How to Use the Landbot MCP in LangChain

Q: Can LangChain search for specific Landbot users?

Yes, it looks them up directly. The agent calls searchcustomers with an email address to find the exact user ID before triggering other actions.

Build multi-step conversational routing pipelines with the Landbot MCP Server and LangChain.

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Connect Landbot MCP to LangChain

Create your Vinkius account to connect Landbot 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 Landbot with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build dynamic routing chains in LangChain

Your LangChain agent pulls exact conversational context using `get_messages` and `get_customer`. Instead of guessing why a user is stuck, the chain retrieves the exact chat sequence and metadata. ReAct agents evaluate this payload immediately. You define the logic that triggers the next step. If the agent detects high frustration in the chat history, it hands the session off automatically. The output of the fetch operation becomes the exact input for your routing node.

Intervene with live agents via MCP Server

The `assign_agent` tool lets your agent pull a human into the loop based on chain conditions. When a specific threshold is met, the system bypasses the automated flow. You map the exact agent ID to the request. LangSmith tracks every handoff attempt. You see the latency of the assignment call and verify if the routing logic fired correctly. This makes debugging complex escalation paths straightforward.

Dispatch targeted responses

The `send_text_message` operation pushes text directly into a live Landbot conversation from your LangChain pipeline. The agent constructs the response based on vector store lookups or database queries, then fires it back to the user. You string together `search_customers` to find the user by email, generate the answer, and send it out via the MCP connection. The entire sequence runs autonomously while maintaining full auditability in your tracing dashboard.

Setup guide

Set up Landbot 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 Landbot 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({
    "landbot-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 Landbot 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 Landbot. 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

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Real-time monitoring

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Built-in savings

60%

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Single dashboard

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place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Landbot MCP in LangChain

Install the langchain-mcp-adapters package. Pass your endpoint URL to MultiServerMCPClient and call get_tools() to bind the operations to your agent.

Yes, it looks them up directly. The agent calls search_customers with an email address to find the exact user ID before triggering other actions.

LangSmith captures every operation. You get full visibility into the token consumption and latency of calls like list_customers or get_bot.

You build the ReAct logic. The agent evaluates the output from get_messages and decides whether to resolve the query or trigger an escalation.

The V8 Isolate Sandbox drops all memory after execution. When your chain pulls chat sequences via get_messages, that conversation data passes strictly to your local environment. Vinkius retains zero customer records.

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