lilbee 0.6.66b485

A batteries-included terminal app for local AI: a browsable model catalog, a search engine over your own files and code, and a chat that cites its sources. Per-project libraries, semantic and hybrid search, vision OCR, auto-built wiki. CLI, TUI, MCP server, REST API, and Python library in one process; no model server, no database server.

Run and manage local AI models, and search everything you own with them, all in one program.

Project site  ·  PyPI  ·  Obsidian plugin  ·  REST API

A batteries-included local search engine you can talk to: it runs the AI models, indexes your files and code, crawls the web, and plugs into your coding agent, so there's nothing else to install or set up. Ask in plain English; every answer cites the file and line.

It's all one program: you never stand up a separate model server, a vector database, or a container. lilbee runs the models and keeps the index itself. Reach it as a full-screen terminal app, a command-line tool, a Model Context Protocol server, an HTTP API, or a Python library. Close it and it's gone, or run it as a service if you'd rather keep it warm. It runs on your computer; lilbee uses a cloud model only when you pick one.

Already running Ollama or LM Studio? Point lilbee at either one and keep managing your models there. lilbee also comes with first-class model management built in, so you can browse Hugging Face, download a model, and give it a role without leaving the app. Use whichever you prefer.

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 →

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

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Quick start

Two recommended ways to use lilbee, depending on whether you're the one driving:

• Run lilbee for the full-screen terminal app. A welcome wizard picks a chat and embedding model, then you index files, search, and chat without leaving the TUI. The Settings screen exposes every retrieval knob (search depth, distance threshold, reranker, chunking) so you can tune lilbee to your library shape.
• Wire it into your agent over MCP. Any MCP-aware coding agent calls lilbee_search / lilbee_add and gets back cited snippets it can quote. Agents can also fine-tune lilbee on the fly via lilbee_settings_set. Drop in the lilbee-mcp skill and the agent reads the full surface: every tool, every retrieval knob, and when to widen for prose vs narrow for code. See Agent integration.

Defaults are sane for chatting with code, documentation, crawled sites, and long PDFs. Every retrieval setting is writable from the TUI Settings screen, the /set slash command, MCP lilbee_settings_set, or config.toml. When answers feel thin or noisy, the usual knobs are top_k, max_distance, or diversity_max_per_source.

CLI, the HTTP API, env vars, and config.toml are there for scripting, headless runs, and custom integrations. See the usage guide.

Highlights

• Answers cite the source line. Click a citation, jump to the file at the exact line. When the answer isn't in your library, lilbee says so instead of inventing one.
• It works, and the demos prove it. Every GIF and tutorial reel here is recorded live on real hardware, nothing staged. Backed by 100% test coverage, full typing, and CI on macOS, Linux, and Windows.
• Up and running in one command. Install, run lilbee, and a first-run wizard pulls a model and drops you straight into chat.
• Reads almost anything you point it at. Documents, scanned pages, spreadsheets, ebooks, web pages, and source code: 90+ formats and 150+ languages in all. Whatever you give it becomes searchable.
• Splits it into pieces that stand on their own. Prose and code are chunked differently, so each piece keeps its meaning instead of getting cut mid-thought. This is where most of the quality lives. A search engine is only as good as the chunks underneath it.
• A sophisticated search engine on top, built on published research. It ranks every result by how well it answers you, so the best match comes back first. 50+ knobs to tune from the Settings screen or hand to your agent, with sane defaults if you'd rather not.
• It brings and runs the models itself. Browse Hugging Face, pull a model, give it a role (chat, embedding, vision, reranking); lilbee runs it on Metal, Vulkan, or CUDA. You never point it at a server you set up.
• Your hardware, put to work. Your machine can do a lot more than you're using it for. lilbee runs local models on hardware you already own, no cloud account required.
• Per-project libraries. Keep one library for everything, or give each project its own.
• One install, many surfaces. TUI, CLI, MCP server, REST API, and Python library. Nothing to stand up: it loads on demand and runs as a service only if you want it warm.
• Everything in one file. The standalone binary is 250-365 MB and bundles the whole thing: search engine, web crawler, MCP server, HTTP server, and terminal UI, with Python and llama.cpp included. Comparable desktop AI apps (often Electron-based) ship hundreds of MB to several GB and do less.
• Works with your coding agent. Connect lilbee to your AI coding assistant and it answers from your actual files and code, with citations, instead of guessing. It can even adjust its own search as it works.

