How to Use the Yitu Technology / 依图科技 MCP in LangChain

Q: Can I use Yitu Technology / 依图科技's liveness tools in LangChain?

Yeah, you can. You can chain face detection with both detectactiveliveness and detectsilentliveness. This is perfect for building robust anti-spoofing checkpoints into your user flow.

Build complex identity verification chains using Yitu Technology / 依图科技 with LangChain.

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Connect Yitu Technology / 依图科技 MCP to LangChain

Create your Vinkius account to connect Yitu Technology / 依图科技 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 Yitu Technology / 依图科技 with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-step Identity Verification

Need to verify a user? You can build an agent that first calls `detect_face` on an image, then uses the resulting data to check liveness with `detect_active_liveness`. The output of these two tools becomes the input for the next step in your chain. This process lets you create complex workflows where one tool's result dictates which other function runs. It’s about building a decision tree, not just running commands.

Repository Management and Search

The agent can manage user records entirely through the MCP Server. You use `create_face_repo` to set up storage, then populate it with faces using `add_face_to_repo`. When verification is needed, the chain calls `search_face_in_repo`, returning top matches and confidence scores. It’s a full loop: setup the repo, add data, and run complex searches. The whole sequence flows through your LangChain agent.

Data Extraction and Moderation

Don't just process faces; handle surrounding data too. An agent can use `ocr_id_card` to pull text from an ID image, then send that image through `moderate_image`. The resulting structured data (the extracted text) gets passed along the chain for further processing. This sequence lets you build systems that validate both the person and the content they are interacting with.

Setup guide

Set up Yitu Technology / 依图科技 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 Yitu Technology / 依图科技 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({
    "yitu-technology-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 Yitu Technology / 依图科技 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 Yitu Technology / 依图科技. 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 Yitu Technology / 依图科技 MCP in LangChain

It gives your AI client structured access to computer vision tools. Instead of just calling a single API endpoint, you build multi-step chains where the agent decides which function to call next based on intermediate results.

This MCP Server handles biometric data (faces), document text (via `ocr_id_card`), and image content for moderation. The results are all passed as structured outputs through your LangChain chains.

Yeah, you can. You can chain face detection with both `detect_active_liveness` and `detect_silent_liveness`. This is perfect for building robust anti-spoofing checkpoints into your user flow.

Yes. The client supports aggregating multiple servers, so you can run this Yitu Technology / 依图科技 server alongside other services in a single complex chain.

The primary data types are image files (for detection and moderation), structured facial vectors, and text strings extracted from documents like ID cards.

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