Multi-Agent-Orchestrator

This Blog and usecase we develop is based on https://awslabs.github.io/multi-agent-orchestrator/

• Flexible Framework: The Multi-Agent Orchestrator is a versatile framework designed to manage and coordinate multiple AI agents, each specializing in different tasks or domains.

• Intelligent Query Routing: It intelligently routes user queries to the most appropriate agent based on the context, intent, and the agent’s capabilities.

• Modular Design: The framework supports the integration of various agents, including LLMs, API-based agents, and custom agents, enabling seamless collaboration between them.

• Context Management: It maintains conversation history and context across interactions, allowing agents to provide coherent and personalized responses.

• Scalable Architecture: The orchestrator is designed to scale from simple single-agent deployments to complex, multi-agent systems handling a wide range of tasks.

• Customizable and Extensible: Developers can easily add new agents, customize existing ones, and integrate additional data sources or APIs, making the orchestrator adaptable to diverse use cases.

Overview

This Personalized Health & Wellness Assistant leverages the Multi-Agent Orchestrator framework to provide users with tailored health and wellness advice. The assistant covers various aspects of health, including diet, exercise, mental health, and sleep management. It integrates with external data sources such as Apple Watch, You Health tracking applications, and sends personalized recommendations via WhatsApp.

• Comprehensive Health Management: The assistant integrates specialized agents for diet, exercise, mental health, and sleep management, providing users with personalized and holistic wellness advice tailored to their individual needs.

• Seamless Data Integration: By connecting with external data sources like Apple Watch, MyFitnessPal, and mental health tracking apps, the assistant offers real-time monitoring and progress tracking, ensuring that users receive accurate and up-to-date recommendations.

• Personalized Daily Insights: The assistant sends end-of-day summaries and next-day reminders via WhatsApp, offering actionable insights based on the user’s daily activities and progress, enhancing their health and wellness journey with continuous, personalized support.

GitHub URL

https://github.com/jayyanar/personalize-health-wellness-agent

Step-by-Step Implementation

Step 1: Define the Scope and Objectives

• Scope: The assistant focuses on diet, exercise, mental health, and sleep management, providing personalized advice and tracking progress.

• Objectives: Offer daily personalized recommendations, integrate with wearable devices, and provide real-time health monitoring.

Step 2: Identify and Develop Specialized Agents

Each aspect of health and wellness is managed by a specialized agent. Below are examples of agents and their configurations:

A. Diet Agent

• Purpose: Provide meal suggestions, track calories, and ensure nutritional balance.

• Data Source: Integration with the USDA Food Database and user dietary preferences from a connected health tracking app like MyFitnessPal.

• Example Configuration:

    typescriptCopy codeimport { BedrockLLMAgent } from 'multi-agent-orchestrator';
  
    const dietAgent = new BedrockLLMAgent({
      name: "Diet Agent",
      description: "Provides personalized meal plans, tracks calorie intake, and ensures nutritional balance.",
      streaming: false,
      inferenceConfig: {
        temperature: 0.2,
      },
      toolConfig: {
        tool: dietToolDescription,
        useToolHandler: dietToolHandler,
      }
    });
  
    const dietToolDescription = [
      {
        toolSpec: {
          name: "Diet_Tool",
          description: "Generates personalized meal plans based on user preferences and nutritional requirements.",
          inputSchema: {
            json: {
              type: "object",
              properties: {
                calorieGoal: { type: "number", description: "Daily calorie goal" },
                dietaryPreferences: { type: "string", description: "User's dietary preferences (e.g., vegan, keto)" }
              },
              required: ["calorieGoal", "dietaryPreferences"],
            }
          },
        }
      }
    ];
  
    async function dietToolHandler(response, conversation) {
      // Handle diet tool requests, integrate with a nutritional database, and provide meal plans
    }
  

B. Exercise Agent

• Purpose: Generate workout plans, track physical activity, and suggest exercises.

• Data Source: Integration with Apple Watch or Fitbit to monitor physical activity and adjust workout plans.

• Example Configuration:

    typescriptCopy codeconst exerciseAgent = new BedrockLLMAgent({
      name: "Exercise Agent",
      description: "Generates workout plans, tracks physical activity, and suggests exercises based on user fitness goals.",
      streaming: false,
      inferenceConfig: {
        temperature: 0.3,
      },
      toolConfig: {
        tool: exerciseToolDescription,
        useToolHandler: exerciseToolHandler,
      }
    });
  
    const exerciseToolDescription = [
      {
        toolSpec: {
          name: "Exercise_Tool",
          description: "Provides customized workout plans based on user fitness level and goals.",
          inputSchema: {
            json: {
              type: "object",
              properties: {
                fitnessLevel: { type: "string", description: "User's fitness level (e.g., beginner, intermediate)" },
                workoutType: { type: "string", description: "Type of workout (e.g., cardio, strength)" }
              },
              required: ["fitnessLevel", "workoutType"],
            }
          },
        }
      }
    ];
  
    async function exerciseToolHandler(response, conversation) {
      // Handle exercise tool requests, generate workout routines, and provide fitness tracking
    }
  

C. Mental Health Agent

• Purpose: Provide mental health support, suggest relaxation techniques, and track mood.

• Data Source: Integration with mental health tracking apps like You Health or Calm to monitor mood and stress levels.

