2025年12月14日 | 5分钟阅读

如何在 Make 中使用轮换代理构建可扩展的网页数据工作流

了解如何自动从任意网站大规模采集商品数据，整理至 Google 表格，并利用 AI 进行分析——整个过程由轮换代理自动应对访问限制和速率限制。

![Guest Post_Oxylabs](__CODE_BLOCK_0__ Web data is one of the most valuable resources for understanding markets, tracking competitors, or employing advanced analytics. Yet, collecting it at scale can quickly become a major challenge due to access blocks or rate limits. That’s where Make and Oxylabs Web Scraper API come together. Make acts as the automation hub, connecting dozens of apps and services into one workflow, while Oxylabs ensures stable and scalable web data access through built-in proxy rotation and parsing. In this guide, you’ll learn how to set up a Make scenario that: * Collects product data from Amazon using Oxylabs Web Scraper API * Automatically saves structured results to Google Sheets * Analyzes the data using AI agents in Make

By the end, you’ll have a fully functional, repeatable workflow you can adapt to your own use case.

Why rotating proxies matter for automation

When you send repeated requests to websites, you can quickly hit blocks or rate limits enforced by various anti-bot systems. Rotating proxies solve this by automatically cycling through different IPs, making each request appear to come from a different user. In these situations, you should look for tools such as the Oxylabs Web Scraper API , which handles proxy management and rotation automatically, routing every request through a large, high-quality proxy pool. This allows your Make scenarios to scale safely and run reliably, even when scraping complex sites like Amazon.

Setting up the workflow in Make

Before we start building, make sure you have: * A Make account (access free plan [here](__CODE_BLOCK_1__ * An Oxylabs Web Scraper API account (get free trial [here](__CODE_BLOCK_2__ * A Google account for connecting to Google Sheets * (Optional) 访问 Make 中的 AI Agents（AI 智能体）（可通过 30 天试用或 Core 套餐获得）

准备就绪后，让我们从零开始构建自动化流程。

步骤 1：创建场景

如果您是 Make 新用户，请注册一个免费账户——每月可享 1,000 次免费操作。登录后： 1. 从左侧菜单中选择场景。 2. 点击 创建新场景。 3. 选择 从头开始构建，即可开始设计您的工作流。

![Oxylabs scenario 1](__CODE_BLOCK_3__

Step 2. Add the Oxylabs module

In the scenario editor, click the plus (+) 图标以添加第一个模块。搜索 Oxylabs 并选择其中一个可用模块——例如 Scrape Amazon Search（抓取亚马逊搜索结果）。

![Oxylabs scenario 2](__CODE_BLOCK_4__ If you haven’t yet, claim your [free trial](__CODE_BLOCK_5__ for Oxylabs Web Scraper API on the Oxylabs dashboard. Once you have credentials, create a new connection by entering your Oxylabs Web Scraper API username and password. ![Oxylabs scenario 3](__CODE_BLOCK_6__ Then, configure your module parameters. For this example, we’ll use: * Query : Lenovo laptop * Geo location : 98104 (Seattle downtown ZIP) * 解析：是 * 页数：2

![Oxylabs scenario 4](__CODE_BLOCK_7__ These settings will instruct the scraper to search Amazon for “Lenovo laptop,” localize results to Seattle, and parse two pages of structured results. On top of that, when you run the module, Oxylabs will automatically rotate proxies and parse results into JSON with no manual proxy setup required.

Step 3. Add the Set variable tool

Next, we’ll process the results into a single array. 1. Add another module, then search for "Set variable". 2. Set the Variable name to flattened. 3. Paste this function into the Variable value field:

_{{flatten(map(map(map(1.results; "content"); "results"); "organic"))}}_ ![Oxylabs scenario 6](__CODE_BLOCK_8__ This formula gathers all organic search results from all pages into a single array of items. You can try running the scenario to see the results.

Step 4. Add Iterator, Array Aggregator, and Google Sheets

While you could push your results directly to Google Sheets in bulk, that wouldn’t let you map specific columns. Instead, we’ll use Iterator and Array Aggregator to handle individual rows before sending data to Google Sheets. #### 4.1 Add Iterator ![Oxylabs scenario 7](__CODE_BLOCK_9__ * Connect the Iterator to your Set variable tool. * Set its value to {{2.flattened}} or select flattened[] from the items menu.

