How to integrate BotPenguin MCP with Mastra AI

This guide walks you through connecting BotPenguin to Mastra AI using the Composio tool router. By the end, you'll have a working BotPenguin agent that can import your contacts, list inbox contacts, review segments, browse bots, and check template fields through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a BotPenguin account through Composio's BotPenguin MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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BotPenguin is an AI chatbot platform for customer messaging, contacts, and conversations. It helps teams manage bot-led support and lead engagement across channels.

6 Tools

Introduction

This guide walks you through connecting BotPenguin to Mastra AI using the Composio tool router. By the end, you'll have a working BotPenguin agent that can import your contacts, list inbox contacts, review segments, browse bots, and check template fields through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a BotPenguin account through Composio's BotPenguin MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate BotPenguin with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKLlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes BotPenguin tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch BotPenguin tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your BotPenguin agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the BotPenguin MCP server, and what's possible with it?

The BotPenguin MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your BotPenguin account. It provides structured and secure access to your contacts, segments, WhatsApp bots, and message templates, so your agent can import contacts, review inbox contacts, inspect segments, find WhatsApp bots, and retrieve template details on your behalf.

  • Bulk contact imports: Have your agent import groups of contacts into BotPenguin for future customer messaging.
  • Inbox contact reviews: Let the agent list inbox contacts and filter them based on whether they have interacted with a bot.
  • Contact segment insights: Direct your agent to list configured segments, review their filters, and check how many contacts each one contains.
  • WhatsApp bot and template discovery: Instruct your agent to find available WhatsApp bots and list the message templates associated with a selected bot.
  • Template field preparation: Have your agent retrieve the dynamic fields required to complete a BotPenguin WhatsApp template.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach BotPenguin through MCP.
3

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({
  apiKey: composioAPIKey as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session
6

Create a Tool Router session for BotPenguin

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["botpenguin"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("BotPenguin MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "botpenguin" for BotPenguin access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the BotPenguin toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "botpenguin-mastra-agent",
    instructions: "You are an AI agent with BotPenguin tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM
9

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\n");

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: "> ",
});

rl.prompt();

rl.on("line", async (userInput: string) => {
  const trimmedInput = userInput.trim();

  if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) {
    console.log("\nGoodbye!");
    rl.close();
    process.exit(0);
  }

  if (!trimmedInput) {
    rl.prompt();
    return;
  }

  messages.push({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        botpenguin: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and BotPenguin toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with BotPenguin and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set");
if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set");
if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set");

const composio = new Composio({ apiKey: composioAPIKey as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["botpenguin"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      botpenguin: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "botpenguin-mastra-agent",
    instructions: "You are an AI agent with BotPenguin tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { botpenguin: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with BotPenguin through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows
TOOLS

Supported Tools

Every BotPenguin action and event your agent gets out of the box.

Get Template Dynamic Fields

Return the dynamic key-value fields required by a BotPenguin WhatsApp template.

Import Contacts

Import contacts in bulk into BotPenguin.

List Inbox Contacts

Return one page of BotPenguin inbox contacts filtered by whether the contact has interacted with a bot.

List Segments

List the contact segments configured in the connected BotPenguin account, including each segment's filter definition and contact count.

List WhatsApp Bots

List WhatsApp bots available to the connected BotPenguin account for use with plugin template tools.

List WhatsApp Templates

List WhatsApp templates available to a BotPenguin bot.

FRAMEWORKS

How to build BotPenguin MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use BotPenguin MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use BotPenguin MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use BotPenguin MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use BotPenguin MCP with Claude Cowork

Codex logo

Codex

Use BotPenguin MCP with Codex

OpenClaw logo

OpenClaw

Use BotPenguin MCP with OpenClaw

Hermes logo

Hermes

Use BotPenguin MCP with Hermes

CLI logo

CLI

Use BotPenguin MCP with CLI

Google ADK logo

Google ADK

Use BotPenguin MCP with Google ADK

LangChain logo

LangChain

Use BotPenguin MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use BotPenguin MCP with Vercel AI SDK

LlamaIndex logo

LlamaIndex

Use BotPenguin MCP with LlamaIndex

CrewAI logo

CrewAI

Use BotPenguin MCP with CrewAI

FAQ

Frequently asked questions

With a standalone BotPenguin MCP server, the agents and LLMs can only access a fixed set of BotPenguin tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from BotPenguin and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Mastra AI fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right BotPenguin tools.

Yes, absolutely. You can configure which BotPenguin scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your BotPenguin data and credentials are handled as safely as possible.

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