
BrowserAI
Run local LLMs inside your browser
Stars: 590

BrowserAI is a tool that allows users to run large language models (LLMs) directly in the browser, providing a simple, fast, and open-source solution. It prioritizes privacy by processing data locally, is cost-effective with no server costs, works offline after initial download, and offers WebGPU acceleration for high performance. It is developer-friendly with a simple API, supports multiple engines, and comes with pre-configured models for easy use. Ideal for web developers, companies needing privacy-conscious AI solutions, researchers experimenting with browser-based AI, and hobbyists exploring AI without infrastructure overhead.
README:
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 |
- π 100% Private: All processing happens locally in your browser
- π WebGPU Accelerated: Near-native performance
- π° Zero Server Costs: No complex infrastructure needed
- π Offline Capable: Works without internet after initial download
- π― Developer Friendly: Simple sdk with multiple engine support
- π¦ Production Ready: Pre-optimized popular models
- Web developers building AI-powered applications
- Companies needing privacy-conscious AI solutions
- Researchers experimenting with browser-based AI
- Hobbyists exploring AI without infrastructure overhead
- π― Run AI models directly in the browser - no server required!
- β‘ WebGPU acceleration for blazing fast inference
- π Seamless switching between MLC and Transformers engines
- π¦ Pre-configured popular models ready to use
- π οΈ Easy-to-use API for text generation and more
npm install @browserai/browserai
OR
yarn add @browserai/browserai
import { BrowserAI } from '@browserai/browserai';
const browserAI = new BrowserAI();
browserAI.loadModel('llama-3.2-1b-instruct');
const response = await browserAI.generateText('Hello, how are you?');
console.log(response);
const ai = new BrowserAI();
await ai.loadModel('llama-3.2-1b-instruct', {
quantization: 'q4f16_1' // Optimize for size/speed
});
const response = await ai.generateText('Write a short poem about coding', {
temperature: 0.8,
maxTokens: 100
});
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?' }
]);
const ai = new BrowserAI();
await ai.loadModel('whisper-tiny-en');
// Using the built-in recorder
await ai.startRecording();
const audioBlob = await ai.stopRecording();
const transcription = await ai.transcribeAudio(audioBlob);
const ai = new BrowserAI();
await ai.loadModel('kokoro-tts');
const audioBuffer = await ai.textToSpeech('Hello, how are you today?');
// 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);
});
More models will be added soon. Request a model by creating an issue.
- 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
- Llama-3.2-1b-Instruct
- Whisper-tiny-en (Speech Recognition)
- Kokoro-TTS (Text-to-Speech)
- π― Simplified model initialization
- π Basic monitoring and metrics
- π Simple RAG implementation
- π οΈ Developer tools integration
- π Enhanced RAG capabilities
- Hybrid search
- Auto-chunking
- Source tracking
- π Advanced observability
- Performance dashboards
- Memory profiling
- Error tracking
- π Security features
- π Advanced analytics
- π€ Multi-model orchestration
We welcome contributions! Feel free to:
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature
) - Commit your changes (
git commit -m 'Add amazing feature'
) - Push to the branch (
git push origin feature/amazing-feature
) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- 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 β€οΈ for the AI community
- 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
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