OpenJarvis README

The project's README from github.com/open-jarvis/OpenJarvis. As one markdown file: README.md.

OpenJarvis

Personal AI, On Personal Devices.

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OpenJarvis demo reel

Documentation

Project Site

Paper

Leaderboard

Roadmap

Why OpenJarvis?

Personal AI agents are exploding in popularity, but nearly all of them still route intelligence through cloud APIs. Your "personal" AI continues to depend on someone else's server. At the same time, our Intelligence Per Watt research showed that local language models already handle 88.7% of single-turn chat and reasoning queries, with intelligence efficiency improving 5.3× from 2023 to 2025. The models and hardware are increasingly ready. What has been missing is the software stack to make local-first personal AI practical.

OpenJarvis is that stack. It is a framework for local-first personal AI, built around three core ideas: shared primitives for building on-device agents; evaluations that treat energy, FLOPs, latency, and dollar cost as first-class constraints alongside accuracy; and a learning loop that improves models using local trace data. The goal is simple: make it possible to build personal AI agents that run locally by default, calling the cloud only when truly necessary. OpenJarvis aims to be both a research platform and a production foundation for local AI, in the spirit of PyTorch.

Installation

Pick your platform and run one command. Each installer handles uv, the Python venv, Ollama, and a starter model — about 3 minutes on broadband.

Platform One-liner
macOS · Linux · WSL2 curl -fsSL https://open-jarvis.github.io/OpenJarvis/install.sh | bash
Native Windows irm https://open-jarvis.github.io/OpenJarvis/install.ps1 | iex
Desktop GUI Download .exe / .dmg / .deb / .rpm / .AppImage from the latest release

Then jarvis to start. The Rust extension and larger models continue downloading in the background; jarvis doctor shows status.

Platform-specific notes (WSL2 setup, native-Windows scheduled-task service, desktop prerequisites, manual / contributor install): see the installation docs.

Quick Start

jarvis                          # start chatting (default: chat-simple)
jarvis init --preset <name> --force  # replace config with a starter preset

Prefix jarvis ... with uv run, or source .venv/bin/activate first.

Preset What it does
morning-digest-mac / morning-digest-linux / morning-digest-minimal Spoken daily briefing from email, calendar, health, news
deep-research Multi-hop research across indexed docs with citations
code-assistant Agent with code execution, file I/O, and shell access
scheduled-monitor Stateful agent on a schedule with memory
chat-simple Lightweight conversation, no tools

Example:

jarvis init --preset morning-digest-mac --force
jarvis connect gdrive          # one OAuth covers Gmail / Calendar / Tasks
jarvis digest --fresh          # generate and play your first briefing

Per-preset deep dives: morning digest · deep research · code assistant · scheduled monitor · chat simple · or the full quickstart guide.

Skills

Skills teach agents how to better use tools and improve their reasoning. Every skill is a tool — agents discover them from a catalog and invoke them on demand.

# Install skills from public sources
jarvis skill install hermes:arxiv
jarvis skill sync hermes --category research

# Use skills with any agent
jarvis ask "Use the code-explainer skill to explain this Python code: for i in range(5): print(i*2)"

# Optimize skills from your trace history
jarvis optimize skills --policy dspy

# Benchmark the impact
jarvis bench skills --max-samples 5 --seeds 42

Import from Hermes Agent (150 skills), OpenClaw (13,700 community skills), or any GitHub repo. Skills follow the agentskills.io open standard.

See the Skills User Guide and Skills Tutorial for details.

Built-in Agents

OpenJarvis ships with eight built-in agents across three execution modes (on-demand, scheduled, continuous):

Agent Type What it does
morning_digest Scheduled Daily briefing from email, calendar, health, news — with TTS audio
deep_research On-demand Multi-hop research with citations across web and local docs
monitor_operative Continuous Long-horizon monitoring with memory, compression, and retrieval
orchestrator On-demand Multi-turn reasoning with automatic tool selection
native_react On-demand ReAct (Thought-Action-Observation) loop agent
operative Continuous Persistent autonomous agent with state management
native_openhands On-demand CodeAct — generates and executes Python code
simple On-demand Single-turn chat, no tools

See the User Guide and Tutorials for detailed setup instructions.

