Product Hunt Scraper with Founders & Emails

Slug: fatihtahta/product-hunt-scraper-fast-reliable-4-1k

Overview

Product Hunt Scraper with Founders & Emails collects structured Product Hunt launch records, including product names, taglines, launch URLs, vote and comment counts, rankings, topics, websites, media, launch history, maker profiles, social links, and optional founder email data. Product Hunt is a public discovery platform for new products, startups, and maker-led launches, making its launch data useful for market intelligence, trend analysis, founder discovery, and competitive monitoring. The actor produces repeatable JSON output that can be used in automation workflows, dashboards, enrichment pipelines, and operational reporting. It is designed for dependable recurring data acquisition from selected dates or Product Hunt leaderboard URLs while keeping output structured and consistent across runs. Users can choose the scope, enrichment depth, comment collection, and maximum number of product records to match validation, monitoring, or production data workflows.

Why Use This Actor

• Market research and analytics: build structured extraction workflows for launch activity, topic movement, voting patterns, maker participation, and category-level market intelligence.
• Product and content teams: discover emerging products, launch narratives, maker profiles, and discussion context for editorial planning, product research, and competitive briefings.
• Developers and data engineering pipelines: feed normalized Product Hunt records into downstream systems, warehouses, search indexes, internal tools, and monitoring workflows.
• Lead generation and enrichment teams: create founder-oriented datasets with public maker profiles, websites, social links, and optional email enrichment for responsible outreach workflows.
• Monitoring and competitive tracking teams: schedule repeatable collection for daily, monthly, or custom leaderboard views and compare rankings, engagement, launches, and topic coverage over time.

Common Use Cases

• Market intelligence: monitor Product Hunt launch volume, engagement, rankings, topics, and category movement across selected leaderboard views.
• Lead generation: build targeted prospect lists from public product launches, maker profiles, websites, and optional founder email fields.
• Competitive monitoring: track new launches, product positioning, social presence, and engagement signals in markets relevant to your company.
• Catalog and directory building: populate internal databases with structured public records for products, makers, launch metadata, and media assets.
• Data enrichment: add current public Product Hunt attributes to existing CRM, BI, or analytics datasets.
• Recurring reporting: schedule periodic runs for dashboards, alerts, trend analysis, and launch ecosystem reporting.

Quick Start

1. Choose a Product Hunt launch date or provide one or more direct leaderboard urls.
2. Set a small maxProducts value for the first validation run.
3. Choose whether to include makers, comments, and founder email enrichment.
4. Review the pricing impact of product records, comments, saved founders, and email enrichment before scaling.
5. Run the actor in Apify Console.
6. Inspect the first dataset records to confirm the shape matches your use case.
7. Increase coverage, add targeted leaderboard URLs, or schedule the actor once the output is verified.

Input Parameters

This actor accepts a Product Hunt leaderboard date or direct leaderboard URLs, plus enrichment and collection limit options.

Parameter	Type	Description	Default
date	string	Product Hunt launch date to collect, formatted as YYYY-MM-DD. Future dates are not supported. If no date or urls are provided, the actor collects the current Product Hunt day.	-
scrapeOnlyFeatured	boolean	When enabled, collects Product Hunt featured launches only. When disabled, includes the broader list of products for the selected leaderboard target.	false
urls	array of strings	One or more direct Product Hunt leaderboard URLs. When provided, these take priority over date and can be used for daily, monthly, yearly, or custom leaderboard views.	-
scrapeMakers	boolean	Includes makers associated with each Product Hunt launch, such as names, profile URLs, headlines, avatars, and available social or website links.	true
getComments	boolean	Collects comments associated with each product when available. This can add useful launch discussion context and may increase output volume.	false
enrichWithEmail	boolean	Attempts to add potential founder email addresses to maker records when enough public product and maker information is available.	false
includeRiskyEmails	boolean	Includes additional email matches that may be less certain. Email results are labeled by confidence where available so downstream systems can filter them later.	true
maxProducts	integer	Maximum number of product records to collect across the selected leaderboard target. Use a small value for validation and a larger value for broader coverage.	10000

Choosing Inputs

Use date when you need the Product Hunt launch leaderboard for a specific day, or leave it empty to collect the current Product Hunt day. Use urls when you already know the exact Product Hunt leaderboard pages you want to collect, such as monthly, yearly, or custom leaderboard views; direct URLs take priority over date.

