How do I find my Lanhu Access Token?

Log in to Lanhu, go to [User Settings] → [Access Token], and generate a new token. Copy it immediately as it won't be shown again.

Can I read design layer information through the agent?

Yes. Use the `list_layers` tool with the design file ID. This allows your agent to retrieve the layer structure and design nodes for that specific file.

Does Lanhu support team-based organization?

Yes. You can list all your teams using the `list_teams` tool and then explore projects and members specifically for each team using their IDs.

Lanhu MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Lanhu
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Lanhu through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "lanhu": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Lanhu, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Lanhu MCP Server

Empower your AI agent to orchestrate your product design workflow with Lanhu (蓝湖), the premier design collaboration platform for high-performance teams. By connecting Lanhu to your agent, you transform complex design handoffs and project coordination into a natural conversation. Your agent can instantly list your projects, retrieve design file information, audit layer structures, and even browse team comments without you needing to navigate the web interface. Whether you are managing a mobile app design or a large-scale enterprise system, your agent acts as a real-time design coordinator, keeping your assets organized and your production moving.

LangChain's ecosystem of 500+ components combines seamlessly with Lanhu through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Project Orchestration — List all accessible design projects and files across your Lanhu workspace.
Design Auditing — Retrieve detailed metadata about design files, including layers and node structures.
Collaboration Monitoring — Browse file comments and discussions to stay informed about team feedback.
Board Management — Access design boards to understand project organization and milestones.
Team Coordination — List teams and members to manage assignments and participation effectively.

The Lanhu MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Lanhu to LangChain via MCP

Follow these steps to integrate the Lanhu MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Lanhu via MCP

Why Use LangChain with the Lanhu MCP Server

LangChain provides unique advantages when paired with Lanhu through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Lanhu MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Lanhu queries for multi-turn workflows

Lanhu + LangChain Use Cases

Practical scenarios where LangChain combined with the Lanhu MCP Server delivers measurable value.

RAG with live data: combine Lanhu tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Lanhu, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Lanhu tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Lanhu tool call, measure latency, and optimize your agent's performance

Lanhu MCP Tools for LangChain (10)

These 10 tools become available when you connect Lanhu to LangChain via MCP:

get_board

Get board details

get_comments

Get file comments

get_file

Get design file info

get_project

Get project details

list_boards

List project boards

list_layers

List file layers

list_members

List team members

list_project_files

g., from Sketch, Figma, XD) within a specific project. List project design files

list_team_projects

List team projects

list_teams

List all Lanhu teams

Example Prompts for Lanhu in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Lanhu immediately.

"List all my design projects on Lanhu."

"Show me the comments for design file 'checkout-v1'."

"List the layers for file 'homepage-main'."

Troubleshooting Lanhu MCP Server with LangChain

Common issues when connecting Lanhu to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Lanhu + LangChain FAQ

Common questions about integrating Lanhu MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.