Standalone Activities - Python SDK

SUPPORT, STABILITY, and DEPENDENCY INFO

Temporal Python SDK support for Standalone Activities is at Pre-release.

All APIs are experimental and may be subject to backwards-incompatible changes.

Standalone Activities are Activities that run independently, without being orchestrated by a Workflow. Instead of starting an Activity from within a Workflow Definition, you start a Standalone Activity directly from a Temporal Client.

The way you write the Activity and register it with a Worker is identical to Workflow Activities. The only difference is that you execute a Standalone Activity directly from your Temporal Client.

This page covers the following:

• Get Started with Standalone Activities
• Run a Worker with the Activity registered
• Execute a Standalone Activity
• Start a Standalone Activity without waiting for the result
• Get a handle to an existing Standalone Activity
• Wait for the result of a Standalone Activity
• List Standalone Activities
• Count Standalone Activities
• Run Standalone Activities with Temporal Cloud

note

This documentation uses source code from the hello_standalone_activity sample.

Get Started with Standalone Activities

Prerequisites:

• Install the Temporal CLI (Standalone Activity Prerelease version)

• Temporal Python SDK (v1.23.0 or higher)

  uv add temporalio or see the Python Quickstart for full instructions.

The first step in running a Standalone Activity involves starting a Temporal server:

temporal server start-dev

This command automatically starts the Temporal development server with the Web UI, and creates the default Namespace. It uses an in-memory database, so do not use it for real use cases.

Temporal Cloud

All code samples on this page use ClientConfig.load_client_connect_config() to configure the Temporal Client connection. It responds to environment variables and TOML configuration files, so the same code works against a local dev server and Temporal Cloud without changes. See Run Standalone Activities with Temporal Cloud below.

The Temporal Server will now be available for client connections on localhost:7233, and the Temporal Web UI will now be accessible at http://localhost:8233. Standalone Activities are available from the nav bar item located towards the top left of the page:

Clone the samples-python repository to follow along:

git clone https://github.com/temporalio/samples-python.git
cd samples-python

The sample project is structured as follows:

hello_standalone_activity/
├── my_activity.py
├── worker.py
├── execute_activity.py
├── start_activity.py
├── list_activities.py
└── count_activities.py

Write an Activity Function

An Activity in the Temporal Python SDK is just a normal function with the @activity.defn decorator. It can optionally be an async def. The way you write a Standalone Activity is identical to how you write an Activity to be orchestrated by a Workflow. In fact, an Activity can be executed both as a Standalone Activity and as a Workflow Activity.

hello_standalone_activity/my_activity.py

# my_activity.py
from dataclasses import dataclass

from temporalio import activity


@dataclass
class ComposeGreetingInput:
    greeting: str
    name: str


@activity.defn
def compose_greeting(input: ComposeGreetingInput) -> str:
    activity.logger.info("Running activity with parameter %s" % input)
    return f"{input.greeting}, {input.name}!"

Run a Worker with the Activity registered

Running a Worker for Standalone Activities is the same as running a Worker for Workflow Activities — you create a Worker, register the Activity, and run the Worker. The Worker doesn't need to know whether the Activity will be invoked from a Workflow or as a Standalone Activity. See How to run a Worker for more details on Worker setup and configuration options.

hello_standalone_activity/worker.py

import asyncio
from concurrent.futures import ThreadPoolExecutor

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

from hello_standalone_activity.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)
    worker = Worker(
        client,
        task_queue="my-standalone-activity-task-queue",
        activities=[compose_greeting],
        activity_executor=ThreadPoolExecutor(5),
    )
    print("worker running...", end="", flush=True)
    await worker.run()


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

Run the Worker in its own terminal:

uv run hello_standalone_activity/worker.py

Execute a Standalone Activity

Use client.execute_activity() to execute a Standalone Activity. Call this from your application code, not from inside a Workflow Definition. This durably enqueues your Standalone Activity in the Temporal Server, waits for it to be executed on your Worker, and then fetches the result.

hello_standalone_activity/execute_activity.py

import asyncio
from datetime import timedelta

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

from hello_standalone_activity.my_activity import ComposeGreetingInput, compose_greeting


