apache-airflow

Programmatically author, schedule and monitor data pipelines

apache-airflow Permissive license Apache-2.0 Active ★ 46,471 v3.3.1 released 2026-08-12

Install

apache-airflow on PyPI

pip

pip install apache-airflow

uv

uv add apache-airflow

poetry

poetry add apache-airflow

Package facts

License	Apache-2.0 (permissive)
Python support	supports the current Python release (!=3.15,>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	2 — apache-airflow-core, apache-airflow-task-sdk
Maintenance	actively maintained — 1 days since the last release
Last repo commit	2026-08-13
First released	2017-05-09
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: apache_airflow-3.3.1-py3-none-any.whl

Keywords: airflow, automation, dag, data, orchestration, pipelines, workflow

Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowIntended 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

About apache-airflow

from the package's own PyPI description — quoted content, verbatim

<!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->

Apache Airflow

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AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows as directed acyclic graphs (DAGs), with a scheduler that executes tasks on workers while respecting dependencies and a web UI for visualization and troubleshooting.

Low install friction with a pure-wheel distribution and only two runtime dependencies (apache-airflow-core and apache-airflow-task-sdk). Actively maintained with a release 1 day old and strong community backing.

Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for both proprietary and open-source projects.

Usage

pip install apache-airflow==3.3.1

import apache-airflow-core
import apache-airflow-task-sdk

Requires Python 3.10–3.14 (not 3.15); a database backend (PostgreSQL, MySQL, or SQLite) must be configured; the scheduler and web server are typically run as separate services.

Verdict: Apache Airflow 3.3.1 is a production-ready, actively maintained orchestration platform with no known vulnerabilities and permissive licensing. Low installation friction and top-1000 PyPI tier reflect widespread adoption. Suitable for teams building complex, dependency-aware data pipelines at scale.

Needs verification

Whether apache-airflow-core and apache-airflow-task-sdk are stable and actively maintained at the same cadence as the main package.
Performance characteristics and resource requirements for typical production deployments.
Compatibility matrix details between Airflow 3.3.1 and specific versions of PostgreSQL, MySQL, and Kubernetes beyond the listed ranges.
Actual usage patterns and API surface area available in the runtime dependencies.

workflow orchestration platformdag scheduler and monitordata pipeline automationtask dependency managementairflow dag orchestrationdistributed workflow executionpipeline scheduling and monitoring