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 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
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)
Anti-Patterns
❌ Vague Agent Roles
Why bad: Agent doesn't know its specialty.
Overlapping responsibilities.
Poor task delegation.
Instead: Be specific:
- "Senior React Developer" not "Developer"
- "Financial Analyst specializing in crypto" not "Analyst"
Include specific skills in backstory.
❌ Missing Expected Outputs
Why bad: Agent doesn't know done criteria.
Inconsistent outputs.
Hard to chain tasks.
Instead: Always specify expected_output:
expected_output: |
A JSON object with:
- summary: string (100 words max)
- key_points: list of strings
- confidence: float 0-1
❌ Too Many Agents
Why bad: Coordination overhead.
Inconsistent communication.
Slower execution.
Instead: 3-5 agents with clear roles.
One agent can handle multiple related tasks.
Use tools instead of agents for simple actions.
Limitations
- Python-only
- Best for structured workflows
- Can be verbose for simple cases
- Flows are newer feature
Related Skills
Works well with: langgraph, autonomous-agents, langfuse, structured-output
