WhatsApp Chat Bot

ANTHROPIC_API_KEY=<your-anthropic-api-key>
WHATSAPP_VERIFY_TOKEN=<your-verify-token>
WHATSAPP_ACCESS_TOKEN=<your-whatsapp-access-token>
WHATSAPP_BUSINESS_PHONE_NUMBER_ID=<your-phone-number-id>
WHATSAPP_API_VERSION=v22.0

// Simple WhatsApp Business API client for sending messages
 
interface SendMessageParams {
  to: string;
  message: string;
}
 
export async function sendWhatsAppMessage({ to, message }: SendMessageParams) {
  // Get environment variables for WhatsApp API
  const apiVersion = process.env.WHATSAPP_API_VERSION || "v22.0";
  const phoneNumberId = process.env.WHATSAPP_BUSINESS_PHONE_NUMBER_ID;
  const accessToken = process.env.WHATSAPP_ACCESS_TOKEN;
 
  // Check if required environment variables are set
  if (!phoneNumberId || !accessToken) {
    return false;
  }
 
  // WhatsApp Business API endpoint
  const url =
    `https://graph.facebook.com/${apiVersion}/${phoneNumberId}/messages`;
 
  // Message payload following WhatsApp API format
  const payload = {
    messaging_product: "whatsapp",
    recipient_type: "individual",
    to: to,
    type: "text",
    text: {
      body: message,
    },
  };
 
  try {
    // Send message via WhatsApp Business API
    const response = await fetch(url, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${accessToken}`,
      },
      body: JSON.stringify(payload),
    });
 
    const result = await response.json();
 
    if (response.ok) {
      console.log(`✅ WhatsApp message sent to ${to}: "${message}"`);
      return true;
    } else {
      console.error("❌ Failed to send WhatsApp message:", result);
      return false;
    }
  } catch (error) {
    console.error("❌ Error sending WhatsApp message:", error);
    return false;
  }
}

import { anthropic } from "@ai-sdk/anthropic";
import { Agent } from "@mastra/core/agent";
import { Memory } from "@mastra/memory";
import { LibSQLStore } from "@mastra/libsql";
 
export const chatAgent = new Agent({
  name: "Chat Agent",
  instructions: `
    You are a helpful, friendly, and knowledgeable AI assistant that loves to chat with users via WhatsApp.
 
    Your personality:
    - Warm, approachable, and conversational
    - Enthusiastic about helping with any topic
    - Use a casual, friendly tone like you're chatting with a friend
    - Be concise but informative
    - Show genuine interest in the user's questions
 
    Your capabilities:
    - Answer questions on a wide variety of topics
    - Provide helpful advice and suggestions
    - Engage in casual conversation
    - Help with problem-solving and creative tasks
    - Explain complex topics in simple terms
 
    Guidelines:
    - Keep responses informative but not overwhelming
    - Ask follow-up questions when appropriate
    - Be encouraging and positive
    - If you don't know something, admit it honestly
    - Adapt your communication style to match the user's tone
    - Remember this is WhatsApp, so keep it conversational and natural
 
    Always aim to be helpful while maintaining a friendly, approachable conversation style.
  `,
  model: anthropic("claude-4-sonnet-20250514"),
  memory: new Memory({
    storage: new LibSQLStore({
      url: "file:../mastra.db",
    }),
  }),
});

import { anthropic } from "@ai-sdk/anthropic";
import { Agent } from "@mastra/core/agent";
import { Memory } from "@mastra/memory";
import { LibSQLStore } from "@mastra/libsql";
 
export const textMessageAgent = new Agent({
  name: "Text Message Agent",
  instructions: `
    You are a text message converter that takes formal or lengthy text and breaks it down into natural, casual text messages.
 
    Your job is to:
    - Convert any input text into 5-8 short, casual text messages
    - Each message should be 1-2 sentences maximum
    - Use natural, friendly texting language (contractions, casual tone)
    - Maintain all the important information from the original text
    - Make it feel like you're texting a friend
    - Use appropriate emojis sparingly to add personality
    - Keep the conversational flow logical and easy to follow
 
    Think of it like you're explaining something exciting to a friend via text - break it into bite-sized, engaging messages that don't overwhelm them with a long paragraph.
 
    Always return exactly 5-8 messages in the messages array.
  `,
  model: anthropic("claude-4-sonnet-20250514"),
  memory: new Memory({
    storage: new LibSQLStore({
      url: "file:../mastra.db",
    }),
  }),
});

import { createStep, createWorkflow } from "@mastra/core/workflows";
import { z } from "zod";
import { sendWhatsAppMessage } from "../../whatsapp-client";
 
const respondToMessage = createStep({
  id: "respond-to-message",
  description: "Generate response to user message",
  inputSchema: z.object({ userMessage: z.string() }),
  outputSchema: z.object({ response: z.string() }),
  execute: async ({ inputData, mastra }) => {
    const agent = mastra?.getAgent("chatAgent");
    if (!agent) {
      throw new Error("Chat agent not found");
    }
 
    const response = await agent.generateVNext(
      [{ role: "user", content: inputData.userMessage }],
    );
 
