# Product Hunt Scraper: Launches, Topics, Makers & Votes (`f0rty7even/producthunt-scraper`) Actor Scrape Product Hunt launches via the official API: name, tagline, description, votes, comments, topics, makers, and links. Filter by topic, date, and ranking. Clean JSON for research and trend tracking. - **URL**: https://apify.com/f0rty7even/producthunt-scraper.md - **Developed by:** [Michael Yousrie](https://apify.com/f0rty7even) (community) - **Categories:** Developer tools, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 launch scrapeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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: Launches, Topics, Makers & Votes **Export Product Hunt launches into clean, structured data.** This **Product Hunt scraper** pulls launches straight from the official Product Hunt API and returns each product's name, tagline, description, vote and comment counts, topics, makers, and links, normalized into one tidy schema. Sort by the daily leaderboard, newest, or most upvoted, and filter by topic and date. Powered by Product Hunt's official API. No login for you to manage, no anti-bot workarounds. ### Why this actor - **Official API, clean data**: structured launch records, not scraped HTML that breaks. - **Real sorting and filtering**: ranking (the leaderboard), newest, most votes, or feature date; narrow to a topic or a date range. - **Rich records**: votes, comments, review count and rating, topics, and the makers behind each launch. - **Simple pricing**: you are charged per launch returned, nothing else. ### Use cases - **Trend and market research**: track what is launching in your space and how it performs. - **Competitive intelligence**: monitor launches in a topic like `artificial-intelligence` or `developer-tools`. - **Content and newsletters**: pull the day's or week's top launches for roundups. - **Datasets**: build a structured feed of products, taglines, and topics for analysis or AI pipelines. ### Input | Field | Description | |---|---| | `order` | Sort order: Ranking (leaderboard), Newest, Most votes, or Featured date. | | `topic` | Optional topic slug (e.g. `artificial-intelligence`, `developer-tools`, `productivity`). | | `postedAfter` / `postedBefore` | Optional date bounds (`YYYY-MM-DD`). | | `featuredOnly` | Keep only launches featured on the homepage. | | `maxItems` | Cap on launches returned (main cost lever). | | `developerToken` | Optional. Use your own Product Hunt token instead of the built-in credentials. | ### Output Each launch becomes one dataset item: ```json { "id": "123456", "name": "Osaurus", "tagline": "Open source agents that run 100% locally on your Mac", "description": "...", "url": "https://www.producthunt.com/posts/osaurus", "website": "https://osaurus.example", "slug": "osaurus", "votesCount": 580, "commentsCount": 89, "reviewsCount": 12, "reviewsRating": 4.8, "topics": ["Open Source", "Privacy", "Artificial Intelligence"], "makers": [ { "name": "Jared Goodman", "username": "j_osaurus", "headline": "10x Creative." } ], "thumbnail": "https://ph-files.imgix.net/....png", "createdAt": "2026-07-13T07:01:00Z", "featuredAt": "2026-07-13T07:01:00Z" } ```` ### Pricing Pay per result: you are charged **per launch returned**. There is no monthly fee. ### Notes - **Public data only.** This actor reads publicly available launch data through Product Hunt's official API. It does not access private messages, email addresses, or anything behind a login. - **Topic slugs** are the lowercase, hyphenated form from a topic's URL (e.g. `producthunt.com/topics/artificial-intelligence` → `artificial-intelligence`). # Actor input Schema ## `order` (type: `string`): How to order launches. Ranking = the daily leaderboard order; Newest = most recent; Votes = most upvoted; Featured = by feature date. ## `topic` (type: `string`): Only launches in this topic, by slug (e.g. artificial-intelligence, developer-tools, productivity). Leave empty for all topics. ## `postedAfter` (type: `string`): Only launches posted on/after this date (YYYY-MM-DD). Leave empty for no lower bound. ## `postedBefore` (type: `string`): Only launches posted on/before this date (YYYY-MM-DD). Leave empty for no upper bound. ## `featuredOnly` (type: `boolean`): Keep only launches that were featured on the Product Hunt homepage. ## `maxItems` (type: `integer`): Cap on launches returned (main cost lever). ## `developerToken` (type: `string`): Optional: your own Product Hunt developer token to use instead of the built-in credentials (e.g. to use your own rate-limit quota). ## Actor input object example ```json { "order": "RANKING", "featuredOnly": false, "maxItems": 100 } ``` # Actor output Schema ## `launches` (type: `string`): One normalized launch per item. Export as JSON, JSONL, CSV, or Excel. # 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("f0rty7even/producthunt-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("f0rty7even/producthunt-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 f0rty7even/producthunt-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=f0rty7even/producthunt-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Product Hunt Scraper: Launches, Topics, Makers & Votes", "description": "Scrape Product Hunt launches via the official API: name, tagline, description, votes, comments, topics, makers, and links. Filter by topic, date, and ranking. Clean JSON for research and trend tracking.", "version": "0.1", "x-build-id": "4Tao8ybh1qOXUIUG1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/f0rty7even~producthunt-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-f0rty7even-producthunt-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/f0rty7even~producthunt-scraper/runs": { "post": { "operationId": "runs-sync-f0rty7even-producthunt-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/f0rty7even~producthunt-scraper/run-sync": { "post": { "operationId": "run-sync-f0rty7even-producthunt-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "order": { "title": "Sort order", "enum": [ "RANKING", "NEWEST", "VOTES", "FEATURED_AT" ], "type": "string", "description": "How to order launches. Ranking = the daily leaderboard order; Newest = most recent; Votes = most upvoted; Featured = by feature date.", "default": "RANKING" }, "topic": { "title": "Topic slug (optional)", "type": "string", "description": "Only launches in this topic, by slug (e.g. artificial-intelligence, developer-tools, productivity). Leave empty for all topics." }, "postedAfter": { "title": "Posted after (optional)", "type": "string", "description": "Only launches posted on/after this date (YYYY-MM-DD). Leave empty for no lower bound." }, "postedBefore": { "title": "Posted before (optional)", "type": "string", "description": "Only launches posted on/before this date (YYYY-MM-DD). Leave empty for no upper bound." }, "featuredOnly": { "title": "Featured launches only", "type": "boolean", "description": "Keep only launches that were featured on the Product Hunt homepage.", "default": false }, "maxItems": { "title": "Max launches", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Cap on launches returned (main cost lever).", "default": 100 }, "developerToken": { "title": "Product Hunt token (optional)", "type": "string", "description": "Optional: your own Product Hunt developer token to use instead of the built-in credentials (e.g. to use your own rate-limit quota)." } } }, "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 } } } } } } } } } } ```