For early validation, set maxProducts to a small value and inspect the dataset before increasing coverage. Enable scrapeOnlyFeatured when you want a more focused dataset around featured launches, and keep it disabled when discovery coverage matters more. Enable scrapeMakers, getComments, and enrichWithEmail only when those fields are useful for your workflow, since richer records can take longer and produce larger datasets.

Pricing

Pricing is based on records and enrichment results saved to the dataset. Product launch and detail records are charged at approximately $2 to $1 per 100 saved product launches, depending on your Apify subscription tier. Comments gathered and saved to the dataset are charged at approximately $2 to $1 per 1,000 saved comments, using the same tier-based pricing model as saved product launches. Founder records discovered from collected products and saved to the dataset are charged at $1 per 1,000 founders. Founder email enrichment is charged at $2 per founder email discovered and saved to the dataset.

For cost control, start with a small maxProducts value, verify the output, and enable getComments or enrichWithEmail only when comment collection or email discovery is required for the run. Pricing may vary by plan or actor listing configuration, so review the pricing shown in Apify Console before running large jobs.

Example Inputs

Daily launch validation

{
  "date": "2026-02-01",
  "scrapeOnlyFeatured": false,
  "scrapeMakers": true,
  "getComments": false,
  "enrichWithEmail": false,
  "maxProducts": 25
}

Direct leaderboard URL collection

{
  "urls": [
    "https://www.producthunt.com/leaderboard/monthly/2026/2"
  ],
  "scrapeOnlyFeatured": false,
  "scrapeMakers": true,
  "getComments": true,
  "enrichWithEmail": false,
  "maxProducts": 100
}

Founder enrichment run

{
  "date": "2026-02-01",
  "scrapeOnlyFeatured": true,
  "scrapeMakers": true,
  "getComments": true,
  "enrichWithEmail": true,
  "includeRiskyEmails": false,
  "maxProducts": 50
}

Output

Output destination

The actor writes results to an Apify dataset as JSON records. The dataset is designed for direct consumption by analytics tools, ETL pipelines, and downstream APIs with minimal post-processing.

When multiple entity types or record shapes exist, this README documents each shape separately based on the provided Example Output.

Record envelope and stable identifiers

Each dataset item is a JSON object with a type field. In the provided output contract, the record type is Product.

The recommended idempotency key is productUrl, because it is the strongest stable top-level identifier available in the example output. If your downstream system also stores launch history, launchHistory.id can be used as a secondary launch-level identifier. For deduplication and upserts, use productUrl as the primary key and update the existing record when later runs return the same product with refreshed engagement, maker, or launch fields. Stable identifiers make records easier to merge, deduplicate, and sync across repeated runs.

Examples

Example: Product (type = "Product")

