Which is more popular, Firecrawl or Notte?

Firecrawl has more GitHub stars (187,756 vs 2,009).

Which is more actively developed, Firecrawl or Notte?

Firecrawl had more commits in the last 90 days (578 vs 169).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Firecrawl is growing faster: +14,055 GitHub stars in the last 30 days vs +13 for Notte.
Pick Firecrawl for: the Web Data API for AI - Turn entire websites into LLM-ready markdown or structured data. Pick Notte for: best framework to build web agents, and deploy serverless web automation functions on reliable browser infra.

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🔥 The Web Data API for AI - Turn entire websites into LLM-ready markdown or structured data

🌸 Best framework to build web agents, and deploy serverless web automation functions on reliable browser infra.

+Industry-leading reliability with >80% success rate on complex websites including JavaScript-heavy and dynamic content
+AI-optimized output formats with clean markdown and structured data specifically designed for LLM consumption
+Comprehensive feature set including media parsing, interactive actions, batch processing, and authentication support

-Repository is still in development and not fully ready for self-hosted deployment
-API-based service likely requires subscription pricing for production use
-As a relatively new tool, long-term stability and support ecosystem may be uncertain

•Building AI agents that need real-time web context and competitor intelligence
•Creating training datasets for LLMs by scraping and cleaning large volumes of web content
•Automating content monitoring and change detection for business intelligence applications

Which is more popular, Firecrawl or Notte?: Firecrawl has more GitHub stars (187,756 vs 2,009).
Which is more actively developed, Firecrawl or Notte?: Firecrawl had more commits in the last 90 days (578 vs 169).
Should I use Firecrawl or Notte?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.