OpenJarvis: 개인용 AI, 개인용 장치

TL;DR: OpenJarvis is an open-source framework for personal AI agents that runs entirely on-device. It provides shared primitives for building on-device agents, efficiency-aware evaluations, and a learning loop that improves models using local trace data. 🚨 Download OpenJarvis today and top the ENERGY leaderboard for a chance to win a Mac Mini! 🚨 In the 1970s and 80s, computing moved from mainframes to personal computers. Not because PCs were more powerful, but because they became efficient enough for what people actually needed. AI is reaching a similar moment. In our recent Intelligence Per Watt study, we found that local language models and local accelerators can accurately service 88.7% of single-turn chat and reasoning queries at interactive latencies, with intelligence efficiency improving 5.3× from 2023 to 2025. At the same time, personal AI is exploding. Frameworks like OpenClaw have attracted more than 250,000 GitHub stars, inspiring a wave of agents (PicoClaw, NanoBot, IronClaw, TinyClaw, MimicLaw, ZeroClaw, etc) all built around the same idea: AI that operates over your personal context, interacting through the platforms you already use. Put these together and the architecture seems obvious: personal AI should run on your personal device. In nearly all of today's personal AI projects, the local component is a thin orchestration layer, while the "brain" lives in someone else's data center. Your most personal data routes through cloud APIs, with their latency, their cost, and their terms of service. We built OpenJarvis to fix this. OpenJarvis is an opinionated framework for personal AI running on your personal devices. It provides shared primitives for building on-device agents, evaluations that treat metrics such as energy, FLOPs, latency, and dollar cost as first-class constraints, and a learning loop that improves models using local trace data. The goal is simple: make it possible to build personal AI agents that run locally by default, calling the cloud only when truly necessary. OpenJarvis aims to be both a research platform and production foundation for local AI, in the spirit of PyTorch. 🏢 OpenJarvis: The Local-First Personal AI Stack OpenJarvis emerged from a simple question: what's standing in the way of personal AI running locally today? We believe the answer comes down to three missing pieces in today's local AI systems: - Shared abstractions. Teams assemble bespoke stacks, choosing independently among model servers, orchestration frameworks, memory stores, tool interfaces, and adaptation pipelines. The result is duplicated effort and brittle, non-interoperable systems. There is no agreed-upon "local AI software stack" the way there is for web development or mobile apps. - Efficiency-aware evaluations: Systems are tuned for task quality alone, even though on-device deployments must jointly satisfy constraints on latency, energy, memory footprint, and dollar cost. Efficiency isn't a nice-to-have on a laptop running on battery; it's a hard requirement. - Closed-loop optimization Because most AI systems run in the cloud, the pieces needed for local improvement don't exist: trace data isn't available, model weights are closed, and the runtime isn't tunable. This makes it nearly impossible to study or build personal AI agents that improve over time. To close these gaps, we built OpenJarvis. OpenJarvis is the open-source stack for personal AI agents that runs entirely on-device. Designed to serve as both a research platform and deployment-ready infrastructure, OpenJarvis does three things: - Defines a set of composable primitives that replace ad hoc integration with an opinionated framework of five primitives — Intelligence, Engine, Agents, Tools & Memory, and Learning — providing the shared abstractions the ecosystem currently lacks. These primitives can be studied individually or as an integrated whole. - Makes efficiency a first-class evaluation target by tracking energy, dollar cost, FLOPs, latency, and related system metrics alongside accuracy. These measurements are essential for optimizing edge deployments, where resource constraints are fundamental. - Provides an optimization harness for deploying optimization strategies across the complete local AI stack: 1) model weights, 2) LM prompts, 3) agentic logic, and 4) inference engine. By learning from local trace data, the harness applies the best optimization strategies to-date while giving researchers a testbed to explore new approaches tailored to the trace signatures that distinguish personal AI (i.e., long-horizon sessions, persistent cross-session context, non-stationary user preferences). 🧱 Primitives for On-Device AI OpenJarvis is structured around five composable primitives. Each primitive can be benchmarked, substituted, and optimized independently, or analyzed within the context of the full system. Collectively, these primitives define modular, hardware-aware abstractions that support both standalone use and system-level composit