How to Use the Imagine.io MCP in LangChain

Q: How do I configure the Imagine.io MCP Server in LangChain?

Install the adapter using pip install langchain-mcp-adapters langgraph. Then, initialize MultiServerMCPClient pointing to your Vinkius endpoint, fetch the tools with client.gettools(), and pass them to your agent.

Q: Can LangChain handle long-running Imagine.io render jobs?

Yes. Your agent calls createrenderjob to start the task, then uses a loop with getjobstatus to check the progress. LangChain manages this state across chain steps, letting you pause or branch logic based on the status.

Q: How do I inspect materials before rendering in Imagine.io with LangChain?

Your agent uses listmaterials to fetch the available options. It then feeds those material IDs directly into the createrenderjob tool within the same chain to ensure the correct texture is applied.

Build multi-step 3D rendering pipelines in LangChain using direct API access to your Imagine.io assets.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Imagine.io MCP to LangChain

Create your Vinkius account to connect Imagine.io to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Imagine.io with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate 3D render queues inside LangChain chains

The `create_render_job` tool starts asynchronous 3D product render jobs directly from your agentic workflow. Your LangChain agent triggers the render, grabs the job ID, and feeds it into the next link in your chain to monitor progress without manual intervention. You can track these operations by passing the output to `get_job_status`. Since LangChain supports LangSmith tracing, you can monitor the latency and token usage of these render loops in real-time.

Query and inspect active 3D scenes

The `list_scenes` tool fetches all available templates from your account, giving your agent the context it needs to choose the right background. It pairs with `get_scene` to retrieve specific spatial layouts and dimensions before you write any rendering code. This setup lets you build complex ReAct loops where the agent inspects the scene parameters, checks the available materials with `list_materials`, and decides which assets to load. You get a clean, trace-ready pipeline that handles the logic step-by-step.

Manage product assets with this MCP Server

The `list_products` tool retrieves your catalog inventory along with associated metadata and render statuses. Your LangChain agent uses this data to verify which items need updated visuals before calling downstream tools. This MCP Server connects your catalog to over 500 integrations in the LangChain ecosystem. You can pull product details with `get_product` and immediately pipe them into external databases or inventory management systems.

Setup guide

Set up Imagine.io MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Imagine.io tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "imagineio-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Imagine.io transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Imagine.io. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Imagine.io now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Imagine.io MCP in LangChain

Install the adapter using `pip install langchain-mcp-adapters langgraph`. Then, initialize `MultiServerMCPClient` pointing to your Vinkius endpoint, fetch the tools with `client.get_tools()`, and pass them to your agent.

Yes. Your agent calls `create_render_job` to start the task, then uses a loop with `get_job_status` to check the progress. LangChain manages this state across chain steps, letting you pause or branch logic based on the status.

Your agent uses `list_materials` to fetch the available options. It then feeds those material IDs directly into the `create_render_job` tool within the same chain to ensure the correct texture is applied.

By default, the client is stateless. If you need to preserve rendering context or previous job IDs across multiple steps, use `client.session()` to maintain a persistent context window.

Vinkius runs the server in an isolated V8 sandbox, ensuring your account credentials and 3D scene data never leak. Only the specific job parameters and scene metadata requested by your tool calls are processed, keeping your proprietary models safe.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript