Longevity-Agents: A Multi-Agent Framework for Personalized Health & Longevity Insights

Overview

The Longevity-Agents is an asynchronous multi-agent framework built to provide personalized health data insights, tailored longevity recommendations, and complex decision-making processes to enhance well-being. It integrates various tools such as web search, domain-specific knowledge retrieval (RAG), code execution, and real-time market data to enable holistic, data-driven health guidance.

Features

Achieved

• Asynchronous Multi-Agent Framework: A scalable and efficient agent-based system that allows multiple agents to work concurrently to provide personalized health and longevity insights.

• Tool Calling Abilities: The framework is capable of leveraging external tools for:

  • Web Search: Retrieve up-to-date information from the web.
  • Domain Knowledge Retrieval (RAG): Utilize domain-specific knowledge for context-aware reasoning.
  • Code Execution: Execute code to perform custom health-related calculations, models, or simulations.

• Longevity Expert Agents: A set of specialized agents designed to provide advice on longevity based on cutting-edge research and real-time health data.

Todo

• Framework

  • Complex Task Execution: Enhancing the ability of agents to execute more intricate, multi-step tasks requiring complex reasoning and interaction between multiple agents.
  • Cutting-Edge Reasoning Approaches: Implement state-of-the-art AI models and reasoning methods to enhance decision-making and provide more accurate and personalized health advice.

• Longevity Data Process

  • Personalized Health Data Processing with Privacy Security: Integrate personalized health data (e.g., biometric data from wearables) and process it to generate health reports and insights.
  • Personalized Longevity Research Updates: Keep users updated with the latest research in longevity and health, providing relevant and curated articles or findings.
  • Social and Environmental Factors Integration: Analyze how environmental factors (e.g., air quality, water quality, climate) and social factors (e.g., social connections, community engagement) affect user longevity.
  • Genomic Data Integration: Incorporate genetic information to offer more personalized health recommendations.

• Customized Personal Health Management

  • Dietary Recommendation System: Provide personalized dietary advice based on user health data, goals, and longevity research.
  • Fitness Plan Generator: Design personalized workout routines based on user fitness level and longevity goals.
  • Mental Health and Stress Management: Offer advice and tools for stress management and mental well-being.
  • Sleep Optimization: Optimize the user’s sleep patterns based on their health data and scientific research on sleep.
  • Long-Term Health Forecasting: Use machine learning models to predict the user’s long-term health outcomes based on current habits and genetic predisposition.

Installation

To install and set up the Longevity-Agent framework, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/longevity-agent.git

2. Navigate to the project directory:

   cd longevity-agent

3. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

4. Install dependencies:

   pip install -r requirements.txt

Contributing

We welcome contributions to the Longevity-Agent project! If you'd like to contribute, follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Write tests for any new functionality.
4. Submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• The agents are based on the state-of-the-art in AI and multi-agent systems.
• Special thanks to the contributors of the libraries and tools integrated into this project.

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