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How to integrate Docker hub MCP with LangChain

Connect LangChain to Docker hub MCP. Create a new docker hub repository, add a member to your docker organization, and more using natural language, with authentication handled for you.

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Docker Hub is a cloud-based registry for finding and sharing container images. It simplifies container collaboration and deployment for individuals and teams.

24 Tools

Introduction

This guide walks you through connecting Docker hub to LangChain using the Composio tool router. By the end, you'll have a working Docker hub agent that can create and delete repositories, inspect images and tags, manage organizations and teams, and set up webhooks through natural language commands.

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

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

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Docker hub with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKGrok BotCodexOpenClawHermesAtomic AgentGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAIPydantic AIAutoGen

TL;DR

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

The Docker hub MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Docker Hub account. It provides structured and secure access to your container repositories and organizations, so your agent can perform actions like creating repositories, managing organization members, deleting images, setting up webhooks, and cleaning up tags on your behalf.

  • Repository and image management: Let your agent create new Docker Hub repositories, delete existing ones, and remove specific images or tags as needed.
  • Organization and team automation: Easily add members to organizations, create new Docker Hub organizations, or delete organizations and teams directly from your workflows.
  • Webhook configuration: Set up or remove repository webhooks to automate external integrations and keep your CI/CD pipelines in sync.
  • Tag and resource cleanup: Direct your agent to delete outdated tags or unused resources, helping you maintain a tidy container registry.
  • Secure role management: Invite users with specific roles to your organizations, ensuring the right access for collaborators and teams.

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 Docker hub 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 Docker hub 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: ['docker_hub']
    }
);

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

Configure the agent with the MCP URL

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

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

Add Organization Member

Invite a user to join a Docker Hub organization.

Create Docker Hub Organization

Create a new Docker Hub organization.

Create Docker Hub Repository

Creates a new Docker Hub repository under the specified namespace.

Create Docker Hub Webhook

Create a webhook on a Docker Hub repository for image push notifications, then add its hook URL; repository admin permissions are required.

Delete Repository Images

Delete up to 100 image manifests from a Docker Hub namespace by repository and SHA256 digest, or use a dry run to validate the request without deleting.

Delete Docker Hub Organization

Permanently deletes a Docker Hub organization.

Delete Docker Hub Repository

Permanently delete a Docker Hub repository and its images, tags, and metadata.

Delete Repository Tag

Permanently delete a specific tag from a Docker Hub repository.

Delete Docker Hub Team

Permanently deletes a team from a Docker Hub organization.

Delete Docker Hub repository webhook

Deletes a specific webhook from a Docker Hub repository.

Get Docker Hub Image

Retrieve a platform-specific image variant by digest by searching repository tags.

Get Docker Hub Repository

Retrieve detailed metadata for a Docker Hub repository, including available description, counts, permissions, and configuration.

Get Docker Hub Tag

Retrieve details of a specific Docker Hub repository tag by namespace, repository, and tag name.

Get Docker Hub Team

Retrieve details of a specific team (group) within a Docker Hub organization.

Get Docker Hub Webhook

Retrieves details of a specific Docker Hub webhook by its ID.

List Organization Access Tokens

List a page of organization access tokens for a Docker Hub organization, with pagination.

List Docker Hub Organizations

List Docker Hub organizations associated with the authenticated user, with available organization details in paginated results.

List Docker Hub Organization Members

List members of a Docker Hub organization with available roles and details, using paginated results.

List Docker Hub Repositories

List repositories under a Docker Hub namespace with optional filtering and pagination.

List Team Members

List members of a Docker Hub team (group) within an organization, returning paginated user details.

List Organization Teams

List all teams (groups) within a Docker Hub organization.

List Docker Hub repository webhooks

List webhooks configured for a Docker Hub repository, returning paginated configuration details.

Remove Organization Member

Remove a member from a Docker Hub organization.

Remove Team Member

Remove a user from a Docker Hub organization team (group), revoking that team membership.

FRAMEWORKS

How to build Docker hub MCP Agent with another framework

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Claude Code

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Claude Cowork

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OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

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Grok Bot logo

Grok Bot

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Codex logo

Codex

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OpenClaw logo

OpenClaw

Use Docker hub MCP with OpenClaw

Hermes logo

Hermes

Use Docker hub MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Docker hub MCP with Atomic Agent

Google ADK logo

Google ADK

Use Docker hub MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Docker hub MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Docker hub MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Docker hub MCP with LlamaIndex

CrewAI logo

CrewAI

Use Docker hub MCP with CrewAI

Pydantic AI logo

Pydantic AI

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AutoGen logo

AutoGen

Use Docker hub MCP with AutoGen

FAQ

Frequently asked questions

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