Run your literature review from one searchable library

Somewhere between year one and the confirmation milestone, every PhD student’s literature review stops fitting in their head. Two hundred sources in, you no longer have a reading problem — you have a memory problem. Was it Kaplan or Kap-something who found the effect reversed in longitudinal data? Which of the four papers on your “definitely cite” list actually addressed the measurement critique? The review chapter isn’t hard because reading is hard; it’s hard because retrieval degrades exactly as the corpus grows.

Marqly is built for that failure mode. It’s an AI bookmark manager that turns everything you read — publisher pages, preprints, blog posts, recorded talks — into one library you can search by meaning and, on Pro, interrogate directly through chat.

Build the corpus without building a filing system

During the collection phase, friction is fatal: any workflow that demands filing decisions per paper gets abandoned by month three. With Marqly, saving is one click from the toolbar, and AI auto-tagging does the filing — every save gets tagged automatically, so thematic structure (“attachment theory,” “panel methods,” “null results”) accumulates on its own. When a database search session ends with a browser full of candidate sources, the tab saver stores every open tab in one action.

Boards give the review its macro-structure. Make one per chapter section or per research question, and drag sources and highlights onto them as your taxonomy of the field firms up. The approach is a close cousin of the method in how to build a second brain, applied to a single, high-stakes project.

Chat with what you’ve read

The distinctive Pro feature for literature-review work is chat with your saves: questions answered from your saved content. This turns the corpus from a pile into something you can query in natural language. In practice, PhD-shaped questions look like:

• “Which of my saved sources used administrative data rather than surveys?”
• “Where did I save something criticizing the standard operationalization of burnout?”
• “Summarize the disagreement between my sources on selection effects.”

The answers come from what you actually saved — pages, highlights, transcripts — which is exactly the scope you want while drafting: your review has to represent your reading, not the internet’s.

The same applies to video. Methods lectures and conference keynotes get saved with transcripts attached via the YouTube AI card, and on Pro you can chat with a specific video’s transcript — useful when a 90-minute talk contains one four-minute segment you need to paraphrase accurately.

Highlights become the skeleton of the chapter

A literature review is ultimately written from passages, not papers. Marqly’s highlighter marks text on any web page in six colors with notes attached, and everything syncs to your library. A workable convention: one color for findings, one for methods details, one for direct quotes you may use verbatim, one for claims you intend to dispute — with a note recording why you marked it. Come drafting time, you write from a curated stream of flagged passages with your past self’s commentary, instead of re-skimming two hundred sources. Highlights persist on the original pages too, so returning to a source shows you exactly what mattered last time.

Retrieval when you half-remember a finding

Semantic search finds saves by meaning across titles, content, highlights, and transcripts — the technical background is in what is semantic search. For a PhD student this is the difference between “search works” and “search doesn’t”: your queries at thesis stage are almost never titles. They’re things like “the study where the effect disappeared after controlling for cohort” — and that query works here. How to search bookmarks with AI has concrete examples worth stealing.

AI summaries earn their keep during screening: when a supervisor forwards eleven “you should probably read this” links, summaries let you triage in minutes and reserve deep reads for what’s actually relevant. Independent comparisons of the category, like the best AI bookmark managers in 2026, cover how Marqly’s approach stacks up against alternatives.

Getting started

1. Install the browser extension — Chrome, Edge, Firefox, and Safari are all supported. Sign up free at app.marqly.com; no card needed.
2. Import the backlog you’ve already accumulated. Marqly ingests standard browser bookmark HTML exports, Pocket archives, and Raindrop.io collections — likely everywhere your first-year reading is currently buried.
3. Let the AI tag it all. Auto-tagging processes the imports and every save from here on, so the corpus stays organized while your attention stays on reading.

Fit check, honestly: Marqly does not format citations or manage BibTeX — keep your reference manager for the bibliography. There’s no offline mode for reading in dead zones, no Android app (the web app runs in Android browsers), and no self-hosting for institutions that mandate it. What it replaces is the graveyard of bookmarks, open tabs, and “papers_final_v2” folders — the part of the PhD workflow nobody’s reference manager ever fixed.