0cfc745954
* feat: Introduce structured error handling for llamacpp extension This commit introduces a structured error handling system for the `llamacpp` extension. Instead of returning simple string errors, we now use a custom `LlamacppError` struct with a specific `ErrorCode` enum. This allows the frontend to display more user-friendly and actionable error messages based on the code, rather than raw debug logs. The changes include: - A new `ErrorCode` enum to categorize errors (e.g., `OutOfMemory`, `ModelArchNotSupported`, `BinaryNotFound`). - A `LlamacppError` struct to encapsulate the code, a user-facing message, and optional detailed logs. - A static method `from_stderr` that intelligently parses llama.cpp's standard error output to identify and map common issues like Out of Memory errors to a specific error code. - Refactored `ServerError` enum to wrap the new `LlamacppError` and provide a consistent serialization format for the Tauri frontend. - Updated all relevant functions (`load_llama_model`, `get_devices`) to return the new structured error type, ensuring a more robust and predictable error flow. - A reduced timeout for model loading from 300 to 180 seconds. This work lays the groundwork for a more intuitive and helpful user experience, as the application can now provide clear guidance to users when a model fails to load. * Update src-tauri/src/core/utils/extensions/inference_llamacpp_extension/server.rs Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com> * Update src-tauri/src/core/utils/extensions/inference_llamacpp_extension/server.rs Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com> * chore: update FE handle error object from extension * chore: fix property type --------- Co-authored-by: ellipsis-dev[bot] <65095814+ellipsis-dev[bot]@users.noreply.github.com> Co-authored-by: Faisal Amir <urmauur@gmail.com>
Jan - Local AI Assistant
Getting Started - Docs - Changelog - Bug reports - Discord
Jan is an AI assistant that can run 100% offline on your device. Download and run LLMs with full control and privacy.
Installation
The easiest way to get started is by downloading one of the following versions for your respective operating system:
| Platform | Stable | Nightly |
| Windows | jan.exe | jan.exe |
| macOS | jan.dmg | jan.dmg |
| Linux (deb) | jan.deb | jan.deb |
| Linux (AppImage) | jan.AppImage | jan.AppImage |
Download from jan.ai or GitHub Releases.
Features
- Local AI Models: Download and run LLMs (Llama, Gemma, Qwen, etc.) from HuggingFace
- Cloud Integration: Connect to OpenAI, Anthropic, Mistral, Groq, and others
- Custom Assistants: Create specialized AI assistants for your tasks
- OpenAI-Compatible API: Local server at
localhost:1337for other applications - Model Context Protocol: MCP integration for enhanced capabilities
- Privacy First: Everything runs locally when you want it to
Build from Source
For those who enjoy the scenic route:
Prerequisites
- Node.js ≥ 20.0.0
- Yarn ≥ 1.22.0
- Make ≥ 3.81
- Rust (for Tauri)
Run with Make
git clone https://github.com/menloresearch/jan
cd jan
make dev
This handles everything: installs dependencies, builds core components, and launches the app.
Available make targets:
make dev- Full development setup and launchmake build- Production buildmake test- Run tests and lintingmake clean- Delete everything and start fresh
Run with Mise (easier)
You can also run with mise, which is a bit easier as it ensures Node.js, Rust, and other dependency versions are automatically managed:
git clone https://github.com/menloresearch/jan
cd jan
# Install mise (if not already installed)
curl https://mise.run | sh
# Install tools and start development
mise install # installs Node.js, Rust, and other tools
mise dev # runs the full development setup
Available mise commands:
mise dev- Full development setup and launchmise build- Production buildmise test- Run tests and lintingmise clean- Delete everything and start freshmise tasks- List all available tasks
Manual Commands
yarn install
yarn build:core
yarn build:extensions
yarn dev
System Requirements
Minimum specs for a decent experience:
- macOS: 13.6+ (8GB RAM for 3B models, 16GB for 7B, 32GB for 13B)
- Windows: 10+ with GPU support for NVIDIA/AMD/Intel Arc
- Linux: Most distributions work, GPU acceleration available
For detailed compatibility, check our installation guides.
Troubleshooting
If things go sideways:
- Check our troubleshooting docs
- Copy your error logs and system specs
- Ask for help in our Discord
#🆘|jan-helpchannel
Contributing
Contributions welcome. See CONTRIBUTING.md for the full spiel.
Links
- Documentation - The manual you should read
- API Reference - For the technically inclined
- Changelog - What we broke and fixed
- Discord - Where the community lives
Contact
- Bugs: GitHub Issues
- Business: hello@jan.ai
- Jobs: hr@jan.ai
- General Discussion: Discord
License
Apache 2.0 - Because sharing is caring.
Acknowledgements
Built on the shoulders of giants: