Why AI needs "eyes" on the web

Large Language Models (LLMs) have a fundamental weakness: their knowledge ends on the day they were trained. GPT-5.4, Claude 5, Gemini 2.5 Pro — all suffer from the same problem. If you ask them about the current stock price, today's news, or a change in an open-source library a week ago, they will fail without web access. And this is where a new category of infrastructure tools comes in, serving as a bridge between LLMs and the live internet.

In 2026, it's no longer just about AI "finding an answer." It's about finding it reliably, quickly, and in a format it can effectively process. This means cleaning HTML of clutter, converting content to Markdown, bypassing anti-bot protections, and most importantly — extracting the essential information from a web page.

The Problem: The Web Was Built for Humans, Not Machines

Web pages in 2026 are technologically complex. JavaScript frameworks generate content dynamically, Cloudflare protects websites from bots, content is scattered across dozens of <div> tags. Simply downloading HTML via curl or requests.get() often returns only an empty skeleton. And even if you get the content, LLM token windows are limited, and every kilobyte of HTML clutter costs something.

OpenAI, when launching ChatGPT search in October 2024, openly admitted that getting useful answers from the web "often requires multiple searches and link traversals." And we're talking about a company with virtually unlimited resources. For smaller developers and startups, the problem is even more pressing.

Fortunately, a number of tools have emerged in the community to solve this problem. From simple APIs to full-fledged scraping frameworks. Let's look at the most interesting ones.

Scrapling: A Python framework that survives website redesigns

One of the most interesting open-source tools in this area is Scrapling — a framework by Egyptian developer Karim Shoair, which has already gained thousands of developers on GitHub. Its main advantage? Adaptive parsing.

A typical web scraper breaks when a website changes its CSS classes or HTML structure. Scrapling works differently: when you set adaptive=True, the framework uses intelligent similarity algorithms that find an element on the page again, even if its selector has changed. In benchmarks, its similarity search is 5× faster than AutoScraper, which is a common alternative in this category.

But Scrapling is not just a parser. It offers four types of "fetchers":

• Fetcher — fast HTTP requests with the ability to impersonate browser TLS fingerprints (Chrome, Firefox) and HTTP/3 support
• StealthyFetcher — a headless browser that bypasses Cloudflare Turnstile and other anti-bot protections
• DynamicFetcher — full browser automation via Playwright or Chrome, suitable for JS-heavy websites
• Async variants — for all of the above, with pooling and parallel request support

For more demanding tasks, Scrapling offers a spider framework (similar to Scrapy) with support for concurrent crawls, pause/resume via checkpoints, streaming output, and automatic proxy server rotation. Another interesting feature is the built-in MCP server, which allows AI assistants like Claude or Cursor to directly control scraping via a standardized interface.

Scrapling is available as pip install scrapling (Python 3.10+), has 92% test coverage, and full type annotations. It's free, open-source under the BSD-3 license. It also works in Docker.

Jina Reader: One-line web access

If you don't want to manage your own scraping infrastructure, there's a more elegant way. Jina AI Reader — a service from the German-American startup Jina AI (based in Berlin and Sunnyvale) — works on a principle that is almost ridiculously simple:

Just prepend https://r.jina.ai/ to any URL and you get clean, LLM-ready content. For example:

curl https://r.jina.ai/https://github.com/D4Vinci/Scrapling

Returns the page content in clean Markdown — no menus, footers, ads, or script tags. The Reader automatically renders JavaScript, extracts the main content, and optionally even describes images on the page using a vision model, so downstream LLMs "see" what's in the illustrations.

The Reader is free for basic use (20 RPM without an API key, 500 RPM with a free key). Paid plans start at a few dollars per million tokens. For context: an average web page consumes around 5,000–15,000 tokens. In addition to the Reader, Jina also offers a Search API (s.jina.ai), which searches the web and returns the top 5 results along with their content.

In the EU context, it's important that Jina AI offers an EU Compliance mode, where all infrastructure and data processing remains within EU jurisdiction. This is crucial for companies that must comply with GDPR and other European regulations.

