# How to integrate Scrapingant MCP with LangChain

```json
{
  "title": "How to integrate Scrapingant MCP with LangChain",
  "toolkit": "Scrapingant",
  "toolkit_slug": "scrapingant",
  "framework": "LangChain",
  "framework_slug": "langchain",
  "url": "https://composio.dev/toolkits/scrapingant/framework/langchain",
  "markdown_url": "https://composio.dev/toolkits/scrapingant/framework/langchain.md",
  "updated_at": "2026-05-12T10:24:50.976Z"
}
```

## Introduction

This guide walks you through connecting Scrapingant to LangChain using the Composio tool router. By the end, you'll have a working Scrapingant agent that can extract product prices from amazon search page, convert a blog post to markdown format, get api usage stats for your account through natural language commands.
This guide will help you understand how to give your LangChain agent real control over a Scrapingant account through Composio's Scrapingant MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Scrapingant with

- [OpenAI Agents SDK](https://composio.dev/toolkits/scrapingant/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/scrapingant/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/scrapingant/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/scrapingant/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/scrapingant/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/scrapingant/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/scrapingant/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/scrapingant/framework/cli)
- [Google ADK](https://composio.dev/toolkits/scrapingant/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/scrapingant/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/scrapingant/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/scrapingant/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/scrapingant/framework/crew-ai)

## TL;DR

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

The Scrapingant MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Scrapingant account. It provides structured and secure access to powerful web scraping tools, so your agent can extract web data, render dynamic pages, convert content to markdown, and monitor API usage on your behalf.
- AI-powered data extraction: Direct your agent to pull structured data from web pages using custom AI queries—ideal for capturing tables, lists, prices, or other key information.
- Dynamic web page scraping: Scrape complex sites with JavaScript rendering, proxy rotation, and support for cookies or browser emulation to handle modern web content.
- Content conversion to markdown: Have your agent extract and convert page content into markdown format for clean, ready-to-use text in downstream applications or LLM pipelines.
- Extended scraping with rich JSON output: Fetch detailed responses including raw HTML, headers, cookies, and other metadata to support advanced automation and analysis tasks.
- API credit monitoring: Let your agent check and report on your current Scrapingant API credit usage, helping you manage quotas and avoid interruptions.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SCRAPINGANT_EXTRACT_CONTENT_AS_MARKDOWN` | Extract Content as Markdown | This tool extracts content from a given URL and converts it into Markdown format. It is particularly useful for preparing text for Language Learning Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It supports GET, POST, PUT, and DELETE methods. |
| `SCRAPINGANT_EXTRACT_DATA_WITH_AI` | Extract Data with AI | This tool allows you to extract structured data from a web page using ScrapingAnt's AI-powered extraction capabilities. You provide a URL and an AI query (prompt) describing what data you want to extract, and the tool returns the extracted data in a structured format. It supports additional parameters for browser rendering, proxies, and cookies to handle dynamic content and localization. |
| `SCRAPINGANT_GET_API_CREDITS_USAGE` | Get API Credits Usage | This tool retrieves the current API credit usage status for the authenticated ScrapingAnt account. It enables users to monitor their consumption of API credits, check their current usage against the subscription limits, and manage their API credits effectively. |
| `SCRAPINGANT_SCRAPE_WEB_PAGE` | Scrape Web Page | This tool scrapes a web page using the ScrapingAnt API. It fetches the HTML content of the specified URL. Users can customize the scraping behavior by enabling a headless browser, using proxies, waiting for specific elements, executing JavaScript, passing cookies, and blocking certain resources. |
| `SCRAPINGANT_SCRAPE_WEBPAGE_POST` | Scrape Webpage via POST | Tool to perform a POST request through ScrapingAnt's proxy to scrape a webpage. Use when you need to scrape pages that require POST method, such as form submissions or APIs that only accept POST requests. Data is forwarded transparently to the target web page. |
| `SCRAPINGANT_SCRAPE_WEBPAGE_PUT` | Scrape Webpage with PUT | Tool to perform a PUT request through ScrapingAnt's proxy to scrape a webpage that requires PUT method. Use when the target webpage requires PUT method for data submission. Data is forwarded transparently to the target web page. |
| `SCRAPINGANT_SCRAPE_WITH_EXTENDED_JSON_OUTPUT` | Scrape with Extended JSON Output | Scrapes a web page and returns comprehensive data including HTML content, plain text, cookies, HTTP headers, XHR/Fetch requests, and iframe content. This tool uses ScrapingAnt's extended endpoint which provides much richer data than standard scraping: - Full HTML and extracted plain text content - All cookies and HTTP response headers from the target page - Captured XHR/Fetch API requests made by the page (useful for finding hidden APIs) - Content from embedded iframes Best used when you need more than just the HTML - such as analyzing cookies, headers, or JavaScript API calls made by a page. For simple HTML scraping, consider using the basic scrape tool instead for lower API credit usage. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Scrapingant MCP server is an implementation of the Model Context Protocol that connects your AI agent to Scrapingant. It provides structured and secure access so your agent can perform Scrapingant operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