Why lilbee

A small local model is fun, but there's only so much you can do with one on its own. Give it properly processed documents and a search engine over them, and it suddenly becomes incredibly powerful. Without those, it never gets past being a fun novelty.

lilbee does all of it, in one install: it finds and runs the models for you, processes your documents and crawls the web pages you point it at, and searches it all with a real engine. Use it yourself in the terminal, or wire it into your coding agent so it answers from your files with citations instead of guessing.

The long-term goal: make local AI genuinely useful on hardware you already own, with no token budgets to ration and no provider to depend on; the cloud's there only when you want it. The same engine works two ways. It's an Encarta 99 you build for yourself, over your files and the web pages you save, that you read and ask questions of. And it's a reference layer for code: point it at your project, your dependencies, and your API docs, and your coding agent answers from what's actually there instead of guessing function names. Read it yourself, or have your agent read it for you.

What you can do with it

A library of your own files

Point lilbee at a folder of PDFs, notes, ebooks, or code and it builds a searchable library, with citations that click back to the source line. The pattern works for anything you have a lot of text about: a shelf of appliance manuals, a field's research papers, a car's service manuals, your company's internal wiki. Whatever you give it becomes searchable, and you can talk to it.

Already using an MCP-aware agent? Hand setup to it.

If you've already got an MCP-aware coding agent running, it can do the setup for you: browse the model catalog, pull picks, wire them into the embedding / reranker / vision roles, and tune retrieval for your library and question style. No TUI, no config file, no restart. Agents already understand search engines, so the right knobs to move are obvious to them. See the lilbee-mcp skill for the workflow and example prompts.

Opencode integration (coming)

Local-model opencode support is coming in #267, with tool-calling working across many GGUF families.

The demo shows a small local model (Qwen) given a specific instruction: when its first search comes back thin, widen lilbee's search settings and search again. The second pass returns the full function bodies with file:line citations. A more capable model would do the same from a higher-level prompt like "improve your search results." Read the lilbee-mcp skill to teach your own model the pattern.

A reference for AI agents

Once configured, lilbee plugs into whatever agent you use, over MCP. Feed it your project's docs, your dependency source, your API docs, your design notes; the agent stops making up function names and instead reads the actual code, cites file and line, and says it doesn't know when the answer isn't in your library.

Your files, the search index, and the embeddings stay on your computer. The agent calls lilbee_search and gets back cited snippets. The demo below is lilbee talking to lilbee: an agent indexes lilbee's own source, then answers questions about how lilbee works with file:line citations.

Offline copies of websites

Install the [crawler] extra, point lilbee at a docs site, a wiki, or a vendor's API reference, and the pages get fetched, converted to markdown, and added to your library. From then on you can search or chat with that copy of the site offline, even after it changes or goes down.

Documents, code, and scanned images

lilbee splits indexing by what's being read:

• Prose and structured documents (PDFs, Office files, ebooks, HTML, 90+ formats) go through Kreuzberg with heading-aware chunking, so each chunk keeps its section context.
• Code goes through tree-sitter's AST-aware splitter across 150+ languages, so chunks map to functions, classes, and modules instead of arbitrary line ranges.
• Scanned PDFs and photos go through OCR: Tesseract for plain text, or a local / remote vision model that keeps tables and layout as markdown.

Retrieval returns things that make sense on their own, not fragments cut through an argument or a function signature.

Pick and tune your models

Chat, embedding, vision, and reranking models are installed and switched from inside the terminal: browse the catalog, pull a model, pick a role. Retrieval and generation expose 50+ settings (chunk size, search strictness, reranker depth, and more), editable from the TUI, env vars, or a project-local config file. Sane defaults.

See when a model won't load before you download it

Hugging Face has thousands of GGUFs, but the bundled llama.cpp only supports a subset of architectures and brand-new ones take time to reach the pinned runtime. lilbee tags incompatible models in the catalog and refuses the download (with an override confirm), so you don't wait through a multi-GB pull only to hit "unsupported architecture" at load.

