How to integrate Expofp MCP with Autogen

This guide walks you through connecting Expofp to AutoGen using the Composio tool router. By the end, you'll have a working Expofp agent that can add new category 'workshops' to event 1023, list all extras available for expo 2048, update category #7 name to 'vip sessions' through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Expofp account through Composio's Expofp MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Expofp is interactive floor plan software for expos and conferences. It helps organizers and attendees navigate events and manage booth assignments with ease.

18 Tools

Introduction

This guide walks you through connecting Expofp to AutoGen using the Composio tool router. By the end, you'll have a working Expofp agent that can add new category 'workshops' to event 1023, list all extras available for expo 2048, update category #7 name to 'vip sessions' through natural language commands.

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

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

Also integrate Expofp 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 Expofp
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Expofp tools
  • Run a live chat loop where you ask the agent to perform Expofp 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 Expofp MCP server, and what's possible with it?

The Expofp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Expofp account. It provides structured and secure access to your expo and floor plan data, so your agent can manage categories, list expo details, and streamline event organization tasks for you.

  • Seamless expo management: Instantly list all expos linked to your Expofp account, making it easy to keep track of multiple events.
  • Effortless category creation: Direct your agent to add new categories to any expo, supporting better organization and event structure in seconds.
  • Category updates and edits: Quickly update existing category names or details for any event, so your event structure always matches your latest needs.
  • Streamlined category removal: Have the agent remove outdated or unnecessary categories from your expos, keeping your event data clean and relevant.
  • Comprehensive extras listing: Retrieve all extras for a specific expo, helping you review and manage additional event options or features at a glance.

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 Expofp 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 Expofp 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 Expofp 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 Expofp session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["expofp"]
    )
    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 Expofp 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 Expofp assistant agent with MCP tools
    agent = AssistantAgent(
        name="expofp_assistant",
        description="An AI assistant that helps with Expofp 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 Expofp 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 Expofp 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 Expofp 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 Expofp 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 Expofp session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["expofp"]
    )
    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 Expofp assistant agent with MCP tools
        agent = AssistantAgent(
            name="expofp_assistant",
            description="An AI assistant that helps with Expofp 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 Expofp 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 Expofp 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 Expofp, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Expofp action and event your agent gets out of the box.

Add Category

Tool to add a new category to an expo.

Delete Exhibitor

Tool to delete an exhibitor from an expo by their ID.

Delete Session Speakers

Tool to delete session speakers by their IDs.

Delete Session Tracks

Tool to delete session tracks by IDs from an expo.

Get Bulk Read Exhibitors Template

Tool to get the template structure for bulk reading exhibitors from an expo.

Get Offline Archive

Lightweight retrieval of offline archive state that never starts a build.

Get or Create Offline Archive

Retrieve the offline archive state for a specific expo version.

Get Session Tracks

Retrieve all session tracks for a specific expo event.

List All Expo Extras

Retrieves all extras (additional services/items) available for a specific expo event, including general extras and booth-specific extras with exhibitor details.

List All Expos

Retrieve all expos (events/exhibitions) accessible with the authenticated ExpoFP account.

List Categories

Retrieve all categories for a specific expo event.

Bulk Read Exhibitors

Tool to bulk read exhibitors from an expo with customizable response template.

Remove Category

Tool to remove a category from an expo.

Set Exhibitor Logo

Tool to set or remove an exhibitor logo using multipart/form-data.

Update Category

Tool to update an existing category.

Update Exhibitor

Tool to update an existing exhibitor.

Upsert Sessions

Tool to create or update sessions in bulk for an expo event.

Upsert Session Tracks

Tool to create or update session tracks in bulk for an expo.

FRAMEWORKS

How to build Expofp MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Expofp MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Expofp MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Expofp MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Expofp MCP with Claude Cowork

Codex logo

Codex

Use Expofp MCP with Codex

OpenClaw logo

OpenClaw

Use Expofp MCP with OpenClaw

Hermes logo

Hermes

Use Expofp MCP with Hermes

CLI logo

CLI

Use Expofp MCP with CLI

Google ADK logo

Google ADK

Use Expofp MCP with Google ADK

LangChain logo

LangChain

Use Expofp MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Expofp MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Expofp MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Expofp MCP with LlamaIndex

CrewAI logo

CrewAI

Use Expofp MCP with CrewAI

FAQ

Frequently asked questions

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

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

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