How to integrate Mailtrap MCP with LangChain

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Introduction

This guide walks you through connecting Mailtrap to LangChain using the Composio tool router. By the end, you'll have a working Mailtrap agent that can send a test email to marketing team, list all emails sent from mailtrap today, create a new inbox for transactional testing through natural language commands.

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

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

Supported Tools & Triggers

Tools
Clean InboxTool to clean an inbox in Mailtrap by deleting all messages.
Create ContactTool to create a new contact in Mailtrap.
Create Contact EventTool to create a contact event in Mailtrap.
Create Contact ExportTool to create a contact export job for a Mailtrap account.
Create Contact FieldTool to create a custom contact field in Mailtrap.
Create Contact ListTool to create a new contact list in Mailtrap.
Create Email TemplateTool to create a new email template in Mailtrap account.
Create Sending DomainTool to create a new sending domain in Mailtrap.
Delete ContactTool to delete a contact from a Mailtrap account.
Delete Contact FieldTool to delete a contact field by its ID.
Delete Contact ListTool to delete a contact list by its ID.
Delete Email TemplateTool to delete an email template from a Mailtrap account.
Delete ProjectTool to delete a project from Mailtrap.
Delete Sending DomainTool to delete a sending domain from a Mailtrap account.
Get Billing UsageTool to retrieve current billing cycle usage for an account.
Get ContactTool to retrieve a contact by UUID or email address from Mailtrap.
Get Contact ExportTool to retrieve the status of a contact export.
Get Contact FieldTool to retrieve contact field details by field ID.
Get Contact Import StatusTool to retrieve the status of a contact import operation.
Get Contact ListTool to retrieve a specific contact list by its ID.
Get Email TemplateTool to retrieve details of a specific email template by ID.
Get Inbox AttributesTool to retrieve inbox attributes from Mailtrap.
Get Message HTML BodyTool to retrieve the HTML body of a message from Mailtrap.
Get Permission ResourcesTool to retrieve all resources in account for permission management.
Get Project by IDTool to retrieve project details from Mailtrap by project ID.
Get Sending DomainTool to retrieve sending domain details from Mailtrap.
Get Sending StatsTool to retrieve email sending statistics from Mailtrap for a specific account.
Get Sending Stats by CategoriesTool to retrieve email sending statistics grouped by categories.
Get Sending Stats by DateTool to retrieve email sending statistics aggregated by date.
Get Sending Stats by DomainsTool to retrieve sending statistics grouped by domains for a Mailtrap account.
Get Sending Stats by ESPTool to retrieve email sending statistics grouped by email service providers (ESPs) for a specified date range.
Import ContactsTool to import contacts in bulk to Mailtrap.
List AccountsTool to list all Mailtrap accounts you have access to.
List Contact FieldsTool to get all contact fields for a Mailtrap account.
List Contact ListsTool to retrieve all contact lists for a Mailtrap account.
List Email TemplatesTool to retrieve all email templates for a Mailtrap account.
List InboxesTool to get a list of inboxes for a Mailtrap account.
List Messages in InboxTool to get messages from a Mailtrap inbox.
List ProjectsTool to get a list of projects for a Mailtrap account.
List Sending DomainsTool to list all sending domains for a Mailtrap account.
List Email SuppressionsTool to list suppressed email addresses for a Mailtrap account.
Mark Inbox as ReadTool to mark all messages in a Mailtrap inbox as read.
Reset Inbox CredentialsTool to reset SMTP credentials for a Mailtrap inbox.
Update contactTool to update an existing contact in Mailtrap.
Update Contact FieldTool to update a contact field in Mailtrap.
Update Contact ListTool to update a contact list's name in Mailtrap.
Update Email TemplateTool to update an existing email template in Mailtrap account.
Update inboxTool to update an inbox's settings in Mailtrap.
Update projectTool to update a project's name in Mailtrap.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

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Claude Agent SDK

Claude Agent SDK

Use Mailtrap MCP with Claude Agent SDK

Google ADK

Google ADK

Use Mailtrap MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Mailtrap MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Mailtrap MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Mailtrap MCP with LlamaIndex

CrewAI

CrewAI

Use Mailtrap MCP with CrewAI

Claude Code

Claude Code

Use Mailtrap MCP with Claude Code

Codex

Codex

Use Mailtrap MCP with Codex

FAQ

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

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

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

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

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