# Product Hunt Daily Scraper *Cheap* (`data_alchemist/producthunt-daily-scraper`) Actor - **URL**: https://apify.com/data\_alchemist/producthunt-daily-scraper.md - **Developed by:** [Dennis](https://apify.com/data_alchemist) (community) - **Categories:** Lead generation, Social media, E-commerce - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 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. 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 ### What does Product Hunt Scraper do? **Product Hunt Scraper** extracts daily product listings from [Product Hunt](https://www.producthunt.com) — including full product details, all historical launches, and user reviews — without relying on slow browser rendering. It hits Product Hunt's internal GraphQL API directly, making it extremely fast and cost-efficient compared to traditional scrapers. Try it on the Apify platform with no setup required. Use it to monitor what's trending in tech, track competitor launches, or feed your AI pipelines with fresh startup data every morning. ### Why use Product Hunt Scraper? - **Daily fresh data** — schedule a run each morning to capture yesterday's top products automatically - **Launches history** — every product's full launch timeline with upvotes, daily/weekly rank, and media - **Deep reviews** — structured review data with ratings, pros/cons, and Q&A per product - **Integrations** — export via Apify API, connect to Google Sheets, Zapier, Make, Slack, and more ### How to use Product Hunt Scraper 1. Go to the Actor page on Apify and click **Try for free** 2. In the **Input** tab, set **Last N days** to `1` to scrape yesterday's products (the default is today) 3. Choose whether to include launches and how many reviews per product 4. Click **Start** and wait for the run to finish (usually under 5 minutes for 100 products) 5. Download your results from the **Output** tab as JSON, CSV, or Excel > **Tip:** Click **Schedule** on the Actor page and set it to run daily at 06:00 UTC to automatically get fresh products every morning in your dataset. ### Input Configure the scraper from the **Input** tab or via the API. | Field | Type | Default | Description | |---|---|---|---| | Max Results | integer | `100` | Maximum number of products to return (1–1000) | | dateToScrape | date | — | Scrape products launched on this date | | N days ago | integer | `1` | Scrape products from N days ago (`0` = today, `1` = yesterday). Used only if `dateToScrape` is not set | | Scrape launches | boolean | `false` | Whether to fetch launches for each product | | Scrape reviews | boolean | `false` | Whether to fetch reviews for each product | | Output format | select | `simplified` | `Simplified` returns clean flat fields; `Full` returns the raw Product Hunt API response | #### Output format - **Simplified** (default) — a clean, flat object with the most useful fields extracted and normalized. Best for spreadsheets, dashboards, and most use cases. - **Full (raw API response)** — the complete GraphQL response from Product Hunt, unmodified. Use this when you need fields not included in the simplified format or want to do your own parsing. **Example input for yesterday's products with reviews and launches:** ```json { "nDaysAgo": 1, "maxResults": 500, "scrapeLaunches": true, "scrapeReviews": true, "outputFormat": "simplified" } ```` ### Output Results are stored in the Apify dataset and available via the **Output** tab or the [Apify API](https://docs.apify.com/api/v2). Download in **JSON, CSV, Excel, or HTML** format. Each item in the dataset represents one product: ```json { "slug": "cursor-ai", "name": "Cursor", "tagline": "The AI code editor", "description": "Cursor is an AI-first code editor designed to make you extraordinarily productive.", "url": "https://www.producthunt.com/products/cursor-ai", "thumbnailUrl": "https://ph-files.imgix.net/...", "categories": ["Developer Tools", "Artificial Intelligence"], "followers": 42800, "reviewsCount": 312, "launchesCount": 4, "createdAt": "2023-03-14", "aggregateRating": { "ratingValue": 4.8, "ratingCount": 312 }, "links": ["https://cursor.sh"], "xAccountHandle": "@cursor_ai", "imageUrls": ["https://ph-files.imgix.net/..."], "team": [{ "name": "Arvid Lunnemark", "url": "https://www.producthunt.com/@arvid_lunnemark" }], "launches": [ { "slug": "cursor-ai-2", "name": "Cursor 0.40", "tagline": "The AI code editor, now 10x faster", "upvotesCount": 3842, "dailyRank": 1, "weeklyRank": 2, "featuredAt": "2024-09-26", "topics": ["Developer Tools", "Productivity"], "makers": [{ "name": "Michael Truell", "username": "michaeltruell" }] } ], "reviews": [ { "rating": { "overall": 5, "easeOfUse": 5, "reliability": 4 }, "overallExperience": "Cursor has completely changed how I write code...", "positiveFeedback": "The tab completion is incredibly smart.", "negativeFeedback": "Occasional latency on large files.", "pros": ["AI suggestions", "Speed", "VS Code compatible"], "cons": ["Subscription price"], "author": { "name": "Jane Doe", "username": "janedoe", "isVerified": true } } ] } ``` ### Data fields #### Product fields | Field | Description | |---|---| | `slug` | Unique product identifier | | `name` | Product name | | `tagline` | Short product description | | `description` | Full product description | | `url` | Product Hunt URL | | `thumbnailUrl` | Logo/thumbnail image URL | | `categories` | List of categories | | `followers` | Number of followers | | `reviewsCount` | Total review count | | `launchesCount` | Number of Product Hunt launches | | `aggregateRating` | Structured rating summary | | `team` | Maker profiles | | `links` | External website URLs | | `xAccountHandle` | Twitter/X handle | | `imageUrls` | Screenshot URLs | | Social URLs | `twitterUrl`, `linkedinUrl`, `facebookUrl`, `instagramUrl`, etc. | #### Launch fields (when `scrapeLaunches` is enabled) | Field | Description | |---|---| | `name` / `tagline` | Launch title and tagline | | `upvotesCount` | Total upvotes received | | `launchDayScore` | Score on launch day | | `dailyRank` / `weeklyRank` | Product Hunt rankings | | `featuredAt` | Launch date | | `topics` | Topic tags | | `makers` / `hunter` | Maker and hunter profiles | | `media` | Images and video links | | `pricingType` | Free / Paid / Freemium | #### Review fields (when `reviewsLimit > 0`) | Field | Description | |---|---| | `rating` | Overall, ease of use, reliability, value, customization scores | | `overallExperience` | Full review text | | `positiveFeedback` / `negativeFeedback` | Structured feedback | | `pros` / `cons` | Tagged pros and cons | | `questionAnswers` | Structured Q\&A | | `alternatives` | Compared alternative products | | `author` | Reviewer profile and verification status | ### Pricing and cost Product Hunt Scraper is **very cheap to run** because it calls Product Hunt's GraphQL API directly rather than rendering full browser pages. A typical run fetching 100 products with reviews and launches completes in under 5 minutes and costs only a few Apify compute units. ### Tips - **Daily automation:** Set **Last N days** to `1` and schedule the Actor to run at 06:00 UTC every day — you'll always have yesterday's full Product Hunt leaderboard in your dataset without lifting a finger. - **API access:** Use the [Apify API](https://docs.apify.com/api/v2) or [JavaScript/Python SDK](https://docs.apify.com/sdk) to trigger runs and fetch results programmatically. - **Integrations:** Connect to Google Sheets, Slack, Zapier, or Make directly from the Actor's **Integrations** tab. ### FAQ and support **Is scraping Product Hunt legal?** This Actor accesses publicly available data. Always review [Product Hunt's Terms of Service](https://www.producthunt.com/terms-of-service) and use the data responsibly. Do not store personal data without a legitimate purpose. **The Actor returned fewer results than expected. Why?** Product Hunt may not have published that many products for the selected date. **Need a custom solution?** If you need custom data, a different output format, or higher volume, open a ticket in the **Issues** tab. # Actor input Schema ## `maxResults` (type: `integer`): Maximum number of products to return ## `dateToScrape` (type: `string`): Scrape products launched on this date ## `nDaysAgo` (type: `integer`): Scrape products launched N days ago. 0 = today, 1 = yesterday, etc. ## `scrapeLaunches` (type: `boolean`): Whether to scrape launches for each product ## `scrapeReviews` (type: `boolean`): Whether to scrape reviews for each product ## `outputFormat` (type: `string`): Full returns the raw Product Hunt API response. Simplified returns a clean, flat structure with the most useful fields. ## Actor input object example ```json { "maxResults": 100, "nDaysAgo": 1, "scrapeLaunches": false, "scrapeReviews": false, "outputFormat": "simplified" } ``` # 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("data_alchemist/producthunt-daily-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("data_alchemist/producthunt-daily-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 data_alchemist/producthunt-daily-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=data_alchemist/producthunt-daily-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Product Hunt Daily Scraper *Cheap*", "version": "0.1", "x-build-id": "gZ2WNMGPuDSjSrF1f" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/data_alchemist~producthunt-daily-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-data_alchemist-producthunt-daily-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/data_alchemist~producthunt-daily-scraper/runs": { "post": { "operationId": "runs-sync-data_alchemist-producthunt-daily-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/data_alchemist~producthunt-daily-scraper/run-sync": { "post": { "operationId": "run-sync-data_alchemist-producthunt-daily-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of products to return", "default": 100 }, "dateToScrape": { "title": "Date to Scrape", "type": "string", "description": "Scrape products launched on this date" }, "nDaysAgo": { "title": "N days ago", "minimum": 0, "maximum": 30, "type": "integer", "description": "Scrape products launched N days ago. 0 = today, 1 = yesterday, etc.", "default": 1 }, "scrapeLaunches": { "title": "Scrape launches?", "type": "boolean", "description": "Whether to scrape launches for each product", "default": false }, "scrapeReviews": { "title": "Scrape reviews?", "type": "boolean", "description": "Whether to scrape reviews for each product", "default": false }, "outputFormat": { "title": "Output format", "enum": [ "simplified", "full" ], "type": "string", "description": "Full returns the raw Product Hunt API response. Simplified returns a clean, flat structure with the most useful fields.", "default": "simplified" } } }, "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 } } } } } } } } } } ```