# Continue-As-New - Python SDK

> For the complete documentation index, see [llms.txt](https://docs.temporal.io/llms.txt).
> Any documentation page is available as raw Markdown by appending `.md` to its URL.

> Use Temporal's Continue-As-New in Python to manage large Event Histories by atomically creating new Workflow Executions with the same Workflow Id and fresh parameters.

This page covers the following for Python developers:

- [What is Continue-As-New?](#what)
- [Use Continue-As-New](#how)
- [When is it right to Continue-As-New?](#when)
- [Test Continue-As-New](#how-to-test)

## What is Continue-As-New? 

[Continue-As-New](/workflow-execution/continue-as-new) lets a Workflow Execution close successfully and creates a new Workflow Execution.
You can think of it as a checkpoint when your Workflow gets too long or approaches certain scaling limits.

The new Workflow Execution is in the same [chain](/workflow-execution#workflow-execution-chain); it keeps the same Workflow Id but gets a new Run Id and a fresh Event History.
It also receives your Workflow's usual parameters.

## Use Continue-As-New with the Python SDK 

First, design your Workflow parameters so that you can pass in the "current state" when you Continue-As-New into the next Workflow run.
This state is typically set to `None` for the original caller of the Workflow.

[View the source code](https://github.com/temporalio/samples-python/blob/main/message_passing/safe_message_handlers/workflow.py) in the context of the rest of the application code.

```python
@dataclass
class ClusterManagerInput:
    state: Optional[ClusterManagerState] = None
    test_continue_as_new: bool = False

@workflow.run
async def run(self, input: ClusterManagerInput) -> ClusterManagerResult:

````
The test hook in the above snippet is covered [below](#how-to-test).

Inside your Workflow, call the [`continue_as_new()`](https://python.temporal.io/temporalio.workflow.html#continue_as_new) function with the same type.
This stops the Workflow right away and starts a new one.

[View the source code](https://github.com/temporalio/samples-python/blob/main/message_passing/safe_message_handlers/workflow.py) in the context of the rest of the application code.

```python
workflow.continue_as_new(
  ClusterManagerInput(
      state=self.state,
      test_continue_as_new=input.test_continue_as_new,
  )
)
````

### Considerations for Workflows with Message Handlers 

If you use Updates or Signals, don't call Continue-as-New from the handlers.
Instead, wait for your handlers to finish in your main Workflow before you run `continue_as_new`.
See the [`all_handlers_finished`](message-passing#wait-for-message-handlers) example for guidance.

## When is it right to Continue-As-New with the Python SDK? 

Use Continue-as-New when your Workflow might encounter degraded performance or [Event History Limits](/workflow-execution/event#event-history).

Temporal tracks your Workflow's progress against these limits to let you know when you should Continue-as-New.
Call `workflow.info().is_continue_as_new_suggested()` to check if it's time.

## Test Continue-As-New with the Python SDK 

Testing Workflows that naturally Continue-as-New may be time-consuming and resource-intensive.
Instead, add a test hook to check your Workflow's Continue-as-New behavior faster in automated tests.

For example, when `test_continue_as_new == True`, this sample creates a test-only variable called `self.max_history_length` and sets it to a small value.
A helper method in the Workflow checks it each time it considers using Continue-as-New:

[View the source code](https://github.com/temporalio/samples-python/blob/main/message_passing/safe_message_handlers/workflow.py) in the context of the rest of the application code.

```python
def should_continue_as_new(self) -> bool:
    if workflow.info().is_continue_as_new_suggested():
        return True
    # For testing
    if (
        self.max_history_length
        and workflow.info().get_current_history_length() > self.max_history_length
    ):
        return True
    return False
```