Input Humano na Execução

Input humano na execução dos agentes

O input humano é fundamental em vários cenários de execução de agentes, permitindo que os agentes solicitem informações adicionais ou esclarecimentos quando necessário. Esse recurso é especialmente útil em processos complexos de tomada de decisão ou quando os agentes precisam de mais detalhes para concluir uma tarefa de forma eficaz.

Usando input humano com CrewAI

Para integrar input humano durante a execução do agente, defina o parâmetro human_input na definição da tarefa. Quando ativado, o agente solicitará informações ao usuário antes de fornecer sua resposta final. Esse input pode oferecer contexto extra, esclarecer ambiguidades ou validar a saída produzida pelo agente.

Exemplo:

pip install crewai

import os
from crewai import Agent, Task, Crew
from crewai_tools import SerperDevTool

os.environ["SERPER_API_KEY"] = "Your Key"  # serper.dev API key
os.environ["OPENAI_API_KEY"] = "Your Key"

# Loading Tools
search_tool = SerperDevTool()

# Define your agents with roles, goals, tools, and additional attributes
researcher = Agent(
    role='Senior Research Analyst',
    goal='Uncover cutting-edge developments in AI and data science',
    backstory=(
        "You are a Senior Research Analyst at a leading tech think tank. "
        "Your expertise lies in identifying emerging trends and technologies in AI and data science. "
        "You have a knack for dissecting complex data and presenting actionable insights."
    ),
    verbose=True,
    allow_delegation=False,
    tools=[search_tool]
)
writer = Agent(
    role='Tech Content Strategist',
    goal='Craft compelling content on tech advancements',
    backstory=(
        "You are a renowned Tech Content Strategist, known for your insightful and engaging articles on technology and innovation. "
        "With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
    ),
    verbose=True,
    allow_delegation=True,
    tools=[search_tool],
    cache=False,  # Disable cache for this agent
)

# Create tasks for your agents
task1 = Task(
    description=(
        "Conduct a comprehensive analysis of the latest advancements in AI in 2025. "
        "Identify key trends, breakthrough technologies, and potential industry impacts. "
        "Compile your findings in a detailed report. "
        "Make sure to check with a human if the draft is good before finalizing your answer."
    ),
    expected_output='A comprehensive full report on the latest AI advancements in 2025, leave nothing out',
    agent=researcher,
    human_input=True
)

task2 = Task(
    description=(
        "Using the insights from the researcher\'s report, develop an engaging blog post that highlights the most significant AI advancements. "
        "Your post should be informative yet accessible, catering to a tech-savvy audience. "
        "Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
    ),
    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2025',
    agent=writer,
    human_input=True
)

# Instantiate your crew with a sequential process
crew = Crew(
    agents=[researcher, writer],
    tasks=[task1, task2],
    verbose=True,
    memory=True,
    planning=True  # Enable planning feature for the crew
)

# Get your crew to work!
result = crew.kickoff()

print("######################")
print(result)