Overview
Collaboration in CrewAI enables agents to work together as a team by delegating tasks and asking questions to leverage each other’s expertise. When allow_delegation=True
, agents automatically gain access to powerful collaboration tools.
Quick Start: Enable Collaboration
from crewai import Agent, Crew, Task
# Enable collaboration for agents
researcher = Agent(
role="Research Specialist",
goal="Conduct thorough research on any topic",
backstory="Expert researcher with access to various sources",
allow_delegation=True, # 🔑 Key setting for collaboration
verbose=True
)
writer = Agent(
role="Content Writer",
goal="Create engaging content based on research",
backstory="Skilled writer who transforms research into compelling content",
allow_delegation=True, # 🔑 Enables asking questions to other agents
verbose=True
)
# Agents can now collaborate automatically
crew = Crew(
agents=[researcher, writer],
tasks=[...],
verbose=True
)
How Agent Collaboration Works
When allow_delegation=True
, CrewAI automatically provides agents with two powerful tools:
Allows agents to assign tasks to teammates with specific expertise.
# Agent automatically gets this tool:
# Delegate work to coworker(task: str, context: str, coworker: str)
Enables agents to ask specific questions to gather information from colleagues.
# Agent automatically gets this tool:
# Ask question to coworker(question: str, context: str, coworker: str)
Collaboration in Action
Here’s a complete example showing agents collaborating on a content creation task:
from crewai import Agent, Crew, Task, Process
# Create collaborative agents
researcher = Agent(
role="Research Specialist",
goal="Find accurate, up-to-date information on any topic",
backstory="""You're a meticulous researcher with expertise in finding
reliable sources and fact-checking information across various domains.""",
allow_delegation=True,
verbose=True
)
writer = Agent(
role="Content Writer",
goal="Create engaging, well-structured content",
backstory="""You're a skilled content writer who excels at transforming
research into compelling, readable content for different audiences.""",
allow_delegation=True,
verbose=True
)
editor = Agent(
role="Content Editor",
goal="Ensure content quality and consistency",
backstory="""You're an experienced editor with an eye for detail,
ensuring content meets high standards for clarity and accuracy.""",
allow_delegation=True,
verbose=True
)
# Create a task that encourages collaboration
article_task = Task(
description="""Write a comprehensive 1000-word article about 'The Future of AI in Healthcare'.
The article should include:
- Current AI applications in healthcare
- Emerging trends and technologies
- Potential challenges and ethical considerations
- Expert predictions for the next 5 years
Collaborate with your teammates to ensure accuracy and quality.""",
expected_output="A well-researched, engaging 1000-word article with proper structure and citations",
agent=writer # Writer leads, but can delegate research to researcher
)
# Create collaborative crew
crew = Crew(
agents=[researcher, writer, editor],
tasks=[article_task],
process=Process.sequential,
verbose=True
)
result = crew.kickoff()
Collaboration Patterns
Pattern 1: Research → Write → Edit
research_task = Task(
description="Research the latest developments in quantum computing",
expected_output="Comprehensive research summary with key findings and sources",
agent=researcher
)
writing_task = Task(
description="Write an article based on the research findings",
expected_output="Engaging 800-word article about quantum computing",
agent=writer,
context=[research_task] # Gets research output as context
)
editing_task = Task(
description="Edit and polish the article for publication",
expected_output="Publication-ready article with improved clarity and flow",
agent=editor,
context=[writing_task] # Gets article draft as context
)
Pattern 2: Collaborative Single Task
collaborative_task = Task(
description="""Create a marketing strategy for a new AI product.
Writer: Focus on messaging and content strategy
Researcher: Provide market analysis and competitor insights
Work together to create a comprehensive strategy.""",
expected_output="Complete marketing strategy with research backing",
agent=writer # Lead agent, but can delegate to researcher
)
Hierarchical Collaboration
For complex projects, use a hierarchical process with a manager agent:
from crewai import Agent, Crew, Task, Process
# Manager agent coordinates the team
manager = Agent(
role="Project Manager",
goal="Coordinate team efforts and ensure project success",
backstory="Experienced project manager skilled at delegation and quality control",
allow_delegation=True,
verbose=True
)
# Specialist agents
researcher = Agent(
role="Researcher",
goal="Provide accurate research and analysis",
backstory="Expert researcher with deep analytical skills",
allow_delegation=False, # Specialists focus on their expertise
verbose=True
)
writer = Agent(
role="Writer",
goal="Create compelling content",
backstory="Skilled writer who creates engaging content",
allow_delegation=False,
verbose=True
)
# Manager-led task
project_task = Task(
description="Create a comprehensive market analysis report with recommendations",
expected_output="Executive summary, detailed analysis, and strategic recommendations",
agent=manager # Manager will delegate to specialists
)
# Hierarchical crew
crew = Crew(
agents=[manager, researcher, writer],
tasks=[project_task],
process=Process.hierarchical, # Manager coordinates everything
manager_llm="gpt-4o", # Specify LLM for manager
verbose=True
)
Best Practices for Collaboration
1. Clear Role Definition
# ✅ Good: Specific, complementary roles
researcher = Agent(role="Market Research Analyst", ...)
