README.md

airflow-pydantic

Typed, serializable models for Apache Airflow DAGs, tasks, sensors, schedules, and runtime resources.

from airflow_pydantic import BashTask, Dag

dag = Dag(
    dag_id="daily-report",
    schedule="0 6 * * *",
    start_date="2025-01-01",
    catchup=False,
    tasks={
        "report": BashTask(bash_command="python /opt/jobs/report.py"),
    },
)

print(dag.render())

Models validate configuration before Airflow parses it, support Python and YAML workflows, render standalone DAG source, and instantiate native Airflow objects. airflow-config is the recommended YAML entry point.

Documentation

• Tutorial: build a declarative DAG
• How-to guides
• Why model Airflow configuration
• API reference

Published documentation is available at airflow-laminar.github.io/airflow-pydantic.

Ecosystem

• airflow-config loads Hydra/YAML configuration and materializes these models.
• airflow-supervisor and supervisor-pydantic manage supervisord jobs.
• airflow-systemd and systemd-pydantic manage systemd services.
• airflow-cron and cron-pydantic convert crontabs into DAG models.
• airflow-balancer selects hosts, ports, pools, and queues.
• airflow-common provides reusable operators and orchestration helpers.

Note

This library was generated using copier from the Base Python Project Template repository.