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Engine for development teams that simplifies the roll-out and operation of container-based applications on Kubernetes. It introduces a service-centric abstraction, which allows users to compose applications from multiple connected components. It supports components packaged using Helm, ksonnet, k8s YAMLs, and any other Kubernetes-friendly way!

This approach to application delivery becomes especially powerful in a multi-team setup, where components owned by different teams must be put together into a service. With ownership boundaries, Dev teams can specify multi-cluster and multi-env (e.g. dev, stage, prod) service behavior, as well as control the lifecycle and updates of their respective services.

It also provides contextual visibility into teams and services, allowing our users to visualize complex dependencies and accurately assess the impact of changes.


What is

You can also read this blog post about the project.




Table of contents



Step #1: Installation

The simplest installation mode is Compact, but you may pick one that suits your needs:

Installation Mode Complexity Description
Compact Easy Installation on local machine (binaries or in a single Docker container)
Kubernetes Medium Deployed to k8s via Helm chart

You can also install it in a stripped-down mode with a fake executor:

Installation Mode Complexity Description
Concepts Easy Use this only if you want get familiar with the key concepts, API and UI. App deployment to k8s is DISABLED

Step #2: Setting up k8s Cluster

You will need to have access to a k8s cluster to deploy apps from the provided examples.

Having a powerful k8s cluster with a good internet connection will definitely provide a better experience compared to a local, single-node k8s cluster. We consider GKE to be the best option if you don't have your own k8s cluster.

Kubernetes Cluster When to use How to run
Your own If you already have a k8s cluster set up Configure on existing k8s cluster
Google Kubernetes Engine If you have a Google account and free credits Configure on GKE
k8s / Minikube Single-node, local machine with 16GB+ RAM Configure on Minikube
k8s / Docker For Mac Single-node, local machine with 16GB+ RAM Configure on Docker For Mac

Step #3: Running Examples

Once the server is up and your k8s cluster is ready, you can get started by running the following examples:

Example Description
twitter-analytics Twitter Analytics Application, multiple services, multi-cloud, based on Helm
twitter-analytics-with-concourse-ci Twitter Analytics Application, integrated with Concourse CI/CD pipelines
guestbook K8S Guestbook Application, multi-cloud, based on K8S YAMLs

Step #4: Send us a note on Slack

Give us your feedback on #general in Slack Status. If you run into any issues, we are always happy to help you resolve them!

How It Works



See artchitecture documentation



See language documentation

How to contribute

The very least you can do is report a bug!

If you want to make a pull request for a bug fix or contribute a feature, see our Development Guide for how to develop, run and test your code.

We are always looking for feedback on how to make the project better. Join our Slack to discuss Slack Status.


Feature Backlog, as well as weekly project milestones, are good places to look at the roadmap items.

If you have any questions, please contact us on Slack Status.

K8s App Engine

Application delivery engine for k8s

K8s App Engine Info

⭐ Stars 164
🔗 Source Code
🕒 Last Update a year ago
🕒 Created 5 years ago
🐞 Open Issues 56
➗ Star-Issue Ratio 3
😎 Author Aptomi