/crewai

name: crewai
description: Expert in CrewAI - the leading role-based multi-agent framework
  used by 60% of Fortune 500 companies.
risk: unknown
source: vibeship-spawner-skills (Apache 2.0)
date_added: 2026-02-27

CrewAI

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams.

**Role**: CrewAI Multi-Agent Architect

You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration. You know when to use sequential vs hierarchical processes.

Expertise

• Agent persona design
• Task decomposition
• Crew orchestration
• Process selection
• Memory configuration
• Flow design

Capabilities

• Agent definitions (role, goal, backstory)
• Task design and dependencies
• Crew orchestration
• Process types (sequential, hierarchical)
• Memory configuration
• Tool integration
• Flows for complex workflows

Prerequisites

• 0: Python proficiency
• 1: Multi-agent concepts
• 2: Understanding of delegation
• Required skills: Python 3.10+, crewai package, LLM API access

Scope

• 0: Python-only
• 1: Best for structured workflows
• 2: Can be verbose for simple cases
• 3: Flows are newer feature

Ecosystem

Primary

• CrewAI framework
• CrewAI Tools

Common_integrations

• OpenAI / Anthropic / Ollama
• SerperDev (search)
• FileReadTool, DirectoryReadTool
• Custom tools

Platforms

• Python applications
• FastAPI backends
• Enterprise deployments

Patterns

Basic Crew with YAML Config

Define agents and tasks in YAML (recommended)

**When to use**: Any CrewAI project

config/agents.yaml

researcher: role: "Senior Research Analyst" goal: "Find comprehensive, accurate information on {topic}" backstory: | You are an expert researcher with years of experience in gathering and analyzing information. You're known for your thorough and accurate research. tools:

• SerperDevTool
• WebsiteSearchTool

verbose: true

writer: role: "Content Writer" goal: "Create engaging, well-structured content" backstory: | You are a skilled writer who transforms research into compelling narratives. You focus on clarity and engagement. verbose: true

config/tasks.yaml

research_task: description: | Research the topic: {topic}

Focus on: 1. Key facts and statistics 2. Recent developments 3. Expert opinions 4. Contrarian viewpoints

Be thorough and cite sources. agent: researcher expected_output: | A comprehensive research report with:

• Executive summary
• Key findings (bulleted)
• Sources cited

writing_task: description: | Using the research provided, write an article about {topic}.

Requirements:

• 800-1000 words
• Engaging introduction
• Clear structure with headers
• Actionable conclusion

agent: writer expected_output: "A polished article ready for publication" context:

• research_task # Uses output from research

crew.py

from crewai import Agent, Task, Crew, Process from crewai.project import CrewBase, agent, task, crew

@CrewBase class ContentCrew: agents_config = 'config/agents.yaml' tasks_config = 'config/tasks.yaml'

@agent def researcher(self) -> Agent: return Agent(config=self.agents_config['researcher'])

@agent def writer(self) -> Agent: return Agent(config=self.agents_config['writer'])

@task def research_task(self) -> Task: return Task(config=self.tasks_config['research_task'])

@task def writing_task(self) -> Task: return Task(config=self.tasks_config['writing_task'])

@crew def crew(self) -> Crew: return Crew( agents=self.agents, tasks=self.tasks, process=Process.sequential, verbose=True )

main.py

crew = ContentCrew() result = crew.crew().kickoff(inputs={"topic": "AI Agents in 2025"})

Hierarchical Process

Manager agent delegates to workers

**When to use**: Complex tasks needing coordination

from crewai import Crew, Process

Define specialized agents

researcher = Agent( role="Research Specialist", goal="Find accurate information", backstory="Expert researcher..." )

analyst = Agent( role="Data Analyst", goal="Analyze and interpret data", backstory="Expert analyst..." )

writer = Agent( role="Content Writer", goal="Create engaging content", backstory="Expert writer..." )

Hierarchical crew - manager coordinates

crew = Crew( agents=[researcher, analyst, writer], tasks=[research_task, analysis_task, writing_task], process=Process.hierarchical, manager_llm=ChatOpenAI(model="gpt-4o"), # Manager model verbose=True )

Manager decides:

- Which agent handles which task

- When to delegate

- How to combine results

result = crew.kickoff()

Planning Feature

Generate execution plan before running

**When to use**: Complex workflows needing structure

from crewai import Crew, Process

Enable planning

crew = Crew( agents=[researcher, writer, reviewer], tasks=[research, write, review], process=Process.sequential, planning=True, # Enable planning planning_llm=ChatOpenAI(model="gpt-4o") # Planner model )

With planning enabled:

1. CrewAI generates step-by-step plan

2. Plan is injected into each task

3. Agents see overall structure

4. More consistent results

result = crew.kickoff()

Access the plan

print(crew.plan)

Memory Configuration

Enable agent memory for context

**When to use**: Multi-turn o