# How to integrate Melo MCP with LangChain

```json
{
  "title": "How to integrate Melo MCP with LangChain",
  "toolkit": "Melo",
  "toolkit_slug": "melo",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/melo/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/melo/framework/langchain.md",
  "updated_at": "2026-05-12T10:18:36.103Z"
}
```

## Introduction

This guide walks you through connecting Melo to LangChain using the Composio tool router. By the end, you'll have a working Melo agent that can find cities with active listings in paris region, suggest location matches for 'boulogne' input, list all saved property search queries through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Melo account through Composio's Melo MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Melo with

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

## TL;DR

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

The Melo MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Melo account. It provides structured and secure access to real-time, deduplicated French real estate listings and market analytics, so your agent can perform actions like searching cities, suggesting locations, retrieving saved searches, and testing webhook integrations on your behalf.
- City discovery and filtering: Instantly fetch and filter lists of cities to explore specific property markets or narrow down search locations.
- Location autocomplete and suggestions: Have your agent suggest matching locations for partial queries, ensuring accurate and user-friendly real estate searches.
- Personalized search retrieval: Retrieve and manage your saved searches to quickly revisit or update your real estate criteria.
- Webhook event simulation: Test and validate webhook integrations by simulating events, making real estate workflow automation seamless.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `MELO_GET_CITIES` | Get Cities | Tool to retrieve a list of cities with optional filters. Use when you need to fetch available cities before processing location-specific data. |
| `MELO_LIST_SEARCHES` | List Searches | Retrieves all property searches saved by the authenticated user from the Melo API. Use this tool to: - View all saved property searches with their filtering criteria - Check notification settings for each search - Paginate through large lists of searches - Filter searches by title or notification status - Sort searches alphabetically by title Returns search details including location filters, budget ranges, property types, and notification configurations. The API uses Hydra collection format for pagination. |
| `MELO_SIMULATE_WEBHOOK` | Simulate Webhook | Simulates sending a Melo webhook event to a specified endpoint for testing webhook integrations. This tool triggers the Melo API to send a sample webhook payload (either a property match or event notification) to your specified HTTPS endpoint. Useful for testing webhook handlers before going live. The API will send realistic property data including advert details, contact information, and geographic data to your endpoint. Use this when you need to test how your webhook endpoint handles Melo notifications. |

## Supported Triggers

None listed.

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

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

url = session.mcp.url
```

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

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

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

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

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

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

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

## Related Toolkits

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- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
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- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.

## Frequently Asked Questions

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

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

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

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

---
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