How to integrate Cabinpanda MCP with Autogen

This guide walks you through connecting Cabinpanda to AutoGen 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 AutoGen 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 AutoGen 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 AutoGen 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 ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Cabinpanda
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Cabinpanda tools
  • Run a live chat loop where you ask the agent to perform Cabinpanda operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Cabinpanda account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Cabinpanda via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Cabinpanda connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Cabinpanda session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["cabinpanda"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Cabinpanda tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Cabinpanda assistant agent with MCP tools
    agent = AssistantAgent(
        name="cabinpanda_assistant",
        description="An AI assistant that helps with Cabinpanda operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Cabinpanda tools from the workbench
8

Run the interactive chat loop

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

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

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

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Cabinpanda tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Cabinpanda and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Cabinpanda session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["cabinpanda"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Cabinpanda assistant agent with MCP tools
        agent = AssistantAgent(
            name="cabinpanda_assistant",
            description="An AI assistant that helps with Cabinpanda operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

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

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

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

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

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

Conclusion

You now have an Autogen assistant wired into Cabinpanda through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Cabinpanda, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
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

LangChain logo

LangChain

Use Cabinpanda MCP with LangChain

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. Autogen 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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