How to integrate Cloudlayer MCP with LangChain

This guide walks you through connecting Cloudlayer to LangChain using the Composio tool router. By the end, you'll have a working Cloudlayer agent that can generate pdf from a contract html template, convert a marketing webpage to a png image, list your most recent generated assets through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Cloudlayer account through Composio's Cloudlayer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Get started for free Get a demo

Cloudlayer

Api Key

Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work.

16 Tools

Managed auth

Connect Cloudlayer without auth hassles

We manage OAuth, API keys, token refresh, and scopes — you just build.

Try for free

Introduction

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

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

Also integrate Cloudlayer with

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK Vercel AI SDK Mastra AI LlamaIndex CrewAI

TL;DR

Here's what you'll learn:

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

The Cloudlayer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Cloudlayer account. It provides structured and secure access to dynamic document and asset generation, so your agent can perform actions like converting HTML or URLs to PDFs or images, managing assets, and configuring storage on your behalf.

Automated PDF and image generation: Instantly convert HTML content or public URLs into professional PDFs and images for reporting, documentation, or sharing.
Asset management and retrieval: Let your agent fetch metadata or download links for generated assets, or list your most recent document and image creations.
Dynamic storage configuration: Seamlessly add and manage external storage buckets or containers for organizing generated files and assets.
Real-time API health monitoring: Enable your agent to check Cloudlayer API status, ensuring your integrations are always up and running.
Flexible screenshot and rendering tasks: Capture dynamic webpage screenshots as images or PDFs, with full control over conversion parameters, for advanced automation workflows.

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:

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

Step-by-step Guide

Step by step10 STEPS

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

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.

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

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

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 Cloudlayer functionality through MCP

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 Cloudlayer tools
Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

const url = session.mcp.url;

What's happening:

We're creating a Tool Router session that gives your agent access to Cloudlayer 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 Cloudlayer tools as needed

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "cloudlayer-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 Cloudlayer MCP server via HTTP
The client is configured with a name and the URL from our Tool Router session
getTools() retrieves all available Cloudlayer tools that the agent can use
We're creating a LangChain agent using the GPT-5 model

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 Cloudlayer 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

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 Cloudlayer 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: ['cloudlayer']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "cloudlayer-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 Cloudlayer 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 Cloudlayer 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 Cloudlayer action and event your agent gets out of the box.

Add Storage

Add a user-owned S3-compatible storage configuration for storing generated assets.

Convert HTML to Image (V2)

Convert HTML content to an image (PNG, JPG, or WebP) using the v2 API endpoint.

Convert HTML to PDF (v2)

Tool to convert HTML content to PDF using CloudLayer v2 API.

Convert URL to PDF (Simple)

Tool to convert a URL to PDF using GET request.

Delete Storage Configuration

Tool to delete a specific user storage configuration.

Get Account Info

Tool to retrieve Cloudlayer account usage, credits, and document counts.

Get Asset

Tool to retrieve a specific asset by its ID.

Get Job By ID

Retrieve details of a specific Cloudlayer job by its ID.

Get API Status

Tool to test API reachability.

Get Storage Configuration by ID

Tool to retrieve a specific storage configuration by its ID.

List Assets

List assets in your CloudLayer account with cursor-based pagination.

List Jobs

List jobs in your CloudLayer account with cursor-based pagination.

List Storage Configurations

Retrieves all user storage configurations (S3-compatible buckets) for the authenticated Cloudlayer account.

Template to PDF

Generate a PDF document from an HTML/Nunjucks template with dynamic data.

Convert URL to Image

Converts a webpage URL to an image (PNG, JPG, or WebP).

Convert URL to PDF

Tool to convert a URL to PDF with full parameter support.

FRAMEWORKS

How to build Cloudlayer MCP Agent with another framework

OpenAI Agents SDK

Use Cloudlayer MCP with OpenAI Agents SDK

Claude Agent SDK

Use Cloudlayer MCP with Claude Agent SDK

Claude Code

Use Cloudlayer MCP with Claude Code

Claude Cowork

Use Cloudlayer MCP with Claude Cowork

Codex

Use Cloudlayer MCP with Codex

OpenClaw

Use Cloudlayer MCP with OpenClaw

Hermes

Use Cloudlayer MCP with Hermes

CLI

Use Cloudlayer MCP with CLI

Google ADK

Use Cloudlayer MCP with Google ADK

Vercel AI SDK

Use Cloudlayer MCP with Vercel AI SDK

Mastra AI

Use Cloudlayer MCP with Mastra AI

LlamaIndex

Use Cloudlayer MCP with LlamaIndex

CrewAI

Use Cloudlayer MCP with CrewAI

MORE TOOLKITS

Explore Other Toolkits

Toolkit marketplace

Google Drive

Oauth2

Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.

Google Docs

Oauth2

Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.

Google Super

Oauth2Api Key

Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.

Affinda

Api Key

Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.

FAQ

Frequently asked questions

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

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

Start with Cloudlayer.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Cloudlayer tool your agent needs.Free to start.

Start building

Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche

Co-Founder, Ingram Technologies

How to integrate Cloudlayer MCP with LangChain

Connect Cloudlayer without auth hassles

Introduction

Also integrate Cloudlayer with

TL;DR

What is LangChain?

What is the Cloudlayer MCP server, and what's possible with it?

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

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Initialize Composio client

Create a Tool Router session

Configure the agent with the MCP URL

Set up interactive chat interface

Run the application

Complete Code

Conclusion

Supported Tools

How to build Cloudlayer MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

Vercel AI SDK

Mastra AI

LlamaIndex

CrewAI

Explore Other Toolkits

Google Drive

Google Docs

Google Super

Affinda

Frequently asked questions

What are the differences in Tool Router MCP and Cloudlayer MCP?+

Can I use Tool Router MCP with LangChain?+

Can I manage the permissions and scopes for Cloudlayer while using Tool Router?+

How safe is my data with Composio Tool Router?+

Start with Cloudlayer.It takes 30 seconds.

Teams ship real agents on Composio