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Watch: Building CrewAI Agents & Flows with Coding Agent Skills

Install our coding agent skills (Claude Code, Codex, …) to quickly get your coding agents up and running with CrewAI. You can install it with npx skills add crewaiinc/skills
In this guide you will create a Flow that sets a research topic, runs a crew with one agent (a researcher using web search), and ends with a markdown report on disk. Flows are the recommended way to structure production apps: they own state and execution order, while agents do the work inside a crew step. If you have not installed CrewAI yet, follow the installation guide first.

Prerequisites

  • Python environment and the CrewAI CLI (see installation)
  • An LLM configured with the right API keys — see LLMs
  • A Serper.dev API key (SERPER_API_KEY) for web search in this tutorial

Build your first Flow

1

Create a Flow project

From your terminal, scaffold a Flow project (the folder name uses underscores, e.g. latest_ai_flow):
crewai create flow latest-ai-flow
cd latest_ai_flow
This creates a Flow app under src/latest_ai_flow/, including a starter crew under crews/content_crew/ that you will replace with a minimal single-agent research crew in the next steps.
2

Configure one agent in `agents.yaml`

Replace the contents of src/latest_ai_flow/crews/content_crew/config/agents.yaml with a single researcher. Variables like {topic} are filled from crew.kickoff(inputs=...).
agents.yaml
# src/latest_ai_flow/crews/content_crew/config/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}. You find the most relevant information and
    present it clearly.
3

Configure one task in `tasks.yaml`

tasks.yaml
# src/latest_ai_flow/crews/content_crew/config/tasks.yaml
research_task:
  description: >
    Conduct thorough research about {topic}. Use web search to find current,
    credible information. The current year is 2026.
  expected_output: >
    A markdown report with clear sections: key trends, notable tools or companies,
    and implications. Aim for 800–1200 words. No fenced code blocks around the whole document.
  agent: researcher
  output_file: output/report.md
4

Wire the crew class (`content_crew.py`)

Point the generated crew at your YAML and attach SerperDevTool to the researcher.
content_crew.py
# src/latest_ai_flow/crews/content_crew/content_crew.py
from typing import List

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


@CrewBase
class ResearchCrew:
  """Single-agent research crew used inside the Flow."""

  agents: List[BaseAgent]
  tasks: List[Task]

  agents_config = "config/agents.yaml"
  tasks_config = "config/tasks.yaml"

  @agent
  def researcher(self) -> Agent:
    return Agent(
      config=self.agents_config["researcher"],  # type: ignore[index]
      verbose=True,
      tools=[SerperDevTool()],
    )

  @task
  def research_task(self) -> Task:
    return Task(
      config=self.tasks_config["research_task"],  # type: ignore[index]
    )

  @crew
  def crew(self) -> Crew:
    return Crew(
      agents=self.agents,
      tasks=self.tasks,
      process=Process.sequential,
      verbose=True,
    )
5

Define the Flow in `main.py`

Connect the crew to a Flow: a @start() step sets the topic in state, and a @listen step runs the crew. The task’s output_file still writes output/report.md.
main.py
# src/latest_ai_flow/main.py
from pydantic import BaseModel

from crewai.flow import Flow, listen, start

from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew


class ResearchFlowState(BaseModel):
  topic: str = ""
  report: str = ""


class LatestAiFlow(Flow[ResearchFlowState]):
  @start()
  def prepare_topic(self, crewai_trigger_payload: dict | None = None):
    if crewai_trigger_payload:
      self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
    else:
      self.state.topic = "AI Agents"
    print(f"Topic: {self.state.topic}")

  @listen(prepare_topic)
  def run_research(self):
    result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
    self.state.report = result.raw
    print("Research crew finished.")

  @listen(run_research)
  def summarize(self):
    print("Report path: output/report.md")


def kickoff():
  LatestAiFlow().kickoff()


def plot():
  LatestAiFlow().plot()


if __name__ == "__main__":
  kickoff()
If your package name differs from latest_ai_flow, change the import of ResearchCrew to match your project’s module path.
6

Set environment variables

In .env at the project root, set:
7

Install and run

crewai install
crewai run
crewai run executes the Flow entrypoint defined in your project (same command as for crews; project type is "flow" in pyproject.toml).
8

Check the output

You should see logs from the Flow and the crew. Open output/report.md for the generated report (excerpt):
# AI Agents in 2026: Landscape and Trends

## Executive summary


## Key trends
- **Tool use and orchestration** — …
- **Enterprise adoption** — …

## Implications

Your actual file will be longer and reflect live search results.

How this run fits together

  1. FlowLatestAiFlow runs prepare_topic first, then run_research, then summarize. State (topic, report) lives on the Flow.
  2. CrewResearchCrew runs one task with one agent: the researcher uses Serper to search the web, then writes the structured report.
  3. Artifact — The task’s output_file writes the report under output/report.md.
To go deeper on Flow patterns (routing, persistence, human-in-the-loop), see Build your first Flow and Flows. For crews without a Flow, see Crews. For a single Agent and kickoff() without tasks, see Agents.
You now have an end-to-end Flow with an agent crew and a saved report — a solid base to add more steps, crews, or tools.

Naming consistency

YAML keys (researcher, research_task) must match the method names on your @CrewBase class. See Crews for the full decorator pattern.

Deploying

Push your Flow to CrewAI AMP once it runs locally and your project is in a GitHub repository. From the project root:
crewai login
The first deploy usually takes around 1 minute. Full prerequisites and the web UI flow are in Deploy to AMP.

Deploy guide

Step-by-step AMP deployment (CLI and dashboard).

Join the Community

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