writer = Agent(role="Technical Content Writer", ...)
# ❌ Avoid: Overlapping or vague roles
agent1 = Agent(role="General Assistant", ...)
agent2 = Agent(role="Helper", ...)
2. Strategic Delegation Enabling
# ✅ Enable delegation for coordinators and generalists
lead_agent = Agent(
role="Content Lead",
allow_delegation=True, # Can delegate to specialists
...
)
# ✅ Disable for focused specialists (optional)
specialist_agent = Agent(
role="Data Analyst",
allow_delegation=False, # Focuses on core expertise
...
)
3. Context Sharing
# ✅ Use context parameter for task dependencies
writing_task = Task(
description="Write article based on research",
agent=writer,
context=[research_task], # Shares research results
...
)
4. Clear Task Descriptions
# ✅ Specific, actionable descriptions
Task(
description="""Research competitors in the AI chatbot space.
Focus on: pricing models, key features, target markets.
Provide data in a structured format.""",
...
)
# ❌ Vague descriptions that don't guide collaboration
Task(description="Do some research about chatbots", ...)
Troubleshooting Collaboration
Issue: Agents Not Collaborating
Symptoms: Agents work in isolation, no delegation occurs
# ✅ Solution: Ensure delegation is enabled
agent = Agent(
role="...",
allow_delegation=True, # This is required!
...
)
Issue: Too Much Back-and-Forth
Symptoms: Agents ask excessive questions, slow progress
# ✅ Solution: Provide better context and specific roles
Task(
description="""Write a technical blog post about machine learning.
Context: Target audience is software developers with basic ML knowledge.
Length: 1200 words
Include: code examples, practical applications, best practices
If you need specific technical details, delegate research to the researcher.""",
...
)
Issue: Delegation Loops
Symptoms: Agents delegate back and forth indefinitely
# ✅ Solution: Clear hierarchy and responsibilities
manager = Agent(role="Manager", allow_delegation=True)
specialist1 = Agent(role="Specialist A", allow_delegation=False) # No re-delegation
specialist2 = Agent(role="Specialist B", allow_delegation=False)
Advanced Collaboration Features
Custom Collaboration Rules
# Set specific collaboration guidelines in agent backstory
agent = Agent(
role="Senior Developer",
backstory="""You lead development projects and coordinate with team members.
Collaboration guidelines:
- Delegate research tasks to the Research Analyst
- Ask the Designer for UI/UX guidance
- Consult the QA Engineer for testing strategies
- Only escalate blocking issues to the Project Manager""",
allow_delegation=True
)
Monitoring Collaboration
def track_collaboration(output):
"""Track collaboration patterns"""
if "Delegate work to coworker" in output.raw:
print("🤝 Delegation occurred")
if "Ask question to coworker" in output.raw:
print("❓ Question asked")
crew = Crew(
agents=[...],
tasks=[...],
step_callback=track_collaboration, # Monitor collaboration
verbose=True
)
Memory and Learning
Enable agents to remember past collaborations:
agent = Agent(
role="Content Lead",
memory=True, # Remembers past interactions
allow_delegation=True,
verbose=True
)
With memory enabled, agents learn from previous collaborations and improve their delegation decisions over time.
Next Steps
- Try the examples: Start with the basic collaboration example
- Experiment with roles: Test different agent role combinations
- Monitor interactions: Use
verbose=True
to see collaboration in action
- Optimize task descriptions: Clear tasks lead to better collaboration
- Scale up: Try hierarchical processes for complex projects
Collaboration transforms individual AI agents into powerful teams that can tackle complex, multi-faceted challenges together.