Lector vs LinguaCafe
Lector
lector.devLinguaCafe
github.comSide by side
| Figure | Lector | LinguaCafe |
|---|---|---|
| Price from | $5/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| iOS | Yes | — |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 4 | 1 |
| Spec score · Language Immersion Apps | 9.1 (#1) | 7.9 (#13) |
| Integrations | The documentation lists AnkiConnect, Claude or OpenAI-compatible AI servers, optional Google Cloud Text-to-Speech, and Docker. | Vocabulary lookups can show DeepL translations when enabled, and imported reading material can include Jellyfin external subtitles. |
| Support | The contact page offers email replies, Discord live chat, and GitHub issues for bugs. | The project lists GitHub, Discord, Reddit, and email as contact channels, and says the maker will try to help with update problems reported on GitHub or Discord. |
| Vocabulary | Saved words and phrases are tracked with known and learning states in a vocabulary database. | Readers can look up words and phrases, save translations, mark words as known or ignored, and track vocabulary by learning level. |
Plans
In their own words
LinguaCafe
LinguaCafe is a free, open-source web app for learning languages through reading and vocabulary review. It is self-hosted, so users can run it on their own machine or private web server and control their data. Reading material can come from plain text, text files, ebooks, YouTube subtitles, subtitle files, Jellyfin external subtitles, or webpages. Readers can look up words and phrases, save translations, mark items as known or ignored, and track vocabulary by learning level. Spaced-repetition reviews follow an approach similar to the Leitner system, and highlighted cards can be exported to Anki or other SRS software. DeepL translations are available for lookups when enabled. Text-to-speech uses browser SpeechSynthesis, with voices dependent on the browser. The project lists many supported languages and supports Progressive Web App installation on mobile devices; it works in browsers on screens at least 340 pixels wide. Setup uses Docker Compose. The project supports x64, with an additional step for Apple silicon, but other Armv8 devices such as Raspberry Pi 3 and newer do not currently work. One user per server is supported.