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Example Use-Cases: Multi-source research, parallel analysis, concurrent data processing Parallel workflows maintain deterministic results while dramatically reducing execution time for independent operations. Workflows parallel steps diagram

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Example

parallel_workflow.py
from agno.workflow import Parallel, Step, Workflow

workflow = Workflow(
    name="Parallel Research Pipeline",
    steps=[
        Parallel(
            Step(name="HackerNews Research", agent=hn_researcher),
            Step(name="Web Research", agent=web_researcher),
            Step(name="Academic Research", agent=academic_researcher),
            name="Research Step"
        ),
        Step(name="Synthesis", agent=synthesizer),  # Combines the results and produces a report
    ]
)

workflow.print_response("Write about the latest AI developments", markdown=True)

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Handling Session State Data in Parallel Steps

When using custom Python functions in your steps, you can access and update the Worfklow session state via the run_context parameter. If you are performing session state updates in Parallel Steps, be aware that concurrent access to shared state will require coordination to avoid race conditions.

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Developer Resources

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Reference

For complete API documentation, see Parallel Steps Reference.