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Data extraction: guides & tutorials
Selectors, headless rendering, and APIs for reliable structured fields. Build extraction pipelines faster with Apify scrapers, schemas, and retries.
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Data extraction is the step that turns a fetched page into reliable structured fields: prices, titles, dates, contacts, and more. These guides cover CSS and XPath selectors, headless rendering for JavaScript content, and calling site APIs directly when raw HTML is too brittle.
Robust extraction survives layout changes through resilient selectors, schema validation, and retries. Apify scrapers return clean JSON or CSV you can load straight into a database or warehouse. Below you will find tutorials for building extraction pipelines that stay accurate as target sites evolve.
LLMs did not replace CSS overnight, but they did change where teams spend time: instead of nursing a selector file per theme, you can often send cleaned HTML (or markdown) to a model and ask for JSON against a schema—handy when layouts drift or you touch many domains. Large language models reason about semantics, so fields like price, rating, and stock can come back without hand-built paths—at the cost of latency, tokens, and occasional guesses. The sections that follow spell out four common roles for LLMs in scraping (selector help, structured extraction, classification, validation), where that breaks down, and when traditional selectors or pre-built Actors still win. Try Firecrawl extract · Apify Store
Apify is a full scraping platform with 30,000+ Actors. ScrapingBee is a proxy/rendering API. Choose Apify for pre-built scrapers; choose ScrapingBee if you already have scraper code and need a rendering proxy.
ScrapingBee sells a simple HTTP API: send a URL, add flags for JavaScript rendering and proxy country, get HTML back—you parse it.
Apify is a cloud automation platform: 30,000+ pre-built Actors (scrapers and workflows), hosted execution, datasets, scheduling, webhooks, and integrations.
Both can sit in a modern data stack; they optimize for different layers of the problem. Below is a single comparison table, then when to pick which, pricing notes, and FAQ.
MCP (Model Context Protocol) is an open standard (originated by Anthropic) that lets assistants like Claude call external tools over a defined protocol. The Apify MCP server exposes thousands of Apify Actors as those tools so Claude can start real scrapes and automations on Apify's cloud and read the results back into chat. Claude Desktop setup: the recommended path in 2026 is to add the hosted remote connector at https://mcp.apify.com/?fpr=use-apify and authenticate via OAuth on first use (no token to paste, auto-updating). The local alternative runs npx -y @apify/actors-mcp-server with your APIFY_TOKEN and a short --actors list. Either way, restart Claude fully after editing config. Runs bill like normal Apify usage (free tier includes monthly credits).
Large language models do not browse the web on their own. MCP fixes that gap: the client (Claude Desktop) talks to a small MCP server, which runs tools on your behalf. With Apify, those tools are Actors (pre-built scrapers and automations in the Apify Store), so Claude can request live data instead of guessing from training cutoffs. MCP is the same open standard that Claude Code, ChatGPT, VS Code, and Cursor use, so the Apify connector works across those clients too.
This guide focuses on Claude Desktop, compares hosted vs local Apify MCP, shows example prompts, contrasts Firecrawl and Bright Data MCP-style workflows, and covers troubleshooting. New to Claude Desktop? You can try Claude free for a week and wire up the connector before deciding on a paid plan.
Start scraping on Apify for free · Apify MCP docs
