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apache-airflow-core

library3.3.1pypypi✓ verified 24d ago

Apache Airflow Core (part of the main `apache-airflow` distribution) provides the foundational components for the Apache Airflow platform, a powerful tool for programmatically authoring, scheduling, and monitoring data workflows. It includes the scheduler, API server, DAG file processor, and triggerer. Workflows, known as Directed Acyclic Graphs (DAGs), are defined as Python code, making them maintainable, versionable, and collaborative. Airflow follows semantic versioning, with minor releases typically every 2-3 months and patch releases as needed. Major versions, such as 3.x, introduce backwards-incompatible changes and are released on an irregular schedule. The current version of `apache-airflow` is 3.2.0.

workflow data devops aws gcp

pip install "apache-airflow[standard]==3.2.0" --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-3.2.0/constraints-3.10.txt"

github ↗package ↗homepage ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · APACHE-AIRFLOW-COR

apache-airflow-core

workflowpythonv3.3.1

Install

—

Import

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Disk

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Pass rate

0/ 10

Env Coverage0 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v3.2.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

1/2 runs

py 3.11

1/2 runs

py 3.12

1/2 runs

py 3.13

1/2 runs

py 3.9

✕ build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

DAG

✓ from airflow import DAG

datetime

✓ from datetime import datetime

BashOperator

✓ from airflow.operators.bash import BashOperator

PythonOperator

✓ from airflow.operators.python import PythonOperator

This quickstart defines a simple 'Hello World' DAG. To run it locally, first install Apache Airflow (see 'install' section). Then, initialize the database and start Airflow components using `airflow standalone`. Place this Python file in your configured `AIRFLOW_HOME/dags` directory, then access the Airflow UI at `localhost:8080`, log in (admin/admin by default for `standalone`), and unpause the `hello_world_dag`.

from airflow import DAG from airflow.operators.bash import BashOperator from datetime import datetime, timedelta with DAG( dag_id='hello_world_dag', start_date=datetime(2026, 1, 1), schedule_interval=timedelta(days=1), catchup=False, # Default in Airflow 3.x is False tags=['example'], ) as dag: start_task = BashOperator( task_id='start', bash_command='echo "Starting hello_world_dag"', ) hello_task = BashOperator( task_id='hello', bash_command='echo "Hello, Airflow!"', ) world_task = BashOperator( task_id='world', bash_command='echo "This is a simple DAG."', ) end_task = BashOperator( task_id='end', bash_command='echo "Finished hello_world_dag"', ) start_task >> hello_task >> world_task >> end_task

airflow --version

Debug

Known issues

breakingDirect database access from worker nodes and tasks has been removed in Airflow 3.x. Tasks and custom operators must now communicate through the REST API instead of direct database connections. Existing DAGs and custom operators that accessed the metadata database directly will require refactoring.

fix

Refactor DAGs and custom operators to use the Airflow REST API or the Task Execution API for runtime interactions (state transitions, heartbeats, XComs, resource fetching). Utilize the Airflow Python Client where appropriate.

affects: 3.x and greater

breakingSubDAGs have been removed in Airflow 3.x. They are replaced by `TaskGroups`, Assets, and Data Aware Scheduling.

fix

Migrate existing SubDAGs to use `TaskGroups` for grouping tasks, or explore Assets and Data Aware Scheduling for alternative architectural patterns.

affects: 3.x and greater

breakingThe `/api/v1` REST API has been replaced by the modern FastAPI-based `/api/v2`.

fix

Update any integrations or custom tools that interact with the Airflow REST API to use the `/api/v2` endpoints. Refer to the Airflow API v2 documentation.

affects: 3.x and greater

breakingThe `catchup_by_default` DAG parameter is now `False` by default in Airflow 3.x, changing the default behavior for DAGs scheduled to run for past intervals.

fix

Explicitly set `catchup=True` in your DAG definition if you intend for it to run for past missed schedules. Review existing DAGs to ensure the desired catchup behavior is maintained.

affects: 3.x and greater

deprecatedAirflow 2.x reaches its end-of-life in April 2026. Staying on Airflow 2.x after this date will introduce security risks as CVEs cannot be patched anymore due to old dependencies.

fix

Plan and execute an upgrade to Airflow 3.x. Utilize tools like Ruff with AIR rules (version 0.13.1 or later) to check DAG compatibility and automate parts of the migration.

affects: <3.0.0

gotchaPlain `pip install apache-airflow` might occasionally lead to an unusable installation due to dependency conflicts. Airflow keeps its dependencies open (in `pyproject.toml`) for user flexibility, but this requires careful management.

fix

Always use the official constraint files for reproducible Airflow installations. Example: `pip install "apache-airflow[EXTRAS]==AIRFLOW_VERSION" --constraint "https://raw.githubusercontent.com/apache/airflow/constraints-AIRFLOW_VERSION/constraints-PYTHON_VERSION.txt"`.

affects: All

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Version history

3.3.1latest on PyPI · released Aug 12, 2026

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Resources

githubhttps://github.com/apache/airflow ↗

packagehttps://pypi.org/project/apache-airflow-core/ ↗

homepagehttps://airflow.apache.org/ ↗

docshttps://airflow.apache.org/docs/ ↗

changeloghttps://airflow.apache.org/docs/apache-airflow/stable/release_notes.html ↗