How to integrate Browseai MCP with LangChain

This guide walks you through connecting Browseai to LangChain using the Composio tool router. By the end, you'll have a working Browseai agent that can extract product prices from competitor websites, monitor job postings for new remote roles, get detailed status of last robot run through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Browseai account through Composio's Browseai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping.

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Introduction

This guide walks you through connecting Browseai to LangChain using the Composio tool router. By the end, you'll have a working Browseai agent that can extract product prices from competitor websites, monitor job postings for new remote roles, get detailed status of last robot run through natural language commands.

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

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

Also integrate Browseai with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKVercel AI SDKMastra AILlamaIndexCrewAI

TL;DR

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

The Browseai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Browseai account. It provides structured and secure access to your robots, tasks, and monitoring workflows, so your agent can perform actions like running robots, extracting website data, monitoring for changes, and managing web automation tasks on your behalf.

  • On-demand web data extraction: Trigger your Browseai robots to collect data from any website instantly, using custom input parameters for targeted web scraping.
  • Automated website monitoring: Set up and manage recurring monitors that watch websites for changes, so your agent can alert you to updates or new information as soon as they happen.
  • Bulk task execution: Launch thousands of data extraction tasks at once for large-scale or repeated scraping jobs, streamlining high-volume automation for your workflows.
  • Task and robot management: Retrieve lists of your robots, fetch detailed results for specific tasks, check execution history, and delete obsolete tasks or monitors to keep your automation setup organized.
  • Real-time notifications and workflow integration: Configure webhooks so your agent receives instant updates when tasks complete, fail, or when website changes are detected, enabling seamless integration with your broader systems.

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 Browseai 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 Browseai 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: ['browseai']
    }
);

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

Configure the agent with the MCP URL

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

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

Bulk Run Tasks

This action allows users to bulk run up to 1,000 tasks per API call using a specified robot.

Create Monitor

Creates a new monitor for a Browse AI robot to automatically track website changes over time.

Create Webhook

This tool creates a new webhook for a Browse AI robot.

Delete a specific monitor

This tool allows users to delete a specific monitor from their Browse AI account.

Delete a specific task

This tool allows you to delete a specific task in BrowseAI by its task ID.

Get Robots List

Retrieves a list of all robots (automated web tasks) under your Browse AI account.

Get Robot Tasks

Retrieves a paginated list of all tasks (execution runs) for a specific Browse AI robot.

Get System Status

Tool to check the operational status of Browse AI infrastructure, including the tasks queue condition.

Get Task Details

Retrieves detailed information about a specific Browse AI task by robot ID and task ID.

List Robot Webhooks

Tool to retrieve all webhooks configured for a specific Browse AI robot.

Run Robot

Triggers the execution of a Browse.

FRAMEWORKS

How to build Browseai MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Browseai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Browseai MCP with Claude Agent SDK

Claude Code logo

Claude Code

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Claude Cowork logo

Claude Cowork

Use Browseai MCP with Claude Cowork

Codex logo

Codex

Use Browseai MCP with Codex

OpenClaw logo

OpenClaw

Use Browseai MCP with OpenClaw

Hermes logo

Hermes

Use Browseai MCP with Hermes

CLI logo

CLI

Use Browseai MCP with CLI

Google ADK logo

Google ADK

Use Browseai MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Browseai MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Browseai MCP with Mastra AI

LlamaIndex logo

LlamaIndex

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CrewAI logo

CrewAI

Use Browseai MCP with CrewAI

FAQ

Frequently asked questions

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

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

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