How to integrate Chatforma MCP with Autogen

This guide walks you through connecting Chatforma to AutoGen using the Composio tool router. By the end, you'll have a working Chatforma agent that can list your bots, review dialog messages, find user variables, browse segments, and send messages through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Chatforma account through Composio's Chatforma MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Chatforma is a chatbot automation platform for building and managing bots. It helps teams run dialogs, segments, broadcasts, forms, and user updates from one place.

10 Tools

Introduction

This guide walks you through connecting Chatforma to AutoGen using the Composio tool router. By the end, you'll have a working Chatforma agent that can list your bots, review dialog messages, find user variables, browse segments, and send messages through natural language commands.

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

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

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

The Chatforma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Chatforma account. It provides structured and secure access to your bots, users, segments, dialogs, messages, and variables, so your agent can find audiences, review conversations, inspect user details, and send targeted messages on your behalf.

  • Bot and audience discovery: Have your agent list your bots, browse their users, and find people within a segment or with open dialogs.
  • Dialog review and replies: Let the agent read messages from a user's bot dialog and send a direct text reply into the conversation.
  • Targeted message dispatches: Direct your agent to send a text dispatch to one user or a selected audience segment.
  • Predefined message delivery: Instruct your agent to review configured bot messages and send the right one to an individual user or segment.
  • User variable lookup: Have your agent review available bot variables and retrieve the current value of a chosen variable for a specific user.

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

Supported Tools

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

Get User Variable

Get the current value of one bot variable for one bot user.

List Bots

List bots belonging to the connected Chatforma account so their IDs can be used in bot-scoped tools.

List Dialog Messages

Return one page of messages exchanged in a specific user's bot dialog, with a cursor for continuing the history.

List Predefined Messages

List predefined messages configured in a bot so a message ID can be selected for sending.

List Segments

List the user segments configured for a bot, including segment IDs needed for targeted sends.

List Users

List a bot's users, members of one segment, or users with open dialogs; choose the view that matches the task.

List Variables

List variables configured for a bot, including IDs and types needed to read user-specific values.

Send Dialog Message

Immediately send a user-visible text message into a specific user's bot dialog.

Send Predefined Message

Send a configured bot message to one user or dispatch it to a segment.

Send Text Dispatch

Create a user-visible text dispatch for one bot user or a segment.

FRAMEWORKS

How to build Chatforma MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Chatforma MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Chatforma MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Chatforma MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Chatforma MCP with Claude Cowork

Codex logo

Codex

Use Chatforma MCP with Codex

OpenClaw logo

OpenClaw

Use Chatforma MCP with OpenClaw

Hermes logo

Hermes

Use Chatforma MCP with Hermes

CLI logo

CLI

Use Chatforma MCP with CLI

Google ADK logo

Google ADK

Use Chatforma MCP with Google ADK

LangChain logo

LangChain

Use Chatforma MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Chatforma MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Chatforma MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Chatforma MCP with LlamaIndex

CrewAI logo

CrewAI

Use Chatforma MCP with CrewAI

FAQ

Frequently asked questions

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

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

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