How to Use the Yonyou / 用友 MCP in LangChain

Q: What happens when I query Yonyou / 用友 sales data with LangChain?

The agent pulls the details using listsalesorders. You get a list of all orders, and you can then refine that search by calling getsalesorder for specific IDs to see line-item breakdowns.

Build Complex Business Logic Chains with LangChain using Yonyou / 用友’s ERP Data

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Connect Yonyou / 用友 MCP to LangChain

Create your Vinkius account to connect Yonyou / 用友 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 Yonyou / 用友 with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Execute multi-step finance workflows

You'll use `get_purchase_order` to pull details on a specific order. Then, your agent can take those results and pass them into `list_vouchers` to find related financial documentation. This allows the agent to chain actions together: first check the purchase order status, then query for associated vouchers, giving you deep visibility into the transaction lifecycle.

Map customer accounts and suppliers

Start by listing all known clients using `list_customers`. You can follow that up with a call to `get_sales_order` to see exactly which transactions were run against those account IDs. If you need to know who supplied the goods, your agent pulls data from `list_suppliers`, keeping the entire context of customer relationships and supply chains in one continuous chain.

Check inventory and master materials

The process starts with querying current stock levels via `get_inventory`. You don't need to guess what material code means; you check the definitive list using `list_materials` first. This lets your agent build a reliable chain: identify the correct material ID, check its inventory level, and then potentially pull up all historical sales records for that item via `list_sales_orders`.

Setup guide

Set up Yonyou / 用友 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 Yonyou / 用友 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({
    "yonyou-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 Yonyou / 用友 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 Yonyou / 用友. 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 Yonyou / 用友 MCP in LangChain

Your agent decides the flow. You start by calling a tool like `list_purchase_orders`, and the output of that call feeds directly into another step, such as using `get_org_info` to filter results geographically.

Yes. Because the MCP Server is observable via LangSmith tracing, you can pinpoint exactly how long each tool call takes and which step slows down your overall chain.

The agent pulls the details using `list_sales_orders`. You get a list of all orders, and you can then refine that search by calling `get_sales_order` for specific IDs to see line-item breakdowns.

The client supports stateless operation by default, but you can use `client.session()` if you need the agent to remember context across multiple consecutive tool calls.

The server touches detailed records about financial voucher details, including associated IDs and transaction summaries. This is crucial for auditing purposes.

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