How to integrate Cloudinary MCP with Autogen

This guide walks you through connecting Cloudinary to AutoGen using the Composio tool router. By the end, you'll have a working Cloudinary agent that can create a new folder for event photos, delete derived assets with ids [123,456], set up upload preset with watermarking through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Cloudinary account through Composio's Cloudinary MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Cloudinary is a cloud-based platform for managing, uploading, and transforming images and videos. It streamlines media workflows and delivers optimized assets globally.

108 Tools

Introduction

This guide walks you through connecting Cloudinary to AutoGen using the Composio tool router. By the end, you'll have a working Cloudinary agent that can create a new folder for event photos, delete derived assets with ids [123,456], set up upload preset with watermarking through natural language commands.

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

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

Also integrate Cloudinary with

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

The Cloudinary MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Cloudinary account. It provides structured and secure access to your digital asset management system, so your agent can perform actions like organizing folders, creating metadata fields, managing upload presets, and handling asset deletion on your behalf.

  • Automated folder and asset organization: Easily instruct your agent to create new asset folders or remove empty ones, keeping your Cloudinary library tidy and structured.
  • Metadata management: Let your agent create custom metadata fields or delete obsolete ones, extending and refining your asset tagging and search capabilities.
  • Preset and upload mapping creation: Have your agent set up upload presets with specific options or define dynamic folder mappings, automating consistent upload processes across your assets.
  • Resource and derived asset cleanup: Direct your agent to permanently delete assets by ID or remove unnecessary derived resources, ensuring your storage stays efficient and clutter-free.
  • Datasource entry management: Ask your agent to inactivate or delete specific datasource entries from metadata fields, keeping your metadata schema accurate and up to date.

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

Supported Tools

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

Activate Live Stream

Tool to manually activate a Cloudinary live stream.

Create Asset Relations by Asset ID

Tool to add related assets by asset ID.

Create Asset Relations by Public ID

Tool to create relations between assets by public ID.

Create Folder

Tool to create a new asset folder.

Create Image from Text

Tool to create an image from text using Cloudinary's text generation API.

Create Live Stream

Tool to create a new live stream in Cloudinary.

Create Live Stream Output

Tool to create a new live stream output configuration.

Create Metadata Field

Tool to create a new metadata field definition.

Create Metadata Rule

Tool to create a new conditional metadata rule.

Create Multi-Resource Animation

Tool to create an animated image, video, or PDF from a set of images.

Create Slideshow

Tool to create an auto-generated video slideshow from existing Cloudinary assets.

Create Streaming Profile

Tool to create a new adaptive streaming profile in your Cloudinary account.

Create Transformation

Tool to create a new named transformation by assigning a custom name to a set of transformation parameters.

Create Trigger

Tool to create a new webhook trigger for a specified event type.

Create Upload Mapping

Tool to create a new upload mapping folder and URL template.

Create Upload Preset

Tool to create a new upload preset.

Delete Asset Relations by Asset ID

Tool to delete asset relations by asset ID.

Delete Asset Relations by Public ID

Tool to delete asset relations by public ID.

Delete Derived Resources

Tool to delete derived assets.

Delete Metadata Field Datasource Entries

Tool to delete datasource entries for a specified metadata field.

Delete Folder

Tool to delete an empty asset folder.

Delete Live Stream

Tool to delete a live stream from Cloudinary.

Delete Live Stream Output

Tool to delete a live stream output from Cloudinary.

Delete Metadata Field

Tool to delete a metadata field by external ID.

Delete Metadata Rule

Tool to delete a conditional metadata rule by its ID.

Delete Resources by Asset ID

Tool to delete resources by asset IDs.

Delete Resources by Public ID

Tool to delete Cloudinary resources by public ID, prefix, or all resources.

Delete Resources by Tags

Tool to delete Cloudinary assets by tag.

Delete Streaming Profile

Tool to delete a custom streaming profile or revert a built-in profile to original settings.

Delete Transformation (v2)

Tool to delete a named transformation from your Cloudinary account.

Delete Trigger

Tool to delete a trigger (webhook notification).

Delete Upload Mapping

Tool to delete a folder upload mapping.

Delete Upload Preset

Tool to delete an upload preset from the account.

Destroy Asset

Tool to permanently destroy a Cloudinary asset/resource by public ID.

Destroy Asset by ID

Tool to delete an asset by its immutable asset ID.

Explicit Resource Update

Tool to update an existing asset and/or eagerly generate derived transformations using Cloudinary's Explicit API.

Explode Multi-Page Resource

Tool to create derived images from multi-page files (PDF, PSD, TIFF, animated GIF) by exploding them into separate images.

Generate Archive

Tool to create an archive (ZIP or TGZ file) containing a set of assets from your Cloudinary environment.

Get Adaptive Streaming Profiles

Tool to list adaptive streaming profiles.

Get Analysis Task Status

Tool to get the status of an analysis task.

Get product environment config details

Tool to get product environment config details.

Get Live Stream

Tool to get details of a single live stream by ID.

Get Live Stream Output

Tool to get details of a single live stream output.

Get Live Stream Outputs

Tool to get a list of live stream outputs.

Get Live Streams

Tool to get a list of live streams from Cloudinary.

Get Metadata Field By ID

Tool to get a single metadata field definition by external ID.

Get Resource by Asset ID

Get Resource by Asset ID

Get Resource by Public ID

Tool to get details of a single resource by public ID.

