# How to integrate All images ai MCP with Autogen

```json
{
  "title": "How to integrate All images ai MCP with Autogen",
  "toolkit": "All images ai",
  "toolkit_slug": "all_images_ai",
  "framework": "AutoGen",
  "framework_slug": "autogen",
  "url": "https://composio.dev/toolkits/all_images_ai/framework/autogen",
  "markdown_url": "https://composio.dev/toolkits/all_images_ai/framework/autogen.md",
  "updated_at": "2026-05-12T10:00:52.074Z"
}
```

## Introduction

This guide walks you through connecting All images ai to AutoGen using the Composio tool router. By the end, you'll have a working All images ai agent that can list all ai-generated images from this week, check if your all-images.ai api key is valid, add webhook to receive image generation updates through natural language commands.
This guide will help you understand how to give your AutoGen agent real control over a All images ai account through Composio's All images ai MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate All images ai with

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

The All images ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, and others directly to your All images ai account. It provides structured and secure access to your AI-powered image generation and management tools, so your agent can perform actions like validating API keys, managing webhooks, and reviewing automated image batches on your behalf.
- API key verification and management: Instantly check and validate your API key status to ensure secure and uninterrupted access for your agent-driven workflows.
- Webhook endpoint creation: Set up custom webhook endpoints so your agent can automatically receive image generation event callbacks and updates as soon as they happen.
- Automated image batch listing: Quickly retrieve and review all images generated in bulk, making it easy to manage, audit, or process large-scale image batches programmatically.
- Bulk image management: Allow your agent to organize, monitor, and keep track of all AI-generated images created for various projects, enabling efficient large-scale media operations.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `ALL_IMAGES_AI_CHECK_API_KEY` | Check API Key | Validates the current API key and retrieves associated account information. Returns the email address and optional name associated with the API key. Use this action to verify your API key is valid before making other API calls. |
| `ALL_IMAGES_AI_CREATE_WEBHOOK_ENDPOINT` | Create webhook endpoint | Tool to add a webhook endpoint to an API key. Use when you want to receive print event callbacks at a custom endpoint. |
| `ALL_IMAGES_AI_DELETE_IMAGE_GENERATIONS` | Delete Image Generations | Tool to delete one or more image generation batches by their print IDs. Use when you need to remove image generations that are no longer needed. |
| `ALL_IMAGES_AI_GET_CREDITS` | Get Credits Balance | Tool to retrieve the current credits balance for your API key. Use this action to check available credits before making API calls that consume credits. |
| `ALL_IMAGES_AI_GET_WEBHOOK` | Get Webhook | Tool to retrieve webhook details by its ID. Use when you need to check the configuration of an existing webhook endpoint, including its URL and subscribed events. |
| `ALL_IMAGES_AI_LIST_AUTOMATED_IMAGES` | List Automated Images | Tool to list all images generated automatically for bulk use. Use after creating automated-image batches to review and manage them. |
| `ALL_IMAGES_AI_LIST_DOWNLOADED_IMAGES` | List Downloaded Images | Tool to list images that have been downloaded from All-Images.ai. Use this action to retrieve and filter downloaded images with optional date range filtering and pagination support. |

## Supported Triggers

None listed.

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

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

## How to build All images ai MCP Agent with another framework

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

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and All images ai MCP?

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

### Can I manage the permissions and scopes for All images ai while using Tool Router?

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

---
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