How to connect Phantombuster and OpenAI Vision
Create a New Scenario to Connect Phantombuster and OpenAI Vision
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 Phantombuster, triggered by another scenario, or executed manually (for testing purposes). In most cases, Phantombuster or OpenAI Vision will be your first step. To do this, click "Choose an app," find Phantombuster or OpenAI Vision, and select the appropriate trigger to start the scenario.

Add the Phantombuster Node
Select the Phantombuster node from the app selection panel on the right.

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

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Authenticate OpenAI Vision
Now, click the OpenAI Vision node and select the connection option. This can be an OAuth2 connection or an API key, which you can obtain in your OpenAI Vision settings. Authentication allows you to use OpenAI Vision through Latenode.
Configure the Phantombuster and OpenAI Vision 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 Phantombuster and OpenAI Vision 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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OpenAI Vision
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Save and Activate the Scenario
After configuring Phantombuster, OpenAI Vision, 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 Phantombuster and OpenAI Vision integration works as expected. Depending on your setup, data should flow between Phantombuster and OpenAI Vision (or vice versa). Easily troubleshoot the scenario by reviewing the execution history to identify and fix any issues.
Most powerful ways to connect Phantombuster and OpenAI Vision
Phantombuster + OpenAI Vision + Google Sheets: Launch a Phantombuster Phantom to extract product images. Use OpenAI Vision (placeholder, assuming an action exists) to analyze the images and extract details. Finally, log the extracted details into a Google Sheet.
Phantombuster + OpenAI Vision + Slack: Launch a Phantombuster Phantom to find brand mentions in images online. Use OpenAI Vision (placeholder, assuming an action exists) to verify relevance of brand mentions. If the mention is relevant, notify the marketing team on Slack.
Phantombuster and OpenAI Vision integration alternatives
About Phantombuster
Automate web data extraction with Phantombuster in Latenode. Scrape social media, websites, and directories to enrich your workflows. Build scalable lead generation or market research processes by integrating Phantombuster's agents directly into Latenode's visual, no-code environment. Use Latenode’s flexible tools for data transformation and routing.
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About OpenAI Vision
Use OpenAI Vision in Latenode to automate image analysis tasks. Detect objects, read text, or classify images directly within your workflows. Integrate visual data with databases or trigger alerts based on image content. Latenode's visual editor and flexible integrations make it easy to add AI vision to any process. Scale automations without per-step pricing.
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See how Latenode works
FAQ Phantombuster and OpenAI Vision
How can I connect my Phantombuster account to OpenAI Vision using Latenode?
To connect your Phantombuster account to OpenAI Vision on Latenode, follow these steps:
- Sign in to your Latenode account.
- Navigate to the integrations section.
- Select Phantombuster and click on "Connect".
- Authenticate your Phantombuster and OpenAI Vision accounts by providing the necessary permissions.
- Once connected, you can create workflows using both apps.
Can I analyze scraped product images for brand consistency?
Yes, you can! Latenode lets you visually design workflows, combining Phantombuster's scraping with OpenAI Vision's image analysis. Ensure uniform brand representation across e-commerce platforms quickly.
What types of tasks can I perform by integrating Phantombuster with OpenAI Vision?
Integrating Phantombuster with OpenAI Vision allows you to perform various tasks, including:
- Automatically identify logos in scraped website screenshots.
- Extract text from images found during web scraping.
- Categorize products based on image analysis of scraped data.
- Detect inappropriate content in scraped images for brand safety.
- Compare visual elements across competitor websites.
How does Latenode enhance Phantombuster data processing?
Latenode adds AI-powered image analysis to scraped data, extending Phantombuster's capabilities with visual insights, all without needing extensive coding.
Are there any limitations to the Phantombuster and OpenAI Vision integration on Latenode?
While the integration is powerful, there are certain limitations to be aware of:
- Rate limits of both Phantombuster and OpenAI Vision apply.
- Large image processing can consume significant resources.
- Accuracy of image analysis depends on image quality.