OpenJarvis/docs/downloads.md

title, description

title	description
Downloads	Download the OpenJarvis desktop app, browser app, CLI, or Python SDK

Downloads

OpenJarvis runs entirely on your hardware. Choose the interface that fits your workflow.

---

Desktop App

The native desktop app bundles Ollama (the inference engine) and the OpenJarvis Python backend into a single installer. Download, open, and start chatting — no terminal required.

Download

Platform	Download	Notes
macOS (Apple Silicon)	:material-download: OpenJarvis.dmg	M1/M2/M3/M4 Macs
macOS (Intel)	:material-download: OpenJarvis.dmg	Intel Macs (2020 and earlier)
Windows (64-bit)	:material-download: OpenJarvis-setup.exe	Windows 10+
Linux (DEB)	:material-download: OpenJarvis.deb	Ubuntu, Debian
Linux (RPM)	:material-download: OpenJarvis.rpm	Fedora, RHEL

!!! tip "All releases" Browse all versions on the GitHub Releases page.

What's included

The desktop app ships with:

• Ollama sidecar — inference engine runs automatically in the background
• OpenJarvis backend — Python API server managed by the app
• Full chat UI — same interface as the browser app
• Energy monitoring — real-time power consumption tracking
• Telemetry dashboard — token throughput, latency, and cost comparison

Build from source

git clone https://github.com/HazyResearch/OpenJarvis.git
cd OpenJarvis/desktop
npm install
npm run tauri build

The built installer will be in desktop/src-tauri/target/release/bundle/.

---

Browser App

Run the full chat UI in your browser. Everything stays local — the backend runs on your machine and the frontend connects via localhost.

One-command setup

git clone https://github.com/HazyResearch/OpenJarvis.git
cd OpenJarvis
./scripts/quickstart.sh

The script handles everything:

1. Checks for Python 3.10+ and Node.js 22+
2. Installs Ollama if not present and pulls a starter model
3. Installs Python and frontend dependencies
4. Starts the backend API server and frontend dev server
5. Opens http://localhost:5173 in your browser

Manual setup

If you prefer to run each step yourself:

=== "Step 1: Clone and install"

```bash
git clone https://github.com/HazyResearch/OpenJarvis.git
cd OpenJarvis
uv sync --extra server
cd frontend && npm install && cd ..
```

=== "Step 2: Start Ollama"

```bash
# Install from https://ollama.com if not already installed
ollama serve &
ollama pull qwen3:0.6b
```

=== "Step 3: Start backend"

```bash
uv run jarvis serve --port 8000
```

=== "Step 4: Start frontend"

```bash
cd frontend
npm run dev
```

Then open http://localhost:5173.

What you get

• Chat interface — markdown rendering, streaming responses, conversation history
• Tool use — calculator, web search, code interpreter, file I/O
• System panel — live telemetry, energy monitoring, cost comparison vs. cloud models
• Dashboard — energy graphs, trace debugging, cost breakdown
• Settings — model selection, agent configuration, theme toggle

---

CLI

The command-line interface is the fastest way to interact with OpenJarvis programmatically. Every feature is accessible from the terminal.

Install

=== "uv (recommended)"

```bash
uv pip install openjarvis
```

=== "pip"

```bash
pip install openjarvis
```

=== "From source"

```bash
git clone https://github.com/HazyResearch/OpenJarvis.git
cd OpenJarvis
uv sync
```

Verify

jarvis --version
# jarvis, version 1.0.0

First commands

# Ask a question
jarvis ask "What is the capital of France?"

# Use an agent with tools
jarvis ask --agent orchestrator --tools calculator "What is 137 * 42?"

# Start the API server
jarvis serve --port 8000

# Run diagnostics
jarvis doctor

# List available models
jarvis model list

# Interactive chat
jarvis chat

!!! info "Inference backend required" The CLI requires a running inference backend (e.g., Ollama). See the Installation guide for setup instructions.

---

Python SDK

For programmatic access, the Jarvis class provides a high-level sync API.

Install

pip install openjarvis

Quick example

from openjarvis import Jarvis

j = Jarvis()
print(j.ask("Explain quicksort in two sentences."))
j.close()

With agents and tools

result = j.ask_full(
    "What is the square root of 144?",
    agent="orchestrator",
    tools=["calculator", "think"],
)
print(result["content"])       # "12"
print(result["tool_results"])  # tool invocations
print(result["turns"])         # number of agent turns

Composition layer

For full control, use the SystemBuilder:

from openjarvis import SystemBuilder

system = (
    SystemBuilder()
    .engine("ollama")
    .model("qwen3:8b")
    .agent("orchestrator")
    .tools(["calculator", "web_search", "file_read"])
    .enable_telemetry()
    .enable_traces()
    .build()
)

result = system.ask("Summarize the latest AI news.")
system.close()

See the Python SDK guide for the full API reference.