Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Workflow Engine for Kubernetes
| Date | Stars |
|---|---|
| 2026-07-24 | 16845 |
| 2026-07-25 | 16847 |
| 2026-07-28 | 16854 |
| 2026-07-30 | 16857 |
| 2026-08-06 | 16878 |
Today
+21 stars today
This week
+21 stars this week
This month
— stars this month
Momentum
140.0
growth rate 0.13%/day
<!-- markdownlint-disable-next-line MD041 -->
[](https://github.com/argoproj/argo-workflows/actions/workflows/snyk.yml?query=branch%3Amain)
[](https://bestpractices.coreinfrastructure.org/projects/3830)
[](https://api.securityscorecards.dev/projects/github.com/argoproj/argo-workflows)
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fargoproj%2Fargo-workflows?ref=badge_shield)
[](https://argoproj.github.io/community/join-slack)
[](https://x.com/argoproj)
[](https://www.linkedin.com/company/argoproj/)
[](https://bsky.app/profile/argoproj.bsky.social)
[](https://github.com/argoproj/argo-workflows/releases/latest)
[](https://artifacthub.io/packages/helm/argo/argo-workflows)
## What is Argo Workflows?
Argo Workflows is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes.
Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition).
* Define workflows where each step is a container.
* Model multi-step workflows as a sequence of tasks or capture the dependencies between tasks using a directed acyclic graph (DAG).
* Easily run compute intensive jobs for machine learning or data processing in a fraction of the time using Argo Workflows on Kubernetes.
Argo is a [Cloud Native Computing Foundation (CNCF)](https://cncf.io/) graduated project.
## Use Cases
* [Machine Learning pipelines](https://argo-workflows.readthedocs.io/en/latest/use-cases/machine-learning/)
* [Data and batch processing](https://argo-workflows.readthedocs.io/en/latest/use-cases/data-processing/)
* [Infrastructure automation](https://argo-workflows.readthedocs.io/en/latest/use-cases/infrastructure-automation/)
* [CI/CD](https://argo-workflows.readthedocs.io/en/latest/use-cases/ci-cd/)
* [Other use cases](https://argo-workflows.readthedocs.io/en/latest/use-cases/other/)
## Why Argo Workflows?
* Argo Workflows is the most popular workflow execution engine for Kubernetes.
* Light-weight, scalable, and easier to use.
* Including for Python users through [the Hera Python SDK for Argo Workflows](https://hera.readthedocs.io/en/stable/).
* Designed from the ground up for containers without the overhead and limitations of legacy VM and server-based environments.
* Cloud agnostic and can run on any Kubernetes cluster.
[Read what people said in our latest survey](https://blog.argoproj.io/argo-workflows-events-2023-user-survey-results-82c53bc30543)
## Try Argo Workflows
You can try Argo Workflows via one of the following:
1. [Interactive Training Material](https://killercoda.com/argoproj/course/argo-workflows/)
1. [Access the demo environment](https://workflows.apps.argoproj.io/workflows/argo)
## Who uses Argo Workflows?
[About 200+ organizations are officially using Argo Workflows](USERS.md)
## Ecosystem
Just some of the projects that use or rely on Argo Workflows (complete list [here](https://github.com/akuity/awesome-argo#ecosystem-projects)):
* [Argo Events](https://github.com/argoproj/argo-events)
* [Hera]Excerpt of 10,018 characters
Read on GitHub
Alex Collins · @intuit · United States
831
632
Yuan Tang · Red Hat · United States
395
361
354
Jesse Suen · @akuity · United States
338
Alan Clucas · @argoproj, @pipekit, @open-telemetry, @nixos, @crumbhole · United Kingdom
325
Anton Gilgur
225
Saravanan Balasubramanian · Intuit · United States
216
146
Tianchu Zhao · Australia
103
shuangkun tian · aliyun · China
91
Isitha Subasinghe · @pipekit · Australia
85
Mason Malone · @adobe · United States
75
Julie Vogelman · Intuit
70
Daisuke Taniwaki · Japan
62
Alexander Matyushentsev · @akuity · United States
61
William Van Hevelingen · @acquia · United States
42
Tim Collins · United Kingdom
40
Dillen Padhiar · @intuit
36
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:51367592ef405377, topic:workflow, desc:workflow engine, readme:workflow engine
matched fp:51367592ef405377, topic:mlops
matched fp:51367592ef405377, topic:data-engineering
matched fp:51367592ef405377, topic:kubernetes