How to integrate Starshipit MCP with LangChain

This guide walks you through connecting Starshipit to LangChain using the Composio tool router. By the end, you'll have a working Starshipit agent that can create your orders, compare shipping rates, create labels, track parcels, and manage manifests through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Starshipit account through Composio's Starshipit MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Starshipit is a shipping and fulfilment platform for ecommerce orders, labels, tracking, and warehouse operations. It helps teams automate delivery workflows across carriers and sales channels.

15 Tools

Introduction

This guide walks you through connecting Starshipit to LangChain using the Composio tool router. By the end, you'll have a working Starshipit agent that can create your orders, compare shipping rates, create labels, track parcels, and manage manifests through natural language commands.

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

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

Also integrate Starshipit with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Starshipit MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Starshipit account. It provides structured and secure access to your orders, shipments, tracking, inventory, and warehouse data, so your agent can create orders, quote shipping rates, generate labels, track deliveries, and review fulfilment performance on your behalf.

  • Order creation and management: Have your agent create unshipped orders, find existing orders, update editable order details, and review orders waiting for fulfilment.
  • Shipping rates and labels: Let the agent compare available shipping services and prices, then generate labels for one order or multiple orders.
  • Tracking and manifests: Direct your agent to check shipment statuses and carrier events, create finalized manifest documents, or review recent manifests.
  • Inventory and product visibility: Instruct your agent to search the product catalogue and review warehouse stock, reserved quantities, and available inventory by product or location.
  • Warehouse performance insights: Have your agent report on recent order cycle times and fulfilment SLA performance across the past 1 to 90 days.

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 step10 STEPS
1

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
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

npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters 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/core is the core agent framework
  • dotenv/config loads environment variables
4

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
5

Import dependencies

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

dotenv.config();
What's happening:
  • We're importing LangChain's MCP adapter and Composio SDK
  • The dotenv/config import loads environment variables from your .env file
  • This setup prepares the foundation for connecting LangChain with Starshipit functionality through MCP
6

Initialize Composio client

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });
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 Starshipit tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding
7

Create a Tool Router session

const session = await composio.create(
    userId as string,
    {
        toolkits: ['starshipit']
    }
);

const url = session.mcp.url;
What's happening:
  • We're creating a Tool Router session that gives your agent access to Starshipit 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 Starshipit tools as needed
8

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "starshipit-agent": {
        transport: "http",
        url: url,
        headers: {
            "x-api-key": process.env.COMPOSIO_API_KEY
        }
    }
});

const tools = await client.getTools();

const agent = createAgent({ model: "gpt-5", tools });
What's happening:
  • We're creating a MultiServerMCPClient that connects to our Starshipit MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Starshipit tools that the agent can use
  • We're creating a LangChain agent using the GPT-5 model
9

Set up interactive chat interface

let conversationHistory: any[] = [];

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

const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: '
});

rl.prompt();

rl.on('line', async (userInput: string) => {
    const trimmedInput = userInput.trim();

    if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
        console.log("\nGoodbye!");
        rl.close();
        process.exit(0);
    }

    if (!trimmedInput) {
        rl.prompt();
        return;
    }

    conversationHistory.push({ role: "user", content: trimmedInput });
    console.log("\nAgent is thinking...\n");

    const response = await agent.invoke({ messages: conversationHistory });
    conversationHistory = response.messages;

    const finalResponse = response.messages[response.messages.length - 1]?.content;
    console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\n👋 Session ended.');
        process.exit(0);
    });
What's happening:
  • We initialize an empty conversationHistory list to maintain context across interactions
  • A readline interface is used to continuously accept 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 invoke() method with the full conversation history
  • Users can type 'exit', 'quit', or 'bye' to end the chat session gracefully
10

Run the application

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
What's happening:
  • We call the main() function to start the application

