Introducing Firecrawl Research Index
AI/ML research moves fast, and the work that matters is split between new papers and the code that implements them. Most search providers omit or misrank key papers, leaving you to review sources by hand without ever being sure you’ve caught everything.
Today we’re launching Firecrawl Research Index, a specialized index for agents pushing the frontier of AI/ML research.
Benchmark-leading performance
On arXivQA, the index has state-of-the-art recall, 18% above the next best provider at similar cost. It also scores 0.750 MRR, meaning the correct paper lands in the top two results. Your agent finds the right papers, right away.
Search millions of papers alongside their code
The index includes all 3M+ arXiv papers, plus GitHub artifacts from top research repos (issues, merged PRs, READMEs), refreshed daily so agents always stay current.
A complete toolset for research loops
The built-in toolset lets agents run research end-to-end, retrieving the right papers, verifying claims against full text, and pulling code. Agents can go from literature to implementation in one query, with no manual filtering, cross-referencing, or review required.
Methodology
We benchmarked on roughly 200 queries from alphaXiv’s ArXivQA, each labeled with up to 10 ground-truth arXiv IDs. To measure recall, we let Opus 4.8 run each provider through its MCP and SKILL.md, then scored the papers it surfaced against those labels.
Availability
Firecrawl Research Index is available now in the API via /search/research, CLI, MCP, and SDKs, and plugs into any harness you already run (Codex, Claude Code, or Grok Build).
Read the documentation to get started.