OpenJarvis/docs/index.md

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OpenJarvis	Programming abstractions for on-device AI	navigation

Programming abstractions for on-device AI

OpenJarvis is a modular framework for building, running, and learning from local AI systems. Five composable pillars — each with a clear ABC interface and decorator-based registry. Everything runs on your hardware. Cloud APIs are optional.

> pip install openjarvis

---

Get Started

=== "Browser App"

Run the full chat UI locally with one script:

```bash
git clone https://github.com/HazyResearch/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 chat UI. Start the backend first,
then open the app.

**1.** Start backend: `git clone ... && cd OpenJarvis && ./scripts/quickstart.sh`

**2.** Download the app:

[Download for macOS (Apple Silicon)](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_aarch64.dmg){ .md-button .md-button--primary }

Also available for [Windows](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_x64-setup.exe), [Linux (DEB)](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_amd64.deb), and [Linux (RPM)](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis-1.0.0-1.x86_64.rpm). See [Installation](getting-started/installation.md#desktop-app) for details.

=== "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 Pillars

1. Intelligence — The LM: model catalog, generation defaults, quantization, preferred engine.
2. Agents — The agentic harness: system prompt, tools, context, retry and exit logic. Seven agent types.
3. Tools — MCP interface: web search, calculator, file I/O, code interpreter, retrieval, and any external MCP server.
4. Engine — The inference runtime: Ollama, vLLM, SGLang, llama.cpp, cloud APIs. Same InferenceEngine ABC.
5. Learning — Improvement loop: SFT weight updates, agent advisor, ICL updater. Trace-driven feedback.

---

Key Features

• Five Composable Pillars

  ---

  Intelligence, Agents, Tools, Engine, and Learning — each with a clear ABC interface and decorator-based registry.

• 5 Engine Backends

  ---

  Ollama, vLLM, SGLang, llama.cpp, and cloud (OpenAI/Anthropic/Google). Same InferenceEngine ABC.

• 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.

• Trace-Driven Learning

  ---

  Every interaction is traced. The learning system improves models (SFT) and agents (prompt, tools, logic).

---

Documentation

• Getting Started

  ---

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

• User Guide

  ---

  CLI, Python SDK, agents, memory, tools, telemetry, and benchmarks.

• Architecture

  ---

  Five-pillar 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.