How to integrate Callpage MCP with LangChain

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Introduction

This guide walks you through connecting Callpage to LangChain using the Composio tool router. By the end, you'll have a working Callpage agent that can list all widgets with enabled status, get sms statistics for widget 12345, create a custom voice message for spanish visitors, show all managers assigned to widget 67890 through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Callpage project to Composio
  • Create a Tool Router MCP session for Callpage
  • Initialize an MCP client and retrieve Callpage tools
  • Build a LangChain agent that can interact with Callpage
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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

The Callpage MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Callpage account. It provides structured and secure access to your Callpage lead management tools, so your agent can perform actions like retrieving widgets, managing users, monitoring SMS activity, and customizing voice messages on your behalf.

  • Retrieve and manage widgets: Quickly list all your Callpage widgets, access detailed widget configurations, and monitor widget status statistics to optimize lead capture.
  • View and audit account users: Effortlessly fetch lists of all users and managers, including status summaries, to keep your team structure up to date and compliant.
  • Monitor SMS and messaging activity: Access all SMS messages and gather statistics on SMS usage for specific widgets, helping you track engagement and campaign reach.
  • Create custom voice messages: Enable your agent to generate personalized voice greetings for widgets, tailoring communication for both managers and visitors with ease.
  • API connectivity and health checks: Let your agent verify API connectivity and status, ensuring seamless and reliable integration between Callpage and your workflows.

Supported Tools & Triggers

Tools
Get All ManagersTool to retrieve a list of managers for a specific widget with pagination.
Get All SMS MessagesTool to retrieve all sms messages for a widget.
Get All UsersTool to retrieve a list of all users with pagination.
Get All WidgetsTool to retrieve a list of widgets with pagination.
Get API RootTool to get the root api greeting.
Get Manager Status StatisticsTool to retrieve statistical data about manager statuses.
Get SMS StatisticsTool to retrieve statistical data about sms messages.
Get WidgetTool to retrieve details of a specific widget by id.
Get Widget Status StatisticsTool to retrieve statistical data about widget statuses.
Create Voice MessageTool to create a custom voice message for a widget.
Create WidgetTool to create a new widget.
Delete Widget by IdTool to delete a widget by id.
Reset SMSTool to reset sms messages to default for a widget.
Reset Voice MessagesTool to reset voice messages to default for a widget.
Update ManagerTool to update an existing manager.
Update WidgetTool to update an existing widget.
Widget Call or ScheduleTool to initiate or schedule a call via widget.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming

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.

Install dependencies

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_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's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Callpage functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Callpage tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Callpage
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['callpage']
)

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Callpage tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
  • This approach allows the agent to dynamically load and use Callpage tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "callpage-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Callpage MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Callpage tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

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

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Callpage and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['callpage']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "callpage-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Callpage related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Callpage through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

How to build Callpage MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Callpage MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Callpage MCP with Claude Agent SDK

Google ADK

Google ADK

Use Callpage MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Callpage MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Callpage MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Callpage MCP with LlamaIndex

CrewAI

CrewAI

Use Callpage MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Callpage MCP?

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

Can I use Tool Router MCP with LangChain?

Yes, you can. LangChain 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 Callpage tools.

Can I manage the permissions and scopes for Callpage while using Tool Router?

Yes, absolutely. You can configure which Callpage 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.

How safe is my data with Composio Tool Router?

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 Callpage data and credentials are handled as safely as possible.

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Callpage MCP Integration with LangChain | Composio