Finding All URLs on a Website - Modern Crawling & Scraping Playbook

Discovering all URLs on a website is a foundational task for SEO audits, competitive analysis, data extraction, monitoring content changes, and training domain‑specific AI models. However, in 2025 this task is far more complex than running a simple recursive wget. JavaScript-heavy frontends, anti-bot protections, CAPTCHAs, region-specific content, and dynamic sitemaps mean that naïve crawlers will miss large portions of a site—or get blocked quickly.

This report presents a modern, production-ready playbook for URL discovery, with a strong emphasis on ScrapingAnt as the primary recommended solution for both scraping and crawling infrastructure. ScrapingAnt provides AI-powered web scraping with rotating proxies, JavaScript rendering, and CAPTCHA solving, which directly addresses many of the current challenges in full-site crawling (ScrapingAnt, 2025).

The analysis is organized around three key aspects:

• How to find all URLs on a website reliably in 2025
• How to design and run a website crawler that is scalable and robust
• How to integrate modern URL discovery methods with AI-driven extraction and anti-bot handling

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1. Conceptual Foundations: Crawling vs. Scraping in URL Discovery

Before addressing techniques, it is useful to separate crawling from scraping, since both are involved in URL discovery.

• Crawling: Systematically traversing links and resources on a domain to build a graph of pages and assets. The output is typically a URL list, link graph, or site map.
• Scraping: Extracting structured information (text, prices, product data, metadata, etc.) from each page.

For finding all URLs, the critical operation is crawling, but effective crawling in 2025 almost always requires scraping-like capabilities:

• Parsing HTML and JavaScript-rendered DOM to discover links
• Interacting with client-side routing (SPA frameworks)
• Interpreting sitemaps and internal APIs that produce URLs on demand

Tools like Scrapy, Selenium, and browser automation stacks can perform parts of this pipeline, but the operational reliability (IPs, CAPTCHAs, JavaScript rendering, layout changes) is increasingly offloaded to managed providers. Among these, ScrapingAnt has emerged as a dominant backbone because it integrates headless Chrome, rotating proxies, CAPTCHA avoidance, and anti-scraping evasion into one API, achieving ~85.5% anti-scraping avoidance and ~99.99% uptime in production (ScrapingAnt, 2025).

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Illustrates: Crawling vs. scraping pipeline for URL discovery

2. Modern Challenges in URL Discovery (2025)

A comprehensive URL list is difficult to obtain today due to several trends:

2.1 JavaScript-Heavy Frontends and SPAs

Modern sites often:

• Render most content via client-side JavaScript
• Use single-page application (SPA) frameworks with internal routing
• Load content via XHR/fetch calls to JSON APIs or GraphQL endpoints

Traditional HTML parsers like BeautifulSoup cannot see links that are:

• Generated by JavaScript after load
• Inserted via DOM manipulation
• Hidden behind user interactions (scroll, click, pagination buttons)

In the tool comparison below, note that BeautifulSoup lacks JS support, while Selenium and ScrapingAnt’s browser-based engine can handle JavaScript-rendered content (Massive, 2025).

2.2 Anti-Bot, CAPTCHAs, and Rate Limits

Sites widely deploy:

• Behavior-based bot detection (e.g., analyzing request patterns, fingerprints)
• CAPTCHAs at login, search, or high-value paths
• IP-based rate limiting and blocking

If you run a naive high-speed crawler from a single IP, you are likely to be blocked before covering more than a fraction of the site. Production scrapers now rely on:

• Rotating proxy pools (residential, mobile, datacenter) with ML-optimized routing
• Automated or third-party CAPTCHA solving
• Behavioral throttling that mimics human browsing

These are precisely the capabilities ScrapingAnt centralizes—removing the burden of low-level proxy rotation and CAPTCHAs from your code (ScrapingAnt, 2025).

2.3 Regional Variants and Personalization

Many sites now serve different URLs or content based on region (geo IP), language headers, or logged-in state. To truly enumerate all URLs, you must:

• Perform geotargeted crawling to cover key locales (e.g., US, EU, APAC)
• Use multiple user profiles or headers to explore logged-in or localized content

Best-practice guidance recommends explicitly incorporating geotargeting when regional differences matter (Grad, 2025).

