sauravpanda/BrowserAI

Health v2 · maintenance only

Adoption not part of the score

1452 stars · 137 forks observed · 2026-09-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BrowserAI is a TypeScript library for running LLMs, speech recognition, text-to-speech, and audio separation models directly in the browser using WebGPU and WebAssembly, with no server required. It provides a unified SDK over multiple engines (MLC, Transformers, Flare, Demucs) with Web Worker support, structured output, and built-in storage.

Use cases

• run llama or deepseek models locally in the browser without a server
• build a privacy-focused chatbot with no API keys or cloud costs
• add speech recognition and text-to-speech voice chat to a web app
• generate structured JSON output from an LLM client-side
• build AI agents that run entirely in the user's browser
• create an offline-capable AI web app
• separate vocals and stems from audio in the browser

When to choose

• you need client-side LLM inference with zero server or API costs
• privacy requires that user data never leaves the device
• you want a simple SDK abstracting multiple browser AI engines
• you're building chat, voice, or agent experiences in a web app

When to avoid

• you need large models that exceed browser memory or WebGPU limits
• you require server-grade throughput or heavy batch inference
• your users are on browsers without WebGPU/WASM support
• you need fine-tuning or training rather than inference

Facets

library · maturity active

llm-inference speech-recognition tts agent-framework rag sdk chatbot large-language-models artificial-intelligence web-development frontend speech-processing browser wasm webgpu local-llm in-browser-ai privacy-first web-workers structured-output mlc transformers-js audio-separation indexeddb ai-agents web-server nodejs

Member repositories

For agents

markdown · JSON · MCP: product_card(name="sauravpanda/BrowserAI")