How to integrate Zenrows MCP with Autogen

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Introduction

This guide walks you through connecting Zenrows to AutoGen using the Composio tool router. By the end, you'll have a working Zenrows agent that can download a pdf of this news article, extract plain text from the given webpage, get latest property data from zillow, show my current zenrows api usage stats through natural language commands.

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

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

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 Zenrows
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Zenrows tools
  • Run a live chat loop where you ask the agent to perform Zenrows 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 Zenrows MCP server, and what's possible with it?

The Zenrows MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Zenrows account. It provides structured and secure access to advanced web scraping capabilities, so your agent can extract structured data, bypass CAPTCHAs, convert pages to PDF, and monitor your API usage on your behalf.

  • Intelligent web data extraction: Direct your agent to scrape and extract plain text or structured data from dynamic websites, including specialized real estate property data from platforms like Zillow or Idealista.
  • PDF and content generation: Ask your agent to convert any web page into a PDF or retrieve clean, formatted plain text for archiving, documentation, or offline reading.
  • Seamless CAPTCHA and block bypassing: Enable your agent to gather data from sites protected by CAPTCHAs or anti-bot systems without manual intervention.
  • Real-time API usage monitoring: Have the agent check your account’s current API usage, concurrency status, and limits to help manage credits and avoid interruptions.
  • Session and compression management: Instruct your agent to maintain consistent scraping sessions, handle compression to optimize bandwidth, and retrieve detailed response headers for debugging and performance optimization.

Supported Tools & Triggers

Tools
Get ZenRows API Usage StatisticsThis tool retrieves the current api usage statistics and limits for your zenrows account.
Get Concurrency StatusThis tool retrieves the current concurrency status of your zenrows api usage.
Get Detailed Concurrency StatusThis tool provides detailed information about the current concurrency status and limits of your zenrows account by making a request to the api and analyzing the response headers.
Get Original Status CodeThis tool retrieves the original http status code returned by the target website, which is useful for debugging purposes.
Get PDF from URLThis tool generates a pdf version of the scraped content from a given url.
Get Plaintext ResponseThis tool extracts plain text content from a webpage using the zenrows api.
Get Real Estate Property DataA specialized tool for extracting structured data from real estate platforms like zillow and idealista.
Get Response with CompressionA tool to fetch content from a url using the zenrows api with compression enabled to optimize bandwidth usage and improve performance.
Get response headersA tool to retrieve and parse response headers from zenrows api requests.
Get Session IDThis tool implements zenrows' session management functionality to maintain the same ip address across multiple requests for up to 10 minutes.
Get Walmart Product DetailsThis tool allows users to extract detailed product information from walmart using zenrows' specialized e-commerce scraping api.
Scrape urlScrape and extract data from a specified url.
Scrape url autoparseThe zenrows scrape url autoparse tool automatically parses and extracts structured data from any given url using intelligent parsing capabilities.
Scrape URL HTMLThis tool extracts raw html data from a given url using zenrows' universal scraper api.
Scrape URL with CSS SelectorsThis tool allows users to scrape specific elements from a webpage using css selectors.
Screenshot URLA tool to capture screenshots of web pages using zenrows api.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Tool Router works

The Tool Router 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

You will need:

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

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 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 Zenrows via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

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 Zenrows connections to use

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 Zenrows session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["zenrows"]
    )
    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 Zenrows tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Zenrows assistant agent with MCP tools
    agent = AssistantAgent(
        name="zenrows_assistant",
        description="An AI assistant that helps with Zenrows 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 Zenrows tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Zenrows 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 Zenrows 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 Zenrows 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 Zenrows session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["zenrows"]
    )
    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 Zenrows assistant agent with MCP tools
        agent = AssistantAgent(
            name="zenrows_assistant",
            description="An AI assistant that helps with Zenrows 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 Zenrows 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 Zenrows 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 Zenrows, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Zenrows MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Zenrows MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Zenrows MCP with Claude Agent SDK

Google ADK

Google ADK

Use Zenrows MCP with Google ADK

LangChain

LangChain

Use Zenrows MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Zenrows MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Zenrows MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Zenrows MCP with LlamaIndex

CrewAI

CrewAI

Use Zenrows MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Zenrows MCP?

With a standalone Zenrows MCP server, the agents and LLMs can only access a fixed set of Zenrows tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Zenrows and many other apps based on the task at hand, all through a single MCP endpoint.

Can I use Tool Router MCP with Autogen?

Yes, you can. Autogen 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 Zenrows tools.

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

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

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