Module IDs in Make auto-increment, so double-check the correct reference number if you’ve added or deleted modules. #### 4.2 Add Array Aggregator ![Oxylabs scenario 8](__CODE_BLOCK_10__ Add an Array Aggregator module, select Iterator [3] as the Source Module, and save the default settings for now. #### 4.3 Add Google Sheets ![Oxylabs scenario 9](__CODE_BLOCK_11__ Add the Google Sheets module and selectBulk Add Rows. For this example, let’s create a spreadsheet in the Google Drive with the following details: * Title : Amazon Search Results * Sheet name : Organic * Columns : ASIN, TITLE, PRICE, PRICE STRIKETHROUGH, RATING, REVIEWS COUNT, SALES VOLUME, IS PRIME, IS AMAZON'S CHOICE, BEST SELLER, SHIPPING, URL

![Oxylabs scenario 10](__CODE_BLOCK_12__ Then, in Make, use the ID finder to locate your spreadsheet, as shown in the example below. ![Oxylabs scenario 11](__CODE_BLOCK_13__ Now disable the map settings, select your Sheet name (Organic)，将 列范围 设置为 [A-Z]，并在行中输入 {{4.array}} 或从菜单中选择 Array[]。 ![Oxylabs scenario 12](__CODE_BLOCK_14__ #### 4.4 Modify Array Aggregator ![Oxylabs scenario 13](__CODE_BLOCK_15__ * Return to the Array Aggregator. * In the Target structure type , select Rows from the Google Sheets - Bulk Add Rows module. * Map each field from the Iterator to match your column names from your sheet.

You can also clean up the URLs before saving. To do so, add __CODE_BLOCK_16__ to the start and remove everything starting from the /ref= part with this function: ___CODE_BLOCK_17__ "/ref="))}}_ ![Oxylabs scenario 14](__CODE_BLOCK_18__ This will trim unnecessary URL parameters and leave a clean link to the product page.

Step 5. Run the scenario

Now run your whole workflow. In this scenario, Make will perform the following actions: 1. Trigger the Oxylabs module to scrape Amazon results using rotating proxies. 2. Present and process data into a structured format. 3. Push all rows into Google Sheets.

Your new sheet should now contain two pages of organized Amazon search results.

Step 6. Create an AI agent with Make

Now that you have a reliable workflow for collecting and organizing data, let's go a step further and use Make's AI Agents to analyze it. We'll create an AI agent to process our data and provide a summary report. #### 6.1 Create the AI Agent First, we need to create the agent that will perform the analysis. Keep in mind that Make offers a 30-day free trial to test this feature, and you can access it anytime with the Core plan. Click the AI Agents button in the left-side menu to access the page. There, select the Create agent button, give your agent a name, and choose your LLM provider (e.g., Google Gemini, OpenAI, Anthropic)。 ![Oxylabs scenario 15](__CODE_BLOCK_19__ To tell the AI its exact role and objective, use this system prompt in the Instructions field: _# Role and Objective_ _You are an expert e-commerce product assistant. Your goal is to analyze scraped product data to identify the best deals based on product specifics, price, availability, and overall value._ _# Instructions & Rules_ _- Provide a comprehensive report of your main findings, highlighting the best product options._ Once saved, this agent will be available as a module you can add to any scenario. #### 6.2 Use the AI Agent in your workflow While in your scenario screen, you can add the newly created AI Agent to your workflow. First, click on the Oxylabs module and change the Pages from 2 to 10. This will get your AI a larger dataset to analyze. Now, add a Router module between your Set variable tool and your Iterator. This will let you branch the scenario, sending the data to both Google Sheets and the AI agent. The original path to the Iterator and Google Sheets will become your first branch, just like in the example below. ![Oxylabs scenario 16](__CODE_BLOCK_20__ As we branch out, click the router to add a new path, then select the Make AI Agents module and choose our new AI Agent we just created. To send the collected data to it, select the flattened[] item or write {{2.flattened}} in the Messages field. ![Oxylabs scenario 18](__CODE_BLOCK_21__ To finish the scenario, jump to your Amazon Search Results sheet and create a new sheet/tab named Analysis. After that, return to the Make scenario, search for Google Sheets , and select the Update a cell module. There, select your spreadsheet and the Analysis sheet, specify the cell where to place the AI response (e.g., A1)，并将值字段设置为 {{7.response}}。

!Oxylabs scenario 19

现在，当您运行该场景时，工作流将抓取 10 页数据，将所有单个商品项保存到主表格中，同时将完整数据集发送给您的 AI 智能体。AI 智能体将分析这些数据，并在另一个独立的工作表中返回完整的响应结果。

!Oxylabs scenario 20

后续步骤

您现在已经构建了一个完整的自动化工作流，可在 Make 内自动完成网页数据的采集、整理与分析。但此设置还有很大的拓展空间：

1. 扩展您的自动化规模 – 通过增加抓取页数或添加新的搜索关键词来扩展现有场景。Oxylabs 内置的轮换代理可确保即使在处理更大规模数据集或更频繁运行时，您的工作流依然稳定可靠。 2. 将其转化为周期性任务 – 设置场景按天或按周自动运行，使您的 Google 表格持续更新最新数据。这样，您无需任何手动操作，即可监控价格、库存或商品排名的变化。 3. 适配其他应用场景 – 此方法不仅限于电商领域。您还可以用于追踪竞争对手网站、监控职位发布、跟踪房地产列表，或采集新闻内容——全部基于 Make 的自动化逻辑和 Oxylabs 可靠的数据交付能力。

结合 Make 的灵活性与 Oxylabs 的网络智能，构建可扩展、数据驱动的工作流，灵活适应您的业务发展需求。

how-to-build-scalable-web-data-workflows-in-make-with-rotating-proxies