Crew YAML Configuration and @CrewBase

Full agents.yaml, tasks.yaml and crew.py examples for the classic crew layout - the crews/<name>/ folder inside a crewai create flow project, or a crewai create crew <name> --classic project.

Verified against crewai 1.15.23 on 2026-10-01: the YAML and crew.py below load as written (two agents, two tasks, reporting_task.context set), and the scaffolded classic crew of the same researcher / reporting_analyst shape ran end to end with anthropic/claude-haiku-4-5 via crewai run.

1. YAML Configuration (agents.yaml & tasks.yaml)

The scaffold uses YAML files for agent and task definitions. This separates configuration from code and supports {variable} interpolation.

agents.yaml

researcher:
  role: >
    {topic} Senior Data Researcher
  goal: >
    Uncover cutting-edge developments in {topic}
  backstory: >
    You're a seasoned researcher with a knack for uncovering
    the latest developments in {topic}.
  # Optional overrides:
  # llm: openai/gpt-4o
  # max_iter: 20
  # max_rpm: 10

reporting_analyst:
  role: >
    {topic} Reporting Analyst
  goal: >
    Create detailed reports based on {topic} research findings
  backstory: >
    You're a meticulous analyst known for turning complex data
    into clear, actionable reports.

tasks.yaml

research_task:
  description: >
    Conduct thorough research about {topic}.
    Identify key trends, breakthrough technologies,
    and potential industry impacts.
  expected_output: >
    A detailed report with analysis of the top 5
    developments in {topic}, with sources and implications.
  agent: researcher

reporting_task:
  description: >
    Review the research and create a comprehensive report about {topic}.
  expected_output: >
    A polished report formatted in markdown with sections
    for each key finding.
  agent: reporting_analyst
  output_file: output/report.md

Key rules:

• {variable} placeholders are replaced at runtime via crew.kickoff(inputs={...})
• expected_output is always a string (never a Pydantic class name); set output_pydantic=Model in the @task method in Python
• agent value must match an agent key in agents.yaml
• In Process.sequential, each task auto-receives all prior task outputs as context
• For non-sequential deps, use context=[other_task] to explicitly pass output

2. Wiring It Together - crew.py

The @CrewBase decorator auto-loads YAML config files and collects @agent and @task methods.

from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool

@CrewBase
class ResearchCrew:
    """Research and reporting crew."""

    agents_config = "config/agents.yaml"   # these two paths are also the defaults
    tasks_config = "config/tasks.yaml"

    @agent
    def researcher(self) -> Agent:
        return Agent(
            config=self.agents_config["researcher"],
            tools=[SerperDevTool()],
        )

    @agent
    def reporting_analyst(self) -> Agent:
        return Agent(
            config=self.agents_config["reporting_analyst"],
        )

    @task
    def research_task(self) -> Task:
        return Task(config=self.tasks_config["research_task"])

    @task
    def reporting_task(self) -> Task:
        return Task(
            config=self.tasks_config["reporting_task"],
            context=[self.research_task()],  # Explicit dependency (optional in sequential)
            output_file="output/report.md",
        )

    @crew
    def crew(self) -> Crew:
        return Crew(
            agents=self.agents,  # auto-collected by @agent
            tasks=self.tasks,    # auto-collected by @task
            process=Process.sequential,
            verbose=True,
        )

Important: Method names must match YAML keys. def researcher(self) maps to the researcher: key in agents.yaml.

Structured output stays in Python: Task(config=self.tasks_config["research_task"], output_pydantic=Report). An output_pydantic: key in tasks.yaml is not a substitute (see the check-crewai-api skill).