Personal AI, On Personal Devices.

--- > **[Documentation](https://open-jarvis.github.io/OpenJarvis/)** > > **[Project Site](https://scalingintelligence.stanford.edu/blogs/openjarvis/)** > > **[Leaderboard](https://open-jarvis.github.io/OpenJarvis/leaderboard/)** > > **[Roadmap](https://open-jarvis.github.io/OpenJarvis/development/roadmap/)** ## Why OpenJarvis? Personal AI agents are exploding in popularity, but nearly all of them still route intelligence through cloud APIs. Your "personal" AI continues to depend on someone else's server. At the same time, our [Intelligence Per Watt](https://www.intelligence-per-watt.ai/) research showed that local language models already handle 88.7% of single-turn chat and reasoning queries, with intelligence efficiency improving 5.3× from 2023 to 2025. The models and hardware are increasingly ready. What has been missing is the software stack to make local-first personal AI practical. OpenJarvis is that stack. It is an opinionated framework for local-first personal AI, built around three core ideas: shared primitives for building on-device agents; evaluations that treat energy, FLOPs, latency, and dollar cost as first-class constraints alongside accuracy; 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 a production foundation for local AI, in the spirit of PyTorch. ## Installation ```bash curl -fsSL https://openjarvis.ai/install.sh | bash ``` That's it. The installer handles everything: uv, the Python venv, Ollama, and pulling a small starter model. About 3 minutes on a typical broadband connection. Then: ```bash jarvis ``` The Rust extension and bigger models continue downloading in the background while you chat. Run `jarvis doctor` to see status. **Platforms:** macOS (Intel + Apple Silicon), Linux, WSL2 on Windows. **Manual install / contributors:** see [docs/getting-started/install.md](docs/getting-started/install.md). ## Quick Start ```bash curl -fsSL https://openjarvis.ai/install.sh | bash jarvis ``` `jarvis init --preset ` switches to a starter config. Available presets: `morning-digest-mac`, `morning-digest-linux`, `morning-digest-minimal`, `deep-research`, `code-assistant`, `scheduled-monitor`, `chat-simple`. ## Starter Configs Install any preset with one command: ```bash uv run jarvis init --preset morning-digest-mac # or any preset below ``` > Prefix every `jarvis ...` invocation with `uv run`, or activate the venv first (`source .venv/bin/activate`) so plain `jarvis ...` works for the rest of your shell session. | Preset | Use Case | What it does | |--------|----------|-------------| | `morning-digest-mac` | Daily Briefing (Mac) | Spoken briefing from email, calendar, health, news with Jarvis voice | | `morning-digest-linux` | Daily Briefing (Linux) | Same, with vLLM support for GPU servers | | `morning-digest-minimal` | Daily Briefing (minimal) | Just Gmail + Calendar, runs on any machine | | `deep-research` | Research Assistant | Multi-hop research across indexed docs with citations | | `code-assistant` | Code Companion | Agent with code execution, file I/O, and shell access | | `scheduled-monitor` | Persistent Monitor | Stateful agent that runs on a schedule with memory | | `chat-simple` | Simple Chat | Lightweight conversation, no tools needed | ```bash # Example: Morning Digest on Mac uv run jarvis init --preset morning-digest-mac uv run jarvis connect gdrive # one OAuth flow covers Gmail, Calendar, Tasks uv run jarvis digest --fresh # generate and play your first briefing # Example: Deep Research uv run jarvis init --preset deep-research uv run jarvis memory index ./docs/ # requires the Rust extension — see Setup above uv run jarvis ask "Summarize all emails about Project X" ``` ### Skills Skills teach agents how to better use tools and improve their reasoning. Every skill is a tool — agents discover them from a catalog and invoke them on demand. ```bash # Install skills from public sources jarvis skill install hermes:arxiv jarvis skill sync hermes --category research # Use skills with any agent jarvis ask "Use the code-explainer skill to explain this Python code: for i in range(5): print(i*2)" # Optimize skills from your trace history jarvis optimize skills --policy dspy # Benchmark the impact jarvis bench skills --max-samples 5 --seeds 42 ``` Import from [Hermes Agent](https://github.com/NousResearch/hermes-agent) (~150 skills), [OpenClaw](https://github.com/openclaw/skills) (~13,700 community skills), or any GitHub repo. Skills follow the [agentskills.io](https://agentskills.io/specification) open standard. See the [Skills User Guide](https://open-jarvis.github.io/OpenJarvis/user-guide/skills/) and [Skills Tutorial](https://open-jarvis.github.io/OpenJarvis/tutorials/skills-workflow/) for details. ### Built-in Agents | Agent | Type | What it does | |-------|------|-------------| | `morning_digest` | Scheduled | Daily briefing from email, calendar, health, news — with TTS audio | | `deep_research` | On-demand | Multi-hop research with citations across web and local docs | | `monitor_operative` | Continuous | Long-horizon monitoring with memory, compression, and retrieval | | `orchestrator` | On-demand | Multi-turn reasoning with automatic tool selection | | `native_react` | On-demand | ReAct (Thought-Action-Observation) loop agent | | `operative` | Continuous | Persistent autonomous agent with state management | | `native_openhands` | On-demand | CodeAct — generates and executes Python code | | `simple` | On-demand | Single-turn chat, no tools | See the [User Guide](https://open-jarvis.github.io/OpenJarvis/user-guide/morning-digest/) and [Tutorials](https://open-jarvis.github.io/OpenJarvis/tutorials/) for detailed setup instructions. Full documentation — including Docker deployment, cloud engines, development setup, and tutorials — at **[open-jarvis.github.io/OpenJarvis](https://open-jarvis.github.io/OpenJarvis/)**. ## Contributing We welcome contributions! See the [Contributing Guide](CONTRIBUTING.md) for incentives, contribution types, and the PR process. Quick start for contributors: ```bash git clone https://github.com/open-jarvis/OpenJarvis.git cd OpenJarvis uv sync --extra dev uv run pre-commit install uv run pytest tests/ -v ``` Browse the [Roadmap](https://open-jarvis.github.io/OpenJarvis/development/roadmap/) for areas where help is needed. Comment **"take"** on any issue to get auto-assigned. ## About OpenJarvis is part of [Intelligence Per Watt](https://www.intelligence-per-watt.ai/), a research initiative studying the efficiency of on-device AI systems. The project is developed at [Hazy Research](https://hazyresearch.stanford.edu/) and the [Scaling Intelligence Lab](https://scalingintelligence.stanford.edu/) at [Stanford SAIL](https://ai.stanford.edu/). ## Sponsors

Laude Institute • Stanford Marlowe • Google Cloud Platform • Lambda Labs • Ollama • IBM Research • Stanford HAI

## Citation ```bibtex @misc{saadfalcon2026openjarvis, title={OpenJarvis: Personal AI, On Personal Devices}, author={Jon Saad-Falcon and Avanika Narayan and Herumb Shandilya and Hakki Orhun Akengin and Robby Manihani and Gabriel Bo and John Hennessy and Christopher R\'{e} and Azalia Mirhoseini}, year={2026}, howpublished={\url{https://scalingintelligence.stanford.edu/blogs/openjarvis/}}, } ``` ## License [Apache 2.0](LICENSE)