{
  "type": "Product",
  "productName": "Reavion",
  "productTagline": "Autonomous browser agents for outbound and GTM execution",
  "productUrl": "https://www.producthunt.com/posts/reavion",
  "productThumbnailUrl": "https://ph-files.imgix.net/f7a55d51-1e3c-44fd-924d-e63cf8f5d616.png",
  "votesCount": 151,
  "commentsCount": 26,
  "dailyRank": "6",
  "topics": [
    "SaaS",
    "Artificial Intelligence",
    "Marketing automation"
  ],
  "websiteUrl": "https://reavion.com",
  "followersCount": 157,
  "productDescription": "Reavion is an AI-powered browser that navigates, reads, decides, and acts for you. Automate outbound and go-to-market tasks, save hours on repetitive workflows, and scale growth without extra hires. Designed for founders, marketers, and teams who want smarter, faster, reliable automation.",
  "mediaGallery": [
    "https://ph-files.imgix.net/f566765d-bdc4-40e2-bcfc-3a42e625cd82.png",
    "https://ph-files.imgix.net/d0ceb80f-4aa9-4481-b837-ca1403de923d.png",
    "https://ph-files.imgix.net/1b68bec3-1ec6-4142-a6ac-a20323a689b1.png",
    "https://ph-files.imgix.net/2cc5a575-3190-4555-99d4-fe2a4fbf92b6.png",
    "https://youtu.be/YGcSZAr7NVU"
  ],
  "launchHistory": [
    {
      "id": "1056377",
      "slug": "reavion",
      "name": "Reavion",
      "tagline": "Autonomous browser agents for outbound and GTM execution",
      "launchNumber": null,
      "scheduledAt": null,
      "createdAt": "2026-02-01T00:01:00-08:00",
      "featuredAt": "2026-02-01T00:01:00-08:00",
      "updatedAt": "2026-04-27T22:01:24-07:00",
      "dailyRank": "6",
      "weeklyRank": "51",
      "monthlyRank": "181",
      "commentsCount": 26,
      "latestScore": 151,
      "launchDayScore": 147,
      "featured": null,
      "launchState": null,
      "launchingToday": null,
      "launchedThisWeek": null,
      "badges": []
    }
  ],
  "makerName1": "Helder Perez",
  "makerUsername1": "helderperez",
  "makerHeadline1": "Founder and Software Engineer",
  "isGoldMaker1": false,
  "makerAvatarUrl1": "https://ph-avatars.imgix.net/8560339/0f0de845-59f0-4a97-97d6-f0f7eea202da.png",
  "makerProfileUrl1": "https://www.producthunt.com/@helderperez",
  "maker1twitter": "https://x.com/helderbuilds",
  "maker1linkedin": "https://www.linkedin.com/in/helderperez/",
  "maker1website": "https://www.reavion.com",
  "makerName2": null,
  "makerUsername2": null,
  "makerHeadline2": null,
  "isGoldMaker2": null,
  "makerAvatarUrl2": null,
  "makerProfileUrl2": null,
  "maker2twitter": null,
  "maker2linkedin": null,
  "maker2website": null,
  "makerName3": null,
  "makerUsername3": null,
  "makerHeadline3": null,
  "isGoldMaker3": null,
  "makerAvatarUrl3": null,
  "makerProfileUrl3": null,
  "maker3twitter": null,
  "maker3linkedin": null,
  "maker3website": null,
  "makerEmail1": "helder@reavion.com",
  "makerEmailStatus1": "verified"
}

Field Reference

Product

type (string, required): Record type. In this contract, the value is Product.

productName (string, required): Product name.

productTagline (string, optional): Short Product Hunt tagline.

productUrl (string, required): Public Product Hunt product page URL and recommended idempotency key.

productThumbnailUrl (string, optional): Product thumbnail image URL.

votesCount (integer, optional): Number of votes shown for the product.

commentsCount (integer, optional): Number of comments shown for the product.

dailyRank (string, optional): Product rank for the selected daily leaderboard context.

topics (array of strings, optional): Product Hunt topics associated with the launch.

websiteUrl (string, optional): Public website URL associated with the product.

followersCount (integer, optional): Number of Product Hunt followers shown for the product.

productDescription (string, optional): Product description text.

mediaGallery (array of strings, optional): Product media URLs, such as images or videos.

launchHistory (array of objects, optional): Launch history entries associated with the product.

launchHistory.id (string, optional): Launch-level identifier.

launchHistory.slug (string, optional): Product Hunt launch slug.

launchHistory.name (string, optional): Launch name.

launchHistory.tagline (string, optional): Launch tagline.

launchHistory.launchNumber (integer or null, optional): Launch number when available.

launchHistory.scheduledAt (string or null, optional): Scheduled launch timestamp when available.

launchHistory.createdAt (string, optional): Launch creation timestamp.

launchHistory.featuredAt (string or null, optional): Featured timestamp when available.

launchHistory.updatedAt (string, optional): Last updated timestamp shown for the launch entry.

launchHistory.dailyRank (string, optional): Daily rank for the launch entry.

launchHistory.weeklyRank (string, optional): Weekly rank for the launch entry.

launchHistory.monthlyRank (string, optional): Monthly rank for the launch entry.

launchHistory.commentsCount (integer, optional): Comment count for the launch entry.

launchHistory.latestScore (integer, optional): Latest score shown for the launch entry.

launchHistory.launchDayScore (integer, optional): Score shown for the launch day.

launchHistory.featured (boolean or null, optional): Featured status when available.

launchHistory.launchState (string or null, optional): Launch state when available.

launchHistory.launchingToday (boolean or null, optional): Whether the product is launching today when available.