• Example Configuration:

    typescriptCopy codeconst mentalHealthAgent = new BedrockLLMAgent({
      name: "Mental Health Agent",
      description: "Provides mental health support, suggests relaxation techniques, and tracks user mood.",
      streaming: false,
      inferenceConfig: {
        temperature: 0.1,
      },
      toolConfig: {
        tool: mentalHealthToolDescription,
        useToolHandler: mentalHealthToolHandler,
      }
    });
  
    const mentalHealthToolDescription = [
      {
        toolSpec: {
          name: "Mental_Health_Tool",
          description: "Suggests mental health exercises and tracks user mood.",
          inputSchema: {
            json: {
              type: "object",
              properties: {
                mood: { type: "string", description: "Current mood of the user" },
                stressLevel: { type: "number", description: "User's stress level (1-10)" }
              },
              required: ["mood", "stressLevel"],
            }
          },
        }
      }
    ];
  
    async function mentalHealthToolHandler(response, conversation) {
      // Handle mental health tool requests, suggest relaxation techniques, and log mood
    }
  

D. Sleep Management Agent

• Purpose: Track sleep patterns, suggest improvements, and provide tips for better sleep.

• Data Source: Integration with Apple Watch, Oura Ring, or other sleep tracking devices.

• Example Configuration:

    typescriptCopy codeconst sleepManagementAgent = new BedrockLLMAgent({
      name: "Sleep Management Agent",
      description: "Tracks sleep patterns, suggests improvements, and provides tips for better sleep.",
      streaming: false,
      inferenceConfig: {
        temperature: 0.2,
      },
      toolConfig: {
        tool: sleepToolDescription,
        useToolHandler: sleepToolHandler,
      }
    });
  
    const sleepToolDescription = [
      {
        toolSpec: {
          name: "Sleep_Tool",
          description: "Monitors sleep patterns and suggests improvements.",
          inputSchema: {
            json: {
              type: "object",
              properties: {
                sleepDuration: { type: "number", description: "Number of hours slept" },
                sleepQuality: { type: "string", description: "Quality of sleep (e.g., good, poor)" }
              },
              required: ["sleepDuration", "sleepQuality"],
            }
          },
        }
      }
    ];
  
    async function sleepToolHandler(response, conversation) {
      // Handle sleep tool requests, analyze sleep data, and suggest improvements
    }
  

Step 3: Integrate Data Sources and APIs

Each agent requires integration with external data sources or APIs:

• Diet Agent: Connect with USDA Food Database API or MyFitnessPal API.

• Exercise Agent: Integrate with Apple Watch or Fitbit API for real-time activity tracking.

• Mental Health Agent: Utilize APIs from Calm or You Health apps for mood and stress tracking.

• Sleep Management Agent: Integrate with sleep tracking devices like Oura Ring or Apple Watch.

Step 4: Set Up the Multi-Agent Orchestrator

typescriptCopy codeimport { MultiAgentOrchestrator } from "multi-agent-orchestrator";

const orchestrator = new MultiAgentOrchestrator();

orchestrator.addAgent(dietAgent);
orchestrator.addAgent(exerciseAgent);
orchestrator.addAgent(mentalHealthAgent);
orchestrator.addAgent(sleepManagementAgent);

Step 5: Implement Context Management

• Conversation History: Maintain a history of interactions to personalize advice.

• User Profiles: Store user-specific data such as dietary preferences, fitness goals, mood logs, and sleep patterns.

• Dynamic Routing: The orchestrator routes queries to the relevant agent based on user input and context.

Step 6: Create a User Interface (UI) with Amplify UI

• Amplify UI: Utilize AWS Amplify UI components to create a responsive and interactive web application.

• Features:

  • Chat interface for interacting with the assistant.

  • Dashboard to display personalized insights and progress.

  • Integration with wearables for real-time health data.

Step 7: Implement Amazon Lex for Conversational Interface

Replace the chatbot with Amazon Lex for natural language understanding and processing:

• Amazon Lex: Configure Lex to handle user queries and route them to the appropriate agents via the orchestrator.

• Integration: Integrate Lex with the Amplify UI to create a seamless conversational experience.

Step 8: Daily Personalized Recommendations via WhatsApp

• WhatsApp Integration: Use AWS SNS or Twilio API to send daily progress summaries and next-day reminders.

• Personalized Messages:

  • End of Day: Summarize today’s progress, such as calories burned, mood improvements, and sleep quality.

  • Next Day Reminder: Provide actionable insights for tomorrow, like suggested workouts, meal plans, or mental health exercises.

• Example:

    typescriptCopy codeasync function sendDailySummary(userId) {
      const summary = generateSummaryForUser(userId); // Generate summary based on user data
      await sendWhatsAppMessage(userId, summary); // Send the summary via WhatsApp
    }
  
    async function sendWhatsAppMessage(userId, message) {
      // Integrate with Twilio API or AWS SNS to send WhatsApp messages
      // Example: await twilio.messages.create({ ... });
    }
  

Conclusion

By following these steps, you can create a robust, context-aware Health & Wellness Assistant. The assistant integrates with various data sources and devices, utilizes Amazon Lex for natural language interactions, and leverages Amplify UI for a seamless user experience. Daily personalized recommendations are sent via WhatsApp, ensuring users receive actionable insights to improve their health and wellness.