apache-airflow-providers-celery
Provider package apache-airflow-providers-celery for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you are running Apache Airflow and need to scale task execution beyond a single machine. The package is actively maintained, has no known vulnerabilities, and is production-stable. Install only if you have a Celery broker already running or planned; it is not useful without one. The low install friction and permissive Apache-2.0 license make it a straightforward addition to an existing Airflow deployment.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an existing Airflow installation (>=2.11.0) and a Celery broker (e.g., Redis or RabbitMQ) to be running separately.
- Low friction install as a pure Python wheel.
- Actively maintained with a release 6 days old.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use without restriction, typical for Apache Foundation projects.
last release 2026-08-08 (6 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,867,146 downloads/mo, #3,478 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-celery
from airflow.providers.celery.executors.celery_executor import CeleryExecutor
# Configure in airflow.cfg or environment
# executor = CeleryExecutor
# celery_broker_url = redis://localhost/0- Whether this provider supports Kubernetes pod operators or only traditional Celery workers
- Performance characteristics and scaling limits for typical cluster sizes
- Specific monitoring capabilities beyond what flower provides
What it is and what it does
This is an Apache Airflow provider package that plugs Celery into Airflow as a distributed task executor. Instead of running all DAG tasks on a single Airflow scheduler, it routes them to remote Celery workers across a cluster, letting you scale horizontally by adding more worker nodes. The package wraps Celery's task queue mechanics and integrates them with Airflow's DAG scheduling, dependency tracking, and monitoring.
You install it alongside an existing Airflow setup, configure it to point to a Celery broker (typically Redis or RabbitMQ), and then set Airflow to use CeleryExecutor. Airflow will then push tasks to the broker, workers pull and execute them, and results flow back. The package includes integration with flower, Celery's monitoring tool, so you can observe worker status and task progress from a web UI.
Use it for
- Scale Airflow task execution across multiple machines by running workers on separate nodes and routing DAG tasks through Celery.
- Process long-running or CPU-intensive tasks in parallel without blocking the Airflow scheduler.
- Monitor distributed task execution and worker health using flower's web dashboard.
- Run Airflow in a multi-tenant environment where different teams or projects need isolated worker pools.
- Integrate Airflow with an existing Celery infrastructure already in use for other applications.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are running Apache Airflow and need to scale task execution beyond a single machine.
The package is actively maintained, has no known vulnerabilities, and is production-stable. Install only if you have a Celery broker already running or planned; it is not useful without one. The low install friction and permissive Apache-2.0 license make it a straightforward addition to an existing Airflow deployment.
Install
apache-airflow-providers-celery on PyPI
Before you install
Low friction install as a pure Python wheel. Actively maintained with a release 6 days old. Requires apache-airflow >=2.11.0, apache-airflow-providers-common-compat >=1.15.0, celery[redis] >=5.5.0,<6, and flower >=1.0.0.
Requires an existing Airflow installation (>=2.11.0) and a Celery broker (e.g., Redis or RabbitMQ) to be running separately.
License in practice
Apache-2.0 permissive license allows commercial and private use without restriction, typical for Apache Foundation projects.
Quickstart
pip install apache-airflow-providers-celery
from airflow.providers.celery.executors.celery_executor import CeleryExecutor
# Configure in airflow.cfg or environment
# executor = CeleryExecutor
# celery_broker_url = redis://localhost/0Verify before relying
- Whether this provider supports Kubernetes pod operators or only traditional Celery workers
- Performance characteristics and scaling limits for typical cluster sizes
- Specific monitoring capabilities beyond what flower provides
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesapache-airflowapache-airflow-providers-common-compatceleryflower |
| Maintenance | Actively maintained 6 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,867,146 / month, #3,478 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_celery-3.23.1-py3-none-any.whl
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See also apache-airflow-providers-cncf-kubernetes · apache-airflow-providers-apache-spark · apache-airflow-providers-edge3 · apache-airflow-task-sdk · flower · apache-airflow-providers-apache-flink · apache-airflow-providers-asana · apache-airflow-providers-git · celery · apache-airflow-core