# How to integrate Respond io MCP with LangChain

```json
{
  "title": "How to integrate Respond io MCP with LangChain",
  "toolkit": "Respond io",
  "toolkit_slug": "respond_io",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/respond_io/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/respond_io/framework/langchain.md",
  "updated_at": "2026-05-12T10:24:00.473Z"
}
```

## Introduction

This guide walks you through connecting Respond io to LangChain using the Composio tool router. By the end, you'll have a working Respond io agent that can add internal note to latest conversation, create a new contact named alex kim, list all channels connected to workspace through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Respond io account through Composio's Respond io MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Respond io with

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

## TL;DR

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

The Respond io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Respond io account. It provides structured and secure access to your customer conversation management platform, so your agent can perform actions like managing contacts, adding internal comments, creating and updating tags, and retrieving messages on your behalf.
- Create and manage contacts: Easily have your agent add new customer contacts to your workspace, ensuring your CRM is always up to date.
- Add internal comments to conversations: Let your agent insert internal notes into customer conversations, keeping your team informed and collaborating seamlessly.
- Retrieve and organize channels: Direct your agent to list all messaging channels connected to your workspace, making it simple to audit or assign channels for support.
- Tag and categorize conversations: Enable your agent to create new tags or update existing ones, helping you organize contacts and conversations for efficient follow-up.
- Fetch specific messages: Ask your agent to pull up particular messages for review or context, streamlining support and follow-up actions.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `RESPOND_IO_CREATE_COMMENT` | Add internal comment to conversation | Tool to add a comment (internal note) to a contact's conversation. Use after verifying the contact identifier. |
| `RESPOND_IO_CREATE_CONTACT` | Create Contact | Creates a new contact in the respond.io workspace with the specified details. The contact is identified by email, phone number, or contact ID. Supports adding profile information, language preferences, and custom fields that have been pre-configured in the workspace. |
| `RESPOND_IO_CREATE_SPACE_TAG` | Create Space Tag | Creates a new tag in the Respond.io workspace for organizing and categorizing contacts and conversations. Tags help with segmentation, filtering, and workflow automation. Each tag must have a unique name within the workspace. |
| `RESPOND_IO_GET_MESSAGE` | Get Message | Tool to retrieve a specific message. Use when you need the details of a message sent to or received from a contact. |
| `RESPOND_IO_LIST_CHANNELS` | List channels | Tool to retrieve a list of channels connected to the workspace. Use when you need to enumerate all messaging channels with pagination support. |
| `RESPOND_IO_LIST_USERS` | List users | Tool to retrieve a list of users in the workspace. Use when you need to fetch all workspace users for auditing or assignment. |
| `RESPOND_IO_UPDATE_SPACE_TAG` | Update Space Tag | Updates an existing workspace tag by its current name. You can modify the tag's name, description, or emoji. Note: Color codes are not currently supported by the API and will be rejected if provided. At least one field besides currentName must be provided to update. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

No description provided.
```python
client = MultiServerMCPClient({
    "respond_io-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({
    "respond_io-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 Respond io 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 Respond io 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=['respond_io']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "respond_io-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 Respond io 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: ['respond_io']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "respond_io-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 Respond io 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 Respond io 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 Respond io MCP Agent with another framework

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

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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Respond io MCP?

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

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

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

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