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Comprehensive Estrutura Leve Tools for Every Need

Get access to Estrutura Leve solutions that address multiple requirements. One-stop resources for streamlined workflows.

Estrutura Leve

  • MLC Web LLM Assistant

    A browser-based AI assistant enabling local inference and streaming of large language models with WebGPU and WebAssembly.
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    What is MLC Web LLM Assistant?
    Web LLM Assistant is a lightweight open-source framework that transforms your browser into an AI inference platform. It leverages WebGPU and WebAssembly backends to run LLMs directly on client devices without servers, ensuring privacy and offline capability. Users can import and switch between models such as LLaMA, Vicuna, and Alpaca, chat with the assistant, and see streaming responses. The modular React-based UI supports themes, conversation history, system prompts, and plugin-like extensions for custom behaviors. Developers can customize the interface, integrate external APIs, and fine-tune prompts. Deployment only requires hosting static files; no backend servers are needed. Web LLM Assistant democratizes AI by enabling high-performance local inference in any modern web browser.
    MLC Web LLM Assistant Core Features
    • Local LLM inference with WebGPU backend
    • WebAssembly support for broad device compatibility
    • Real-time streaming of AI responses
    • Model switching (LLaMA, Vicuna, Alpaca, etc.)
    • Customizable React-based user interface
    • Conversation history and system prompt management
    • Extensible plugin architecture for custom behaviors
    • Offline operation without server dependencies

  • Minimalist Agent

    A minimalist Python AI agent that uses OpenAI's LLM for multi-step reasoning and task execution via LangChain.
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    What is Minimalist Agent?
    Minimalist Agent provides a bare-bones framework for building AI agents in Python. It leverages LangChain’s agent classes and OpenAI’s API to perform multi-step reasoning, dynamically select tools, and execute functions. You can clone the repository, configure your OpenAI API key, define custom tools or endpoints, and run the CLI script to interact with the agent. The design emphasizes clarity and extensibility, making it easy to study, modify, and extend core agent behaviors for experimentation or teaching.
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