Crewai Workflow

Build multi-agent workflows using CrewAI patterns

CrewAI Workflow Skill

Build production-ready multi-agent workflows using CrewAI best practices.

CrewAI Workflow Skill

Build production-ready multi-agent workflows using CrewAI best practices.

Process

Step 1: Define Agents

Create specialized agents with clear roles:

python

from crewai import Agent

researcher = Agent(
    role='Research Analyst',
    goal='Find accurate information on topics',
    backstory='Expert researcher with attention to detail',
    verbose=True,
    memory=True,
    max_iter=3
)

writer = Agent(
    role='Content Writer',
    goal='Create engaging content from research',
    backstory='Skilled writer who transforms data into stories',
    verbose=True
)

Step 2: Define Tasks

Create tasks with clear expected outputs:

python

from crewai import Task

research_task = Task(
    description='Research the topic: {topic}',
    agent=researcher,
    expected_output='Comprehensive research report with key findings'
)

writing_task = Task(
    description='Write an article based on research',
    agent=writer,
    expected_output='Engaging article in markdown format',
    context=[research_task]  # Depends on research
)

Step 3: Create Crew

Assemble agents and tasks into a crew:

python

from crewai import Crew, Process

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

result = crew.kickoff(inputs={"topic": "AI Agents"})
print(result.raw)

Step 4: Add Flows (Optional)

For complex state management:

python

from crewai.flow.flow import Flow, listen, start

class MyFlow(Flow):
    @start()
    def begin(self):
        return crew.kickoff(inputs={"topic": "AI"})
    
    @listen(begin)
    def process_result(self, result):
        return result.raw

flow = MyFlow()
final_result = flow.kickoff()

python

from crewai import Agent

researcher = Agent(
    role='Research Analyst',
    goal='Find accurate information on topics',
    backstory='Expert researcher with attention to detail',
    verbose=True,
    memory=True,
    max_iter=3
)

writer = Agent(
    role='Content Writer',
    goal='Create engaging content from research',
    backstory='Skilled writer who transforms data into stories',
    verbose=True
)

python

from crewai import Task

research_task = Task(
    description='Research the topic: {topic}',
    agent=researcher,
    expected_output='Comprehensive research report with key findings'
)

writing_task = Task(
    description='Write an article based on research',
    agent=writer,
    expected_output='Engaging article in markdown format',
    context=[research_task]  # Depends on research
)

python

from crewai import Crew, Process

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

result = crew.kickoff(inputs={"topic": "AI Agents"})
print(result.raw)

python

from crewai.flow.flow import Flow, listen, start

class MyFlow(Flow):
    @start()
    def begin(self):
        return crew.kickoff(inputs={"topic": "AI"})
    
    @listen(begin)
    def process_result(self, result):
        return result.raw

flow = MyFlow()
final_result = flow.kickoff()

What Gets Created

File	Purpose
`agents/`	Agent definitions with roles
`tasks/`	Task definitions with dependencies
`crews/`	Crew configurations
`flows/`	Flow orchestrations (optional)

Process Types

Type	Use Case
`Process.sequential`	Tasks run one after another
`Process.hierarchical`	Manager delegates to workers

Best Practices

•Clear Roles: Each agent should have ONE clear responsibility
•Detailed Backstories: Guide agent behavior with context
•Set max_iter: Prevent infinite loops (typically 3-5)
•Use Context: Chain tasks with the context parameter
•Enable Memory: Use memory=True for better context

Common Patterns

Research → Write → Review Pipeline

python

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

Hierarchical with Manager

python

crew = Crew(
    agents=[manager, worker1, worker2],
    tasks=[complex_task],
    process=Process.hierarchical,
    manager_llm=ChatOpenAI(model='gpt-4')
)

python

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

python

crew = Crew(
    agents=[manager, worker1, worker2],
    tasks=[complex_task],
    process=Process.hierarchical,
    manager_llm=ChatOpenAI(model='gpt-4')
)

Fallback Procedures

Issue	Solution
Agent loops infinitely	Set `max_iter=3`
Wrong task order	Use `context` parameter
Tool failures	Add error handling in tools
LLM errors	Use `max_retry_limit`

Related Artifacts

•Knowledge: knowledge/crewai-patterns.json
•Templates: templates/ai/crewai/
•Examples: docs/examples/04-multi-agent-research-system/

Prerequisites

[!IMPORTANT] Requirements:

•Packages: crewai, crewai-tools

•Knowledge: crewai-patterns.json