How to integrate Altoviz MCP with LangChain

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Introduction

This guide walks you through connecting Altoviz to LangChain using the Composio tool router. By the end, you'll have a working Altoviz agent that can find customer details by email address, update a client's company information, retrieve current vat rates for invoices, list all available product measurement units through natural language commands.

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

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

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Connect your Altoviz project to Composio
  • Create a Tool Router MCP session for Altoviz
  • Initialize an MCP client and retrieve Altoviz tools
  • Build a LangChain agent that can interact with Altoviz
  • 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 Altoviz MCP server, and what's possible with it?

The Altoviz MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Altoviz account. It provides structured and secure access to your billing, invoicing, and customer management data, so your agent can manage products, find customers, update records, and retrieve financial information on your behalf.

  • Product management and creation: Instruct your agent to create new products, update details, or delete products from your Altoviz catalog with ease.
  • Customer and contact lookup: Effortlessly find customers or contacts by email, enabling quick access to client details and supporting streamlined communication.
  • Financial classification and VAT management: Let your agent fetch available classifications and VAT rates, ensuring accurate tax handling and financial document setup.
  • Unit retrieval for transactions: Retrieve all available measurement units in your system, supporting precise product and invoice management.
  • Customer information updates: Have your agent modify or update customer records, keeping your business data up-to-date without manual intervention.

Supported Tools & Triggers

Tools
Create ProductCreates a new product in the altoviz system.
Delete ProductThis tool allows you to delete an existing product from altoviz.
Find Contact by EmailThis tool allows searching for contacts in altoviz using an email address.
Find Customer by EmailThis tool allows you to find a customer in altoviz by their email address.
Find Product by NumberThis tool allows users to find products in altoviz using the product number.
Get Classifications ListThis tool retrieves a list of classifications from the altoviz platform.
Get Units ListThis tool retrieves a list of all available units in the altoviz system.
Get VAT RatesThis tool retrieves a list of all available vat rates from altoviz.
Update Customer InformationThis tool updates an existing customer's information in altoviz.

What is the Composio tool router, and how does it fit here?

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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 Altoviz 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 Altoviz tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

url = session.mcp.url
What's happening:
  • We're creating a Tool Router session that gives your agent access to Altoviz 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 Altoviz tools as needed

Configure the agent with the MCP URL

client = MultiServerMCPClient({
    "altoviz-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 Altoviz MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • get_tools() retrieves all available Altoviz 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 Altoviz 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 Altoviz 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=['altoviz']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "altoviz-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 Altoviz 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 Altoviz 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.

How to build Altoviz MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Altoviz MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Altoviz MCP with Claude Agent SDK

Google ADK

Google ADK

Use Altoviz MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Altoviz MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Altoviz MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Altoviz MCP with LlamaIndex

CrewAI

CrewAI

Use Altoviz MCP with CrewAI

FAQ

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

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

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

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

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