# How to integrate Bestbuy MCP with LangChain

```json
{
  "title": "How to integrate Bestbuy MCP with LangChain",
  "toolkit": "Bestbuy",
  "toolkit_slug": "bestbuy",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/bestbuy/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/bestbuy/framework/langchain.md",
  "updated_at": "2026-05-12T10:02:51.488Z"
}
```

## Introduction

This guide walks you through connecting Bestbuy to LangChain using the Composio tool router. By the end, you'll have a working Bestbuy agent that can find top-rated laptops under $1000, list best buy stores near seattle, show reviews for samsung galaxy s24 through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Bestbuy account through Composio's Bestbuy MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Bestbuy with

- [OpenAI Agents SDK](https://composio.dev/toolkits/bestbuy/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/bestbuy/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/bestbuy/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/bestbuy/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/bestbuy/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/bestbuy/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/bestbuy/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/bestbuy/framework/cli)
- [Google ADK](https://composio.dev/toolkits/bestbuy/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/bestbuy/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/bestbuy/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/bestbuy/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/bestbuy/framework/crew-ai)

## TL;DR

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

The Bestbuy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Best Buy account. It provides structured and secure access to product, store, category, and review data, so your agent can perform actions like searching for products, retrieving store information, fetching detailed product specs, and analyzing customer reviews on your behalf.
- Browse and filter product listings: Instantly fetch Best Buy product catalogs, apply category filters, or sort by price and relevance to help users discover the right items.
- Retrieve detailed product information: Let your agent pull in-depth specifications, pricing, and availability for any product using its SKU, ensuring accurate recommendations or comparisons.
- Analyze and summarize customer reviews: Access and summarize customer feedback on products, or drill down into specific reviews to guide purchasing decisions.
- Explore store locations and details: Find Best Buy store locations near you, retrieve detailed store info, and assist with in-store shopping logistics or pickup arrangements.
- Navigate product categories and metadata: List, search, and explore Best Buy's extensive product categories, making it easy to organize options or narrow down shopping journeys.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BESTBUY_GET_CATEGORIES` | Get Categories | Tool to retrieve Best Buy product categories. Use when you need to list or filter categories in the catalog. |
| `BESTBUY_GET_CATEGORY_DETAILS` | Get Category Details | Tool to retrieve detailed information about a Best Buy category by its ID. Use when you need enriched metadata about a specific category. |
| `BESTBUY_GET_PRODUCT_DETAILS` | Get Product Details by SKU | Tool to retrieve detailed information about a specific product by SKU. Use after obtaining a valid SKU to fetch its attributes. |
| `BESTBUY_GET_PRODUCTS` | Get Products | Tool to retrieve products from Best Buy. Use when you need to fetch product listings with optional filters and sorting. Example: 'Get products for category abcat0101000 sorted by salePrice.dsc.' |
| `BESTBUY_GET_REVIEW_DETAILS` | Get Review Details | Tool to retrieve detailed information for a specific review by ID. Use after confirming the review ID. |
| `BESTBUY_GET_REVIEWS` | Get Product Reviews | Tool to retrieve product reviews. Use when you need customer feedback with filters or sorting. Use after obtaining product SKUs. |
| `BESTBUY_GET_STORE_DETAILS` | Get Store Details | Tool to retrieve detailed information about a specific Best Buy store. Use when you have a valid store ID. |
| `BESTBUY_GET_STORES` | Get Best Buy Stores | Tool to retrieve a list of Best Buy stores. Use when you need store listings with optional filters or geo-search after confirming your API key. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Bestbuy MCP server is an implementation of the Model Context Protocol that connects your AI agent to Bestbuy. It provides structured and secure access so your agent can perform Bestbuy operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Bestbuy tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Bestbuy 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 Bestbuy tools as needed
```python
# Create Tool Router session for Bestbuy
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['bestbuy']
)

url = session.mcp.url
```

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

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "bestbuy-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)
```

```typescript
const client = new MultiServerMCPClient({
    "bestbuy-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Bestbuy 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")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Bestbuy 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

