# How to integrate Sendloop MCP with LangChain

```json
{
  "title": "How to integrate Sendloop MCP with LangChain",
  "toolkit": "Sendloop",
  "toolkit_slug": "sendloop",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/sendloop/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/sendloop/framework/langchain.md",
  "updated_at": "2026-05-06T08:27:39.298Z"
}
```

## Introduction

This guide walks you through connecting Sendloop to LangChain using the Composio tool router. By the end, you'll have a working Sendloop agent that can show open and scheduled campaigns this week, list all subscribers in your main list, get summary report for last email blast through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Sendloop account through Composio's Sendloop MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Sendloop with

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

## TL;DR

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

The Sendloop MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Sendloop account. It provides structured and secure access to your email marketing campaigns, subscriber lists, and detailed reports, so your agent can list campaigns, analyze subscriber data, retrieve account information, and review campaign performance on your behalf.
- Comprehensive campaign management: Ask your agent to list all existing email campaigns, filter them by status, and handle pagination to easily browse through your marketing efforts.
- Subscriber list access and reporting: Retrieve all your mailing lists and get detailed reports on subscriber growth, engagement, and performance after sending campaigns.
- Targeted subscriber insights: Let the agent fetch subscribers for any given list, filter them by status, and manage large lists with effortless pagination.
- Account information retrieval: Have your agent pull up-to-date details about your Sendloop account, keeping you informed about your overall setup and usage.
- Performance analytics: Quickly get summary metrics for specific subscriber lists to evaluate campaign success and optimize your email marketing strategy.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SENDLOOP_GET_OVERALL_LIST_REPORT` | Get Overall List Report | Tool to retrieve overall report for a subscriber list. use after sending campaigns to get summary metrics. |
| `SENDLOOP_LIST_CAMPAIGNS` | List Campaigns | Tool to list campaigns. use when you need to filter by campaign status and handle pagination for campaign retrieval. |
| `SENDLOOP_LIST_LISTS` | List SendLoop Lists | Tool to retrieve subscriber lists. use when you need to get all mailing lists with optional pagination. |
| `SENDLOOP_LIST_SUBSCRIBERS` | List SendLoop Subscribers | Tool to list subscribers in a specified sendloop list with pagination. use when you need to retrieve subscribers for a given list id, optionally filtering by status, page number, and page size. |
| `SENDLOOP_SENDLOOP_GET_ACCOUNT_INFO` | Get Sendloop Account Information | Tool to retrieve account information. use when you need details about the current sendloop account. |

## Supported Triggers

None listed.

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

The Sendloop MCP server is an implementation of the Model Context Protocol that connects your AI agent to Sendloop. It provides structured and secure access so your agent can perform Sendloop 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 Sendloop 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 Sendloop 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 Sendloop tools as needed
```python
# Create Tool Router session for Sendloop
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['sendloop']
)

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "sendloop-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({
    "sendloop-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 Sendloop 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 Sendloop 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=['sendloop']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "sendloop-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 Sendloop 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: ['sendloop']
        }
    );

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

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

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## Frequently Asked Questions

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

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

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

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

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