launchHistory.launchedThisWeek (boolean or null, optional): Whether the product launched this week when available.

launchHistory.badges (array, optional): Badge values associated with the launch entry.

makerName1 / makerName2 / makerName3 (string or null, optional): Maker names for up to three makers represented as convenience fields.

makerUsername1 / makerUsername2 / makerUsername3 (string or null, optional): Product Hunt usernames for the corresponding makers.

makerHeadline1 / makerHeadline2 / makerHeadline3 (string or null, optional): Maker profile headlines.

isGoldMaker1 / isGoldMaker2 / isGoldMaker3 (boolean or null, optional): Product Hunt Gold maker status when available.

makerAvatarUrl1 / makerAvatarUrl2 / makerAvatarUrl3 (string or null, optional): Maker avatar image URLs.

makerProfileUrl1 / makerProfileUrl2 / makerProfileUrl3 (string or null, optional): Public Product Hunt maker profile URLs.

maker1twitter / maker2twitter / maker3twitter (string or null, optional): Public X or Twitter profile URLs for the corresponding makers.

maker1linkedin / maker2linkedin / maker3linkedin (string or null, optional): Public LinkedIn profile URLs for the corresponding makers.

maker1website / maker2website / maker3website (string or null, optional): Public maker website URLs.

makerEmail1 (string, optional): Email address associated with the first maker when email enrichment is enabled and a result is available.

makerEmailStatus1 (string, optional): Confidence or verification status for makerEmail1.

Data Quality, Guarantees, And Handling

• Structured records: results are normalized into predictable JSON objects for downstream use.
• Best-effort extraction: fields may vary by region, session, availability, and Product Hunt interface experiments.
• Optional fields: null-check optional fields in downstream code, especially maker, social, email, media, and launch history attributes.
• Deduplication: use productUrl as the strongest stable top-level key available from the output; use launchHistory.id as a secondary launch-level key where helpful.
• Freshness: results reflect the publicly available data at run time.
• Repeated runs: use the recommended idempotency key when syncing data into warehouses, CRMs, or search indexes.

Tips For Best Results

• Start with a small maxProducts value to validate the output shape before scaling up.
• Use date for daily launch monitoring and urls for specific leaderboard views.
• Use one date or a focused set of leaderboard URLs per run when you need cleaner segmentation.
• Leave scrapeOnlyFeatured disabled when the goal is broader launch discovery.
• Enable getComments only when launch discussion and engagement context are part of the analysis.
• Enable enrichWithEmail for founder outreach workflows, and use includeRiskyEmails based on your tolerance for broader versus stricter email matching.
• Schedule recurring runs for monitoring workflows and use productUrl for deduplication over time.

How to Run on Apify

1. Open the actor in Apify Console.
2. Configure the available input fields for the target scope.
3. Set the maximum number of outputs to collect with maxProducts.
4. Click Start and wait for the run to finish.
5. Open the dataset and inspect the first records.
6. Download results in JSON, CSV, Excel, or other supported formats.

Scheduling & Automation

Scheduling

Automated Data Collection

You can schedule runs to keep Product Hunt launch, maker, and enrichment datasets fresh without manual collection. Scheduled runs are useful for daily launch monitoring, recurring market intelligence, and repeatable CRM or warehouse syncs.

• Navigate to Schedules in Apify Console
• Create a new schedule, such as daily, weekly, or custom cron
• Configure input parameters
• Enable notifications for run completion
• Add webhooks for automated processing

Integration Options

• CRM enrichment: sync public product, maker, website, social, and email fields into account or lead records.
• Google Sheets or Airtable: review launch lists, founder contacts, topics, and engagement metrics in lightweight operating workflows.
• Webhooks: trigger validation, notification, or ingestion workflows after each completed run.
• BI dashboards: monitor Product Hunt launch activity, ranking movement, votes, comments, topics, and founder coverage over time.
• Data enrichment pipelines: append Product Hunt context to existing company, product, founder, or market intelligence datasets.
• Slack, Discord, or email alerts: notify teams when new launches or target leaderboard records are collected.

Export Formats And Downstream Use

Apify datasets can be exported or consumed by downstream systems for operational reporting, analytics, enrichment, and automated ingestion.

