How to integrate Classmarker MCP with LangChain

This guide walks you through connecting Classmarker to LangChain using the Composio tool router. By the end, you'll have a working Classmarker agent that can add student to biology exam access list, create a new question for math quiz, delete user account for withdrawn student through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Classmarker account through Composio's Classmarker MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

Get started for freeGet a demo
Classmarker logoClassmarker
Api Key

ClassMarker is a professional online quiz maker for business and education. It provides instant grading, flexible test design, and in-depth reporting.

28 Tools

Introduction

This guide walks you through connecting Classmarker to LangChain using the Composio tool router. By the end, you'll have a working Classmarker agent that can add student to biology exam access list, create a new question for math quiz, delete user account for withdrawn student through natural language commands.

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

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

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

The Classmarker MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Classmarker account. It provides structured and secure access to your quiz management tools, so your agent can create tests, manage users and groups, add questions, and control access codes—without manual intervention.

  • Automated user and group management: Let your agent create new users, add them to groups, or delete users and groups for streamlined participant organization.
  • Dynamic question and category creation: Instruct your agent to add new questions or categories to your exams, helping you build tests faster and keep content organized.
  • Access code and permissions control: Enable your agent to generate, assign, or delete access codes for specific exams, giving or revoking test access instantly as needed.
  • Test link and API key management: Allow your agent to manage test links or revoke API keys to maintain secure and up-to-date exam distribution.
  • Efficient data cleanup: Ask your agent to remove users, groups, test links, or access codes, keeping your Classmarker account tidy and up to date with minimal effort.

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 Classmarker 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 Classmarker 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: ['classmarker']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Access List Item

Tool to add one or more access codes to an access list.

Create a new question category

Tool to create a new question category.

Create Group

Tool to create a new group.

Create Question

Tool to create a new question with specified text, type, and category in ClassMarker.

Create ClassMarker User

Tool to create a new user in ClassMarker.

Delete Access List Item

Removes one or more access codes from a ClassMarker access list.

Delete API Key

Tool to delete an API key by its ID.

Delete Group

Tool to delete a group by its ID.

Delete Test Link

Deletes a specific link assignment from a test/exam in ClassMarker.

Delete User

Tool to delete a specific user by ID.

Delete Webhook

Deletes a webhook listener from your ClassMarker account.

Get Group Details

Retrieve detailed information about a specific group by ID.

Get Initial Finished After Timestamp

Compute the initial finishedAfterTimestamp for paginating ClassMarker test results.

Get Question

Tool to retrieve a specific question by its ID.

Get Recent Results For Group Exam

Tool to fetch recent test results for a specific group and exam.

Get Recent Results Link Exam

Fetch recent exam results for a specific link and test combination.

Get Test Details

Retrieves detailed information for a specific test (exam) including its name and all assignment contexts.

Get User Details

Tool to retrieve detailed information about a specific user.

List Question Categories

Retrieves all question categories organized in a hierarchical structure.

List Certificates

Tool to list all certificates.

List Questions

Retrieve a paginated list of questions from your question bank.

List Recent Results For Groups

Retrieve recent exam results for all groups in your ClassMarker account.

List Recent Results for Links

Retrieves recent test results from ALL links accessible to your API key.

List Users

Tool to list all users.

List Webhooks

Tool to retrieve all configured webhooks.

Update Sub-Category

Tool to update an existing sub-category.

Update an existing parent category

Tool to update an existing parent category.

Update Question

Updates an existing question in the ClassMarker question bank.

FRAMEWORKS

How to build Classmarker MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Classmarker MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Classmarker MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Classmarker MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Classmarker MCP with Claude Cowork

Codex logo

Codex

Use Classmarker MCP with Codex

OpenClaw logo

OpenClaw

Use Classmarker MCP with OpenClaw

Hermes logo

Hermes

Use Classmarker MCP with Hermes

CLI logo

CLI

Use Classmarker MCP with CLI

Google ADK logo

Google ADK

Use Classmarker MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Classmarker MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Classmarker MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Classmarker MCP with LlamaIndex

CrewAI logo

CrewAI

Use Classmarker MCP with CrewAI

FAQ

Frequently asked questions

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

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

Start with Classmarker.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Classmarker tool your agent needs.Free to start.

Start building
Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREEBOOK A DEMO
agent.aiZoomLettaGleanHubSpotAWSWabi

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

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.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave
Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

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.

Tomisin Jenrola
Tomisin Jenrola
Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

Jerome Leclanche
Jerome Leclanche
Co-Founder, Ingram Technologies