Use page.locator("#catalog > .product") to find repeated elements in Python Playwright. Narrow to an intended record before reading a single value, or call all() to iterate the matches after the list has finished loading. To build records in the browser, use evaluate_all() with a JavaScript projection.

Finding a match is only part of extraction. In the examples below, .first returned a sponsored product, while an early count() found a loading placeholder. Both operations worked; both selected the wrong data. The runnable example waits for the catalog's ready state and checks every SKU, title, currency, price and link against independently specified records.

Use page.$() to get the first matching element and page.$$() to get all matching elements. When you want data rather than handles, page.$eval() runs a callback on the first match, and page.$$eval() passes all matches to a callback that can return JSON records.

The selector still needs to identify the right records, and the fields must be populated before you read them. This guide demonstrates both problems with a local catalogue: a promotional card that matches an overly broad selector, and product cards that exist before their titles and prices arrive.

You ask Claude Code to fetch a website and summarize it. Instead of useful content, you get back an empty <div> and a bunch of script tags. If you've used Claude Code for web scraping for more than a week, you've hit this wall. Here's why it happens and how to fix it in 30 seconds.

Traditional web scraping pipelines rely heavily on brittle, hand-crafted rules – CSS selectors, XPath queries, and regular expressions – that tend to break as soon as a website’s layout or DOM structure changes. With the rapid evolution of front-end frameworks, A/B testing, and personalized content, these brittle approaches impose high maintenance costs and limit scalability.

Healthcare market mapping increasingly depends on granular, up‑to‑date data on provider networks and drug formularies. Payers, health systems, digital health companies, and analytics firms use these data to understand network adequacy, competitive positioning, product design, and patient access. However, much of this information is not available via clean, official APIs; instead, it resides in heterogeneous, JavaScript-heavy web portals that were built for human browsing, not machine consumption.

Robots.txt has long been the core mechanism for expressing crawl preferences and constraints on the web. Yet, the file format is intentionally simple and underspecified, while real-world websites exhibit complex, context-dependent expectations around crawling, scraping, and automated interaction. In parallel, large language models (LLMs) and agentic AI workflows are transforming how scraping systems reason about and adapt to such expectations.

Large language models (LLMs) are changing how organizations interpret digital signals into meaningful narratives. Instead of manually interpreting search data, social chatter, and web content, analysts can now use LLMs to convert raw, noisy signals into structured insights and strategic recommendations. When combined with web scraping pipelines and tools like Google Trends, this creates a powerful stack for continuous trend detection, interpretation, and communication.

HTTP header–based fingerprinting and bot detection have become core defenses in modern web infrastructures. For anyone building large-scale web crawlers, competitive intelligence systems, or AI-powered data pipelines, understanding and manipulating HTTP headers is often the difference between reliable access and constant blocking.

In 2025, the debate between headless and headful browsers is no longer academic. It sits at the core of how organizations approach web automation, testing, AI agents, and scraping under increasingly aggressive bot-detection regimes. At the same time, AI-driven scraping backbones like ScrapingAnt - which combine headless Chrome clusters, rotating proxies, and CAPTCHA avoidance - have reshaped what “production-ready” scraping looks like.

Web scraping has become a foundational capability for analytics, competitive intelligence, and training data pipelines. Yet the raw output of scraping—HTML, JSON fragments, inconsistent text blobs—is notoriously messy. Normalizing this data into clean, structured, analysis‑ready tables is typically where projects stall: field formats vary, schemas drift, and edge cases proliferate. Traditional approaches rely heavily on regular expressions, handcrafted parsers, and brittle heuristics that quickly devolve into “regex hell.”