How to integrate Scrapegraph ai MCP with LangChain

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Introduction

This guide walks you through connecting Scrapegraph ai to LangChain using the Composio tool router. By the end, you'll have a working Scrapegraph ai agent that can extract product prices from amazon search results, summarize latest news headlines from bbc homepage, convert wikipedia article to markdown format through natural language commands.

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

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

Also integrate Scrapegraph ai with

TL;DR

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

The Scrapegraph 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 Scrapegraph ai account. It provides structured and secure access to powerful web scraping and data extraction tools, so your agent can perform actions like running AI-powered scrapers, converting webpages to markdown, monitoring job statuses, and managing your account usage with ease.

  • AI-powered web scraping and search: Instruct your agent to extract structured data from any website or perform detailed web searches with parsed, organized results.
  • Webpage to markdown conversion: Let your agent instantly convert any webpage into clean, readable markdown for easy documentation or analysis.
  • Automated job status tracking: Check on the progress and results of ongoing scraping, crawling, or conversion jobs to stay updated without manual effort.
  • Smart multi-page crawling: Direct the agent to launch intelligent crawlers that gather data across multiple linked pages in a single workflow.
  • Account usage monitoring and feedback: Retrieve your remaining credits, track API usage, and submit feedback on completed tasks—all through your AI agent.

Supported Tools & Triggers

Tools
Convert Webpage to Markdown (V2)Tool to convert any webpage into clean, well-formatted Markdown with full parameter control.
Generate SchemaGenerate or modify a JSON schema based on a search query for structured data extraction.
Get Agentic Scraper HistoryRetrieve paginated history of agentic scraper jobs.
Get Crawler HistoryRetrieve the history of crawler jobs for your account.
Get CreditsRetrieve remaining and used credits for your ScrapeGraphAI account.
Get Endpoint SuggestionsTool to get AI-powered suggestions for creating scraping endpoints.
Get Live Session URLTool to get a URL for a live browser session.
Get Markdownify HistoryTool to retrieve the history of markdownify webpage-to-Markdown conversion jobs.
Get Scrape HistoryRetrieve the history of scrape jobs from your ScrapeGraphAI account.
Get Searchscraper HistoryGet the history of searchscraper jobs with pagination support.
Get Sitemap HistoryTool to retrieve the history of sitemap extraction jobs.
Get Smartscraper HistoryTool to retrieve the history of smartscraper jobs.
Get Usage TimelineTool to retrieve usage timeline statistics for your ScrapeGraphAI account.
Get Webhook LogsTool to retrieve webhook delivery logs for a crawler job.
List Scheduled JobsRetrieve a paginated list of all scheduled scraping jobs for your account.
Markdownify StatusCheck the status and retrieve results of a Markdownify webpage-to-Markdown conversion job.
Save Endpoint ConfigurationTool to save custom scraping endpoint configurations to ScrapeGraphAI.
Search ScraperPerform AI-powered web searches with structured, parsed results.
Check SearchScraper StatusCheck the status and results of an asynchronous SearchScraper job.
SmartCrawler StatusCheck the status and retrieve results of a SmartCrawler web crawling job.
Start Smart ScraperStart AI-powered web scraping with natural language extraction prompts.
SmartScraper StatusCheck the status and retrieve results of a SmartScraper web scraping job.
Start Smart Crawler (Async)Tool to start a multi-page web crawl using SmartCrawler for AI-powered data extraction.
Submit FeedbackSubmit feedback and ratings for completed ScrapeGraphAI requests.
Submit Product FeedbackSubmit product feedback for ScrapeGraphAI.
Convert JSON to TOON FormatTool to convert JSON data to TOON (Token-Oriented Object Notation) format.
Validate API KeyValidate your ScrapeGraphAI API key to ensure it is active and authorized.

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 Scrapegraph ai 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 Scrapegraph ai tools
  • Validating that COMPOSIO_USER_ID is also set before proceeding

Create a Tool Router session

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

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

Configure the agent with the MCP URL

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

    url = session.mcp.url
    
    client = MultiServerMCPClient({
        "scrapegraph_ai-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 Scrapegraph ai 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 Scrapegraph 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.

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FAQ

What are the differences in Tool Router MCP and Scrapegraph ai MCP?

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

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

Yes, absolutely. You can configure which Scrapegraph 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.

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 Scrapegraph ai data and credentials are handled as safely as possible.

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