AI Agent Data Pipelines
Feed your AI agents live web data with structured extraction and multi-source research.
Quick Answer
Connect CrawlForge to your agent over MCP and it gains 27 web tools it can call directly. Use deep_research (10 credits) for multi-source analysis with conflict detection and extract_content (2 credits) for clean, structured output -- no custom scrapers to build or maintain. A typical research task runs about 12 credits.
The Problem
AI agents reason well but are blind to live web data. Building custom scrapers for every data source is slow, fragile, and expensive to maintain. Agents need structured, real-time information to make accurate decisions.
The Solution
CrawlForge gives your agents direct access to web data through MCP. Use deep_research for multi-source analysis with conflict detection, and extract_content for clean, structured output -- no custom scrapers needed.
Code Example
// AI agent fetches and synthesizes live market data
const research = await mcp.deep_research({
query: "Latest funding rounds in AI infrastructure startups Q1 2026",
sources: 5,
conflict_detection: true,
});
// Extract structured data from a specific source
const content = await mcp.extract_content({
url: research.sources[0].url,
format: "markdown",
});
console.log(research.summary);
console.log(content.main_content);Tools Used
Estimated cost: ~12 credits per research task
Frequently Asked Questions
How do I give an AI agent live web data without building custom scrapers?
Connect CrawlForge over MCP and the agent gains 27 tools it can call directly. Use deep_research for multi-source analysis with conflict detection and extract_content for clean, structured output — no per-source scraper code to write or maintain.
What is an AI agent data pipeline?
It is the flow that feeds an autonomous agent structured, real-time web data so it can reason on current facts. With CrawlForge the pipeline is just MCP tool calls: the agent searches, researches, and extracts on demand instead of relying on a stale training cutoff.
How many credits does a typical agent research task cost?
About 12 credits — roughly 10 for one deep_research call plus 2 for an extract_content follow-up. Every plan including the free 1,000-credit tier can run it, and you pay per call, so cost scales with how much research the agent does.
Does CrawlForge work with LangChain and LlamaIndex agents?
Yes. CrawlForge is MCP-native and also exposes a REST API, so it plugs into LangChain, LlamaIndex, the Vercel AI SDK, and any MCP client like Claude or Cursor. The agent auto-discovers all 27 tools once connected.
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