Eval directory
Evals for Firecrawl
8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Firecrawl AI products.
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.
How complete this published benchmark library is across datasets, metrics, rubrics, use-case maps, and pack context. This is library coverage, not an agent performance score.
Test datasets
8/8 packs
Scoring metrics
0/8 packs
Judge rubrics
8/8 packs
Use-case maps
0/8 packs
Pack context
8/8 packs
Available eval packs for Firecrawl
8 packs ready to run.
Actions And Dynamic Pages
Evaluates Firecrawl's Actions & Dynamic Pages across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.
Auth Rate Limits Credits Webhooks
Evaluates Firecrawl's Auth, Rate Limits, Credits & Webhooks across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.
Crawl Whole Site
Evaluates Firecrawl's Crawl (whole site) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.
Extract Llm Structured
Evaluates Firecrawl's Extract (LLM structured) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.
Map Url Discovery
Evaluates Firecrawl's Map (URL discovery) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.
Safety Legality And Governance
Evaluates Firecrawl's Safety, Legality & Governance across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.
Scrape Single Url
Evaluates Firecrawl's Scrape (single URL) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.
Search
Evaluates Firecrawl's Search across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Web Data for AI eval coverage.
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.