A structured literature tracker built for graduate students and researchers. You do the reading. You do the thinking. LitFlow handles the organization — and lets AI synthesize your notes, not replace them.
Single HTML file. No account. No server. Your data never leaves your machine.
Most PhD students read sporadically, take notes inconsistently, and spend hours reconstructing what a paper said when it's time to write. AI tools promise to help — but end up doing the reading for you, which defeats the point.
One paper per day. Six structured note fields. A streak counter that makes consistency feel earned. At 365 papers a year, the habit compounds into a breadth of knowledge that sets your writing and thinking apart.
LitFlow's synthesis feature works only from your notes — it is explicitly instructed not to add information from its training data. The AI surfaces what you already captured. The thinking remains yours.
LitFlow is a single HTML file. It runs in your browser, stores everything locally, and works offline. In Chrome or Edge, you can connect a data file on your hard drive — your library persists even if you clear browser data. No account, no server, no company holding your literature database hostage.
This came out of a real PhD workflow — MUSC Systems Neuroscience, multimodal behavioral phenotyping, the kind of lit review that spans five fields simultaneously. It solves the problem I actually had.
Every paper gets the same six fields: Study Design, Key Methods, Main Findings, Relevance to Your Project, Open Questions, and Notes. Consistent structure makes your notes actually useful when you write — and makes AI synthesis dramatically more reliable than unstructured prose.
Define your own literature domains — give each a name and a color. Papers can be filtered, sorted, and synthesized by track. Useful whether you're building depth in one field or managing the five-literature problem that most interdisciplinary PhD students face.
Select a track and a synthesis mode: draft literature review section, gap analysis, methods summary, key findings table, or a custom prompt. The prompt engineering is built in — every synthesis call instructs the model to work only from your captured notes, not from its training data. Your API key goes directly to your chosen provider; it never touches any server.
Claude · OpenAI · Local LLMs (Ollama, LM Studio)While reading and taking notes, paste a title, DOI, or URL into the "Additional Readings" field and hit Enter. The paper is instantly added to your library as Unassigned / To Read — no navigation, no interruption. DOIs auto-resolve to title and citation via CrossRef.
A day streak counter, per-track progress bars, and a progress line across the top of the page that fills as you complete papers. Small things, but consistency needs feedback loops — especially in the long middle of a PhD where progress is invisible.
Back up your entire library as JSON (re-importable) or export completed notes as formatted Markdown — ready to drop into Obsidian, Notion, a dissertation chapter, or a grant application. The Markdown export is the bridge between your reading habit and your writing.
In Chrome or Edge, go to ⚙ Settings → Data → Connect data file. LitFlow links to a .json file on your hard drive and writes every save directly to it. Clear cookies, reinstall the browser, switch computers — your library is still in the file. Firefox and Safari users keep the standard behavior with regular JSON export as backup.
Click "Download LitFlow" above. You get a single litflow.html file — no install, no dependencies, no build step.
Double-click the file or drag it into Chrome, Edge, Safari, or Firefox. All data is saved automatically in your browser's local storage — close and reopen anytime, your notes are still there.
File → Open → litflow.html
⚠️ Protect your library — don't rely on the browser alone.
LitFlow stores your notes in your browser's local storage by default. Clearing "cookies and site data" or resetting your browser will permanently delete everything. Do one or both of the following:
.json file on your hard drive. Every save writes to disk — your library survives any browser wipe.Go to ⚙ Settings and define your reading tracks — the literature domains your project spans. Name them, pick a color. This takes two minutes and shapes everything else.
Click "+ Add Paper," fill in the title and link, assign a track. Then open it, take notes in the six fields, and mark it done. That's the daily loop. One paper. Every day.
In Settings, pick a provider — Claude, OpenAI, or a local model running on your machine. Enter your API key. It's stored only in your browser, sent only to the API you chose. Once set, the ✦ Synthesize tab turns your completed notes into draft literature review sections, gap analyses, and more.
"The goal was never to automate the reading. The goal was to remove every friction point that makes the reading feel impossible on a Tuesday morning before clinic."
— Mike Martino, MD/PhD candidate, MUSC
Every synthesis prompt in LitFlow contains an explicit instruction: work only from the researcher's notes — do not add information from training data. This is a deliberate design choice, not a limitation.
The cognitive work of reading a paper — deciding what matters, connecting it to what you know, noticing what's missing — is where expertise is built. LitFlow keeps that work firmly with you. The AI's job is retrieval and synthesis of what you've already processed, not a shortcut around the processing itself.
Mike Martino is an MD/PhD candidate in Systems Neuroscience at the Medical University of South Carolina. His research spans fear sensitization, computational psychiatry, and multimodal behavioral phenotyping — which means he has to maintain a reading habit across five literatures simultaneously.
LitFlow grew out of the FIXATE project — a pipeline for extracting acoustic, kinematic, and facial behavioral features from naturalistic clinical video. The same frustration that drove that pipeline (too many good ideas, too little organized knowledge) drove this tool.
He believes researchers shouldn't have to choose between using AI tools and developing genuine expertise. LitFlow is his attempt to prove you don't have to.
LitFlow is planned for submission to the Journal of Open Source Software (JOSS) alongside a methods paper on structured AI-assisted literature review in graduate science education. If you use this tool and would be willing to participate in a brief survey study, keep an eye on The Neuro-Humanist for updates.
If LitFlow has been useful to your research, a citation will eventually be possible — and deeply appreciated.
One file. No account. No server. Works offline. Your notes stay yours.
LitFlow is and will always be free. If it saves you time, a coffee is appreciated and helps fund future tools.