---
id: airflow-dbt-python
version: "3.6.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# airflow-dbt-python — A collection of Airflow operators, hooks, and utilities to execute dbt commands

License: permissive · Maintenance: active · Downloads: 300.3K/mo

## 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 above — verify before relying.

Provides Airflow operators, hooks, and utilities to execute dbt commands within Airflow workflows, treating dbt as a library rather than a CLI wrapper.

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

pip install airflow-dbt-python
uv add airflow-dbt-python
poetry add airflow-dbt-python

## Installing airflow-dbt-python

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.

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'
)

Requires Airflow 3.0 or later, dbt-core 1.8 or later, and Python 3.10 or later.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 300.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

airflow dbt integration, dbt operators for airflow, run dbt in airflow, airflow dbt orchestration, dbt task execution airflow, airflow dbt hooks, dbt-integration, workflow-orchestration, data-pipeline

[View on SkillFed](https://skillfed.io/packages/airflow-dbt-python) · [View on PyPI](https://pypi.org/project/airflow-dbt-python/)