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How to integrate Yelp MCP with LangChain

Connect LangChain to Yelp MCP. Find top-rated coffee shops nearby, show best pizza places open now, and more using natural language, with authentication handled for you.

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Yelp is a local business directory and review platform with millions of listings. Instantly access rich business data, ratings, and customer reviews for location-based services.

12 Tools

Introduction

This guide walks you through connecting Yelp to LangChain using the Composio tool router. By the end, you'll have a working Yelp agent that can search businesses, read reviews and highlights, get business details, find events, and look up businesses by phone through natural language commands.

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

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

Also integrate Yelp with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAIPydantic AIAutoGen

TL;DR

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

The Yelp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, and more directly to Yelp's extensive business data. It provides structured and secure access to business search, reviews, ratings, and local business details, so your agent can help you find businesses, compare ratings, read reviews, and discover local favorites on your behalf.

  • Business discovery and search: Ask your agent to find restaurants, shops, or services by location, category, or specific business name with up-to-date Yelp data.
  • Detailed review retrieval: Have your agent fetch and summarize customer reviews for any business, making it easier to choose where to go.
  • Ratings and reputation checks: Let your agent provide business ratings, number of reviews, and popularity insights before you make a decision.
  • Local business information access: Get detailed information like address, hours, contact info, and amenities for businesses near you or in any city.
  • Personalized recommendations: Enable your agent to suggest top-rated options based on your preferences, trending spots, or special occasions.

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 Yelp 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 Yelp 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: ['yelp']
    }
);

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

Configure the agent with the MCP URL

const client = new MultiServerMCPClient({
    "yelp-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 Yelp MCP server via HTTP
  • The client is configured with a name and the URL from our Tool Router session
  • getTools() retrieves all available Yelp 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 Yelp 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 Yelp 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: ['yelp']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "yelp-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 Yelp 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 Yelp 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 Yelp action and event your agent gets out of the box.

Autocomplete Search

Get autocomplete suggestions for businesses, categories, and search terms on Yelp based on partial text input.

Get Business Details

Get detailed information about a Yelp business by ID or alias, including location, photos, rating, review count and, when available, hours and phone number.

Get Business Reviews

Get reviews for a specific business on Yelp using its business ID or alias.

Get Category Details

Get detailed information about a specific Yelp category by its alias.

Get Event Details

Get details of a Yelp event by its event ID, including dates, location, cost, attendance counts and, when available, the associated business ID.

Get Featured Event

Get the featured event for a given location on Yelp.

Get Review Highlights

Get review highlights for a specific business on Yelp using its business ID or alias.

List Categories

List all Yelp business categories across all locales by default.

Search and Chat

Chat with Yelp's AI assistant to search for businesses, get recommendations, and ask questions.

Search Businesses

Search Yelp businesses by location, term, categories and other filters, with up to 50 results per call and offset pagination.

Search Business by Phone

Search for a business by phone number on Yelp.

Search Events

Search for events on Yelp by location, categories, and other filters.

FRAMEWORKS

How to build Yelp MCP Agent with another framework

Claude Code logo

Claude Code

Use Yelp MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Yelp MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Yelp MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Yelp MCP with Claude Agent SDK

Codex logo

Codex

Use Yelp MCP with Codex

OpenClaw logo

OpenClaw

Use Yelp MCP with OpenClaw

Hermes logo

Hermes

Use Yelp MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Yelp MCP with Atomic Agent

Google ADK logo

Google ADK

Use Yelp MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Yelp MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Yelp MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Yelp MCP with LlamaIndex

CrewAI logo

CrewAI

Use Yelp MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Yelp MCP with Pydantic AI

AutoGen logo

AutoGen

Use Yelp MCP with AutoGen

FAQ

Frequently asked questions

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

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

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