    return { response: response.text };
  },
});
 
const breakIntoMessages = createStep({
  id: "break-into-messages",
  description: "Breaks response into text messages",
  inputSchema: z.object({ prompt: z.string() }),
  outputSchema: z.object({ messages: z.array(z.string()) }),
  execute: async ({ inputData, mastra }) => {
    const agent = mastra?.getAgent("textMessageAgent");
    if (!agent) {
      throw new Error("Text Message agent not found");
    }
 
    const response = await agent.generateVNext(
      [{ role: "user", content: inputData.prompt }],
      {
        structuredOutput: {
          schema: z.object({
            messages: z.array(z.string()),
          }),
        },
      },
    );
 
    if (!response.object) throw new Error("Error generating messages");
 
    return response.object;
  },
});
 
const sendMessages = createStep({
  id: "send-messages",
  description: "Sends text messages via WhatsApp",
  inputSchema: z.object({
    messages: z.array(z.string()),
    userPhone: z.string(),
  }),
  outputSchema: z.object({ sentCount: z.number() }),
  execute: async ({ inputData }) => {
    const { messages, userPhone } = inputData;
 
    console.log(
      `\n🔥 Sending ${messages.length} WhatsApp messages to ${userPhone}...`,
    );
 
    let sentCount = 0;
 
    // Send each message with a small delay for natural flow
    for (let i = 0; i < messages.length; i++) {
      const success = await sendWhatsAppMessage({
        to: userPhone,
        message: messages[i],
      });
 
      if (success) {
        sentCount++;
      }
 
      // Add delay between messages for natural texting rhythm
      if (i < messages.length - 1) {
        await new Promise((resolve) => setTimeout(resolve, 1000));
      }
    }
 
    console.log(
      `\n✅ Successfully sent ${sentCount}/${messages.length} WhatsApp messages\n`,
    );
 
    return { sentCount };
  },
});
 
export const chatWorkflow = createWorkflow({
  id: "chat-workflow",
  inputSchema: z.object({ userMessage: z.string() }),
  outputSchema: z.object({ sentCount: z.number() }),
})
  .then(respondToMessage)
  .map(async ({ inputData }) => ({
    prompt:
      `Break this AI response into 3-8 casual, friendly text messages that feel natural for WhatsApp conversation:\n\n${inputData.response}`,
  }))
  .then(breakIntoMessages)
  .map(async ({ inputData, getInitData }) => {
    // Parse the original stringified input to get user phone
    const initData = getInitData();
    const webhookData = JSON.parse(initData.userMessage);
    const userPhone =
      webhookData.entry?.[0]?.changes?.[0]?.value?.messages?.[0]?.from ||
      "unknown";
 
    return {
      messages: inputData.messages,
      userPhone,
    };
  })
  .then(sendMessages);
 
chatWorkflow.commit();

import { Mastra } from "@mastra/core/mastra";
import { registerApiRoute } from "@mastra/core/server";
import { PinoLogger } from "@mastra/loggers";
import { LibSQLStore } from "@mastra/libsql";
 
import { chatWorkflow } from "./workflows/chat-workflow";
import { textMessageAgent } from "./agents/text-message-agent";
import { chatAgent } from "./agents/chat-agent";
 
export const mastra = new Mastra({
  workflows: { chatWorkflow },
  agents: { textMessageAgent, chatAgent },
  storage: new LibSQLStore({
    url: ":memory:",
  }),
  logger: new PinoLogger({
    name: "Mastra",
    level: "info",
  }),
  server: {
    apiRoutes: [
      registerApiRoute("/whatsapp", {
        method: "GET",
        handler: async (c) => {
          const verifyToken = process.env.WHATSAPP_VERIFY_TOKEN;
          const {
            "hub.mode": mode,
            "hub.challenge": challenge,
            "hub.verify_token": token,
          } = c.req.query();
 
          if (mode === "subscribe" && token === verifyToken) {
            return c.text(challenge, 200);
          } else {
            return c.status(403);
          }
        },
      }),
      registerApiRoute("/whatsapp", {
        method: "POST",
        handler: async (c) => {
          const mastra = c.get("mastra");
          const chatWorkflow = mastra.getWorkflow("chatWorkflow");
 
          const body = await c.req.json();
 
          const workflowRun = await chatWorkflow.createRunAsync();
          const runResult = await workflowRun.start({
            inputData: { userMessage: JSON.stringify(body) },
          });
 
          return c.json(runResult);
        },
      }),
    ],
  },
});

import "dotenv/config";
 
import { mastra } from "./mastra";
 
// Simulate a WhatsApp webhook payload
const mockWebhookData = {
  entry: [
    {
      changes: [
        {
          value: {
            messages: [
              {
                from: "1234567890", // Test phone number
                text: {
                  body: "Hello! How are you today?"
                }
              }
            ]
          }
        }
      ]
    }
  ]
};
 
const workflow = mastra.getWorkflow("chatWorkflow");
const workflowRun = await workflow.createRunAsync();
 
const result = await workflowRun.start({
  inputData: { userMessage: JSON.stringify(mockWebhookData) }
});
 
console.log("Workflow completed:", result);

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