Firecrawl: Infrastructure for AI agents in production

The biggest player in the "web-to-LLM" space is undoubtedly Firecrawl — a Y Combinator startup with over 113,000 stars on GitHub, used by companies like Apple, Canva, Shopify, and DoorDash. Unlike Scrapling, which is primarily a Python library, Firecrawl is a cloud infrastructure layer with an API, official SDKs for Python, Node.js, Go, Rust, Java, and Elixir, and native MCP integration.

Firecrawl offers three basic operations:

• Search — searches the web and returns results, including full content in Markdown. One API call = query → relevant pages → content.
• Scrape — from any URL, you get clean Markdown, HTML, a screenshot, or structured JSON according to your schema.
• Interact — allows AI agents to click, scroll, fill out forms, and navigate multi-page processes. Useful for content behind logins or complex single-page applications.

Firecrawl claims to cover 96% of the web (including JS-heavy pages) with a P95 latency of 3.4 seconds. It offers 500 free credits per month (1 credit = 1 page), with paid plans starting at $19/month (Hobby) up to $249/month (Growth). For context: one search query costs 1 credit per result, scrape 1 credit per page, interact 5 credits per action.

MCP as a universal connector

A key role in the entire ecosystem is played by the Model Context Protocol (MCP) — an open standard originally from Anthropic, now supported by Claude, ChatGPT, Cursor, Windsurf, Visual Studio Code, and dozens of other tools. MCP acts as a "USB-C for AI applications" — a standardized interface through which AI can communicate with external systems.

In practice, this means that when you add an MCP server for Scrapling or Firecrawl to your Claude Code, the AI assistant gains the ability to read web pages, search the web, and extract data from them — all without you having to program anything. Firecrawl, for example, reports over 400,000 installed MCP servers.

For developers, this means a fundamental change in workflow: instead of switching between the browser and the editor, the AI agent itself finds the necessary information — current documentation, GitHub issues, Stack Overflow — and works with it directly.

How Claude Code, OpenCode, and other AI coders use the web

Specific implementations vary, but the principle is similar. Claude Code (from Anthropic) uses a WebSearch tool, which first performs a search and then downloads the content of relevant pages — usually via an API like Jina Reader or through its own fetching infrastructure. It receives results in clean text or Markdown, saving tokens and speeding up processing.

OpenCode (an open-source AI coding agent) uses WebFetch and WebSearch tools — WebFetch downloads a specific URL and converts it into a readable format, WebSearch performs a full-text search and returns relevant results. In both cases, it's automation that saves the developer minutes of context switching.

Cursor and Windsurf (other popular AI editors) integrate web scraping via MCP — a developer can add an MCP server for Firecrawl or Scrapling to their project, and the editor automatically gains the ability to read documentation, search for bug solutions, or analyze external repositories.

Another interesting trend is "agent skills" — for example, both Scrapling and Firecrawl offer SKILL.md files, which AI agents (like Claude Code or OpenCode) automatically load and learn how to control scraping based on them. It's essentially documentation written for AI, not for humans.

Pricing and Availability: What to choose?

For a Czech developer or a small company, the key question is: what to invest time and possibly money in? Here's a quick comparison:

To start, I recommend a combination of Jina Reader for quick reading of individual pages and Scrapling for anything that requires scaling or bypassing protections. Firecrawl makes sense when a project grows to a stage where you need reliable infrastructure without worrying about proxy servers, rendering, and rate limiting.

What this means for Czech developers and companies

All the described tools are available in the Czech Republic without restrictions. Jina Reader, thanks to its EU Compliance mode and Berlin headquarters, offers fully GDPR-compliant data processing — which is essential for companies that process personal data or customer data. Firecrawl has SOC 2 certification, but its infrastructure primarily runs in the USA, which may be an obstacle for some Czech companies during audits.

For Czech AI startups and development teams, a practical recommendation follows: don't waste tokens on HTML clutter. Every token that an LLM consumes parsing navigation and ads is a token you paid for unnecessarily. Using the Reader API or Scrapling can reduce LLM call costs by 60–80% — simply because the model only receives relevant content.

The impact on Czech companies building RAG (Retrieval-Augmented Generation) systems is also interesting — whether it's internal documentation search, customer support, or competitor monitoring. Instead of demanding their own scraping infrastructure, they can leverage existing tools and focus on what truly differentiates them from the competition.