How to integrate Zenserp MCP with LangChain

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Introduction

This guide walks you through connecting Zenserp to LangChain using the Composio tool router. By the end, you'll have a working Zenserp agent that can find top news articles on ai ethics, get trending keywords for electric cars, list local coffee shops in brooklyn, search product prices for wireless earbuds through natural language commands.

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

The Zenserp MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zenserp account. It provides structured and secure access to real-time search engine results, so your agent can perform actions like running Google searches, grabbing news headlines, pulling images, analyzing trends, and even fetching local business data on your behalf.

  • Comprehensive Google and Bing search: Instantly run structured web searches and retrieve up-to-date SERP data from Google or Bing for any query.
  • Automated news and trend analysis: Have your agent fetch recent Google News articles or analyze keyword popularity over time using Google Trends data.
  • Reverse image and visual content search: Perform reverse image lookups or image searches to discover where an image appears online or find relevant pictures for any topic.
  • Shopping and video discovery: Search Google Shopping for product offers or Google Video for relevant multimedia results, all via agent-driven queries.
  • Local and map-based business lookup: Let your agent use Google Maps search to find businesses or places based on location, keywords, or coordinates for local intelligence.

Supported Tools & Triggers

Tools
Bing SearchTool to obtain bing search results.
Google News SearchTool to perform a google news search.
Google Reverse Image SearchTool to perform a reverse image search on google.
Google Shopping SearchTool to perform a google shopping search.
Google TrendsTool to retrieve google trends data.
Google Video SearchTool to perform a google video search via zenserp.
Yandex Search via ZenserpTool to obtain yandex search results via zenserp api.
Zenserp Google Image SearchTool to perform a google image search via zenserp.
Google Maps SearchTool to perform a google maps (local) search.
Zenserp Google SearchTool to perform a standard google search via zenserp.
Google Shopping SearchTool to perform a google shopping search.

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

OpenAI Agents SDK

OpenAI Agents SDK

Use Zenserp MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Zenserp MCP with Claude Agent SDK

Google ADK

Google ADK

Use Zenserp MCP with Google ADK

Vercel AI SDK

Vercel AI SDK

Use Zenserp MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Zenserp MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Zenserp MCP with LlamaIndex

CrewAI

CrewAI

Use Zenserp MCP with CrewAI

FAQ

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

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

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

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

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