Cloud models, when you want them

lilbee runs entirely on your machine by default. Two ways to use a cloud model when you want one:

• Bring your own key. Install the [litellm] extra, add an API key, then point any role (chat, embedding, vision, rerank) at a cloud model from the same catalog. The TUI shows a warning the whole time a cloud model is on.
• Pair lilbee with a cloud agent over MCP. Your files, the embeddings, and the index stay local. Any MCP-aware agent calls lilbee_search / lilbee_add and gets back cited snippets.

Either way, your files and the index stay on your computer. Only what you ask and the snippets needed to answer it get sent to the cloud model.

TUI

lilbee (no args) launches a full Textual terminal app: streaming chat with clickable citations, a model bar with searchable pickers and a Search/Chat toggle, a Task Center for background jobs, and screens for the model catalog, settings, the setup wizard, and the auto-built wiki. Type / for the command list; tab completion works everywhere.

Ctrl+P opens the Textual command palette, ? toggles the keybinding cheat sheet, /help opens the slash-command catalog. Every action lilbee can take is reachable from one of those three.

Every GIF on this page (plus the extras that don't fit here) is at lilbee.sh/tutorial as an embedded video with long-form captions. Tape sources are in demos/. For commands and settings, see the usage guide.

Hardware requirements

Standalone mode runs entirely on your machine. No cloud required. Minimum: Apple Silicon Mac, or a 64-bit Intel/AMD CPU from 2013+, or an ARMv8 Linux box; 8 GB RAM, 2 GB disk.

Full platform and resource breakdown

Platform	Minimum	Recommended
macOS arm64	Apple Silicon (M1 or newer), macOS 11+	M-series Pro / Max / Ultra
Linux x86_64	64-bit Intel/AMD from 2013+ (x86-64-v3)	Modern Intel/AMD CPU + an NVIDIA, AMD, or Intel Arc GPU
Windows x86_64	64-bit Intel/AMD from 2013+ (x86-64-v3), Windows 10/11	Modern desktop / workstation CPU + GPU
Linux ARM64	ARMv8 NEON-capable (Raspberry Pi 4+, AWS Graviton, Ampere Altra)	Modern ARM server with 16+ GB RAM

Resource	Minimum	Recommended
RAM	8 GB	16 to 32 GB to keep several local models warm at once (chat + embed + rerank + vision); actual footprint scales with the sizes and quantizations you pick
GPU / Accelerator	none required (CPU-only works)	Apple Silicon (Metal) · NVIDIA / AMD / Intel Arc (Vulkan) · NVIDIA + CUDA toolkit (opt-in CUDA wheels, see Install)
Disk	2 GB	10+ GB for multiple models

Install

Two routes, and the difference matters:

• Into your own Python with pip or uv (Python 3.11 to 3.14). Uses the Python and tooling you already have, picks the fastest CPU code path for your machine at runtime, and upgrades like any other package. Recommended if you have Python.
• A self-contained bundle: the standalone binary, or the Homebrew / AUR / Nix / Docker builds that wrap it. Nothing else to install. The trade-off is a single large download (it bundles its own Python runtime, llama.cpp, and the optional extras) and a small cold-start cost the first time it self-extracts. Recommended if you'd rather not deal with Python.

Have an NVIDIA GPU? Both routes have a CUDA build that's faster than the default Vulkan path. Skip to On NVIDIA hardware.

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.

How	Command	Notes
pip	pip install --pre lilbee	Recommended. The default wheel runs on any x86_64 CPU and uses your GPU via Vulkan / Metal automatically. Intel Mac: add --extra-index-url https://lilbee.sh/cpu/ (browse wheels).
uv	uv tool install --prerelease=allow lilbee	Same wheel as pip; fetches a Python for you if you need one.
Homebrew	brew tap tobocop2/lilbee && brew install lilbee	macOS arm64 / Linux x86_64. Bundled build; clears the macOS quarantine flag for you.
AUR	paru -S lilbee	Arch Linux. Wraps the Linux x86_64 binary; works with yay / pacaur / any helper.
Docker	docker run --rm -v lilbee-data:/home/lilbee/data ghcr.io/tobocop2/lilbee:latest --help	GHCR image, tagged by version and latest. Data lives at /home/lilbee/data. Mount a volume there.
Nix	nix run github:tobocop2/lilbee	NixOS, nix-darwin, or any host with nix. On Linux the flake bundles glibc, libgomp, and vulkan-loader so it runs on bare NixOS.
Standalone binary	download for your platform →	One file, own Python runtime, no pip needed. Linux needs glibc 2.28+; the macOS / Windows builds are unsigned (xattr -d com.apple.quarantine ./lilbee-macos-arm64 if Gatekeeper blocks it).
From source	git clone https://github.com/tobocop2/lilbee && cd lilbee && uv sync && uv run lilbee	For hacking on it. Needs git and uv.

