Advanced CrewAI Patterns

👶 Explained like I'm 5

You know how to build simple crews with a few agents. But what if you need something more complex? Like a company with managers, teams, and workers all working together?

Advanced CrewAI is like building a whole organization of AI agents - some manage others, some work in parallel, some review work, and they all coordinate together!

❓ Why we need this

Simple crews are great, but real-world problems need:

• Hierarchical structures: Managers coordinating teams
• Parallel processing: Multiple agents working simultaneously
• Review loops: Quality assurance through multiple passes
• Dynamic workflows: Agents that adapt based on results
• Error recovery: Handling failures gracefully

Advanced patterns make crews more powerful and reliable!

🧠 How it works

Pattern 1: Hierarchical Crews

Managers coordinate specialized teams:

┌─────────────────┐
│  Manager Agent  │
└────────┬────────┘
         │
    ┌────┴────┐
    │         │
┌───▼───┐ ┌──▼────┐
│ Team A│ │Team B │
│ Agent1│ │Agent1 │
│ Agent2│ │Agent2 │
└───────┘ └───────┘

Code Example:

from crewai import Agent, Task, Crew

# Manager agent
manager = Agent(
    role='Project Manager',
    goal='Coordinate teams and ensure quality',
    backstory='You are an experienced project manager',
    verbose=True
)

# Team A agents
researcher_a = Agent(role='Researcher A', goal='Research topic A')
writer_a = Agent(role='Writer A', goal='Write about topic A')

# Team B agents
researcher_b = Agent(role='Researcher B', goal='Research topic B')
writer_b = Agent(role='Writer B', goal='Write about topic B')

# Manager coordinates both teams
coordination_task = Task(
    description='Coordinate research and writing teams',
    agent=manager
)

crew = Crew(
    agents=[manager, researcher_a, writer_a, researcher_b, writer_b],
    tasks=[coordination_task],
    process='hierarchical',
    manager_llm=manager.llm
)

Pattern 2: Review Loops

Content goes through multiple review cycles:

# Create → Review → Revise → Final Review

writer = Agent(role='Writer', goal='Create content')
reviewer = Agent(role='Reviewer', goal='Improve content')
final_reviewer = Agent(role='Final Reviewer', goal='Approve content')

write_task = Task(description='Write article', agent=writer)
review_task = Task(
    description='Review and suggest improvements',
    agent=reviewer,
    context=[write_task]
)
revise_task = Task(
    description='Revise based on feedback',
    agent=writer,
    context=[review_task]
)
final_task = Task(
    description='Final review and approval',
    agent=final_reviewer,
    context=[revise_task]
)

crew = Crew(
    agents=[writer, reviewer, final_reviewer],
    tasks=[write_task, review_task, revise_task, final_task]
)

Pattern 3: Parallel Processing with Synthesis

Multiple agents work simultaneously, then results are combined:

# Parallel research → Synthesis

market_researcher = Agent(role='Market Researcher', goal='Analyze market')
tech_researcher = Agent(role='Tech Researcher', goal='Research technology')
competitor_researcher = Agent(role='Competitor Analyst', goal='Study competitors')
synthesizer = Agent(role='Synthesizer', goal='Combine findings')

# Parallel tasks (no dependencies)
market_task = Task(description='Research market', agent=market_researcher)
tech_task = Task(description='Research tech', agent=tech_researcher)
competitor_task = Task(description='Analyze competitors', agent=competitor_researcher)

# Synthesis task (depends on all three)
synthesis_task = Task(
    description='Synthesize all research into comprehensive report',
    agent=synthesizer,
    context=[market_task, tech_task, competitor_task]
)

crew = Crew(
    agents=[market_researcher, tech_researcher, competitor_researcher, synthesizer],
    tasks=[market_task, tech_task, competitor_task, synthesis_task]
)

🧪 Advanced Examples

Example: Multi-Stage Content Creation

from crewai import Agent, Task, Crew

# Stage 1: Research
researcher = Agent(
    role='Researcher',
    goal='Gather comprehensive information',
    tools=[DuckDuckGoSearchRun()],
    verbose=True
)

# Stage 2: Outline
outliner = Agent(
    role='Content Outliner',
    goal='Create structured outline',
    verbose=True
)

# Stage 3: Writing
writer = Agent(
    role='Writer',
    goal='Write engaging content',
    verbose=True
)

# Stage 4: Fact-checking
fact_checker = Agent(
    role='Fact Checker',
    goal='Verify all facts',
    tools=[DuckDuckGoSearchRun()],
    verbose=True
)

# Stage 5: Editing
editor = Agent(
    role='Editor',
    goal='Polish content',
    verbose=True
)

# Tasks with dependencies
research_task = Task(description='Research topic', agent=researcher)
outline_task = Task(description='Create outline', agent=outliner, context=[research_task])
write_task = Task(description='Write content', agent=writer, context=[outline_task])
fact_check_task = Task(description='Verify facts', agent=fact_checker, context=[write_task, research_task])
edit_task = Task(description='Edit and polish', agent=editor, context=[fact_check_task])

crew = Crew(
    agents=[researcher, outliner, writer, fact_checker, editor],
    tasks=[research_task, outline_task, write_task, fact_check_task, edit_task],
    verbose=True
)

result = crew.kickoff()

🎯 Real-World Case Studies

🛠 Hands-on Task

Design an advanced crew for a complex problem:

1. Choose Complex Problem: Something requiring multiple stages
2. Design Hierarchy: Manager and teams
3. Identify Parallel Tasks: What can run simultaneously?
4. Plan Dependencies: What must happen in order?
5. Add Review Loops: Where is quality critical?
6. Error Handling: What if something fails?

✅ Checklist

Understand advanced patterns:

🚨 Common Pitfalls & Solutions

Pitfall 1: Over-Complex Hierarchies

Problem: Too many management layers slow things down.

Solution: Keep hierarchies flat. Only add managers when truly needed.

Pitfall 2: Ignoring Parallel Opportunities

Problem: Running everything sequentially wastes time.

Solution: Identify independent tasks and run them in parallel.

Pitfall 3: Missing Error Handling

Problem: One failure stops entire crew.

Solution: Add error handling and retry logic.

💡 Best Practices

1. Start Simple: Begin with basic crew, add complexity gradually
2. Identify Parallelism: Look for independent tasks
3. Use Hierarchies Sparingly: Only when coordination is needed
4. Add Review Loops: For quality-critical outputs
5. Handle Errors: Plan for failures
6. Monitor Performance: Track what works
7. Document Patterns: Help others understand your design

🔗 Additional Resources

CrewAI Advanced Concepts

Advanced patterns and orchestration processes for multi-agent systems

AdvancedPatterns

🚀 Challenge for GitHub

Build an advanced crew with:

1. Hierarchical structure
2. Parallel processing
3. Review loops
4. Error handling
5. Complete documentation

Share your code and explain your design decisions!

🎓 Next Steps

Continue learning:

1. Explore: Memory & State Management - Persistent agent memory
2. Learn: Testing & Debugging - Ensure crew reliability
3. Review: Building with CrewAI - Refresh basics