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Evals for Firecrawl

8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Firecrawl AI products.

AI Platform
Use evals for Firecrawl

About Firecrawl

Firecrawl is a web-data API for AI — it turns websites into clean, LLM-ready markdown or structured data via scrape, crawl, map, search, and LLM-powered extract endpoints, with JS rendering, browser actions, and proxies. Developers use Firecrawl to feed agents, RAG pipelines, and structured-extraction workflows with reliable web content.

Employees

~30

Industry

Web Data / Scraping

Headquarters

San Francisco, CA

Available eval packs for Firecrawl

8 packs ready to run.

Why eval Firecrawl AI

Firecrawl's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.

The Corsac eval library for Firecrawl measures four dimensions teams care about most when deploying ai platform agents:

  • Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
  • Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
  • Safety gates — does it escalate or refuse when it should, and only then?
  • Operator quality — does it preserve analyst trust by surfacing the right context at the right time?

Every eval pack above is hand-authored against Firecrawl's public product surface and runnable in Corsac with your own data.