Full documentation — including Docker deployment, cloud engines, development setup, and tutorials — at open-jarvis.github.io/OpenJarvis.

Community

Contributing

We welcome contributions! See the Contributing Guide for incentives, contribution types, and the PR process.

Quick start for contributors:

git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
uv sync --extra dev
uv run pre-commit install
uv run pytest tests/ -v

Browse the Roadmap for areas where help is needed. Comment "take" on any issue to get auto-assigned.

About

OpenJarvis is part of Intelligence Per Watt, a research initiative studying the intelligence efficiency of AI systems. The project is developed at Hazy Research and the Scaling Intelligence Lab at Stanford SAIL.

Sponsors

Laude Institute • Stanford Marlowe • Google Cloud Platform • Lambda Labs • Ollama • IBM Research • Stanford HAI

Citation

@misc{saadfalcon2026openjarvispersonalaipersonal,
      title={OpenJarvis: Personal AI, On Personal Devices}, 
      author={Jon Saad-Falcon and Avanika Narayan and Robby Manihani and Tanvir Bhathal and Herumb Shandilya and Hakki Orhun Akengin and Gabriel Bo and Andrew Park and Matthew Hart and Caia Costello and Chuan Li and Christopher Ré and Azalia Mirhoseini},
      year={2026},
      eprint={2605.17172},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2605.17172}, 
}

License

Apache 2.0

OpenJarvis (open-jarvis/OpenJarvis)

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11.4K ·GitHub
OpenJarvis
What is this?OpenJarvis is an open-source framework for personal AI that runs on your own devices: agents use local models by default and call the cloud only when truly necessary.
Is it a framework, a CLI, or a desktop app?Primarily a framework — in the spirit of PyTorch — that you drive through the jarvis CLI, and it also ships a packaged desktop GUI app for Mac, Windows, and Linux.
How do I get it running?Run the one-line installer for your platform — curl on macOS/Linux/WSL2, irm on native Windows — or download a desktop installer. Setup takes about 3 minutes, then run jarvis to start.
Do I need to bring my own model or an API key?No — the installer sets up Ollama and a starter model for you, so you can run fully on local models without any API key. Cloud engines are supported, but only as an optional fallback.
Can local models really handle my everyday requests?Mostly yes: the team's research measured local models already handling 88.7% of single-turn chat and reasoning queries. OpenJarvis is built around that, keeping the cloud for the remaining hard cases.
What platforms does it run on?macOS, Linux, and Windows (native via PowerShell or WSL2), each with a one-line installer, plus packaged desktop GUI builds (.exe, .dmg, .deb, .rpm, .AppImage) for all three.
What can the built-in agents do?Eight agents across three modes (on-demand, scheduled, continuous): morningdigest delivers a spoken daily briefing from email, calendar, health, and news; deepresearch does multi-hop research with citations; nativeopenhands generates and executes Python code; others cover long-horizon monitoring, tool orchestration, ReAct reasoning, and plain chat.
What are skills?Skills teach agents to use tools better — each skill is itself a tool that agents discover in a catalog and invoke on demand. They follow the agentskills.io open standard, and you can import ~150 from Hermes Agent, ~13,700 from OpenClaw, or any GitHub repo.
How is its approach to evaluation different?Its evaluations treat energy, FLOPs, latency, and dollar cost as first-class constraints alongside accuracy, and a learning loop keeps improving models using local trace data.
What is Intelligence Per Watt?A research initiative studying the intelligence efficiency of AI systems. OpenJarvis is part of it and was developed at Hazy Research and the Scaling Intelligence Lab at Stanford SAIL.
What are similar projects?Raven, XRAG, opensre, Second-Me, leon, EverOS, CUAWright, yovoice
Which topics is it in?personal-ai, on-device, text-to-speech, evaluation, ollama, skills, local-first, agent-skills, framework, gui, privacy, terminal
Creator
open-jarvis
Size
86.6 MB
Stars
11.4K
Updated
CommitOct 17Releasedesktop-v1.0.2May 25
Age
8mo
Commits · 90 Days