Crews

from crewai import Crew, Process

crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research_task, writing_task, editing_task],
    process=Process.sequential,  # Task 1 → Task 2 → Task 3
    verbose=True
)

result = crew.kickoff()

# Sequential flow:
# 1. Researcher completes research_task
# 2. Writer receives research output, completes writing_task
# 3. Editor receives writing output, completes editing_task
# → Final result = editing_task output

crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research_task, writing_task, editing_task],
    process=Process.hierarchical,  # Manager delegates tasks
    manager_llm="gpt-4o",            # LLM for the manager agent
    verbose=True
)

result = crew.kickoff()

# Hierarchical flow:
# 1. Manager analyzes all tasks
# 2. Manager delegates research_task to researcher
# 3. Manager reviews output, delegates writing_task to writer
# 4. Manager may request revisions or re-delegation
# 5. Manager delegates editing_task to editor
# → Final result curated by manager

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential,
    memory=True,          # Enable memory across runs
    verbose=True
)

# Memory types:
# Short-term: conversation within a single crew run
# Long-term: persisted across multiple crew runs
# Entity: remembers key entities and facts

# First run - crew learns about the topic
result1 = crew.kickoff(inputs={"topic": "AI in Healthcare"})

# Second run - crew remembers previous research
result2 = crew.kickoff(inputs={"topic": "AI in Healthcare Updates"})

# Use {placeholders} in task descriptions
research_task = Task(
    description="Research {topic} for the {market} market.",
    expected_output="Detailed report on {topic}.",
    agent=researcher
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.sequential
)

# Pass inputs at runtime
result = crew.kickoff(inputs={
    "topic": "Electric Vehicles",
    "market": "European"
})

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

# Agents
researcher = Agent(
    role="SEO Research Specialist",
    goal="Find trending topics and keywords for {topic}",
    backstory="Expert in SEO research and trend analysis.",
    tools=[SerperDevTool()],
)

writer = Agent(
    role="Senior Content Writer",
    goal="Write SEO-optimized articles that rank and engage",
    backstory="Award-winning writer specializing in tech content.",
)

editor = Agent(
    role="Chief Editor",
    goal="Ensure all content is accurate, engaging, and polished",
    backstory="Former newspaper editor with an eye for detail.",
)

# Tasks
research = Task(
    description="Research {topic}. Find 5 key angles, trending keywords, and competitor content.",
    expected_output="Research brief with angles, keywords, and sources.",
    agent=researcher,
)

write = Task(
    description="Write a 1000-word article on {topic} using the research. Include H2 headings, code examples, and a call-to-action.",
    expected_output="A complete article in markdown format.",
    agent=writer,
    context=[research],
)

edit = Task(
    description="Edit the article for grammar, accuracy, SEO, and readability. Ensure claims match the research.",
    expected_output="Final polished article ready for publication.",
    agent=editor,
    context=[write, research],
    output_file="output/article.md",
)

# Crew
content_crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research, write, edit],
    process=Process.sequential,
    verbose=True,
)

result = content_crew.kickoff(inputs={"topic": "AI Code Assistants"})