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This tutorial demonstrates one of the most powerful features of Jsonify: building a multi-level workflow. We will tackle a common real-world problem: scraping sugar content information from the menus of different fast-food chains (McDonald’s and Wendy’s). The challenge is that each website uses a different navigation structure. McDonald’s uses standard links and spoilers, while Wendy’s might use JavaScript-driven navigation for its menu items. By creating parallel execution rows, we can build a single workflow that handles both scenarios and combines the data into one clean, final list.

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Goal

To scrape the item_name and sugar_grams from the menu category pages of McDonald’s and Wendy’s. The key challenge is that each site requires a different automation approach:
  1. McDonald’s (Standard Site): We will find all product links, follow them, and then click a spoiler to reveal nutritional information.
  2. Wendy’s (JavaScript Site): We will use the Open sub-pages block to navigate the menu, as direct links might not be available.

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Key Blocks Used

  • Open datasets (Input)
  • Layout menu (Add new row, Link actions)
  • Find Links (Transform)
  • Follow Links (Input)
  • Open sub-pages (Transform)
  • Interact with Page (Transform)
  • Extract Data (Transform)

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What You’ll Learn

  • How to structure your input data to handle different automation scenarios.
  • How to use the Layout menu to create parallel execution branches.
  • How to build different automation logics for different types of websites.
  • How to link and merge the results from parallel branches into a single, unified Extract Data block.

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Steps

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1. Prepare Your Input Data

The key is to prepare your data to guide each branch.
  • Create a new workflow and start with an Open datasets block.
  • Inside the dataset, create two columns: URL and contain.
  • The URL column will hold the links to the menu category pages.
  • The contain column will hold a text snippet that is unique to the product links on that site, which helps the Find Links block.

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2. Build the First Row (McDonald’s - Standard Navigation)

This branch will handle the McDonald’s links.
  1. Start with an Open datasets block containing the data from the table after.
URLcontain
https://www.mcdonalds.com/gb/en-gb/menu/whats-new.html/product/
https://www.mcdonalds.com/gb/en-gb/menu/burgers.html/product/
  1. Add a Find Links block. Configure it with a dynamic goal: Find all links to each product's page that must contain '{{contain}}'. This uses the variable from our dataset.
  2. Add a Follow Links block next. Configure it to Follow each link found by the previous step.
  3. Add an Interact with Page block to click the spoiler. Configure it with the action: Click on "Nutritional Information".

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3. Add a Parallel Row

Now we’ll create the separate branch for Wendy’s.
  1. Click on the Layout menu in the top action bar.
  2. Select Add new row. A new “start” placeholder will appear on your canvas. Add Open datasets block from the top menu.

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4. Build the Second Row (Wendy’s - JavaScript Navigation)

  1. Select the new Open datasets block. It will use the same data as the first one.
URL
https://order.wendys.com/category/100/hamburgers?lang=en_US
https://order.wendys.com/category/149/english-muffins?lang=en_US
  1. Add an Open sub-pages block after it. This block will handle the JavaScript-driven menu.
    • What pages do you want to open?: open each menu item on the page
    • How many pages to extract?: Set a reasonable limit, e.g., 20.
Now, we will direct the output of both branches into a single, final Extract Data block. This block will have the final context from whichever branch was active for a given input row.
  1. Click on the Layout menu and select Link actions.
  2. Click on the Open sub-pages block (the last block of Row 2), then click on the Extract Data block, it’s at the end of the first brunch. Now, second branch feed into it.
Screenshot: A multi-level workflow showing two parallel rows being linked into a single, final Extract Data block

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6. Configure the Final Extract Data Block

This block will now receive the final product page from either of the two branches.
  • Select the final Extract Data block.
  • What information…?: Choose A single item.
  • Attributes: Define a single, unified schema that works for both McDonald’s and Wendy’s product pages.
    NAMEDESCRIPTION
    item_name<the name of the menu item>
    sugar_grams<the sugar content in grams (g)>

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7. Run and Check Results

  • Run the workflow. The agent will process each row from your dataset. It will intelligently choose the correct path based on which blocks can execute.
  • The final output will be a single, clean list of menu item names and their sugar content, collected from both websites.
Congratulations! You have successfully built a multi-level workflow to handle real-world variations in website design.