The Modern Guide to AI-Powered Web Scraping and Automation
The Modern Guide to AI-Powered Web Scraping and Automation
Hands-On Strategies for Stealth, Structured, and AI-Driven Web Scraping
About the Book
Unlock the full potential of web scraping with modern, AI-driven techniques and automation. The "Modern Web Scraping using AI and automation" is a practical guide that teaches you how to build robust, scalable, and intelligent scraping workflows using Python, Playwright, undetected-chromedriver, and the Chrome DevTools MCP. Learn how to extract structured data from complex, JavaScript-heavy websites, handle anti-bot defenses, and integrate Large Language Models (LLMs) for automated content understanding, summarization, and semantic reasoning.
Packed with hands-on examples, this book covers proxy strategies, CAPTCHA handling, network-level scraping, and serverless deployment, providing you with the tools to transform raw web content into actionable insights. Whether you’re a software engineer, data scientist, or AI enthusiast, this guide shows how to build ethical, compliant, and future-proof scraping pipelines that power analytics, machine learning, and AI applications.
Keywords: web scraping, AI-powered scraping, Playwright, browser automation, undetected-chromedriver, Chrome DevTools MCP, LLM scraping, Python scraping, data extraction, automation pipelines, ethical web scraping
About the Author
Table of Contents
- Preface
- Legal Disclaimer
- Introduction
- Chapter 1: Legacy Approach vs Modern Approach
- Legacy Example: requests + BeautifulSoup
- Modern Approach: Playwright
- Stealth Browsers and undetected-chromedriver
- Trade-offs and operational risks
- Summary
- Chapter 2: Building Robust Foundations with Modern Tooling
- Choosing the right browser automation stack
- Why Playwright and undetected-chromedriver outperform Selenium
- Setting Up Playwright for Stealth and Bypassing Detection
- Network-level scraping
- Common CDP patterns in scrapers
- Operational considerations
- Integrating Chrome DevTools MCP for Stable, Standardized Scraping Control
- Handling Modern Defenses
- AI-Assisted CAPTCHA Solvers
- Chapter 3: Structured Data Extraction and Smart Parsing
- DOM parsing at scale
- Parsing Dynamic HTML with
parsel,lxml, and Playwright Selectors
- Dealing with Shadow DOM
- Handling Infinite Scroll
- Schema Discovery and Auto-Field Extraction
- Using ScrapeGraph for LLM-driven Schema Inference
- Text and Visual Extraction
- Extracting text, image attributes, and video metadata
- Data Cleaning and Structuring
- Chapter 4: Integrating LLMs into the Scraping Workflow
- LLMs as Scraping Assistants
- LLMs for Summarization and DOM Navigation
- AI-Native Scraping Frameworks
- Semantic Post-Processing
- Chapter 5: Automation Pipelines and Scaling Strategies
- Job Orchestration and Scheduling
- Parallel Scraping
- Async Scraping
- Managing Concurrency and Rate-Limiting
- Storage and Integration
- Monitoring and Self-Healing
- Chapter 6: Ethical, Compliant, and Future-Safe Scraping
- Legal Boundaries and Compliance
- The Future of Web Scraping
- Emerging Trends
- Key Tools Shaping the Future
- Closing Reflection
- Glossary
- Resources and links
- Browser Automation & Scraping Frameworks
- AI-Native Scraping Tools
- LLMs and AI Tools
- Data Processing & Storage
- OCR and Visual Extraction
- Job Scheduling & Orchestration
- Monitoring & Observability
- Learning Resources & Documentation
- Miscellaneous Tools
- Additional GitHub Resources for AI and Web Scraping
- AI-Driven Scraping Repositories
- Awesome Lists and Curated Resources
- Preface
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