A research thinking environment for academics. Import your Zotero library, read PDFs and EPUBs in-app, take notes that link back to the source, and export to an Obsidian-compatible vault or your own Git repo.
A startup from Bishop's Stortford, the United Kingdom, founded by Alberto Zuin.
This page is designed to help you find out whether ScholarThread is good and if it is the right choice for you.
ScholarThread is a research thinking environment for postgraduates and academics. It works alongside Zotero rather than replacing it: connect once, and your collections arrive in a reader for PDFs and EPUBs, with highlights in five colours, a note on every highlight and Apple Pencil on iPad. Notes link to papers, passages and each other, and export as plain Markdown to Obsidian or a Git repository. A knowledge graph shows what cites what and what is still unconnected. Chat answers questions from your own library and cites the passage behind each claim, and Explain turns a dense passage into plain language. Files are stored in the EU, AI runs on Amazon Bedrock in EU regions, and nothing you upload is used to train models. Free plan; Pro with an academic price.
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Import and Export Options
Zotero and Obsidian import with background sync
PDF Reader
PDF and EPUB reader
Apple Pencil Support
Apple Pencil handwriting on iPad
Note Taking
Notes that link back to the exact passage
Literature Review
Literature and concept note templates
Literature Discovery and Organization
OpenAlex literature search with one-click import
Knowledge Graph
Knowledge graph of papers, notes and citations
AI Chat
Chat that answers from your own library, with citations
AI Summary
Explain: plain-language explanations of a selected passage
Searchability
Search by meaning across papers, notes and chats
Citation Generator
Citation export (BibTeX, RIS, CSL styles)
Chrome Extension
Chrome extension to save papers from the browser
iPhone & iPad App
iPhone and iPad app with cross-device sync
MCP Server
MCP server for Claude and other AI assistants
Privacy
EU data storage, no training on your content
Newly launched in 2026, used by individual PhD candidates and researchers. No institutional customers yet.
ScholarThread brings reading, notes and AI together around your own papers. Every highlight and note points back to the exact passage it came from. A knowledge graph shows how your papers and notes connect, and what is still unconnected. Chat answers only from your library, with a citation for every claim, and Explain turns a dense passage into plain language. Your notes stay yours as plain Markdown (Obsidian, Git), your files are stored in the EU, and nothing you upload is used to train AI models.
Because everything is connected. Most researchers juggle a reference manager, a PDF reader, a notes app and a chatbot that has never seen their papers. In ScholarThread they are one workspace: a highlight becomes a note, the note links back to its passage, the graph shows how it all connects, and chat answers from that same library with citations. It also fits the tools you already use: Zotero, Obsidian, Git, OpenAlex, your browser, and AI assistants like Claude through MCP. Web, iPhone and iPad, all in sync.
PhD candidates and postgraduate researchers, plus the academics who supervise them: people who read dozens of papers for a thesis or a project and need to turn that reading into notes, arguments and writing they can trace back to the source. It suits anyone with a growing library, from a first literature review to a viva, especially if they already use tools like Zotero or Obsidian.
I'm Alberto. I work full time, and my research happens around it: early mornings, evenings, the odd free hour at the weekend. My papers lived in Zotero, my books on a Kobo and my notes in Obsidian. Each tool did its job well, but none of them knew the others existed. My workaround was screenshots: I pasted the passage or diagram into a note so the connection was visible somewhere, with no link back to the source. Obsidian's graph was the closest I had to seeing how my thinking fit together, but it stopped at the edge of my notes; it had no idea a note was really about page 214 of a PDF. So I built ScholarThread: one place where reading, notes and sources are connected, and where AI helps you find what you've already read instead of doing the thinking for you, so the little time I have goes into understanding, not re-finding.
Web app: Next.js and React, TypeScript, Tailwind CSS, Apollo GraphQL client; react-pdf (PDF.js) and epub.js for the reader; Cytoscape for the knowledge graph.
Backend: Python, FastAPI with Strawberry GraphQL, Celery workers with Redis/Valkey, Neo4j graph database.
AI: Amazon Bedrock in EU regions (Anthropic Claude and Amazon Nova as standard models), with retrieval over your own library. On iOS, meaning-based search runs on the device with Core ML.
iPhone and iPad app: Swift, SwiftUI, SwiftData, PDFKit, PencilKit (Apple Pencil), Readium for EPUB.
Browser extension: WXT, React, TypeScript.
Infrastructure: AWS (EKS, S3, KMS; data in Frankfurt), OpenTofu, Flux GitOps, Cloudflare.
Accounts and payments: Clerk, Stripe, Apple in-app purchase.
Monitoring and analytics: Sentry, PostHog, Google Analytics (consent-based).
We have collected here some useful links to help you find out if ScholarThread is good.
Check the traffic stats of ScholarThread on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of ScholarThread on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of ScholarThread's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of ScholarThread on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
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