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.
Hey Product Hunt 👋 Eric, Caleb, and Nick from Firecrawl here.
AI agents rely on search tools to find answers on the web, but those answers are often buried in noise. Navigation, boilerplate, and tangents wrap around the excerpt that matters, so an agent processes far more content than it needs to. That makes search slower, more expensive, and more likely to return the wrong answer.
Firecrawl /search now runs every result through a new custom relevance model. It scores every paragraph, list, and table against your query, then returns the excerpts that best answer it.
We evaluated the improved /search on SimpleQA, OpenAI's factuality benchmark for short, fact-seeking questions. AI agents using Firecrawl /search achieve state-of-the-art accuracy at 94.7%, the highest score of any search provider we tested.
By returning relevant excerpts instead of full pages, each response uses 10x fewer tokens. That leaves more of your agent's context window free and makes the results cheaper and faster to reason over.
AI agents with web search keep their context lean across long chains of searches. RAG pipelines get source-linked excerpts ready for a prompt without separate chunking or reranking. Research and enrichment workflows can pull the specific fact, figure, or quote they need.
The upgrade is live across the API, SDKs, CLI, and MCP, and the API shape hasn't changed, so existing /search calls start returning better results automatically!
If you're building products with agentic search, we'd love to hear how it improves your results.
About The new Firecrawl /search on Product Hunt
“Our most accurate Search API for AI agents.”
The new Firecrawl /search launched on Product Hunt on July 24th, 2026 and earned 283 upvotes and 32 comments, placing #4 on the daily leaderboard. 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.
On the analytics side, The new Firecrawl /search competes within Developer Tools, GitHub and Search — topics that collectively have 576.2k followers on Product Hunt. The dashboard above tracks how The new Firecrawl /search performed against the three products that launched closest to it on the same day.
Who hunted The new Firecrawl /search?
The new Firecrawl /search was hunted by Eric Ciarla. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Reviews
The new Firecrawl /search has received 14 reviews on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
For a complete overview of The new Firecrawl /search including community comment highlights and product details, visit the product overview.
Eric Ciarla