How to integrate Docker hub MCP with LangChain

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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 a new docker hub repository, add a member to my docker organization, delete an old image from a repository, set up a webhook for repository updates 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.

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.

Supported Tools & Triggers

Tools
Add Organization MemberTool to send an invitation for a user to join a Docker Hub organization.
Create Docker Hub OrganizationTool to create a Docker Hub organization.
Create Docker Hub RepositoryTool to create a Docker Hub repository under a namespace.
Create Docker Hub WebhookTool to create a webhook on a Docker Hub repository.
Delete Repository ImageTool to delete a specific image within a Docker Hub repository.
Delete Docker Hub OrganizationTool to delete a specific Docker Hub organization.
Delete Docker Hub RepositoryTool to delete a specific Docker Hub repository.
Delete Repository TagTool to delete a specific tag from a Docker Hub repository.
Delete Docker Hub TeamTool to delete a specific team from an organization.
Delete Docker Hub repository webhookTool to delete a specific webhook from a repository.
Get Docker Hub ImageTool to retrieve detailed information about a specific image within a Docker Hub repository.
Get Organization DetailsTool to retrieve details of a specific organization namespace.
Get Docker Hub RepositoryTool to retrieve details of a specific Docker Hub repository.
Get Docker Hub TagTool to retrieve details of a specific Docker Hub repository tag.
Get Docker Hub TeamTool to retrieve a specific Docker Hub team.
Get Docker Hub WebhookTool to retrieve details of a specific Docker Hub webhook.
List Repository ImagesTool to list image variants for a specific Docker Hub repository.
List Docker Hub OrganizationsTool to list organizations (namespaces) for the authenticated user.
List Docker Hub Organization MembersTool to list members of a Docker Hub organization.
List Docker Hub RepositoriesTool to list repositories under a namespace.
List Repository TagsTool to list tags for a Docker Hub repository.
List Team MembersTool to list members of a Docker Hub team.
List Organization TeamsTool to list teams in a specific organization.
List Docker Hub repository webhooksTool to list webhooks for a Docker Hub repository.
Remove Organization MemberTool to remove a user from a Docker Hub organization.
Remove Team MemberTool to remove a user from a Docker Hub team.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

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

pip install composio-langchain langchain-mcp-adapters langchain python-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 is the core agent framework
  • python-dotenv 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

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Docker hub functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
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

Create a Tool Router session

# Create Tool Router session for Docker hub
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['docker_hub']
)

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

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "docker_hub-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("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
  • get_tools() retrieves all available Docker hub tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Docker hub related question or task to the agent.\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['exit', 'quit', 'bye']:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts 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 ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Docker hub and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['docker_hub']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "docker_hub-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Docker hub related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

if __name__ == "__main__":
    asyncio.run(main())

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.

How to build Docker hub MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Docker hub MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Docker hub MCP with Claude Agent SDK

Google ADK

Google ADK

Use Docker hub MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Docker hub MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Docker hub MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Docker hub MCP with LlamaIndex

CrewAI

CrewAI

Use Docker hub MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Docker hub MCP?

With a standalone Docker hub MCP server, the agents and LLMs can only access a fixed set of Docker hub tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Docker hub and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with LangChain?

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 Docker hub tools.

Can I manage the permissions and scopes for Docker hub while using Tool Router?

Yes, absolutely. You can configure which Docker hub 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.

How safe is my data with Composio Tool Router?

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 Docker hub data and credentials are handled as safely as possible.

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