How to integrate Hyperbrowser MCP with LangChain

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Introduction

This guide walks you through connecting Hyperbrowser to LangChain using the Composio tool router. By the end, you'll have a working Hyperbrowser agent that can start a browser session with stealth mode, extract all product titles from this url, check status of your ongoing scrape job through natural language commands.

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

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

Also integrate Hyperbrowser with

TL;DR

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

The Hyperbrowser MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Hyperbrowser account. It provides structured and secure access to automated browser sessions, web scraping, and browser-based task management, so your agent can launch sessions, extract data, manage automation jobs, and monitor progress on your behalf.

  • Automated browser session creation: Let your agent spin up new browser sessions with custom privacy, stealth, and proxy settings for tailored automation tasks.
  • Scalable web scraping and extraction: Easily initiate and manage scrape jobs to extract structured content from any target website, with support for session and scrape customization.
  • Real-time job status monitoring: Have your agent check, track, and report the live status of browser-use, crawl, or data extraction jobs, ensuring you always know what's happening.
  • Retrieve results from automation jobs: Fetch and review the outputs of completed crawl or extract jobs, including paginated data and detailed results, right inside your workflow.
  • Profile and automation management: Create or delete Hyperbrowser profiles as needed, giving you flexible control over your automation environment and resources.

Supported Tools & Triggers

Tools
Add ExtensionTool to add a new browser extension to Hyperbrowser for use in sessions.
Create Hyperbrowser ProfileCreates a new persistent Hyperbrowser profile for storing browser state (cookies, sessions, etc.
Create Scrape JobTool to initiate a new scrape job.
Create SessionTool to create a new browser session with custom stealth, proxy, and privacy settings.
Delete ProfileTool to delete a profile.
Fetch Web PageTool to fetch a web page and return content in various formats (HTML, Markdown, JSON, screenshot, etc.
Get browser-use task statusTool to retrieve the current status of a browser-use task.
Get Claude Computer Use Task ResultTool to retrieve the complete result and status of a Claude Computer Use task.
Get Claude Computer Use Task StatusPoll the execution status of a Claude Computer Use task.
Get Crawl Job StatusTool to retrieve the status and results of a specific crawl job.
Get Crawl StatusTool to retrieve the current status of a specific crawl job.
Get CUA Task ResultTool to retrieve the status and results of a CUA (Claude User Agent) task.
Get CUA Task StatusPoll the execution status of a CUA task.
Get Extract Job ResultTool to fetch the status and results of a specific extract job.
Get Extract Job StatusRetrieve the status of an extract job.
Get Gemini Computer Use task resultTool to retrieve the current status and results of a Gemini Computer Use task.
Get HyperAgent Task ResultTool to retrieve the status and results of a HyperAgent task.
Get Profile By IDRetrieves details of a specific Hyperbrowser profile by its UUID.
Get Scrape Job ResultRetrieves the status and results of a scrape job.
Get Scrape Job StatusTool to retrieve the current status of a specific scrape job.
Get Session DetailsRetrieve detailed information about a Hyperbrowser session by its ID.
Get Session Downloads URLTool to retrieve the downloads URL for a session.
Get Session RecordingRetrieve the recording URL for a browser session.
Get Session Video Recording URLTool to retrieve the video recording URL for a browser session.
Get Web Crawl ResultTool to retrieve the status and results of a web crawl job.
Get Web Crawl StatusTool to retrieve just the status of a web crawl job without the full results.
List ExtensionsTool to list all browser extensions.
List ProfilesTool to list profiles.
List SessionsTool to list sessions with optional status filter.
Search WebTool to perform a web search and retrieve results with titles, URLs, and descriptions.
Start Browser Use TaskTool to start an asynchronous browser-use task.
Start Claude Computer Use TaskTool to start a Claude Computer Use task.
Start Crawl JobTool to start a new crawl job for a specified URL.
Start CUA TaskTool to start an OpenAI CUA (Computer-Using Agent) task.
Start Extract JobStart an AI-powered data extraction job from one or more web pages.
Start Gemini Computer Use TaskTool to start a Gemini Computer Use task for browser automation using Google's Gemini.
Start Web CrawlTool to start an asynchronous web crawl job that follows links from a starting URL and returns content from each page.
Stop Browser Use TaskTool to stop a running browser-use task.
Stop Claude Computer Use TaskTool to stop a running Claude computer use task.
Stop CUA TaskTool to stop a running CUA task.
Stop Gemini Computer Use TaskTool to stop a running Gemini computer use task.
Stop SessionTool to stop a running session by ID.

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

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

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

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

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FAQ

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

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

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

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

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