The simplest way to run workflows in Python, powered by Argo Workflows

Define, run, and manage Kubernetes-native workflows effortlessly with Python. Automate complex pipelines without the YAML headache.

What is Hera?

Hera is an intuitive SDK that extends Argo Workflows, allowing you to define and submit jobs entirely in Python. Use Hera to create and scale DAG, step-wise, and parallelized workflows with ease.

See Hera in Action!

The @script decorator lets you run any function on Kubernetes, with native integration into Argo Workflows.

dag_diamond.py


from hera.workflows import DAG, Workflow, script

@script()
def echo(message: str):
    print(message)

with Workflow(
    generate_name="dag-diamond-",
    entrypoint="diamond",
) as w:
    with DAG(name="diamond"):
        A = echo(name="A", arguments={"message": "A"})
        B = echo(name="B", arguments={"message": "B"})
        C = echo(name="C", arguments={"message": "C"})
        D = echo(name="D", arguments={"message": "D"})
        A >> [B, C] >> D

w.create()

Python-first

Write native, testable Python functions using Pydantic, NumPy, TensorFlow, and more.

Cloud Native Scale

Argo Workflows runs up to 50% faster than Airflow. Parallelize steps by default.

No More YAML!

Define workflows as Python functions. Inherently reusable. Easier to write, test, and maintain.

Why Hera?

Combine the simplicity of Python with Kubernetes power.
Keep workflow orchestration code separate from business logic.
Effortlessly run ML and data processing on Kubernetes.
Open source and freely available under Apache 2.0 license.

Get Started Now

Join Our Developer Community

Hera is built by developers for developers. Our open-source community is creating the future of cloud-native Python orchestration. Contribute code, report issues, or just star the repo to show support.

... Github Stars

... Contributors

... Pull Requests

... Commits

Testimonials from Hera's anonymous 2025 developer survey

"Massively simpler than trying to manage complex YAML files."

- DevOps Engineer

"For me: It just works"

- Data Scientist

"It is 1000x better than Kubeflow SDK and easier than Argo YAML to author pipelines."

- Machine Learning Engineer

"Harness the power of Argo Workflows directly from Python, low effort to create workflows."

- DevOps Engineer