# Product Hunt Scraper – Products, Launches, Makers & Reviews (`abotapi/product-hunt-launches-scraper`) Actor Extract Product Hunt data including products, launches, keyword search results, maker profiles, reviews, categories, media, social links, product websites, and optional public contact emails. - **URL**: https://apify.com/abotapi/product-hunt-launches-scraper.md - **Developed by:** [AbotAPI](https://apify.com/abotapi) (community) - **Categories:** Lead generation, E-commerce, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Product Hunt Launches Scraper Extract Product Hunt products, launches, keyword search results, maker profiles, reviews, categories, media, social links, product websites, and optional public contact emails. ### Input Choose `mode` first: | Mode | Uses | Ignores | |---|---|---| | `url` | `startUrls` | `searchTerms` | | `keyword` | `searchTerms`, `searchSort`, `searchCategories`, `searchCategorySlugs` | `startUrls` | For URL mode, paste Product Hunt URLs in `startUrls`: | URL shape | Output | |---|---| | `https://www.producthunt.com/` | Recent product rows | | `/search?q={keyword}` | Product rows matching the keyword | | `/leaderboard/daily/YYYY/M/D` | Ranked product rows | | `/leaderboard/weekly/YYYY/WW` | Ranked product rows | | `/leaderboard/monthly/YYYY/M` | Ranked product rows | | `/leaderboard/yearly/YYYY` | Ranked product rows | | `/topics/{slug}` | Product rows for that topic | | `/collections/{slug}` | Product rows from the collection | | `/collections` | Product rows from listed collections | | `/products/{slug}` | One product row | | `/@username` | One maker profile row | Useful options: | Field | Description | |---|---| | `mode` | Selects URL list or keyword search | | `searchTerms` | Keyword searches to expand into product rows | | `searchSort` | Sort generated keyword searches by relevance, popular, or newest | | `searchCategories` | Multi-select common Product Hunt categories for keyword searches | | `searchCategorySlugs` | Advanced custom Product Hunt category slugs | | `enrichEmails` | Checks product websites for public contact emails | | `includeReviews` | Includes public Product Hunt review details where present | | `outputReviewsAsRows` | Saves extracted reviews as separate rows | | `maxReviewsPerProduct` | Limits reviews attached to each product | | `minReviewsCount` | Keeps only products with at least this many reviews | | `minFollowersCount` | Keeps only products with at least this many followers | | `websiteRequired` | Keeps only products with an external website URL | | `socialLinkRequired` | Keeps only products with a social or source-code URL | | `maxItems` | Hard cap on total dataset rows | | `maxSearchResultsPerTerm` | Search candidates scanned per keyword before filters are applied | | `proxy` | Optional connection settings | `maxItems` is the only finite default cap. Leave proxy settings simple for light runs; enable proxy or choose Residential if your run sees rate limits. Datacenter or free proxy configurations can be less reliable for larger runs. ### Send results into your apps (MCP connectors) Optionally pipe the scraped results into the apps you already use, via Model Context Protocol (MCP) connectors. This is an extra delivery step **after** the scrape — the Apify dataset is never changed. **What gets written to the connector:** a condensed, human-readable **summary** of each record — not the full JSON. Each item becomes one entry with a **title** and its key fields flattened to plain text. The **complete record always stays in the Apify dataset**. 1. Authorize a connector once under **Apify → Settings → Integrations** (Notion, Linear, Airtable, or Apify). 2. Select it in the **"Pipe results into your apps"** input field. (If the picker is empty, you haven't authorized a connector yet.) 3. For **Notion**, also set `notionParentPageUrl` to the page where items should be created. The connection is mediated by Apify's MCP proxy, so this actor never sees your third-party credentials. Leave the field empty to skip. ### Output Product rows include: - `rowType`, `slug`, `name`, `tagline`, `description` - `canonicalUrl`, `sourceUrl`, `websiteUrl`, social links, app links - `votesCount`, `commentsCount`, `followersCount`, `reviewsCount` - `logoUrl`, `media[]`, `mediaCount` - `categories[]` - `reviews[]`, `reviewsExtracted`, `averageRating` - `harvestedEmails[]` when email enrichment is enabled - `searchContext` with the discovery URL and result position Maker rows include: - `rowType`, `username`, `name`, `headline`, `avatarUrl` - `productUrls[]`, `productsCount` Review rows, when enabled, include: - `productSlug`, `productUrl`, `reviewId`, `reviewUrl` - `rating`, `ratingMax`, `reviewText` - `positiveNotes[]`, `negativeNotes[]` - `authorName`, `authorUrl`, `authorImage` ### Notes The Actor reads public Product Hunt pages only. Product website email discovery is best-effort: many sites do not publish email addresses, hide them behind scripts, or use forms instead. ### Legal Scrape only publicly available data and comply with Product Hunt's Terms of Service and applicable privacy laws when using personal data such as maker names or public email addresses. # Actor input Schema ## `mode` (type: `string`): Select URL list for fixed Product Hunt pages or Keyword search for generated search pages. ## `startUrls` (type: `array`): Paste Product Hunt homepage, search, leaderboard, topic, collection, product, or maker profile URLs. Listing pages expand into product records. ## `searchTerms` (type: `array`): Product Hunt keywords to search. Each term is converted to a Product Hunt search URL and expanded into product rows. ## `searchSort` (type: `string`): Sort applied to generated keyword search URLs. ## `searchCategories` (type: `array`): Restrict generated keyword searches to one or more common Product Hunt categories. Leave empty to search all categories. ## `searchCategorySlugs` (type: `array`): Advanced: Product Hunt category slugs not listed above, for example ai-engineer. Combined with Keyword categories. ## `enrichEmails` (type: `boolean`): When enabled, the Actor checks each product website for public contact emails. Disable for faster Product Hunt-only metadata. ## `includeReviews` (type: `boolean`): Include review summaries that are publicly present on Product Hunt product pages. ## `outputReviewsAsRows` (type: `boolean`): When enabled, each extracted review is also saved as its own dataset row after the product row. ## `maxReviewsPerProduct` (type: `integer`): Maximum embedded or separate review rows per product. Leave empty for a compact default; set 0 to omit review details while keeping review counts. ## `maxItems` (type: `integer`): Hard cap on total dataset rows. This is the only finite default cap. ## `maxSearchResultsPerTerm` (type: `integer`): Maximum search candidates scanned per keyword before record-level filters are applied. Leave empty to scan enough candidates to satisfy Max items. ## `minReviewsCount` (type: `integer`): Only keep product rows with at least this many Product Hunt reviews. Leave empty for no review-count filter. ## `minFollowersCount` (type: `integer`): Only keep product rows with at least this many Product Hunt followers. Leave empty for no follower-count filter. ## `websiteRequired` (type: `boolean`): Only keep products that expose an external website URL. ## `socialLinkRequired` (type: `boolean`): Only keep products that expose at least one external social or source-code link. ## `proxy` (type: `object`): Apify Proxy is optional for light runs. If Product Hunt rate-limits your run, enable proxy or choose Residential. ## `mcpConnectors` (type: `array`): Optionally send the scraped results into the apps you already use, via Model Context Protocol (MCP) connectors. Authorize a connector once under Apify → Settings → Integrations, then select it here. The connector receives a condensed, human-readable summary per item (title + key fields), not the full JSON — the complete record stays in the dataset. Leave empty to skip. Supported: Notion (https://mcp.notion.com/mcp), Linear (https://mcp.linear.app/sse), Airtable (https://mcp.airtable.com/mcp), Apify (https://mcp.apify.com). ## `notionParentPageUrl` (type: `string`): URL (or id) of the Notion page under which item pages are created. Required to enable the Notion export; ignored by other connectors. ## `maxNotifyListings` (type: `integer`): Cap on items written to each connector per run. Does not affect the dataset. ## Actor input object example ```json { "mode": "keyword", "startUrls": [ { "url": "https://www.producthunt.com/search?q=ai" }, { "url": "https://www.producthunt.com/leaderboard/daily/2026/5/22" }, { "url": "https://www.producthunt.com/topics/artificial-intelligence" }, { "url": "https://www.producthunt.com/products/bolt-new" }, { "url": "https://www.producthunt.com/@ericsimons40" } ], "searchTerms": [ "ai" ], "searchSort": "relevance", "searchCategories": [], "enrichEmails": true, "includeReviews": true, "outputReviewsAsRows": false, "maxItems": 20, "websiteRequired": false, "socialLinkRequired": false, "proxy": { "useApifyProxy": true }, "maxNotifyListings": 50 } ```` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ { "url": "https://www.producthunt.com/search?q=ai" }, { "url": "https://www.producthunt.com/leaderboard/daily/2026/5/22" }, { "url": "https://www.producthunt.com/topics/artificial-intelligence" }, { "url": "https://www.producthunt.com/products/bolt-new" }, { "url": "https://www.producthunt.com/@ericsimons40" } ], "searchTerms": [ "ai" ], "proxy": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("abotapi/product-hunt-launches-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrls": [ { "url": "https://www.producthunt.com/search?q=ai" }, { "url": "https://www.producthunt.com/leaderboard/daily/2026/5/22" }, { "url": "https://www.producthunt.com/topics/artificial-intelligence" }, { "url": "https://www.producthunt.com/products/bolt-new" }, { "url": "https://www.producthunt.com/@ericsimons40" }, ], "searchTerms": ["ai"], "proxy": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("abotapi/product-hunt-launches-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrls": [ { "url": "https://www.producthunt.com/search?q=ai" }, { "url": "https://www.producthunt.com/leaderboard/daily/2026/5/22" }, { "url": "https://www.producthunt.com/topics/artificial-intelligence" }, { "url": "https://www.producthunt.com/products/bolt-new" }, { "url": "https://www.producthunt.com/@ericsimons40" } ], "searchTerms": [ "ai" ], "proxy": { "useApifyProxy": true } }' | apify call abotapi/product-hunt-launches-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=abotapi/product-hunt-launches-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Product Hunt Scraper – Products, Launches, Makers & Reviews", "description": "Extract Product Hunt data including products, launches, keyword search results, maker profiles, reviews, categories, media, social links, product websites, and optional public contact emails.", "version": "0.1", "x-build-id": "nM3a2wwI4iHEHrf4L" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/abotapi~product-hunt-launches-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-abotapi-product-hunt-launches-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/abotapi~product-hunt-launches-scraper/runs": { "post": { "operationId": "runs-sync-abotapi-product-hunt-launches-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/abotapi~product-hunt-launches-scraper/run-sync": { "post": { "operationId": "run-sync-abotapi-product-hunt-launches-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Input mode", "enum": [ "url", "keyword" ], "type": "string", "description": "Select URL list for fixed Product Hunt pages or Keyword search for generated search pages.", "default": "keyword" }, "startUrls": { "title": "Product Hunt URLs", "type": "array", "description": "Paste Product Hunt homepage, search, leaderboard, topic, collection, product, or maker profile URLs. Listing pages expand into product records.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "searchTerms": { "title": "Keyword search terms", "type": "array", "description": "Product Hunt keywords to search. Each term is converted to a Product Hunt search URL and expanded into product rows.", "items": { "type": "string" } }, "searchSort": { "title": "Keyword search sort", "enum": [ "relevance", "popular", "newest" ], "type": "string", "description": "Sort applied to generated keyword search URLs.", "default": "relevance" }, "searchCategories": { "title": "Keyword categories", "type": "array", "description": "Restrict generated keyword searches to one or more common Product Hunt categories. Leave empty to search all categories.", "items": { "type": "string", "enum": [ "ai-coding-agents", "vibe-coding", "ai-code-editors", "llms", "ai-chatbots", "ai-infrastructure", "prompt-engineering-tools", "productivity", "ai-workflow-automation", "lead-generation", "marketing-automation", "design-creative", "video-editing", "design-resources", "graphic-design-tools", "social-networking", "professional-networking", "finance", "accounting", "fundraising-resources", "investing", "no-code-platforms", "figma-plugins", "static-site-generators", "automation", "engineering-development" ], "enumTitles": [ "AI Coding Agents", "Vibe Coding Tools", "AI Code Editors", "LLMs", "AI Chatbots", "AI Infrastructure Tools", "Prompt Engineering Tools", "Productivity", "AI Workflow Automation", "Lead generation software", "Marketing automation platforms", "Design & Creative", "Video editing", "Design resources", "Graphic design tools", "Social Networking", "Professional networking platforms", "Finance", "Accounting software", "Fundraising resources", "Investing", "No-code Platforms", "Figma Plugins", "Static site generators", "Automation tools", "Engineering & Development" ] }, "default": [] }, "searchCategorySlugs": { "title": "Custom keyword category slugs", "type": "array", "description": "Advanced: Product Hunt category slugs not listed above, for example ai-engineer. Combined with Keyword categories.", "items": { "type": "string" } }, "enrichEmails": { "title": "Find contact emails on product websites", "type": "boolean", "description": "When enabled, the Actor checks each product website for public contact emails. Disable for faster Product Hunt-only metadata.", "default": true }, "includeReviews": { "title": "Include product reviews", "type": "boolean", "description": "Include review summaries that are publicly present on Product Hunt product pages.", "default": true }, "outputReviewsAsRows": { "title": "Output reviews as separate rows", "type": "boolean", "description": "When enabled, each extracted review is also saved as its own dataset row after the product row.", "default": false }, "maxReviewsPerProduct": { "title": "Max reviews per product", "minimum": 0, "maximum": 100, "type": "integer", "description": "Maximum embedded or separate review rows per product. Leave empty for a compact default; set 0 to omit review details while keeping review counts." }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Hard cap on total dataset rows. This is the only finite default cap.", "default": 20 }, "maxSearchResultsPerTerm": { "title": "Max search candidates per keyword", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Maximum search candidates scanned per keyword before record-level filters are applied. Leave empty to scan enough candidates to satisfy Max items." }, "minReviewsCount": { "title": "Minimum reviews", "minimum": 0, "type": "integer", "description": "Only keep product rows with at least this many Product Hunt reviews. Leave empty for no review-count filter." }, "minFollowersCount": { "title": "Minimum followers", "minimum": 0, "type": "integer", "description": "Only keep product rows with at least this many Product Hunt followers. Leave empty for no follower-count filter." }, "websiteRequired": { "title": "Require product website", "type": "boolean", "description": "Only keep products that expose an external website URL.", "default": false }, "socialLinkRequired": { "title": "Require social or source link", "type": "boolean", "description": "Only keep products that expose at least one external social or source-code link.", "default": false }, "proxy": { "title": "Proxy configuration", "type": "object", "description": "Apify Proxy is optional for light runs. If Product Hunt rate-limits your run, enable proxy or choose Residential." }, "mcpConnectors": { "title": "Pipe results into your apps (optional)", "type": "array", "description": "Optionally send the scraped results into the apps you already use, via Model Context Protocol (MCP) connectors. Authorize a connector once under Apify → Settings → Integrations, then select it here. The connector receives a condensed, human-readable summary per item (title + key fields), not the full JSON — the complete record stays in the dataset. Leave empty to skip. Supported: Notion (https://mcp.notion.com/mcp), Linear (https://mcp.linear.app/sse), Airtable (https://mcp.airtable.com/mcp), Apify (https://mcp.apify.com)." }, "notionParentPageUrl": { "title": "Notion parent page (Notion connector only)", "type": "string", "description": "URL (or id) of the Notion page under which item pages are created. Required to enable the Notion export; ignored by other connectors." }, "maxNotifyListings": { "title": "Max items to export per connector", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Cap on items written to each connector per run. Does not affect the dataset.", "default": 50 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```