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

📋 Overview

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

GitHub Repository: https://github.com/AvinasiLabs/longevity-agents

✨ Core Features

🔄 Asynchronous Multi-Agent Framework

• Scalable and efficient agent-based system
• Allows multiple agents to work concurrently, providing personalized health and longevity insights
• Supports decomposition and collaborative execution of complex tasks

🛠️ Tool Calling Capabilities

The framework can leverage 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 specially designed agents aimed at providing longevity advice based on cutting-edge research and real-time health data.

🏗️ Technical Architecture

🔄 Complex Task Execution

• Enhances agents' ability to execute more complex, multi-step tasks
• Supports tasks requiring complex reasoning and interaction between multiple agents

🧠 Advanced Reasoning Methods

• Implements state-of-the-art AI models and reasoning methods
• Enhances decision-making capabilities to provide more accurate and personalized health advice

📊 Longevity Data Processing

• 🔒 Personalized Health Data Processing with Privacy Security: Integrates personalized health data (such as biometric data from wearables) and processes it to generate health reports and insights
• 📰 Personalized Longevity Research Updates: Keeps users informed about the latest research in longevity and health, providing relevant and curated articles or findings
• 🌍 Social and Environmental Factors Integration: Analyzes how environmental factors (such as air quality, water quality, climate) and social factors (such as social connections, community engagement) affect user longevity
• 🧬 Genomic Data Integration: Incorporates genetic information to provide more personalized health recommendations

🏥 Customized Personal Health Management

🍽️ Dietary Recommendation System

• Provides personalized dietary advice based on user health data, goals, and longevity research

💪 Fitness Plan Generator

• Designs personalized workout routines based on user fitness level and longevity goals

🧘 Mental Health and Stress Management

• Offers advice and tools for stress management and mental well-being

😴 Sleep Optimization

• Optimizes sleep patterns based on user health data and scientific research on sleep

📈 Long-Term Health Forecasting

• Uses machine learning models to predict long-term health outcomes based on current habits and genetic predisposition

🔧 Technical Implementation

The Longevity-Agents framework employs the following key technologies:

• 🔄 ReAct + Function Call Tools: Combines Reasoning and Acting patterns, implementing tool usage through function calls, ReAct method and MCP (Model Context Protocol).
• 🤖 Broad LLM Compatibility: Supports various large language models, improving system flexibility and adaptability
• ⚡ Asynchronous Concurrent Multi-Agent Framework: Supports multiple agents working in parallel, improving system efficiency
• 🔍 Test-Time Scaling Techniques:
  • Chain of Thought (CoT): Enhances reasoning capabilities
  • Retrieval-Augmented Generation (RAG): Integrates external knowledge
  • Agent Teams: Collaborates to solve problems
  • Task Graph and Tree Search (such as MCTS): Optimizes decision processes

🗺️ Development Roadmap

The Longevity-Agents development roadmap spans 1-2 years, divided into 4 phases, covering AI and Web3 technologies, focusing on the longevity domain. The platform targets both the general public and medical experts, providing decentralized data collection, consultation management, and research tools. Web3 technologies (such as blockchain, IPFS) will be used for data storage and incentives, while AI will support personalized health and research.

Phase 1: Infrastructure and Core Development

• [x] Longevity Expert AI Agent
• [x] Hybrid Retrieval RAG Framework
• [x] RAG Framework Deployment
• [x] Automated Paper Update and Storage
• [x] Paper Agent Release
• [x] Knowledge Base Graph Structure Storage Development and Deployment
• [ ] RAG Framework Graph Structure Data Enhancement
• [x] Diagnostic Agent Release
• [ ] Agent Framework Introduction of Advanced Reasoning Methods
• [ ] Reasoning Enhancement + Primary Knowledge Graph RAG Workflow Release
• [ ] Primary Knowledge Graph Initial Mapping to Ontology Graph
• [ ] Prototype Agent Testing with Ontology Graph Integration

Phase 2: Advanced AI and Web3 Integration

• [ ] RAG Framework Performance Optimization for High-Concurrency Scenarios
• [ ] Prototype Product Release with Ontology Graph Integration
• [ ] AI and Decentralized Data Collection Automated Workflow Development
• [ ] AI Agent Integration with Decentralized Data Storage and Computing
• [ ] Decentralized AI Agent Prototype Release, Implementing Health Data AI Analysis and Personalized Longevity Plan Generation
• [ ] Agent Framework Technical Iteration
• [ ] Longevity Domain LLM Fine-tuning
• [ ] Launch of Longevity AI Research Tool Library Construction
• [ ] First Longevity AI Research Tool Release
• [ ] Initial User Health Management Agent Release

Phase 3: Platform Expansion and User Engagement

• [ ] Expand Decentralized AI Agent Product Features for the General Public
• [ ] Release Approximately 10 Longevity AI Research Tools
• [ ] Seek Collaboration with Longevity Research Institutions and Healthcare Providers for Customized Data and AI Agent Tools
• [ ] Develop AI Agent Framework Multimodal Data Processing Capabilities
• [ ] Upgrade Infrastructure, Ensure Service Load, Optimize AI-Related Product Operation Mechanisms, Achieve Elastic Scalable Deployment
• [ ] AI-Enhanced Data Collection and Standardized Data Processing Workflow

Phase 4: Frontier Technology Integration, Feature Marketplace, and User Participation

• [ ] Agent Framework Technical Iteration, Introducing Latest Base Models and Logical Reasoning Frameworks
• [ ] Second Round of Domain LLM Fine-tuning
• [ ] User Participation in Building Personal Longevity Agent Marketplace
• [ ] Decentralized Longevity AI Research Tool Marketplace

🎯 Conclusion

The Longevity-Agents framework represents an innovative application of AI in the health and longevity domain, providing comprehensive health management and longevity recommendations through the combination of multi-agent systems, advanced reasoning methods, and personalized data processing. As the development roadmap progresses, the framework will continuously integrate frontier technologies, expand functionality, and ultimately form a decentralized health and longevity ecosystem.