# How to integrate Cloudflare browser rendering MCP with Autogen

```json
{
  "title": "How to integrate Cloudflare browser rendering MCP with Autogen",
  "toolkit": "Cloudflare browser rendering",
  "toolkit_slug": "cloudflare_browser_rendering",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/cloudflare_browser_rendering/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/cloudflare_browser_rendering/framework/autogen.md",
  "updated_at": "2026-05-12T10:06:45.818Z"
}
```

## Introduction

This guide walks you through connecting Cloudflare browser rendering to AutoGen using the Composio tool router. By the end, you'll have a working Cloudflare browser rendering agent that can capture a full-page screenshot of example.com, extract all product prices from a category page, get the html and image of a login page through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Cloudflare browser rendering account through Composio's Cloudflare browser rendering MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Cloudflare browser rendering with

- [ChatGPT](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/chatgpt)
- [OpenAI Agents SDK](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/codex)
- [Cursor](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/cursor)
- [VS Code](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/vscode)
- [OpenCode](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/opencode)
- [OpenClaw](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/cli)
- [Google ADK](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/cloudflare_browser_rendering/framework/crew-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 Cloudflare browser rendering
- Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
- Configure an Autogen AssistantAgent that can call Cloudflare browser rendering tools
- Run a live chat loop where you ask the agent to perform Cloudflare browser rendering 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 Cloudflare browser rendering MCP server, and what's possible with it?

The Cloudflare browser rendering MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Cloudflare browser rendering account. It provides structured and secure access to headless browser automation and rendering on Cloudflare’s global infrastructure, so your agent can capture screenshots, extract data, generate snapshots, and automate browser tasks on your behalf.
- Automated webpage screenshot capture: Instantly instruct your agent to capture high-quality screenshots of any web page or HTML content with custom viewport and clipping options.
- Combined DOM and visual snapshot generation: Direct your agent to create a full webpage snapshot with both the rendered HTML and an image, perfect for archiving or analysis.
- Precise HTML element scraping: Ask your agent to extract specific text, HTML, attributes, or box metrics from rendered web pages using CSS selectors—ideal for detailed data collection or monitoring changes.
- Account management automation: Enable your agent to fetch and manage all accessible Cloudflare accounts, making it easy to orchestrate browser rendering tasks across different environments.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CLOUDFLARE_BROWSER_RENDERING_CAPTURE_SCREENSHOT` | Capture Screenshot | Tool to capture a webpage screenshot. Use when you need a visual snapshot of a URL or HTML with optional viewport and clipping. Always validate screenshot content — the tool returns a successful result even when the captured page is a 404 or error page, with no error signal raised. |
| `CLOUDFLARE_BROWSER_RENDERING_LIST_ACCOUNTS` | List Accounts | List all Cloudflare accounts accessible to the authenticated API token. Returns account IDs, names, types, and settings. Use this to retrieve a valid account_id required by other browser-rendering actions like capture_screenshot, scrape_html_elements, and take_webpage_snapshot. |
| `CLOUDFLARE_BROWSER_RENDERING_SCRAPE_HTML_ELEMENTS` | Scrape HTML Elements | Tool to scrape HTML elements for text, HTML, attributes, and box metrics. Use when you need detailed data of matched selectors after rendering a page. |
| `CLOUDFLARE_BROWSER_RENDERING_TAKE_WEBPAGE_SNAPSHOT` | Take Webpage Snapshot | Capture both rendered HTML content and a screenshot of a webpage in a single request. Returns the full DOM content as a string and a Base64-encoded screenshot image. Useful when you need both visual representation and page content for analysis. |

## Supported Triggers

None listed.

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

The Cloudflare browser rendering MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Cloudflare browser rendering. Instead of manually wiring Cloudflare browser rendering APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely.
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

You will need:
- A Composio API key
- An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
- A Cloudflare browser rendering account you can connect to Composio
- Some basic familiarity with Autogen and Python async

### 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

Install Composio, Autogen extensions, and dotenv.
What's happening:
- composio connects your agent to Cloudflare browser rendering via MCP
- autogen-agentchat provides the AssistantAgent class
- autogen-ext-openai provides the OpenAI model client
- autogen-ext-tools provides MCP workbench support
```bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools
```

### 3. Set up environment variables

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 Cloudflare browser rendering connections to use
```bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com
```

### 4. Import dependencies and create Tool Router session

What's happening:
- load_dotenv() reads your .env file
- Composio(api_key=...) initializes the SDK
- create(...) creates a Tool Router session that exposes Cloudflare browser rendering tools
- session.mcp.url is the MCP endpoint that Autogen will connect to
```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 Cloudflare browser rendering session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["cloudflare_browser_rendering"]
    )
    url = session.mcp.url
```

### 5. Configure MCP parameters for Autogen

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
```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")}
)
```

### 6. Create the model client and agent

What's happening:
- OpenAIChatCompletionClient wraps the OpenAI model for Autogen
- McpWorkbench connects the agent to the MCP tools
- AssistantAgent is configured with the Cloudflare browser rendering tools from the workbench
```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 Cloudflare browser rendering assistant agent with MCP tools
    agent = AssistantAgent(
        name="cloudflare_browser_rendering_assistant",
        description="An AI assistant that helps with Cloudflare browser rendering operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )
```

### 7. Run the interactive chat loop

What's happening:
- The script prompts you in a loop with You:
- Autogen passes your input to the model, which decides which Cloudflare browser rendering 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
```python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Cloudflare browser rendering 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")
```

## Complete Code

```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 Cloudflare browser rendering session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["cloudflare_browser_rendering"]
    )
    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 Cloudflare browser rendering assistant agent with MCP tools
        agent = AssistantAgent(
            name="cloudflare_browser_rendering_assistant",
            description="An AI assistant that helps with Cloudflare browser rendering 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 Cloudflare browser rendering 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 Cloudflare browser rendering 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 Cloudflare browser rendering, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Cloudflare browser rendering MCP Agent with another framework

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

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- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
- [Backendless](https://composio.dev/toolkits/backendless) - Backendless is a backend-as-a-service platform for mobile and web apps, offering database, file storage, user authentication, and APIs. It helps developers ship scalable applications faster without managing server infrastructure.
- [Baserow](https://composio.dev/toolkits/baserow) - Baserow is an open-source no-code database platform for building collaborative data apps. It makes it easy for teams to organize data and automate workflows without writing code.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Cloudflare browser rendering MCP?

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

### Can I manage the permissions and scopes for Cloudflare browser rendering while using Tool Router?

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

---
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