Control task execution order

Introduction

In aiida-workgraph, a task typically runs as soon as its inputs are available. However, you sometimes need to control the execution order more precisely, forcing a task to wait for another unrelated task to complete. This is useful when a task relies on data that is not passed directly as an input but is, for instance, updated in a shared context.

Note

For dependencies between different WorkGraphs, use the monitor task. Please refer to the monitor HowTo for more details.

from aiida import load_profile
from aiida_workgraph import group, task

load_profile()

Profile<uuid='2c6af82ef2b241408a4c27c7c32d5490' name='presto'>

Dependency operators >> and <<

The simplest way to create an explicit dependency is with the >> and << operators. They let you enforce that one task must finish before another begins, even if there’s no data link between them.

• task_A >> task_B means “task A, THEN task B”.

• task_B << task_A means “task B WAITS ON task A” - equivalent to task_A >> task_B.

Note

These operators can be used to create a chain of dependencies.

# task_A THEN task_B THEN task_C
task_A >> task_B >> task_C

For many dependencies, the group utility can be used:

from aiida_workgraph import group

# tasks A AND B (in parallel), THEN task C
group(task_A, task_B) >> task_C

Simple example

Let’s define a workflow where an add task must wait for two multiply tasks to finish first.

@task
def add(x, y):
    return x + y


@task
def multiply(x, y):
    return x * y

Next, we define a WorkGraph in which a group of multiply tasks run in parallel, then (>>) an add task executes, returning its result.

@task.graph
def wait_graph(x, y):
    return (
        group(
            multiply(x=y, y=2),
            multiply(x=y, y=3),
        )
        >> add(x=x, y=1).result  # `add` waits for both `multiply` tasks to complete
    )


wg = wait_graph.build(x=1, y=2)
wg.run()

06/29/2026 09:09:33 AM <2622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [148|WorkGraphEngine|continue_workgraph]: tasks ready to run: multiply,multiply1
06/29/2026 09:09:33 AM <2622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [148|WorkGraphEngine|update_task_state]: Task: multiply, type: PYFUNCTION, finished.
06/29/2026 09:09:33 AM <2622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [148|WorkGraphEngine|continue_workgraph]: tasks ready to run: multiply1
06/29/2026 09:09:33 AM <2622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [148|WorkGraphEngine|update_task_state]: Task: multiply1, type: PYFUNCTION, finished.
06/29/2026 09:09:34 AM <2622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [148|WorkGraphEngine|continue_workgraph]: tasks ready to run: add
06/29/2026 09:09:34 AM <2622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [148|WorkGraphEngine|update_task_state]: Task: add, type: PYFUNCTION, finished.
06/29/2026 09:09:34 AM <2622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [148|WorkGraphEngine|continue_workgraph]: tasks ready to run:
06/29/2026 09:09:34 AM <2622> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [148|WorkGraphEngine|finalize]: Finalize workgraph.

{'result': <Int: uuid: c23ce0a6-72d9-478a-9aa0-135ad095f0cd (pk: 154) value: 2>}

By checking the REPORT logs from AiiDA, you will see that both multiply tasks complete before the add task begins, as specified.

Conclusion

You now know two effective methods to control task execution order in aiida-workgraph:

• Use the wait operators (>>, <<) for simple, direct dependencies between individual tasks.

• Use a Zone to group tasks that should be treated as a single execution block, simplifying complex dependency chains.

These tools give you fine-grained control over your workflows, ensuring your calculations run in the correct sequence.