crewai

name	crewai
description	Expert skill for role-based multi-agent orchestration with CrewAI. Agents with Role/Goal/Backstory, task design, crew composition (sequential or hierarchical), tool integration, callbacks, and production deployment. Use when orchestrating multi-agent teams or comparing agent frameworks.
license	MIT
metadata	{"author":"Magnus Hedemark","version":"1.1.0","source":"https://docs.crewai.com"}

CrewAI Expert Skill

CrewAI is a framework for role-based multi-agent orchestration. Unlike LangGraph's low-level state-machine graphs, CrewAI provides a higher abstraction: agents are defined as Roles with Goals and Backstories, crews are composed with built-in sequential or hierarchical workflows, and inter-agent delegation is built into the framework.

Core Paradigm

from crewai import Agent, Task, Crew, Process
from crewai.tools import tool

@tool("search")
def search_web(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

researcher = Agent(
    role="Senior Researcher",
    goal="Find accurate information on any topic",
    backstory="Expert researcher with 10 years of experience",
    tools=[search_web],
    verbose=True,
)

writer = Agent(
    role="Technical Writer",
    goal="Write clear reports from research findings",
    backstory="Experienced technical writer",
    verbose=True,
)

research_task = Task(
    description="Research the topic thoroughly",
    expected_output="A detailed research brief",
    agent=researcher,
)

write_task = Task(
    description="Write a report based on research",
    expected_output="A well-structured report",
    agent=writer,
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    verbose=True,
)

result = crew.kickoff()

Core Principles

1. Agents are Roles, not functions. Role + Goal + Backstory defines the agent's identity. Strong role definitions reduce hallucination.
2. Tasks declare what, not how. Description + expected_output defines the task. The agent figures out execution.
3. Sequential is for pipelines, Hierarchical is for complexity. Sequential runs tasks in order. Hierarchical uses a manager agent to delegate and validate.
4. Manager LLM is required for Hierarchical. Without manager_llm, hierarchical process fails silently.
5. Delegation loops are real. allow_delegation=True without max_iter bounds can cause infinite handoffs.
6. Tool errors don't raise. A failed tool call marks the task as failed but doesn't raise an exception. Check task output.

Where to Start

You already have...	Start here
Nothing — exploring CrewAI	Sequential crew with 2 agents (research → write)
Agents you want to coordinate	Build a Hierarchical crew with manager_llm
Tools you want to integrate	Use @tool decorator, add tools to relevant agents
A production deployment	Add callbacks, memory, error handling

Quick Reference

Task	Approach	Reference
Define agent	Agent(role, goal, backstory)	references/agent-design.md
Define task	Task(description, expected_output, agent)	references/task-design.md
Sequential crew	Crew(process=Process.sequential)	references/crew-patterns.md
Hierarchical crew	Crew(process=Process.hierarchical, manager_llm=...)	references/crew-patterns.md
Create tool	@tool("name") decorator	references/tool-integration.md
Add callbacks	step_callback=fn on Agent	references/callbacks.md
Enable memory	memory=True on Crew or Agent	references/crew-patterns.md

Framework Routing Guide

Scenario	Reach for	Why
Role-based multi-agent teams	CrewAI	Role/Goal/Backstory is the native abstraction
State-machine multi-agent	LangGraph	Graph topology, subgraphs, human-in-the-loop
Conversational multi-agent	AutoGen	Agent chat as orchestration primitive
Chain/agent composition	LangChain	LCEL pipe operator for general chains
Documents to query / RAG	LlamaIndex	Data ingestion is the primary primitive

Reference Files

Reference	Load when	File
Agent Design	Defining agents with roles, goals, backstories	references/agent-design.md
Task Design	Creating tasks with descriptions and outputs	references/task-design.md
Crew Patterns	Sequential, hierarchical, consensual crews	references/crew-patterns.md
Tool Integration	Creating tools with @tool decorator	references/tool-integration.md
Callbacks	Monitoring agent and task execution	references/callbacks.md
Memory System	Unified Memory class, cross-agent context	references/memory-system.md
Flows	Event-driven orchestration connecting crews	references/flows.md
FAQ & Troubleshooting	Common errors and fixes	references/faq-and-troubleshooting.md

Templates

Template	When to use	File
Research Crew	Sequential: researcher → writer → reviewer	templates/research-crew.py
Hierarchical Crew	Manager with specialist agents	templates/hierarchical-crew.py
Customer Support	Triage → specialist → response	templates/support-crew.py

Troubleshooting

Symptom	Likely cause	Fix	Reference
Crew runs but no output	Agent stuck in delegation loop	Set max_iter=15 on agent	references/agent-design.md
Hierarchical crew fails	No manager_llm set	Add manager_llm=ChatOpenAI(model="gpt-4")	references/crew-patterns.md
Task never completes	Agent exceeds max_iter	Increase max_iter or simplify task	references/agent-design.md
Tool not being called	Tool not added to agent	Add tools=[my_tool] to Agent definition	references/tool-integration.md
High token usage	Hierarchical mode	Manager processes all outputs — use cheaper LLM	references/crew-patterns.md
Memory between tasks not working	Crew-level memory not set	Add memory=True to Crew	references/crew-patterns.md

When NOT to Use CrewAI

• Single-agent task — too much abstraction for one agent
• Need fine-grained graph control (cycles, conditional branching) — use LangGraph
• Need conversational agent interactions — use AutoGen
• Need simple chain composition — use LangChain LCEL