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

## Complete Code

```python
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=['bestbuy']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "bestbuy-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 Bestbuy 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())
```

```typescript
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: ['bestbuy']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "bestbuy-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 Bestbuy 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 Bestbuy 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 Bestbuy MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/bestbuy/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/bestbuy/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/bestbuy/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/bestbuy/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/bestbuy/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/bestbuy/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/bestbuy/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/bestbuy/framework/cli)
- [Google ADK](https://composio.dev/toolkits/bestbuy/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/bestbuy/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/bestbuy/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/bestbuy/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/bestbuy/framework/crew-ai)

## Related Toolkits

- [Addresszen](https://composio.dev/toolkits/addresszen) - Addresszen is a real-time address autocomplete and verification service. It helps capture accurate, deliverable addresses with instant suggestions and validation.
- [Asin data api](https://composio.dev/toolkits/asin_data_api) - Asin data api gives you detailed, real-time product data from Amazon, including price, rank, and reviews. Perfect for e-commerce pros and data-driven marketers who need instant marketplace insights.
- [Baselinker](https://composio.dev/toolkits/baselinker) - BaseLinker is an all-in-one e-commerce management platform connecting stores, marketplaces, carriers, and more. It streamlines order processing, inventory control, and automates your sales operations.
- [Btcpay server](https://composio.dev/toolkits/btcpay_server) - BTCPay Server is a free, open-source, self-hosted Bitcoin payment processor. It lets merchants accept Bitcoin payments directly, cutting out middlemen and boosting privacy.
- [Cdr platform](https://composio.dev/toolkits/cdr_platform) - Cdr platform is an API for purchasing carbon dioxide removal services. It enables businesses to offset emissions by accessing verified carbon removal projects.
- [Cloudcart](https://composio.dev/toolkits/cloudcart) - CloudCart is an e-commerce platform for building and managing online stores. It helps businesses streamline product listings, orders, and customer engagement.
- [Countdown api](https://composio.dev/toolkits/countdown_api) - Countdown API gives you real-time, structured eBay product data, reviews, and seller feedback. Perfect for powering price monitoring, product research, or marketplace analytics workflows.
- [Dpd2](https://composio.dev/toolkits/dpd2) - Dpd2 is a robust email management platform for handling, sorting, and automating email workflows. Streamline your communications and boost productivity with advanced sorting, labeling, and response tools.
- [Finerworks](https://composio.dev/toolkits/finerworks) - FinerWorks is an online platform for fine art and photo printing services. Artists and photographers use it to order custom prints and manage print inventory efficiently.
- [Fingertip](https://composio.dev/toolkits/fingertip) - Fingertip is a business management platform for selling, booking, and customer engagement—all from a single link. It helps businesses streamline operations and connect with customers across social channels.
- [Fraudlabs pro](https://composio.dev/toolkits/fraudlabs_pro) - FraudLabs Pro is an online payment fraud detection service for e-commerce and merchants. It helps minimize chargebacks and revenue loss by detecting and preventing fraudulent transactions.
- [Gift up](https://composio.dev/toolkits/gift_up) - Gift Up! is a digital platform for selling, managing, and redeeming gift cards online. It streamlines promotions and gift card transactions for businesses and their customers.
- [Goody](https://composio.dev/toolkits/goody) - Goody is a gifting platform that lets users send gifts and physical products without handling logistics. It streamlines gifting by managing delivery, fulfillment, and recipient experience.
- [Gumroad](https://composio.dev/toolkits/gumroad) - Gumroad is a platform for selling digital products, physical goods, and memberships with a simple checkout and marketing tools. It streamlines creator payouts and helps you grow your audience effortlessly.
- [Instacart](https://composio.dev/toolkits/instacart) - Instacart is an online grocery delivery and pickup service platform. It lets you discover local retailers and create shoppable lists and recipes with ease.
- [Junglescout](https://composio.dev/toolkits/junglescout) - Junglescout is an Amazon product research and analytics platform for sellers. It delivers sales estimates, competitive insights, and optimization tools to boost your Amazon business.
- [Ko fi](https://composio.dev/toolkits/ko_fi) - Ko-fi is a platform that lets creators receive donations, memberships, and sales from fans. It helps creators monetize their work and grow their audience with minimal friction.
- [Lemon squeezy](https://composio.dev/toolkits/lemon_squeezy) - Lemon Squeezy is a payments and subscription platform built for software companies. It makes managing payments, taxes, and customer subscriptions effortless.
- [Loyverse](https://composio.dev/toolkits/loyverse) - Loyverse is a point-of-sale (POS) platform for small businesses, offering tools for sales, inventory, and customer loyalty. It helps streamline retail operations and boost customer engagement.
- [Memberstack](https://composio.dev/toolkits/memberstack) - Memberstack lets you add user authentication, payments, and member management to your website—no backend code required. Easily manage your site's members and subscriptions from a single platform.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Bestbuy MCP?

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

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

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

---
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