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

    activity_result = await client.execute_activity(
        compose_greeting,
        args=[ComposeGreetingInput("Hello", "World")],
        id="my-standalone-activity-id",
        task_queue="my-standalone-activity-task-queue",
        start_to_close_timeout=timedelta(seconds=10),
    )
    print(f"Activity result: {activity_result}")


if __name__ == "__main__":
    asyncio.run(my_application())

Run it (with the Worker running in another terminal):

uv run hello_standalone_activity/execute_activity.py

Or use the Temporal CLI:

temporal activity execute \
  --type compose_greeting \
  --activity-id my-standalone-activity-id \
  --task-queue my-standalone-activity-task-queue \
  --start-to-close-timeout 10s \
  --input '{"greeting": "Hello", "name": "World"}'

Start a Standalone Activity without waiting for the result

Starting a Standalone Activity means sending a request to the Temporal Server to durably enqueue your Activity job, without waiting for it to be executed by your Worker.

Use client.start_activity() to start your Standalone Activity and get a handle:

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

Run the full example (with the Worker running in another terminal):

uv run hello_standalone_activity/start_activity.py

Or use the Temporal CLI:

temporal activity start \
  --type compose_greeting \
  --activity-id my-standalone-activity-id \
  --task-queue my-standalone-activity-task-queue \
  --start-to-close-timeout 10s \
  --input '{"greeting": "Hello", "name": "World"}'

Get a handle to an existing Standalone Activity

You can also use client.get_activity_handle() to create a handle to a previously started Standalone Activity:

activity_handle = client.get_activity_handle(
    activity_id="my-standalone-activity-id",
    run_id="the-run-id",
)

You can now use the handle to wait for the result, describe, cancel, or terminate the Activity.

Wait for the result of a Standalone Activity

Under the hood, calling client.execute_activity() is the same as calling client.start_activity() to durably enqueue the Standalone Activity, and then calling await activity_handle.result() to wait for the activity to be executed and fetch the result:

activity_result = await activity_handle.result()

List Standalone Activities

Use client.list_activities() to list Standalone Activity Executions that match a List Filter query. The result is an async iterator that yields ActivityExecution entries.

hello_standalone_activity/list_activities.py

import asyncio

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


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

    activities = client.list_activities(
        query="TaskQueue = 'my-standalone-activity-task-queue'",
    )

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


if __name__ == "__main__":
    asyncio.run(my_application())

Run it:

uv run hello_standalone_activity/list_activities.py

Or use the Temporal CLI:

temporal activity list

The query parameter accepts the same List Filter syntax used for Workflow Visibility. For example, "ActivityType = 'MyActivity' AND Status = 'Running'".

Count Standalone Activities

Use client.count_activities() to count Standalone Activity Executions that match a List Filter query.

hello_standalone_activity/count_activities.py

import asyncio

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


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

    resp = await client.count_activities(
        query="TaskQueue = 'my-standalone-activity-task-queue'",
    )

    print("Total activities:", resp.count)

    for group in resp.groups:
        print(f"Group {group.group_values}: {group.count}")


if __name__ == "__main__":
    asyncio.run(my_application())

Run it:

uv run hello_standalone_activity/count_activities.py

Or use the Temporal CLI:

temporal activity count

Run Standalone Activities with Temporal Cloud

The code samples on this page use ClientConfig.load_client_connect_config(), so the same code works against Temporal Cloud - just configure the connection via environment variables or a TOML profile. No code changes are needed.

For full details on connecting to Temporal Cloud, including Namespace creation, certificate generation, and authentication options, see Connect to Temporal Cloud.

Connect with mTLS

export TEMPORAL_ADDRESS=<your-namespace>.<your-account-id>.tmprl.cloud:7233
export TEMPORAL_NAMESPACE=<your-namespace>.<your-account-id>
export TEMPORAL_TLS_CLIENT_CERT_PATH='path/to/your/client.pem'
export TEMPORAL_TLS_CLIENT_KEY_PATH='path/to/your/client.key'

Connect with an API key

export TEMPORAL_ADDRESS=<region>.<cloud_provider>.api.temporal.io:7233
export TEMPORAL_NAMESPACE=<your-namespace>.<your-account-id>
export TEMPORAL_API_KEY=<your-api-key>

Then run the Worker and starter code as shown in the earlier sections.