Product Hunt Scraper — Apify Actor

Scrape Product Hunt for daily and top products, including upvotes, comment counts, descriptions, maker information, tags, and more. This actor is perfect for:

• Competitive analysis — track new product launches in your niche.
• Market research — discover trending products and categories.
• Founder intelligence — build lead lists of makers and their projects.
• Content curation — aggregate Product Hunt data for newsletters or dashboards.

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How it works

1. The actor sends a standard HTTP GET request to the Product Hunt collection page of your choice (e.g. https://www.producthunt.com/tech).
2. Product Hunt is built with Next.js, so the initial page state is serialised into a <script id="__NEXT_DATA__"> tag.
3. The actor parses this JSON payload to extract products without needing a headless browser — making it fast and resource-efficient.
4. Each product is normalised, filtered (by minimum upvotes), limited, and pushed to the Apify dataset.

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Input parameters

Field	Type	Default	Description
date	string	today	The date to scrape (YYYY-MM-DD). Used for reference; Product Hunt often returns currently featured products.
collection	enum	tech	Which Product Hunt collection to scrape.
min_votes	int	0	Minimum number of upvotes — products below this threshold are dropped.
limit	int	25	Maximum number of products to return (capped at 100).

Available collections

• tech
• games
• podcasts
• books
• developer-tools
• artificial-intelligence
• all (home page / trending)

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Example output

Each item in the dataset is a JSON object like this:

{
  "name": "Not Diamond",
  "tagline": "The AI model router that improves every LLM output",
  "description": "Not Diamond automatically routes each query to the best LLM, saving costs and improving quality.",
  "url": "https://www.producthunt.com/posts/not-diamond",
  "slug": "not-diamond",
  "upvotes": 987,
  "comments_count": 42,
  "maker_name": "Tommy Sun",
  "thumbnail_url": "https://ph-files.imgix.net/...",
  "website_url": "https://notdiamond.ai",
  "tags": ["artificial-intelligence", "developer-tools", "open-source"],
  "featured_at": "2025-05-26T07:01:00Z",
  "id": "123456"
}

Field descriptions

Field	Type	Description
name	string	Product name
tagline	string	Short one-line description
description	string	Full description (often same as tagline)
url	string	Product Hunt post URL
slug	string	URL slug
upvotes	integer	Number of upvotes ▲
comments_count	integer	Number of comments
maker_name	string	Name of the first listed maker
thumbnail_url	string	Thumbnail image URL
website_url	string	Product's external website
tags	array	List of topic/category tags
featured_at	string	ISO 8601 timestamp when featured
id	string	Unique Product Hunt ID

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Example usage (Apify client)

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run_input = {
    "collection": "tech",
    "min_votes": 50,
    "limit": 10,
    "date": "2025-05-26",
}

run = client.actor("your-actor-id").call(run_input=run_input)
dataset_items = client.dataset(run["defaultDatasetId"]).list_items().items

for item in dataset_items:
    print(f"{item['name']} — ▲ {item['upvotes']} by {item['maker_name']}")

JavaScript / TypeScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const input = {
  collection: 'developer-tools',
  min_votes: 100,
  limit: 5,
};

const run = await client.actor('your-actor-id').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

items.forEach(item => {
  console.log(`${item.name} — ▲ ${item.upvotes}`);
});

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Local development

# 1. Clone the repository
git clone <repo-url> product-hunt-scraper
cd product-hunt-scraper

# 2. Create a virtual environment
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the actor locally
python -m src

Note: When running locally without the Apify platform, Actor.push_data() and Actor.get_input() require either the Apify CLI (apify run) or environment variables. For quick tests, you can set APIFY_LOCAL_EMULATION=true or use the Apify CLI.

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Technical details

• Language: Python 3.14
• HTTP client: httpx (async)
• HTML parsing: BeautifulSoup is declared as a dependency, but the primary extraction path uses the __NEXT_DATA__ JSON payload — no full DOM parsing needed.
• Scraping approach: Static HTTP request with a real browser User-Agent. No headless browser (Puppeteer/Playwright) required.
• Dataset: Each product is pushed individually via Actor.push_data().

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Limitations

• Product Hunt rate-limits aggressive requests. Adding randomised delays between multiple runs is recommended.
• The __NEXT_DATA__ JSON structure may change when Product Hunt updates their frontend. The actor includes fallback logic to find product data by key name, but occasional breakage is possible.
• Only publicly visible data on collection pages is extracted. Authentication-gated data (e.g. private follow feeds) is not supported.

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License

MIT