# 'Hello world' example of Flower Framework

**URL:** <https://discuss.flower.ai/t/hello-world-example-of-flower-framework/1260>\
**Category:** Flower Framework\
**Tags:** flower\
**Created:** [July 2, 2026, 3:46pm UTC](https://discuss.flower.ai/t/hello-world-example-of-flower-framework/1260 "2026-07-02T15:46:15Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![herman](https://dub1.discourse-cdn.com/flex013/user_avatar/discuss.flower.ai/herman/32/331_2.png) [@herman](https://discuss.flower.ai/u/herman)\
**Post date:** [July 2, 2026, 3:46pm UTC](https://discuss.flower.ai/t/hello-world-example-of-flower-framework/1260/1 "2026-07-02T15:46:15Z")

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Hi All, I am looking for a very basis ‘Hello World’ example of the Flower Framework without diving into Machine learning or AI (yet). I want to get the basics of the framework first (ClientApp, ServerApp, strategy, etc). I imagine two clients who have number 5 and 8. And the server Aggregates those numbers (13). From there I want to build on top of the basic (adding vectors, matrices, etc). I cannot find any such an example on the Internet.

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<div class="post-metadata">

**Author:** ![javier](https://dub1.discourse-cdn.com/flex013/user_avatar/discuss.flower.ai/javier/32/116_2.png) [@javier](https://discuss.flower.ai/u/javier)\
**Post date:** [July 6, 2026, 1:07pm UTC](https://discuss.flower.ai/t/hello-world-example-of-flower-framework/1260/2 "2026-07-06T13:07:25Z")

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Hi @herman , the closest to the super simple example you mention is the [`@flwrlabs/quickstart-numpy`](https://flower.ai/apps/flwrlabs/quickstart-numpy/). It does something very similar to what you describe. The `ClientApp` will return a random scalar in its `train` method, and a vector of three random numbers in its `evaluate` method. The `ServerApp` aggregates those received scalar/vector following the standard `FedAvg` strategy.

I’d recommend you to first clone the example in your directory after installing flower:

```shell
 pip install -U flwr

# get the app
flwr new @flwrlabs/quickstart-numpy

```

Then you can open the `quickstart-numpy` directory to inspect the code. Or run it directly via:

```shell
cd quickstart-numpy
flwr run . --stream

```

This simple app is equivalent to the one presented in the Flower Tutorial. I recommend going through the first three sections:

1. [What is federated learning ?](https://flower.ai/docs/framework/tutorial-series-what-is-federated-learning.html)
2. [Get started with Flower (no code)](https://flower.ai/docs/framework/tutorial-series-get-started-with-flower.html)
3. [Write your first Flower App](https://flower.ai/docs/framework/tutorial-series-write-your-first-flower-app.html)