Make it accessible and engaging for a general audience.""",
    expected_output='A 4-paragraph blog post about AI advancements',
    agent=writer,
    context=[research_task],  # Depends on research_task output
    output_file='blog_post.md'
)

Task Key Properties

task = Task(
    description='Detailed task description',
    expected_output='Clear output format',
    agent=agent_instance,
    tools=[tool1, tool2],           # Task-specific tools
    context=[previous_task],        # Dependencies
    async_execution=False,          # Run asynchronously
    output_json=OutputClass,        # Structured output (Pydantic)
    output_pydantic=OutputClass,    # Pydantic validation
    output_file='result.txt',       # Save output to file
    callback=callback_function,     # Callback on completion
    human_input=False              # Request human feedback
)

Crews: Organizing Agent Teams

Crews orchestrate agents working together toward a common goal.

from crewai import Crew, Process

# Create a crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,  # or Process.hierarchical
    verbose=True,
    memory=True,
    cache=True,
    max_rpm=10,
    share_crew=False
)

# Kickoff the crew
result = crew.kickoff()
print(result)

# Kickoff with custom inputs
result = crew.kickoff(inputs={
    'topic': 'Artificial Intelligence',
    'audience': 'developers'
})

Process Types

# Sequential process (tasks run one after another)
crew = Crew(
    agents=[agent1, agent2],
    tasks=[task1, task2],
    process=Process.sequential
)

# Hierarchical process (manager delegates to agents)
crew = Crew(
    agents=[agent1, agent2],
    tasks=[task1, task2],
    process=Process.hierarchical,
    manager_llm='gpt-4'  # Required for hierarchical
)

Flows: Structured Workflow Orchestration

Flows provide event-driven, deterministic control over execution paths.

from crewai.flow.flow import Flow, listen, start

class BlogPostFlow(Flow):

    @start()
    def fetch_topic(self):
        """Entry point - fetch the topic to write about"""
        print("Starting blog post generation")
        return "AI advancements in 2024"

    @listen(fetch_topic)
    def research_topic(self, topic):
        """Research the topic"""
        print(f"Researching: {topic}")
        # Integrate with Crew for autonomous research
        research_c