Pricing

Rating

Developer

Actor stats

Categories

Share

Product Hunt Scraper — Apify Actor

👁 Apify Actor
👁 Python

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.

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.

Input parameters

Available collections

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

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

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 =newApifyClient({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}`);
});

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.

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().

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.

License

MIT