by llm-guy · Agent Tool · ★ 282
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🧠 Jarvis – Local Voice-Controlled AI Assistant Jarvis is a voice-activated, conversational AI assistant powered by a local LLM (Qwen via Ollama). It listens for a wake word, processes spoken commands using a local language model with LangChain, and responds out loud via TTS. It supports tool-calling for dynamic functions like checking the current time. 🚀 Features 🗣 Voice-activated with wake word "Jarvis" 🧠 Local language model (Qwen 3 via Ollama) 🔧 Tool-calling with LangChain 🔊 Text-to-speech responses via 🌍 Example tool: Get the current time in a given city 🔐 Optional support for OpenAI API integration ▶️ How It Works () Startup & local LLM Setup Initializes a local Ollama model () via Registers tools () using LangChain Wake Word Listening Listens via microphone (e.g., ) If it hears the word "Jarvis", it enters "conversation mode" Voice Command Handling Records the user’s spoken command Passes the command to the LLM, which may invoke tools Responds using text-to-speech (with optional custom voice) Timeout If the user is inactive for more than 30 seconds in conversation mode, it resets to wait for the wake word again.
| Stars | 282 |
| Forks | 98 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 54.5038246203858/100 |
| Open Issues | 4 |
| Last Updated | 2025-09-08 |
| Created | 2025-07-23 |
| Platforms | python |
| Est. Tokens | ~1k |
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jarvis is Jarvis is a voice-activated, conversational AI assistant powered by a local LLM (Qwen via Ollama). It listens for a wake word, processes spoken commands using a local language model with LangChain, an. It is categorized as a Agent Tool with 282 GitHub stars.
jarvis is primarily written in Python.
You can find installation instructions and usage details in the jarvis GitHub repository at github.com/llm-guy/jarvis. The project has 282 stars and 98 forks, indicating an active community.
The top alternatives to jarvis on Agent Skills Hub include claude-forge, excalidraw-diagram-skill, agent-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.