OpenJarvis is an open-source framework for building personal AI agents that run on your own hardware. It’s built by Stanford’s Hazy Research and Scaling Intelligence labs, as part of their “Intelligence Per Watt” research into efficient local AI.
Local models can already handle most day-to-day chat and reasoning, yet most personal AI still sends every request to the cloud. OpenJarvis makes local-first the default. Models run locally, and the cloud is optional. Energy, cost, and latency are tracked alongside accuracy.
Version 1.0 is now available with built-in support for Ollama.
Get started
Install Ollama
Download Ollama for macOS, Windows, or Linux.
Install OpenJarvis
On macOS or Linux, the install script sets up everything you need and auto-detects your existing Ollama installation:
curl -fsSL https://open-jarvis.github.io/OpenJarvis/install.sh | bash
On Windows, run that command inside WSL2, or install the desktop app.
Then run jarvis to start.
Choosing models
The install script sets up a starter model so you can begin right away, but you can choose your own. Pull any model through Ollama and use it for a query:
jarvis model pull qwen3.5:35b
jarvis ask -m qwen3.5:35b "Your prompt"
To set a default model, add it to ~/.openjarvis/config.toml:
[intelligence]
default_model = "qwen3.5:35b"
preferred_engine = "ollama"
Built-in agents
OpenJarvis ships with ready-to-run presets. Each one bundles an agent with the engines and tools it needs to run.
Morning briefing
Generate a morning briefing agent using your calendar, email, and the day’s news:
jarvis init --preset morning-digest-mac
jarvis connect gdrive
jarvis digest --fresh
Research across files
Ask a question to perform research across the web and your local documents, returning an answer with citations:
jarvis init --preset deep-research
jarvis memory index ./docs/
jarvis ask "Summarize all emails about Project X"
Local coding agent
A code agent that writes and runs Python on your machine to get tasks done:
jarvis init --preset code-assistant
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