Drag & drop a .txt or .bib file here
% Your generated citations will appear here % Enter papers on the left and click Generate
% Raw data will appear here
Drag & drop a .txt or .bib file here
% Your generated citations will appear here % Enter papers on the left and click Generate
% Raw data will appear here
Built for researchers who need accurate BibTeX, fast. No accounts, no servers, no compromises.
Auto-fetches complete citation data — authors, year, journal, DOI — across three academic APIs with automatic failover.
Crossref for accuracy, arXiv for preprints, Semantic Scholar for breadth. Each paper tries all three sources before giving up.
Handles special characters, diacritics, and non-Latin scripts in paper titles and author names without corruption.
Everything runs in your browser. No paper titles, citations, or usage data ever reaches our servers. Local storage only.
Process hundreds of papers in one go with automatic rate-limiting and smart retry for failed lookups.
Every citation you generate is saved locally. Search, filter by source, organize into folders, and export anytime.
From paper titles to formatted citations in under a minute.
Paste titles, DOIs, or arXiv IDs — or drag a .bib/.txt file.
One click fetches citations from Crossref, arXiv, and Semantic Scholar.
Copy, download as BibTeX/RIS/APA/MLA, or share via link.
Drag the button to your bookmarks bar. When you're on a journal page or arXiv abstract, click it to jump straight into Citation Fetcher with the paper pre-filled.
Drag the "Cite This!" button to your browser's bookmarks bar.
When browsing a journal page or arXiv abstract, click the bookmarklet.
Citation Fetcher opens with the paper's DOI or title pre-filled.
Three trusted sources, automatically queried in your preferred order.
High-quality metadata from thousands of academic publishers. Most accurate citation data available.
Open-access preprints in physics, math, computer science, and related fields. Free with no rate limits.
AI-powered academic search with comprehensive coverage, citation graphs, and related paper recommendations.