airflow-dbt-python
A collection of Airflow operators, hooks, and utilities to execute dbt commands
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and low install friction. It is production-stable (Development Status 5) and directly addresses the common need to orchestrate dbt within Airflow. Install it if you run dbt workflows in Airflow and want native operator support rather than CLI wrapping.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Airflow 3.0 or later, dbt-core 1.8 or later, and Python 3.10 or later.
- Low install friction; pure Python wheel with three runtime dependencies.
- Actively maintained as of 2026-05-24 with recent releases.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; safe for most production environments.
last release 2026-05-24 (82 days) · last repo commit 2026-05-24 · 215 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 300,271 downloads/mo, #7,844 on PyPI
Alternatives
Verify before relying
pip install airflow-dbt-python
from airflow_dbt_python.operators.dbt import DbtRunOperator
run_dbt = DbtRunOperator(
task_id='dbt_run',
project_dir='/path/to/dbt/project'
)- Specific version compatibility matrix between airflow-dbt-python and Airflow/dbt-core patch versions beyond the stated minima.
- Performance characteristics when handling large dbt projects or high-frequency task execution.
- Support scope for dbt adapters beyond the documented redshift extra.
What it is and what it does
airflow-dbt-python bridges Airflow and dbt by providing native operators and hooks that invoke dbt-core as a library rather than wrapping CLI calls. It enables you to orchestrate dbt workflows directly within Airflow DAGs, with each task executing in an isolated temporary directory to work with Airflow's independent task execution model.
The package handles the practical challenges of integrating these tools: it can fetch dbt projects and profiles from remote storage (S3, git), push dbt artifacts to Airflow XCom for downstream use, and map Airflow connections directly to dbt targets without requiring a profiles.yml file. It supports Python 3.10 through 3.13 and is actively maintained against the latest Airflow and dbt-core releases.
Use it for
- Orchestrate multi-step dbt workflows (run, test, snapshot) as independent Airflow tasks with conditional logic between steps.
- Fetch dbt projects from git repositories or S3 at task runtime and execute them in isolated environments across distributed executors.
- Extract dbt artifacts (manifest, run_results) to XCom and use them in downstream Airflow tasks for alerting, reporting, or dynamic DAG generation.
- Use Airflow connections to configure dbt targets without maintaining separate profiles.yml files in your deployment.
- Deploy dbt workflows to managed Airflow services (AWS MWAA, GCP Cloud Composer) by adding the package to requirements or PyPI packages.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and low install friction. It is production-stable (Development Status 5) and directly addresses the common need to orchestrate dbt within Airflow. Install it if you run dbt workflows in Airflow and want native operator support rather than CLI wrapping.
Install
airflow-dbt-python on PyPI
Before you install
Low install friction; pure Python wheel with three runtime dependencies. Actively maintained as of 2026-05-24 with recent releases. Requires Airflow 3.0 or later and dbt-core 1.8 or later.
Requires Airflow 3.0 or later, dbt-core 1.8 or later, and Python 3.10 or later.
License in practice
MIT license permits commercial and private use with minimal restrictions; safe for most production environments.
Quickstart
pip install airflow-dbt-python
from airflow_dbt_python.operators.dbt import DbtRunOperator
run_dbt = DbtRunOperator(
task_id='dbt_run',
project_dir='/path/to/dbt/project'
)Verify before relying
- Specific version compatibility matrix between airflow-dbt-python and Airflow/dbt-core patch versions beyond the stated minima.
- Performance characteristics when handling large dbt projects or high-frequency task execution.
- Support scope for dbt adapters beyond the documented redshift extra.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.14,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesapache-airflowcontextlib-chdirdbt-core |
| Maintenance | Actively maintained 82 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 300,271 / month, #7,844 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/StableIntended Audience :: DevelopersProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: airflow_dbt_python-3.6.0-py3-none-any.whl
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See also airflow-dbt · astronomer-cosmos · apache-airflow-providers-dbt-cloud · apache-airflow-providers-standard · apache-airflow-providers-arangodb · apache-airflow-providers-oracle · airflow-mcd · apache-airflow-providers-http · apache-airflow-providers-weaviate · apache-airflow-providers-odbc