How to integrate Emailoctopus MCP with LangChain

This guide walks you through connecting Emailoctopus to LangChain using the Composio tool router. By the end, you'll have a working Emailoctopus agent that can add new subscribers to your newsletter list, unsubscribe a user from marketing emails, list all recent email campaigns sent through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Emailoctopus account through Composio's Emailoctopus MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Emailoctopus logoEmailoctopus
Api Key

EmailOctopus is a straightforward email marketing platform for creating, sending, and analyzing campaigns. It helps you grow and engage your audience with affordable, easy-to-use tools.

20 Tools

Introduction

This guide walks you through connecting Emailoctopus to LangChain using the Composio tool router. By the end, you'll have a working Emailoctopus agent that can add new subscribers to your newsletter list, unsubscribe a user from marketing emails, list all recent email campaigns sent through natural language commands.

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

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

Also integrate Emailoctopus with

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Emailoctopus project to Composio
  • Create a Tool Router MCP session for Emailoctopus
  • Initialize an MCP client and retrieve Emailoctopus tools
  • Build a LangChain agent that can interact with Emailoctopus
  • 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 Emailoctopus MCP server, and what's possible with it?

The Emailoctopus MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Emailoctopus account. It provides structured and secure access to your subscriber lists, contacts, and campaigns, so your agent can perform actions like managing lists, adding contacts, launching campaigns, and handling unsubscriptions on your behalf.

  • Seamless contact management: Your agent can create new contacts, update details, or remove subscribers from your marketing lists in seconds.
  • Mailing list creation and organization: Effortlessly set up new mailing lists and keep your audience segmented for targeted campaigns.
  • Campaign insights and retrieval: Instantly access details about your recent email campaigns, including summaries and performance data.
  • Automated unsubscriptions and compliance: Quickly unsubscribe contacts or delete them to keep your lists clean and privacy-compliant.
  • Bulk list management: Retrieve and organize all mailing lists in your account, making it easy to scale and update your marketing efforts.

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 step10 STEPS
1

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
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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
  • dotenv/config loads environment variables
4

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
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Emailoctopus functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
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 Emailoctopus tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['emailoctopus']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Emailoctopus 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 Emailoctopus tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "emailoctopus-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Emailoctopus MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Emailoctopus tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

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

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

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;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept 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 invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['emailoctopus']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "emailoctopus-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Emailoctopus related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    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;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Emailoctopus 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.
TOOLS

Supported Tools

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

Create Contact

This tool creates a new contact in EmailOctopus.

Create Field

Tool to create a new custom field on an EmailOctopus mailing list.

Create List

Creates a new mailing list in EmailOctopus for organizing and managing email contacts.

Create Tag

Tool to create a new tag on an EmailOctopus mailing list.

Delete Contact

Permanently deletes a contact from a specified EmailOctopus list.

Delete Field

Permanently deletes a custom field from a specified EmailOctopus list.

Delete List

This tool allows you to delete an existing mailing list from your EmailOctopus account.

Delete Tag

Tool to delete a tag from a mailing list in EmailOctopus.

Get All Lists

This tool retrieves all the mailing lists associated with the EmailOctopus account.

Get Contact

Tool to retrieve details of a specific contact from an EmailOctopus list.

Get List

Retrieves details of a specific mailing list by ID.

Get Recent Campaigns

This tool retrieves a list of recent campaigns from the EmailOctopus account.

List Contacts

Tool to retrieve contacts from an EmailOctopus list.

List Tags

Tool to retrieve all tags from a mailing list.

Unsubscribe Contact

Unsubscribes a contact from an EmailOctopus mailing list.

Batch Update Contacts

Tool to update multiple contacts in an EmailOctopus list in a single batch operation.

Update Field

Updates an existing custom field on an EmailOctopus list including its label, tag, type, and fallback value.

Update List

Tool to update an existing mailing list's name in EmailOctopus.

Update Tag

Tool to update an existing tag on a mailing list.

Create or Update Contact

Tool to create or update a contact in EmailOctopus.

FAQ

Frequently asked questions

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

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 Emailoctopus tools.

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

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