How to integrate Serpapi MCP with LangChain

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Introduction

This guide walks you through connecting Serpapi to LangChain using the Composio tool router. By the end, you'll have a working Serpapi agent that can find latest job postings for python developers, show recent stock news for apple inc, list concerts happening in new york this week, compare ebay prices for nintendo switch through natural language commands.

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

The Serpapi MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your SerpApi account. It provides structured and secure access to real-time search engine results, so your agent can perform actions like scraping search data, analyzing trends, retrieving product listings, and exploring local business information on your behalf.

  • Real-time web search across engines: Instantly fetch structured search results from Google, Bing, Baidu, and DuckDuckGo for any query, including organic results, ads, and rich snippets.
  • Product and marketplace data extraction: Automatically search eBay for products and retrieve detailed, structured product data to power research or price comparison workflows.
  • Event and job listings discovery: Let your agent search Google Events and Google Jobs to uncover upcoming events, conferences, or relevant job postings with granular location and keyword filters.
  • Financial and stock information retrieval: Seamlessly pull the latest company details, stock prices, market news, and trends from Google Finance using a simple query.
  • Location and map-based search: Enable your agent to perform Google Maps searches to find local businesses, attractions, or venues—complete with structured location data and optional GPS-based results.

Supported Tools & Triggers

Tools
Baidu SearchBaidu search
Bing SearchRetrieve bing search engine results.
DuckDuckGo searchPerforms a duckduckgo search via serpapi to retrieve serp data, including organic results, ads, and structured information.
eBay SearchRetrieve ebay search results.
Search Google EventsSearches for events (e.
Search financeRetrieves structured financial information (e.
Google Domains ListRetrieve the list of supported google domains for search queries.
Google Jobs SearchRetrieve google jobs search results.
Google Light SearchRetrieve google light search results.
Google maps searchPerforms a google maps search via serp api for a given query, optionally using specific gps coordinates and pagination, returning structured location data.
Hotel SearchRetrieve google hotel search results.
Image searchSearches google images via serp api for a given query, returning structured image results.
List LocationsList locations
Search for news articlesSearches google news (via serpapi, using the `tbm=nws` parameter) for articles matching a query; precise queries yield best results.
Google Play SearchRetrieve google play store search results.
Search Google ScholarSearches google scholar via serpapi for academic literature, papers, articles, and citations based on a query.
Serp API searchPerforms a real-time google search via the serp api for a non-empty query.
Shopping searchSearches google shopping for a specific product or item to retrieve structured product listings.
Google Trends searchFetches google trends data; the `query`'s format (single/multiple terms) must comply with the selected `data type` (see its field description for details).
Walmart SearchRetrieve walmart search results.
Yahoo SearchRetrieve yahoo!
Yandex SearchRetrieve yandex search results.
YouTube SearchRetrieve youtube search results.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Serpapi MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Serpapi MCP with Claude Agent SDK

Google ADK

Google ADK

Use Serpapi MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Serpapi MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Serpapi MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Serpapi MCP with LlamaIndex

CrewAI

CrewAI

Use Serpapi MCP with CrewAI

FAQ

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

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

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

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

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