airflow-exporter
Airflow plugin to export dag and task based metrics to Prometheus.
Decision gist · record as of 2026-08-14
Yes. The package solves a clear problem—exposing Airflow metrics to Prometheus—with low install friction, active maintenance, permissive licensing, and no known vulnerabilities. It is suitable for any Airflow deployment that uses Prometheus for monitoring. Verify that your Airflow version (2.* or 3.*) matches the exporter version branch before installing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.9 and a running Airflow instance (2.* for versions <2.0.0, 3.* for >=2.0.0).
- Low friction: pure Python wheel with only two runtime dependencies (apache-airflow and prometheus-client).
- Actively maintained with a recent release; last commit 2026-07-05 and 279 repository stars suggest stable ongoing support.
License · maintenance · safety
permissive license (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and open-source deployments alike.
last release 2026-07-05 (40 days) · last repo commit 2026-07-05 · 279 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 290,393 downloads/mo, #7,990 on PyPI
Alternatives
Verify before relying
pip install airflow-exporter
# Metrics automatically available at:
# http://<your_airflow_host_and_port>/admin/metrics/
# Optional: add labels to DAG params
dag = DAG(
'dummy_dag',
params={'labels': {'env': 'test'}}
)- Whether the exporter automatically registers itself as an Airflow plugin or requires manual configuration steps beyond pip install.
- Performance overhead when exporting metrics from DAGs with hundreds or thousands of tasks.
What it is and what it does
airflow-exporter is an Airflow plugin that bridges workflow orchestration and observability by publishing DAG and task metrics to Prometheus. It exposes metrics like task status counts, DAG status, DAG run duration, and the last DAG run state, each labeled with dag_id, task_id, owner, and status information. Metrics are served at a standard HTTP endpoint on your Airflow instance.
The package integrates directly into Airflow with minimal setup—a single pip install—and supports adding custom labels to metrics via DAG params, enabling environment-specific or team-specific metric segmentation. It requires apache-airflow and prometheus-client as runtime dependencies and supports current Python versions (3.9+), with separate version branches for Airflow 2.* and 3.* compatibility.
Use it for
- Monitor DAG execution status and task completion rates in real time via Prometheus dashboards and alerts.
- Track DAG run duration to identify performance regressions or bottlenecks in workflow execution.
- Add environment or team labels to metrics for multi-tenant or multi-environment Airflow deployments.
- Integrate Airflow observability into existing Prometheus-based monitoring stacks without custom instrumentation.
- Alert on DAG failures or paused workflows by querying airflow_dag_last_status metrics in Prometheus rules.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package solves a clear problem—exposing Airflow metrics to Prometheus—with low install friction, active maintenance, permissive licensing, and no known vulnerabilities. It is suitable for any Airflow deployment that uses Prometheus for monitoring. Verify that your Airflow version (2.* or 3.*) matches the exporter version branch before installing.
Install
airflow-exporter on PyPI
Before you install
Low friction: pure Python wheel with only two runtime dependencies (apache-airflow and prometheus-client). Actively maintained with a recent release; last commit 2026-07-05 and 279 repository stars suggest stable ongoing support.
Requires Python >=3.9 and a running Airflow instance (2.* for versions <2.0.0, 3.* for >=2.0.0).
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and open-source deployments alike.
Quickstart
pip install airflow-exporter
# Metrics automatically available at:
# http://<your_airflow_host_and_port>/admin/metrics/
# Optional: add labels to DAG params
dag = DAG(
'dummy_dag',
params={'labels': {'env': 'test'}}
)Verify before relying
- Whether the exporter automatically registers itself as an Airflow plugin or requires manual configuration steps beyond pip install.
- Performance overhead when exporting metrics from DAGs with hundreds or thousands of tasks.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesapache-airflowprometheus-client |
| Maintenance | Actively maintained 40 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 290,393 / month, #7,990 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: Web EnvironmentIntended Audience :: System AdministratorsNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Topic :: System :: Monitoring |
Evidence: airflow_exporter-2.1.0-py3-none-any.whl
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See also prometheus-flask-exporter · starlette-exporter · mwaa-dr · opentelemetry-exporter-prometheus · prometheus-fastapi-instrumentator · openlineage-airflow · apache-airflow-task-sdk · acryl-datahub-airflow-plugin · starlette-prometheus · astronomer-starship