astronomer-cosmos
Orchestrate your dbt projects in Airflow
Decision gist · record as of 2026-08-14
Yes. The package is production-stable (Development Status 5), actively maintained with recent releases, has no known vulnerabilities, and solves a real integration gap for teams using both dbt and Airflow. Install friction is low, and the Apache 2.0 license is permissive. Recommended if you orchestrate dbt workflows in Airflow.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow to be installed and configured; Python >= 3.10.
- Low friction install with a wheel distribution.
- Depends on apache-airflow and 8 other packages; all are standard Python libraries or Airflow ecosystem components.
License · maintenance · safety
Apache-2.0 (permissive) — Apache License 2.0 (permissive). You can use, modify, and distribute this package freely in commercial and open-source projects, provided you include the license notice.
last release 2026-08-04 (10 days) · last repo commit 2026-08-14 · 1,247 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 9,680,418 downloads/mo, #1,506 on PyPI
Alternatives
Verify before relying
pip install astronomer-cosmos
from cosmos import DbtDag
my_dag = DbtDag(
dag_id='my_dbt_dag',
project_dir='/path/to/dbt/project'
)- Whether virtual environment isolation for dbt is automatic or requires explicit configuration.
- Supported dbt Core versions and any version compatibility constraints.
- Performance characteristics when handling large dbt projects with many models.
What it is and what it does
Astronomer Cosmos is a bridge between dbt Core and Apache Airflow that converts dbt projects into native Airflow DAGs and Task Groups. Instead of running dbt through profiles and CLI commands, you define your dbt project in Python code and Cosmos generates Airflow tasks for each model, test, and snapshot, complete with dependencies, retries, and alerting.
The package runs dbt in isolated virtual environments by default to avoid dependency conflicts with Airflow, and integrates with Airflow's connection system so you can manage data warehouse credentials through Airflow rather than dbt profiles. It enables data-aware scheduling—running dbt models immediately after upstream data ingestion—and lets you inspect and retry individual models as Airflow tasks. It depends on apache-airflow, jinja2, attrs, packaging, and several other standard libraries.
Use it for
- Convert an existing dbt project into an Airflow DAG with minimal code changes to enable Airflow-native scheduling and monitoring.
- Run dbt tests immediately after model completion to catch data quality issues early in the pipeline.
- Manage dbt credentials and connections through Airflow's connection system instead of maintaining separate dbt profiles.
- Orchestrate dbt alongside non-dbt tasks (API calls, Python scripts, SQL queries) in a single Airflow DAG.
- Use Airflow's data-aware scheduling to trigger dbt models only after upstream data sources are refreshed.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is production-stable (Development Status 5), actively maintained with recent releases, has no known vulnerabilities, and solves a real integration gap for teams using both dbt and Airflow. Install friction is low, and the Apache 2.0 license is permissive. Recommended if you orchestrate dbt workflows in Airflow.
Install
astronomer-cosmos on PyPI
Before you install
Low friction install with a wheel distribution. Depends on apache-airflow and 8 other packages; all are standard Python libraries or Airflow ecosystem components. Actively maintained with a release 10 days ago and 1247 repository stars.
Requires Apache Airflow to be installed and configured; Python >= 3.10.
License in practice
Apache License 2.0 (permissive). You can use, modify, and distribute this package freely in commercial and open-source projects, provided you include the license notice.
Quickstart
pip install astronomer-cosmos
from cosmos import DbtDag
my_dag = DbtDag(
dag_id='my_dbt_dag',
project_dir='/path/to/dbt/project'
)Verify before relying
- Whether virtual environment isolation for dbt is automatic or requires explicit configuration.
- Supported dbt Core versions and any version compatibility constraints.
- Performance characteristics when handling large dbt projects with many models.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesaenumapache-airflowattrsdeprecationjinja2msgpackopenlineage-integration-commonpackagingvirtualenv |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 9,680,418 / month, #1,506 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/StableEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: astronomer_cosmos-1.15.1-py3-none-any.whl
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See also airflow-dbt · astronomer-starship · airflow-dbt-python · apache-airflow-core · apache-airflow · apache-airflow-providers-apache-beam · prefect-dbt · apache-airflow-providers-dbt-cloud · apache-airflow-providers-git · apache-airflow-task-sdk