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Feed your model the
cleanest data on the web.

Qcrawl is built for AI teams. Pull clean markdown from any URL, build training corpora at scale, give your agents a single tool for the public web — all from one API key.

Markdown that LLMs actually want

Built-in Readability extraction strips navigation, ads, sidebars, footers, and cookie banners before the page leaves our system. What lands in your prompt is the body content — nothing else. Token budgets stop being a fight.

Structured signals alongside the text

Every scrape also returns OpenGraph, Twitter Card, JSON-LD, and Microdata when available. Use the prose for embeddings and the structured layer for filtering, faceting, or knowledge graph construction.

Tool-use ready

Plug the scrape endpoint into any agent framework — Anthropic tool use, OpenAI function calling, LangChain, LlamaIndex. Predictable JSON, fast response times, and a hard timeout so agents never hang.

Crawl at training scale

Full-site crawls with budget, depth, and concurrency controls. Stream results to a webhook. Build a 100,000-page training corpus in an afternoon, not a sprint.

Drop into any AI stack.

RAG ingestion, agent tool-use, training corpus build — same API, three patterns.

import httpx
from openai import OpenAI

ds = httpx.Client(headers={"Authorization": "Bearer osk_..."})
openai = OpenAI()

# Pull clean markdown ready for embedding
page = ds.post("https://api.qcrawl.com/v1/scrape",
               json={"url": "https://example.com/article", "format": "markdown"}).json()

# Hand straight to an embedding model
embedding = openai.embeddings.create(
    model="text-embedding-3-large",
    input=page["content"],
).data[0].embedding

Used by AI teams for

Retrieval-augmented generation

Index documentation, knowledge bases, and competitor sites with clean markdown that embeds cleanly and retrieves predictably.

Pretraining and continued pretraining

Build domain-specific corpora — finance, legal, medical, technical — without writing custom scrapers per site.

Agentic workflows

Give your agent a single tool for the entire public web. One key, one schema, no fragile per-site adapters.

Evaluation and grounding

Fact-check model outputs against live sources. Pull the same URL on demand to verify or rebut.

AI engineering questions, answered

How is Qcrawl's markdown different from raw HTML or BeautifulSoup output? ▾
We run a Readability-style extraction that identifies the main content block and discards everything around it. The resulting markdown is typically 80 to 95 percent smaller than the original HTML while preserving headings, links, and prose structure. For LLM ingestion this means lower token costs and higher signal-to-noise in your embeddings.
Can I use Qcrawl as a tool in agent frameworks? ▾
Yes. The scrape, batch scrape, and intelligence endpoints all return predictable JSON with stable schemas. Wrapping them as a tool for Anthropic's tool-use, OpenAI's function calling, LangChain, or LlamaIndex is a one-function exercise. We publish example wrappers in the docs.
Does Qcrawl respect robots.txt? ▾
Our crawler endpoint respects robots.txt by default and exposes a flag to override for cases where you have explicit permission. Individual scrape calls fetch a single page as a user agent would, which is the same pattern used by every browser, search engine bot, and link previewer.
What about copyright and fair use for training data? ▾
Training data legality varies by jurisdiction and use case. We give you the tools to fetch public web content; the licensing decisions remain with you. For commercial training runs we recommend working with your legal team on a source policy and using our crawl endpoint's exclude rules to honor it at scale.
Do you support streaming responses? ▾
Individual scrape responses are JSON, returned in one shot when the page is fully rendered. For very large jobs we recommend the async pattern with webhook delivery — your server gets a clean POST when each batch completes, no long-held connections.
Is the output deterministic enough for embedding-based search? ▾
Yes for content. The markdown extraction is deterministic given the same HTML — the same URL fetched twice returns the same markdown body unless the source page itself changes. Dynamic content like timestamps will naturally vary.
Can I scrape PDFs and other non-HTML formats? ▾
We focus on HTML and JavaScript-rendered pages today. PDFs are on the roadmap — talk to us if you have specific needs.
How does this compare to Firecrawl for LLM workflows? ▾
Firecrawl popularized this category and produces excellent markdown output. Qcrawl matches the markdown quality and adds two things on top: a domain intelligence layer that returns DNS, WHOIS, SSL, and tech-stack alongside the content, and a growing catalog of vertical actors for sites like Amazon and Zillow where generic scraping returns inconsistent data.

Production recipes for AI teams

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How to build a RAG knowledge base from the web in 2026

The 2026 playbook for ingesting public web content into a retrieval-augmented generation pipeline — clean markdown, structured metadata, and freshness without infrastructure pain.

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Pull clean article bodies, JSON-LD, OpenGraph, Twitter Cards, and reading-time metadata from any news or blog page — the modern alternative to building a Readability fork.

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How to crawl an entire website in 2026

The full-site crawler playbook — depth controls, budget caps, robots.txt obedience, sitemap unrolling, and webhook-based delivery for crawls that finish hours later.

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