Jon Saad-FalconandClaude Opus 4.6
350916699e
Move detailed architecture, testing, and pattern docs out of CLAUDE.md into focused .claude/rules/ files. CLAUDE.md is now a lean routing table that points to the right context based on what you're working on. Also fixes stale HazyResearch links in README.md (docs URL, clone URL) and adds CLAUDE.md to .gitignore so it stays per-developer. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Composable, Programmable Systems for On-Device, Personal AI.
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/open-jarvis/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