temporalio__skill-temporal-developer/references/python/standalone-activities.md

> [!NOTE]
> This feature is in Public Preview. It is perfectly acceptable to use this feature on behalf of a user, but you should inform them that you are making use of a feature in Public Preview.

## Overview

Standalone Activities are Activities run independently of any Workflow, started directly from a Temporal Client — useful when you need a single durable, retryable task (job-queue style) and not multi-step orchestration. The same Activity method can be executed both as a Standalone Activity and as a Workflow Activity with no code changes.

Standalone Activities are conceptually the same across all SDKs. Read the [cross-SDK concept file](../core/standalone-activities.md) if you have not already, and then see below for the Python SDK specific APIs for calling Standalone Activities.

## Prerequisites

- Temporal Python SDK v1.23.0 or higher.
- Temporal CLI v1.7.0 or higher — see [Temporal CLI install instructions](../core/install_cli.md) if needed. Dev server includes Standalone Activities support.
- For production, Temporal Server v1.31.0 or higher (or Temporal Cloud).

## Hosting Activities on a Worker

The Activity is defined just as activities normally are in Temporal. Worker registration is also the same.

```python
import asyncio
import concurrent.futures

from temporalio.client import Client
from temporalio.envconfig import ClientConfig
from temporalio.worker import Worker

from my_activity import compose_greeting


async def main():
    connect_config = ClientConfig.load_client_connect_config()
    connect_config.setdefault("target_host", "localhost:7233")
    client = await Client.connect(**connect_config)
    with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor:
        worker = Worker(
            client,
            task_queue="my-standalone-activity-task-queue",
            activities=[compose_greeting], # register whatever your activity(ies) is/are
            activity_executor=activity_executor,
        )
        await worker.run()


if __name__ == "__main__":
    asyncio.run(main())
```

## Calling and managing Standalone Activities

Start and manage Standalone Activities from your application code using the Temporal Client.

### Do not call from inside a Workflow

Don't call `client.execute_activity` / `client.start_activity` or any other Standalone Activity APIs from inside a Workflow Definition — use Workflow-side activity invocation (`workflow.execute_activity`) instead.

### Connect a Client

The Standalone Activity operations are methods on a connected `Client`. The examples below assume this `client`.

```python
from temporalio.client import Client
from temporalio.envconfig import ClientConfig

connect_config = ClientConfig.load_client_connect_config()
connect_config.setdefault("target_host", "localhost:7233")
client = await Client.connect(**connect_config)
```

### Execute (wait for result)

Use `client.execute_activity(...)` to durably enqueue the Activity, wait for it to run on a Worker, and return the result. Required arguments: the activity (first positional), `args=[...]`, `id`, `task_queue`, and a timeout such as `start_to_close_timeout`.

#### With type checking

Use when activity definitions are available in this language. Pass the activity function reference; the SDK infers the result type from its signature.

```python
import uuid
from datetime import timedelta

# In practice, use a meaningful business identifier, like customer or transaction identifier
activity_id = str(uuid.uuid4())

activity_result = await client.execute_activity(
    compose_greeting,
    args=[ComposeGreetingInput("Hello", "World")],
    id=activity_id,
    task_queue="my-standalone-activity-task-queue",
    start_to_close_timeout=timedelta(seconds=10),
)
```

#### Without type checking

Use when activity definitions are unavailable in this language (i.e. you can't import them). Pass the activity type name as a string; optionally set `result_type` to decode the result.

```python
from datetime import timedelta

activity_result = await client.execute_activity(
    "compose_greeting",
    args=[ComposeGreetingInput("Hello", "World")],
    id=activity_id,
    task_queue="my-standalone-activity-task-queue",
    start_to_close_timeout=timedelta(seconds=10),
    result_type=str,
)
```

### Start (do not wait for result)

Use `client.start_activity(...)` to durably enqueue the Activity and get back a handle without waiting for completion. This takes the **exact same arguments as `execute_activity`**.

```python
activity_handle = await client.start_activity(...)
```

### Get a handle to an existing Activity execution

Use `client.get_activity_handle(...)` to attach a handle to a previously started Standalone Activity. Omitting `run_id` (or passing `None`) targets the latest run of that Activity ID.

```python
activity_handle = client.get_activity_handle(activity_id="my-standalone-activity-id")
```

### Wait for the result of a handle

```python
result = await activity_handle.result()
```

Calling `execute_activity` is equivalent to `start_activity` followed by `await activity_handle.result()`.

### List Standalone Activities

```python
activities = client.list_activities(
    query="TaskQueue = 'my-standalone-activity-task-queue'",
)  # returns an async iterator of ActivityExecution

async for info in activities:
    print(f"ActivityID: {info.activity_id}, Type: {info.activity_type}, Status: {info.status}")
```

Only Standalone Activity Executions are returned; Activities running inside Workflows are not included.

### Count Standalone Activities

Use `client.count_activities(query=...)` to count matching executions; this takes the **exact same arguments as `list_activities`**.

```python
resp = await client.count_activities(
    query="TaskQueue = 'my-standalone-activity-task-queue'",
)
print("Total activities:", resp.count)
```