Complete Code

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

import { Composio } from '@composio/core';
import { LangchainProvider } from '@composio/langchain';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";  
import { createAgent } from "langchain";
import * as readline from 'readline';
import 'dotenv/config';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.COMPOSIO_USER_ID;

if (!composioApiKey) throw new Error('COMPOSIO_API_KEY is not set');
if (!userId) throw new Error('COMPOSIO_USER_ID is not set');

async function main() {
    const composio = new Composio({
        apiKey: composioApiKey as string,
        provider: new LangchainProvider()
    });

    const session = await composio.create(
        userId as string,
        {
            toolkits: ['starshipit']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "starshipit-agent": {
            transport: "http",
            url: url,
            headers: {
                "x-api-key": process.env.COMPOSIO_API_KEY
            }
        }
    });
    
    const tools = await client.getTools();
  
    const agent = createAgent({ model: "gpt-5", tools });
    
    let conversationHistory: any[] = [];
    
    console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
    console.log("Ask any Starshipit related question or task to the agent.\n");
    
    const rl = readline.createInterface({
        input: process.stdin,
        output: process.stdout,
        prompt: 'You: '
    });

    rl.prompt();

    rl.on('line', async (userInput: string) => {
        const trimmedInput = userInput.trim();
        
        if (['exit', 'quit', 'bye'].includes(trimmedInput.toLowerCase())) {
            console.log("\nGoodbye!");
            rl.close();
            process.exit(0);
        }
        
        if (!trimmedInput) {
            rl.prompt();
            return;
        }
        
        conversationHistory.push({ role: "user", content: trimmedInput });
        console.log("\nAgent is thinking...\n");
        
        const response = await agent.invoke({ messages: conversationHistory });
        conversationHistory = response.messages;
        
        const finalResponse = response.messages[response.messages.length - 1]?.content;
        console.log(`Agent: ${finalResponse}\n`);
        
        rl.prompt();
    });

    rl.on('close', () => {
        console.log('\nSession ended.');
        process.exit(0);
    });
}

main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});

Conclusion

You've successfully built a LangChain agent that can interact with Starshipit 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.
TOOLS

Supported Tools

Every Starshipit action and event your agent gets out of the box.

Create Manifest

Create and finalize a Starshipit manifest PDF for only the explicitly listed shipments.

Create Order

Create one unshipped Starshipit order with destination, line items, packages, sender, and customs details.

Create Shipping Label

Create shipping labels for one Starshipit order.

Create Shipping Labels

Create shipping labels for multiple existing Starshipit orders in one irreversible batch.

Get Order

Retrieve one order by its Starshipit numeric ID or source-platform order number.

Get Shipping Rates

Quote available Starshipit shipping services and prices for a destination and one or more packages.

Get Tracking

Get current shipment status and carrier tracking events by tracking number or order number.

Get WMS Performance

Return Starshipit WMS order-cycle and SLA performance metrics for the previous 1 to 90 days.

List Manifests

Return one page of manifests in reverse chronological order.

List Products

Search and page through the Starshipit product catalogue with optional sorting.

List Tags

Return one page of user-defined order tags, optionally filtered by tag name.

List Unshipped Orders

Return one page of unshipped orders, optionally limited to orders created or updated after a UTC timestamp.

List WMS Inventory

Return one page of Starshipit WMS inventory grouped by product or location, including stock, reservation, and availability quantities.

Search Orders

Search orders by a phrase and optional field, status, or child-account inclusion filter.

Update Order

Update editable fields on one Starshipit order identified by order_id or order_number.

FRAMEWORKS

How to build Starshipit MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Starshipit MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Starshipit MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Starshipit MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Starshipit MCP with Claude Cowork

Codex logo

Codex

Use Starshipit MCP with Codex

OpenClaw logo

OpenClaw

Use Starshipit MCP with OpenClaw

Hermes logo

Hermes

Use Starshipit MCP with Hermes

CLI logo

CLI

Use Starshipit MCP with CLI

Google ADK logo

Google ADK

Use Starshipit MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Starshipit MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Starshipit MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Starshipit MCP with LlamaIndex

CrewAI logo

CrewAI

Use Starshipit MCP with CrewAI

FAQ

Frequently asked questions

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

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 Starshipit tools.

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

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As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

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