crewAI/docs/pt-BR/concepts/collaboration.mdx

---
title: Colaboração
description: Como permitir que agentes trabalhem juntos, deleguem tarefas e se comuniquem de forma eficaz em equipes CrewAI.
icon: screen-users
---

## Visão Geral

A colaboração no CrewAI permite que agentes trabalhem juntos como uma equipe, delegando tarefas e fazendo perguntas para aproveitar a expertise uns dos outros. Quando `allow_delegation=True`, os agentes automaticamente têm acesso a poderosas ferramentas de colaboração.

## Guia Rápido: Habilite a Colaboração

```python
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
)
```

## Como Funciona a Colaboração entre Agentes

Quando `allow_delegation=True`, o CrewAI automaticamente fornece aos agentes duas ferramentas poderosas:

### 1. **Ferramenta de Delegação de Trabalho**
Permite que agentes designem tarefas para colegas com expertise específica.

```python
# Agent automatically gets this tool:
# Delegate work to coworker(task: str, context: str, coworker: str)
```

### 2. **Ferramenta de Fazer Pergunta**
Permite que agentes façam perguntas específicas para obter informações de colegas.

```python
# Agent automatically gets this tool:
# Ask question to coworker(question: str, context: str, coworker: str)
```

## Colaboração em Ação

Veja um exemplo completo onde agentes colaboram em uma tarefa de criação de conteúdo:

```python
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()
```

## Padrões de Colaboração

### Padrão 1: Pesquisa → Redação → Edição
```python
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
)
```

### Padrão 2: Tarefa Única Colaborativa
```python
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
)
```

## Colaboração Hierárquica

Para projetos complexos, utilize um processo hierárquico com um agente gerente:

```python
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
)
```

## Melhores Práticas para Colaboração

### 1. **Definição Clara de Papéis**
```python
# ✅ Bom: papéis específicos e complementares
researcher = Agent(role="Market Research Analyst", ...)
writer = Agent(role="Technical Content Writer", ...)

# ❌ Evite: Papéis sobrepostos ou vagos  
agent1 = Agent(role="General Assistant", ...)
agent2 = Agent(role="Helper", ...)
```

### 2. **Delegação Estratégica Habilitada**
```python
# ✅ Habilite delegação para coordenadores e generalistas
lead_agent = Agent(
    role="Content Lead",
    allow_delegation=True,  # Can delegate to specialists
    ...
)

# ✅ Desative para especialistas focados (opcional)
specialist_agent = Agent(
    role="Data Analyst", 
    allow_delegation=False,  # Focuses on core expertise
    ...
)
```

### 3. **Compartilhamento de Contexto**
```python
# ✅ Use o parâmetro context para dependências entre tarefas
writing_task = Task(
    description="Write article based on research",
    agent=writer,
    context=[research_task],  # Shares research results
    ...
)
```

### 4. **Descrições Claras de Tarefas**
```python
# ✅ Descrições específicas e acionáveis
Task(
    description="""Research competitors in the AI chatbot space.
    Focus on: pricing models, key features, target markets.
    Provide data in a structured format.""",
    ...
)

# ❌ Descrições vagas que não orientam a colaboração
Task(description="Do some research about chatbots", ...)
```

## Solução de Problemas em Colaboração

### Problema: Agentes Não Colaboram
**Sintomas:** Agentes trabalham isoladamente, sem ocorrer delegação
```python
# ✅ Solução: Certifique-se que a delegação está habilitada
agent = Agent(
    role="...",
    allow_delegation=True,  # This is required!
    ...
)
```

### Problema: Troca Excessiva de Perguntas
**Sintomas:** Agentes fazem perguntas em excesso, progresso lento
```python
# ✅ Solução: Forneça melhor contexto e papéis específicos
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.""",
    ...
)
```

### Problema: Loops de Delegação
**Sintomas:** Agentes delegam tarefas repetidamente uns para os outros indefinidamente
```python
# ✅ Solução: Hierarquia e responsabilidades bem definidas
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)
```

## Recursos Avançados de Colaboração

### Regras Personalizadas de Colaboração
```python
# 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
)
```

### Monitoramento da Colaboração
```python
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
)
```

## Memória e Aprendizado

Permita que agentes se lembrem de colaborações passadas:

```python
agent = Agent(
    role="Content Lead",
    memory=True,  # Remembers past interactions
    allow_delegation=True,
    verbose=True
)
```

Com a memória ativada, os agentes aprendem com colaborações anteriores e aprimoram suas decisões de delegação ao longo do tempo.

## Próximos Passos

- **Teste os exemplos**: Comece pelo exemplo básico de colaboração
- **Experimente diferentes papéis**: Teste combinações variadas de papéis de agentes  
- **Monitore as interações**: Use `verbose=True` para ver a colaboração em ação
- **Otimize descrições de tarefas**: Tarefas claras geram melhor colaboração
- **Escale**: Experimente processos hierárquicos para projetos complexos

A colaboração transforma agentes de IA individuais em equipes poderosas capazes de enfrentar desafios complexos e multifacetados juntos.