No description provided.

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Navigate to your API settings and generate a new API key.
- Store this key securely as you'll need it for authentication.

### 2. Install dependencies

No description provided.
```python
pip install composio-langchain langchain-mcp-adapters langchain python-dotenv
```

```typescript
npm install @composio/langchain @langchain/core @langchain/openai @langchain/mcp-adapters dotenv
```

### 3. Set up environment variables

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
```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
```

### 4. Import dependencies

No description provided.
```python
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()
```

```typescript
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();
```

### 5. Initialize Composio client

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 Scrapingant tools
- Validating that COMPOSIO_USER_ID is also set before proceeding
```python
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")
```

```typescript
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()
    });
```

### 6. Create a Tool Router session

What's happening:
- We're creating a Tool Router session that gives your agent access to Scrapingant 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 Scrapingant tools as needed
```python
# Create Tool Router session for Scrapingant
session = composio.create(
    user_id=os.getenv("COMPOSIO_USER_ID"),
    toolkits=['scrapingant']
)

url = session.mcp.url
```

```typescript
const session = await composio.create(
    userId as string,
    {
        toolkits: ['scrapingant']
    }
);

const url = session.mcp.url;
```

### 7. Configure the agent with the MCP URL

No description provided.
```python
client = MultiServerMCPClient({
    "scrapingant-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)
```

```typescript
const client = new MultiServerMCPClient({
    "scrapingant-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 });
```

### 8. Set up interactive chat interface

No description provided.
```python
conversation_history = []

print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Scrapingant 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")
```

```typescript
let conversationHistory: any[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n");
console.log("Ask any Scrapingant 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);
    });
```

### 9. Run the application

No description provided.
```python
if __name__ == "__main__":
    asyncio.run(main())
```

```typescript
main().catch((err) => {
    console.error('Fatal error:', err);
    process.exit(1);
});
```

## Complete Code

```python
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=['scrapingant']
    )

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "scrapingant-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 Scrapingant 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())
```

```typescript
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: ['scrapingant']
        }
    );

    const url = session.mcp.url;
    
    const client = new MultiServerMCPClient({
        "scrapingant-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 Scrapingant 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 Scrapingant 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 Scrapingant MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/scrapingant/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/scrapingant/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/scrapingant/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/scrapingant/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/scrapingant/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/scrapingant/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/scrapingant/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/scrapingant/framework/cli)
- [Google ADK](https://composio.dev/toolkits/scrapingant/framework/google-adk)
- [Vercel AI SDK](https://composio.dev/toolkits/scrapingant/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/scrapingant/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/scrapingant/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/scrapingant/framework/crew-ai)

## Related Toolkits

- [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. It's the go-to tool for calculations, reporting, and flexible data management.
- [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters.
- [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort.
- [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time.
- [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. It streamlines data collection, monitoring, and repetitive online actions.
- [Ambee](https://composio.dev/toolkits/ambee) - Ambee is an environmental data platform providing real-time, hyperlocal APIs for air quality, weather, and pollen. Get precise environmental insights to power smarter decisions in your apps and workflows.
- [Ambient weather](https://composio.dev/toolkits/ambient_weather) - Ambient Weather is a platform for personal weather stations with a robust API for accessing local, real-time, and historical weather data. Get detailed environmental insights directly from your own sensors for smarter apps and automations.
- [Anonyflow](https://composio.dev/toolkits/anonyflow) - Anonyflow is a service for encryption-based data anonymization and secure data sharing. It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements.
- [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints.
- [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots.
- [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches.
- [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand.
- [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance.
- [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows.
- [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses.
- [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research.
- [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction.
- [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation.
- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Scrapingant MCP?

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

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

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

---
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