What is Hashbrown for React?

Hashbrown is a React library designed to simplify the development of LLM-powered applications, offering specialized hooks for chat, structured data extraction, and generative UI.

What is the purpose of the sandboxed JavaScript execution?

It provides a safe WASM-based QuickJS environment where an LLM can generate and execute code for complex calculations or data visualizations without compromising host security.

Does Hashbrown support multiple LLM providers?

Yes, through its Node.js backend adapters, it integrates with OpenAI, Anthropic, Google, Azure, Bedrock, Ollama, and other custom providers.

Do I need a backend server to use this skill?

Yes, Hashbrown requires a Node.js backend adapter to securely manage API keys and proxy communication between the React frontend and the LLM provider.

How does the generative UI feature work?

It allows developers to 'expose' specific React components to the LLM, which the AI can then choose to render dynamically within the application UI based on user context.

Hashbrown LLM React Development

Name: Hashbrown LLM React Development
Author: aaronbassett

byaaronbassett

데이터 과학 및 ML

Simplifies building generative UI and LLM-powered React applications with client-side tool calling and sandboxed execution.

소개

Hashbrown is a comprehensive React framework designed to integrate Large Language Models directly into frontend applications through specialized hooks and backend adapters. It enables developers to create sophisticated generative user interfaces where the AI can render React components, execute sandboxed JavaScript for data visualization, and perform client-side actions via tool calling. By abstracting the complexities of streaming, structured data validation with Skillet schemas, and multi-provider connectivity, Hashbrown allows developers to rapidly build AI agents, adaptive dashboards, and multi-threaded chat interfaces with minimal boilerplate.

주요 기능

Pre-built component and server templates for rapid scaffolding
Type-safe structured data extraction using the Skillet schema language
Client-side tool calling to interact with application state and browser APIs
Generative UI for rendering React components directly from LLM output
0 GitHub stars
Sandboxed JavaScript execution via WASM-based QuickJS for safe data processing

사용 사례

Automating form generation and data extraction from natural language into structured TypeScript objects
Creating sophisticated chat interfaces with real-time streaming and multi-threaded conversations
Building AI-powered browser agents and dynamic dashboards that adapt to user intent

소개

주요 기능

Pre-built component and server templates for rapid scaffolding
Type-safe structured data extraction using the Skillet schema language
Client-side tool calling to interact with application state and browser APIs
Generative UI for rendering React components directly from LLM output
0 GitHub stars
Sandboxed JavaScript execution via WASM-based QuickJS for safe data processing

사용 사례

Automating form generation and data extraction from natural language into structured TypeScript objects
Creating sophisticated chat interfaces with real-time streaming and multi-threaded conversations
Building AI-powered browser agents and dynamic dashboards that adapt to user intent