# How to integrate Cabinpanda MCP with LangChain

```json
{
  "title": "How to integrate Cabinpanda MCP with LangChain",
  "toolkit": "Cabinpanda",
  "toolkit_slug": "cabinpanda",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/cabinpanda/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/cabinpanda/framework/langchain.md",
  "updated_at": "2026-05-12T10:04:40.604Z"
}
```

## 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 SDK](https://composio.dev/toolkits/cabinpanda/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/cabinpanda/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/cabinpanda/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/cabinpanda/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/cabinpanda/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/cabinpanda/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/cabinpanda/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/cabinpanda/framework/cli)
- [Google ADK](https://composio.dev/toolkits/cabinpanda/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/cabinpanda/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/cabinpanda/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/cabinpanda/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/cabinpanda/framework/crew-ai)

## 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.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CABINPANDA_CREATE_FORM` | Create Form | Create a new form in CabinPanda using a template. The form will be created with default fields based on the selected template. Use template_id '1' for a blank form or other IDs for themed templates. |
| `CABINPANDA_DELETE_FORM` | Delete Form | Permanently deletes a form from CabinPanda using its unique key. This action is irreversible - use with caution. Requires the form's alphanumeric key (not the numeric ID) which can be obtained from the List Forms action's 'key' field. |
| `CABINPANDA_DELETE_INTEGRATION` | Delete Integration | Deletes a form integration configuration by its ID. Use this to disconnect a specific integration (e.g., Slack, Google Sheets, Stripe) from a form. First use CABINPANDA_LIST_INTEGRATIONS to find the 'form_integration.id' of the configured integration you want to remove. This is a destructive and idempotent operation. |
| `CABINPANDA_GET_FORM_DETAILS` | Get Form Details | Tool to retrieve details of a specific form by its key (32-character hex string). Use when you need form metadata (fields, labels, timestamps) before processing or submitting data. Get form keys from the list_forms endpoint. |
| `CABINPANDA_GET_INTEGRATION_DETAILS` | Get Integration Details | Retrieve detailed information about a specific integration by its numeric ID. Returns integration configuration, category, OAuth settings, and form/team integration status. Use List Integrations first to discover available IDs. |
| `CABINPANDA_GET_PROFILE` | Get Profile | Retrieves the authenticated user's CabinPanda profile including account details, workspace information, available features, usage statistics, and billing plan. Use this action to get the current user's identity, check plan limits, or verify account setup status. |
| `CABINPANDA_LIST_FORMS` | List Forms | Tool to retrieve a list of all forms associated with the account. Use when you need to fetch form details for management or analysis. |
| `CABINPANDA_LIST_INTEGRATIONS` | List Integrations | Tool to retrieve a list of all integrations for the account. Use when you need to inspect available or active integrations before managing them. |
| `CABINPANDA_LIST_SUBMISSIONS` | List form submissions | Retrieve all submissions for a specific form. The form_id parameter requires the 32-character hex key (not the numeric ID). First use List Forms to get available form keys, then pass one to this action. |
| `CABINPANDA_LIST_USERS` | List Users | Retrieves a list of all team members (users) associated with your CabinPanda account. Use this tool to: - View all team members in your workspace - Get user IDs for other team management operations - Check user roles, permissions, and current usage statistics - Audit team membership and billing plan information Returns detailed user information including email, workspace details, available features, and usage statistics. |
| `CABINPANDA_UPDATE_FORM` | Update Form | Tool to update the details of an existing form including name, template, fields and settings. Use when you need to modify form properties, update field configuration, or change form behavior. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Cabinpanda MCP server is an implementation of the Model Context Protocol that connects your AI agent to Cabinpanda. It provides structured and secure access so your agent can perform Cabinpanda operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

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
```python
# Create Tool Router session for Cabinpanda
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['cabinpanda']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['cabinpanda']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "cabinpanda-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)
```

```typescript
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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Cabinpanda 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")
```

```typescript
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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
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=['cabinpanda']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "cabinpanda-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 Cabinpanda 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())
```

```typescript
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.

## How to build Cabinpanda MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/cabinpanda/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/cabinpanda/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/cabinpanda/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/cabinpanda/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/cabinpanda/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/cabinpanda/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/cabinpanda/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/cabinpanda/framework/cli)
- [Google ADK](https://composio.dev/toolkits/cabinpanda/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/cabinpanda/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/cabinpanda/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/cabinpanda/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/cabinpanda/framework/crew-ai)

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- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Cabinpanda MCP?

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.

### 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 Cabinpanda tools.

### Can I manage the permissions and scopes for Cabinpanda while using Tool Router?

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.

### 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 Cabinpanda data and credentials are handled as safely as possible.

---
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