jarvis

# Jarvis **A 100% private AI voice assistant that lives on your computer** (works offline). Talk naturally as if Jarvis is a third person in the room — say its name anywhere in your sentence and get conversational, context-aware responses. It remembers everything, always knows the current location and time, can search the web, read your screen, control Chrome, track nutrition, and much more with support for unlimited MCPs and tools without context rot. Sensitive info is automatically redacted before anything is saved to disk. 100% local processing. No subscriptions. No data harvesting. Automatic redaction of sensitive info. Free offline dictation included. --- **Support Jarvis** [](https://github.com/sponsors/isair) [](https://ko-fi.com/isair) ---

## Why Jarvis? ** Your data stays yours** - 100% local AI processing. No cloud, no subscriptions, no data harvesting. Automatic redaction of sensitive info. This is non-negotiable. **️ A third person in the room** - Unlike voice assistants that only respond to rigid commands, Jarvis understands conversations. It maintains a short temporary rolling context of what's being discussed, so when you ask "Jarvis, what do you think?" it knows exactly what you're talking about. Have it chime into discussions with friends, help debug code while you talk through problems, or weigh in on decisions. ** Never forgets** - Unlimited memory across conversations. Adapts tone naturally to the topic. Learns your preferences over time. **️ Free dictation** - Hold a hotkey, speak, release — your words appear in any app as text. Like WisprFlow, but free, offline, and private. No subscription, no cloud transcription. ** Extensible** - MCP integration connects Jarvis to thousands of tools: smart home, GitHub, Slack, databases, and more. Smart tool selection means adding more tools won't slow things down. ** Transparent progress** - We track what works (and what doesn't) with automated evals. [See current accuracy →](EVALS.md) ** Known limitations:** Jarvis is under active development. Primary development happens on macOS. Windows/Linux support may lag behind. We're building in the open, [issues](https://github.com/isair/jarvis/issues) and [contributions](https://github.com/isair/jarvis/pulls) welcome! - Voice-only for now—no text chat interface yet ([#35](https://github.com/isair/jarvis/issues/35)) - No mobile apps ([#17](https://github.com/isair/jarvis/issues/17)) - "Stop" commands during speech sometimes get filtered as echo ([#24](https://github.com/isair/jarvis/issues/24)) - Dictation is not available on macOS 26+ (Tahoe) due to a pynput incompatibility ([#172](https://github.com/isair/jarvis/issues/172)) See it in action (example conversations) **Chiming into conversations** (the magic moment): ``` Alice: I wonder what the weather will be like tomorrow Bob: Yeah, we should check before planning the picnic Alice: Jarvis, what do you think? Heard: "What do you think Jarvis?" Intent (wake word): directed → "what do you think about the weather for the picnic" ✨ Working on it: what do you think about the weather for the picnic Tool: getWeather… Generating response... Jarvis Tomorrow looks great for a picnic! Sunny with highs around 22°C... ``` Jarvis understood the entire conversation and gave a contextual answer — no need to repeat the question. **Browser automation via MCP:** ``` Heard: "Open YouTube Jarvis." Intent (wake word): directed → "open YouTube" ✨ Working on it: open YouTube Generating response... Jarvis I have opened YouTube for you. ``` **Health-aware advice:** ``` Heard: "Should I order pizza tonight considering my health goals Jarvis?" Intent (wake word): directed → "should I order pizza tonight considering my health goals" ✨ Working on it: should I order pizza tonight considering my health goals Tool: fetchMeals… Generating response... Jarvis Looking at your week — you mentioned wanting to lose 5kg by June... Today you've had about 860 calories so far. Given your 1,800 calorie target, you've got room for pizza! But maybe consider thin crust to save calories. ``` **Personalised news search:** ``` Heard: "What are some news from today that might interest me Jarvis?" Intent (wake word): directed → "what are some news from today that might interest me" ✨ Working on it: what are some news from today that might interest me Tool: searchMemory… Tool: webSearch… Generating response... Jarvis Here's a quick snapshot of today's headlines... ``` **Natural wake word placement** (say "Jarvis" anywhere): ``` Heard: "Give me a random topic to discuss Jarvis." Intent (wake word): directed → "give me a random topic to discuss" ✨ Working on it: give me a random topic to discuss Generating response... Jarvis How about the Fermi Paradox? Given the vast number of stars... ``` **Echo detection** (Jarvis ignores its own speech): ``` Jarvis I have opened YouTube for you. Listening for follow-up (3s)... Heard: "I have opened YouTube for you." Heard (echo): "i have opened youtube for you." Returning to wake word mode ``` ## Quick Install ### 1. Install Prerequisites | Platform | Requirement | |----------|-------------| | **All** | [Ollama](https://ollama.com/download) (default), or any OpenAI-compatible server you already run (LM Studio, Jan, llama.cpp, vLLM, oMLX, LocalAI, …) — see [Configuration → LLM Provider](#configuration) | ### 2. Download Jarvis Get the latest from [GitHub Releases](https://github.com/isair/jarvis/releases): | Platform | Download | Run | |----------|----------|-----| | **Windows** | `Jarvis-Windows-x64.zip` | Extract → Run `Jarvis.exe` | | **macOS** | `Jarvis-macOS-arm64.zip` | Extract → Move to Applications → Right-click → Open | | **Linux** | `Jarvis-Linux-x64.tar.gz` | `tar -xzf` → Run `./Jarvis/Jarvis` | Jarvis starts listening automatically — just say "Jarvis" and talk!

