# How to integrate Remarkety MCP with LangChain

```json
{
  "title": "How to integrate Remarkety MCP with LangChain",
  "toolkit": "Remarkety",
  "toolkit_slug": "remarkety",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/remarkety/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/remarkety/framework/langchain.md",
  "updated_at": "2026-05-12T10:23:39.704Z"
}
```

## Introduction

This guide walks you through connecting Remarkety to LangChain using the Composio tool router. By the end, you'll have a working Remarkety agent that can list all active email campaigns this week, fetch recent customers who abandoned carts, upload a new batch of newsletter contacts through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Remarkety account through Composio's Remarkety MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Remarkety with

- [OpenAI Agents SDK](https://composio.dev/toolkits/remarkety/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/remarkety/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/remarkety/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/remarkety/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/remarkety/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/remarkety/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/remarkety/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/remarkety/framework/cli)
- [Google ADK](https://composio.dev/toolkits/remarkety/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/remarkety/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/remarkety/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/remarkety/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/remarkety/framework/crew-ai)

## TL;DR

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

The Remarkety MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Remarkety account. It provides structured and secure access to your marketing automation data, so your agent can manage campaigns, sync customer lists, track cart events, and update contacts on your behalf.
- Campaign management and insights: Retrieve and review detailed lists of your marketing campaigns, making it easy to analyze performance or schedule new sends.
- Customer data access: Fetch paginated customer lists from your Remarkety store, enabling targeted outreach or audience segmentation for your campaigns.
- Product catalog retrieval: Let your agent pull product details from your store, perfect for creating personalized marketing messages or promotions.
- Cart tracking for remarketing: Automatically sync customer cart updates to trigger remarketing flows and recover abandoned carts with personalized offers.
- Bulk contact import and sync: Upload or update large batches of contacts at once, keeping your lists current and campaigns reaching the right people.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `REMARKETY_GET_CAMPAIGNS` | Get Remarkety Campaigns | Tool to retrieve a list of marketing campaigns. Use when you need to page through campaigns with optional limit and skip. |
| `REMARKETY_GET_CUSTOMERS` | Get Remarkety Customers | Tool to retrieve customers. Use when you need to fetch a paginated list of customers from a Remarkety store. |
| `REMARKETY_REMARKETY_GET_PRODUCTS` | Get Remarkety Products | Tool to retrieve products. Use when you need to fetch a paginated list of products from a Remarkety store. |
| `REMARKETY_TRACK_CART` | Track Remarkety Cart | Tool to track cart events in Remarkety. Use after a customer updates their cart to sync cart data for remarketing. |
| `REMARKETY_UPLOAD_CONTACTS_BATCH` | Upload Contacts Batch | Tool to upload or update multiple contacts in batch. Use when you need to import or synchronize many contacts at once. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Remarkety MCP server is an implementation of the Model Context Protocol that connects your AI agent to Remarkety. It provides structured and secure access so your agent can perform Remarkety operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Remarkety tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Remarkety 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 Remarkety tools as needed
```python
# Create Tool Router session for Remarkety
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['remarkety']
)

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "remarkety-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)
```

```typescript
const client = new MultiServerMCPClient({
    "remarkety-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Remarkety 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")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Remarkety 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
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=['remarkety']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "remarkety-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 Remarkety 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())
```

```typescript
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: ['remarkety']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "remarkety-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 Remarkety 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 Remarkety 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 Remarkety MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/remarkety/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/remarkety/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/remarkety/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/remarkety/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/remarkety/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/remarkety/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/remarkety/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/remarkety/framework/cli)
- [Google ADK](https://composio.dev/toolkits/remarkety/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/remarkety/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/remarkety/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/remarkety/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/remarkety/framework/crew-ai)

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- [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Remarkety MCP?

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

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)