How to Use the Lanhu MCP in LangChain

Q: Can Lanhu and LangChain handle complex project hierarchies?

Yes. You chain listteams into listteamprojects to traverse large organizations. The agent manages the state between these calls to map out the entire project tree.

Chain your design data into automated pipelines with the Lanhu MCP Server and LangChain.

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Connect Lanhu MCP to LangChain

Create your Vinkius account to connect Lanhu 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 Lanhu with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build agentic design-to-code chains

Feed your design metadata directly into reasoning chains. Use `list_teams` and `list_team_projects` to locate assets, then pass those IDs into `get_project` to trigger automated build sequences. Your agent handles the logic between steps. It knows exactly when to call `list_layers` to inspect component properties before generating code, keeping your repository in sync with the latest design changes.

Trace design handoff logic

Monitor every tool interaction through your existing observability stack. When the agent calls `get_file` to pull assets, LangSmith captures the full input and output context for your audit trail. Debugging becomes a matter of checking the chain. If a component fails to render, you see exactly which `list_project_files` output caused the issue, allowing for surgical fixes in your pipeline.

Automate feedback loops

Connect design comments to your development workflow. Your agent uses `get_comments` to pull feedback from designers and injects that text into your documentation or issue tracking system. This closes the loop between design intent and implementation. By linking `get_board` data with your internal tools, your agent ensures no design critique goes ignored during the build process.

Setup guide

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

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Common questions about Lanhu MCP in LangChain

It exposes design tools as executable functions for your agents. You register these tools via the adapter, allowing the agent to call them whenever it needs live design data.

Yes. You chain `list_teams` into `list_team_projects` to traverse large organizations. The agent manages the state between these calls to map out the entire project tree.

Absolutely. Since every tool call is a standard function execution, your tracing middleware logs the arguments and results of every request, including those from `get_file`.

Provide your endpoint token during client initialization. The server handles the handshake, keeping your credentials out of the agent's logic layer.

Your design files remain within the Lanhu infrastructure. The server only transmits the specific metadata or asset links you request during a session.

Use it with your favorite AI tools

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

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