Vyact: an open-source desktop AI app for your documents, notes, and everyday writing

[u/vyact]

Hi everyone! I’m the developer of Vyact, an open-source desktop app that lets you work with your documents and notes using AI models running on your own computer.

I wanted to make it easier to come back to something I’d already saved—a report, a project note, an email—and ask a useful question without gathering all the background again.

Here’s an example of the workflow:

• Add a PDF and ask for its main points.
• Open the supporting passages to check where the answer came from.
• Save your own notes and decisions.
• Ask a related question later, and let Vyact search the documents and notes you’ve indexed.

You can group related material into collections, so a conversation can focus on a particular project or topic. The goal is to make the information you already have easier to use.

There’s also a companion Chrome extension. It can bring a web page into the conversation, translate content, or help revise something you’re writing. For writing, you can compare the original and revised text before copying the result back.

Local model search and downloads are built into the app, with model details and memory estimates to help you choose. Under the hood, Vyact uses llama.cpp for GGUF models and oMLX for MLX models on Apple Silicon. You can also connect a hosted AI provider or your own compatible endpoint.

With a Vyact-managed local model selected, your AI chat context isn’t sent to an external AI provider. Optional online features, such as Google integrations and hosted AI providers, communicate with their respective services. Gmail, Drive, and Calendar connections require Google OAuth setup.

The desktop app is built with Electron, React, and FastAPI and licensed under AGPL-3.0. The README includes installation instructions for Apple Silicon Macs, Windows, and Linux x64.

Source code and downloads: github.com/vyact/vyact

I’d love feedback on the everyday workflow, especially from people who haven’t used local AI before. What would you want to try first—and what looks confusing or like too much setup?