nativelab 0.3.1

NativeLab AI framework

NativeLab

A fully local, privacy-first LLM workbench powered by llama.cpp - desktop GUI, terminal CLI, and an experimentation layer.

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NativeLab is a desktop and terminal client for running large language models entirely on your machine. No API keys, no cloud, no data leaving your computer. It wraps llama.cpp behind a polished PyQt6 GUI and a Claude-Code-style terminal CLI, with first-class support for multi-model pipelines, document references, long-document summarization, and a brand-new Labs experimentation layer.

pip install nativelab
nativelab            # GUI
nativelab --cli      # terminal control center (setup, chat, models, labs, integrations)

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App screenshots

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_{Light chat workspace}

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_{Dark mode}

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_{Dev workspace}

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_{Pipeline builder}

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_{Server controls}

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_Skills

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_Appearance

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_{NativeLab UI}

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✨ Highlights

• 🖥️ Desktop GUI - Chat, model library, visual pipeline builder, MCP, HuggingFace downloader, Labs, theming.
• ⌨️ Terminal CLI - nativelab --cli opens a full terminal control center for chat, local/API models, skills, Labs, saved pipelines, integrations, endpoint serving, @file embedding, slash commands, and linting.
• 🧪 Labs - A dedicated experimentation layer with a shared endpoint API. New lab features get engine status, model swap, context change, and LLM calls for free.
• 🔌 Integrations - Local JSON endpoint, route browser, and saved Discord/WhatsApp bot connector profiles.
• 🔗 Visual Pipeline Builder - 20 node types (model, transform, branch, loop, custom Python), live execution log, save/load.
• 🌐 API + local mixing - OpenAI-compatible and Anthropic endpoints work side-by-side with local GGUFs.
• ⚡ Parallel + pipeline mode - Run reasoning + coding engines simultaneously and chain them automatically.
• 🧠 Auto family detection - 20+ model families recognised from filename; correct prompt template applied.
• 📦 HuggingFace downloader - Search any GGUF repo and pull files without leaving the app.

See changelog.txt for the latest release notes and docs/architecture.md for the layered design.

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📚 Documentation

The docs are split into short, focused files so you can jump straight to what you need.

Page	What's inside
docs/README.md	Documentation index with one-line summaries.
docs/installation.md	Install, llama.cpp setup, first-time workspace.
docs/cli.md	nativelab --cli - quick reference + link to the beginner guide.
docs/features.md	Full feature catalogue; latest release notes are in changelog.txt.
docs/architecture.md	Layered architecture, project structure, data flow.
docs/labs.md	The Labs experimentation layer + how to add a feature.
docs/integrations.md	Integration endpoint routes, local HTTP bridge, Discord and WhatsApp bot connectors.
docs/models.md	Model registry, families, quantization, API models.
docs/workflows.md	Pipelines, references, summarization, MCP, HF downloads.
docs/ui.md	GUI tour, theming, shortcuts, data persistence.
docs/troubleshooting.md	Common errors and their fixes.

Beginner-friendly walkthroughs:

• 🆕 Never used a terminal LLM tool? Start with nativelab/cli/cli_guide.md.
• 🆕 Want to add a lab feature? Read docs/labs.md.

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⚡ Quick start

GUI

pip install nativelab
nativelab

The first launch opens the desktop app. Use the Download tab to install llama.cpp binaries and grab a GGUF model - no manual setup required.

CLI

pip install nativelab
nativelab --cli

The CLI runs an interactive wizard the first time:

1. Verifies llama-server / llama-cli are present (or guides you to install them).
2. Lets you pick or download a GGUF model from HuggingFace.
3. Asks for a context size with sensible defaults.
4. Opens the terminal control center with Chat, Models, API Models, Skills, Labs, Pipelines, Integrations, Status, and Setup.

nativelab --cli models list
nativelab --cli api-models list
nativelab --cli skills chat-on
nativelab --cli endpoint /snapshot --json
nativelab --cli chat

Full beginner walkthrough: nativelab/cli/cli_guide.md.

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🧪 Labs - the experimentation layer

The nativelab/labs/ package is a sandbox for new features. Every lab panel receives a single LabEndpoints instance and uses it for all engine interaction:

from nativelab.labs import LabEndpoints

# Read state
endpoints.status_text     # "🟢 Server  :8612"
endpoints.model_path      # "/abs/path/to/mistral-7b.Q4_K_M.gguf"
endpoints.snapshot()      # {model_name, ctx_value, server_port, …}

# Synchronous LLM call - auto-routes API > server > CLI
endpoints.call_llm(messages=[...], system_prompt="…")

# Reverse routing - ask the host app to change state
endpoints.request_load_model("/path/to/other.gguf")
endpoints.request_context(8192)
endpoints.request_unload()

Add a lab feature by dropping nativelab/labs/<feature>.py with a QWidget panel that has LAB_NAME, LAB_ICON, and a set_endpoints(...) method, then appending it to LAB_FEATURES. Full guide in docs/labs.md.

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🛠️ Requirements

• Python 3.10+
• PyQt6 (installed automatically as a dependency)
• llama.cpp binaries - llama-server / llama-cli. The GUI's Download tab installs these for you, or you can drop them in ./llama/bin/.
• Optional: psutil (RAM monitor), PyPDF2 (PDF summarization), pyflakes / flake8 / pylint (CLI lint).

Detailed instructions in docs/installation.md.

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🤝 Contributing

Issues and PRs welcome. See CONTRIBUTING.md and CODE_OF_CONDUCT.md.

For security disclosures, see SECURITY.md.

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📜 License

AGPL v3 - see LICENSE. NativeLab depends on llama.cpp (MIT) and PyQt6 (GPL/commercial).

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Built for people who want their LLMs local, fast, and under their own control.

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