How to integrate Webscraping ai MCP with LangChain

This guide walks you through connecting Webscraping ai to LangChain using the Composio tool router. By the end, you'll have a working Webscraping ai agent that can extract product prices from amazon listings, get latest headlines from bbc homepage, fetch rendered job postings from linkedin through natural language commands. This guide will help you understand how to give your LangChain agent real control over a Webscraping ai account through Composio's Webscraping ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Webscraping ai is a powerful API for extracting website data with JS rendering and proxy rotation. It lets you reliably scrape and parse dynamic web content at scale.

7 Tools

Introduction

This guide walks you through connecting Webscraping ai to LangChain using the Composio tool router. By the end, you'll have a working Webscraping ai agent that can extract product prices from amazon listings, get latest headlines from bbc homepage, fetch rendered job postings from linkedin through natural language commands.

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

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

Also integrate Webscraping ai 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 Webscraping ai project to Composio
  • Create a Tool Router MCP session for Webscraping ai
  • Initialize an MCP client and retrieve Webscraping ai tools
  • Build a LangChain agent that can interact with Webscraping ai
  • 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 Webscraping ai MCP server, and what's possible with it?

The Webscraping ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Webscraping ai account. It provides structured and secure access to web scraping capabilities, so your agent can retrieve raw HTML, extract rendered web content, parse text, and monitor your API usage on your behalf.

  • Retrieve raw HTML from any webpage: Let your agent fetch the unprocessed HTML content of any website for analysis or processing.
  • Extract fully rendered HTML with JavaScript support: Have your agent capture the final, JavaScript-rendered HTML—great for sites that rely on dynamic content.
  • Get plain text from web pages: Direct your agent to extract just the readable text from any URL, skipping all the markup and scripts.
  • Monitor API usage and quota: Your agent can check your account's API call limits and current usage, so you always know where you stand.

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 Webscraping ai 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 Webscraping ai 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: ['webscraping_ai']
    }
);

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

Configure the agent with the MCP URL

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

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

Get account usage and quota

Tool to retrieve account API call quota and usage.

Ask Question About Web Page

Tool to get an answer to a question about a given web page using LLM.

Extract Fields with AI

Tool to extract structured data fields from a web page using AI.

Get Rendered HTML

Tool to retrieve fully rendered HTML of a webpage.

Get Selected HTML

Tool to extract HTML from specific page elements using CSS selectors.

Get Selected Multiple Elements

Tool to extract HTML of multiple page areas by URL and CSS selectors.

Get Text

Tool to retrieve raw text content from a specified web page.

FRAMEWORKS

How to build Webscraping ai MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Webscraping ai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Webscraping ai MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Webscraping ai MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Webscraping ai MCP with Claude Cowork

Codex logo

Codex

Use Webscraping ai MCP with Codex

OpenClaw logo

OpenClaw

Use Webscraping ai MCP with OpenClaw

Hermes logo

Hermes

Use Webscraping ai MCP with Hermes

CLI logo

CLI

Use Webscraping ai MCP with CLI

Google ADK logo

Google ADK

Use Webscraping ai MCP with Google ADK

Vercel AI SDK logo

Vercel AI SDK

Use Webscraping ai MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Webscraping ai MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Webscraping ai MCP with LlamaIndex

CrewAI logo

CrewAI

Use Webscraping ai MCP with CrewAI

FAQ

Frequently asked questions

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

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

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