flower
Celery Flower
Decision gist · record as of 2026-08-14
Yes. Flower is actively maintained, has no known vulnerabilities, and is the de facto standard web UI for Celery cluster management. Install it if you run Celery in production or development and need visibility into task execution. The low install friction and permissive license make it a straightforward addition to any Celery setup.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Celery broker (e.g., RabbitMQ, Redis) and a Celery application to monitor.
- Low friction installation as a pure Python wheel.
- Actively maintained with recent commits and a mature codebase (first released in 2012).
License · maintenance · safety
BSD (permissive) — BSD permissive license allows use in commercial and proprietary projects with minimal restrictions. Suitable for most deployment scenarios without licensing concerns.
last release 2023-08-13 (1097 days) · last repo commit 2026-08-14 · 7,231 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 13,562,935 downloads/mo, #1,273 on PyPI
Alternatives
Verify before relying
pip install flower
celery -A tasks.app flower
# Then access the web UI at http://localhost:5555- Whether the REST API supports all task management operations described in the excerpt or if some are web-UI-only.
- Performance characteristics when monitoring very large numbers of workers or tasks.
- Whether OAuth providers (Google, Github, Gitlab, Okta) require additional configuration beyond basic auth.
What it is and what it does
Flower is a web application that connects to a Celery message broker and provides a real-time dashboard for monitoring and controlling distributed task clusters. It displays worker status, task progress, queue statistics, and scheduled tasks, and exposes both a web UI and a REST API for cluster management.
The package depends on Celery for task queue integration, Tornado for the web server, Prometheus-client for metrics export, humanize for readable output formatting, and pytz for timezone handling. It runs on port 5555 by default and supports multiple authentication methods including HTTP Basic Auth and OAuth providers. The tool is designed for developers and operators who need visibility into asynchronous task execution across multiple workers.
Use it for
- Monitor task execution progress and history across a distributed Celery cluster in real time.
- Diagnose worker failures or performance issues by viewing worker statistics and currently running tasks.
- Programmatically manage worker pools, restart workers, or revoke/terminate stuck tasks via the REST API.
- Export Celery metrics to Prometheus for integration with existing monitoring and alerting systems.
- Control task rate limits, autoscaling, and queue assignments for individual workers from a central dashboard.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Flower is actively maintained, has no known vulnerabilities, and is the de facto standard web UI for Celery cluster management. Install it if you run Celery in production or development and need visibility into task execution. The low install friction and permissive license make it a straightforward addition to any Celery setup.
Install
flower on PyPI
Before you install
Low friction installation as a pure Python wheel. Actively maintained with recent commits and a mature codebase (first released in 2012). Depends on five runtime packages including celery, tornado, and prometheus-client, all well-established libraries.
Requires a running Celery broker (e.g., RabbitMQ, Redis) and a Celery application to monitor.
License in practice
BSD permissive license allows use in commercial and proprietary projects with minimal restrictions. Suitable for most deployment scenarios without licensing concerns.
Quickstart
pip install flower
celery -A tasks.app flower
# Then access the web UI at http://localhost:5555Verify before relying
- Whether the REST API supports all task management operations described in the excerpt or if some are web-UI-only.
- Performance characteristics when monitoring very large numbers of workers or tasks.
- Whether OAuth providers (Google, Github, Gitlab, Okta) require additional configuration beyond basic auth.
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagescelerytornadoprometheus-clienthumanizepytz |
| Maintenance | Actively maintained 1,097 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 13,562,935 / month, #1,273 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: System :: Distributed Computing |
Evidence: flower-2.0.1-py2.py3-none-any.whl
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See also celery · flwr · aa-taskmonitor · apache-airflow-providers-celery · celery-progress · Glances · taskiq · opentelemetry-instrumentation-celery · celery-redbeat · pytest-celery