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firecrawl v2.11.0 — Firecrawl v2.11.0

Source
rhys-firecrawl
Author
rhys-firecrawl
Date
Key takeaways · AI-distilled
  • Browser sessions now return the raw CDP (Chrome DevTools Protocol) WebSocket URL, so you can attach Playwright or Puppeteer to a live Firecrawl browser and drive it yourself instead of being limited to Firecrawl's own actions.
  • The monitor's judge used to invent before/after text on long pages. The fix was evidentiary: give it the full unified diff as its only input, so it can only describe changes that actually appear.
  • JSON-mode monitor diffs now use order-insensitive equality. Arrays with identical values in a different order were previously reported as changes, firing an alert on every single run.
  • The research index bills as search: 2 credits per 10 results, or 10 credits per 10 for zero-data-retention teams. Keeping your data out of retention costs 5x on this endpoint.
Terms in this piece · Glossary
  • MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
  • LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
Why it matters

Firecrawl's v2.11 adds an arXiv+GitHub research index with SOTA arXivQA recall, keyless /CLI/SDK access, and a deterministicJson format that caches a per-site extractor so you skip per-request LLM calls for cheaper, consistent structured scrapes. Useful if you're building agents that need clean web/research data or change monitoring with LLM-judged relevance filtering.

Key quotes

“It has state-of-the-art recall on arXivQA, outperforming the next best provider by 18% at comparable cost.”

“Added a `redactPII` option that strips personal and sensitive data like names, emails, phone numbers, addresses, and secrets out of scraped content before it's returned.”

“Firecrawl generates a reusable extractor for your schema and caches it per site, so repeat scrapes are cheaper and return consistent results.”

“Added a `goal` to monitors so an LLM judges each detected change as meaningful or noise against what you actually care about, cutting alert spam and surfacing the changes that matter first in summary emails.”

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