# Meet OpenJarvis: A Local-First Framework for On-Device Personal AI Agents with Tools, Memory, and Learning > Source: > Published: 2026-06-04 06:23:10+00:00 Researchers at Stanford University and Lambda Labs, have published the [research paper for OpenJarvis](https://arxiv.org/pdf/2605.17172v1), an open-source framework that runs inference, agents, memory, and learning entirely on-device. The open-weight models configured through OpenJarvis land within 3.2 percentage points of the best cloud model on average, at roughly 800× lower marginal API cost per query and roughly 4× lower latency under the research’s benchmark protocol. This research work builds on the research team’s earlier [ Intelligence Per Watt study](https://arxiv.org/pdf/2511.07885), which reported that local models already handle 88.7% of single-turn chat and reasoning queries at interactive latency, with intelligence efficiency improving 5.3× from 2023 to 2025. **Model Overview & Access** OpenJarvis is not a single model. It is a framework that composes any supported model with a configurable agent stack, evaluated across 11 local models from four families. | Property | Value | |---|---| License | Apache 2.0 | Framework release | March 12, 2026 | Paper | arXiv:2605.17172 (posted May 16, 2026) | Repository | github.com/open-jarvis/OpenJarvis | Stars / forks | ~5.4k / ~1.2k (June 2026) | Languages | Python (~83%), Rust (~9%), TypeScript (~7%) | Evaluated models | 11 local models across 4 families: Qwen3.5, Gemma4, Nemotron, Granite | Cloud baselines | Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro | Supported engines | Ollama, vLLM, SGLang, llama.cpp, Apple Foundation Models, Exo (among others) | Context window | Model-dependent | Installation | Single command; ~3 minutes on broadband | Hardware | Tested on 7 platforms, from Mac Mini M4 to NVIDIA DGX Spark | **Architecture: Five Primitives and a Spec** OpenJarvis decomposes a personal AI system into five typed primitives, composed through a single declarative configuration object called a **spec**. **Intelligence**— the model, weights, generation parameters, and quantization format.** Engine**— the inference runtime (Ollama, vLLM, SGLang, etc.), batching, KV-cache settings, and hardware path.** Agents**— the reasoning loop (ReAct or CodeAct), system prompts, tool-use policy, and turn limits.** Tools & Memory**— external interfaces, retrieval backends, 25+ data connectors, and 32+ messaging channels, with native MCP support and interchangeable memory backends.**Learning**— the optimizer that updates the spec from traces. This slot accepts LoRA, DSPy, GEPA, or LLM-guided spec search. Each primitive is independently swappable, and a spec serializes all five into a TOML file. Two specs can share the same agent and tool configuration and differ only in model and engine, so the same behavior runs on a Mac Mini and a workstation without rewriting prompts. **LLM-guided spec search** is the second contribution. It is a local–cloud collaboration: a frontier cloud model acts as a teacher at search time, reading traces, diagnosing failure clusters, and proposing edits across Intelligence, Engine, Agents, and Tools & Memory. An edit is accepted only if it improves the target failure cluster without causing meaningful regressions elsewhere — the research team calls this the **gate** (default tolerance 1%). The optimized spec then runs entirely on-device at inference time, with zero cloud calls. The teacher is used only at search time; at 100 queries per day, the amortized teacher cost falls below $0.001 per query within six months. Prior work (GEPA, DSPy, LoRA) optimizes one primitive at a time, and prompt optimizers alone recover only about 5 pp of the cloud–local gap. LLM-guided spec search recovers 13–32 pp because it edits across primitives jointly, at 7–11× lower optimization cost than single-primitive baselines. The four-primitive move space contributes 5.5–16.5 pp, and the LLM proposer adds about 10 pp on average over an evolutionary search at the same move space. **Capabilities & Performance** OpenJarvis was evaluated across 8 benchmarks spanning 508 tasks: tool calling (ToolCall-15), agentic workflows (PinchBench), coding (LiveCodeBench), customer service (τ-Bench