celery
Distributed Task Queue.
Decision gist · record as of 2026-08-14
Yes. Celery is production-stable, actively maintained, permissively licensed, and has minimal install friction. It is the de facto standard for async task queues in Python. Install it if you need background job processing, task scheduling, or distributed work coordination. The main gotcha is that you must run a separate message broker; Celery itself is not a standalone queue.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a message broker (RabbitMQ, Redis, or similar) running separately; Celery itself is just the client and worker framework.
- Low friction install with a pure-Python wheel and active maintenance.
- The package is in production/stable status with recent commits and a large community (28785 GitHub stars), though it carries 10 runtime dependencies that will be pulled in automatically.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive and poses no restrictions on commercial or proprietary use. You can use, modify, and distribute Celery with minimal licensing obligations.
last release 2026-03-26 (141 days) · last repo commit 2026-08-13 · 28,785 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 58,245,954 downloads/mo, #516 on PyPI
Alternatives
Verify before relying
pip install celery
from celery import Celery
app = Celery('myapp', broker='amqp://guest@localhost//')
@app.task
def add(x, y):
return x + y
result = add.delay(4, 6)- Whether the 10 runtime dependencies introduce any known security issues beyond the OSV scan.
- Performance characteristics under specific workload patterns claimed in the description.
What it is and what it does
Celery is a distributed task queue framework that decouples task execution from the main application flow. You define tasks as decorated Python functions, then submit them to a message broker (such as RabbitMQ or Redis) where worker processes pick them up and execute them asynchronously. This pattern is useful for offloading long-running operations, scheduling periodic work, and scaling task processing across multiple machines.
The package handles the full lifecycle: task serialization, routing to workers, result storage, and automatic retry on failure. It integrates with popular web frameworks like Django and Flask without requiring additional packages, and supports multiple concurrency models (prefork, gevent, eventlet). The framework is mature, actively maintained, and widely used in production systems.
Use it for
- Offload long-running computations (image processing, report generation) from web request handlers to background workers.
- Schedule periodic tasks (cleanup jobs, data synchronization) using Celery Beat scheduler.
- Distribute CPU-intensive work across multiple machines to parallelize processing.
- Implement reliable job queues with automatic retry and dead-letter handling for failed tasks.
- Decouple microservices by using a message broker to coordinate async work between applications.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Celery is production-stable, actively maintained, permissively licensed, and has minimal install friction. It is the de facto standard for async task queues in Python. Install it if you need background job processing, task scheduling, or distributed work coordination. The main gotcha is that you must run a separate message broker; Celery itself is not a standalone queue.
Install
celery on PyPI
Before you install
Low friction install with a pure-Python wheel and active maintenance. The package is in production/stable status with recent commits and a large community (28785 GitHub stars), though it carries 10 runtime dependencies that will be pulled in automatically.
Requires a message broker (RabbitMQ, Redis, or similar) running separately; Celery itself is just the client and worker framework.
License in practice
BSD-3-Clause is permissive and poses no restrictions on commercial or proprietary use. You can use, modify, and distribute Celery with minimal licensing obligations.
Quickstart
pip install celery
from celery import Celery
app = Celery('myapp', broker='amqp://guest@localhost//')
@app.task
def add(x, y):
return x + y
result = add.delay(4, 6)Verify before relying
- Whether the 10 runtime dependencies introduce any known security issues beyond the OSV scan.
- Performance characteristics under specific workload patterns claimed in the description.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesbilliardkombuvineclickclick-didyoumeanclick-replclick-pluginspython-dateutilexceptiongrouptzlocal |
| Maintenance | Actively maintained 141 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 58,245,954 / month, #516 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/StableFramework :: CeleryOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: Object BrokeringTopic :: System :: Distributed Computing |
Evidence: celery-5.6.3-py3-none-any.whl
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See also celery-batches · dvc-task · flower · pytest-celery · render_sdk · taskiq · django-celery-results · dramatiq · vercel-workers · celery-singleton