What does the featured flag do?

Set featured to true to return only featured launches.

Do I pay for lookups that come back empty?

No. You are charged per result, so a request that finds nothing costs nothing. Testing this against a messy list is free to get wrong.

What does a request cost?

1 point per returned record, which is $1 per 1,000, and only records that come back are charged. Points come out of your monthly plan allowance.

Startup & Funding Data

Product Hunt Leaderboards API and MCP — Ranked Launches by Day, Week, Month or Year

Send a period and a date and get that leaderboard back as JSON, one record per launch in rank order: rank, name, tagline, votes, comments, topics, product slug and link, and when it was featured. The date format follows the period: 2024/1/15 for daily, 2024/3 for weekly or monthly, 2024 for yearly.

Run it in the app Read the API docs

You send: a period (daily, weekly, monthly or yearly) and a date in the matching format, such as 2024/1/15 for a day, plus an optional featured-only flag and a max_items cap (up to 250)
You get back: the ranked launches for that period with rank, name, tagline, votes, comments, topics and product link
Coverage: Product Hunt records with a public page.
Price: 1 point per returned record — $1 per 1,000; Billed per record actually returned, capped by max_items. A request that returns nothing costs nothing.

Comparison

Ways to get this data

Three approaches teams use today, and where each one runs into trouble.

Approach	What it takes	Cost	Trade-off
Product Hunt directly	Open the page on Product Hunt and scroll through the list	Free, and slow	Fine for one record, useless across a list, and the layout differs on every platform
Building a scraper	Write it, host it, and repair it when Product Hunt changes its pages	Engineering time	A maintenance commitment for something that isn't your product
NeuralVerge	One request, the same shape as every other source in the catalog	$1 per 1,000, misses not billed	Returns what Product Hunt publishes at request time. Other platforms are separate sources with the same request format

Response

What you get back

Every field in the example response below.

Field	Type	Description
`session_id`	string	Id of this run, for tracing.
`kind`	string	Source type of the response.
`period`	string	The period used for the run; daily in the sample.
`date`	string	The date you sent.
`max_items`	number	The cap on returned records, as run.
`start_page`	number	Page the run started from.
`partial`	boolean	True when the run stopped before reaching max_items.
`human`	string	Readable summary: the query, how many results came back and how many were found.
`total_points`	number	Points charged for the run.
`machine.total_items`	number	Number of launches returned in this response.
`machine.total_found`	null	Total matches reported by the platform, when it reports one. Null in the sample.
`machine.first_item.id`	string	Launch slug.
`machine.first_item.name`	string	Launch name.
`machine.first_item.url`	string	Link to the launch page.
`machine.first_item.alias`	string	Launch slug as Product Hunt uses it.
`machine.first_item.launch_id`	string	Numeric launch id, as a string.
`machine.first_item.rank`	number	Position on the leaderboard.
`machine.first_item.tagline`	string	One-line tagline.
`machine.first_item.votes_count`	number	Votes on the launch.
`machine.first_item.comments_count`	number	Comments on the launch.
`machine.first_item.topics`	array	Topics, each with name, slug and url.
`machine.first_item.image`	string	Launch image URL.
`machine.first_item.product_slug`	string	Slug of the product behind the launch.
`machine.first_item.product_url`	string	Link to the product page.
`machine.first_item.featured_at`	string	When the launch was featured, ISO 8601 with offset.
`machine.items[]`	array	Launches in rank order, same shape as first_item.
`machine.page_count`	number	Pages read for the run.
`machine.blocked`	boolean	Whether the platform refused the request.
`machine.proxy_traffic_bytes`	number	Size of the data transferred for the run, in bytes.

Response

Example response

A real response, as returned.

