How to Use the Leonardo.ai (Generative AI & Models) MCP in OpenAI Agents SDK

Q: Can an agent in OpenAI Agents SDK perform image-to-image tasks using Leonardo.ai (Generative AI & Models)?

Yes. The agent calls uploadinitimage to secure a presigned URL, uploads your source asset, and then passes that reference to the image generation tool to start the run.

Q: How does Leonardo.ai (Generative AI & Models) handle long-running generation polling in OpenAI Agents SDK?

The agent initiates the run with generateimage and receives a job ID. It then schedules periodic checks using getgeneration until the status changes from pending to completed, returning the final image array.

Q: Can I restrict which Leonardo models my agents can access?

You configure this by parsing the output of listplatformmodels within your agent's system prompt or guardrail functions, ensuring the agent only selects approved base models.

Build production agent networks using OpenAI Agents SDK to programmatically generate and manage Leonardo.ai visual assets.

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Connect Leonardo.ai (Generative AI & Models) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Leonardo.ai (Generative AI & Models) to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Leonardo.ai (Generative AI & Models) with OpenAI Agents SDK

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Control image generation loops in OpenAI Agents SDK

Your OpenAI Agents SDK python agents can kick off Leonardo.ai visual generations directly during conversational flows. By calling `generate_image`, the agent gets a tracking identifier, allowing it to poll `get_generation` asynchronously without blocking the main event loop. Here's the thing: instead of waiting on slow Leonardo.ai image rendering times, the system manages state transitions behind the scenes while keeping user interactions fast and fluid.

Track custom model parameters across agent handoffs

When delegating tasks between specialized OpenAI Agents SDK agents, you need to pass specific Leonardo.ai model configurations. The agent uses `list_custom_models` to find fine-tuned styles on your Leonardo workspace, then inspects parameters with `get_model` to verify compatibility before running inference. Because OpenAI Agents SDK enforces strict guardrails, you can programmatically validate that the selected model matches the creative brief before executing the generation step.

Clean up generation history with this MCP Server

Keep your Leonardo.ai workspace clean by letting your OpenAI Agents SDK agents manage their own storage footprints. The agent checks recent assets using `list_user_generations` and removes unwanted variations or test runs by invoking `delete_generation`. This MCP Server integration ensures your OpenAI Agents SDK agent acts as a responsible tenant, pruning old Leonardo.ai assets automatically based on your retention rules.

Setup guide

Set up Leonardo.ai (Generative AI & Models) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Leonardo.ai (Generative AI & Models) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Leonardo.ai (Generative AI & Models) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Leonardo.ai (Generative AI & Models) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Leonardo.ai (Generative AI & Models) Agent",
            instructions="You have access to Leonardo.ai (Generative AI & Models) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Leonardo.ai. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Leonardo.ai (Generative AI & Models) MCP in OpenAI Agents SDK

You pass your Vinkius endpoint token when configuring the HTTP server parameters. The SDK handles the transport layer, letting your agents auto-discover tools like `get_user` with zero manual schema definition.

Yes. The agent calls `upload_init_image` to secure a presigned URL, uploads your source asset, and then passes that reference to the image generation tool to start the run.

The agent initiates the run with `generate_image` and receives a job ID. It then schedules periodic checks using `get_generation` until the status changes from pending to completed, returning the final image array.

You configure this by parsing the output of `list_platform_models` within your agent's system prompt or guardrail functions, ensuring the agent only selects approved base models.

Your credentials stay secured in the Vinkius vault, never exposed to the agent runtime. Only temporary presigned URLs for image uploads and output image links pass through the connection.

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