How to integrate Botpress MCP with Pydantic AI

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Introduction

This guide walks you through connecting Botpress to Pydantic AI using the Composio tool router. By the end, you'll have a working Botpress agent that can list all active conversations for your bot, show issues reported for a specific bot, delete a file from a bot workspace through natural language commands.

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

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

Also integrate Botpress with

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 Botpress
  • 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 Botpress 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 Botpress MCP server, and what's possible with it?

The Botpress MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Botpress account. It provides structured and secure access to your chatbot platform, so your agent can perform actions like listing conversations, managing bot files, tracking issues, and exploring workspaces on your behalf.

  • Comprehensive conversation management: Retrieve and paginate through all chatbot conversations, making it easy to review chat history and analyze user interactions.
  • Bot issue tracking and diagnostics: List and investigate issues related to specific bots, helping you stay informed about errors or configuration problems as they arise.
  • Workspace discovery and organization: Browse both public and private workspaces, making it seamless to manage your bot environments or explore new collaborative spaces.
  • File and tag oversight: List, manage, and categorize bot files and their associated tags or tag values, streamlining bot asset organization.
  • Account information access: Instantly fetch authenticated account details so your agent always works with the latest profile and permission data.

Supported Tools & Triggers

Tools
Break Down Workspace Usage By BotTool to break down workspace usage by bot.
BOTPRESS_CHARGE_WORKSPACE_UNPAID_INVOICESTool to charge unpaid invoices for a specific Botpress workspace.
Check Handle AvailabilityTool to check if a workspace handle is available in Botpress.
BOTPRESS_CREATE_ADMIN_INTEGRATIONTool to create a new integration in a Botpress workspace via the Admin API.
BOTPRESS_CREATE_ADMIN_WORKSPACETool to create a new workspace in Botpress via the Admin API.
BOTPRESS_CREATE_BOTTool to create a new bot in a Botpress workspace via the Admin API.
BOTPRESS_CREATE_CONVERSATIONTool to create a new conversation in Botpress via the Runtime API.
Delete Admin WorkspaceTool to permanently delete a workspace from Botpress admin.
Delete FilePermanently deletes a file from a Botpress bot's storage by its file ID.
Delete Integration Shareable IDTool to delete a shareable ID for an integration installed in a Botpress bot.
Delete Knowledge BasePermanently deletes a knowledge base from Botpress by its knowledge base ID.
Get AccountTool to get details of the authenticated account.
Get Account PreferenceTool to get a preference of the account.
Get All Workspace Quota CompletionTool to get a map of workspace IDs to their highest quota completion rate.
Get Dereferenced Public Plugin By IDTool to get a public plugin by ID with all interface entity references resolved to the corresponding entities as extended by the backing integrations.
Get IntegrationTool to get a specific Botpress integration by name and version.
Get Public IntegrationTool to retrieve a public integration by name and version from the Botpress hub.
Get Public Integration By IDTool to retrieve detailed information about a public Botpress integration by its ID.
Get Public InterfaceTool to get a public interface by name and version from the Botpress Hub.
Get Public Interface by IDTool to retrieve a public interface by its ID from the Botpress Hub.
Get Public PluginTool to retrieve detailed information about a public plugin from Botpress Hub by name and version.
Get Public Plugin By IDTool to retrieve details of a public plugin by its unique ID.
Get Public Plugin CodeTool to retrieve public plugin code from Botpress Hub.
Get Table RowTool to fetch a specific row from a table using the row's unique identifier.
Get Upcoming InvoiceTool to get the upcoming invoice for a workspace.
Get WorkspaceTool to get detailed information about a specific Botpress workspace by ID.
Get Workspace QuotaTool to get workspace quota information for a specific usage type.
LIST_ACTION_RUNSTool to list action runs for a specific integration of a bot.
LIST_BOT_ISSUESTool to list issues associated with a specific bot.
LIST_CONVERSATIONSTool to list all Conversations.
LIST_FILE_TAGSTool to list all tags used across all bot files.
LIST_FILE_TAG_VALUESTool to list all values for a given file tag across all files.
LIST_HUB_INTEGRATIONSTool to list public integrations from the Botpress hub.
LIST_INTEGRATION_API_KEYSTool to list Integration API Keys (IAKs) for a specific integration.
List IntegrationsTool to list integrations with filtering and sorting capabilities.
LIST_KNOWLEDGE_BASESTool to list knowledge bases for a bot.
List PluginsTool to list Botpress plugins.
List Public InterfacesTool to retrieve a list of public interfaces available in the Botpress Hub.
LIST_PUBLIC_PLUGINSTool to retrieve a list of public plugins available in the Botpress hub.
LIST_PUBLIC_WORKSPACESTool to retrieve a list of public workspaces.
LIST_USAGE_HISTORYTool to retrieve usage history for a bot or workspace.
List Workspace InvoicesTool to list all invoices billed to a workspace.
LIST_WORKSPACESList all Botpress workspaces accessible to the authenticated user.
Request Integration VerificationTool to request verification for a Botpress integration via the Admin API.
BOTPRESS_RUN_VRLTool to execute a VRL (Vector Remap Language) script against input data using the Botpress Admin API.
BOTPRESS_SEND_MESSAGETool to send a message to an existing Botpress conversation via the Runtime API.
Set Account PreferenceTool to set a preference for the account.
Set Workspace PreferenceTool to set a preference for a Botpress workspace.
Update AccountTool to update details of the authenticated account.
BOTPRESS_UPDATE_ADMIN_BOTSTool to update an existing bot in a Botpress workspace via the Admin API.
UPDATE_ADMIN_WORKSPACETool to update a Botpress workspace via the Admin API.
BOTPRESS_UPDATE_WORKFLOWTool to update a workflow object in Botpress by setting parameter values.
BOTPRESS_VALIDATE_INTEGRATION_UPDATETool to validate an integration update request in Botpress Admin API.

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

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

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.

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 Botpress
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file

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

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 Botpress
  • MCPServerStreamableHTTP connects to the Botpress MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant

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 Botpress
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["botpress"],
    )
    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 Botpress 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

Initialize the Pydantic AI Agent

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

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 Botpress.\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
  • Botpress API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns

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 Botpress 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 Botpress
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["botpress"],
    )
    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
    botpress_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[botpress_mcp],
        instructions=(
            "You are a Botpress assistant. Use Botpress 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 Botpress.\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 Botpress through Composio's Tool Router. With this setup, your agent can perform real Botpress 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 + Botpress for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.

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FAQ

What are the differences in Tool Router MCP and Botpress MCP?

With a standalone Botpress MCP server, the agents and LLMs can only access a fixed set of Botpress tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Botpress and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Pydantic AI?

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 Botpress tools.

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

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

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