# Jan - Run your own AI

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> ⚠️ **Jan is currently in Development**: Expect breaking changes and bugs! Jan lets you run AI on your own hardware, with helpful tools to manage models and monitor your hardware performance. In the background, Jan runs [Nitro](https://nitro.jan.ai), a C++ inference engine. It runs various model formats (GGUF/TensorRT) on various hardware (Mac M1/M2/Intel, Windows, Linux, and datacenter-grade Nvidia GPUs) with optional GPU acceleration. > See the Nitro codebase at https://nitro.jan.ai. ## Demo

Jan Web GIF

## Quicklinks - Developer documentation: https://jan.ai/docs (Work in Progress) - Desktop app: Download at https://jan.ai/ - Mobile app shell: Download via [App Store](https://apps.apple.com/us/app/jan-on-device-ai-cloud-ais/id6449664703) | [Android](https://play.google.com/store/apps/details?id=com.jan.ai) - Nitro (C++ AI Engine): https://nitro.jan.ai ## Plugins Jan supports core & 3rd party extensions: - [x] **LLM chat**: Self-hosted Llama2 and LLMs - [x] **Model Manager**: 1-click to install, swap, and delete models - [x] **Storage**: Optionally store your conversation history and other data in SQLite/your storage of choice - [ ] **3rd-party AIs**: Connect to ChatGPT, Claude via API Key (in progress) - [ ] **Cross device support**: Mobile & Web support for custom shared servers (in progress) - [ ] **File retrieval**: User can upload private and run a vectorDB (planned) - [ ] **Multi-user support**: Share a single server across a team/friends (planned) - [ ] **Compliance**: Auditing and flagging features (planned) ## Hardware Support Nitro provides both CPU and GPU support, via [llama.cpp](https://github.com/ggerganov/llama.cpp) and [TensorRT](https://github.com/NVIDIA/TensorRT), respectively. - [x] Nvidia GPUs (accelerated) - [x] Apple M-series (accelerated) - [x] Linux DEB - [x] Windows x64 Not supported yet: Apple Intel, Linux RPM, Windows x86|ARM64, AMD ROCm > See [developer docs](https://docs.jan.ai/docs/) for detailed installation instructions. ## Contributing Contributions are welcome! Please read the [CONTRIBUTING.md](CONTRIBUTING.md) file ### Pre-requisites - node >= 20.0.0 - yarn >= 1.22.0 ### Use as complete suite (in progress) ### For interactive development Note: This instruction is tested on MacOS only. 1. **Clone the Repository:** ``` git clone https://github.com/janhq/jan git checkout feature/hackathon-refactor-jan-into-electron-app cd jan ``` 2. **Install dependencies:** ``` yarn install # Packing base plugins yarn build:plugins ``` 4. **Run development and Using Jan Desktop** ``` yarn dev ``` This will start the development server and open the desktop app. In this step, there are a few notification about installing base plugin, just click `OK` and `Next` to continue. ### For production build ```bash # Do step 1 and 2 in previous section git clone https://github.com/janhq/jan cd jan yarn install yarn build:plugins # Build the app yarn build ``` This will build the app MacOS m1/m2 for production (with code signing already done) and put the result in `dist` folder. ## License Jan is free, [open core](https://en.wikipedia.org/wiki/Open-core_model), and Sustainable Use Licensed. ## Acknowledgements Jan builds on top of other open-source projects: - [llama.cpp](https://github.com/ggerganov/llama.cpp) - [TensorRT](https://github.com/NVIDIA/TensorRT) - [Keycloak Community](https://github.com/keycloak/keycloak) (Apache-2.0) ## Contact - Bugs & requests: file a Github ticket - For discussion: join our Discord [here](https://discord.gg/FTk2MvZwJH) - For business inquiries: email hello@jan.ai - For jobs: please email hr@jan.ai