---
title: OpenJarvis
description: Programming abstractions for on-device AI
hide:
  - navigation
---

# _Programming abstractions_ for on-device AI

<p class="hero-tagline">
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.
</p>

<div class="install-cmd">> pip install openjarvis</div>

---

## 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"

    Download the native desktop app — it bundles Ollama and the Python backend
    so everything works out of the box.

    [Download for macOS (Apple Silicon)](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_aarch64.dmg){ .md-button .md-button--primary }

    Also available for [macOS (Intel)](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_x64.dmg), [Windows](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_x64-setup.exe), [Linux (DEB)](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_amd64.deb), and [Linux (RPM)](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_amd64.rpm). See the [Downloads](downloads.md) page 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

<div class="grid cards" markdown>

-   **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).

</div>

---

## Documentation

<div class="grid cards" markdown>

-   **[Getting Started](getting-started/installation.md)**

    ---

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

-   **[User Guide](user-guide/cli.md)**

    ---

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

-   **[Architecture](architecture/overview.md)**

    ---

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

-   **[API Reference](api/index.md)**

    ---

    Auto-generated reference for every module.

-   **[Deployment](deployment/docker.md)**

    ---

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

-   **[Development](development/contributing.md)**

    ---

    Contributing guide, extension patterns, roadmap, and changelog.

</div>