{
  "session_id": "11111111-1111-1111-1111-111111111111",
  "kind": "producthunt_leaderboards",
  "period": "daily",
  "date": "2024/1/15",
  "max_items": 2,
  "start_page": 1,
  "partial": false,
  "human": "# Product Hunt — leaderboards\n\n- **Query:** period=daily, date=2024/1/15\n- **Results:** 2\n- **Proxy traffic:** 31.4 KB",
  "machine": {
    "total_items": 2,
    "first_item": {
      "id": "ai-picasso-ai-dance",
      "name": "AI Picasso - AI dance",
      "url": "https://www.producthunt.com/posts/ai-picasso-ai-dance",
      "alias": "ai-picasso-ai-dance",
      "launch_id": "434214",
      "rank": 1,
      "tagline": "Full body AI dance from photos",
      "votes_count": 314,
      "comments_count": 275,
      "topics": [
        {
          "name": "Android",
          "slug": "android",
          "url": "https://www.producthunt.com/topics/android"
        },
        {
          "name": "iOS",
          "slug": "ios",
          "url": "https://www.producthunt.com/topics/ios"
        }
      ],
      "image": "https://ph-files.imgix.net/6fac80c0-fafa-4033-9374-9c7d1e30437d.png?auto=format&fit=crop&w=128&h=128",
      "product_slug": "ai-picasso",
      "product_url": "https://www.producthunt.com/products/ai-picasso",
      "featured_at": "2024-01-15T00:23:23-08:00"
    },
    "items": [
      {
        "id": "ai-picasso-ai-dance",
        "name": "AI Picasso - AI dance",
        "url": "https://www.producthunt.com/posts/ai-picasso-ai-dance",
        "alias": "ai-picasso-ai-dance",
        "launch_id": "434214",
        "rank": 1,
        "tagline": "Full body AI dance from photos",
        "votes_count": 314,
        "comments_count": 275,
        "topics": [
          {
            "name": "Android",
            "slug": "android",
            "url": "https://www.producthunt.com/topics/android"
          },
          {
            "name": "iOS",
            "slug": "ios",
            "url": "https://www.producthunt.com/topics/ios"
          }
        ],
        "image": "https://ph-files.imgix.net/6fac80c0-fafa-4033-9374-9c7d1e30437d.png?auto=format&fit=crop&w=128&h=128",
        "product_slug": "ai-picasso",
        "product_url": "https://www.producthunt.com/products/ai-picasso",
        "featured_at": "2024-01-15T00:23:23-08:00"
      },
      {
        "id": "team-analytics-by-codemonk-ai",
        "name": "Team Analytics by CodeMonk.ai",
        "url": "https://www.producthunt.com/posts/team-analytics-by-codemonk-ai",
        "alias": "team-analytics-by-codemonk-ai",
        "launch_id": "434333",
        "rank": 2,
        "tagline": "Uncover gaps in your tech team and hire the best fit with AI",
        "votes_count": 279,
        "comments_count": 264,
        "topics": [
          {
            "name": "Hiring",
            "slug": "hiring",
            "url": "https://www.producthunt.com/topics/hiring"
          },
          {
            "name": "Freelance",
            "slug": "freelance",
            "url": "https://www.producthunt.com/topics/freelance"
          }
        ],
        "image": "https://ph-files.imgix.net/34c51888-34fb-4b51-b6e9-9ec03661319d.png?auto=format&fit=crop&w=128&h=128",
        "product_slug": "codemonk",
        "product_url": "https://www.producthunt.com/products/codemonk",
        "featured_at": "2024-01-15T03:59:30-08:00"
      }
    ],
    "total_found": null,
    "page_count": 1,
    "blocked": false,
    "proxy_traffic_bytes": 32196
  },
  "total_points": 2
}

Integration

How to run it

The same lookup works from the app, the API, or as a tool an agent can call mid-task.

Results come back in the same envelope as every other source: a session id, a readable summary, the structured result and the points charged. max_items caps how many records are returned and billed.

curl -X POST https://api.neuralverge.ai/functions/v1/run-producthunt-leaderboards \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"date":"2024/1/15","max_items":2}'

Use cases

What teams use it for

Where this field shows up in an actual workflow.

Launch-day benchmarks

See what votes and comment counts it took to place on a given day.

Historical trend reports

Pull the top launches of each month or year to track which product types appear.

Category and topic scans

Read a period's list and filter by topic to find launches in your space.

Coverage & limits

Coverage, freshness and limits

What this source covers, how fresh it is, and where it stops.

Coverage

Product Hunt records with a public page.

Freshness

Read at request time, so the answer reflects the platform as it stands when you ask.

Handling

This source returns information Product Hunt shows publicly. Every response carries the session id you can trace.

What it will not do

—The date format must match the period (day, week or month, or year); a mismatched date will not resolve.
—The source returns the leaderboard as the platform shows it for the period, with no extra scoring.
—total_found is null in the sample, so the total length of a leaderboard is not reported.

FAQ

Questions, answered

Daily 2024/1/15, weekly 2024/3, monthly 2024/3, yearly 2024.

Related sources

Product Hunt launch

One launch with votes, ranks and product.

Product Hunt launches search

Search launches by keyword.

Product Hunt topic products

Products listed under one topic.

All startup & funding data

Startup directories, funding databases, launch platforms and investor data.

Try product hunt leaderboards on your own data

One request format across every source in the catalog.

Get started