ScraperAPI

Welcome to “Ask ScraperAPI”, the weekly series where we tackle your biggest web scraping questions. This week’s question came from our Discord Channel: Before we answer this, join us for some cool scraping discussions: https://lnkd.in/gA6htKNJ

The toughest blockers to bypass keep getting tougher. - Amazon WAF Bot Control. - Cloudflare Bot Management. - DataDome. They’re the big 3 when it comes to protecting websites from scrapers. And they evolve like crazy, what worked yesterday might get you blocked today. We’ve seen devs share stories like: 👉 “I scraped a Cloudflare site with curl_cffi in Python… then Cloudflare rolled out new protection requiring a cf_clearance cookie tied to a rayId and my script died overnight.” Sound familiar? So, we've broken down how each of them works (AWS, Cloudflare, DataDome), and at the end, shared a smarter way to automate scraping against all three. ➡ ️ Swipe through the carousel to see the breakdown.

Welcome to “Ask ScraperAPI”, the weekly series where we tackle your biggest web scraping questions. This week’s question came straight from Reddit: Question: “BeautifulSoup, Selenium, Playwright, or Puppeteer? I’m trying to scrape 10,000–20,000 laptop specs.” Check the answer….

Major publishers such as Reddit, Yahoo, and Medium have launched the Really Simple Licensing (RSL) protocol, a new web standard that enables AI companies to pay for content they scrape from online platforms. Key takeaways: - RSL upgrades the old robots.txt file, allowing publishers to embed machine-readable licensing terms that bill AI companies through models like subscriptions, pay-per-crawl, or pay-per-inference. - This development is led by tech visionaries like RSS co-creator Eckart Walther and Tim O'Reilly, who claim the standard aims to give creators leverage over their content. - RSL Collective, a non-profit coalition, will act as a rights-management clearinghouse, negotiating fees much like ASCAP does for music. - This move comes amid a surge of copyright lawsuits from entities such as The New York Times and Getty Images against major AI labs. ScraperAPI is built for this new reality, ensuring your scraping is compliant and respects publisher policies. What are our thoughts on this? Will the new protocol finally force AI to pay its dues? Let's discuss in the comments! #AI #WebScraping #RSL #DataEthics #Publishing

Developers are supercharging their LLMs with real-time web data using ScraperAPI's MCP Server. The aim is to move beyond an LLM’s limited, pre-trained data and connect AI assistants like Claude to real-time, dynamic, web information. Connecting an LLM to the web is one thing, but then the real challenge is pulling clean data from heavily protected sites, bypassing their anti-bot systems, and delivering it straight into your chat box. ScraperAPI’s MCP server handles it for you through: - Advanced anti-bot bypass systems - A massive rotating proxy network - JavaScript Rendering - Geo-Targeting Premium IP Tiers Full Request Customization, etc. Here’s what you get right out of the box: ✅ A simple setup via pip install and a config file ✅ Built-in proxy rotation, CAPTCHA handling, and JS rendering  ✅ An intuitive, prompt-based approach (start prompts with “scrape” to access the server) ✅ Real-time data returned directly into your Claude chat. Read the full guide on integrating with Claude here: https://lnkd.in/e7NW38zG

If you love scraping reviews and analyzing them with an LLM to make an informed decision, you'll love this new blog. It's a tutorial on how you can scrape Walmart reviews, analyze them with ScraperAPI, VADER, Gemini, and Streamlit! You can read here: https://lnkd.in/g4yB_ddh Happy hacking!

Request failures, at scale, can feel like a mystery costing you time, resources, and data. ScraperAPI’s Error Logs feature alleviates this burden with granular insights into every failed scrape, so that you can troubleshoot issues quickly. Here’s what that means for your debugging workflow: - Get full failure details, including URL, status code, and retries.  - See High/Medium/Low severity labels to attack critical issues first.  - Filter logs by domain, status code, or severity.  - Customize columns and export logs.  - Spot patterns (WAF, rate limits, etc) to stop future failures. Learn more 👉 https://lnkd.in/eeGgzfrX

Your Python scraping script works… until the site uses AWS WAF. AWS WAF isn’t just another firewall. It: - Tracks the whole scraper behavior (IPs, user-agents, headers, and more) - Uses machine learning to flag unusual patterns - Dynamically validates requests So if the site owner has an advanced tool like AWS WAF, the scraper should also fight back with a smarter solution like ScraperAPI. ScraperAPI handles everything behind the scenes — rotating proxies, managing headers, solving captchas — and simply returns the raw HTML. All you do is pass the target URL through a simple API call. Check out the full tutorial here: https://lnkd.in/ed44MzYb

A 98% overall success rate can easily hide that your most important domain is failing 50% of the time or that another is quietly draining your credits. Our new Domain Analytics Dashboard addresses this issue by providing a domain-by-domain breakdown, so you can easily identify and resolve inefficiencies. Here’s how it optimizes your scraping strategy: ✅ You get a full breakdown of requests, success rates, and credits used per domain. ✅ Quickly identify average latency and concurrency for any target site or domain. ✅ Visualize success vs. failure patterns with domain-specific charts. ✅ Isolate data through powerful filters: product type (API, Async), location, domains, or specific parameters. ✅ Customize columns to see only the data you need for your reports. Learn more here 👉 https://lnkd.in/eeGgzfrX