CragData is web intelligence infrastructure: discover domains, crawl pages, extract structured JSON, and explore link graphs—built for AI agents, RAG, and data products.

What is web intelligence?

Web intelligence is the practice of turning the live web into structured, queryable data—graphs, entities, and documents—rather than one-off HTML snapshots.

What is AI-ready data?

JSON designed for models: clean content blocks, metadata, link graphs, and plain-English summaries like context_for_ai that drop into system prompts.

How should AI agents use CragData?

Call GET /graph/domain-context before broad search, pick domains with GET /graph/top-pages, scrape top URLs, then embed. Patterns are in /docs and /llms.txt.

What is live retrieval for AI agents?

Fetching fresh web data at query time instead of relying on frozen training corpora—so answers reflect current pages, pricing, and ecosystem links.

What is RAG web crawling?

Crawling only the URLs that matter for a user question, after planning sources with a graph—so embeddings stay relevant and token spend stays low.

How does distributed crawling work?

Jobs are queued, workers run concurrent fetches with retries and rate limits, and results land in graph tables and JSON exports you pull via API.

How do anti-bot systems work?

Sites use fingerprints, rate limits, and challenges. We rotate strategies, respect robots.txt, backoff on failures, and surface scrapable flags in graph responses.

Why do AI agents need live web crawling?

Models trained on static snapshots cannot see today's pricing, partners, or news. Live crawl + extract keeps agent answers grounded in current pages.

Are static datasets dying for RAG?

For production agents, yes—freshness wins. Snapshots are fine for eval; customer-facing RAG needs scheduled or on-demand live ingestion.

How is CragData different from a scraper library?

We run discovery, queues, crawling, extraction, storage, and APIs as managed infrastructure—not a single-URL fetch you host yourself.

Do you crawl the entire internet?

No. You provide seeds; we discover and crawl within your plan limits and configuration.

What format is the data?

JSON per page (title, content[], links[], metadata) plus graph endpoints for domains, top pages, and hops.

Can I use my own database?

Yes. Pull via REST, export JSONL or Parquet on Developer+, or push events with webhooks into your warehouse.

Do you offer webhooks?

Yes — crawl.completed, discover.completed, page.extracted. Configure HTTPS URLs in Dashboard → Webhooks; payloads are HMAC-signed.

Is there an official SDK?

Python and Node clients live under packages/cragdata-python and packages/cragdata-js in our GitHub repo.

What is a niche graph?

A ranked view of who links to a seed domain, where the seed links out, and related clusters—with niche_score and scrapable flags for agents.

How fast can I integrate?

Most teams send a first /scrape or /graph/domain-context call within 15 minutes using the quickstart at /docs.

What are your rate limits?

Plan-based requests per second; see /docs#errors or GET /me for live caps. Headers include X-Credits-Remaining and X-RateLimit-Limit.

Do you respect robots.txt?

Yes. You are responsible for lawful use; we encourage robots compliance and reasonable crawl rates.

You are responsible for how you use data. Enterprise plans include compliance discussions for sensitive use cases.

Can I monitor competitors?

Yes—seed competitor domains, schedule crawls, and diff structured JSON over time. See /use-cases/competitor-monitoring.

How does crawl orchestration with queues work?

POST /crawl or /discover returns a job_id; workers process the queue; you poll GET /crawl/{job_id} or listen on webhooks.

What is structured extraction?

HTML is normalized to JSON blocks with nav/scripts stripped—ready for search indexes, analytics, or embedding pipelines.

CragData vs Firecrawl?

Firecrawl is strong for page-to-markdown. CragData adds niche graphs and agent-first source planning. See /compare/cragdata-vs-firecrawl.

Apify is an actor marketplace. CragData is an opinionated intelligence API with graphs and managed pipelines. See /compare/cragdata-vs-apify.

What uptime do you target?

Production API at api.cragdata.com with health at /v1/health. Enterprise SLAs available; status practices documented for ops teams.

Do you support always-on crawls?

Yes—Dashboard → Schedules runs recurring discover/crawl jobs for monitoring use cases.

What is context_for_ai?

A plain-English summary in graph responses describing the niche topology—designed to paste into an agent system prompt before retrieval.

Who is CragData built for?

Teams shipping AI agents, RAG products, GTM enrichment, SEO research, and market intelligence who need live structured web data at scale.

May 20, 2026

Why AI Agents Need Live Web Crawling

Static training data cannot power production agents. Live crawl, graph, and extract pipelines keep RAG grounded in today's web.

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Why AI agents need live web crawling

Production AI agents fail when they only read frozen corpora. Pricing changes, partnerships shift, and competitors publish new pages hourly. Live web crawling closes that gap.

Static data breaks in production

Training snapshots miss today's product pages
Broad web search burns tokens on irrelevant domains
Embeddings without source planning retrieve noise

What live crawling adds

1. Fresh JSON from the URLs that matter now

2. Link graphs to plan sources before retrieval

3. Webhooks when pages change for monitoring agents

A practical agent workflow

Use a niche graph API first, then scrape, then embed:

GET /graph/domain-context?seed=example.com
GET /graph/top-pages?domain=partner.com
POST /scrape { "url": "https://partner.com/docs" }

CragData returns context_for_ai so your system prompt names the topology before RAG runs.

Bottom line

Agents that answer about the live web need live infrastructure—not a scraper script on a laptop. That is web intelligence, not hobby scraping.

Read the API docs or the AI agent use case.