SIGN UPLOG IN
X (Twitter)LinkedInGitHubDiscordYouTube

How to integrate Scrapegraph ai MCP with Autogen

Connect Autogen to Scrapegraph ai MCP. Extract product prices from amazon search results, summarize latest news headlines from bbc homepage, and more using natural language, with authentication handled for you.

Get started for freeGet a demo
Scrapegraph ai logoScrapegraph ai
Api Key

Scrapegraph ai is an AI-powered web scraping API that lets you extract structured data from any website using natural language prompts. Easily turn web pages into ready-to-use data for your apps and agents.

27 Tools

Introduction

This guide walks you through connecting Scrapegraph ai to AutoGen using the Composio tool router. By the end, you'll have a working Scrapegraph ai agent that can extract structured data with AI, crawl websites, convert pages to Markdown, scrape search results, and check your credits through natural language commands.

This guide will help you understand how to give your AutoGen 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.

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Scrapegraph ai with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAIPydantic AI

TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Scrapegraph ai
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Scrapegraph ai tools
  • Run a live chat loop where you ask the agent to perform Scrapegraph ai operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

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.

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 step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Scrapegraph ai account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
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

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Scrapegraph ai via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Scrapegraph ai connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Scrapegraph ai session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["scrapegraph_ai"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Scrapegraph ai tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Scrapegraph ai assistant agent with MCP tools
    agent = AssistantAgent(
        name="scrapegraph_ai_assistant",
        description="An AI assistant that helps with Scrapegraph ai operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Scrapegraph ai tools from the workbench
8

Run the interactive chat loop

python
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")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Scrapegraph ai tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Scrapegraph ai and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Scrapegraph ai session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["scrapegraph_ai"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Scrapegraph ai assistant agent with MCP tools
        agent = AssistantAgent(
            name="scrapegraph_ai_assistant",
            description="An AI assistant that helps with Scrapegraph ai operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        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")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Scrapegraph ai through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Scrapegraph ai, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Scrapegraph ai action and event your agent gets out of the box.

Convert Webpage to Markdown (V2)

Tool to convert any webpage into clean, well-formatted Markdown with full parameter control.

Generate Schema

Generate or modify a JSON schema based on a search query for structured data extraction.

Get Agentic Scraper History

Retrieve paginated history of agentic scraper jobs.

Get Crawler History

Retrieve the history of crawler jobs for your account.

Get Credits

Retrieve credit information for your ScrapeGraphAI account, including remaining credits and usage.

Get Endpoint Suggestions

Tool to get AI-powered suggestions for creating scraping endpoints.

Get Live Session URL

Tool to get a URL for a live browser session.

Get Markdownify History

Tool to retrieve the history of markdownify webpage-to-Markdown conversion jobs.

Get Scrape History

Retrieve the history of scrape jobs from your ScrapeGraphAI account.

Get Searchscraper History

Get the history of searchscraper jobs with pagination support.

Get Sitemap History

Tool to retrieve the history of sitemap extraction jobs.

Get Smartscraper History

Tool to retrieve the history of smartscraper jobs.

Get Usage Timeline

Tool to retrieve usage timeline statistics for your ScrapeGraphAI account.

Get Webhook Logs

Retrieve webhook delivery logs for a crawler job.

List Scheduled Jobs

Retrieve a paginated list of all scheduled scraping jobs for your account.

Markdownify Status

Check the status and retrieve results of a Markdownify webpage-to-Markdown conversion job.

Save Endpoint Configuration

Tool to save custom scraping endpoint configurations to ScrapeGraphAI.

Search Scraper

Perform web searches and scrape results, with AI-powered structured extraction or raw Markdown content.

Check SearchScraper Status

Check the status and results of an asynchronous SearchScraper job.

SmartCrawler Status

Check the status and retrieve results of a SmartCrawler web crawling job.

Start Smart Scraper

Extract structured JSON from one URL, HTML document, or Markdown document using ScrapeGraphAI v2.

Look Up SmartScraper Request

Retrieve the v2 History record for a SmartScraper Extract request ID.

Start Smart Crawler (Async)

Start a multi-page SmartCrawler job for AI-powered structured data extraction or markdown conversion.

Submit Feedback

Submit feedback and ratings for completed ScrapeGraphAI requests.

Submit Product Feedback

Submit product feedback for ScrapeGraphAI.

Convert JSON to TOON Format

Convert a JSON object to TOON (Token-Oriented Object Notation) for compact LLM processing while maintaining data structure.

Validate API Key

Check whether your ScrapeGraphAI API key is valid and authorized for use before making other API calls.

FRAMEWORKS

How to build Scrapegraph ai MCP Agent with another framework

Claude Code logo

Claude Code

Use Scrapegraph ai MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Scrapegraph ai MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Scrapegraph ai MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Scrapegraph ai MCP with Claude Agent SDK

Codex logo

Codex

Use Scrapegraph ai MCP with Codex

OpenClaw logo

OpenClaw

Use Scrapegraph ai MCP with OpenClaw

Hermes logo

Hermes

Use Scrapegraph ai MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Scrapegraph ai MCP with Atomic Agent

Google ADK logo

Google ADK

Use Scrapegraph ai MCP with Google ADK

LangChain logo

LangChain

Use Scrapegraph ai MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Scrapegraph ai MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Scrapegraph ai MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Scrapegraph ai MCP with LlamaIndex

CrewAI logo

CrewAI

Use Scrapegraph ai MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Scrapegraph ai MCP with Pydantic AI

FAQ

Frequently asked questions

Start with Scrapegraph ai.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Scrapegraph ai tool your agent needs.Free to start.

Start building