crewai-agents

Overview

CrewAI is a framework for orchestrating role-playing AI agents that collaborate to complete complex tasks. Agents have roles, goals, and backstories, and work together in crews with configurable processes (sequential, hierarchical).

Capabilities

• Define agents with roles, goals, and tools
• Create tasks with expected outputs
• Organize crews with sequential or hierarchical processes
• Add custom tools for web search, file I/O, APIs
• Enable memory for context across tasks
• Use delegation for agent-to-agent communication

When to Use

Trigger phrases:

• "crewai agents"

• "CrewAI multi-agent orchestration — agents, tasks, crews, tools, memory, delegati"

• Building multi-agent systems for research, writing, or analysis

• Needing role-specialized agents collaborating on tasks

• Wanting structured task delegation with accountability

• Building autonomous workflows with human-in-the-loop options

When NOT to Use

• Task is outside your authorization scope
• You need to implement controls (use implementing-* skills)
• Task is about analysis, not action (use analyzing-* skills)
• You don't have access to target systems
• Task requires compliance expertise (consult professionals)
• Task is about defense, not offense (use defensive skills)

Pseudo Code

# Example workflow for this skill
def execute(input_data):
    # Step 1: Validate input
    if not input_data:
        raise ValueError("Input data is required")

    # Step 2: Process core logic
    result = process(input_data)

    # Step 3: Validate output
    validate_output(result)

    return result

Agent and Crew Definition

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

# Tools
search_tool = SerperDevTool()
file_tool = FileReadTool()

# Agents
researcher = Agent(
    role="Senior Research Analyst",
    goal="Find comprehensive information on the given topic",
    backstory="You are an experienced researcher with expertise in finding and synthesizing information from multiple sources.",
    tools=[search_tool],
    verbose=True,
    allow_delegation=False,
)

writer = Agent(
    role="Content Writer",
    goal="Write engaging, well-structured content based on research",
    backstory="You are a skilled writer who transforms research into compelling narratives.",
    tools=[file_tool],
    verbose=True,
    allow_delegation=False,
)

reviewer = Agent(
    role="Quality Reviewer",
    goal="Ensure content is accurate, well-structured, and meets standards",
    backstory="You are a meticulous editor with years of experience in content quality assurance.",
    verbose=True,
    allow_delegation=True,  # Can delegate back to writer
)

# Tasks
research_task = Task(
    description="Research the latest trends in AI agents for 2026",
    expected_output="A comprehensive report with key findings, trends, and data points",
    agent=researcher,
)

writing_task = Task(
    description="Write a blog post based on the research findings",
    expected_output="A 1500-word blog post with introduction, key sections, and conclusion",
    agent=writer,
    context=[research_task],  # Depends on research
)

review_task = Task(
    description="Review the blog post for accuracy and quality",
    expected_output="A quality assessment with specific improvement suggestions",
    agent=reviewer,
    context=[writing_task],
)

# Crew
crew = Crew(
    agents=[researcher, writer, reviewer],
    tasks=[research_task, writing_task, review_task],
    process=Process.sequential,  # Execute tasks in order
    verbose=True,
    memory=True,  # Enable shared memory
)

# Execute
result = crew.kickoff()
print(result)