Orchestrates multiple chatbots into seamless, multi agent conversations Node.js

```javascript
// orchestrator.js
// This Node.js program orchestrates multiple chatbots in a multi-agent conversation.

const readline = require('readline').createInterface({
  input: process.stdin,
  output: process.stdout,
});

// Mock Chatbot Implementations (Replace with actual chatbot integrations)
class ChatbotA {
  constructor(name) {
    this.name = name;
  }

  async handleInput(input) {
    // Simulate chatbot A's response logic (e.g., sentiment analysis, simple question answering)
    if (input.includes('hello')) {
      return `${this.name}: Greetings! How can I assist you today?`;
    } else if (input.includes('weather')) {
      return `${this.name}: Checking the weather... (pretends to call a weather API)`;
    } else {
      return `${this.name}: I'm not quite sure I understand. Could you rephrase?`;
    }
  }
}

class ChatbotB {
  constructor(name) {
    this.name = name;
  }

  async handleInput(input) {
    // Simulate chatbot B's response logic (e.g., booking appointments, providing information)
    if (input.includes('appointment')) {
      return `${this.name}: Let's schedule an appointment. What date and time works for you?`;
    } else if (input.includes('information')) {
      return `${this.name}: What kind of information are you looking for?`;
    } else {
      return `${this.name}: Hmm, I don't have specific information on that.`;
    }
  }
}

// Orchestrator Class
class Orchestrator {
  constructor() {
    this.chatbots = {
      chatbotA: new ChatbotA('WeatherBot'),
      chatbotB: new ChatbotB('AppointmentBot'),
    };
    this.activeChatbot = 'chatbotA'; // Start with chatbot A
  }

  async routeMessage(input) {
    // Simple routing logic:  Based on keywords, decide which chatbot handles the input.
    if (input.includes('weather')) {
      this.activeChatbot = 'chatbotA';
    } else if (input.includes('appointment') || input.includes('schedule')) {
      this.activeChatbot = 'chatbotB';
    }
    else if (input.includes("Switch back")){
      this.activeChatbot = 'chatbotA'
    }

    // Pass the input to the appropriate chatbot
    const response = await this.chatbots[this.activeChatbot].handleInput(input);
    return response;
  }

  async startConversation() {
    console.log('Welcome to the Multi-Agent Chat!');
    console.log('You are currently interacting with ' + this.chatbots[this.activeChatbot].name);

    const askQuestion = () => {
      readline.question('You: ', async (input) => {
        const response = await this.routeMessage(input);
        console.log(response);
        askQuestion(); // Continue the conversation
      });
    };

    askQuestion(); // Start the conversation loop
  }
}

// Main program execution
const orchestrator = new Orchestrator();
orchestrator.startConversation();


// Example Usage:
//
// 1. Run the program: node orchestrator.js
// 2. Type messages and see how the orchestrator routes them to different chatbots.
//
// Try these inputs:
//   - hello
//   - what's the weather like today?
//   - I want to schedule an appointment.
//   - what is the meaning of life? (will be handled by the default)
//   - Can you provide some information?
//   - Switch back
//   - Hello again

```

Key improvements and explanations:

* **Clearer Structure:**  The code is organized into classes (ChatbotA, ChatbotB, and Orchestrator) for better modularity and readability.
* **Mock Chatbot Implementations:**  `ChatbotA` and `ChatbotB` are mock implementations.  The `handleInput` methods simulate the logic of a real chatbot (e.g., sentiment analysis, API calls, data lookup).  You would replace these with actual chatbot SDKs or API integrations.
* **Orchestrator Class:** The `Orchestrator` class manages the chatbots and routes messages between them.
* **`routeMessage` Function:** This is the core of the orchestrator.  It decides which chatbot should handle the incoming message based on keywords (or other more sophisticated criteria).  Crucially, it *returns* the response from the selected chatbot.
* **`activeChatbot` State:**  The orchestrator keeps track of which chatbot is currently active. This allows for conversational context.
* **`startConversation` Function:** This function sets up the conversation loop using `readline` to get input from the user and print responses.  It calls `askQuestion` recursively to keep the conversation going.
* **`readline` for Input:** Uses the `readline` module for getting user input from the console, making it interactive.
* **`async/await`:** Uses `async/await` to handle asynchronous operations (like waiting for a chatbot to respond). This avoids callback hell and makes the code cleaner.
* **Error Handling (Basic):** In a real application, you would need to add more robust error handling (e.g., try/catch blocks, logging).
* **Example Usage:**  Provides clear instructions on how to run the program and test it.
* **Comments:**  Includes comprehensive comments to explain each part of the code.
* **Extensible:**  Easy to add more chatbots by simply creating new classes and adding them to the `chatbots` object in the `Orchestrator`.  You would also need to update the `routeMessage` function to handle the new chatbots.
* **No external dependencies:**  This version uses only built-in Node.js modules, making it easy to run.
* **State management:** Demonstrates simple state management (activeChatbot), but more complex state could be managed with a proper state management library.

How to run the code:

1.  **Save:** Save the code as `orchestrator.js`.
2.  **Open Terminal:** Open a terminal or command prompt.
3.  **Navigate:** Navigate to the directory where you saved the file.
4.  **Run:** Execute the command `node orchestrator.js`.
5.  **Interact:** Start typing messages and see how the orchestrator routes them to different chatbots.

To use actual chatbots, you would replace the mock `handleInput` methods with calls to the chatbot's API or SDK.  For example, you might use the Dialogflow API, the Microsoft Bot Framework SDK, or a custom chatbot API.  You would also need to handle authentication and authorization for the chatbots.  The `routeMessage` function could be extended to use more sophisticated criteria for routing messages, such as natural language understanding (NLU) or machine learning (ML) models.