# astronomer/astronomer-cosmos

Run your dbt Core or dbt Fusion projects as Apache Airflow DAGs and Task Groups with a few lines of code

Repository: https://github.com/astronomer/astronomer-cosmos
Canonical: https://ross.abutalabs.com/products/astronomer-cosmos
Homepage: https://astronomer.github.io/astronomer-cosmos/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: airflow, airflow-operators, apache-airflow, dbt, python, workflow
Last push: 2026-09-02T06:44:45+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 100, release rhythm 96, longevity 97
- inputs: {"age_days": 1359, "days_push": 0, "days_rel": 29, "gap_med": 23.5, "n_releases_24m": 25}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1254, forks 319 (observed 2026-09-03T02:15:09.336653+00:00)

## What it is
Astronomer Cosmos is an open-source Python library that converts dbt Core or dbt Fusion projects into Apache Airflow DAGs and task groups with minimal code. It lets teams orchestrate dbt transformations using Airflow's scheduling, monitoring, and data-aware features.

## Use cases
- run dbt projects as airflow dags
- orchestrate dbt models with airflow
- convert dbt models into airflow tasks
- schedule dbt transformations in airflow
- run dbt tests after each model in airflow
- use airflow connections instead of dbt profiles
- integrate dbt with airflow without dependency conflicts

## When to choose
- you already run Apache Airflow and want dbt models as first-class Airflow tasks
- you want per-model task granularity with retries, alerting, and data-aware scheduling
- you need to run dbt against Airflow connections rather than dbt profiles
- you want dbt isolated in a virtual environment to avoid dependency conflicts

## When to avoid
- you don't use Airflow and just need to run dbt on its own
- you prefer dbt's own scheduler or another orchestrator like Dagster or Prefect
- you only need simple one-shot dbt runs without orchestration

## Facets
- artifact type: library
- maturity: active
- function: workflow-automation, etl, scheduling, developer-tools
- domain: analytics, developer-tools
- platform: python, cloud, self-hosted
- tags: dbt, apache-airflow, data-transformation, orchestration, dags, analytics-engineering, data-engineering, automation, docker

## Member repositories
- astronomer/astronomer-cosmos (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:09.336653+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:07:38.081133+00:00, confidence not recorded.
  - readme: https://github.com/astronomer/astronomer-cosmos (fetched 2026-09-03T02:15:09.336653+00:00, sha e3a5045a59cc)
  - homepage: https://astronomer.github.io/astronomer-cosmos/ (fetched 2026-08-29T12:18:35.065669+00:00, sha 090a0af330c8)
  - registry_pypi: https://pypi.org/pypi/astronomer-cosmos/json (fetched 2026-08-29T12:18:35.068264+00:00, sha 9b527fae4fe8)
- Data as of 2026-08-30T08:39:29.467469+00:00.