How to connect Google Vertex AI and Github
Create a New Scenario to Connect Google Vertex AI and Github
In the workspace, click the “Create New Scenario” button.

Add the First Step
Add the first node – a trigger that will initiate the scenario when it receives the required event. Triggers can be scheduled, called by a Google Vertex AI, triggered by another scenario, or executed manually (for testing purposes). In most cases, Google Vertex AI or Github will be your first step. To do this, click "Choose an app," find Google Vertex AI or Github, and select the appropriate trigger to start the scenario.

Add the Google Vertex AI Node
Select the Google Vertex AI node from the app selection panel on the right.

Google Vertex AI
Configure the Google Vertex AI
Click on the Google Vertex AI node to configure it. You can modify the Google Vertex AI URL and choose between DEV and PROD versions. You can also copy it for use in further automations.
Add the Github Node
Next, click the plus (+) icon on the Google Vertex AI node, select Github from the list of available apps, and choose the action you need from the list of nodes within Github.

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Authenticate Github
Now, click the Github node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your Github settings. Authentication allows you to use Github through Latenode.
Configure the Google Vertex AI and Github Nodes
Next, configure the nodes by filling in the required parameters according to your logic. Fields marked with a red asterisk (*) are mandatory.
Set Up the Google Vertex AI and Github Integration
Use various Latenode nodes to transform data and enhance your integration:
- Branching: Create multiple branches within the scenario to handle complex logic.
- Merging: Combine different node branches into one, passing data through it.
- Plug n Play Nodes: Use nodes that don’t require account credentials.
- Ask AI: Use the GPT-powered option to add AI capabilities to any node.
- Wait: Set waiting times, either for intervals or until specific dates.
- Sub-scenarios (Nodules): Create sub-scenarios that are encapsulated in a single node.
- Iteration: Process arrays of data when needed.
- Code: Write custom code or ask our AI assistant to do it for you.

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Trigger on Webhook
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Save and Activate the Scenario
After configuring Google Vertex AI, Github, and any additional nodes, don’t forget to save the scenario and click "Deploy." Activating the scenario ensures it will run automatically whenever the trigger node receives input or a condition is met. By default, all newly created scenarios are deactivated.
Test the Scenario
Run the scenario by clicking “Run once” and triggering an event to check if the Google Vertex AI and Github integration works as expected. Depending on your setup, data should flow between Google Vertex AI and Github (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Google Vertex AI and Github
Github + Google Vertex AI + Slack: When a new commit is made to a Github repository, analyze the committed code using Google Vertex AI to detect potential security vulnerabilities. If vulnerabilities are found, send a notification to a designated Slack channel.
Github + Google Vertex AI + Jira: When a new issue is created in a Github repository, use Google Vertex AI to analyze the issue description and classify its severity. Based on the severity, create a new Jira ticket with the appropriate priority.
Google Vertex AI and Github integration alternatives
About Google Vertex AI
Use Vertex AI in Latenode to build AI-powered automation. Quickly integrate machine learning models for tasks like sentiment analysis or image recognition. Automate data enrichment or content moderation workflows without complex coding. Latenode’s visual editor makes it easier to chain AI tasks and scale them reliably, paying only for the execution time of each flow.
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About Github
Automate code management with Github in Latenode. Trigger workflows on commits, pull requests, or issues. Build automated CI/CD pipelines, track code changes, and sync repo data with project management tools. Scale code-related automations easily and add custom logic with JavaScript nodes.
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See how Latenode works
FAQ Google Vertex AI and Github
How can I connect my Google Vertex AI account to Github using Latenode?
To connect your Google Vertex AI account to Github on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Google Vertex AI and click on "Connect".
- Authenticate your Google Vertex AI and Github accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I automate code review feedback using AI?
Yes, you can! Latenode lets you trigger Google Vertex AI from Github commits to automatically analyze code and provide feedback. Improve code quality and accelerate the review process with AI.
What types of tasks can I perform by integrating Google Vertex AI with Github?
Integrating Google Vertex AI with Github allows you to perform various tasks, including:
- Automatically generate commit messages using AI based on code changes.
- Analyze code quality metrics and identify potential bugs using AI models.
- Trigger AI-powered code documentation updates based on new commits.
- Automate sentiment analysis of issue comments using Vertex AI.
- Train custom AI models on your Github repository's codebase.
How can I use JavaScript to extend Google Vertex AI capabilities?
Latenode allows you to use JavaScript code blocks to pre-process data before sending it to Google Vertex AI, or to post-process the results for complex workflows.
Are there any limitations to the Google Vertex AI and Github integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Rate limits imposed by Google Vertex AI and Github APIs may affect performance.
- Complex data transformations may require advanced JavaScript coding within Latenode.
- Real-time code analysis on very large repositories can be resource-intensive.