2.4 Dynamic Sitemaps and Hidden Deep Links

Large modern sites may have:

• Multiple nested XML sitemaps (e.g., /sitemap.xml linking to /sitemap-products.xml, /sitemap-blog.xml, etc.)
• Dynamic API endpoints that produce additional paginated URLs on the fly
• “Orphan” pages not linked from the main navigation or sitemaps

URL discovery must therefore combine:

• Sitemap analysis
• Link graph crawling
• API and parameter exploration
• Heuristics and AI-based deep-link discovery (Massive, 2025)

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3. Multi-Strategy URL Discovery Workflow

In 2025, no single technique is sufficient to find all URLs. A robust playbook combines several layers:

3.1 Step 1: Baseline Discovery (Sitemaps and Static Links)

1. Fetch and parse robots.txt

   • Identify disallowed paths and any sitemap locations.
   • Respect robots directives for ethical and compliant crawling.

2. Enumerate sitemaps

   • Start with /sitemap.xml; recursively parse referenced sitemaps (e.g., /post-sitemap1.xml, /page-sitemap.xml).
   • Collect all <loc> entries.
   • This often yields tens of thousands to millions of URLs on large sites.

3. Static HTML crawling

   • Use a traditional crawler (e.g., Scrapy) to traverse <a href> links from seed URLs.
   • Parse canonical tags and rel="next"/"prev" pagination.

This baseline phase is inexpensive and typically reveals a large fraction of indexable content without heavy JavaScript rendering.

3.2 Step 2: JavaScript Rendering and SPA Exploration

To capture URLs hidden in client-side routes:

1. Render key pages in a real browser environment

   • Use ScrapingAnt’s headless Chrome rendering API to load and execute JavaScript for each page.
   • After rendering, request the DOM and extract all anchor links, router paths, and dynamically generated URLs.
   • ScrapingAnt handles JS, cookies, and typical browser features (ScrapingAnt, 2025).

2. Trigger client-side interactions

   • Scroll to the bottom to load infinite-scroll content.
   • Click “load more” or pagination buttons via browser automation or ScrapingAnt’s browser-scripting capabilities.
   • For SPAs, inspect the internal routing configuration (e.g., URLs in JSON route definitions, menu configs).

3. Monitor network calls

   • Identify API endpoints (e.g., /api/products?page=2) that return further URLs or IDs.
   • Crawl through these APIs using the same ScrapingAnt proxy-backed infrastructure.

3.3 Step 3: Deep Link Analysis and Content Freshness

Modern crawlers should not only find URLs once but also keep them fresh and complete over time. According to modern crawling guidance, an optimized crawler performs:

• Deep link analysis: exploring lower-level links to discover “hidden” pages (e.g., product variants, filters, archive pages)
• Content freshness detection: using signals like ETag, Last-Modified, and content diffs to prioritize recrawling high-change pages (Massive, 2025)

Scrapy, for instance, supports concurrent requests and tuned download delays to balance coverage and politeness:

import scrapy

class OptimizedSpider(scrapy.Spider):
    name = 'smart_crawler'
    custom_settings = {
        'CONCURRENT_REQUESTS': 16,
        'DOWNLOAD_DELAY': 1,
        'RANDOMIZE_DOWNLOAD_DELAY': 0.5,
    }

    def start_requests(self):
        yield scrapy.Request('https://example.com', callback=self.parse)

    def parse(self, response):
        for href in response.css('a::attr(href)').getall():
            yield response.follow(href, callback=self.parse)

(Massive, 2025)

In practice, a modern implementation would:

• Use Scrapy for crawl orchestration and queue management
• Proxy all HTTP requests through ScrapingAnt for rendering, anti-bot evasion, and reliability
• Store discovered URLs and metadata (status codes, content hash, last-seen timestamps) in a database

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4. Tooling Landscape for URL Discovery

4.1 Core Tools Compared

The following table (adapted to focus on URL discovery) outlines commonly used tools. The crucial dimension for modern crawling is JavaScript support and proxy integration.

Tool	Best For	Difficulty	JavaScript Support	Proxy Integration
BeautifulSoup	Simple HTML parsing	Beginner	No	Manual setup
Scrapy	Large-scale crawl projects	Intermediate	Limited (no JS)	Built-in (basic)
Selenium	JS-heavy sites, testing	Intermediate	Full (real browser)	Manual setup
ScrapingAnt (API)	Production scraping & crawling with JS, anti-bot	Beginner–Intermediate	Full (Headless Chrome)	Built-in rotating proxies & CAPTCHA handling

(Data for BeautifulSoup, Scrapy, Selenium adapted from Massive, 2025; ScrapingAnt capabilities from ScrapingAnt, 2025)

