How to integrate Baselinker MCP with LangChain

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Introduction

This guide walks you through connecting Baselinker to LangChain using the Composio tool router. By the end, you'll have a working Baselinker agent that can list all orders from this week, find orders placed by john@example.com, show pending receipts waiting to be issued, get all purchase orders for supplier abc through natural language commands.

This guide will help you understand how to give your LangChain agent real control over a Baselinker account through Composio's Baselinker 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 Baselinker project to Composio
  • Create a Tool Router MCP session for Baselinker
  • Initialize an MCP client and retrieve Baselinker tools
  • Build a LangChain agent that can interact with Baselinker
  • 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 Baselinker MCP server, and what's possible with it?

The Baselinker MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Baselinker account. It provides structured and secure access to your e-commerce data, so your agent can perform tasks like managing orders, tracking inventory, processing receipts, and retrieving sales information on your behalf.

  • Order management and retrieval: Let your agent fetch, filter, and download order details—including by date, status, or customer email—for fast access to sales and customer data.
  • Inventory and purchase order tracking: Automatically retrieve current inventory documents, view purchase orders, and get document series to keep your stock and supply chain in check.
  • External storage and warehouse discovery: Seamlessly list all external stores and warehouses connected to your Baselinker account, so your agent knows where your products live.
  • Order status and custom field mapping: Ask your agent to pull order statuses or extra field definitions, making it easy to interpret, categorize, and automate order processing flows.
  • Pending receipt management: Quickly fetch receipts that still need to be issued, helping your team stay on top of fiscal compliance and transaction documentation.

Supported Tools & Triggers

Tools
Get External Storages ListTool to retrieve a list of available external storages connected to baselinker.
Get Inventory DocumentsTool to retrieve a list of inventory documents.
Get Inventory Document SeriesTool to retrieve available inventory document series.
Get Inventory Purchase OrdersTool to retrieve a list of purchase orders from baselinker storage.
Get Inventory Purchase Order SeriesTool to retrieve a list of purchase order document series.
Get New ReceiptsTool to retrieve receipts waiting to be issued.
Get Order Extra FieldsTool to retrieve extra fields defined for orders.
Get OrdersTool to download orders from baselinker order manager.
Get Orders by EmailTool to search for orders by e-mail address.
Get Order Status ListTool to retrieve a list of order statuses created in the baselinker order manager.
Get SeriesTool to download invoice/receipt numbering series.
Get Storages ListTool to download a list of available storages accessible via api.

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 Baselinker 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 Baselinker tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "baselinker-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 Baselinker 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 Baselinker 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 Baselinker MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Baselinker MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Baselinker MCP with Claude Agent SDK

Google ADK

Google ADK

Use Baselinker MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Baselinker MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Baselinker MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Baselinker MCP with LlamaIndex

CrewAI

CrewAI

Use Baselinker MCP with CrewAI

FAQ

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

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

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

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

Used by agents from

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Context
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