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Hatchet Python V1 Migration Guide

This guide will help you migrate Hatchet workflows from the V0 SDK to the V1 SDK.

Introductory Example

First, a simple example of how to define a workflow with the V1 SDK:

from hatchet_sdk import Context, EmptyModel, Hatchet

hatchet = Hatchet(debug=True)


@hatchet.task(name="SimpleWorkflow")
def step1(input: EmptyModel, ctx: Context) -> None:
    print("executed step1")


def main() -> None:
    worker = hatchet.worker("test-worker", slots=1, workflows=[step1])
    worker.start()

The API has changed significantly in the V1 SDK. Even in this simple example, there are some notable highlights:

1. Tasks can now be declared with hatchet.task, meaning you no longer have to create a workflow explicitly to define a task. This should feel similar to how e.g. Celery handles task definition. Note that we recommend declaring a workflow in many cases, but the simplest possible way to get set up is to use hatchet.task.
2. Tasks have a new signature. They now take two arguments: input and context. The input is either of type input_validator (a Pydantic model you provide to the workflow), or is an EmptyModel, which is a helper Pydantic model Hatchet provides and uses as a default. The context is once again the Hatchet Context object.
3. Workflows can now be registered on a worker by using the workflows keyword argument to the worker method, although the old register_workflows method is still available.

Pydantic

Hatchet’s V1 SDK makes heavy use of Pydantic models, and recommends you do too! Let’s dive into a more involved example using Pydantic in a fanout example.

class ParentInput(BaseModel):
    n: int = 100


class ChildInput(BaseModel):
    a: str


parent_wf = hatchet.workflow(name="FanoutParent", input_validator=ParentInput)
child_wf = hatchet.workflow(name="FanoutChild", input_validator=ChildInput)


@parent_wf.task(execution_timeout=timedelta(minutes=5))
async def spawn(input: ParentInput, ctx: Context) -> dict[str, Any]:
    print("spawning child")

    result = await child_wf.aio_run_many(
        [
            child_wf.create_bulk_run_item(
                input=ChildInput(a=str(i)),
                options=TriggerWorkflowOptions(
                    additional_metadata={"hello": "earth"}, key=f"child{i}"
                ),
            )
            for i in range(input.n)
        ]
    )

    print(f"results {result}")

    return {"results": result}

In this example, we use a few more new SDK features:

1. Workflows are now declared with hatchet.workflow, and then have their corresponding tasks registered with workflow.task.
2. We define two Pydantic models, ParentInput and ChildInput, and pass them to the parent and child workflows as input_validators. Note that now, the input parameters for the tasks in those two workflows are Pydantic models of those types, and we can treat them as such. This replaces the old context.workflow_input for accessing the input to the workflow / task - now, we just can access the input directly.
3. When we want to spawn the child workflow, we can use the run methods on the child_workflow object, which is a Hatchet Workflow, instead of needing to refer to the workflow by its name (a string). The input field to run() is now also properly typed as ChildInput.
4. The child workflow (see below) makes use of some of Hatchet’s DAG features, such as defining parent tasks. In the new SDK, parents of a task are defined as a list of Task objects as opposed to as a list of strings, so now, process2 has process (the Task) as its parent, as opposed to "process" (the string). This also allows us to use ctx.task_output(process) to access the output of the process task in the process2 task, and know the type of that output at type checking time.

@child_wf.task()
def process(input: ChildInput, ctx: Context) -> dict[str, str]:
    print(f"child process {input.a}")
    return {"status": input.a}


@child_wf.task(parents=[process])
def process2(input: ChildInput, ctx: Context) -> dict[str, str]:
    process_output = ctx.task_output(process)
    a = process_output["status"]

    return {"status2": a + "2"}

See our Pydantic documentation for more.

Other Breaking Changes

There have been a number of other breaking changes throughout the SDK in V1.

Typing improvements:

1. All times and durations, such as timeout and schedule_timeout fields are now datetime.timedelta objects instead of strings (e.g. "10s" becomes timedelta(seconds=10)).
2. External-facing protobuf objects, such as StickyStrategy and ConcurrencyLimitStrategy, have been replaced by native Python enums to make working with them easier.
3. All interactions with the Workflow object are now typed, so you know e.g. what the type of the input to the workflow needs to be at type checking time (we see this in the Pydantic example above).
4. All external-facing types that are used for triggering workflows, scheduling workflows, etc. are now Pydantic objects, as opposed to being TypedDicts.
5. The return type of each Task is restricted to a JSONSerializableMapping or a Pydantic model, to better align with what the Hatchet Engine expects.
6. The ClientConfig now uses Pydantic Settings, and we’ve removed the static methods on the Client for from_environment and from_config in favor of passing configuration in correctly. See the configuration example for more details.
7. The REST API wrappers, which previously were under hatchet.rest, have been completely overhauled.

Naming changes:

1. We no longer have nested aio clients for async methods. Instead, async methods throughout the entire SDK are prefixed by aio_, similar to Langchain’s use of the a prefix to indicate async. For example, to run a workflow, you may now either use workflow.run() or workflow.aio_run().
2. All functions on Hatchet clients are now verbs. For instance the way to list workflow runs is via hatchet.workflows.list.
3. max_runs on the worker has been renamed to slots.

Removals:

1. sync_to_async has been removed. We recommend reading our asyncio documentation for our recommendations on handling blocking work in otherwise async tasks.

Other miscellaneous changes:

1. As shown in the Pydantic example above, there is no longer a spawn_workflow(s) method on the Context. run is now the preferred method for spawning workflows, which will automatically propagate the parent’s metadata to the child workflow.

Other New features

There are a handful of other new features that will make interfacing with the SDK easier, which are listed below.

1. Concurrency keys using the input to a workflow are now checked for validity at runtime. If the workflow’s input_validator does not contain a field that’s used in a key, Hatchet will reject the workflow when it’s created. For example, if the key is input.user_id, the input_validator Pydantic model must contain a user_id field.
2. There is now an on_success_task on the Workflow object, which works just like an on-failure task, but it runs after all upstream tasks in the workflow have succeeded.