Tasks

from crewai import Task

task = Task(
    # What should the agent do?
    description="""Research the current state of AI in healthcare.
    Focus on:
    1. FDA-approved AI diagnostics tools
    2. AI in drug discovery pipelines
    3. Patient outcome improvements
    Include specific statistics and case studies.""",

    # What should the output look like?
    expected_output="""A structured research report with:
    - Executive summary (3 sentences)
    - 3 sections with findings and statistics
    - List of sources referenced""",

    # Which agent handles this?
    agent=researcher,
)

# Task 1: Research (no dependencies)
research_task = Task(
    description="Research AI trends in education for 2026.",
    expected_output="A detailed research report with 5 key trends.",
    agent=researcher
)

# Task 2: Writing (depends on research)
writing_task = Task(
    description="Write a blog post based on the research findings.",
    expected_output="A 800-word blog post in markdown format.",
    agent=writer,
    context=[research_task]  # Receives research output as context
)

# Task 3: Editing (depends on writing)
editing_task = Task(
    description="""Review and edit the blog post.
    Fix grammar, improve clarity, and ensure accuracy
    against the original research.""",
    expected_output="A polished, publication-ready blog post.",
    agent=editor,
    context=[writing_task, research_task]  # Access both previous outputs
)

from pydantic import BaseModel
from typing import List

class TrendReport(BaseModel):
    title: str
    trends: List[str]
    summary: str
    confidence_score: float

task = Task(
    description="Identify the top 5 AI trends for 2026.",
    expected_output="A structured report with trends and confidence.",
    agent=researcher,
    output_pydantic=TrendReport  # Forces structured output
)

# After crew.kickoff(), access structured data:
# task.output.pydantic.trends
# task.output.pydantic.confidence_score

task = Task(
    description="Write a comprehensive market analysis.",
    expected_output="A detailed market analysis report.",
    agent=analyst,
    output_file="reports/market_analysis.md"  # Saves output to file
)

def task_completed(output):
    """Called when a task finishes."""
    print(f"Task completed! Output length: {len(str(output))}")
    # Send notification, log to database, etc.

task = Task(
    description="Analyze customer feedback data.",
    expected_output="Summary of customer sentiment trends.",
    agent=analyst,
    callback=task_completed  # Called after task execution
)