V2, τ²-Bench Telecom), general assistance (GAIA), and deep research (LiveResearchBench, DeepResearchBench). **The swap test**: Replacing the intended cloud model with Qwen3.5-9B in existing frameworks (OpenClaw, Hermes Agent) drops accuracy by 25–39 pp. With the same model under an OpenJarvis spec, the residual drop shrinks to 5.6–16.5 pp — recovering 56–77% of the portability loss. **The accuracy frontier**: The best single local model, Qwen3.5-122B, reaches 80.3% average accuracy versus Claude Opus 4.6 at 83.5% — a 3.2 pp gap. Local specs match or exceed cloud on 4 of 8 benchmarks: ToolCall-15, PinchBench, LiveCodeBench, and τ-Bench V2. **Cost and latency**: Local configurations form the accuracy–efficiency frontier. Qwen3.5-122B delivers its 80.3% at roughly a thousandth of a cent per query, versus $0.009 per query for Claude Opus 4.6 — an approximately 800× marginal API-cost advantage. End-to-end latency drops by roughly 4× on the agentic workloads, though the paper notes single-shot prompts can favor cloud serving. **Search gains**: LLM-guided spec search improves the Qwen3.5-9B student to 100% on PinchBench, 83% on LiveCodeBench, and 91% on LiveResearchBench. Across the full eight-benchmark suite, average gains per student model range from 13.1 to 31.5 pp. The authors report that these gains survive their robustness checks (reward-weight variants, search-seed variance, and random restarts). **How to Use it** Installation is one command. On macOS, Linux, or WSL2: ``` curl -fsSL https://open-jarvis.github.io/OpenJarvis/install.sh | bash ``` Windows users run an equivalent PowerShell script (`irm … | iex` ). The installer provisions `uv` , a Python virtual environment, Ollama, and a starter model in about three minutes on broadband. A desktop GUI ships as a `.dmg` , `.exe` , `.deb` , `.rpm` , or `.AppImage` from the releases page. After install, `jarvis` starts a chat session. Starter presets cover common workflows: ``` jarvis init --preset morning-digest-mac # daily briefing with TTS jarvis init --preset deep-research # multi-hop research with citations jarvis init --preset code-assistant # agent with code execution and shell access jarvis init --preset scheduled-monitor # stateful agent on a schedule ``` The framework ships with eight built-in agents across three execution modes — on-demand, scheduled, and continuous. It connects to 25+ data sources (Gmail, Calendar, iMessage, Notion, Obsidian, Slack, GitHub, and others) and exposes agents over 32+ messaging channels (WhatsApp, Telegram, Discord, iMessage, Signal, and others). Skills can be imported from external catalogs — about 150 from Hermes Agent and about 13,700 community skills from OpenClaw — all following the agentskills.io specification. A `jarvis optimize skills --policy dspy` command refines them from local trace history. **Marktechpost’s Visual Explainer** [marktechpost.com](https://www.marktechpost.com) **Key Takeaways** - OpenJarvis runs inference, agents, memory, and learning fully on-device, landing within 3.2 pp of the best cloud model at ~800× lower marginal API cost and ~4× lower latency. - A typed "spec" decomposes the stack into five swappable primitives — Intelligence, Engine, Agents, Tools & Memory, and Learning — serialized to portable TOML. - LLM-guided spec search uses a frontier cloud model as a search-time teacher to recover 13–32 pp of the cloud–local gap at 7–11× lower optimization cost, then runs locally with zero cloud calls. - Local specs match or exceed cloud on 4 of 8 benchmarks (ToolCall-15, PinchBench, LiveCodeBench, τ-Bench V2); the remaining gap concentrates on reasoning- and research-heavy tasks. Check out the [ Paper ](https://arxiv.org/pdf/2605.17172v1)and **Also, feel free to follow us on** [Repo](https://github.com/open-jarvis/OpenJarvis).**and don’t forget to join our**[Twitter](https://x.com/intent/follow?screen_name=marktechpost) **and Subscribe to** [150k+ ML SubReddit](https://www.reddit.com/r/machinelearningnews/)**. Wait! are you on telegram?** [our Newsletter](https://www.aidevsignals.com/) [now you can join us on telegram as well.](https://t.me/machinelearningresearchnews)Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? [Connect with us](https://forms.gle/wbash1wF6efRj8G58)