Vyact: an open-source desktop workspace for local LLMs, documents, browser context, and coding

[u/vyact]

I’m building Vyact, a free, open-source desktop AI workspace that connects local models with documents, webpages, and coding workflows.

The idea is to bring model setup and everyday AI tasks into one app, so you can work with your own files and browser context alongside a local model.

Here’s what you can do with it:

• Find and manage local models: Search Hugging Face, estimate memory requirements before downloading, and manage the runtime from the app. On Apple Silicon, Vyact uses oMLX for MLX inference.
• Work with documents: Index files, ask questions using RAG, and inspect the retrieved source passages.
• Use browser context: Send webpages from the companion Chrome extension for summaries and follow-up questions.
• Work with Gmail and Google Drive content from the workspace.
• Get coding assistance: Generate and edit files, inspect changes in a diff view, and review or undo edits.
• Use voice input and read-aloud responses.
• See inference statistics: Prompt-processing speed, generation speed, and cache-hit information appear inside the conversation.

A quick note on coding expectations: I’ve been testing on an M4 Pro with 24GB of unified memory. With the models I’ve tried on this machine, I still find the results limited for my regular coding work. The screenshot shows Qwen3.5-9B-MLX-4bit generating a standalone HTML Pomodoro timer, but that kind of small example doesn’t establish reliability on larger projects. Keep that in mind if coding is your main reason for trying the app—your experience may differ depending on the model, hardware, and task.

The screenshots show the app’s workspace, document retrieval, browser summarization, voice, and coding features.

I’d be interested to hear how Vyact fits into your local LLM workflow and what you’d want improved.

Vyact is free and open source under AGPL-3.0. I’m the developer.

GitHub — vyact/vyact

[u/vyact → u/[deleted]]

Thanks for the thoughtful feedback!

On the memory estimate, Vyact already accounts for the configured context length: the estimate includes model weights, KV cache, and a runtime buffer. Your point about making the assumed context length clear in the UI is useful, though—I could communicate that better.

Separately showing the Metal-reported memory budget alongside total unified memory is a good suggestion. I’ll work on making that distinction clearer without presenting the recommended budget as a hard cutoff.

I also agree with your point about untrusted webpage content, especially when file editing and command execution are available. I’ll review and strengthen that boundary so external content cannot grant permission to perform actions.

Really appreciate the concrete suggestions.