OpenJarvis/docs/index.md

title, description, search, hide

title	description	search	hide
OpenJarvis	Personal AI, On Personal Devices	boost 2	navigation

Personal AI, On Personal Devices

OpenJarvis is a research framework for composable, on-device AI systems. Build personal AI that runs on your hardware. Cloud APIs are optional.

• :material-image-multiple:{ .lg .middle } See what people use it for

  ---

  A gallery of real setups — morning briefs that summarize your overnight Slack and email, a Discord companion that knows your calendar, a code reviewer that works at 30,000 feet. Outcome-first, with links to the docs that explain how to build each one.

  :octicons-arrow-right-24: Browse the Showcase

---

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.

---

Get Started

=== "Browser App"

Run the full chat UI locally with one script:

```bash
git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
./scripts/quickstart.sh
```

This installs dependencies, starts Ollama + a local model, launches the backend
and frontend, and opens `http://localhost:5173` in your browser.

=== "Desktop App"

The desktop app is a native window for the OpenJarvis UI.
The backend (Ollama + inference) runs on your machine — start it first, then open the app.

**Step 1.** Start the backend:

```bash
git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
./scripts/quickstart.sh
```

**Step 2.** Download and open the desktop app:

[Download for macOS](https://github.com/open-jarvis/OpenJarvis/releases/download/desktop-v1.0.2/OpenJarvis_1.0.1_universal.dmg){ .md-button .md-button--primary }

Also available for [Windows](https://github.com/open-jarvis/OpenJarvis/releases/download/desktop-v1.0.2/OpenJarvis_1.0.1_x64-setup.exe), [Linux (DEB)](https://github.com/open-jarvis/OpenJarvis/releases/download/desktop-v1.0.2/OpenJarvis_1.0.1_amd64.deb), and [Linux (RPM)](https://github.com/open-jarvis/OpenJarvis/releases/download/desktop-v1.0.2/OpenJarvis-1.0.1-1.x86_64.rpm). See the [Downloads](downloads.md) page for details.

The app connects to `http://localhost:8000` automatically.

!!! warning "macOS first launch"

    Run `xattr -cr /Applications/OpenJarvis.app` if the app shows as "damaged".

=== "Python SDK"

```python
from openjarvis import Jarvis

j = Jarvis()                              # auto-detect engine
response = j.ask("Explain quicksort.")
print(response)
```

For more control, use `ask_full()` to get usage stats, model info, and tool results:

```python
result = j.ask_full(
    "What is 2 + 2?",
    agent="orchestrator",
    tools=["calculator"],
)
print(result["content"])       # "4"
print(result["tool_results"])  # [{tool_name: "calculator", ...}]
```

=== "CLI"

```bash
jarvis ask "What is the capital of France?"

jarvis ask --agent orchestrator --tools calculator "What is 137 * 42?"

jarvis serve --port 8000

jarvis memory index ./docs/
jarvis memory search "configuration options"
```

---

Five Primitives for Personal AI

OpenJarvis is built around five composable layers. Each has a clean interface and can be swapped independently.

1. Intelligence — Pick a model, or let OpenJarvis pick one for your hardware. Manages the full catalog of local models across providers.
2. Engine — The inference runtime: Ollama, vLLM, SGLang, llama.cpp, cloud APIs, and more. Auto-detects your hardware and recommends the best fit.
3. Agents — Multi-step reasoning with tool use. Eight built-in agent types from simple chat to orchestrated workflows.
4. Tools & Memory — Web search, calculator, file I/O, code interpreter, retrieval, persistent local state, and any external MCP server.
5. Learning — Your AI gets better over time. Every interaction generates traces that drive automatic improvements to model weights, prompts, and agent behavior.

---

Key Features

• 10+ Engine Backends

  ---

  Ollama, vLLM, SGLang, llama.cpp, MLX, Exo, LiteLLM, cloud (OpenAI/Anthropic/Google), and more. Same InferenceEngine interface, swap freely.

• Automated Workflows

  ---

  Cron-based agents that monitor, summarize, and act. Code review, email triage, research digests — running 24/7 on your hardware.

• Hardware-Aware

  ---

  Auto-detects GPU vendor, model, and VRAM. Recommends the optimal engine for your hardware.

• Offline-First

  ---

  All core functionality works without a network connection. Cloud APIs are optional extras.

• OpenAI-Compatible API

  ---

  jarvis serve starts a FastAPI server with SSE streaming. Drop-in replacement for OpenAI clients.

• Energy & Cost Tracking

  ---

  Built-in telemetry for GPU power draw, token costs, and latency. See exactly what each query costs in watts and dollars.

---

Documentation

• Getting Started

  ---

  Install OpenJarvis, configure your first engine, and run your first query.

• User Guide

  ---

  CLI, Python SDK, and guides for Morning Digest, Deep Research, Code Assistant, Scheduled Monitor, Simple Chat, agents, memory, tools, and telemetry.

• Architecture

  ---

  Five-primitive design, registry pattern, query flow, and cross-cutting learning.

• API Reference

  ---

  Auto-generated reference for every module.

• Deployment

  ---

  Docker, systemd, launchd. GPU-accelerated container images.

• Development

  ---

  Contributing guide, extension patterns, roadmap, and changelog.

Research

OpenJarvis is part of Intelligence Per Watt, a research initiative studying the efficiency of on-device AI systems. Developed at Hazy Research and the Scaling Intelligence Lab at Stanford SAIL.

Read the blog post for the full research motivation, architecture details, and experimental results.

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}, 
}

Sponsors

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

Follow @OpenJarvisAI on X for updates.