Crewai Multi Agent

CrewAI - Framework de Orquestração Multi-Agent

Construa equipes de agentes de IA autônomos que colaboram para resolver tarefas complexas.

Quando usar CrewAI

Use CrewAI quando:

• Construir sistemas multi-agent com papéis especializados
• Precisar de colaboração autônoma entre agentes
• Quiser delegação de tarefas baseada em papéis (pesquisador, escritor, analista)
• Exigir execução de processo sequencial ou hierárquico
• Construir workflows em produção com memória e observabilidade
• Precisar de configuração mais simples que LangChain/LangGraph

Principais características:

• Independente: Sem dependências de LangChain, footprint enxuto
• Baseado em papéis: Agentes têm papéis, objetivos e históricos
• Duplo paradigma: Crews (autônomas) + Flows (event-driven)
• 50+ ferramentas: Web scraping, busca, bancos de dados, serviços de IA
• Memória: Memória de curto prazo, longo prazo e de entidades
• Pronto para produção: Tracing, recursos corporativos

Use alternativas em vez disso:

• LangChain: Apps LLM de propósito geral, pipelines RAG
• LangGraph: Workflows complexos com estado e ciclos
• AutoGen: Ecossistema Microsoft, conversas multi-agent
• LlamaIndex: Q&A de documentos, recuperação de conhecimento

Início rápido

Instalação

# Framework principal
pip install crewai

# Com 50+ ferramentas built-in
pip install 'crewai[tools]'

Criar projeto com CLI

# Criar novo projeto crew
crewai create crew my_project
cd my_project

# Instalar dependências
crewai install

# Executar o crew
crewai run

Crew simples (somente código)

from crewai import Agent, Task, Crew, Process

# 1. Definir agentes
researcher = Agent(
    role="Senior Research Analyst",
    goal="Discover cutting-edge developments in AI",
    backstory="You are an expert analyst with a keen eye for emerging trends.",
    verbose=True
)

writer = Agent(
    role="Technical Writer",
    goal="Create clear, engaging content about technical topics",
    backstory="You excel at explaining complex concepts to general audiences.",
    verbose=True
)

# 2. Definir tarefas
research_task = Task(
    description="Research the latest developments in {topic}. Find 5 key trends.",
    expected_output="A detailed report with 5 bullet points on key trends.",
    agent=researcher
)

write_task = Task(
    description="Write a blog post based on the research findings.",
    expected_output="A 500-word blog post in markdown format.",
    agent=writer,
    context=[research_task]  # Uses research output
)

# 3. Criar e executar crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,  # Tasks run in order
    verbose=True
)

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

Conceitos principais

Agentes - Trabalhadores autônomos

from crewai import Agent

agent = Agent(
    role="Data Scientist",                    # Job title/role
    goal="Analyze data to find insights",     # What they aim to achieve
    backstory="PhD in statistics...",         # Background context
    llm="gpt-4o",                             # LLM to use
    tools=[],                                 # Tools available
    memory=True,                              # Enable memory
    verbose=True,                             # Show reasoning
    allow_delegation=True,                    # Can delegate to others
    max_iter=15,                              # Max reasoning iterations
    max_rpm=10                                # Rate limit
)

Tarefas - Unidades de trabalho

from crewai import Task

task = Task(
    description="Analyze the sales data for Q4 2024. {context}",
    expected_output="A summary report with key metrics and trends.",
    agent=analyst,                            # Assigned agent
    context=[previous_task],                  # Input from other tasks
    output_file="report.md",                  # Save to file
    async_execution=False,                    # Run synchronously
    human_input=False                         # No human approval needed
)

Crews - Equipes de agentes

from crewai import Crew, Process

crew = Crew(
    agents=[researcher, writer, editor],      # Team members
    tasks=[research, write, edit],            # Tasks to complete
    process=Process.sequential,               # Or Process.hierarchical
    verbose=True,
    memory=True,                              # Enable crew memory
    cache=True,                               # Cache tool results
    max_rpm=10,                               # Rate limit
    share_crew=False                          # Opt-in telemetry
)

# Executar com inputs
result = crew.kickoff(inputs={"topic": "AI trends"})

# Acessar resultados
print(result.raw)                             # Final output
print(result.tasks_output)                    # All task outputs
print(result.token_usage)                     # Token consumption

Tipos de processo

Sequencial (padrão)

As tarefas são executadas em ordem, cada agente completando sua tarefa antes da próxima:

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential  # Task 1 → Task 2 → Task 3
)

Hierárquico

Auto-cria um agente gerenciador que delega e coordena:

crew = Crew(
    agents=[researcher, writer, analyst],
    tasks=[research_task, write_task, analyze_task],
    process=Process.hierarchical,  # Manager delegates tasks
    manager_llm="gpt-4o"           # LLM for manager
)

Usando ferramentas

Ferramentas built-in (50+)

pip install 'crewai[tools]'

from crewai_tools import (
    SerperDevTool,           # Web search
    ScrapeWebsiteTool,       # Web scraping
    FileReadTool,            # Read files
    PDFSearchTool,           # Search PDFs
    WebsiteSearchTool,       # Search websites
    CodeDocsSearchTool,      # Search code docs
    YoutubeVideoSearchTool,  # Search YouTube
)

# Atribuir ferramentas ao agente
researcher = Agent(
    role="Researcher",
    goal="Find accurate information",
    backstory="Expert at finding data online.",
    tools=[SerperDevTool(), ScrapeWebsiteTool()]
)

