How to Use the autoRetouch MCP in LangChain

Chain autoRetouch workflows directly in LangChain to automate your product image processing pipeline.

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Connect autoRetouch MCP to LangChain

Create your Vinkius account to connect autoRetouch 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.

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Setup autoRetouch with LangChain

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Claude

Perplexity

Sequence image processing in LangChain

Trigger automated retouching directly from your reasoning chains. Use `create_execution` to kick off tasks as part of a larger pipeline. The agent handles the logic, moving from job submission to status tracking without manual oversight.

Verify batch status using MCP Server tools

Check the progress of your image processing tasks by hooking into `get_batch` or `list_executions` within your agent graph. This keeps your pipeline aware of state changes, allowing the system to react when an image finishes processing.

Monitor wallet credits via LangChain

Keep tabs on your spending by calling `get_wallet_balance` inside your agent's decision loop. If the balance drops below your threshold, the chain can halt execution to prevent overages.

Setup guide

Set up autoRetouch 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 autoRetouch 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({
    "autoretouch-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 autoRetouch transactions"
    })
    print(result["messages"][-1].content)

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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 autoRetouch. 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.

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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 autoRetouch MCP in LangChain

Install the necessary MCP adapters and initialize the MultiServerMCPClient. Point the client to your Vinkius endpoint to expose all 11 tools to your agent.

Yes, you can pipe tool outputs into LangSmith. This lets you trace every `get_execution` call to see exactly what happened during a failed run.

It is. You can pass the tools directly into your agent definition to let it decide when to list workflows or start a new job.

It returns standard JSON responses for every tool call. Your agent can catch these errors and retry or notify you based on the status code.

We only touch your image metadata and workflow configurations. Your actual product photos stay on the server, and the MCP connection only passes identifiers back to your agent.

Use it with your favorite AI tools

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

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