How to integrate Cabinpanda MCP with LangChain

This guide walks you through connecting Cabinpanda to LangChain using the Composio tool router. By the end, you'll have a working Cabinpanda agent that can list all forms created this month, get details of form by id, show all submissions for a survey through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Cabinpanda account through Composio's Cabinpanda MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

11 Tools

Introduction

This guide walks you through connecting Cabinpanda to LangChain using the Composio tool router. By the end, you'll have a working Cabinpanda agent that can list all forms created this month, get details of form by id, show all submissions for a survey through natural language commands.

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

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

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

The Cabinpanda MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, and more directly to your Cabinpanda account. It provides structured and secure access to your online forms and submissions, so your agent can create forms, fetch submissions, manage integrations, and analyze collected data—all on your behalf.

  • Form creation and management: Let your agent design new forms, set up fields, and update or delete existing forms to streamline data collection workflows.
  • Submission review and analysis: Quickly retrieve and review submissions for any form, enabling your assistant to summarize, filter, or process responses in real time.
  • Integration automation: Have the agent list, inspect, or remove integrations (like webhooks) so your forms can connect to other tools and automate follow-up actions.
  • User and team oversight: Effortlessly fetch account users and profile details, making it easy to manage access and keep your team in sync.
  • Comprehensive form listing: Instantly retrieve all forms in your account to enable bulk operations, reporting, or quick audits through your agent.

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 Cabinpanda 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 Cabinpanda 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: ['cabinpanda']
    }
);

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

Configure the agent with the MCP URL

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

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

Create Form

Create a new form in CabinPanda using a template.

Delete Form

Permanently deletes a form from CabinPanda using its unique key.

Delete Integration

Deletes a form integration configuration by its ID.

Get Form Details

Tool to retrieve details of a specific form by its key (32-character hex string).

Get Integration Details

Retrieve detailed information about a specific integration by its numeric ID.

Get Profile

Retrieves the authenticated user's CabinPanda profile including account details, workspace information, available features, usage statistics, and billing plan.

List Forms

Tool to retrieve a list of all forms associated with the account.

List Integrations

Tool to retrieve a list of all integrations for the account.

List form submissions

Retrieve all submissions for a specific form.

List Users

Retrieves a list of all team members (users) associated with your CabinPanda account.

Update Form

Tool to update the details of an existing form including name, template, fields and settings.

FRAMEWORKS

How to build Cabinpanda MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Cabinpanda MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Cabinpanda MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Cabinpanda MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Cabinpanda MCP with Claude Cowork

Codex logo

Codex

Use Cabinpanda MCP with Codex

OpenClaw logo

OpenClaw

Use Cabinpanda MCP with OpenClaw

Hermes logo

Hermes

Use Cabinpanda MCP with Hermes

CLI logo

CLI

Use Cabinpanda MCP with CLI

Google ADK logo

Google ADK

Use Cabinpanda MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Cabinpanda MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Cabinpanda MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Cabinpanda MCP with LlamaIndex

CrewAI logo

CrewAI

Use Cabinpanda MCP with CrewAI

FAQ

Frequently asked questions

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

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

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