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Apache Airflow 3 provider to run Python callables in Pixi environments.
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Apache Airflow 3 provider to run Python callables, Bash commands and Pixi tasks in Pixi environments, on workers or in Kubernetes pods.
Translation pending
Taken from the repository README
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An Apache Airflow 3 provider that runs tasks inside Pixi-managed environments,
on the worker or in a Kubernetes pod, so a task can use conda-forge and PyPI packages that are not
installed on the worker.
Documentation: https://danplischke.github.io/apache-airflow-providers-pixi/
(for LLMs and coding agents: llms.txt)
Requires Apache Airflow 3.1.2 or newer and Python 3.10 to 3.14 (3.14 needs Airflow 3.2). The
workers need pixi 0.81.0 or newer on PATH; the provider
never installs it. See Requirements
and the deployment guide.Read the full README ↗An Apache Airflow 3 provider that runs tasks inside Pixi-managed environments, on the worker or in a Kubernetes pod, so a task can use conda-forge and PyPI packages that are not installed on the worker.
Documentation: https://danplischke.github.io/apache-airflow-providers-pixi/ (for LLMs and coding agents: llms.txt)
pip install apache-airflow-providers-pixi
pip install "apache-airflow-providers-pixi[cncf.kubernetes]" # adds PixiKubernetesPodOperator
Requires Apache Airflow 3.1.2 or newer and Python 3.10 to 3.14 (3.14 needs Airflow 3.2). The
workers need pixi 0.81.0 or newer on PATH; the provider
never installs it. See Requirements
and the deployment guide.
from airflow.sdk import dag, task
@dag
def my_dag():
@task.pixi(dependencies={"python": "3.12.*"}, pypi_dependencies=["pandas>=2"])
def summarize(n: int) -> float:
import pandas as pd # imports go inside the function: it runs in the Pixi environment
return float(pd.Series(range(n)).mean())
summarize(3)
my_dag()
Pass pixi_project_path or pixi_toml_path instead to use an existing Pixi project. The
Pixi Operator guide
covers the ways to choose an environment, arguments and return values, the Airflow context,
environment variables, errors, timeouts and caches.
| Class / decorator | What it does |
|---|---|
PixiOperator / @task.pixi | runs a Python callable in a Pixi environment on the worker |
PixiBashOperator / @task.pixi_bash | runs a Bash command in a Pixi environment |
PixiTaskOperator | runs a task from the manifest's [tasks] |
PixiKubernetesPodOperator / @task.pixi_kubernetes | runs a Python callable in a Pixi environment in a Kubernetes pod |
PixiBranchOperator / @task.pixi_branch | chooses the downstream tasks to follow |
PixiShortCircuitOperator / @task.pixi_short_circuit | skips downstream tasks when the callable returns a falsy value |
PixiSensor / @task.pixi_sensor | waits until the callable returns a truthy value |
Credentials for private conda channels and PyPI indexes go in a
pixi connection.
Other providers can build on these operators; see
Building on the Pixi Operator.
With uv, just and pixi installed:
just dev installs the package with the dev and docs groups and the pre-commit hooks.just test runs the unit tests, with a fake pixi.just check runs what CI runs: lint, unit tests, docs build, package build and smoke test.just standalone starts a local Airflow at http://localhost:8080 with the DAGs in dev/dags.just lists the other recipes. Integration, system and compatibility tests are described in
System Tests and
Installing from sources.
MIT