ai-agents

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Basic Information

This repository collects code and resources for multiple AI agent projects with an emphasis on practical agent applications. The primary highlighted project is a Smart Health Agent that provides a GPU-accelerated, multi-agent application for delivering personalized health guidance. The repo serves as a centralized place to find project directories, example implementations, and documentation for contributors and users interested in agent-based systems. It is organized to showcase individual projects rather than a single monolithic product, and points readers to project folders for code, configuration, and usage notes. The README emphasizes the Smart Health Agent and indicates ongoing additions of other agents and examples over time, making it useful for discovery and experimentation.

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App Details

Features
Includes a Smart Health Agent that is GPU-accelerated and built around a multi-agent workflow. Supports real-time health metrics collection and processing as part of the agent pipeline. Integrates external context such as weather to inform recommendations. Employs Retrieval Augmented Generation (RAG) techniques to provide personalized health advice by combining retrieved information with generative models. Repository structure contains project-specific directories with code, documentation, and examples to help users inspect and run individual agents. The README documents project scope and directs users to the Smart Health Agent folder for implementation details.
Use Cases
Provides a practical example of an end-user focused AI agent applied to health and wellness that developers and researchers can study or extend. The Smart Health Agent demonstrates combining multi-agent orchestration, GPU acceleration, real-time data inputs, contextual integrations like weather, and RAG for personalized recommendations, which can inform design decisions for similar systems. The repository aggregates code and documentation so users can inspect implementations, reproduce experiments, or adapt components for their own agent projects. It is useful for prototyping health-focused assistants, learning patterns for multi-agent workflows, and exploring integration of retrieval and generative techniques in applied settings.

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