Class-based Executor

Run a workflow step with a class-based executor.

Use a class-based executor in a workflow: define a class with a __call__ method and pass an instance as the step executor.

Install dependencies:

uv pip install agno openai sqlalchemy yfinance

class_based_executor.py

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.yfinance import YFinanceTools
from agno.workflow.step import Step, StepInput, StepOutput
from agno.workflow.workflow import Workflow

# Define agents
hackernews_agent = Agent(
    name="Hackernews Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[HackerNewsTools()],
    instructions="Extract key insights and content from Hackernews posts",
)

finance_agent = Agent(
    name="Finance Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[YFinanceTools()],
    instructions="Get stock prices and financial data",
)

# Define research team for complex analysis
research_team = Team(
    name="Research Team",
    members=[hackernews_agent, finance_agent],
    instructions="Analyze content and create comprehensive social media strategy",
)

content_planner = Agent(
    name="Content Planner",
    model=OpenAIResponses(id="gpt-5.2"),
    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 executor using a class.
# Make sure you define the `__call__` method that receives
# the `step_input`.
class CustomContentPlanning:
    def __call__(self, step_input: StepInput) -> StepOutput:
        """
        Custom function that does intelligent content planning with context awareness
        """
        message = step_input.input
        previous_step_content = step_input.previous_step_content

        # Create intelligent planning prompt
        planning_prompt = f"""
            STRATEGIC CONTENT PLANNING REQUEST:
            Core Topic: {message}
            Research Results: {previous_step_content[:500] if previous_step_content else "No research results"}
            Planning Requirements:
            1. Create a comprehensive content strategy based on the research
            2. Leverage the research findings effectively
            3. Identify content formats and channels
            4. Provide timeline and priority recommendations
            5. Include engagement and distribution strategies
            Please create a detailed, actionable content plan.
        """

        try:
            response = content_planner.run(planning_prompt)

            enhanced_content = f"""
                ## Strategic Content Plan
                **Planning Topic:** {message}
                **Research Integration:** {"Research-based" if previous_step_content else "No research foundation"}
                **Content Strategy:**
                {response.content}
                **Custom Planning Enhancements:**
                - Research Integration: {"High" if previous_step_content else "Baseline"}
                - Strategic Alignment: Optimized for multi-channel distribution
                - Execution Ready: Detailed action items included
            """.strip()

            return StepOutput(content=enhanced_content)

        except Exception as e:
            return StepOutput(
                content=f"Custom content planning failed: {str(e)}",
                success=False,
            )


# Define steps using different executor types

research_step = Step(
    name="Research Step",
    team=research_team,
)

content_planning_step = Step(
    name="Content Planning Step",
    executor=CustomContentPlanning(),
)


# Define and use examples
if __name__ == "__main__":
    content_creation_workflow = Workflow(
        name="Content Creation Workflow",
        description="Automated content creation with custom execution options",
        db=SqliteDb(
            session_table="workflow_session",
            db_file="tmp/workflow.db",
        ),
        # Define the sequence of steps
        # First run the research_step, then the content_planning_step
        # You can mix and match agents, teams, and even regular python functions directly as steps
        steps=[research_step, content_planning_step],
    )
    content_creation_workflow.print_response(
        input="AI trends in 2024",
        markdown=True,
    )