Celery
docs.celeryq.dev
What it is
Celery is a free, open-source distributed task queue for real-time processing and scheduled jobs. It sends task messages through a broker to worker processes, and systems can run multiple workers and brokers for high availability and horizontal scaling. Celery Beat starts tasks at regular intervals for available workers. Documented transports include RabbitMQ, Redis, Amazon SQS, Kafka and Google Pub/Sub, with different capability labels: RabbitMQ and Redis are stable, SQS is stable without monitoring or remote control, and Kafka is experimental without those features. Integrations are documented for Django, Pyramid, Pylons, Flask, web2py, Tornado and FastAPI. Commands inspect and manage workers, while Flower and event utilities provide monitoring options. Celery 5.6 supports Python 3.9 through 3.13 and PyPy 3.9 or newer. It is self-hosted, installable via PyPI or source, and licensed under the New BSD License.
Spec sheet
- Purpose
- Celery is a distributed task queue focused on real-time processing that also supports task scheduling.Read 4 Oct 2026 · docs.celeryq.dev
- Security
- Celery advises treating it as an unsafe component and recommends restricting broker access; supported broker backends can use SSL encryption and authentication.Read 4 Oct 2026 · docs.celeryq.dev
- License
- Celery is open source and licensed under the New BSD License.Read 4 Oct 2026 · docs.celeryq.dev
- Broker integrations
- Documented message transports include RabbitMQ, Redis, Amazon SQS, Kafka, and Google Pub/Sub.Read 4 Oct 2026 · docs.celeryq.dev
- Broker limits
- The documentation marks RabbitMQ and Redis stable, SQS stable without monitoring or remote control, and Kafka experimental without either capability.Read 4 Oct 2026 · docs.celeryq.dev
- Monitoring
- Celery provides commands to inspect and manage worker nodes and documents monitoring tools including Flower and event utilities.Read 4 Oct 2026 · docs.celeryq.dev
- Scaling
- A Celery system can use multiple workers and brokers to support high availability and horizontal scaling.Read 4 Oct 2026 · docs.celeryq.dev
- Scheduling
- Celery Beat starts tasks at regular intervals for execution by available worker nodes.Read 4 Oct 2026 · docs.celeryq.dev
- Support channels
- The project points users to its mailing list, IRC channel, issue tracker, and community resources for help.Read 4 Oct 2026 · github.com
- Supported runtime
- Celery 5.6 runs on Python 3.9 through 3.13 and PyPy 3.9 or newer.Read 4 Oct 2026 · github.com
- Task processing
- Celery sends task messages through a broker, which delivers them to worker processes for execution.Read 4 Oct 2026 · docs.celeryq.dev
- Web framework integrations
- Celery documents integrations for Django, Pyramid, Pylons, Flask, web2py, Tornado, and FastAPI.Read 4 Oct 2026 · github.com
- Windows support
- The project says Windows is not officially supported, although it may work.Read 4 Oct 2026 · github.com
Plans and prices
Celery Free Open-source distributed task queue · install via PyPI or from source Read 4 Oct 2026 · docs.celeryq.dev
Strong points and limits
Strong points
- Supports scheduled jobs with Celery Beat
- Multiple workers and brokers can scale
- Integrates with several Python frameworks
- Runs on Python 3.9–3.13
- Open source under New BSD License
Limits
- Windows is not officially supported
- Kafka transport is experimental
- Broker access requires careful restriction
- Self-hosted deployment is required
Who it suits
Celery suits Python teams that need self-hosted background processing, retries, dead-letter queues or scheduled jobs. It fits systems able to operate and secure their own brokers and workers.
Verdict
Celery covers distributed execution and scheduling with broad broker and framework documentation. Its self-hosted model and broker-security requirements place operational responsibility on the deploying team.
Where Specifiction places it
- Task Queue Software#11 of 378.1
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