How to integrate Quo MCP with LangChain

This guide walks you through connecting Quo to LangChain using the Composio tool router. By the end, you'll have a working Quo agent that can get call details and voicemails, get messages, manage contacts, create tasks, and manage webhooks through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Quo account through Composio's Quo MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Api Key

Quo is a business phone platform for calls, SMS, contacts, and team conversations. It helps teams manage customer communication from one shared, searchable workspace.

36 Tools

Introduction

This guide walks you through connecting Quo to LangChain using the Composio tool router. By the end, you'll have a working Quo agent that can get call details and voicemails, get messages, manage contacts, create tasks, and manage webhooks through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Quo account through Composio's Quo MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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TL;DR

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

The Quo MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Quo account. It provides structured and secure access to your business calls, messages, contacts, conversations, tasks, users, and webhooks, so your agent can review calls, send text messages, manage contacts, organize conversations and tasks, and monitor webhook activity on your behalf.

  • Call and voicemail review: Have your agent review call participants, routing, status, timing, recordings, voicemail transcripts, and recording details.
  • Messaging and conversation management: Direct your agent to find and read messages, send texts from your Quo numbers, and mark conversations as read, open, or done.
  • Contact organization: Let the agent create, find, update, or delete contacts while working with standard details, custom fields, and external source information.
  • Task coordination: Instruct your agent to create and edit tasks, connect them to calls or conversations, assign teammates, set due dates, and complete or reopen work.
  • Webhook monitoring: Have your agent create, update, review, or remove webhook subscriptions and inspect their delivery activity and outcomes.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step10 STEPS
1

Prerequisites

Before starting this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

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

Install the required packages for LangChain with MCP support.

What's happening:

  • @composio/langchain provides Composio integration for LangChain
  • @langchain/mcp-adapters enables MCP client connections
  • @langchain/core is the core agent framework
  • dotenv/config loads environment variables
4

Set up environment variables

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

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
5

Import dependencies

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();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Quo functionality through MCP
6

Initialize Composio client

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()
    });
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 Quo tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

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

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Quo 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 Quo tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "quo-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 });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Quo MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Quo tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Quo 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);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

Here's the complete code to get you started with Quo and LangChain:

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: ['quo']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "quo-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 Quo 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 Quo 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.
TOOLS

Supported Tools

Every Quo action and event your agent gets out of the box.

Create Contact

Create a Quo contact with standard fields, optional custom fields, and external source metadata.

Create Task

Create a task linked to exactly one phone number, conversation, or activity.

Create Webhook

Create one unified webhook subscription for selected Quo events and return its signing secret, which must be stored securely.

Delete Contact

Permanently delete one Quo contact by ID.

Delete Task

Permanently delete one Quo task by ID.

Delete Webhook

Permanently delete one unified Quo webhook and revoke its signing secret.

Get Call

Get one call and its routing, participants, status, and timing details by ID.

Get Call Voicemail

Get voicemail processing status, transcript, duration, and recording URL for a Quo call.

Get Contact

Get one contact with default and custom fields by Quo contact ID.

Get Message

Get one message by Quo message ID, including its content, related IDs, participants, delivery status, and timestamps.

Get Phone Number

Get one Quo phone number and its assignments and restrictions by ID.

Get Task

Get one task and its current links, assignee, due date, and completion state.

Get User

Get one workspace user by Quo user ID.

Get Webhook

Get one unified Quo webhook by its numeric ID.

Get Webhook Delivery

Get the payload and attempt details for one Quo webhook delivery event.

List Call Recordings

List recording segments for a Quo call in chronological order.

List Calls

List calls between a Quo number and one external participant with optional user and creation-time filters.

List Contact Custom Fields

List the workspace's contact custom-field definitions for constructing valid contact values.

List Contacts

List contacts, optionally filtered by external IDs or source, with stable-v1 phone and custom fields.

List Conversations

List inbox conversations with optional phone-number, user, creation-time, update-time, and inactive-state filters.

List Messages

List messages exchanged by a Quo phone number and one or more participants, optionally filtered by sender or creation time.

List Phone Numbers

List Quo phone numbers and their assigned users, calling restrictions, and messaging restrictions.

List Tasks

List workspace tasks with cursor-controlled pagination.

List Users

List workspace users and return one cursor-controlled page.

List Webhook Deliveries

List one page of delivery events for a unified Quo webhook, optionally filtered by status, event type, or dispatch time.

List Webhooks

List unified Quo webhooks across call, message, contact, task, and integration event families.

Send Message

Send an irreversible text message from a Quo number to one or more recipients.

Send Message and Mark Conversation Done

Send an irreversible text message from a Quo number and move the resulting inbox conversation to Done.

Set Conversation Status

Set a Quo conversation to read, done, or open.

Set Task Assignee

Assign or unassign a Quo user on a task.

Set Task Completion

Complete or reopen a Quo task.

Set Task Conversation Link

Link or unlink a Quo task and conversation.

Set Task Due Date

Set or remove a Quo task's due date.

Update Contact

Patch selected standard, custom, or source fields on an existing Quo contact.

Update Task

Replace both the title and description of an existing Quo task.

Update Webhook

Partially update a unified Quo webhook.

FAQ

Frequently asked questions

With a standalone Quo MCP server, the agents and LLMs can only access a fixed set of Quo tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Quo and many other apps based on the task at hand, all through a single MCP endpoint.

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 Quo tools.

Yes, absolutely. You can configure which Quo 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.

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 Quo data and credentials are handled as safely as possible.

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