How to integrate Imagekit io MCP with LangChain

This guide walks you through connecting Imagekit io to LangChain using the Composio tool router. By the end, you'll have a working Imagekit io agent that can move all event photos to new 2024 folder, delete outdated logo file from media library, create custom metadata field for copyright info through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Imagekit io account through Composio's Imagekit io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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ImageKit.io is a cloud-based media management platform for image and video delivery. Instantly optimize, transform, and deliver visuals globally via a lightning-fast CDN.

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Introduction

This guide walks you through connecting Imagekit io to LangChain using the Composio tool router. By the end, you'll have a working Imagekit io agent that can move all event photos to new 2024 folder, delete outdated logo file from media library, create custom metadata field for copyright info through natural language commands.

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

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

Also integrate Imagekit io 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 Imagekit io project to Composio
  • Create a Tool Router MCP session for Imagekit io
  • Initialize an MCP client and retrieve Imagekit io tools
  • Build a LangChain agent that can interact with Imagekit io
  • 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 Imagekit io MCP server, and what's possible with it?

The Imagekit io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your ImageKit.io account. It provides structured and secure access to your media library, so your agent can perform actions like organizing folders, managing files, handling bulk operations, editing metadata, and cleaning up assets on your behalf.

  • Bulk file operations: Effortlessly move, copy, or update tags on multiple files at once to streamline large-scale asset management.
  • Folder organization and management: Ask your agent to create new folders for better asset structuring or delete old folders—including all their contents—when you need to tidy up.
  • Custom metadata control: Let your agent create or delete custom metadata fields, so your media assets stay rich with the information your workflows need.
  • File and version cleanup: Instruct the agent to permanently delete files or remove outdated file versions to keep your storage lean and organized.
  • Bulk job monitoring: Have your agent track the status of ongoing bulk jobs, like folder copies or moves, so you always know what’s happening behind the scenes.

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 Imagekit io 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 Imagekit io 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: ['imagekit_io']
    }
);

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

Configure the agent with the MCP URL

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

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

Bulk Job Status

Retrieve the status of a bulk folder operation.

Bulk Move Files

Tool to move multiple files in bulk.

Bulk Remove Tags

Tool to remove tags from multiple files in bulk.

Copy Folder

Initiate an asynchronous bulk copy of a folder and all its contents to a new location.

Create Custom Metadata Field

Create a new custom metadata field in ImageKit DAM.

Create Folder

Creates a new folder in ImageKit.

Delete Custom Metadata Field

Permanently deletes a custom metadata field from ImageKit.

Delete File

Permanently deletes a file from ImageKit by its unique file ID.

Delete File Version

Permanently deletes a specific non-current file version from ImageKit.

Delete Folder

Permanently delete a folder and all its contents from ImageKit Media Library.

Delete Multiple Files

Permanently delete multiple files from ImageKit media library in a single batch operation.

Get Upload Authentication Parameters

Tool to generate authentication parameters for client-side file uploads.

Get File Details

Tool to retrieve details of a specific file.

Get File Metadata

Tool to retrieve metadata of an uploaded file.

Get File Version Details

Tool to retrieve details of a specific file version.

Get Usage

Retrieve ImageKit account usage metrics for a specified date range.

List and Search Media Assets

List and search media assets (files, folders, file-versions) in your ImageKit media library.

List Custom Metadata Fields

List all custom metadata fields defined in the ImageKit Media Library.

List File Versions

Retrieves all versions of a specific file in ImageKit.

Move Folder

Move a folder from one location to another in your ImageKit media library.

Purge ImageKit Cache

Purge CDN and ImageKit internal caches for a specific URL or URL pattern.

Check purge cache status

Tool to check the status of a cache purge request.

Rename File

Renames an existing file in the ImageKit media library.

Restore File Version

Restores a non-current file version to become the current version in ImageKit.

Update Custom Metadata Field

Updates an existing custom metadata field's label or schema constraints in ImageKit DAM.

Update File Details

Update file details in ImageKit media library.

FRAMEWORKS

How to build Imagekit io MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Imagekit io MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Imagekit io MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Imagekit io MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Imagekit io MCP with Claude Cowork

Codex logo

Codex

Use Imagekit io MCP with Codex

OpenClaw logo

OpenClaw

Use Imagekit io MCP with OpenClaw

Hermes logo

Hermes

Use Imagekit io MCP with Hermes

CLI logo

CLI

Use Imagekit io MCP with CLI

Google ADK logo

Google ADK

Use Imagekit io MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Imagekit io MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Imagekit io MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Imagekit io MCP with LlamaIndex

CrewAI logo

CrewAI

Use Imagekit io MCP with CrewAI

FAQ

Frequently asked questions

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

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

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