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 or complex workflows
from crewai import Crew
Memory types:
- Short-term: Within task execution
- Long-term: Across executions
- Entity: About specific entities
crew = Crew(
agents=[...],
tasks=[...],
memory=True, # Enable all memory types
verbose=True
)
Custom memory config
from crewai.memory import LongTermMemory, ShortTermMemory
crew = Crew(
agents=[...],
tasks=[...],
memory=True,
long_term_memory=LongTermMemory(
storage=CustomStorage() # Custom backend
),
short_term_memory=ShortTermMemory(
storage=CustomStorage()
),
embedder={
"provider": "openai",
"config": {"model": "text-embedding-3-small"}
}
)
Memory helps agents:
- Remember previous interactions
- Build on past work
- Maintain consistency
Flows for Complex Workflows
Event-driven orchestration with state
When to use: Complex, multi-stage workflows
from crewai.flow.flow import Flow, listen, start, and_, or_, router
class ContentFlow(Flow):
# State persists across steps
model_config = {"extra": "allow"}
@start()
def gather_requirements(self):
"""First step - gather inputs."""
self.topic = self.inputs.get("topic", "AI")
self.style = self.inputs.get("style", "professional")
return {"topic": self.topic}
@listen(gather_requirements)
def research(self, requirements):
"""Research after requirements gathered."""
research_crew = ResearchCrew()
result = research_crew.crew().kickoff(
inputs={"topic": requirements["topic"]}
)
self.research = result.raw
return result
@listen(research)
def write_content(self, research_result):
"""Write after research complete."""
writing_crew = WritingCrew()
result = writing_crew.crew().kickoff(
inputs={
"research": self.research,
"style": self.style
}
)
return result
@router(write_content)
def quality_check(self, content):
"""Route based on quality."""
if self.needs_revision(content):
return "revise"
return "publish"
@listen("revise")
def revise_content(self):
"""Revision flow."""
# Re-run writing with feedback
pass
@listen("publish")
def publish_content(self):
"""Final publishing."""
return {"status": "published", "content": self.content}
Run flow
flow = ContentFlow()
result = flow.kickoff(inputs={"topic": "AI Agents"})
Custom Tools
Create tools for agents
When to use: Agents need external capabilities
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
Method 1: Class-based tool
class SearchInput(BaseModel):
query: str = Field(..., description="Search query")
class WebSearchTool(BaseTool):
name: str = "web_search"
description: str = "Search the web for information"
args_schema: type[BaseModel] = SearchInput
def _run(self, query: str) -> str:
# Implementation
results = search_api.search(query)
return format_results(results)
Method 2: Function decorator
from crewai import tool
@tool("Database Query")
def query_database(sql: str) -> str:
"""Execute SQL query and return results."""
return db.execute(sql)
Assign tools to agents
researcher = Agent(
role="Researcher",
goal="Find information",
backstory="...",
tools=[WebSearchTool(), query_database]
)
Collaboration
Delegation Triggers
- langgraph|state machine|graph -> langgraph (Need explicit state management)
- observability|tracing -> langfuse (Need LLM observability)
- structured output|json schema -> structured-output (Need structured responses)
Research and Writing Crew
Skills: crewai, structured-output
Workflow:
1. Define researcher and writer agents
2. Create research → analysis → writing pipeline
3. Use structured output for research format
4. Chain tasks with context
Observable Agent Team
Skills: crewai, langfuse
Workflow:
1. Build crew with agents and tasks
2. Add Langfuse callback handler
3. Monitor agent interactions
4. Evaluate output quality
Complex Workflow with Flows
Skills: crewai, langgraph
Workflow:
1. Design workflow with CrewAI Flows
2. Use LangGraph patterns for state
3. Combine crews in flow steps
4. Handle branching and routing
Related Skills
Works well with: langgraph, autonomous-agents, langfuse, structured-output
When to Use
- User mentions or implies: crewai
- User mentions or implies: multi-agent team
- User mentions or implies: agent roles
- User mentions or implies: crew of agents
- User mentions or implies: role-based agents
- User mentions or implies: collaborative agents
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.