## Features - **Conversational Awareness** - Understands ongoing discussions. Ask "Jarvis, what do you think?" and it knows what you're talking about. Works naturally in multi-person conversations. - **Unlimited Memory** - Never forgets. Searches across all your conversation history. Memory Viewer GUI included. - **Adaptive Tone** - Automatically surgical for code, pragmatic for business, encouraging for wellbeing — no manual mode switching - **Smart Tool Selection** - Embedding-based relevance filtering picks only the tools needed per query — add unlimited MCP tools without performance degradation - **Built-in Tools** - Screenshot OCR, web search (DuckDuckGo → Brave → Wikipedia fallback chain with auto-fetch), weather, file access, nutrition tracking, location awareness, plus a tool-discovery escape hatch the agent uses to widen its own toolset mid-reply - **Knowledge Graph Memory** - Self-organising memory that learns from conversations, auto-splits by topic, and surfaces relevant knowledge automatically - **Natural Voice** - Say "Jarvis" anywhere in your sentence, interrupt with "stop", follow up without repeating the wake word - **Dictation Mode** - Free, offline alternative to WisprFlow — hold a hotkey, speak, release to paste text into any app - **MCP Integration** - Connect to thousands of external tools (Home Assistant, GitHub, Slack, etc.) ## System Requirements | Hardware | VRAM | Model | |----------|------|-------| | Low-VRAM / CPU | 2GB+ | `qwen3.5:0.8b` | | Most users | 8GB+ | `gemma4:e2b` (default) | | Better quality | 16GB+ | `gemma4:e4b` | | High-end | 24GB+ | `gpt-oss:20b` | > **Note:** VRAM requirements include the fast model (`gemma4:e2b`) which is always loaded alongside the chat model for voice intent classification and other real-time work. The default chat model shares this, so no extra VRAM is needed. The setup wizard will guide you through model selection and installation on first launch. ## Configuration Most users won't need to change anything. Open **⚙️ Settings** from the tray menu to configure Jarvis through a graphical interface — no JSON editing required. Settings are saved to `~/.config/jarvis/config.json`.

LLM Provider (Ollama or OpenAI-compatible) By default Jarvis runs everything locally through [Ollama](https://ollama.com): no API keys, nothing leaves your machine. If you already run an OpenAI-compatible server you can point Jarvis at it instead. Your data still only travels to the servers you control. Pick the provider in the Setup Wizard's first step, or under **⚙️ Settings → LLM Provider**. No JSON editing required. On the OpenAI-compatible page the wizard does the legwork for you: it auto-detects running local servers, offers a one-click preset for your app, and when you press **Connect** it loads the server's model list and checks the chosen model for chat, tool calling, and embeddings, so you know it works before you finish setup. Tested local servers (all run on your own machine): | App | Default base URL | Notes | |-----|------------------|-------| | LM Studio | `http://localhost:1234/v1` | Chat, tool calling, and embeddings. | | Ollama (OpenAI API) | `http://localhost:11434/v1` | The native Ollama path is the default; the OpenAI shape works too. | | Jan | `http://localhost:1337/v1` | Chat and tool calling. | | llama.cpp (`llama-server`) | `http://localhost:8080/v1` | Tool calling depends on the model. | | LocalAI | `http://localhost:8080/v1` | Feature support depends on the backend model. | | vLLM | `http://localhost:8000/v1` | Tool calling depends on the model. | | oMLX (Apple Silicon) | varies | No embeddings endpoint, so memory uses keyword search unless you route embeddings to Ollama (below). | For reference, the underlying config keys are: ```json { "llm_provider": "openai_compatible", "llm_base_url": "http://localhost:1234/v1", "llm_api_key": "", "llm_chat_model": "your-served-model-name" } ``` - `llm_base_url`: your server's OpenAI API base URL. - `llm_api_key`: only if your server requires one; leave empty otherwise. - `llm_chat_model`: whatever model name your server exposes. - `fast_model` (optional): the small, quick model used for real-time work (voice intent, tool routing, quick classifications). Leave empty for automatic: `gemma4:e2b` on Ollama, your chat model on an OpenAI-compatible server. Set it to pin a dedicated small model. **Embeddings** (used for memory search) can run on a different backend. If your chat server has no embeddings endpoint, memory falls back to keyword search. To keep full semantic memory, route embeddings to Ollama (the wizard offers this automatically when it detects a server that cannot embed): ```json { "embedding_provider": "ollama", "embedding_model": "nomic-embed-text" } ``` Leave `embedding_provider` empty to use the same provider as chat. With no working embeddings, memory search degrades gracefully to keyword search. Speech Recognition (Whisper) #### Language Modes - **Multilingual** (default, 99 languages): `"whisper_model": "medium"` - **English Only** (slightly better English accuracy): `"whisper_model": "medium.en"` #### Model Sizes | Model | English | Multilingual | Download | VRAM | Speed | |-------|---------|--------------|----------|------|-------| | Tiny | `tiny.en` | `tiny` | ~75 MB | ~1 GB | ~10x | | Base | `base.en` | `base` | ~140 MB | ~1 GB | ~7x | | Small | `small.en` | `small` | ~465 MB | ~2 GB | ~4x | | **Medium** | `medium.en` | `medium` | ~1.5 GB | ~5 GB | ~2x | | Large V3 Turbo | - | `large-v3-turbo` | ~1.5 GB | ~6 GB | ~8x | Speed is relative to the original large model. [Source](https://github.com/openai/w