CrewAI

Complex tasks need specialized expertise — a researcher, writer, and editor working together. CrewAI makes multi-agent collaboration declarative: define agents by role, assign tasks, and let the crew execute with built-in delegation, memory, and process management.

CrewAI is like assembling a film crew — director (manager), cinematographer (researcher), screenwriter (writer) — each with a defined role, collaborating to produce the final movie.

Scenario

Content crew: Researcher agent searches and summarizes AI trends → Writer agent drafts a blog post using research → Editor agent polishes grammar and tone. Sequential process, each task receives prior task output as context.

Solution

In Agent Frameworks, apply CrewAI to this scenario: Content crew: Researcher agent searches and summarizes AI trends → Writer agent drafts a blog post using research → Editor agent polishes grammar and tone. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to CrewAI (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where CrewAI happens in the code.

1from crewai import Agent, Task, Crew, Process  # import dependencies2
3researcher = Agent(4    role="Senior Researcher",5    goal="Find comprehensive information on AI trends",6    backstory="Expert researcher with 10 years in tech journalism",7    tools=[search_tool, scrape_tool],8    verbose=True,9)10
11writer = Agent(12    role="Content Writer",13    goal="Write engaging, accurate blog posts",14    backstory="Award-winning tech blogger",15    verbose=True,16)17
18research_task = Task(19    description="Research the top 5 AI trends in 2025",20    expected_output="Detailed research report with sources",21    agent=researcher,22)23
24write_task = Task(25    description="Write a 1000-word blog post based on the research",26    expected_output="Polished blog post in markdown",27    agent=writer,28    context=[research_task],29)30
31crew = Crew(32    agents=[researcher, writer],33    tasks=[research_task, write_task],34    process=Process.sequential,35    memory=True,36)37
38result = crew.kickoff()

• Too many agents for simple tasks — coordination overhead
• Not using task context — agents lack prior work
• Vague expected_output — agents produce inconsistent results
• Hierarchical process when sequential suffices
• No output parsing — unstructured agent outputs

Quick recap — the most important points from this module.