open-source desktop agent app running its agents on local ollama models: two agents on qwen3.5:9b and qwen3.8 on a 16 gb card, real recording, including the limits i hit

i'm the maintainer of dodaios (formerly personal jarvis), an open-source ai agent app for the desktop. apache 2.0, runs on windows, macos and linux, no account and no cloud of ours between you and your models. it works with hosted models too, but this post is only about running it locally. what it is one app for the things ai can do on a computer: - agents that stay around: each one has its own chat, instructions, tools, memory and schedule - an agentic ide: nine coding clis side by side in real terminal panes, or one at a time as a thread - voice: your own wake phrase, live conversations, dictation into any app. speech recognition (local whisper) and voices (piper) can run fully offline - computer use, a local wiki as memory, routines, and a marketplace with 40+ plugins and mcp servers what the video shows a real screen recording, sped up while the models think. i create two agents and pick ollama as the model provider in the dialog, once with qwen3.5:9b and once with qwen3.8-16gb, on an rtx 5070 ti with 16 gb. each gets a small job: three post drafts, and a reply to a mail. real time to the answer was 71 s for the 9b and 116 s for the larger one. what did not work locally with hosted models an agent sets its own role and schedules its own routine from one message ("every weekday at 8:00 ..."). both local models failed at exactly those tool calls in my tests: the 9b never called the tool, the larger one called the tool that lists routines twice but never the one that saves, and the app ended the turn after eight minutes. so the jobs in the video need no tools. the thread view for coding agents also has no local model picker yet. if you know a local model that is reliable at tool calls on 16 gb, i would like to test it. how it's built - python 3.11 backend on fastapi, react frontend, shown in a native window through pywebview - models are plugins behind one streaming interface: ollama and any openai-compatible local server, next to openai, anthropic, gemini, openrouter and nvidia. nothing is hardcoded to one provider, and you can pick a different model per agent - layers only talk downwards through protocols, and sideways through immutable typed events on an event bus. every event carries a trace id and a flight recorder subscribes to all of them, so a run can be replayed - every action passes a risk check with four levels: safe, monitor, ask, block - memory is a local wiki on your disk that the app reads, writes and cites - keys live in your operating system's credential store, and nothing leaves the machine unless you connect a service that needs it honest notes the pixel creatures in the video come from a branch that is not released yet. it is mostly a one-person project with almost 5,900 commits, so expect rough edges. repo: https://github.com/Dodaios/Dodaios submitted by /u/InternationalGap3698 [link] [comments]

Source: r/LocalLLaMA | 2026-10-13

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