Lilbee

lilbee runs and manages your models: chat, embedding, vision, and rerank, placed across every GPU you have. It puts them to work as a search engine you can talk to, over your files, notes, code, and the web, where every answer cites the exact file and line. It crawls websites into your library, launches your coding agents on local models, and hands any MCP-aware agent cited answers from everything you've indexed. The same engine backs the Obsidian community plugin, so your vault gets all of it without a terminal. Ask in plain English. No containers, no networking, nothing else to install or set up.

And it is private. Your files, the index, the embeddings, your questions, and the answers stay on your machine. lilbee sends no telemetry, needs no account, and makes no cloud call unless you configure a cloud model yourself.

It's all one program: no separate model server, vector database, or container to stand up. lilbee runs the models and keeps the index itself. Reach it as a terminal app, CLI, Model Context Protocol server, HTTP API, or Python library. Close it and it's gone, or run it as a service to keep it warm. Everything runs on your computer; it uses a cloud model only when you pick one.

Models are no different: lilbee has its own model manager and multi-GPU fleet, built on llama.cpp, so one executable does everything (browse Hugging Face, download a model, give it a role, run it on Metal / Vulkan / CUDA). You don't need Ollama or LM Studio at all: the architectures lilbee runs are behind most of the 190,000+ GGUF repos on Hugging Face, with representatives verified on real GPUs. If you already use them, point lilbee at your existing setup and keep your models.

Tutorial reel: every demo on this page (and the extras) as a real video player at lilbee.sh/tutorial.

⚠️ Beta software

lilbee is in active beta development. Every release on PyPI is a pre-release; you must use --pre (or uv's --prerelease=allow) when installing. Interfaces, command names, and on-disk formats may shift between betas. Feedback, bug reports, and issues are very welcome; that's the whole point of the beta.

Latest pre-release (always): lilbee on PyPI →

• Quick start
• Tutorial reel (long-form videos)
• Install
• Hardware requirements
• Highlights
• Why lilbee
• First start
• What you can do with it
• Agents
• TUI
• HTTP Server · REST API reference
• Supported formats
• Wiki
• Built on

Install

Two routes, and the difference matters:

• A self-contained bundle (start here): the standalone binary, or the Homebrew / AUR / Nix / Docker / Flatpak / Snap / Scoop builds that wrap it. It carries its own Python runtime, llama.cpp, the model engine, and the optional extras, so there is nothing else to install and nothing to assemble. The trade-off is one large download and a small cold-start cost the first time it self-extracts.
• Into your own Python with pip or uv (Python 3.11 to 3.14), in its own section below. This is the developer route: it installs lilbee as a library in an environment you manage, and the model engine is a separate extra from a separate index that you have to ask for. Take it if you are working on lilbee or importing it into your own code.

Have a discrete GPU? The bundled builds and the Vulkan engine already use it. There are faster vendor builds too: NVIDIA (CUDA) and AMD (ROCm).

No external services either way; lilbee downloads and runs models locally. Optional, for scanned-PDF / image OCR: Tesseract (brew install tesseract / apt install tesseract-ocr) or a GGUF vision model.

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