# Product Hunt Scraper (`kuromaro/product-hunt-scraper`) Actor Scrape Product Hunt for trending products. Get votes, comments, makers, and trend scores. - **URL**: https://apify.com/kuromaro/product-hunt-scraper.md - **Developed by:** [Takuma](https://apify.com/kuromaro) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Scraper Collects trending products from [Product Hunt](https://www.producthunt.com/) and enriches each result with a composite **trend score** — useful for market research, trend analysis, and B2B lead generation. ### Features - Scrapes **today's**, **weekly**, or **monthly** leaderboards - Optional **topic filter** (e.g. `developer-tools`, `artificial-intelligence`, `productivity`) - Computes a **trend score** (0–100) that blends votes, comment engagement, and recency - Proxy support via Apify Proxy ### Input | Field | Type | Default | Description | |---|---|---|---| | `mode` | `enum` | `today` | `today` / `weekly` / `monthly` | | `topic` | `string` | — | Optional topic slug to filter products | | `maxResults` | `integer` | `30` | Max products returned (1–200) | | `proxyConfiguration` | `object` | — | Apify Proxy settings | ### Output Schema Each item in the dataset has the following fields: | Field | Type | Description | |---|---|---| | `name` | string | Product name | | `tagline` | string | One-line description | | `description` | string | Longer description | | `url` | string | Product Hunt listing URL | | `websiteUrl` | string | Maker's website | | `thumbnail` | string | Thumbnail image URL | | `votesCount` | integer | Upvotes received | | `commentsCount` | integer | Number of comments | | `maker` | object | `{ name, username, headline }` | | `topics` | string[] | Associated topic slugs | | `launchDate` | string | ISO-8601 launch date | | `trendScore` | float | Composite score 0–100 | | `engagementRatio` | float | `commentsCount / votesCount` | | `scrapedMode` | string | Mode used for this run | | `scrapedTopic` | string | Topic filter applied (or empty) | | `rank` | integer | Rank in results (1 = highest trend score) | ### Trend Score Formula ```` trendScore = 100 × ( 0.60 × log\_votes\_score # primary demand signal - 0.25 × engagement\_ratio # community discussion - 0.15 × recency\_bonus # freshness (today=1.0, ≤7d=0.5, ≤30d=0.25) ) ```` ### Use Cases - **Market research**: identify emerging tools and products in a specific niche - **Lead generation**: find startups with high traction for outreach - **Trend monitoring**: track what's popular on Product Hunt week-over-week - **Competitive analysis**: discover competitors in your product category ### Example Input ```json { "mode": "weekly", "topic": "developer-tools", "maxResults": 50 } ```` ### Notes - Product Hunt renders pages server-side (SSR). The scraper extracts the embedded `__NEXT_DATA__` JSON for reliable parsing, with an HTML fallback. - Rate limiting: add Apify Proxy (`proxyConfiguration`) to avoid IP blocks on high-volume runs. - Topic slugs can be found in Product Hunt URLs: `https://www.producthunt.com/topics/` # Actor input Schema ## `mode` (type: `string`): Which Product Hunt leaderboard to scrape. ## `topic` (type: `string`): Filter products by topic slug (e.g. 'developer-tools', 'artificial-intelligence', 'productivity'). Leave blank for all topics. ## `maxResults` (type: `integer`): Maximum number of products to return. ## `proxyConfiguration` (type: `object`): Optional Apify Proxy configuration to avoid rate limiting. ## Actor input object example ```json { "mode": "today", "maxResults": 30 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("kuromaro/product-hunt-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/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("kuromaro/product-hunt-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call kuromaro/product-hunt-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kuromaro/product-hunt-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Product Hunt Scraper", "description": "Scrape Product Hunt for trending products. Get votes, comments, makers, and trend scores.", "version": "0.1", "x-build-id": "38EeSemy2g5z0dtyX" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kuromaro~product-hunt-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kuromaro-product-hunt-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/kuromaro~product-hunt-scraper/runs": { "post": { "operationId": "runs-sync-kuromaro-product-hunt-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/kuromaro~product-hunt-scraper/run-sync": { "post": { "operationId": "run-sync-kuromaro-product-hunt-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", "required": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "today", "weekly", "monthly" ], "type": "string", "description": "Which Product Hunt leaderboard to scrape.", "default": "today" }, "topic": { "title": "Topic Filter (optional)", "type": "string", "description": "Filter products by topic slug (e.g. 'developer-tools', 'artificial-intelligence', 'productivity'). Leave blank for all topics." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of products to return.", "default": 30 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Optional Apify Proxy configuration to avoid rate limiting." } } }, "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 } } } } } } } } } } ```