Academic RSS Reader

Turn scattered academic RSS feeds into a local reading queue that you can continuously screen, translate, and prioritize.

Academic RSS Reader is a desktop Obsidian plugin for the first pass of academic literature screening. It collects RSS and Atom feeds into a local SQLite database, keeps reading decisions in five clear baskets, and learns from those decisions to prioritize unread papers. It does not require a Python sidecar.

The plugin focuses on the step between “new papers arrived” and “I know what deserves a closer read”. It is designed to reduce repetitive triage, not to replace a reference manager or a full-text database.

[!IMPORTANT] Current recommended version: 1.5.0. This version requires Obsidian 1.13.0 or later and is desktop-only. Its bundled runtime must also provide Node.js 22.16 or later, node:sqlite DatabaseSync, and the SQLite Backup API. Update Obsidian before installing or updating when possible.

Why use Academic RSS Reader

One reading queue: bring journal, lab, publisher, and other RSS/Atom feeds into one reader.
Feedback-driven prioritization: mark papers as interested, archived, hidden, or expired, then use those decisions to rank the unread queue.
A small, understandable workflow: collect, scan, classify, and return to the papers worth reading.
Local-first storage: subscriptions, papers, reading states, translations, recommendations, and analysis stay in a Vault directory you choose.
Optional assistance: translate visible titles or ask a configured OpenAI-compatible service to review only the papers that remain pending.
Recoverable and explainable: migrations create a protection backup first; recommendation results retain positive and negative keyword evidence, while subscription defaults and article-level journal metadata stay separate.

The core principle is simple: the plugin handles repetitive collection and sorting; you decide what deserves attention.

How it works

Choose a Vault data directory
        ↓
Add or import RSS/Atom feeds
        ↓
Update feeds and collect new papers
        ↓
Scan the unread basket and mark reading states
        ↓
Refresh local recommendations
        ↓
Optionally review pending papers with an LLM

Who it is for

Academic RSS Reader is a good fit if you:

follow multiple journals, publishers, laboratories, or research topics through RSS;
receive more new papers than you can inspect immediately;
want a queue that reflects your own reading decisions instead of a generic popularity ranking;
prefer to keep research data inside your local Obsidian Vault;
want lightweight title translation without sending every paper to an AI service.

What it can do

Collect and organize feeds

Add, edit, enable, disable, and delete feeds.
Import OPML, XML, TXT, pasted feed lists, or one URL per line.
Update enabled feeds automatically when the reader is first opened after an app launch. Feeds updated successfully within the previous hour are skipped with a visible count; manual updates always run.
Use ETag, Last-Modified, HTTP 304, bounded concurrency, retry delays, timeouts, cancellation, health states, and automatic backoff to make repeated updates less noisy.
Extract titles, authors, source names, years, DOIs, links, and abstracts when provided by the feed.
Preserve stable identities, normalized titles, cross-feed associations, existing reading states, and layered duplicate checks.

Screen papers in five baskets

Manage papers as Unread, Interested, Archived, Hidden, or Expired.
Load long lists continuously in batches of 100 instead of rendering the whole database at once.
Open the original paper in the system browser.
Undo the most recent status action during the current reader session.

Prioritize the unread queue

Train a local TypeScript TF-IDF and logistic-regression model from your reading states.
Use interested and archived papers as positive examples, and hidden and expired papers as negative examples.
Show high-relevance, pending, and low-relevance tiers below a fixed three-line title area, using a compact status badge and two aligned lines for the strongest positive and negative keyword evidence.
Use the same TF-IDF feature scale for training and formal scoring; cancelled background training cannot write stale results.
Manage the learned keyword list by disabling or re-enabling individual terms.
Refresh recommendations automatically after feed updates, while reusing an unchanged model and scoring only new or changed unread papers when possible.
Optionally review only pending papers with a user-configured OpenAI-compatible LLM. The LLM does not run automatically during normal feed updates.

The local model needs at least two positive and two negative training papers. Until there is enough feedback, papers can remain unscored rather than receiving an arbitrary personalized judgment.

Translate and analyze

Translate visible paper titles and prefetch the next eight titles while you scroll.
Cache translations in the local database and keep the original title available at any time.
Review overall reading states and compare interest rates across feeds.
Keep the interface language tied to Obsidian while choosing the content translation target separately.