• JSON: for APIs, applications, and data pipelines
• CSV or Excel: for spreadsheet workflows and manual review
• API access: for automated ingestion into internal systems
• BI and warehouses: for reporting, dashboards, and historical analysis

Performance

Estimated run times:

• Small runs (< 1,000 outputs): ~3-5 minutes
• Medium runs (1,000-5,000 outputs): ~5-15 minutes
• Large runs (5,000+ outputs): ~15-30 minutes

Execution time varies based on filters, result volume, and how much information is returned per record. Highly filtered runs can finish faster, while broad discovery or detail-rich records with makers, comments, and email enrichment may take longer.

Limitations

• Availability depends on what Product Hunt publicly exposes at run time.
• Some optional fields may be missing on sparse records or records with limited public profile information.
• Very broad leaderboard collection may take longer or require higher maxProducts values.
• Target-side changes can affect field availability, naming, or visibility.
• Regional, account, or availability differences may change visible results.
• Email enrichment is best-effort and should be reviewed before use in outreach workflows.

Troubleshooting

• No results returned: check the selected date, direct urls, and whether Product Hunt has matching public leaderboard records for the target scope.
• Fewer results than expected: broaden the scope, raise maxProducts, disable scrapeOnlyFeatured, or verify that the selected target contains enough matching records.
• Some fields are empty: optional fields depend on what each product, launch, maker, or profile publicly provides.
• Run takes longer than expected: reduce scope, lower maxProducts for validation, or split broad collection into smaller runs.
• Output changed: compare the current output with the field reference and report a small sample if support is needed.

FAQ

What data does this actor collect?

It collects public Product Hunt launch records, including product details, rankings, votes, comments count, topics, websites, media, launch history, maker profile fields, social links, and optional founder email data.

Can I filter by location, category, date, price, or other criteria?

The supported scope controls are date, direct leaderboard urls, scrapeOnlyFeatured, and maxProducts. The schema does not include location, price, or category filter fields.

Why did I receive fewer results than my limit?

maxProducts is an upper limit, not a guaranteed count. The selected date or leaderboard URL may contain fewer public records, or the enabled scope may be narrower than the limit.

Can I schedule recurring runs?

Yes. Use Apify schedules to run the actor on a daily, weekly, or custom cadence for recurring monitoring and reporting.

How do I avoid duplicates across runs?

Use productUrl as the recommended idempotency key when storing or syncing results. If you need launch-level tracking, also store launchHistory.id when present.

Can I export the data to CSV, Excel, or JSON?

Yes. Apify datasets can be downloaded in JSON, CSV, Excel, and other supported formats.

How does pricing work?

Pricing is based on saved outputs. Product launch and detail records are charged at approximately $2 to $1 per 100 saved launches depending on your Apify subscription tier. Comments gathered and saved to the dataset use the same tier-based rate, approximately $2 to $1 per 1,000 saved comments. Saved founder records are charged at $1 per 1,000 founders, and founder email enrichment is charged at $2 per discovered email saved to the dataset.

Does this actor collect private data?

The actor is intended to collect publicly available Product Hunt product, launch, maker, and related enrichment information. Users are responsible for using the data lawfully and responsibly.

What should I include when reporting an issue?

Include the input used, with sensitive values redacted, the run ID, expected versus actual behavior, and a small output sample if it helps illustrate the issue.

Compliance & Ethics

Responsible Data Collection

This actor collects publicly available Product Hunt launch, product, maker, and contact enrichment information from https://www.producthunt.com for legitimate business purposes, including:

• Startup ecosystem research and market analysis
• Founder and product discovery for responsible business development
• Competitive and trend monitoring for operational reporting

This section is informational and not legal advice. Users are responsible for ensuring that their use of collected data complies with applicable laws, regulations, platform terms, and internal policies.

Best Practices

• Use collected data in accordance with applicable laws, regulations, and the target site's terms
• Respect individual privacy and personal information
• Use data responsibly and avoid disruptive or excessive collection
• Do not use this actor for spamming, harassment, or other harmful purposes
• Follow relevant data protection requirements where applicable, such as GDPR and CCPA

Support

For help, use the actor page or Issues section. Include the input used with sensitive values redacted, the run ID, expected versus actual behavior, and an optional small output sample so the issue can be reviewed efficiently.