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Basic Information

BrowserAI is a developer-focused open source SDK and runtime for running production-ready large language models directly inside modern web browsers. It enables privacy-preserving local inference using WebGPU acceleration so models run on-device without sending data to servers. The project supports multiple engine backends including MLC and Transformers, provides pre-configured models for text generation, speech recognition, and text-to-speech, and includes utilities for loading models, tracking progress, and generating structured JSON outputs. It offers a simple npm package interface, examples for chat, transcription and TTS, built-in storage for conversations and embeddings, and is positioned to support RAG and multi-model orchestration on the client side. The README targets web developers, researchers, hobbyists and no-code builders who want zero-server, offline-capable AI in browser applications.

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Features
BrowserAI highlights local, private inference with WebGPU acceleration to achieve near-native performance. It supports offline usage after initial download and claims zero server costs. The SDK provides seamless switching between MLC and Transformers engines, pre-optimized model bundles, and an easy-to-use API for text generation and chat. Additional features include Web Worker support for non-blocking UIs, structured JSON schema output, built-in database support for conversations and embeddings, speech recognition and text-to-speech models, progress callbacks during model load, and a maintained list of supported MLC and Transformers models. The project also publishes a roadmap covering RAG, observability, memory profiling, and enterprise-level security and multi-model orchestration.
Use Cases
BrowserAI helps teams and individuals build privacy-conscious AI features directly in browser apps without maintaining inference servers. It reduces infrastructure and hosting costs by enabling on-device model execution and supports offline scenarios. The SDK speeds prototyping and production deployment for chat, transcription and TTS features, and its built-in storage and embedding support help enable retrieval-augmented workflows. WebGPU acceleration and Web Worker integration improve responsiveness and UX. The project is useful to web developers, researchers experimenting with client-side models, hobbyists exploring LLMs without cloud dependencies, and no-code platform builders who need a browser-native AI runtime to power agent builders and interactive demos.

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