CrewAI Basics

from crewai import Agent
 
researcher = Agent(
    role="Research Analyst",
    goal="Collect accurate, citable notes on the topic",
    backstory="You prefer primary sources and short bullet notes over essays.",
    verbose=True,
)
 
writer = Agent(
    role="Technical Writer",
    goal="Turn research notes into a clear brief for engineers",
    backstory="You write tight markdown with headings and no fluff.",
    verbose=True,
)

from crewai import Task
 
research_task = Task(
    description=(
        "Research {topic}. List 5 key facts with why each matters. "
        "Note uncertainty explicitly."
    ),
    expected_output="Markdown bullets: fact, why it matters, confidence.",
    agent=researcher,
)
 
write_task = Task(
    description=(
        "Using the research notes, write a 3-paragraph engineering brief on {topic}. "
        "Audience: senior developers. End with 3 open questions."
    ),
    expected_output="Markdown brief with three paragraphs and three questions.",
    agent=writer,
    context=[research_task],
)

from crewai import Crew, Process
 
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    verbose=True,
)
 
result = crew.kickoff(inputs={"topic": "offline evals for tool-using agents"})
print(result.raw)
print(result.tasks_output)  # intermediate + final task outputs

researcher = Agent(
    role="Research Analyst",
    goal="Collect accurate notes quickly",
    backstory="You stop when you have five solid facts.",
    max_iter=8,
    max_retry_limit=2,
    verbose=True,
)

import os
from crewai import Agent
from crewai_tools import SerperDevTool  # requires SERPER_API_KEY
 
search = SerperDevTool()
 
researcher = Agent(
    role="Research Analyst",
    goal="Find current facts with web search",
    backstory="You verify claims before writing them down.",
    tools=[search],
    verbose=True,
)
 
writer = Agent(
    role="Technical Writer",
    goal="Write from provided notes only",
    backstory="You do not invent sources. You rewrite notes.",
    tools=[],  # intentional
    verbose=True,
)

result = crew.kickoff(inputs={"topic": "agent memory design"})
 
print("--- final ---")
print(result.raw)
 
for task_out in result.tasks_output:
    print(task_out.agent, "=>", (task_out.raw or "")[:200])
 
print("tokens:", result.token_usage)
# If you enabled structured output on a task, also check result.pydantic / json_dict

from crewai import Crew, Process
 
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.hierarchical,
    manager_llm="gpt-4o",  # or a configured LLM instance; required for hierarchical
    verbose=True,
)
 
result = crew.kickoff(inputs={"topic": "RAG evaluation metrics"})
print(result.raw)

from crewai import Crew, Process, Memory
 
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    memory=True,  # or memory=Memory(...) for tuned scoring / storage
    verbose=True,
)
 
crew.kickoff(inputs={"topic": "CrewAI knowledge sources"})

write_task = Task(
    description="Write a 3-paragraph brief on {topic} from the research notes.",
    expected_output="Markdown brief saved for review.",
    agent=writer,
    context=[research_task],
    output_file="output/topic_brief.md",
)
 
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    verbose=True,
    output_log_file="crew_run.json",  # JSON if filename ends with .json
)
 
result = crew.kickoff(inputs={"topic": "agent observability"})
print("wrote", "output/topic_brief.md", "final chars", len(result.raw or ""))
# CLI later: crewai log-tasks-outputs / crewai replay -t <task_id> (verify at build)