Composable, Programmable Systems for On-Device, Personal AI.

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Documentation

Project Site

OpenJarvis is a framework for building AI systems that run entirely on local hardware. Rather than treating intelligence as a cloud service, OpenJarvis provides composable abstractions for local model selection, inference, agentic reasoning, tool use, and learning — all aware of the hardware they run on.

from openjarvis import Jarvis

j = Jarvis()                                      # auto-detect hardware + engine
response = j.ask("Explain backpropagation")       # route to best local model

j.ask("Solve x^2 - 5x + 6 = 0",                  # multi-turn agent with tools
      agent="orchestrator",
      tools=["calculator", "think"])

j.memory.index("./papers/")                       # index documents into local storage
results = j.memory.search("attention mechanism")  # semantic retrieval
j.close()

Installation

pip install openjarvis            # core framework
pip install openjarvis[server]    # + FastAPI server

You also need a local inference backend: Ollama, vLLM, SGLang, or llama.cpp.

Quick Start

The fastest path is Ollama on any machine with Python 3.10+:

# 1. Install OpenJarvis
pip install openjarvis

# 2. Detect hardware and generate config
jarvis init

# 3. Install and start Ollama (https://ollama.com)
curl -fsSL https://ollama.com/install.sh | sh
ollama serve                      # start the Ollama server

# 4. Pull a model
ollama pull qwen3:8b

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

# 6. Verify your setup
jarvis doctor

jarvis init auto-detects your hardware and recommends the best engine. After init, it prints engine-specific next steps. Run jarvis doctor at any time to diagnose configuration or connectivity issues.

Development

From source, you need the Rust extension for full functionality (security, tools, agents, etc.):

# 1. Clone and install Python deps
git clone https://github.com/HazyResearch/OpenJarvis.git
cd OpenJarvis
uv sync --extra dev

# 2. Build and install the Rust extension (requires Rust toolchain)
uv run maturin develop -m rust/crates/openjarvis-python/Cargo.toml

# 3. Run tests
uv run pytest tests/ -v

See Contributing for more.

The Five Pillars

Pillar	What it does	Key abstractions
Intelligence	Model management and routing	RouterPolicy, QueryAnalyzer, ModelCatalog
Engine	Inference runtime abstraction	InferenceEngine ABC — Ollama, vLLM, SGLang, llama.cpp, MLX
Agents	Pluggable reasoning strategies	BaseAgent ABC — Simple, Orchestrator, ReAct, OpenHands, OpenClaw
Tools	Capabilities via MCP	BaseTool ABC — calculator, code interpreter, web search, memory; external MCP servers auto-discovered
Learning	Trace-driven adaptation	LearningPolicy ABC — SFT (model routing), AgentAdvisor (restructuring), ICL (tool usage)

Every interaction produces a Trace — a structured record of the full reasoning chain. Learning policies consume traces to improve model selection, agent behavior, and tool usage over time.

About

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

Sponsors

Laude Institute • Stanford Marlowe • Google Cloud Platform • Lambda Labs

License

Apache 2.0