Firecrawl is a great tool. I want to say that upfront, because what I'm about to describe isn't really Firecrawl's fault. It's just not what it was built for.
I was using it as the data layer behind an AI agent, and the costs kept climbing for a simple reason: every time my agent needed fresh context, I paid to re-fetch the same sources again. Same pages, same data, paid for again and again, just to get a raw dump I then had to clean up myself before my agent could even use it.
It felt wasteful. Not because the tool was bad, but because I was using a scraper to do a knowledge base's job.
The Difference That Was Costing Me Money
A scraper gives you data right now: raw, unstructured, one-time. A knowledge base gives you data that's always current, structured, and queryable.
I didn't need "fetch this page again." I needed something that already knew what I wanted, kept it fresh on its own, and let my agent ask for exactly the slice it needed, not the whole page dumped into context every single call.
Three things were actually breaking my workflow:
No structure. Raw scraped output meant my agent was spending half its context window parsing format instead of using data: nav menus mixed into content, tables that didn't parse cleanly, no consistency between pages.
No memory. I had no way to know what changed between fetches. Did a price update? Did a listing get removed? The only way to find out was diffing two raw dumps by hand.
No queryability. I couldn't ask for just the rows I needed. Every call meant everything, whether my agent needed it or not, straight into the context window, every time.
So I Built Quorel
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Happy to answer questions about the architecture, the MCP server, or how the versioning works under the hood. This is genuinely the tool I use for my own agent's data layer now.
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