# How to integrate Retently MCP with LangChain

```json
{
  "title": "How to integrate Retently MCP with LangChain",
  "toolkit": "Retently",
  "toolkit_slug": "retently",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/retently/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/retently/framework/langchain.md",
  "updated_at": "2026-05-12T10:24:07.004Z"
}
```

## Introduction

This guide walks you through connecting Retently to LangChain using the Composio tool router. By the end, you'll have a working Retently agent that can list all customer feedback from last week, add 'urgent' tag to negative feedback, get latest nps score for your account through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Retently account through Composio's Retently MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Retently with

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

## TL;DR

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

The Retently MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Retently account. It provides structured and secure access to your customer feedback and survey data, so your agent can perform actions like analyzing feedback, managing customers, organizing survey results, and tagging feedback—completely on your behalf.
- Automated customer management: Effortlessly create, update, or delete customers in your Retently workspace, ensuring your CRM data stays up to date.
- Feedback analysis and retrieval: Retrieve recent feedback, pull detailed feedback entries, or get a list of all customer responses for easy sentiment tracking and reporting.
- Survey and campaign insights: Instantly fetch all your Retently campaigns or get the latest NPS score to stay on top of your customer satisfaction metrics.
- Feedback organization with tags and topics: Let your agent categorize and organize feedback by adding tags or topics, so you can quickly identify trends and areas for improvement.
- Advanced customer lookup: Quickly get detailed information about any customer by their unique ID, perfect for personalizing follow-ups or resolving support issues.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `RETENTLY_ADD_FEEDBACK_TAGS` | Add Feedback Tags | Add tags to feedback items by providing feedback IDs and corresponding tags. |
| `RETENTLY_ADD_FEEDBACK_TOPICS` | Add Feedback Topics | Add topics to feedback items by providing feedback IDs and corresponding topics. |
| `RETENTLY_CREATE_OR_UPDATE_CUSTOMERS` | Create or Update Customers | Tool to create new customers or update existing ones by providing their details, including email, name, company, tags, and properties. Use this to manage your customer base in Retently. |
| `RETENTLY_DELETE_CUSTOMERS` | Delete Customers | Delete customers from the workspace by providing their unique IDs. |
| `RETENTLY_GET_CAMPAIGNS` | Get Campaigns | Tool to retrieve a list of campaigns associated with the account. Use when you need to get details about all campaigns. |
| `RETENTLY_GET_CUSTOMER_BY_ID` | Get Customer By ID | Tool to retrieve detailed information about a specific customer by their unique ID. Use when you need to get all the details of a customer. |
| `RETENTLY_GET_CUSTOMERS` | Get Customers | Retrieve a list of customers with optional parameters for pagination, sorting, and filtering by email or date range. |
| `RETENTLY_GET_FEEDBACK` | Get Feedback | Tool to retrieve feedback received from customers. Use when you need to get a list of feedback, with optional parameters for pagination and sorting. |
| `RETENTLY_GET_FEEDBACK_BY_ID` | Get Feedback by ID | Tool to retrieve detailed information about specific feedback by its unique ID. Use when you need to get the details of a single feedback entry. |
| `RETENTLY_GET_LATEST_SCORE` | Get Latest Score | Tool to retrieve the latest NPS score for the account. Use when you need to get the most up-to-date NPS score. |
| `RETENTLY_GET_OUTBOX` | Get Outbox | Retrieve the outbox of surveys that are scheduled to be sent. |
| `RETENTLY_GET_REPORTS` | Get Reports | Tool to retrieve reports related to NPS surveys, including scores and trends. Use when you need to get campaign performance data. |
| `RETENTLY_GET_TEMPLATES` | Get Templates | Tool to retrieve a list of survey templates available in the account. Use when you need to get the available survey templates. |
| `RETENTLY_SEND_TRANSACTIONAL_SURVEY` | Send Transactional Survey | Tool to send a transactional survey to customers. Use when you need to send a survey to a customer after a specific event, with an optional delay. |
| `RETENTLY_UNSUBSCRIBE_CUSTOMERS` | Unsubscribe Customers | Unsubscribe customers from receiving surveys by providing their email addresses. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

- [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources.
- [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research.
- [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows.
- [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research.
- [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data.
- [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds.
- [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights.
- [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly.
- [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools.
- [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping.
- [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights.
- [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space.
- [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently.
- [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
- [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.
- [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business.
- [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets.

## Frequently Asked Questions

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

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

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

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

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