Jon Saad-FalconandClaude Opus 4.6
a4c4081ff4
- Rewrite .github/workflows/desktop.yml: 2-job pipeline (validate + build-and-release) with rolling desktop-latest pre-release on push to main and stable desktop-v* releases - Add UpdateChecker component: checks for updates on startup + every 30 min, background download with progress bar, one-click relaunch - Configure Tauri updater: endpoints pointing to desktop-latest release, pubkey placeholder - Add tauri-plugin-process for relaunch support (Cargo.toml, lib.rs, package.json) - Add macOS Entitlements.plist for notarization (network + file access, no sandbox) - Add scripts/bump-desktop-version.sh for atomic version bumps across 3 config files - Add desktop/README.md with dev setup, auto-update architecture, signing docs - Update .gitignore for desktop/node_modules, dist, target - Configure macOS minimumSystemVersion, Windows timestampUrl - Include all Phase 14-21 work: agent hardening, RBAC, taint tracking, workflows, skills, knowledge graph, sessions, A2A, MCP templates, WASM sandbox, TUI dashboard, production tools, CLI expansion, API expansion, learning productionization, Tauri desktop app, and 10 new channels Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Programming abstractions for on-device 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.
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