A Chrome extension that uses WebLLM to run AI-powered web automation entirely on-device. No cloud APIs, no API keys, fully private.

• On-Device AI: Uses WebLLM with WebGPU acceleration for local LLM inference
• Multi-Agent System: Planner + Navigator agents for intelligent task execution
• Browser Automation: Navigate, click, type, extract data from web pages
• Privacy-First: All AI runs locally, no data leaves your device
• Offline Support: Works offline after initial model download

• Chrome 124+ (required for WebGPU in service workers)
• Node.js 18+ and npm
• GPU with WebGPU support (most modern GPUs work)

1. Clone and install dependencies:

   cd local-browser
   npm install

2. Build the extension:

3. Load in Chrome:

   • Open chrome://extensions
   • Enable "Developer mode" (top right)
   • Click "Load unpacked"
   • Select the dist folder from this project

4. First run:

   • Click the extension icon in your toolbar
   • The first run will download the AI model (~1GB)
   • This is cached for future use

1. Navigate to any webpage
2. Click the Local Browser extension icon
3. Type a task like:
   • "Search for 'WebGPU' on Wikipedia and extract the first paragraph"
   • "Go to example.com and tell me what's there"
   • "Find the search box and search for 'AI news'"
4. Watch the AI execute the task step by step

This watches for changes and rebuilds automatically.

local-browser/
├── manifest.json           # Chrome extension manifest (MV3)
├── src/
│   ├── background/         # Service worker
│   │   ├── index.ts        # Entry point & message handling
│   │   ├── llm-engine.ts   # WebLLM wrapper
│   │   └── agents/         # AI agent system
│   │       ├── base-agent.ts
│   │       ├── planner-agent.ts
│   │       ├── navigator-agent.ts
│   │       └── executor.ts
│   ├── content/            # Content scripts
│   │   ├── dom-observer.ts # Page state extraction
│   │   └── action-executor.ts
│   ├── popup/              # React popup UI
│   │   ├── App.tsx
│   │   └── components/
│   └── shared/             # Shared types & constants
└── dist/                   # Build output

1. User enters a task in the popup UI
2. Planner Agent analyzes the task and creates a high-level strategy
3. Navigator Agent examines the current page DOM and decides on the next action
4. Content Script executes the action (click, type, extract, etc.)
5. Loop continues until task is complete or fails

The extension uses a two-agent architecture inspired by Nanobrowser:

• PlannerAgent: Strategic planning, creates step-by-step approach
• NavigatorAgent: Tactical execution, chooses specific actions based on page state

Both agents output structured JSON that is parsed and executed.

Default model: Qwen2.5-1.5B-Instruct-q4f16_1-MLC (~1GB)

Alternative models (configured in src/shared/constants.ts):

• Phi-3.5-mini-instruct-q4f16_1-MLC (~2GB, better reasoning)
• Llama-3.2-1B-Instruct-q4f16_1-MLC (~0.7GB, smaller)

• Update Chrome to version 124 or later
• Check chrome://gpu to verify WebGPU status
• Some GPUs may not support WebGPU

• Ensure you have enough disk space (~2GB free)
• Check browser console for errors
• Try clearing the extension's storage and reloading

• Some pages block content scripts (chrome://, extension pages)
• Try on a regular webpage like wikipedia.org

• Go to chrome://extensions
• Click the reload button on the extension

• POC Scope: This is a proof-of-concept, not production software
• No Vision: Uses text-only DOM analysis (no screenshot understanding)
• Single Tab: Only works with the currently active tab
• Basic Actions: Supports navigate, click, type, extract, scroll, wait
• Model Size: Smaller models may struggle with complex tasks

• WebLLM: On-device LLM inference with WebGPU
• React: Popup UI
• TypeScript: Type-safe development
• Vite + CRXJS: Chrome extension bundling
• Chrome Extension Manifest V3: Modern extension architecture

This project is inspired by:

• Nanobrowser - Multi-agent web automation (MIT License)
• WebLLM - In-browser LLM inference (Apache-2.0 License)

MIT License - See LICENSE file for details.