Get Result from Asynchronous Celery Tasks in Python

Setting Up the Project

First, let’s set up our Celery instance in a file named This setup involves configuring Celery with RabbitMQ as the message broker and an RPC backend for storing task results:

from celery import Celery
from celery.utils.log import get_task_logger

# Initialize Celery application
app = Celery("tasks", broker='amqp://username:password@localhost', backend='rpc://')

# Create a logger
logger = get_task_logger(__name__)

def add(x, y):'Starting to add {x} + {y}')
        result = x + y'Task completed with result {result}')
        return result
    except Exception as e:
        logger.error('Error occurred', exc_info=True)
        raise e

In the code above, we define a Celery application named tasks configured with a RabbitMQ broker. The logger is utilized to record the operations and any errors encountered during the execution of tasks.

Invoking Asynchronous Tasks

Next, let’s write a to invoke our asynchronous task and handle the result:

from celery.result import AsyncResult
from tasks import add

# Sending an asynchronous task
result: AsyncResult = add.delay(1, 2)

# Checking if the task is ready and retrieving the result
print(result.ready())  # Prints False if the task is not yet ready
print(result.get(timeout=10))  # Waits for the result up to 10 seconds

Here, add.delay(1, 2) sends an asynchronous task to add the numbers 1 and 2. The AsyncResult object allows us to check if the task is completed and to fetch the result once it is available.

Running the Celery Worker

To execute the tasks, we need to run a Celery worker. Due to compatibility issues with Windows, we use the --pool=solo option:

.venv\Scripts\python.exe -m celery -A tasks worker --loglevel=info -E --pool=solo

The --pool=solo option is crucial for running Celery on Windows as it avoids issues that arise from the default prefork pool, which is not fully supported on Windows platforms.