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BrowserAI 🚀

Run Production-Ready LLMs Directly in Your Browser

Simple • Fast • Private • Open Source

Live Demo • Documentation • Discord Community

BrowserAI Demo

🌟 Live Demos

Demo Description Try It
Chat Multi-model chat interface chat.browserai.dev
Voice Chat Full-featured with speech recognition & TTS voice-demo.browserai.dev
Text-to-Speech Powered by Kokoro 82M tts-demo.browserai.dev

⚡ Key Features

  • 🔒 100% Private: All processing happens locally in your browser
  • 🚀 WebGPU Accelerated: Near-native performance
  • 💰 Zero Server Costs: No complex infrastructure needed
  • 🌐 Offline Capable: Works without internet after initial download
  • 🎯 Developer Friendly: Simple sdk with multiple engine support
  • 📦 Production Ready: Pre-optimized popular models

🎯 Perfect For

  • Web developers building AI-powered applications
  • Companies needing privacy-conscious AI solutions
  • Researchers experimenting with browser-based AI
  • Hobbyists exploring AI without infrastructure overhead

✨ Features

  • 🎯 Run AI models directly in the browser - no server required!
  • ⚡ WebGPU acceleration for blazing fast inference
  • 🔄 Seamless switching between MLC and Transformers engines
  • 📦 Pre-configured popular models ready to use
  • 🛠️ Easy-to-use API for text generation and more
  • 🔧 Web Worker support for non-blocking UI performance
  • 📊 Structured output generation with JSON schemas
  • 🎙️ Speech recognition and text-to-speech capabilities
  • 💾 Built-in database support for storing conversations and embeddings

🚀 Quick Start

npm install @browserai/browserai

OR

yarn add @browserai/browserai

Basic Usage

import { BrowserAI } from '@browserai/browserai';

const browserAI = new BrowserAI();

// Load model with progress tracking
await browserAI.loadModel('llama-3.2-1b-instruct', {
  quantization: 'q4f16_1',
  onProgress: (progress) => console.log('Loading:', progress.progress + '%')
});

// Generate text
const response = await browserAI.generateText('Hello, how are you?');
console.log(response);

📚 Examples

Text Generation with Options

const response = await browserAI.generateText('Write a short poem about coding', {
  temperature: 0.8,
  max_tokens: 100,
  system_prompt: "You are a creative poet specialized in technology themes."
});

Chat with System Prompt

const ai = new BrowserAI();
await ai.loadModel('gemma-2b-it');

const response = await ai.generateText([
  { role: 'system', content: 'You are a helpful assistant.' },
  { role: 'user', content: 'What is WebGPU?' }
]);

Chat with System Prompt

const response = await browserAI.generateText('List 3 colors', {
  json_schema: {
    type: "object",
    properties: {
      colors: {
        type: "array",
        items: {
          type: "object",
          properties: {
            name: { type: "string" },
            hex: { type: "string" }
          }
        }
      }
    }
  },
  response_format: { type: "json_object" }
});

Speech Recognition

const browserAI = new BrowserAI();
await browserAI.loadModel('whisper-tiny-en');

// Using the built-in recorder
await browserAI.startRecording();
const audioBlob = await browserAI.stopRecording();
const transcription = await browserAI.transcribeAudio(audioBlob, {
  return_timestamps: true,
  language: 'en'
});

Text-to-Speech

const ai = new BrowserAI();
await ai.loadModel('kokoro-tts');
const audioBuffer = await browserAI.textToSpeech('Hello, how are you today?', {
  voice: 'af_bella',
  speed: 1.0
});// Play the audio using Web Audio API
const audioContext = new AudioContext();
const source = audioContext.createBufferSource();
audioContext.decodeAudioData(audioBuffer, (buffer) => {
  source.buffer = buffer;
  source.connect(audioContext.destination);
  source.start(0);
});

🔧 Supported Models

More models will be added soon. Request a model by creating an issue.

MLC Models

  • Llama-3.2-1b-Instruct
  • SmolLM2-135M-Instruct
  • SmolLM2-360M-Instruct
  • SmolLM2-1.7B-Instruct
  • Qwen-0.5B-Instruct
  • Gemma-2B-IT
  • TinyLlama-1.1B-Chat-v0.4
  • Phi-3.5-mini-instruct
  • Qwen2.5-1.5B-Instruct
  • DeepSeek-R1-Distill-Qwen-7B
  • DeepSeek-R1-Distill-Llama-8B
  • Snowflake-Arctic-Embed-M-B32
  • Snowflake-Arctic-Embed-S-B32
  • Snowflake-Arctic-Embed-M-B4
  • Snowflake-Arctic-Embed-S-B4

Transformers Models

  • Llama-3.2-1b-Instruct
  • Whisper-tiny-en (Speech Recognition)
  • Whisper-base-all (Speech Recognition)
  • Whisper-small-all (Speech Recognition)
  • Kokoro-TTS (Text-to-Speech)

🗺️ Enhanced Roadmap

Phase 1: Foundation

  • 🎯 Simplified model initialization
  • 📊 Basic monitoring and metrics
  • 🔍 Simple RAG implementation
  • 🛠️ Developer tools integration

Phase 2: Advanced Features

  • 📚 Enhanced RAG capabilities
    • Hybrid search
    • Auto-chunking
    • Source tracking
  • 📊 Advanced observability
    • Performance dashboards
    • Memory profiling
    • Error tracking

Phase 3: Enterprise Features

  • 🔐 Security features
  • 📈 Advanced analytics
  • 🤝 Multi-model orchestration

🤝 Contributing

We welcome contributions! Feel free to:

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • MLC AI for their incredible mode compilation library and support for webgpu runtime and xgrammar
  • Hugging Face and Xenova for their Transformers.js library, licensed under Apache License 2.0. The original code has been modified to work in a browser environment and converted to TypeScript.
  • All our contributors and supporters!

Made with ❤️ for the AI community

🚀 Requirements

  • Modern browser with WebGPU support (Chrome 113+, Edge 113+, or equivalent)
  • For models with shader-f16 requirement, hardware must support 16-bit floating point operations