jan README

The project's README from github.com/janhq/jan. As one markdown file: README.md.

Jan - Open-source ChatGPT replacement

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Getting Started - Community - Changelog - Bug reports

Jan is bringing the best of open-source AI in an easy-to-use product. Download and run LLMs with full control and privacy.

Installation

Get it from Microsoft Store Get it on Flathub

The easiest way to get started is by downloading one of the following versions for your respective operating system:

Platform Download
Windows jan.exe
macOS jan.dmg
Linux (deb) jan.deb
Linux (AppImage) jan.AppImage
Linux (Arm64) How-to

Download from jan.ai or GitHub Releases.

Features

Build from Source

For those who enjoy the scenic route:

Prerequisites

Run with Make

git clone https://github.com/janhq/jan
cd jan
make dev

This handles everything: installs dependencies, builds core components, and launches the app.

Available make targets:

Manual Commands

yarn install
yarn build
yarn dev

Building on Windows

Run make dev from Git Bash (installed with Git for Windows) — make dispatches its recipes through sh, so a plain cmd.exe won't work.

You do not need a "Native Tools Command Prompt for VS 2022". The bundled llama.cpp engine builds with Ninja + clang-cl, and clang-cl locates the MSVC toolchain and Windows SDK on its own. What has to be installed (and on PATH for ninja/clang-cl/cmake):

Engine variants are picked with JAN_ENGINE_VARIANT (tokens: cpu, vulkan, metal, cuda12, cuda13, hip/rocm, joined by -), e.g.:

make dev JAN_ENGINE_VARIANT=cuda13

"nvcc fatal : Could not open output file ...fattn-...cu.obj.d" during tauri-plugin-llamacpp(build) means the build path crossed Windows' 260-character MAX_PATH limit — nvcc does not honor the long-path opt-in. The build script now detects this and automatically relocates the llama.cpp build tree to a short directory under %LOCALAPPDATA%\jan-engine. If you hit path-length errors anyway, set JAN_ENGINE_BUILD_DIR to a short path (e.g. C:\jb) or move the checkout closer to the drive root.

System Requirements

Minimum specs for a decent experience:

For detailed compatibility, check our installation guides.

Troubleshooting

If things go sideways:

  1. Check our troubleshooting docs
  2. Copy your error logs and system specs
  3. Ask for help in our Discord #🆘|jan-help channel

Contributing

Contributions welcome. See CONTRIBUTING.md for the full spiel.

Note: Please sign your commits so we can verify your contributions.

Contact

License

Apache 2.0 - Because sharing is caring.

Acknowledgements

Built on the shoulders of giants:

jan (janhq/jan)

llamacpp
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45K ·GitHub
What is this?Jan is an open-source, self-hosted alternative to ChatGPT that runs 100% offline on your own computer, letting you download and run LLMs locally with full control and privacy.
Is this a desktop app or something I run in a terminal?It's a desktop application built with Tauri (Rust backend), with installers for Windows, macOS, and Linux (deb/AppImage/Arm64).
How do I get it running?Download the prebuilt installer for your OS from jan.ai or GitHub Releases, or build it from source with Node.js, Yarn, Make, and Rust using 'make dev'.
Any gotchas when building on Windows?You must run 'make dev' from Git Bash, not cmd.exe, since make dispatches recipes through sh; also long build paths can hit Windows' 260-character MAXPATH limit, which the build script tries to work around automatically.
What hardware do I need to run bigger models smoothly?Minimum specs scale with model size: 8GB RAM for 3B models, 16GB for 7B, and 32GB for 13B models; GPU acceleration is available on Windows (NVIDIA/AMD/Intel Arc) and Linux.
Does my data leave my machine?No — everything runs locally by default, and cloud connections (OpenAI, Anthropic, etc.) are only used if you explicitly set them up.
Can Jan connect to cloud AI providers too, or is it strictly local?It can do both: it runs local models fully offline, but also supports Cloud Integration to connect to GPT via OpenAI, Claude via Anthropic, Mistral, Groq, and MiniMax.
Does Jan expose an API for other apps to use?Yes, it runs an OpenAI-Compatible API on a local server at localhost:1337 so other applications can talk to it.
Can Jan do more than chat, like use tools or agents?Yes, it has Model Context Protocol (MCP) integration for agentic capabilities and lets you create Custom Assistants for specialized tasks.
Where do the local models come from?You download and run LLMs such as Llama, Gemma, Qwen, and GPT-oss directly from HuggingFace.
Creator
janhq
Size
1.5 GB
Stars
45K
Age
3y
Commits · 90 Days