Get Resources by Asset Folder

Tool to list assets stored directly in a specified folder.

Get Resources by Context

Tool to retrieve assets with a specified contextual metadata key/value.

Get Resources in Moderation

Tool to retrieve assets in a moderation queue by status.

Get Root Folders

Tool to list all root folders in the product environment.

Get Streaming Profile Details

Tool to get details of a single streaming profile by name.

Get Resource Tags

Tool to list all tags used for a specified resource type.

Get Transformation

Tool to retrieve details of a specific transformation.

Get Transformations

Tool to list all transformations (named and unnamed).

List Webhook Triggers

Tool to list all webhook triggers for event types in your environment.

Get Upload Mapping Details

Tool to retrieve details of a single upload mapping by folder.

Get Upload Mappings

Tool to list all upload mappings.

Get Upload Preset

Tool to retrieve details of a single upload preset by name.

Get Usage

Tool to get product environment usage details.

Get Video Views

Tool to get video analytics views from Cloudinary.

Idle Live Stream

Tool to manually idle a Cloudinary live stream.

List Images

Tool to list image assets from Cloudinary.

List Metadata Fields

Tool to list all structured metadata fields defined in your Cloudinary product environment.

List Metadata Rules

Tool to retrieve all conditional metadata rules defined in your Cloudinary account.

List Raw Files

Tool to list raw assets from Cloudinary.

List Resources by Asset IDs

Tool to retrieve multiple resources by their asset IDs.

List Resources by External IDs

Tool to retrieve resources by their external IDs.

List Resources by Tag

Tool to list resources (assets) with a specified tag.

List Resources by Type

Tool to retrieve resources (assets) by resource type and storage type.

List Resource Types

Tool to list all available resource types in your Cloudinary product environment.

List Upload Presets

Tool to list all upload presets configured in the account.

List Video Assets

Tool to list video assets from Cloudinary.

Manage Context Metadata

Tool to add or remove contextual metadata on Cloudinary assets.

Order Metadata Field Datasource

Tool to update ordering of a metadata field datasource.

Ping Cloudinary Servers

Tool to ping Cloudinary servers.

Publish Resources

Tool to publish Cloudinary assets by public IDs, prefix, or tag.

Rename or Move Resource Public ID

Tool to rename an asset's public ID using Cloudinary's rename endpoint.

Reorder Metadata Field

Tool to change the position of a specific metadata field.

Reorder Metadata Fields

Tool to reorder all metadata fields in the product environment.

Restore Metadata Field Datasource Entries

Tool to restore previously deleted datasource entries for a metadata field.

Restore Deleted Resources

Tool to restore deleted Cloudinary resources by public IDs.

Restore Resources by Asset IDs

Tool to restore backed up assets by asset IDs.

Search Assets

Tool to search and filter assets using powerful query expressions.

Search Datasource in Metadata Field

Tool to search datasource values in a metadata field.

Search Folders

Tool to search asset folders with filtering, sorting, and pagination.

Search All Metadata Field Datasources

Tool to search across all metadata field datasources.

Visual Search Assets

Tool to find images in your asset library based on visual similarity or content.

Show Folder

Tool to list sub-folders within a specified folder.

Update Asset Metadata

Tool to populate or update metadata field values on one or more Cloudinary assets.

Update Folder

Tool to rename or move an existing asset folder.

Update Live Stream

Tool to update a live stream's configuration in Cloudinary.

Update Live Stream Output

Tool to modify an existing live stream output configuration.

Update Metadata Field

Tool to update a metadata field definition by external ID.

Update Metadata Field Datasource

Tool to update the datasource (allowed values) for a metadata field.

Update Metadata Rule

Tool to update an existing conditional metadata rule.

Update Resource by Asset ID

Tool to update asset properties by asset ID in Cloudinary.

Update Resource by Public ID

Tool to update asset properties by public ID in Cloudinary.

Update Resource Tags

Tool to add, remove, replace, or remove all tags for one or more Cloudinary assets.

Update Streaming Profile

Tool to modify an existing adaptive streaming profile's configuration.

Update Transformation (v2)

Tool to update the definition of an existing named transformation.

Update Trigger

Tool to update the callback URL of an existing webhook trigger.

Update Upload Mapping

Tool to update an existing upload mapping by changing its remote URL template.

Update Upload Preset

Tool to update an existing upload preset's configuration settings.

Upload Asset

Tool to upload media assets (images, videos, raw files) to Cloudinary.

Upload File Chunk

Tool to upload a single chunk of a large file to Cloudinary.

Upload File (Auto Detect)

Tool to upload files with automatic resource type detection.

FRAMEWORKS

How to build Cloudinary MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Cloudinary MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Cloudinary MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Cloudinary MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Cloudinary MCP with Claude Cowork

Codex logo

Codex

Use Cloudinary MCP with Codex

OpenClaw logo

OpenClaw

Use Cloudinary MCP with OpenClaw

Hermes logo

Hermes

Use Cloudinary MCP with Hermes

CLI logo

CLI

Use Cloudinary MCP with CLI

Google ADK logo

Google ADK

Use Cloudinary MCP with Google ADK

LangChain logo

LangChain

Use Cloudinary MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Cloudinary MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Cloudinary MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Cloudinary MCP with LlamaIndex

CrewAI logo

CrewAI

Use Cloudinary MCP with CrewAI

FAQ

Frequently asked questions

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

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 Cloudinary tools.

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

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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.

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