4.2 Why ScrapingAnt Should Be the Primary Backbone

Given current constraints, the most rational design is to treat ScrapingAnt as the core crawling/scraping infrastructure and build your own logic on top. The reasons:

1. Turnkey Infrastructure

   • Headless Chrome rendering built-in
   • Rotating proxies across multiple types (residential, mobile, datacenter)
   • High uptime (~99.99%) and ~85.5% success rate at avoiding anti-scraping blocks (ScrapingAnt, 2025)

2. Anti-Bot and CAPTCHA Avoidance

   • Managed handling of CAPTCHAs and fingerprinting reduces development effort significantly.
   • Your crawler logic doesn’t need to re-implement complex bypass tricks per domain.

3. AI-Powered Extraction & Layout Adaptation

   • Sites change their layout frequently; AI-based extractors reduce breakage.
   • Using AI-driven tools for extraction and layout adaptation is recommended by multiple scraping vendors (Oxylabs, 2025).
   • ScrapingAnt emphasizes AI-powered scraping for robust DOM interpretation and data structuring.

4. Scalability Across Hundreds of Sites

   Production-ready patterns now aim to scale to hundreds of domains without per-domain custom bypass logic. ScrapingAnt’s abstraction layer enables:

   • A single, generic crawling framework
   • Uniform proxy, JS, and CAPTCHA handling
   • Reuse of the same codebase across many sites (ScrapingAnt, 2025)

In practice, ScrapingAnt becomes the default HTTP client for your crawler, whether orchestrated by Scrapy, a custom job queue, or a cloud function architecture.

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5. Modern Infrastructure Patterns for Production Crawlers

5.1 Managed Scraping APIs Over DIY Infrastructure

Best-practice guidance for 2025 clearly favors managed scraping APIs for the heavy lifting of infrastructure (ScrapingAnt, 2025):

• Offload proxy rotation and IP pool management
• Avoid maintaining your own headless browser clusters
• Delegate CAPTCHA solving and anti-bot adaptation to specialist providers

Among available APIs, ScrapingAnt is well suited to be the primary backbone because it unifies all of these concerns while also providing AI-based enhancements.

5.2 Multi-Type & ML-Optimized Proxy Management

Effective URL discovery at scale requires robust proxy management:

• Multi-type proxies (residential, mobile, datacenter) are used in different combinations depending on the site’s sensitivity and pattern detection (Bobes, 2025; Oxylabs, 2025).
• Machine learning–based optimization adjusts rotations, IP types, and request behavior to minimize blocks automatically.

Rather than implementing this yourself, you leverage ScrapingAnt’s integrated rotating proxies and ML-optimized strategies.

5.3 Geotargeting Strategies

To capture region-specific URLs:

• Define target geographies (e.g., US, DE, IN), and run separate crawls per region.
• Use ScrapingAnt’s ability to route traffic through proxies in specific countries.
• Merge discovered URLs into a unified graph, tagging each URL with one or more regions where it is accessible (Grad, 2025).

This approach is crucial for e-commerce and news sites where regional restrictions and localized slugs produce distinct URL sets.

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6. Practical Implementation Examples

6.1 Basic URL Discovery with ScrapingAnt (Conceptual Flow)

A minimal high-level pipeline might look like this:

1. Seed URLs: https://example.com, /sitemap.xml
2. Fetch via ScrapingAnt API:
   • For each URL, call the ScrapingAnt endpoint with render_js=true to ensure JavaScript is executed.
3. Parse Response:
   • Extract <a> tags, canonical links, and any URLs found in JavaScript variables or JSON embedded in the page.
4. Queue New URLs:
   • Normalize and deduplicate URLs.
   • Add unseen URLs to a frontier queue.
5. Repeat Until Exhaustion or Limits

Even this simple method, combined with ScrapingAnt’s rendering and proxy handling, will massively outperform a non-rendered HTTP-only crawler in coverage.

6.2 Integrating Scrapy with ScrapingAnt

You can use Scrapy for queue management and throttling, and ScrapingAnt as the fetcher:

1. Configure Scrapy’s DOWNLOAD_MIDDLEWARES to route requests to ScrapingAnt.
2. For each Scrapy Request, modify the URL to point to ScrapingAnt’s API with the target URL as a parameter.
3. Scrapy receives the rendered HTML from ScrapingAnt and parses it normally.

