The new Firecrawl /search

Our most accurate /search yet that uses 10x fewer tokens.

Developer ToolsGitHubSearch

▲ 247 votes27 commentsLaunched Jul 24, 2026

Daily #6Weekly #23

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.

AI Analysis

📝 Summary

Firecrawl's new /search uses a trained model to return the most relevant excerpts from web pages that best answer a query. This addresses the key pain point of AI agents consuming excessive tokens when reading full pages for grounding. It delivers highly relevant context using 10x fewer tokens and achieves a leading 94.7% score on SimpleQA, outperforming all other providers. The value proposition is more accurate, efficient web search integration for AI agents, available live on every /search call.

📈 Market Timing

In 2025-2026, AI agents and LLM-based applications are experiencing explosive growth with strong demand for efficient, low-cost web grounding and RAG solutions. The technology for specialized extraction models has matured, user needs for token optimization are rising, and the economic environment favors AI productivity tools. This is an excellent time to launch such an innovation.

✅ Feasibility

The feature is already live and integrated into existing Firecrawl API calls. Technical difficulty of training the specialized model has been successfully addressed. Development and operation costs are manageable on current infrastructure with high scalability potential. Minimal supply chain or compliance risks for a SaaS API product. Overall feasibility is High.

🎯 Target Market

Primary users are AI/ML developers and engineers building autonomous agents or LLM-powered applications (demographics: tech professionals aged 25-40). Industries: software development, AI services. Geographically concentrated in US, Europe, and global tech hubs. AI developer tools TAM exceeds $15B with SAM for search/grounding around $2B. Core pain points are token costs and retrieval accuracy; strong willingness to pay for superior performance.

⚔️ Competition

Medium. Direct competitors: Tavily (tavily.com), Exa (exa.ai), Perplexity API (perplexity.ai), SerpAPI (serpapi.com). Advantages: superior accuracy (94.7% SimpleQA), 10x token efficiency, seamless Firecrawl integration. Disadvantages: newer entrant compared to established search APIs, may require users to adopt Firecrawl ecosystem.

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