How to integrate Smugmug MCP with LangChain

This guide walks you through connecting Smugmug to LangChain using the Composio tool router. By the end, you'll have a working Smugmug agent that can show all albums in your travel folder, get details for the europe 2023 album, list all child folders under weddings through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Smugmug account through Composio's Smugmug MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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SmugMug is a photography platform for showcasing, sharing, and selling photos and videos. It lets creators organize, protect, and monetize their work in one place.

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Introduction

This guide walks you through connecting Smugmug to LangChain using the Composio tool router. By the end, you'll have a working Smugmug agent that can show all albums in your travel folder, get details for the europe 2023 album, list all child folders under weddings through natural language commands.

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

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

Also integrate Smugmug with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Smugmug MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Smugmug account. It provides structured and secure access to your photo galleries and user profile, so your agent can perform actions like browsing folders, listing albums, retrieving photo details, and exploring your Smugmug structure on your behalf.

  • Retrieve folder and album details: Instantly fetch information about specific folders or albums, including creation dates, node IDs, and highlight albums.
  • Explore your Smugmug hierarchy: Ask your agent to list all child nodes—albums or folders—within any parent node, helping you navigate your photo organization with ease.
  • List albums in any folder: Let your agent pull comprehensive lists of albums within a designated folder, even handling large collections with pagination.
  • Get public user profile info: Retrieve your Smugmug public profile details to share or review your online photography presence.
  • Access node-specific data: Have your agent look up details for any node (album, folder, or page) using its unique identifier, supporting granular photo management and discovery.

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 Smugmug 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 Smugmug 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: ['smugmug']
    }
);

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

Configure the agent with the MCP URL

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

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

Get Album

Retrieves detailed information about a SmugMug album using its album key.

Get Album Highlight Image

Retrieves the highlight/cover image for a SmugMug album.

Get Album Image

Get an image within a specific album context as an AlbumImage relationship object.

Get Album Images

Tool to retrieve all images in a SmugMug album as AlbumImage relationship objects.

Get Folder Albums

Tool to retrieve albums from a specific folder in a SmugMug user's account by nickname and folder path.

Get Folder by User and Path

Tool to retrieve folder details by user nickname and folder path.

Get Folder Details

Retrieves details of a specific folder in SmugMug using its Node ID.

Get Folder Subfolders

Retrieves all subfolders within a specified folder in a SmugMug user's account.

Get Image

Tool to retrieve details for a specific image (photo or video) by its image key.

Get Image Metadata

Tool to retrieve additional metadata from an image file including EXIF data, camera settings, GPS location, and other embedded information.

Get Image Size Details

Retrieve raw media URLs and dimensions for all available sizes of an image.

Get Image Sizes

Retrieves available image sizes and URLs for a SmugMug image by its unique image key.

Get Node Highlight Image

Tool to get the highlight/cover image for a node (folder, album, or page).

Get Node Parent

Tool to retrieve the parent node of a specified SmugMug node.

Get Node Parents

Tool to retrieve a node and all its ancestor nodes (breadcrumb trail).

Get User

Tool to get a SmugMug user account by their nickname.

Get User Bio Image

Tool to retrieve the bio image for a SmugMug user by their nickname (username).

Get User Featured Albums

Retrieves the featured albums for a SmugMug user by their nickname.

Get User Features

Tool to retrieve a list of features available to a SmugMug user based on their subscription plan.

Get User Profile

Retrieves the public profile information for a SmugMug user by their nickname (username).

Get User Root Node

Tool to retrieve the root node of a user's folder tree on SmugMug.

List Child Nodes

Lists all child nodes (folders and albums) under a specified parent node in SmugMug.

Search User Content

Search for images across a user's SmugMug content.

Unlock Album

Tool to unlock a password-protected SmugMug album.

Unlock User Site

Tool to unlock a user's password-protected SmugMug site.

FRAMEWORKS

How to build Smugmug MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Smugmug MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Smugmug MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Smugmug MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Smugmug MCP with Claude Cowork

Codex logo

Codex

Use Smugmug MCP with Codex

OpenClaw logo

OpenClaw

Use Smugmug MCP with OpenClaw

Hermes logo

Hermes

Use Smugmug MCP with Hermes

CLI logo

CLI

Use Smugmug MCP with CLI

Google ADK logo

Google ADK

Use Smugmug MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Smugmug MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Smugmug MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Smugmug MCP with LlamaIndex

CrewAI logo

CrewAI

Use Smugmug MCP with CrewAI

FAQ

Frequently asked questions

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

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

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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.

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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.

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