The new Firecrawl /search is a specialized Search API built specifically to help AI agents ground themselves in the web efficiently and accurately. Traditionally, enabling AI agents to read full pages for every query burns through context windows and tokens rapidly. Firecrawl solves this by training a custom model that extracts only the specific excerpts from each search result that best answer the user's query.
By providing highly relevant context without the bloat, the tool outperforms processing full pages while using 10x fewer tokens. Notably, on SimpleQA benchmarks, AI agents utilizing Firecrawl /search achieve an impressive score of 94.7%, outperforming competing providers. This feature is live today on every /search call for developers building advanced AI systems.
It is a specialized Search API designed for AI agents. Instead of returning full pages that consume tokens quickly, it uses a trained model to return precise excerpts that best answer a given query.
It uses 10x fewer tokens than traditional methods by extracting only the most relevant content and context excerpts from each search result rather than forcing your AI agent to read full web pages.
On SimpleQA, AI agents utilizing Firecrawl /search score 94.7%, which ranks higher than any other competing provider.
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Co-founder @ Firecrawl (YC S22)