On NVIDIA hardware

The default Vulkan build works on NVIDIA cards, but there's a dedicated CUDA build that's faster on NVIDIA hardware and sidesteps the iGPU + dGPU Vulkan-loader crash on Windows.

	Command
pip	pip install --pre lilbee --extra-index-url https://lilbee.sh/cu125/
Homebrew	brew install tobocop2/lilbee/lilbee-cuda
AUR	paru -S lilbee-cuda
Nix	nix run github:tobocop2/lilbee#lilbee-cuda
Binary	lilbee-linux-x86_64-cu125 or lilbee-windows-x86_64-cu125.exe

Same lilbee command after install. The CUDA runtime is bundled; you only need the NVIDIA driver. Already have the regular lilbee installed? On AUR paru -S lilbee-cuda swaps it automatically; on Homebrew run brew uninstall lilbee first. Older driver? cu124 and cu121 ship via the matching wheel indexes and as direct-download Linux binaries on the release page.

Then check it runs and pick a model:

lilbee self-check    # ~90 MB download; runs an inference + an embedding; "SELF-CHECK PASSED" on success
lilbee               # launch the terminal app; pick a chat + embedding model on the welcome screen

The usage guide covers the rest: TUI screens, slash commands, CLI, HTTP server, MCP, env vars, and config.toml.

Linux runtime requirements

The Linux x86_64 wheel and binary link the Vulkan loader at runtime. Most desktop distros (Ubuntu 22.04+, Pop!_OS, Mint) ship libvulkan1; bare Arch / Fedora / Alpine images don't, and lilbee self-check fails with cannot open shared object file: libvulkan.so.1. Install it once: sudo pacman -S vulkan-icd-loader (Arch / Manjaro), sudo dnf install vulkan-loader (Fedora, RHEL), or sudo apt-get install libvulkan1 (Debian, Ubuntu).

Optional extras

These only matter for a pip or uv install: add the name in brackets, e.g. pip install --pre 'lilbee[crawler,litellm]' (combine multiple, and --extra-index-url still works for CUDA). The standalone binary and the Homebrew / AUR / Nix / Docker builds already include all three. lilbee works without them either way.

Extra	What it adds
[crawler]	Index websites alongside your files: crawl a docs site or wiki to markdown, then search it offline.
[litellm]	Bridge to hosted model providers for chat, vision, or embeddings while other roles stay local. The TUI flags when a hosted role is active.
[graph]	Concept-graph search: extracts the ideas in your documents and uses how they relate to surface matches plain keyword search misses. No extra model calls.

See the full guide on optional extras for configuration.

Upgrading

pip install --upgrade --pre lilbee
# or
uv tool install --reinstall --prerelease=allow lilbee

Agent integration

Drop the lilbee-mcp skill into .opencode/skills/ or .claude/skills/, register lilbee as an MCP server, and any MCP-aware coding agent can search your library, swap models, and tune retrieval. The skill is the single entry point: it documents every tool, the workflows the agent should follow, and points to drop-in AGENTS.md and worker-subagent starters under examples/agent-integration/.

The demos below use opencode driving a cloud model. lilbee stays local; only the queries and the returned chunks cross the wire to the cloud model. Local-model opencode integration is on the way across many GGUF families: see Opencode integration (coming) above.

Live-indexing example: opencode (cloud model) indexes a Godot 4 pathfinding subset (~3s), then lilbee_search-es for AStarGrid2D and answers method-by-method against your local files.

The same shape scales up. Pre-index Godot 4's full class reference (810 XMLs, 3449 chunks) and the same opencode + cloud setup can write a procedural level generator with every API call backed by a godot-classes/<Class>.xml:line citation; the side-by-side benchmark measured 4 hallucinated APIs without lilbee, 0 with.

