Installation¶

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

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/open-jarvis/OpenJarvis.git
cd OpenJarvis
./scripts/quickstart.sh

The script handles everything:

Checks for Python 3.10+ and Node.js 18+
Installs Ollama if not present and pulls a starter model
Installs Python and frontend dependencies
Starts the backend API server and frontend dev server
Opens http://localhost:5173 in your browser

Manual setup¶

If you prefer to run each step yourself:

Step 1: Clone and installStep 2: Start OllamaStep 3: Start backendStep 4: Start frontend

git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
uv sync --extra desktop
uv run maturin develop -m rust/crates/openjarvis-python/Cargo.toml
cd frontend && npm install && cd ..

Prerequisites

Requires Rust (curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh). On Python 3.14+, set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1 before the maturin command.

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

uv run jarvis serve --port 8000

cd frontend
npm run dev

Then open http://localhost:5173.

Desktop App¶

The desktop app is a native window for the OpenJarvis chat UI. All inference and backend processing happens on your local machine — the app connects to the backend you start locally.

Setup¶

Step 1. Start the backend (same as Browser App):

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

Step 2. Download and open the desktop app:

Platform	Download
macOS (Universal)	OpenJarvis.dmg
Windows (64-bit)	OpenJarvis-setup.exe
Linux (DEB)	OpenJarvis.deb
Linux (RPM)	OpenJarvis.rpm
Linux (AppImage)	OpenJarvis.AppImage

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

macOS: \"app is damaged\"

If macOS says the app is damaged, clear the Gatekeeper quarantine flag:

xattr -cr /Applications/OpenJarvis.app

This is normal for open-source apps distributed outside the App Store.

All releases

Browse all versions on the GitHub Releases page.

Build from source¶

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

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

CLI¶

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

Install¶

git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
uv sync
uv run maturin develop -m rust/crates/openjarvis-python/Cargo.toml

Requires Rust. On Python 3.14+, set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1 before the maturin command.

Verify¶

jarvis --version
# jarvis, version 0.1.0

First commands¶

jarvis ask "What is the capital of France?"

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

jarvis serve --port 8000

jarvis doctor

jarvis model list

jarvis chat

Inference backend required

The CLI requires a running inference backend (e.g., Ollama). See Setting up an inference backend below.

Python SDK¶

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

Install¶

git clone https://github.com/open-jarvis/OpenJarvis.git
cd OpenJarvis
uv sync
uv run maturin develop -m rust/crates/openjarvis-python/Cargo.toml

Requires Rust. On Python 3.14+, set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=1 before the maturin command.

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.

Hardware¶

OpenJarvis has no special hardware requirements of its own — the CLI, server, and SDK run anywhere the software Requirements below are met. jarvis init detects your CPU, RAM, and GPU, then recommends an inference engine and local model for the generated config. You can choose a different engine during setup.

jarvis init          # detect hardware, write a matching config

Recommended configurations¶

These are the Qwen3.5 recommendations for llamacpp, mlx, ollama, vllm, and sglang. jarvis init can offer an already-running engine first, and your engine choice can change the model. The bands use whole-number GB values and assume one GPU; the formulas below determine the exact result.

System RAM (no reported VRAM)	GPU VRAM (one GPU)	Recommended model	Download estimate
5–14 GB	1–8 GB	`qwen3.5:2b`	~1.1 GB
15–24 GB	9–17 GB	`qwen3.5:4b`	~2.2 GB
25–44 GB	18–35 GB	`qwen3.5:9b`	~5.0 GB
45 GB or more	36 GB or more	`qwen3.5:27b`	~14.9 GB

The two memory columns are alternatives: when a GPU reports VRAM, the recommendation uses VRAM across all detected GPUs; otherwise it uses system RAM. On Apple Silicon, detection reports unified system memory as GPU memory.

How the model is chosen¶

jarvis init first computes usable memory:

Detected	Usable memory
GPU reporting VRAM	`VRAM × max(GPU count, 1) × 0.9`
No GPU, or VRAM unavailable	`(total RAM − 4 GB) × 0.8`

For the Qwen3.5 tier engines, a positive usable-memory value selects the first tier it fits:

Usable memory	Model
Up to 8 GB	`qwen3.5:2b`
Up to 16 GB	`qwen3.5:4b`
Up to 32 GB	`qwen3.5:9b`
More than 32 GB	`qwen3.5:27b`

All four recommended Qwen3.5 models are dense. The table describes the current selection rule, not a guarantee that a model will fit or run well on every device in a band.

