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. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.
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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. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.
source
vibeship-spawner-skills (Apache 2.0)
CrewAI
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.
Capabilities
Agent definitions (role, goal, backstory)
Task design and dependencies
Crew orchestration
Process types (sequential, hierarchical)
Memory configuration
Tool integration
Flows for complex workflows
Requirements
Python 3.10+
crewai package
LLM API access
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 years of experience
gathering analyzing information. Yoconfig/agents.yamlconfig/tasks.yamlresearchewriteresearch_task
with
in
and
u'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 = '
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 planprint(crew.plan)
