dag-factory
Dynamically build Apache Airflow DAGs from YAML files
Decision gist · record as of 2026-08-14
Yes. dag-factory is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It solves a real problem—reducing boilerplate and enabling declarative DAG definition—for Airflow users. Install it if you manage multiple similar Airflow workflows or want to let non-Python developers define DAGs; skip it only if your DAGs are highly custom or require complex Python logic that YAML cannot express.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow 2.9+ and Python 3.10+ to be installed and configured; Airflow environment must be set up before dag-factory can load DAGs.
- Low install friction with a pure Python wheel.
- Actively maintained as of 2026-08-14 with 1452 repository stars.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary projects.
last release 2026-05-07 (99 days) · last repo commit 2026-08-14 · 1,452 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 679,361 downloads/mo, #5,370 on PyPI
Alternatives
Verify before relying
pip install dag-factory
from dag_factory.dag_factory import load_yml_dags
dags = load_yml_dags(dag_folder='dags', safe_mode=True)- Specific performance characteristics when handling large numbers of DAGs or complex task dependencies.
- Extent of custom operator support and any limitations on operator types that can be declared via YAML.
- Whether all Airflow 3 features are fully supported or if certain advanced features still require Python code.
What it is and what it does
dag-factory is a library that lets you define Apache Airflow DAGs using YAML configuration files instead of writing Python code. It reads YAML files and generates fully functional Airflow DAGs, making it possible to construct workflows without needing to understand Airflow's Python API or object model. The library supports scheduling, dynamically mapped tasks, custom operators, callbacks, and KubernetesPodOperator, covering most common workflow patterns.
It's designed for teams that want to avoid repetitive DAG boilerplate, enable non-Python developers to define workflows, or maintain DAG definitions in a more declarative, version-control-friendly format. The package depends on Apache Airflow, packaging, pathspec, pyyaml, and typer, and is actively maintained with support for modern Python versions (3.10–3.14) and Airflow 2.9+ including Airflow 3.
Use it for
- Define and version-control multiple similar Airflow workflows using YAML templates without duplicating Python code.
- Enable data engineers or domain experts without Python expertise to create and modify Airflow DAGs.
- Rapidly prototype and iterate on workflow definitions by editing YAML files instead of redeploying Python modules.
- Manage large numbers of DAGs with consistent structure and defaults across an organization.
- Reduce maintenance burden by centralizing DAG logic in configuration rather than scattered Python files.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
dag-factory is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It solves a real problem—reducing boilerplate and enabling declarative DAG definition—for Airflow users. Install it if you manage multiple similar Airflow workflows or want to let non-Python developers define DAGs; skip it only if your DAGs are highly custom or require complex Python logic that YAML cannot express.
Install
dag-factory on PyPI
Before you install
Low install friction with a pure Python wheel. Actively maintained as of 2026-08-14 with 1452 repository stars. Requires Apache Airflow 2.9+ and Python 3.10+, both of which are current versions.
Requires Apache Airflow 2.9+ and Python 3.10+ to be installed and configured; Airflow environment must be set up before dag-factory can load DAGs.
License in practice
Licensed under Apache License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary projects.
Quickstart
pip install dag-factory
from dag_factory.dag_factory import load_yml_dags
dags = load_yml_dags(dag_folder='dags', safe_mode=True)Verify before relying
- Specific performance characteristics when handling large numbers of DAGs or complex task dependencies.
- Extent of custom operator support and any limitations on operator types that can be declared via YAML.
- Whether all Airflow 3 features are fully supported or if certain advanced features still require Python code.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesapache-airflowpackagingpathspecpyyamltyper |
| Maintenance | Actively maintained 99 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 679,361 / month, #5,370 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableFramework :: Apache AirflowIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Database |
Evidence: dag_factory-1.1.0-py3-none-any.whl
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See also apache-airflow · mwaa-dr · apache-airflow-core · apache-airflow-task-sdk · apache-airflow-providers-git · apache-airflow-providers-neo4j · apache-airflow-providers-apache-beam · apache-airflow-providers-discord · apache-airflow-providers-openai · apache-hamilton