BrowserAI is an open-source, browser-first AI framework that enables developers to run large language models (LLMs), embeddings, and multimodal models entirely within the user’s browser.

It eliminates the need for backend infrastructure or external APIs by leveraging technologies like WebGPU, WebAssembly, and browser-native AI capabilities to perform inference locally.

The library provides a unified interface for working with multiple in-browser AI engines such as Transformers.js, WebLLM, and native browser models, reducing fragmentation and simplifying integration into modern web applications. It also supports streaming outputs, structured data generation, background processing via Web Workers, and persistent storage using browser databases like IndexedDB and SQLite.

BrowserAI is designed with a strong focus on privacy and cost-efficiency—since all processing happens locally, no user data is sent to external servers and there are no API usage costs. It also enables offline functionality once models are downloaded, making it suitable for edge applications and privacy-sensitive use cases.

Overall, BrowserAI is positioned as a foundational tool for building AI-powered web apps, chatbots, and agents that run entirely on the client side, offering developers a simple and infrastructure-free way to integrate AI into their products.

Key features include:

• Run LLMs and AI models directly in the browser
• Unified API across multiple AI engines (WebLLM, Transformers.js, etc.)
• WebGPU and WebAssembly acceleration for performance
• Streaming responses and structured output support
• Background processing with Web Workers
• Built-in storage using IndexedDB and SQLite
• Offline capability after initial model download
• Privacy-first local execution with no data leaving the device

Common use cases include:

• Building browser-based AI chatbots
• Creating privacy-focused AI tools, running offline AI applications
• Prototyping AI features without backend infrastructure
• Embedding AI directly into web apps and dashboards