How to integrate Botpress MCP with LangChain

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Introduction

This guide walks you through connecting Botpress to LangChain 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 LangChain 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:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Botpress project to Composio
  • Create a Tool Router MCP session for Botpress
  • Initialize an MCP client and retrieve Botpress tools
  • Build a LangChain agent that can interact with Botpress
  • Set up an interactive chat interface for testing

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides tools and abstractions for building agents that can reason, use tools, and maintain conversation context.

Key features include:

  • Agent Framework: Build agents that can use tools and make decisions
  • MCP Integration: Connect to external services through Model Context Protocol adapters
  • Memory Management: Maintain conversation history across interactions
  • Multi-Provider Support: Works with OpenAI, Anthropic, and other LLM providers

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 this tutorial, make sure you have:
  • Python 3.10 or higher installed on your system
  • A Composio account with an API key
  • An OpenAI 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

pip install composio-langchain langchain-mcp-adapters langchain python-dotenv

Install the required packages for LangChain with MCP support.

What's happening:

  • composio-langchain provides Composio integration for LangChain
  • langchain-mcp-adapters enables MCP client connections
  • langchain is the core agent framework
  • python-dotenv loads environment variables

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_composio_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio's API
  • COMPOSIO_USER_ID identifies the user for session management
  • OPENAI_API_KEY enables access to OpenAI's language models

Import dependencies

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Botpress functionality through MCP

Initialize Composio client

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))

    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
What's happening:
  • We're loading the COMPOSIO_API_KEY from environment variables and validating it exists
  • Creating a Composio instance that will manage our connection to Botpress tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

# Create Tool Router session for Botpress
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['botpress']
)

url = session.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
  • This approach allows the agent to dynamically load and use Botpress tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "botpress-agent": {
        "transport": "streamable_http",
        "url": session.mcp.url,
        "headers": {
            "x-api-key": os.getenv("COMPOSIO_API_KEY")
        }
    }
})

tools = await client.get_tools()

agent = create_agent("gpt-5", tools)
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Botpress MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Botpress tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model

Set up interactive chat interface

conversation_history = []

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

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

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

    if not user_input:
        continue

    conversation_history.append({"role": "user", "content": user_input})
    print("\nAgent is thinking...\n")

    response = await agent.ainvoke({"messages": conversation_history})
    conversation_history = response['messages']
    final_response = response['messages'][-1].content
    print(f"Agent: {final_response}\n")
What's happening:
  • We initialize an empty conversation_history list to maintain context across interactions
  • A while loop continuously accepts user input from the command line
  • When a user types a message, it's added to the conversation history and sent to the agent
  • The agent processes the request using the ainvoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully

Run the application

if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • We call the main() function using asyncio.run() to start the application

Complete Code

Here's the complete code to get you started with Botpress and LangChain:

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from dotenv import load_dotenv
from composio import Composio
import asyncio
import os

load_dotenv()

async def main():
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    
    if not os.getenv("COMPOSIO_API_KEY"):
        raise ValueError("COMPOSIO_API_KEY is not set")
    if not os.getenv("COMPOSIO_USER_ID"):
        raise ValueError("COMPOSIO_USER_ID is not set")
    
    session = composio.create(
        user_id=os.getenv("COMPOSIO_USER_ID"),
        toolkits=['botpress']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "botpress-agent": {
            "transport": "streamable_http",
            "url": url,
            "headers": {
                "x-api-key": os.getenv("COMPOSIO_API_KEY")
            }
        }
    })
    
    tools = await client.get_tools()
  
    agent = create_agent("gpt-5", tools)
    
    conversation_history = []
    
    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Ask any Botpress related question or task to the agent.\n")
    
    while True:
        user_input = input("You: ").strip()
        
        if user_input.lower() in ['exit', 'quit', 'bye']:
            print("\nGoodbye!")
            break
        
        if not user_input:
            continue
        
        conversation_history.append({"role": "user", "content": user_input})
        print("\nAgent is thinking...\n")
        
        response = await agent.ainvoke({"messages": conversation_history})
        conversation_history = response['messages']
        final_response = response['messages'][-1].content
        print(f"Agent: {final_response}\n")

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

Conclusion

You've successfully built a LangChain agent that can interact with Botpress through Composio's Tool Router.

Key features of this implementation:

  • Dynamic tool loading through Composio's Tool Router
  • Conversation history maintenance for context-aware responses
  • Async Python provides clean, efficient execution of agent workflows
You can extend this further by adding error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows.

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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 LangChain?

Yes, you can. LangChain 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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