> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# GitHub Actions with Openlayer

> Learn how to use Openlayer with GitHub Actions

Openlayer for GitHub automatically generates model outputs and tests your GitHub projects
with Openlayer, providing feedback on the quality of your AI systems with every change you make.

For **advanced use cases**, you can use **Openlayer with GitHub Actions** as your CI/CD
provider to generate outputs on your datasets for every `git` push. This can then be
pushed to Openlayer to run tests under any conditions you’d like.

This approach is useful for developers who want full control over their CI/CD pipeline,
as well as GitHub Enterprise Server users, who can’t leverage Openlayer’s built-in git
integration. It’s also useful for users whose [execution runtime](/development/openlayer-json#runtime)
may not be supported by Openlayer.

<Tip>
  You can [view a full example
  here](https://github.com/openlayer-ai/templates/tree/main/ci-cd/github-actions)
  or follow this guide to get started.
</Tip>

## Generating Outputs

You can generate your AI system’s outputs locally (or in GitHub Actions)
without giving Openlayer access to the source code through the [Openlayer CLI](/api-reference/cli/commands/batch)
`openlayer batch` command.
This will generate outputs and expect them to be placed in your model’s `outputDirectory`
folder conforming the the [Batch Output specification](/development/configuring-output-generation#providing-a-way-for-openlayer-to-run-your-model-on-your-datasets).

`openlayer batch` allows you to generate your AI system’s outputs within your own CI
setup, either on GitHub Actions or your own CI, and upload *only* the artifacts
(and not the source code) to Openlayer to create a new project version.

## Configuring GitHub Actions for Openlayer

The CLI `openlayer push` command will upload the artifacts in your working directory (
skipping everything in `.openlayerignore`) to Openlayer. Openlayer will auto
detect that your outputs have already been created and go straight to computing
your metrics and running your tests.

Let’s create our Action with a new file called `.github/workflows/openlayer.yaml`

```json theme={null}
name: Openlayer Tests
env:
  OPENLAYER_PROJECT_ID: ${{ secrets.OPENLAYER_PROJECT_ID }}
  // anything else you need for your AI to generate outputs
  OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
on:
  push:
    branches:
      - main
jobs:
  Test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Install Openlayer CLI
        run: curl -o- "https://downloads.openlayer.com/cli/install/linux_64.sh" | sh
      - name: Install Your Requirements
	      run: openlayer install
      - name: Generate Outputs
        run: openlayer batch
      - name: Push Project Artifacts to Openlayer
        run: openlayer push --message ${{ github.event.head_commit.message }} --api-key=${{ secrets.OPENLAYER_API_KEY }}
```

This Action will run when your code is pushed to a git branch.
`openlayer push` will wait for the results and cause the Action to fail if
any of your tests failed.

Let’s add the required values from Openlayer as secrets in GitHub

1. Retrieve your [Openlayer API key](/workspace-and-projects/find-your-api-key)
2. Install the [Openlayer CLI](/api-reference/cli/overview) and run `openlayer login`
3. Inside your folder, run `openlayer link` to create a new Openlayer project
4. Inside the generated `.openlayer` folder, save the `projectId` from the `config.json`
5. Inside GitHub, add `OPENLAYER_API_KEY`, `OPENLAYER_PROJECT_ID` and anything else you need to generate your outputs as [secrets](https://docs.github.com/en/actions/security-guides/using-secrets-in-github-actions)

## Testing Openlayer + your AI with GitHub Actions

Now that your Openlayer AI system is configured with GitHub Actions, you can try
out the workflow:

* Create a new pull request in your GitHub repository
* Merge the pull request into your main branch
* GitHub Actions will recognize the change and use the Openlayer CLI to push your AI
* The Action passes or fails based on the results of your tests

Every merge into your branch of choice will now be tested with Openlayer.