This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 0.6.90b448 instead.
Reason given by maintainers: not supported
lilbee
Beta — feedback and bug reports welcome. Open an issue.
Local knowledge base that handles both code and documents, with a git-like per-project model. Search, ask questions, or chat — standalone or as a retrieval backend for AI agents via MCP. Supports vision OCR for scanned PDFs. Everything stays on your machine, powered by Kreuzberg for text extraction, Ollama for embeddings, chat, and vision OCR, and LanceDB for vector storage.
- Why lilbee
- Demos
- Install
- Quick start · Full usage guide
- Agent integration
- Interactive chat
- Supported formats
Why lilbee
lilbee indexes documents and code into a searchable local knowledge base. Use it standalone — search, ask questions, chat — or plug it into AI coding agents as a retrieval backend via MCP.
Most tools like this only handle code. lilbee handles PDFs, Word docs, spreadsheets, images (OCR) — and code too, with AST-aware chunking.
- Standalone knowledge base — add documents, search, ask questions, or chat interactively with model switching and slash commands
- AI agent backend — MCP server and JSON CLI so coding agents can search your indexed docs as context
- Per-project databases —
lilbee initcreates a.lilbee/directory (like.git/) so each project gets its own isolated index - Documents and code alike — PDFs, Office docs, spreadsheets, images, ebooks, and 150+ code languages via tree-sitter
- Open-source and fully offline — your documents never leave your machine. Runs with Ollama and LanceDB, no cloud APIs or Docker
Add files (lilbee add), then search or ask questions. Once indexed, search works without Ollama — agents use their own LLM to reason over the retrieved chunks.
Demos
Click the ▶ arrows below to expand each demo.
AI agent — lilbee search vs web search (detailed analysis)
opencode + minimax-m2.5-free, single prompt, no follow-ups. The Godot 4.4 XML class reference (917 files) is indexed in lilbee. The baseline uses Exa AI code search instead.
⚠️ Caution: minimax-m2.5-free is a cloud model — retrieved chunks are sent to an external API. Use a local model if your documents are private.
| API hallucinations | Lines | |
|---|---|---|
| With lilbee (code · config) | 0 | 261 |
| Without lilbee (code · config) | 4 (~22% error rate) | 213 |
With lilbee — all Godot API calls match the class reference
Without lilbee — 4 hallucinated APIs (details)
If you spot issues with these benchmarks, please open an issue.
Vision OCR
Scanned PDF → searchable knowledge base
A scanned 1998 Star Wars: X-Wing Collector's Edition manual indexed with vision OCR (LightOnOCR-2), then queried in lilbee's interactive chat (qwen3-coder:30b, fully local). Three questions about dev team credits, energy management, and starfighter speeds — all answered from the OCR'd content.
See benchmarks, test documents, and sample output for model comparisons.
One-shot question from OCR'd content
The scanned Star Wars: X-Wing Collector's Edition guide, queried with a single lilbee ask command — no interactive chat needed.
Standalone
Interactive local offline chat
[!NOTE] Entirely local on a 2021 M1 Pro with 32 GB RAM.
Model switching via tab completion, then a Q&A grounded in an indexed PDF.
Code index and search
Add a codebase and search with natural language. Tree-sitter provides AST-aware chunking.
JSON output
Structured JSON output for agents and scripts.
Hardware requirements
lilbee runs entirely on your local machine — your hardware is the compute.
| Resource | Minimum | Recommended |
|---|---|---|
| RAM | 8 GB | 16–32 GB |
| GPU / Accelerator | — | Apple Metal (M-series), NVIDIA GPU (6+ GB VRAM) |
| Disk | 2 GB (models + data) | 10+ GB if using multiple models |
| CPU | Any modern x86_64 / ARM64 | — |
Ollama handles inference and uses Metal on macOS or CUDA on Linux/Windows. Without a GPU, models fall back to CPU — usable for embedding but slow for chat.
Install
Prerequisites
- Python 3.11+
- Ollama — the embedding model (
nomic-embed-text) is auto-pulled on first sync. If no chat model is installed, lilbee prompts you to pick and download one. - Optional (for scanned PDF/image OCR): Tesseract (
brew install tesseract/apt install tesseract-ocr) or an Ollama vision model (recommended for better quality — see vision OCR)
First-time download: If you're new to Ollama, expect the first run to take a while — models are large files that need to be downloaded once. For example,
qwen3:8bis ~5 GB and the embedding modelnomic-embed-textis ~274 MB. After the initial download, models are cached locally and load in seconds. You can check what you have installed withollama list.
Install
pip install lilbee # or: uv tool install lilbee
Development (run from source)
git clone https://github.com/tobocop2/lilbee && cd lilbee
uv sync
uv run lilbee
Quick start
See the usage guide.
Agent integration
lilbee can serve as a local retrieval backend for AI coding agents via MCP or JSON CLI. See docs/agent-integration.md for setup and usage.
Interactive chat
Running lilbee or lilbee chat enters an interactive REPL with conversation history, streaming responses, and slash commands:
| Command | Description |
|---|---|
/status |
Show indexed documents and config |
/add [path] |
Add a file or directory (tab-completes paths) |
/model [name] |
Switch chat model — no args opens an interactive picker; with a name, switches directly (tab-completes installed models) |
/vision [name|off] |
Switch vision OCR model — no args opens a picker, off disables (tab-completes catalog models) |
/version |
Show lilbee version |
/reset |
Delete all documents and data (asks for confirmation) |
/help |
Show available commands |
/quit |
Exit chat |
Slash commands and paths tab-complete. A spinner shows while waiting for the first token from the LLM.
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 |
— | |
| Scanned PDF | .pdf (no extractable text) |
Tesseract (auto, plain text) or Ollama vision model (recommended — preserves tables, headings, and layout as markdown) |
| Office | .docx, .xlsx, .pptx |
— |
| eBook | .epub |
— |
| Images (OCR) | .png, .jpg, .jpeg, .tiff, .bmp, .webp |
Tesseract |
| Data | .csv, .tsv |
— |
| Structured | .xml, .json, .jsonl, .yaml, .yml |
— |
| Text | .md, .txt, .html, .rst |
— |
| Code | .py, .js, .ts, .go, .rs, .java and 150+ more via tree-sitter (AST-aware chunking) |
— |
See the usage guide for OCR setup and model benchmarks.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for lilbee 0.4.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lilbee-0.4.5.tar.gz | 14.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lilbee-0.4.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 14.5 MB
Release files / lilbee-0.4.5.tar.gz
| Download URL | lilbee-0.4.5.tar.gz |
|---|---|
| Size | 14.5 MB |
| Tags | Source |
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| Tags | Python 3 |
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| Uploaded via |
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