How to integrate Chatforma MCP with Pydantic AI

This guide walks you through connecting Chatforma to Pydantic AI 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 Pydantic AI 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 Pydantic AI 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 Pydantic AI 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:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Chatforma
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Chatforma workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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 step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active 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

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Chatforma
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Chatforma
  • MCPServerStreamableHTTP connects to the Chatforma MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Chatforma
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["chatforma"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Chatforma 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
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
chatforma_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[chatforma_mcp],
    instructions=(
        "You are a Chatforma assistant. Use Chatforma tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Chatforma endpoint
  • The agent uses GPT-5 to interpret user commands and perform Chatforma operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Chatforma.\n")

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", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Chatforma API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Chatforma and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Chatforma
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["chatforma"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    chatforma_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[chatforma_mcp],
        instructions=(
            "You are a Chatforma assistant. Use Chatforma tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Chatforma.\n")

    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", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

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

Conclusion

You've built a Pydantic AI agent that can interact with Chatforma through Composio's Tool Router. With this setup, your agent can perform real Chatforma actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Chatforma for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
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. Pydantic AI 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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