How to integrate Botbaba MCP with Autogen

This guide walks you through connecting Botbaba to AutoGen using the Composio tool router. By the end, you'll have a working Botbaba agent that can deploy new chatbot to whatsapp channel, update chatbot greeting message instantly, fetch conversation logs for last 24 hours through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Botbaba account through Composio's Botbaba MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Botbaba logoBotbaba
Api Key

Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.

42 Tools

Introduction

This guide walks you through connecting Botbaba to AutoGen using the Composio tool router. By the end, you'll have a working Botbaba agent that can deploy new chatbot to whatsapp channel, update chatbot greeting message instantly, fetch conversation logs for last 24 hours through natural language commands.

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

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

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TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Botbaba
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Botbaba tools
  • Run a live chat loop where you ask the agent to perform Botbaba operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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

The Botbaba MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Botbaba account. It provides structured and secure access to your chatbot management platform, so your agent can perform actions like creating bots, updating conversation flows, managing integrations, deploying changes, and monitoring chatbot analytics on your behalf.

  • Bot creation and configuration: Instantly create new chatbots, set up welcome messages, and configure basic settings directly from your agent.
  • Conversational flow management: Update, organize, or refine conversation trees, intents, and responses for smarter, more natural chatbot interactions.
  • Integration with messaging platforms: Enable your agent to connect bots with channels like WhatsApp, Facebook Messenger, and web chat for seamless communication.
  • Real-time deployment and publishing: Push bot changes live or roll back updates—ensuring your chatbots stay current and relevant with minimal effort.
  • Analytics and performance monitoring: Automatically fetch usage statistics, analyze user engagement, and monitor bot performance to optimize conversational experiences.

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Botbaba account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Botbaba via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Botbaba connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Botbaba session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["botbaba"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Botbaba tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Botbaba assistant agent with MCP tools
    agent = AssistantAgent(
        name="botbaba_assistant",
        description="An AI assistant that helps with Botbaba operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Botbaba tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Botbaba related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Botbaba tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Botbaba and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Botbaba session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["botbaba"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Botbaba assistant agent with MCP tools
        agent = AssistantAgent(
            name="botbaba_assistant",
            description="An AI assistant that helps with Botbaba operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Botbaba related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Botbaba through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Botbaba, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

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

Shopify Cart Creation Simulator

Tool to simulate a Shopify cart creation webhook payload.

Cart Creation Shopify Webhook

Tool to receive Shopify Cart Creation webhooks.

Cart Update Shopify Webhook

Tool to forward Shopify cart update events to BotBaba.

Shopify Checkout Creation Webhook Receiver

Tool to receive Shopify checkout creation webhook events.

Checkout Update Shopify Webhook

Tool to forward Shopify checkout/update events to Botbaba.

Delete a broadcast campaign

Tool to delete a broadcast campaign.

Delete Contact

Tool to delete a contact.

Delete a conversation flow

Tool to delete a conversation flow.

Delete Tag

Tool to delete a tag.

Delete Template

Tool to delete a message template.

Delete a webhook subscription

Tool to delete a webhook subscription.

Execute Bot Action

Tool to execute a bot action or workflow.

Execute Bot Action By User

Tool to execute a bot action for specific users.

Get Bot Widget Settings

Tool to retrieve widget configuration settings for a bot.

Get Broadcast

Tool to retrieve details of a specific broadcast.

Get BotBaba Contact

Tool to fetch a BotBaba contact by its ID.

Get Contact Analytics

Tool to retrieve analytics data for contacts.

Get Filename from Path

Tool to extract the filename from a file path.

Get Flow

Tool to retrieve details of a specific flow.

Get Message

Tool to retrieve status of a specific message.

Get Message Analytics

Tool to retrieve analytics data for a specific message.

Get Template

Tool to retrieve details of a specific template.

Get Webhook

Tool to retrieve details of a specific webhook.

List Broadcasts

Tool to list all broadcast campaigns.

List Flows

Tool to list all conversation flows with their IDs and metadata.

List Tags

Tool to list all tags.

List Templates

Tool to retrieve a paginated list of templates.

List Webhook Event Types

Tool to list available webhook event types.

List Webhooks

Tool to list all registered webhooks.

Receive Shopify Order Cancellation Webhook

Tool to receive Shopify order cancellation webhooks.

Order Fulfillment Simulator

Tool to simulate a Shopify order fulfillment webhook payload.

Order Fulfillment Shopify Webhook

Tool to receive Shopify Order Fulfillment webhooks.

Order Payment Shopify Webhook

Tool to receive Shopify Order Payment webhooks.

Send WhatsApp Template Message

Tool to forward/send a WhatsApp template message via Botbaba.

Shopify Checkout Creation Simulator

Tool to simulate a Shopify checkout creation webhook payload.

Shopify Checkout Update Simulator

Tool to simulate a Shopify checkout update webhook payload.

Update Contact

Tool to update an existing contact.

Update Tag

Tool to update an existing tag.

Update Template

Tool to update an existing message template.

Update Webhook

Tool to update an existing webhook.

Gupshup WhatsApp Webhook Event Simulator

Tool to simulate Gupshup WhatsApp webhook events.

Forward Gupshup Webhook Message

Tool to forward inbound WhatsApp webhook events from Gupshup to Botbaba.

FAQ

Frequently asked questions

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

Yes, you can. Autogen 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 Botbaba tools.

Yes, absolutely. You can configure which Botbaba 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 Botbaba data and credentials are handled as safely as possible.

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