Ferramentas customizadas

from crewai.tools import BaseTool
from pydantic import Field

class CalculatorTool(BaseTool):
    name: str = "Calculator"
    description: str = "Performs mathematical calculations. Input: expression"

    def _run(self, expression: str) -> str:
        try:
            result = eval(expression)
            return f"Result: {result}"
        except Exception as e:
            return f"Error: {str(e)}"

# Usar ferramenta customizada
agent = Agent(
    role="Analyst",
    goal="Perform calculations",
    tools=[CalculatorTool()]
)

Configuração em YAML (recomendado)

Estrutura do projeto

my_project/
├── src/my_project/
│   ├── config/
│   │   ├── agents.yaml    # Agent definitions
│   │   └── tasks.yaml     # Task definitions
│   ├── crew.py            # Crew assembly
│   └── main.py            # Entry point
└── pyproject.toml

agents.yaml

researcher:
  role: "{topic} Senior Data Researcher"
  goal: "Uncover cutting-edge developments in {topic}"
  backstory: >
    You're a seasoned researcher with a knack for uncovering
    the latest developments in {topic}. Known for your ability
    to find relevant information and present it clearly.

reporting_analyst:
  role: "Reporting Analyst"
  goal: "Create detailed reports based on research data"
  backstory: >
    You're a meticulous analyst who transforms raw data into
    actionable insights through well-structured reports.

tasks.yaml

research_task:
  description: >
    Conduct thorough research about {topic}.
    Find the most relevant information for {year}.
  expected_output: >
    A list with 10 bullet points of the most relevant
    information about {topic}.
  agent: researcher

reporting_task:
  description: >
    Review the research and create a comprehensive report.
    Focus on key findings and recommendations.
  expected_output: >
    A detailed report in markdown format with executive
    summary, findings, and recommendations.
  agent: reporting_analyst
  output_file: report.md

crew.py

from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool

@CrewBase
class MyProjectCrew:
    """My Project crew"""

    @agent
    def researcher(self) -> Agent:
        return Agent(
            config=self.agents_config['researcher'],
            tools=[SerperDevTool()],
            verbose=True
        )

    @agent
    def reporting_analyst(self) -> Agent:
        return Agent(
            config=self.agents_config['reporting_analyst'],
            verbose=True
        )

    @task
    def research_task(self) -> Task:
        return Task(config=self.tasks_config['research_task'])

    @task
    def reporting_task(self) -> Task:
        return Task(
            config=self.tasks_config['reporting_task'],
            output_file='report.md'
        )

    @crew
    def crew(self) -> Crew:
        return Crew(
            agents=self.agents,
            tasks=self.tasks,
            process=Process.sequential,
            verbose=True
        )

main.py

from my_project.crew import MyProjectCrew

def run():
    inputs = {
        'topic': 'AI Agents',
        'year': 2025
    }
    MyProjectCrew().crew().kickoff(inputs=inputs)

if __name__ == "__main__":
    run()

Flows - Orquestração event-driven

Para workflows complexos com lógica condicional, use Flows:

from crewai.flow.flow import Flow, listen, start, router
from pydantic import BaseModel

class MyState(BaseModel):
    confidence: float = 0.0

class MyFlow(Flow[MyState]):
    @start()
    def gather_data(self):
        return {"data": "collected"}

    @listen(gather_data)
    def analyze(self, data):
        self.state.confidence = 0.85
        return analysis_crew.kickoff(inputs=data)

    @router(analyze)
    def decide(self):
        return "high" if self.state.confidence > 0.8 else "low"

    @listen("high")
    def generate_report(self):
        return report_crew.kickoff()

# Executar flow
flow = MyFlow()
result = flow.kickoff()

Consulte Flows Guide para documentação completa.

Sistema de memória

# Habilitar todos os tipos de memória
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    memory=True,           # Enable memory
    embedder={             # Custom embeddings
        "provider": "openai",
        "config": {"model": "text-embedding-3-small"}
    }
)

Tipos de memória: Curto prazo (ChromaDB), Longo prazo (SQLite), Entidades (ChromaDB)

Provedores de LLM

from crewai import LLM

llm = LLM(model="gpt-4o")                              # OpenAI (default)
llm = LLM(model="claude-sonnet-4-5-20250929")                       # Anthropic
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")  # Local
llm = LLM(model="azure/gpt-4o", base_url="https://...")              # Azure

agent = Agent(role="Analyst", goal="Analyze data", llm=llm)

CrewAI vs alternativas

Boas práticas

1. Papéis claros - Cada agente deve ter uma especialidade distinta
2. Configuração em YAML - Melhor organização para projetos maiores
3. Habilitar memória - Melhora o contexto entre tarefas
4. Definir max_iter - Prevenir loops infinitos (padrão 15)
5. Limitar ferramentas - Máximo 3-5 ferramentas por agente
6. Rate limiting - Definir max_rpm para evitar limites de API

Problemas comuns

Agente preso em loop:

agent = Agent(
    role="...",
    max_iter=10,           # Limit iterations
    max_rpm=5              # Rate limit
)

Tarefa não usando contexto:

task2 = Task(
    description="...",
    context=[task1],       # Explicitly pass context
    agent=writer
)

Erros de memória:

# Use variável de ambiente para storage
import os
os.environ["CREWAI_STORAGE_DIR"] = "./my_storage"

Referências

• Flows Guide - Workflows event-driven, gerenciamento de estado
• Tools Guide - Ferramentas built-in, ferramentas customizadas, MCP
• Troubleshooting - Problemas comuns, debugging

Recursos

• GitHub: https://github.com/crewAIInc/crewAI (25k+ stars)
• Docs: https://docs.crewai.com
• Tools: https://github.com/crewAIInc/crewAI-tools
• Examples: https://github.com/crewAIInc/crewAI-examples
• Version: 1.2.0+
• License: MIT