# How to integrate Cloudlayer MCP with Autogen

```json
{
  "title": "How to integrate Cloudlayer MCP with Autogen",
  "toolkit": "Cloudlayer",
  "toolkit_slug": "cloudlayer",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/cloudlayer/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/cloudlayer/framework/autogen.md",
  "updated_at": "2026-05-06T08:06:31.488Z"
}
```

## Introduction

This guide walks you through connecting Cloudlayer to AutoGen using the Composio tool router. By the end, you'll have a working Cloudlayer agent that can generate pdf from a contract html template, convert a marketing webpage to a png image, list your most recent generated assets through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a Cloudlayer account through Composio's Cloudlayer MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Cloudlayer with

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

The Cloudlayer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Cloudlayer account. It provides structured and secure access to dynamic document and asset generation, so your agent can perform actions like converting HTML or URLs to PDFs or images, managing assets, and configuring storage on your behalf.
- Automated PDF and image generation: Instantly convert HTML content or public URLs into professional PDFs and images for reporting, documentation, or sharing.
- Asset management and retrieval: Let your agent fetch metadata or download links for generated assets, or list your most recent document and image creations.
- Dynamic storage configuration: Seamlessly add and manage external storage buckets or containers for organizing generated files and assets.
- Real-time API health monitoring: Enable your agent to check Cloudlayer API status, ensuring your integrations are always up and running.
- Flexible screenshot and rendering tasks: Capture dynamic webpage screenshots as images or PDFs, with full control over conversion parameters, for advanced automation workflows.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CLOUDLAYER_ADD_STORAGE` | Add Storage | Tool to add a new user storage configuration. use when you need to attach an external bucket or container for file storage. |
| `CLOUDLAYER_GET_ASSET` | Get Asset | Tool to retrieve a specific asset by its id. use when you need to fetch metadata or download url of an existing asset after its generation. |
| `CLOUDLAYER_GET_STATUS` | Get API Status | Tool to test api reachability. use when checking if the cloudlayer api is available. |
| `CLOUDLAYER_HTML_TO_IMAGE` | HTML to Image | Tool to convert base64-encoded html to an image. use when you need to render raw html or a url into png, jpeg, or webp after content load. |
| `CLOUDLAYER_HTML_TO_PDF` | Convert HTML to PDF | Tool to convert html or a public url into a pdf document. use when you need programmatic pdf generation from html content. |
| `CLOUDLAYER_LIST_ASSETS` | List Assets | Tool to list the ten most recent assets. use when you need to retrieve your latest cloudlayer assets. |
| `CLOUDLAYER_LIST_STORAGE` | List Storage Configurations | Tool to retrieve a list of all storage configurations. use after authenticating to cloudlayer to view your account's storage settings. |
| `CLOUDLAYER_URL_TO_IMAGE_POST` | Convert URL to Image | Tool to convert a webpage url to an image. use when dynamic screenshot parameters are needed. |
| `CLOUDLAYER_URL_TO_PDF_POST` | Convert URL to PDF | Tool to convert a url to pdf with full parameter support. use when you need advanced control over paper size, margins, headers/footers, or webhook callbacks. |

## Supported Triggers

None listed.

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

The Cloudlayer MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Cloudlayer. Instead of manually wiring Cloudlayer 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 Cloudlayer 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 Cloudlayer 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 Cloudlayer 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 Cloudlayer 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 Cloudlayer session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["cloudlayer"]
    )
    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 Cloudlayer 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 Cloudlayer assistant agent with MCP tools
    agent = AssistantAgent(
        name="cloudlayer_assistant",
        description="An AI assistant that helps with Cloudlayer 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 Cloudlayer 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 Cloudlayer 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 Cloudlayer session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["cloudlayer"]
    )
    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 Cloudlayer assistant agent with MCP tools
        agent = AssistantAgent(
            name="cloudlayer_assistant",
            description="An AI assistant that helps with Cloudlayer 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 Cloudlayer 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 Cloudlayer 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 Cloudlayer, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

## How to build Cloudlayer MCP Agent with another framework

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

## Related Toolkits

- [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices.
- [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze.
- [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization.
- [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data.
- [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease.
- [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training.
- [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses.
- [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files.
- [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently.
- [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price.
- [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere.
- [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code.
- [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more.
- [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place.
- [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems.
- [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project.
- [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks.
- [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API.
- [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale.
- [Docmosis](https://composio.dev/toolkits/docmosis) - Docmosis generates PDF and Word documents from user-defined templates. It's perfect for merging data fields to quickly produce reports, invoices, and business letters.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Cloudlayer MCP?

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

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

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

---
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