How to integrate Scrapegraph ai MCP with CrewAI

This guide walks you through connecting Scrapegraph ai to CrewAI 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 CrewAI 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.

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Scrapegraph 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.

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Introduction

This guide will help you understand how to give your CrewAI 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

OpenAI Agents SDK Claude Agent SDK Claude Code Claude Cowork Codex OpenClaw Hermes CLI Google ADK LangChain Vercel AI SDK Mastra AI LlamaIndex

TL;DR

Here's what you'll learn:

Get a Composio API key and configure your Scrapegraph ai connection
Set up CrewAI with an MCP enabled agent
Create a Tool Router session or standalone MCP server for Scrapegraph ai
Build a conversational loop where your agent can execute Scrapegraph ai operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

Agent Roles: Define specialized agents with specific goals and backstories
Task Management: Create tasks with clear descriptions and expected outputs
Crew Orchestration: Combine agents and tasks into collaborative workflows
MCP Integration: Connect to external tools through Model Context Protocol

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:

Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS

Prerequisites

Before starting, make sure you have:

Python 3.9 or higher
A Composio account and API key
A Scrapegraph ai connection authorized in Composio
An OpenAI API key for the CrewAI LLM
Basic familiarity with Python

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

bash

pip install composio crewai crewai-tools[mcp] python-dotenv

What's happening:

composio connects your agent to Scrapegraph ai via MCP
crewai provides Agent, Task, Crew, and LLM primitives
crewai-tools[mcp] includes MCP helpers
python-dotenv loads environment variables from .env

Set up environment variables

bash

COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_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 with Composio
USER_ID scopes the session to your account
OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python

import os
from composio import Composio
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
import dotenv

dotenv.load_dotenv()

COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

What's happening:

CrewAI classes define agents and tasks, and run the workflow
MCPServerHTTP connects the agent to an MCP endpoint
Composio will give you a short lived Scrapegraph ai MCP URL

Create a Composio Tool Router session for Scrapegraph ai

python

composio_client = Composio(api_key=COMPOSIO_API_KEY)
session = composio_client.create(user_id=COMPOSIO_USER_ID, toolkits=["scrapegraph_ai"])

url = session.mcp.url

What's happening:

You create a Scrapegraph ai only session through Composio
Composio returns an MCP HTTP URL that exposes Scrapegraph ai tools

Initialize the MCP Server

python

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users search the internet effectively",
        backstory="You are a helpful assistant with access to search tools.",
        tools=tools,
        verbose=False,
        max_iter=10,
    )

What's Happening:

Server Configuration: The code sets up connection parameters including the MCP server URL, streamable HTTP transport, and Composio API key authentication.
MCP Adapter Bridge: MCPServerAdapter acts as a context manager that converts Composio MCP tools into a CrewAI-compatible format.
Agent Setup: Creates a CrewAI Agent with a defined role (Search Assistant), goal (help with internet searches), and access to the MCP tools.
Configuration Options: The agent includes settings like verbose=False for clean output and max_iter=10 to prevent infinite loops.
Dynamic Tool Usage: Once created, the agent automatically accesses all Composio Search tools and decides when to use them based on user queries.

Create a CLI Chatloop and define the Crew

python

print("Chat started! Type 'exit' or 'quit' to end.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Conversation history:\n{conversation_context}\n\n"
            f"Current request: {user_input}"
        ),
        expected_output="A helpful response addressing the user's request",
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], verbose=False)
    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")

What's Happening:

Interactive CLI Setup: The code creates an infinite loop that continuously prompts for user input and maintains the entire conversation history in a string variable.
Input Validation: Empty inputs are ignored to prevent processing blank messages and keep the conversation clean.
Context Building: Each user message is appended to the conversation context, which preserves the full dialogue history for better agent responses.
Dynamic Task Creation: For every user input, a new Task is created that includes both the full conversation history and the current request as context.
Crew Execution: A Crew is instantiated with the agent and task, then kicked off to process the request and generate a response.
Response Management: The agent's response is converted to a string, added to the conversation context, and displayed to the user, maintaining conversational continuity.

