Make AI Web Search
Make AI Web Search is available on paid plans. To use it, upgrade to a paid plan. While this app is in beta, functionality and pricing may change.
Make AI Web Search is a built-in app that lets you find and retrieve up-to-date information from the internet directly within your Make scenarios. It searches across websites, news articles, and other online sources to provide relevant, structured results — perfect for enriching workflows with fresh data, fact-checking, or content discovery. All searches run securely within Make, without depending on external third-party integrations.
The app retrieves and analyzes information from static HTML web pages by reading the initial page source. It acts as a simple HTML fetcher and does not function as a real browser. As a result, it cannot execute JavaScript, render client-side content, or interact with dynamically generated pages such as Single Page Applications (SPAs). It also cannot reliably extract data from sites where content loads dynamically, appears in canvases or SVGs, or is heavily generated by client-side scripts.
Key benefits
- No external accounts: No need to connect or manage a separate account. Everything works within your existing environment.
- Privacy focused: Your data is never stored or used for AI training.
- Instant setup: Start using the app immediately in your platform. There is no need to create any connection.
Credit usage
The module uses GPT-5 nano and the credit usage is based on the amount of input and output tokens consumed in each run. Input tokens depend on the prompt you enter, and output tokens are based on the response. After each run, you can see exactly how many tokens were used in the module's output.
The table below shows how input and output tokens convert to Make credits for OpenAI models:
Model name | Input tokens | Output tokens |
|---|---|---|
GPT-5 nano | 18080 tokens per credit | 2260 tokens per credit |
For more detailed information about credits, refer to the Credits documentation.
Make AI Web Search modules
You can use the following module to build your scenarios.
The current features and functionality are still in beta and are actively being improved. As such, pricing is subject to change and will be updated as the app evolves.
Generate a response
Generates a response from the input provided.
Field | Description |
|---|---|
Text | Enter your search query for the AI. You can include details on how you want the response to be formatted. |
Parse JSON in response | Enable to parse the AI’s output as JSON. If parsing fails, the raw text response will be returned. |
City | Enter a city to refine the search location. For example, San Francisco. |
Country | Select or map a country to prioritize search results relevant to that country. Content from the selected country will appear higher in web search results. To map a country manually, enter its two-letter ISO code. For example, US. |
Region | Enter a region to narrow the search location. For example, California. |
Timezone | Select or map a timezone to adjust results based on local time. For example, America/Los_Angeles. |
In this use case, you’ll use the Make AI Web Search app to retrieve information from a website. You’ll find the most recent posts on the Make Blog, collect their URLs and publication dates, and extract their content. The scenario returns all data in a structured JSON format.
Step 1. Set up your first Make AI Web Search module
First, you need to add the Make AI Web Search app to your scenario and configure it to retrieve one of today’s blog post URLs.
Add a Make AI Web Search > Generate a response module to your scenario.
In the Text field, enter the prompt to retrieve the information you need.
Example prompt:
Get one of today's blog post URLs. Nest these items under the URLs.
Get the date of the last blog post formatted YYYY-MM-DD, blog title.
Output in JSON wrapped by [StartJSON] and [EndJSON].
Do not ask for clarification and do it now.
https://www.make.com/en/blogSwitch on the Advanced settings option.
In the Parse JSON in response field, select Yes to format the response as JSON.
Click Save.
Step 2: Add an Iterator
Next, add an Iterator module to your scenario to handle each blog post result as a separate item.
Add the Iterator module to your scenario.
In the Array field, map the result from the previous Make AI Web Search module.
Click Save.
Step 3: Extract the blog post content
Finally, add another Make AI Web Search module to extract the content of the blog post.
Add a Make AI Web Search > Generate a response module to your scenario after the Iterator.
In the Text field, enter the prompt to extract the content from the URL.
Example prompt:
Extract the blog post body.After entering the prompt, map the URL value from the Iterator module to the Text field to extract the retrieved content.
Click Save.
You’ve now set up the scenario to retrieve and extract content from a website, with the data processed step by step for each URL and returned in JSON format.
Templates
You can look for Make AI Web Search templates in Make's template gallery, where you'll find thousands of pre-created scenarios.