Crawlers for LLM data

Crawlers for LLM data turn web pages into text that a language model can use. They fetch a page or a whole site, strip navigation, ads and scripts, and return the main content as markdown or plain text, often with the title, links and metadata alongside. Some add search, so an agent can find pages and read them in one step.

Teams use them to build retrieval systems over documentation, feed research agents, collect articles for analysis and prepare text for fine-tuning or evaluation. The value is in the cleaning step. Raw HTML wastes model context on menus and markup, and badly cleaned text gives a model the wrong facts.

When you compare tools, run each on pages you care about and read the output. Look at whether headings, lists, tables and code blocks survive, whether the main text is complete, and whether boilerplate is gone. Check how the crawler handles pages that need JavaScript, how it follows links within a site, and how you limit depth and page count so a crawl does not run away.

Some options are hosted APIs and some are open-source libraries you run yourself. Hosted services are quick to start and handle rendering for you. Self-hosted crawlers give you control over cost, data location and crawl behavior. Large open datasets of crawled pages are a third option for research that needs breadth more than freshness.

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7 AI web scraping tools in crawlers for llm data

Firecrawl

API and open-source project that crawls a site and returns each page as clean markdown or structured data for language models.

  • Developer API
  • Open source
  • Free plan or free tier

Spider

Crawler written in Rust, offered as an API and open-source library, that returns pages as markdown, text or HTML.

  • Developer API
  • Returns markdown for LLMs

Jina Reader

by Jina AI

Reader API that takes a URL and returns the main content as markdown text that a language model can read.

  • Developer API
  • Returns markdown for LLMs

Tavily

Search, extract and crawl API built for AI agents, returning cleaned page content alongside search results.

  • Developer API

Exa

Search engine API for AI applications that finds pages by meaning and can return their full text.

  • Developer API

Crawl4AI

Open-source Python crawler built on Playwright that outputs markdown and structured fields for retrieval and agent pipelines.

  • Open source
  • Returns markdown for LLMs

Common Crawl

by Common Crawl Foundation

Nonprofit that crawls the web and publishes the raw pages, metadata and text as free open datasets.

This is where questions about consent and copyright are sharpest. Many sites now say in robots.txt or their terms whether they allow AI crawlers, and some name specific bots. Check how each tool identifies itself and whether it follows robots.txt by default. Keep the source URL with every chunk of text so answers can cite where they came from, and think about whether the content may be stored, summarized or used for training under its license.

Freshness is the last thing to plan. Documentation and news change, so decide how often to recrawl and how to remove pages that no longer exist.

Chunking is the step after crawling. Retrieval systems split each page into pieces small enough for a model, and splits that cut a table or a code block in half produce poor answers. Crawlers that keep heading structure make it easier to split on section boundaries and to show readers the section an answer came from.

Questions about crawlers for llm data

Why convert pages to markdown for a language model?

Markdown keeps the structure that matters, such as headings, lists and links, while dropping markup and scripts. That fits more useful text into the model's context and makes chunking for retrieval easier.

Do these crawlers respect robots.txt?

Many do by default, and some let you turn that off. Check the setting before a crawl and keep it on. Also read the site terms, since many now state rules for AI use specifically.

Can I train a model on crawled pages?

That depends on copyright, the site's terms and the laws where you work, and the answer is not settled everywhere. Ask your counsel before using crawled content for training.

Hosted API or self-hosted crawler?

A hosted API is faster to start and handles rendering for you. A self-hosted crawler gives control over cost, data location and crawl rules. Many teams prototype on an API and move large, steady jobs in-house.

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