How to integrate Templated MCP with LangChain

This guide walks you through connecting Templated to LangChain using the Composio tool router. By the end, you'll have a working Templated agent that can generate invoice pdf from customer data, list all available image templates, clone existing certificate template for edits through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Templated account through Composio's Templated MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Templated is a platform for designing templates and automating the creation of images and PDFs via API. It lets you dynamically generate branded content and documents at scale.

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Introduction

This guide walks you through connecting Templated to LangChain using the Composio tool router. By the end, you'll have a working Templated agent that can generate invoice pdf from customer data, list all available image templates, clone existing certificate template for edits through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Templated account through Composio's Templated 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 Templated project to Composio
  • Create a Tool Router MCP session for Templated
  • Initialize an MCP client and retrieve Templated tools
  • Build a LangChain agent that can interact with Templated
  • 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 Templated MCP server, and what's possible with it?

The Templated MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Templated account. It provides structured and secure access to your templates and renders, so your agent can generate dynamic documents, manage templates, clone or tag designs, and retrieve render results on your behalf.

  • Automated document and image generation: Instantly have your agent render PDFs or images from any saved template, filling in dynamic variables to create invoices, certificates, or marketing materials.
  • Template management and organization: Let your agent list, retrieve, clone, or delete templates, keeping your template library organized and up-to-date without manual effort.
  • Dynamic tagging and categorization: Easily add or remove tags from templates, enabling smarter organization and fast searching for specific designs or document types.
  • Render tracking and retrieval: Ask your agent to list all renders for a template or fetch detailed information about a specific document render, making it simple to track document creation and usage history.
  • Account insights and validation: Have your agent retrieve account information to confirm API access, check account metadata, or audit usage details as needed.

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 Templated 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 Templated 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: ['templated']
    }
);

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

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "templated-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 Templated MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Templated 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 Templated 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 Templated 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: ['templated']
        }
    );

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

Add Tags to Template

Tool to append new tags to an existing template without overwriting existing tags.

Clone Template

Tool to clone an existing template.

Count Template Clones

Tool to get the count of cloned templates for a specific source template.

Create Folder

Tool to create a new folder for organizing templates and renders.

Create Template Render

Create document renders from templates.

Create Template

Tool to create a new template with specified dimensions and layers.

Delete Folder

Tool to delete a folder by its ID.

Delete Render

Tool to delete a specific render by its ID.

Delete Template

Tool to delete a template by its unique ID.

Duplicate Template Render

Tool to duplicate an existing render with the same settings and template.

Get Account Information

Tool to retrieve account information, including email, name, team name, API usage statistics, and subscription plan.

List All Templates

Tool to list all templates of a user.

List Folder Renders

Tool to list all renders in a specific folder.

List Folders

Tool to list all folders in the account.

List Folder Templates

Tool to list all templates in a specific folder.

List Fonts

Tool to list all available fonts including Google Fonts and user-uploaded custom fonts.

List Gallery Templates

Tool to list templates from the public gallery.

List All Renders

Tool to list all renders in the account.

List Template Clones

Tool to list all cloned templates in the account.

List Template Renders

Tool to list all renders of a specific template.

List Uploads

Tool to list all uploaded images in the account.

Merge Template Renders

Tool to merge multiple renders into a single PDF output.

Move Render to Folder

Tool to move a render to a specific folder.

Move Template to Folder

Tool to move a template to a folder.

Remove Template Tags

Tool to remove specific tags from a template.

Retrieve Template Render

Tool to retrieve details of a specific render by its ID.

Retrieve Template

Tool to retrieve a single Template by its unique ID.

Update Folder

Tool to update an existing folder's properties.

Update Template

Tool to update an existing template including its properties and layers.

Update Template Tags

Tool to overwrite all tags on a template.

FRAMEWORKS

How to build Templated MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Templated MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

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Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use Templated MCP with Claude Cowork

Codex logo

Codex

Use Templated MCP with Codex

OpenClaw logo

OpenClaw

Use Templated MCP with OpenClaw

Hermes logo

Hermes

Use Templated MCP with Hermes

CLI logo

CLI

Use Templated MCP with CLI

Google ADK logo

Google ADK

Use Templated MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Templated MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Templated MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Templated MCP with LlamaIndex

CrewAI logo

CrewAI

Use Templated MCP with CrewAI

FAQ

Frequently asked questions

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

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

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