Data model and safety boundaries

Each card shows one journal name: an article-level value refreshed from RSS takes priority, with the earliest associated feed default used only as a fallback.
Paper cards use a fixed 13rem height with increased row spacing. Three title lines are always reserved, with longer titles clipped after the third line without inserting a misleading mid-title ellipsis; actions immediately follow the relevance block.
Title fragments delimited by $...$ , $$...$$, $...$, or \[...\] use Obsidian's native MathJax renderer; invalid fragments fall back to their original text.
Article links accept only http: and https:. RSS/XML rejects DOCTYPE, invalid root structures, and responses over 10 MiB; only known tracking parameters are removed, while business query parameters are preserved.
LLM endpoints must use HTTPS, or HTTP on localhost, 127.0.0.1, or ::1. Requests have a 30-second timeout and bounded retries.

Get started

Open Settings → Community plugins → Browse, search for Academic RSS Reader, and install and enable it.
Open Settings → Academic RSS Reader and choose a data directory inside the current Vault.
Select Create new database for a new setup, or Load database for an existing rss-reader.sqlite3. v1.3 and older databases are upgraded in place; a protection backup is created first and the original database is restored if migration fails.
Open the reader from the RSS ribbon icon, or press Cmd/Ctrl + P and run Open reader.
Add feeds or import an OPML/feed list, then run an update.
Mark papers as you review them. After you have enough examples, open Personalized recommendations and run Update keyword recommendations.
If you want an additional review pass, configure an OpenAI-compatible endpoint, model, and SecretStorage API key in settings, then run Review pending items with LLM explicitly.

If the community directory is unavailable, download only main.js, manifest.json, and styles.css from the matching GitHub Release and place them in:

<Vault>/.obsidian/plugins/academic-rss-reader/
├── main.js
├── manifest.json
└── styles.css

Do not install the source-code archive generated by GitHub. When upgrading manually from the former rss-reader plugin ID, close Obsidian, rename the old plugin directory to academic-rss-reader, replace the three release files, and keep data.json so the legacy LLM key can be migrated to SecretStorage on first load.

Data and privacy

Your runtime database is stored in the Vault-relative directory you select:

<Vault>/<selected data directory>/
├── rss-reader.sqlite3
└── backups/

Subscriptions, papers, reading states, translations, recommendations, and interest analysis are stored locally in SQLite. General settings stay in the plugin's data.json; the LLM API key is stored through Obsidian SecretStorage, while data.json stores only the selected secret entry name.

Academic RSS Reader does not require an account, run telemetry, or operate a developer relay server. Network requests are limited to services you use:

RSS/Atom updates go to the feed URLs you configure.
Title translation uses an unofficial Google web endpoint. When enabled, visible and prefetched titles are sent directly from your device; translations may be rate-limited, inaccurate, or unavailable and should not be used for formal citation.
An LLM request is made only when you explicitly test the connection or review pending papers. The request includes the paper information needed for the review and your optional research-interest description, and is sent to the endpoint you configure.

The plugin does not create or open a database during startup. Database creation and loading begin only after you choose a Vault-relative data directory and explicitly create or load it. Saves use validated temporary and previous snapshots, and the settings page can create protection backups and restore the latest valid backup. The desktop-native SQLite boundary is limited to the database file, WAL/SHM sidecars, temporary files, and backups; other Vault files continue to use Obsidian's DataAdapter boundary.

Compatibility and boundaries

Desktop Obsidian only: macOS, Windows, and Linux desktop installations.
Requires Obsidian 1.13.0 or later plus a bundled Node.js 22.16+ runtime exposing node:sqlite; unsupported runtimes block database loading with an upgrade prompt. There is no sql.js fallback.
No Python, separate Node.js installation, or sidecar process is required; Obsidian supplies the required runtime.
Title translation is an experimental reading aid, not a formal translation or citation service.
The database is intended for one local desktop Obsidian instance at a time; it is not a multi-user sync database.

Continue reading

Development and release commands are documented in the development guide.

License

MIT

Academic RSS Reader

Description

Reviews

Stats

Latest Version

Changelog

Academic RSS Reader v1.4.1

README file from