Apache Airflow

Category	Badges
License
PyPI
Containers
Community
Dev tools

Version	Build Status
Main
3.x

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows.

When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.

Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on Dags a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Table of contents

• Project Focus
• Principles
• Requirements
• Getting started
• Installing from PyPI
• Installation
• Official source code
• Convenience packages
• User Interface
• Semantic versioning
• Version Life Cycle
• Support for Python and Kubernetes versions
• Base OS support for reference Airflow images
• Approach to dependencies of Airflow
• Contributing
• Community standards
• Agent-assisted contribution (apache-magpie)
• Voting Policy
• Who uses Apache Airflow?
• Who maintains Apache Airflow?
• What goes into the next release?
• Can I use the Apache Airflow logo in my presentation?
• Links
• Sponsors

Project Focus

Airflow works best with workflows that are mostly static and slowly changing. When the Dag structure is similar from one run to the next, it clarifies the unit of work and continuity. Other similar projects include Luigi, Oozie and Azkaban.

Beyond traditional data pipelines, Airflow is widely used to orchestrate machine learning workflows — training, retraining, evaluation, and deployment — and increasingly to orchestrate agentic and LLM-based workloads, coordinating the steps of an AI pipeline (data prep, tool calls, model invocation, evaluation) rather than acting as the agent itself. This isn't a new direction: teams have run ML and AI workloads on Airflow for years, and the ecosystem of providers supporting these use cases (see the AI & ML section of the Airflow registry) continues to grow.

Airflow is commonly used to process data, but has the opinion that tasks should ideally be idempotent (i.e., results of the task will be the same, and will not create duplicated data in a destination system), and should not pass large quantities of data from one task to the next (though tasks can pass metadata using Airflow's XCom feature). For high-volume, data-intensive tasks, a best practice is to delegate to external services specializing in that type of work.

Airflow is not a streaming solution, but it is often used to process real-time data, pulling data off streams in batches.

Principles

• Dynamic: Pipelines are defined in code, enabling dynamic dag generation and parameterization.
• Extensible: The Airflow framework includes a wide range of built-in operators and can be extended to fit your needs.
• Flexible: Airflow leverages the Jinja templating engine, allowing rich customizations.

Requirements

Apache Airflow is tested with: