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Sequential workflows ensure predictable execution order and clear data flow between steps. Example Flow: Research → Data Processing → Content Creation → Final Review Sequential workflows ensure predictable execution order and clear data flow between steps.
sequential_workflow.py
from agno.workflow import Step, Workflow, StepOutput

def data_preprocessor(step_input):
    # Custom preprocessing logic

    # Or you can also run any agent/team over here itself
    # response = some_agent.run(...)
    return StepOutput(content=f"Processed: {step_input.input}") # <-- Now pass the agent/team response in content here

workflow = Workflow(
    name="Mixed Execution Pipeline",
    steps=[
        research_team,      # Team
        data_preprocessor,  # Function
        content_agent,      # Agent
    ]
)

workflow.print_response("Analyze the competitive landscape for fintech startups", markdown=True)
For more information on how to use custom functions, refer to the Workflow with custom function step page.
See Example:
StepInput and StepOutput provides standardized interfaces for data flow between steps: So if you make a custom function as an executor for a step, make sure that the input and output types are compatible with the StepInput and StepOutput interfaces. This will ensure that your custom function can seamlessly integrate into the workflow system.Take a look at the schemas for StepInput and StepOutput.