How to integrate Unisender MCP with LangChain

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Introduction

This guide walks you through connecting Unisender to LangChain using the Composio tool router. By the end, you'll have a working Unisender agent that can send an sms campaign to new signups, create a new email list segment, check status of yesterday's email campaign through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Unisender account through Composio's Unisender 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 Unisender project to Composio
  • Create a Tool Router MCP session for Unisender
  • Initialize an MCP client and retrieve Unisender tools
  • Build a LangChain agent that can interact with Unisender
  • 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 Unisender MCP server, and what's possible with it?

The Unisender MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Unisender account. It provides structured and secure access so your agent can perform Unisender operations on your behalf.

Supported Tools & Triggers

Tools
Check Email StatusTool to check the delivery status of emails sent via sendEmail method.
Create Email TemplateTool to create a new email template for mass campaigns in UniSender.
Create FieldTool to create a new custom field for contact data in UniSender.
Create ListTool to create a new contact list for organizing thematic email campaigns in UniSender.
Create Subscriber NoteTool to create a note for a subscriber contact in UniSender.
Delete FieldTool to remove a custom field from the Unisender system.
Delete ListTool to permanently delete a contact list from your UniSender account.
Delete Subscriber NoteTool to delete a subscriber note from UniSender by its ID.
Delete TagTool to delete a tag by its ID and remove it from all associated contacts.
Delete TemplateTool to remove a template from the UniSender account.
Exclude Contact from ListsTool to remove contacts from specified lists or all lists in UniSender.
Export ContactsTool to export contact data from UniSender lists for synchronization.
Get CampaignsTool to retrieve list of campaigns from Unisender within a specified date range.
Get Campaign StatusTool to check the current status of a campaign in UniSender.
Get ContactTool to get information about a single contact from UniSender.
Get Contact Field ValuesTool to retrieve custom field values for a specific contact identified by email address.
Get FieldsTool to retrieve all custom user-defined fields for contact personalization and data management.
Get ListsTool to retrieve all existing mailing lists associated with the account.
Get MessagesTool to retrieve list of all messages with body and attachments.
Get Sender Domain ListTool to retrieve information about sender domains and their DKIM status.
Get TagsTool to retrieve all custom tags/labels for contact segmentation.
Get TemplateTool to retrieve detailed information about a specific email template by its ID.
Get TemplatesTool to retrieve list of all templates with full content including body.
Import Contacts (Bulk)Tool to bulk import contacts to UniSender with maximum 500 contacts per call.
Check if Contact is in ListsTool to check if a contact exists in specified mailing lists based on and/or conditions.
List MessagesTool to list all messages without body and attachments.
List TemplatesTool to list email templates without body content.
Subscribe Contact to ListsTool to add contacts to one or multiple mailing lists with optional tags and field values.
Unsubscribe ContactTool to unsubscribe contacts from mailing lists in UniSender.
Update Email TemplateTool to update an existing email template for mass campaigns.
Update FieldTool to modify parameters of an existing custom field in UniSender.
Update ListTool to update the parameters of an existing contact list in UniSender.
Update Subscriber NoteTool to update the content of an existing subscriber note in UniSender.

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 Unisender 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 Unisender tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Unisender 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 Unisender tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "unisender-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 Unisender MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Unisender 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 Unisender 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 Unisender 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=['unisender']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "unisender-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 Unisender 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 Unisender 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 Unisender MCP Agent with another framework

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Google ADK

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Vercel AI SDK

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Use Unisender MCP with Vercel AI SDK

Mastra AI

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Use Unisender MCP with Mastra AI

LlamaIndex

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Use Unisender MCP with LlamaIndex

CrewAI

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Use Unisender MCP with CrewAI

Claude Code

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Use Unisender MCP with Claude Code

Codex

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Use Unisender MCP with Codex

FAQ

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

With a standalone Unisender MCP server, the agents and LLMs can only access a fixed set of Unisender tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Unisender 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 Unisender tools.

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

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

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