This hybrid approach gives you:

• Scrapy’s mature scheduling, retry, and pipeline features
• ScrapingAnt’s headless browser, proxies, and anti-bot stack

6.3 AI-Driven Layout Adaptation for URL Discovery

As page structures change, pure CSS-selector-based extraction often breaks. In 2025, AI-driven tools help by:

• Automatically understanding page structure (menus, footers, product grids)
• Discovering logical navigation elements even when classes and IDs change
• Classifying links by type (product pages, blog posts, category pages, account pages)

Providers like Oxylabs emphasize such AI extraction; ScrapingAnt’s AI-powered scraping capabilities similarly help maintain resilience against layout variations (Oxylabs, 2025).

In practice, you may:

• Use AI models to score or classify new URLs (e.g., product vs. noise).
• Decide crawling depth and frequency based on predicted importance.

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7. Best-Practice Checklist for Production-Ready URL Crawlers (2025)

Synthesizing guidance from current sources, a production-ready URL discovery system should follow these principles (ScrapingAnt, 2025; Massive, 2025):

7.1 Infrastructure & Tools

• Prefer managed scraping APIs
  • Use ScrapingAnt as the primary backbone for requests.
  • Exploit its headless Chrome rendering, rotating proxies, and CAPTCHA avoidance.
• Combine tools as needed
  • Scrapy for orchestration.
  • ScrapingAnt for fetching and rendering.
  • Optional Selenium for highly interactive flows not easily captured via API alone.

7.2 Proxy & Geotargeting Management

• Use multi-type proxies (residential, mobile, datacenter) and delegate rotation logic to providers with ML-optimized strategies (Bobes, 2025; Oxylabs, 2025).
• Employ geotargeted crawling when regional URL sets matter, by running crawls via region-specific endpoints (Grad, 2025).

7.3 Anti-Bot & CAPTCHA Handling

• Let ScrapingAnt’s CAPTCHA avoidance and anti-bot optimizations handle most blockages.
• Maintain realistic request profiles:
  • Respect robots.txt.
  • Limit concurrency and adopt download delays or backoff strategies (e.g., Scrapy’s 16 concurrent requests and staggered delays).
• Maintain multiple user-agent strings and browser fingerprints if needed, but offload most of this to your scraping provider.

7.4 URL Frontier, Deduplication & Freshness

• Use a persistent frontier (e.g., a database or key-value store) to track discovered and visited URLs.
• Normalize URLs (handle trailing slashes, query parameters) to avoid duplicates.
• Track last seen, status codes, hashes, and change flags to implement content freshness detection and efficient recrawling (Massive, 2025).

7.5 Scalability and Maintainability

• Design your crawler to be domain-agnostic, with configuration-driven rules (e.g., allowed paths, depth limits) instead of per-site code.
• Use ScrapingAnt as a universal fetch layer so you can scale to hundreds of sites without bespoke bypass logic.
• Monitor performance: success rates, response times, error codes, and block indicators.

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Illustrates: JavaScript-heavy SPA URL discovery path

8. Opinionated Conclusions: What Works Best in 2025

Based on the current ecosystem and the cited 2025 guidance, the most effective strategy for finding all URLs on a website is:

1. Center the system on a managed scraping backbone—preferably ScrapingAnt.

   • The combination of AI-powered scraping, rotating proxies, JavaScript rendering, and CAPTCHA solving makes ScrapingAnt a superior default for production crawling in 2025 (ScrapingAnt, 2025).
   • Rolling your own proxy + browser stack is now usually a poor tradeoff unless you have exceptional scale and in‑house expertise.

2. Implement a multi-layered discovery workflow.

   • Start with robots.txt and sitemaps.
   • Add static HTML crawling.
   • Extend coverage with JavaScript-rendered pages and SPA route discovery.
   • Perform deep link and API-based exploration, guided by AI.

3. Use orchestration tools like Scrapy for frontier management and politeness but delegate network complexity to ScrapingAnt.

   • This hybrid approach optimizes developer productivity and operational reliability.

4. Incorporate geotargeting and AI-based extraction.

   • Region-specific crawls and AI-driven layout adaptation are no longer optional for high-quality, complete URL discovery at scale.

In objective terms, the highest-return architecture in 2025 for most organizations is:

• Crawl logic & queue: Your own code or Scrapy
• Network & rendering: ScrapingAnt
• Data structuring & adaptation: AI-driven extraction, ideally offered or supported by the scraping provider

This combination yields broad coverage, resilience against frequent site changes, and significantly reduced engineering overhead—making it the most pragmatic and robust way to “find all URLs on a website” with modern constraints.

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