apache-airflow-providers-openfaas
Provider package apache-airflow-providers-openfaas for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you run Apache Airflow and need to invoke OpenFaaS functions from your DAGs. The package is actively maintained, has no known vulnerabilities, installs cleanly, and carries a permissive Apache-2.0 license. It is a straightforward provider integration with low friction and clear purpose.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires an existing Apache Airflow installation (>=2.11.0) and Python >=3.10.
- Low install friction with a pure-Python wheel.
- Actively maintained as part of the Apache Airflow ecosystem; last commit 2026-08-14 with 46490 repository stars.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 license permits commercial and private use with minimal restrictions; you must include a copy of the license and state material changes, but no copyleft obligations apply to your own code.
last release 2026-06-07 (68 days) · last repo commit 2026-08-14 · 46,490 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 185,834 downloads/mo, #10,000 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-openfaas
from airflow.providers.openfaas.operators.openfaas import OpenFaasOperator
task = OpenFaasOperator(task_id='invoke_function', function_name='my_function')- Whether the package includes connection/authentication helpers for OpenFaaS clusters.
- What OpenFaaS versions are tested and supported by this provider.
- Whether additional dependencies (e.g. HTTP client libraries) are bundled or assumed to be present.
What it is and what it does
This is an Apache Airflow provider package that adds OpenFaaS integration to Airflow workflows. It allows you to define and execute OpenFaaS serverless functions as tasks within your Airflow DAGs, bridging the gap between Airflow's orchestration capabilities and OpenFaaS's function-as-a-service runtime.
The package is maintained as part of the official Apache Airflow ecosystem and depends on apache-airflow and apache-airflow-providers-common-compat. It supports Python 3.10 through 3.14 and is classified as Production/Stable. Installation is straightforward via pip on top of an existing Airflow setup.
Use it for
- Invoke OpenFaaS functions as part of a larger Airflow orchestration pipeline.
- Trigger serverless workloads from Airflow DAGs for event-driven or on-demand processing.
- Integrate OpenFaaS-deployed microservices into scheduled Airflow workflows.
- Build hybrid workflows combining Airflow's scheduling with OpenFaaS's function execution.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run Apache Airflow and need to invoke OpenFaaS functions from your DAGs.
The package is actively maintained, has no known vulnerabilities, installs cleanly, and carries a permissive Apache-2.0 license. It is a straightforward provider integration with low friction and clear purpose.
Install
apache-airflow-providers-openfaas on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained as part of the Apache Airflow ecosystem; last commit 2026-08-14 with 46490 repository stars. Requires apache-airflow >=2.11.0 and apache-airflow-providers-common-compat >=1.10.1.
Requires an existing Apache Airflow installation (>=2.11.0) and Python >=3.10.
License in practice
Apache-2.0 license permits commercial and private use with minimal restrictions; you must include a copy of the license and state material changes, but no copyleft obligations apply to your own code.
Quickstart
pip install apache-airflow-providers-openfaas
from airflow.providers.openfaas.operators.openfaas import OpenFaasOperator
task = OpenFaasOperator(task_id='invoke_function', function_name='my_function')
Verify before relying
- Whether the package includes connection/authentication helpers for OpenFaaS clusters.
- What OpenFaaS versions are tested and supported by this provider.
- Whether additional dependencies (e.g. HTTP client libraries) are bundled or assumed to be present.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesapache-airflowapache-airflow-providers-common-compat |
| Maintenance | Actively maintained 68 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 185,834 / month, #10,000 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 :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_openfaas-3.9.5-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “airflow openfaas provider”
- apache-airflow-providers-openfaasIntegrates OpenFaaS serverless functions into Apache Airflow…
- apache-airflow-providers-common-ioProvides common I/O utilities and operators for Apache Airflow…
- apache-airflow-providers-sqliteIntegrates SQLite databases with Apache Airflow as a provider…
Give your agent the search over MCP, or paste the wish link into any chat.
More Monitoring packages
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
Provides generated Python code for OpenTelemetry semantic conventions, enabling standardized attribute naming and constant definitions for instrumentation and telemetry collection.
Install it if you are using OpenTelemetry and want to follow semantic conventions correctly.
Provides the reference implementation of the OpenTelemetry API for collecting and exporting traces, metrics, and logs from Python applications.
Provides the abstract API and interfaces for OpenTelemetry instrumentation in Python, defining how to emit traces, metrics, and logs without tying code to a specific SDK implementation.
Exports OpenTelemetry observability data to an OpenTelemetry Collector using Protobuf-encoded messages over HTTP.
Install it if you are using OpenTelemetry in Python and need to send data to a Collector over HTTP.
Provides automatic instrumentation commands and programmatic APIs to inject distributed tracing into Python applications without code changes, detecting and instrumenting packages used by your program.
Install it if you need distributed tracing without code changes and have compatible instrumented packages in your environment.
See also apache-airflow-providers-asana · apache-airflow-providers-fab · apache-airflow-providers-vespa · apache-airflow-providers-github · apache-airflow-providers-facebook · apache-airflow-providers-alibaba · apache-airflow-providers-apache-flink · apache-airflow-providers-papermill · apache-airflow-providers-grpc · apache-airflow-providers-singularity