FUTO Swipe
Open-source AI swipe typing models and library for accurate, on-device word prediction.
| What is it | Open-source AI swipe typing models and library for accurate, on-device word prediction. |
|---|---|
| Pricing | Unknown |
| Platform | API |
| API | Yes |
| Best for | integrating swipe typing into apps, building privacy-focused keyboards |
| Domain registered | 2025 |
Data updated Aug. 1, 2026
What does FUTO Swipe do?
FUTO Swipe is a family of open-source AI models and a C++ library designed for accurate swipe typing on mobile devices. It powers FUTO Keyboard, a fully offline Android keyboard app, but the models are available for anyone to build with. The system takes a user's swipe path — the curve traced by a finger across the keyboard — and predicts the intended word, all running on-device with no internet connection needed. This means your typing data never leaves your phone.
The architecture uses three complementary models. The Encoder is layout- and language-agnostic, handling general predictions. The ContextLM is a tiny language model that filters out nonsensical word choices based on the sentence so far. The Decoder is layout- and language-specific, achieving top-tier accuracy — currently only available for QWERTY English. Combined with a beam search of width 300, the system achieves a top-4 fail rate of about 4% (under 1% ignoring out-of-vocabulary words). All models are small: the largest is 1.5 million parameters, and the entire stack runs in milliseconds even on low-end hardware. The C++ library (swipe-library) handles inference, decoding, and beam search, making it straightforward to integrate into any application.
FUTO Swipe is ideal for developers building custom keyboards, accessibility tools, or any text input system that values privacy and performance. It's already used in FUTO Keyboard, but the open models invite experimentation — from swipe typing in VR to using a laptop trackpad. The models are released under the FUTO Model License (attribution required), and the library is GPL. Researchers and hobbyists can download the dataset of 1 million swipes (MIT licensed) from HuggingFace to train their own models.
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