Complete Code

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

python

from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter
from composio import Composio
from dotenv import load_dotenv
import os

load_dotenv()

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY is not set in the environment.")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment.")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment.")

# Initialize Composio and create a session
composio = Composio(api_key=COMPOSIO_API_KEY)
session = composio.create(
    user_id=COMPOSIO_USER_ID,
    toolkits=["scrapegraph_ai"],
)
url = session.mcp.url

# Configure LLM
llm = LLM(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY"),
)

server_params = {
    "url": url,
    "transport": "streamable-http",
    "headers": {"x-api-key": COMPOSIO_API_KEY},
}

with MCPServerAdapter(server_params) as tools:
    agent = Agent(
        role="Search Assistant",
        goal="Help users with internet searches",
        backstory="You are an expert assistant with access to Composio Search tools.",
        tools=tools,
        llm=llm,
        verbose=False,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Conversation history:\n{conversation_context}\n\n"
                f"Current request: {user_input}"
            ),
            expected_output="A helpful response addressing the user's request",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task], verbose=False)
        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

Conclusion

You now have a CrewAI agent connected to Scrapegraph ai through Composio's Tool Router. The agent can perform Scrapegraph ai operations through natural language commands.

Next steps:

Add role-specific instructions to customize agent behavior
Plug in more toolkits for multi-app workflows
Chain tasks for complex multi-step operations

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 remaining and used credits for your ScrapeGraphAI account.

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

Tool to 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 AI-powered web searches with structured, parsed results.

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

Start AI-powered web scraping with natural language extraction prompts.

SmartScraper Status

Check 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 Feedback

Submit feedback and ratings for completed ScrapeGraphAI requests.

Submit Product Feedback

Submit product feedback for ScrapeGraphAI.

Convert JSON to TOON Format

Tool to convert JSON data to TOON (Token-Oriented Object Notation) format.

Validate API Key

Validate your ScrapeGraphAI API key to ensure it is active and authorized.

FRAMEWORKS

How to build Scrapegraph ai MCP Agent with another framework

OpenAI Agents SDK

Use Scrapegraph ai MCP with OpenAI Agents SDK

Claude Agent SDK

Use Scrapegraph ai MCP with Claude Agent SDK

Claude Code

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Claude Cowork

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Codex

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OpenClaw

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Hermes

Use Scrapegraph ai MCP with Hermes

CLI

Use Scrapegraph ai MCP with CLI

Google ADK

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LangChain

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Vercel AI SDK

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Mastra AI

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LlamaIndex

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FAQ

Frequently asked questions

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.

Yes, you can. CrewAI 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.

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.

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.

Start with Scrapegraph ai.It takes 30 seconds.

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

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Customer stories

Teams ship real agents on Composio

GET STARTED FOR FREE BOOK A DEMO

Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati

Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya

Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

Nirman Dave

CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu

Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

Tomisin Jenrola

Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

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How to integrate Scrapegraph ai MCP with CrewAI

Connect Scrapegraph ai without auth hassles

Introduction

Also integrate Scrapegraph ai with

TL;DR

What is CrewAI?

What is the Scrapegraph ai MCP server, and what's possible with it?

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

How the Composio SDK works

Step-by-step Guide

Prerequisites

Getting API Keys for OpenAI and Composio

Install dependencies

Set up environment variables

Import dependencies

Create a Composio Tool Router session for Scrapegraph ai

Initialize the MCP Server

Create a CLI Chatloop and define the Crew

Complete Code

Conclusion

Supported Tools

How to build Scrapegraph ai MCP Agent with another framework

OpenAI Agents SDK

Claude Agent SDK

Claude Code

Claude Cowork

Codex

OpenClaw

Hermes

CLI

Google ADK

LangChain

Vercel AI SDK

Mastra AI

LlamaIndex

Explore Other Toolkits

Excel

21risk

Abstract

Addressfinder

Frequently asked questions

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

Can I use Tool Router MCP with CrewAI?+

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

How safe is my data with Composio Tool Router?+

Start with Scrapegraph ai.It takes 30 seconds.

Teams ship real agents on Composio