HTTP Server

The HTTP server exposes a REST API any tool or GUI can hit: search (with SSE streaming), document lifecycle, crawling, model management, configuration. See the REST API reference and the usage guide for setup.

The Obsidian plugin is a GUI built on it: it starts the HTTP server in the background, and every citation opens a Source Preview scrolled to the exact spot. Install via BRAT; the plugin README has setup.

Running as a service (optional)

For tools that talk to lilbee's HTTP REST API (the Obsidian plugin, custom GUIs, anything hitting /api/*), your OS launcher can keep the HTTP server warm so requests skip the cold-start.

This is the only lilbee surface that benefits from a system daemon. The TUI, lilbee chat, the MCP server, and the rest of the CLI are designed to load on demand and exit when you close them. There's no always-on process to babysit, which is uncommon in this corner of the local-AI ecosystem.

Pull a chat and embedding model first; all recipes pin the server to 127.0.0.1:42697.

Platform	Command
macOS (Homebrew)	brew services start lilbee
Linux (Arch / AUR)	systemctl --user enable --now lilbee (add loginctl enable-linger $USER on headless servers)
NixOS	Import lilbee.nixosModules.lilbee, set services.lilbee.enable = true;

Supported formats

Text extraction powered by Kreuzberg, code chunking by tree-sitter. Structured formats (XML, JSON, CSV) get embedding-friendly preprocessing. This list is not exhaustive; Kreuzberg supports additional formats beyond what's listed here.

Format	Extensions	Requires
PDF	.pdf	none
Scanned PDF	.pdf (no extractable text)	Tesseract (auto, plain text), or a GGUF vision model via the native mtmd backend (recommended, preserves tables, headings, and layout as markdown)
Office	.docx, .xlsx, .pptx	none
eBook	.epub	none
Images (OCR)	.png, .jpg, .jpeg, .tiff, .bmp, .webp	Tesseract
Data	.csv, .tsv	none
Structured	.xml, .json, .jsonl, .yaml, .yml	none
Code	.py, .js, .ts, .go, .rs, .java and 150+ more via tree-sitter (AST-aware chunking)	none

See the usage guide for OCR setup and model benchmarks.

Experimental

Two opt-in features that work but are still finding their final shape: Wiki and semantic chunking. Click to expand.

Generation quality and retrieval behavior depend on your library, models, and knobs; expect to iterate. Feedback is welcome.

Wiki

lilbee analyzes the documents you've indexed and writes a wiki about them. Pages compound across sources: concepts and entities that show up repeatedly get their own page with citations from every source that mentions them. Sections are citation-verified before publish, and plain-text concept references are rewritten to [[wiki link]] form so graph-style markdown viewers can render the connections. Lower-confidence pages land in a drafts/ queue for review rather than publishing direct.

See the Wiki section of the usage guide for the full command list and configuration.

Semantic chunking

A semantic-chunking mode is available as an opt-in alternative to the default fixed-size chunker. It uses embedding similarity to find topic boundaries, so each chunk is one coherent thought instead of a fragment that cuts through an argument. The benefit shows up on prose-heavy collections like novels, essays, long-form research papers, or interview transcripts. The trade-off is roughly 9x more embedding calls during indexing.

See the Semantic chunking section of the usage guide for trade-offs and how to enable it.

Built on

lilbee stands on a stack of established open-source projects, all bundled into one install:

• llama.cpp (via llama-cpp-python) is the local model runtime. Every chat, embedding, vision, and reranker call goes through it. Without llama.cpp there is no lilbee.
• Hugging Face Hub (via huggingface_hub) hosts the model catalog and handles every download. Search, browse, and pull all route through it.
• Kreuzberg parses 90+ document formats with heading-aware chunking.
• LanceDB is the embedded vector store.
• tree-sitter (via tree-sitter-language-pack) chunks code across 150+ languages.
• crawl4ai and Playwright crawl the web; Tesseract is the OCR fallback when no vision model is set.
• LiteLLM bridges cloud model providers (the [litellm] optional extra).
• Textual draws the terminal; Litestar runs the HTTP server.
• MCP Python SDK is the agent surface; Typer is the CLI; Pydantic is the config + validation backbone.

License

Elastic License 2.0 (ELv2). See LICENSE.