This example demonstrates Workflows using named Step objects for better tracking and organization. This pattern provides clear step identification and enhanced logging while maintaining simple sequential execution.

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Pattern: Sequential Named Steps

When to use: Linear processes where you want clear step identification, better logging, and future platform support. Ideal when you have distinct phases that benefit from naming.
sequence_of_steps.py
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.googlesearch import GoogleSearchTools
from agno.tools.hackernews import HackerNewsTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow

# Define agents
hackernews_agent = Agent(
    name="Hackernews Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[HackerNewsTools()],
    role="Extract key insights and content from Hackernews posts",
)
web_agent = Agent(
    name="Web Agent",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[GoogleSearchTools()],
    role="Search the web for the latest news and trends",
)

# Define research team for complex analysis
research_team = Team(
    name="Research Team",
    members=[hackernews_agent, web_agent],
    instructions="Research tech topics from Hackernews and the web",
)

content_planner = Agent(
    name="Content Planner",
    model=OpenAIChat(id="gpt-5-mini"),
    instructions=[
        "Plan a content schedule over 4 weeks for the provided topic and research content",
        "Ensure that I have posts for 3 posts per week",
    ],
)

# Define steps
research_step = Step(
    name="Research Step",
    team=research_team,
)

content_planning_step = Step(
    name="Content Planning Step",
    agent=content_planner,
)

content_creation_workflow = Workflow(
    name="Content Creation Workflow",
    description="Automated content creation from blog posts to social media",
    db=SqliteDb(
        session_table="workflow_session",
        db_file="tmp/workflow.db",
    ),
    steps=[research_step, content_planning_step],
)

# Create and use workflow
if __name__ == "__main__":
    content_creation_workflow.print_response(
        input="AI trends in 2024",
        markdown=True,
    )
This was a synchronous non-streaming example of this pattern. To checkout async and streaming versions, see the cookbooks-