---
title: DAG Workflows
description: Define directed acyclic graph (DAG) Workflows in Python with declarative step dependencies.
image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png
---

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# DAG Workflows

The Python Workflows SDK supports DAG workflows in a declarative way, using `step.do` and parameter names to define dependencies (other steps that must complete before a step can run).

**Python**

```python
from workers import Response, WorkflowEntrypoint, WorkerEntrypoint


class PythonWorkflowStarter(WorkflowEntrypoint):
    async def run(self, event, step):
        async def await_step(fn):
            try:
                return await fn()
            except TypeError as e:
                print(f"Successfully caught {type(e).__name__}: {e}")


        await step.sleep('demo sleep', '10 seconds')


        @step.do()
        async def dep_1():
            # does stuff
            print('executing dep1')
            return 'dep1'


        @step.do()
        async def dep_2():
            # does stuff
            print('executing dep2')
            return 'dep2'


        @step.do(concurrent=True)
        async def final_step(dep_1, dep_2):
            # does stuff
            print(f'{dep_1} {dep_2}')


        await await_step(final_step)


class Default(WorkerEntrypoint):
    async def fetch(self, request):
        await self.env.MY_WORKFLOW.create()
        return Response("Hello world!")
```

In this example, `dep_1` and `dep_2` are run concurrently before execution of `final_step`, which depends on both of them.

Having `concurrent=True` allows dependencies to be resolved concurrently. If a dependency has already completed, it will be skipped and its return value will be reused.

Note

Older compatibility behavior supports explicit dependency lists with `depends=[...]`. For new workflows, use parameter names to express step dependencies.

This pattern is useful for diamond shaped workflows, where a step depends on two or more other steps that can run concurrently.

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