Firecrawl
The Web Data API for AI Agents and Developers
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The new Firecrawl /search - Our most accurate Search API for AI agents.

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Search is how AI agents ground themselves in the web, but reading full pages for every query burns tokens fast. We trained a model that returns the excerpts from each /search result that best answer your query, giving your AI agents highly relevant context from every page. It outperforms processing full pages while using 10x fewer tokens. On SimpleQA, AI agents using Firecrawl /search now score 94.7%, higher than any other provider. It's live today on every /search call.

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That side-by-side cost/accuracy comparison thing is exactly the stuff that ends up getting litigated in G2/Capterra reviews later, when prospects are comparing tools and can't just ask you directly. Curious if you've found review platforms actually surface that kind of signal for you, or if it's still mostly direct sales conversations at this stage?

94.7% on SimpleQA is a strong number, but that benchmark is specifically short factual questions with a fairly clean right answer, which is close to the best case for a relevance model scoring paragraphs against a query. Curious how much that accuracy holds up on queries where the right answer isn't a single fact but requires synthesizing across multiple excerpts, since scoring individual paragraphs and lists against a query is a different task than judging whether the combination of several partial excerpts actually adds up to a complete answer. Also, returning excerpts instead of full pages means the model never sees the surrounding context that a paragraph originally sat in. If a relevant looking paragraph is actually a counterexample, an outdated caveat, or a quote the source page was explicitly refuting, does the excerpt carry any signal that it needs to be read skeptically, or does stripping the surrounding page context risk agents treating pulled quotes as more authoritative than the original author intended.
Maker

 great points!! There are definitely trade-offs, however, you can always turn this option off.

That's a solid benchmark score. Does the relevance model retrain continuously or is it a fixed release?

Maker

 It's fixed for now, but we will retrain in the future.

firecrawl has become the default answer every time someone in the agent space asks "how do i give my agent reliable web access." that kind of organic word of mouth in developer communities is hard to manufacture. the "interact with" part beyond just scraping is where it gets interesting for agentic use cases. filling forms, clicking through flows, handling dynamic content that only renders after javascript executes. curious how the rate limiting and retry logic works when a target site starts throttling requests mid-crawl.

Returning the relevant excerpt instead of making every agent carry the whole page is the practical win. Long agent runs fail in boring ways when context fills with page furniture, so keeping retrieval lean is a reliability feature as much as a token-cost feature.

Returning the relevant excerpt instead of making every agent carry the whole page is the practical win. Long agent runs fail in boring ways when context fills with page furniture, so keeping retrieval lean is a reliability feature as much as a token-cost feature.

The 10x fewer tokens claim is the part that caught my attention. In agent workflows, the expensive failure is often not retrieval itself, but letting the model chew through irrelevant page structure. I’d be curious to see where the relevance model struggles most: tables, long docs, or queries with several equally plausible answers?

94.7% on SimpleQA is impressive. How does the relevance model handle ambiguous queries?

Benchmark scrore is really good. What are the scenarios where it failed to answer correctly?

The excerpt approach is a clean tradeoff for factual lookups where the signal is usually in one paragraph. Curious how the model handles research queries where the evidence is spread across 3-4 short paragraphs on the same page, or where two sources directly contradict, does it surface multiple excerpts per result, or one best-match per page? That gap is where we've seen agents get confidently wrong answers from snippet-based search.

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