How to integrate Imagekit io MCP with Autogen

This guide walks you through connecting Imagekit io to AutoGen using the Composio tool router. By the end, you'll have a working Imagekit io agent that can move all event photos to new 2024 folder, delete outdated logo file from media library, create custom metadata field for copyright info through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Imagekit io account through Composio's Imagekit io MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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ImageKit.io is a cloud-based media management platform for image and video delivery. Instantly optimize, transform, and deliver visuals globally via a lightning-fast CDN.

26 Tools

Introduction

This guide walks you through connecting Imagekit io to AutoGen using the Composio tool router. By the end, you'll have a working Imagekit io agent that can move all event photos to new 2024 folder, delete outdated logo file from media library, create custom metadata field for copyright info through natural language commands.

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

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

Also integrate Imagekit io with

OpenAI Agents SDKClaude Agent SDKClaude CodeClaude CoworkCodexOpenClawHermesCLIGoogle ADKLangChainVercel AI SDKMastra AILlamaIndexCrewAI

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

The Imagekit io MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your ImageKit.io account. It provides structured and secure access to your media library, so your agent can perform actions like organizing folders, managing files, handling bulk operations, editing metadata, and cleaning up assets on your behalf.

  • Bulk file operations: Effortlessly move, copy, or update tags on multiple files at once to streamline large-scale asset management.
  • Folder organization and management: Ask your agent to create new folders for better asset structuring or delete old folders—including all their contents—when you need to tidy up.
  • Custom metadata control: Let your agent create or delete custom metadata fields, so your media assets stay rich with the information your workflows need.
  • File and version cleanup: Instruct the agent to permanently delete files or remove outdated file versions to keep your storage lean and organized.
  • Bulk job monitoring: Have your agent track the status of ongoing bulk jobs, like folder copies or moves, so you always know what’s happening behind the scenes.

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

Supported Tools

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

Bulk Job Status

Retrieve the status of a bulk folder operation.

Bulk Move Files

Tool to move multiple files in bulk.

Bulk Remove Tags

Tool to remove tags from multiple files in bulk.

Copy Folder

Initiate an asynchronous bulk copy of a folder and all its contents to a new location.

Create Custom Metadata Field

Create a new custom metadata field in ImageKit DAM.

Create Folder

Creates a new folder in ImageKit.

Delete Custom Metadata Field

Permanently deletes a custom metadata field from ImageKit.

Delete File

Permanently deletes a file from ImageKit by its unique file ID.

Delete File Version

Permanently deletes a specific non-current file version from ImageKit.

Delete Folder

Permanently delete a folder and all its contents from ImageKit Media Library.

Delete Multiple Files

Permanently delete multiple files from ImageKit media library in a single batch operation.

Get Upload Authentication Parameters

Tool to generate authentication parameters for client-side file uploads.

Get File Details

Tool to retrieve details of a specific file.

Get File Metadata

Tool to retrieve metadata of an uploaded file.

Get File Version Details

Tool to retrieve details of a specific file version.

Get Usage

Retrieve ImageKit account usage metrics for a specified date range.

List and Search Media Assets

List and search media assets (files, folders, file-versions) in your ImageKit media library.

List Custom Metadata Fields

List all custom metadata fields defined in the ImageKit Media Library.

List File Versions

Retrieves all versions of a specific file in ImageKit.

Move Folder

Move a folder from one location to another in your ImageKit media library.

Purge ImageKit Cache

Purge CDN and ImageKit internal caches for a specific URL or URL pattern.

Check purge cache status

Tool to check the status of a cache purge request.

Rename File

Renames an existing file in the ImageKit media library.

Restore File Version

Restores a non-current file version to become the current version in ImageKit.

Update Custom Metadata Field

Updates an existing custom metadata field's label or schema constraints in ImageKit DAM.

Update File Details

Update file details in ImageKit media library.

FRAMEWORKS

How to build Imagekit io MCP Agent with another framework

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Imagekit io MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Imagekit io MCP with Claude Agent SDK

Claude Code logo

Claude Code

Use Imagekit io MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Imagekit io MCP with Claude Cowork

Codex logo

Codex

Use Imagekit io MCP with Codex

OpenClaw logo

OpenClaw

Use Imagekit io MCP with OpenClaw

Hermes logo

Hermes

Use Imagekit io MCP with Hermes

CLI logo

CLI

Use Imagekit io MCP with CLI

Google ADK logo

Google ADK

Use Imagekit io MCP with Google ADK

LangChain logo

LangChain

Use Imagekit io MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Imagekit io MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Imagekit io MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Imagekit io MCP with LlamaIndex

CrewAI logo

CrewAI

Use Imagekit io MCP with CrewAI

FAQ

Frequently asked questions

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

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

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