Inference engine¶

The detected GPU vendor and reported name select the engine:

Detected GPU	Engine
None or unrecognized GPU vendor	`llamacpp`
Apple GPU	`mlx`
NVIDIA name containing A100, H100, H200, L40, A10, or A30	`vllm`
Other NVIDIA	`ollama`
AMD name containing MI300, MI325, MI350, or MI355	`vllm`
Other AMD (including Radeon)	`lemonade`

When lemonade is selected and usable memory is positive, jarvis init instead recommends Qwen3.6-35B-A3B-GGUF. The Qwen3.5 tier and download tables above do not apply to that default.

See Setting Up an Inference Backend for installing the engine jarvis init picks.

Minimum¶

The recommendation code imposes no CPU minimum. CPU-only inference speed depends on your processor and core count.

Memory is the real floor. With 4 GB of RAM or less and no GPU, usable memory is zero or less and no local model is recommended.

Low-memory and headless machines

You do not need a local model at all. Point OpenJarvis at a hosted API with the cloud quick-path and the hardware tiers above stop applying.

Storage¶

Model weights dominate disk usage — the estimates above range from ~1.1 GB to ~14.9 GB for the Qwen3.5 defaults. Budget additional space for the Python environment and whichever inference engine you install.

Overriding the detected defaults

These are defaults, not limits. The generated config records what was detected in a comment at the top of the file, and default_model under [intelligence] in ~/.openjarvis/config.toml can be set to anything larger or smaller — see the Configuration guide. Re-run jarvis init --force to re-detect and overwrite an existing config.

Requirements¶

Requirement	Version	Install	Notes
Python	3.10–3.13	python.org	Required. 3.14+ not yet supported (a core dependency lacks 3.14 wheels).
uv	latest	`curl -LsSf https://astral.sh/uv/install.sh \\| sh` or `brew install uv` (macOS)	Python package & project manager
Git	any	git-scm.com or `brew install git` (macOS)	Required
Rust	stable	`curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs \\| sh`	Required for the Rust extension
Inference backend	any	See below	At least one of Ollama, vLLM, llama.cpp, SGLang, or a cloud API
Node.js	18+	nodejs.org or `brew install node` (macOS)	Required for the browser UI; 22+ for the WhatsApp Baileys channel bridge

macOS users

See the macOS Installation Guide for a complete step-by-step walkthrough covering Homebrew, uv, Rust, llama.cpp, and common pitfalls.

Optional Extras¶

OpenJarvis uses optional extras to keep the base installation lightweight.

Inference Backends¶

Extra	Install Command	Description
`inference-cloud`	`uv sync --extra inference-cloud`	OpenAI and Anthropic APIs
`inference-google`	`uv sync --extra inference-google`	Google Gemini API

Ollama, vLLM, and llama.cpp are HTTP-based

These engines have no additional Python dependencies — OpenJarvis communicates over HTTP. You still need the engine software running on your machine.

Memory Backends¶

Extra	Install Command	Description
`memory-faiss`	`uv sync --extra memory-faiss`	FAISS vector store
`memory-colbert`	`uv sync --extra memory-colbert`	ColBERTv2 late-interaction retrieval
`memory-bm25`	`uv sync --extra memory-bm25`	BM25 sparse retrieval

SQLite memory is always available

The default SQLite/FTS5 memory backend requires no additional dependencies.

Server & Other¶

Extra	Install Command	Description
`desktop`	`uv sync --extra desktop`	Desktop/API server plus local speech input
`server`	`uv sync --extra server`	OpenAI-compatible API server (`jarvis serve`)
`dev`	`uv sync --extra dev`	Development and testing tools
`docs`	`uv sync --extra docs`	Documentation build tools

Combine extras:

uv sync --extra desktop --extra memory-faiss --extra inference-cloud

Setting Up an Inference Backend¶

OpenJarvis requires at least one inference backend. Choose the one that matches your hardware.

Ollama (Recommended)¶

The easiest way to get started. Handles model downloading and serving automatically.

Install from ollama.com
Start the server and pull a model:
```
ollama serve
ollama pull qwen3:0.6b
```
Verify: jarvis model list

Best for: Apple Silicon Macs, consumer NVIDIA GPUs, CPU-only systems

vLLM¶

High-throughput serving optimized for datacenter GPUs.

Install following the official guide
Start: vllm serve Qwen/Qwen2.5-7B-Instruct
Auto-detected at http://localhost:8000

Best for: NVIDIA datacenter GPUs (A100, H100), AMD GPUs

llama.cpp¶

Efficient CPU and GPU inference with GGUF quantized models.

Build from github.com/ggerganov/llama.cpp
Start: llama-server -m /path/to/model.gguf --port 8080
Auto-detected at http://localhost:8080

Cloud APIs¶

uv sync --extra inference-cloud --extra inference-google
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Next Steps¶

Quick Start — Run your first query
Configuration — Customize engine hosts, model routing, memory, and more