How to Use the Typebot MCP in LangChain

Q: Can I monitor bot deployments using LangChain?

Yeah, you can monitor it. After calling publishtypebot, your agent can immediately call gettypebotdetails to verify the structure of the newly deployed version and ensure the schema didn't break.

Q: How does Typebot integrate with LangChain for data analysis?

The listtypebotresults tool gathers all collected user responses. Your LangChain agent reads this structured data, allowing you to analyze lead flow patterns and identify common points of failure in the bot's logic.

Q: What if I need to manage multiple bots with LangChain?

You can list all available conversational typebots using listtypebots or find which workspaces contain them via listworkspaces. Your agent uses this information to scope its actions efficiently.

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Connect Typebot MCP to LangChain

Create your Vinkius account to connect Typebot 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 Typebot with LangChain

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Automating Bot Deployment Pipelines

The `publish_typebot` tool lets your agent push the latest bot changes directly to production. This is critical for building reliable multi-step chains; you can't verify a workflow until it's live. The chain output of a successful deployment (e.g., confirmation ID) becomes the input for the next step, like logging or notifying a user via another API call.

Running Bot Conversation Tests

Need to test a new bot flow before launch? Use `start_chat_session` to programmatically kick off a conversation. Your agent can then observe the full interaction lifecycle, treating it like any other data point in the chain. This lets you build conditional logic into your ReAct agents—if the chat session hits Step 3, call Tool X; otherwise, proceed with step Y.

Analyzing Bot Performance Data

The `list_typebot_results` tool collects user responses for analysis. Your agent can iterate through these results and aggregate metrics—say, counting how often users fail at a specific question. This raw data becomes the input payload that feeds into a downstream analytics service or database write operation.

Setup guide

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

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Common questions about Typebot MCP in LangChain

LangChain uses Typebot's tools as discrete steps in your agent chain. The output from a tool like `list_typebots` becomes the context that determines which subsequent tool should run next, allowing for multi-step reasoning.

Yeah, you can monitor it. After calling `publish_typebot`, your agent can immediately call `get_typebot_details` to verify the structure of the newly deployed version and ensure the schema didn't break.

The `list_typebot_results` tool gathers all collected user responses. Your LangChain agent reads this structured data, allowing you to analyze lead flow patterns and identify common points of failure in the bot's logic.

You can list all available conversational typebots using `list_typebots` or find which workspaces contain them via `list_workspaces`. Your agent uses this information to scope its actions efficiently.

This MCP Server touches collected user responses. When you analyze these results, your agent handles the structured text and metadata associated with those interactions.

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