This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Create production-ready web scraper code in minutes with AI. Paste URLs and our AI analyzes the page structure, maps selectors, and writes complete scraper code in Python, Node.js, Scrapy, Playwright & more. Free trial available.
We're excited to launch ScrapeOps AI Scraper Generator today.
We built this because creating and maintaining scrapers is still too slow, repetitive, and fragile.
The first scraper usually starts simple: open the page, inspect the HTML, find selectors, write the parser, clean the output, test it, then repeat the same boilerplate again for the next page type.
But in production, the real pain comes later:
• Selectors drift • Fields go missing • Page structures change • JavaScript rendering behaves differently • Output can look correct while quietly being wrong
Most scraping tools either sell a black-box API or a polished happy-path demo. We wanted something more useful for developers: a faster way to get to inspectable scraper code while still keeping control of the workflow.
So we built an AI scraper code generator around supported schemas and real developer stacks.
The workflow is simple:
Give a URL → choose your stack → generate scraper code → AI scores how correctly the scraper ran
Today, the generator supports workflows around structured page types like product details, product search, product categories, and prebuilt scraper examples for popular websites.
You can generate scraper code for Python and Node.js stacks, inspect the generated code, watch the generation progress, and then review an AI-powered scoring breakdown across areas like:
• Data accuracy • Critical fields • Data types • Structure • Completeness
That scoring layer is important. We don't want users to just get a blob of extracted data and assume the scraper worked. The AI checks the output and gives a quality breakdown so you can see how correctly the scraper ran.
During beta, ScrapeOps users generated 3,000+ scrapers with this tool. That helped us improve the schema flow, generated code quality, stack selection, and output scoring experience.
Some use cases we're seeing already:
• Ecommerce product data extraction • Product search/category scraping • Competitive pricing research • Market monitoring • Faster scraper prototyping • Building starter code for data pipelines • Creating reusable scraper templates
This is not positioned as "AI scrapes any URL magically." Scraping is messier than that.
Our goal is more practical: reduce repetitive scraper setup, generate code developers can inspect and modify, and use AI scoring to help users understand whether the scraper output is good enough to trust.
Longer term, we're also thinking about guided fixes, selector drift detection, replayable traces, IDE integrations, and tighter workflows with tools developers already use.
If you've ever had a scraper work perfectly on Monday and quietly break by Friday, this is built for you.
Would LOVE your feedback, especially on:
• The generated code quality • Whether the AI scoring breakdown builds trust • What would make this useful in your real scraping workflow
Since the tool generates code based on schemas, how does it handle high-frequency selector drift detection down the line? If a major e-commerce site updates its class names next week, can we just pass the original schema back through 'ScrapeOps' to patch the broken selectors instantly?
This is a very real pain, scrapers usually look simple until the first selector changes or one page quietly returns bad data. Curious, do most users come to ScrapeOps because they want to build scrapers faster? Or because maintaining existing scrapers is the bigger pain?
Every scraper eventually becomes a monitoring problem pretending to be a parsing problem.
Generated scraper code still scares me slightly from a security perspective. People copy AI output into prod environments way too casually now.
About AI Web Scraper Builder on Product Hunt
“Lovable for Scrapers”
AI Web Scraper Builder was submitted on Product Hunt and earned 22 upvotes and 7 comments, placing #35 on the daily leaderboard. Create production-ready web scraper code in minutes with AI. Paste URLs and our AI analyzes the page structure, maps selectors, and writes complete scraper code in Python, Node.js, Scrapy, Playwright & more. Free trial available.
AI Web Scraper Builder was featured in Developer Tools (514k followers), Artificial Intelligence (471.1k followers) and Business Intelligence (3.6k followers) on Product Hunt. Together, these topics include over 174.1k products, making this a competitive space to launch in.
Who hunted AI Web Scraper Builder?
AI Web Scraper Builder was hunted by Ian Kerins. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how AI Web Scraper Builder stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ian Kerins
Hey Product Hunt 👋
Ian here, founder of ScrapeOps.
We're excited to launch ScrapeOps AI Scraper Generator today.
We built this because creating and maintaining scrapers is still too slow, repetitive, and fragile.
The first scraper usually starts simple: open the page, inspect the HTML, find selectors, write the parser, clean the output, test it, then repeat the same boilerplate again for the next page type.
But in production, the real pain comes later:
• Selectors drift
• Fields go missing
• Page structures change
• JavaScript rendering behaves differently
• Output can look correct while quietly being wrong
Most scraping tools either sell a black-box API or a polished happy-path demo. We wanted something more useful for developers: a faster way to get to inspectable scraper code while still keeping control of the workflow.
So we built an AI scraper code generator around supported schemas and real developer stacks.
The workflow is simple:
Give a URL → choose your stack → generate scraper code → AI scores how correctly the scraper ran
Today, the generator supports workflows around structured page types like product details, product search, product categories, and prebuilt scraper examples for popular websites.
You can generate scraper code for Python and Node.js stacks, inspect the generated code, watch the generation progress, and then review an AI-powered scoring breakdown across areas like:
• Data accuracy
• Critical fields
• Data types
• Structure
• Completeness
That scoring layer is important. We don't want users to just get a blob of extracted data and assume the scraper worked. The AI checks the output and gives a quality breakdown so you can see how correctly the scraper ran.
During beta, ScrapeOps users generated 3,000+ scrapers with this tool. That helped us improve the schema flow, generated code quality, stack selection, and output scoring experience.
Some use cases we're seeing already:
• Ecommerce product data extraction
• Product search/category scraping
• Competitive pricing research
• Market monitoring
• Faster scraper prototyping
• Building starter code for data pipelines
• Creating reusable scraper templates
This is not positioned as "AI scrapes any URL magically." Scraping is messier than that.
Our goal is more practical: reduce repetitive scraper setup, generate code developers can inspect and modify, and use AI scoring to help users understand whether the scraper output is good enough to trust.
Longer term, we're also thinking about guided fixes, selector drift detection, replayable traces, IDE integrations, and tighter workflows with tools developers already use.
If you've ever had a scraper work perfectly on Monday and quietly break by Friday, this is built for you.
Would LOVE your feedback, especially on:
• The generated code quality
• Whether the AI scoring breakdown builds trust
• What would make this useful in your real scraping workflow
Thanks for checking it out 🙌