Not a search API.
Not a scraper.
The layer between.
ZapFetch sits where your LLM needs it: one call, three primitives, typed output. Existing tools force you to stitch them yourself.
If you're building an AI agent that needs fresh web data, two categories of tool almost solve the problem. Search APIs like Exa give you ranked URLs for a semantic query over a pre-indexed corpus. Scrapers like Firecrawl take a URL and return its content as markdown or structured JSON. Each does its half well.
The trouble is the seam in between. Agents don't want URLs they have to fetch, and they don't want markdown they have to parse. They want one call that starts from an intent (“news about X”), ends with typed fields, and handles the retrieval in the middle. Stitching search + scrape + schema yourself means two API contracts, two rate limits, two error models, and a lot of glue code in the hot path of every agent turn.
ZapFetch is built for that hot path. Below is what the three tools actually offer when you line them up side by side — capability, latency, and cost per thousand requests.
The verdict is intentionally boring: if you only need semantic search, use Exa. If you only need a URL-to-markdown pipeline, Firecrawl does that cleanly. If you need what an agent actually needs — a query that comes back structured — ZapFetch is the one call that replaces both.