How to Use the Flow XO MCP in LangChain

Q: Where does my Flow XO message history data go when using LangChain?

Your message history, retrieved by listuserhistory, stays strictly within your local LangChain runtime and the Vinkius sandboxed environment. Vinkius runs this MCP Server in an isolated container, ensuring your chatbot user logs are never stored or leaked to third parties.

Trigger Flow XO workflows and manage chatbot users directly within your LangChain reasoning loops.

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Connect Flow XO MCP to LangChain

Create your Vinkius account to connect Flow XO 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 Flow XO with LangChain

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Chain Flow XO Actions with LangChain

This MCP Server exposes `trigger_webhook` and `toggle_workflow` to let your LangChain agents control automation states dynamically. Your agent analyzes the user's intent, decides to toggle a specific sequence, and fires the webhook to initiate the external work. Every step runs as a discrete node in your chain, passing outputs like webhook execution statuses directly to the next prompt. You watch the entire execution path in LangSmith, tracking exactly how your agent handles workflow states without guessing.

Contextual Chatbot User Management

The `get_user_details` tool pulls profile data so your agent can personalize its next conversational turn. If the user profile is missing, the agent runs `create_user` to register them on the fly. You don't have to write custom glue code to connect this MCP Server. The LangChain agent handles the conditional logic, pulling history with `list_user_history` to decide if a profile needs an update via `update_user`.

Data-Driven Broadcast Triggers

The `list_broadcasts` tool gives your chain historical broadcast performance data to guide messaging decisions. When analytics show low engagement via `get_automation_analytics`, your agent initiates corrective campaigns. Using this performance context, the agent fires `send_push_message` to targeted segments returned by `list_chatbot_users`. This creates a closed loop where your LangChain framework measures, decides, and acts.

Setup guide

Set up Flow XO 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 Flow XO 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({
    "flow-xo-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 Flow XO 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 Flow XO. 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 Flow XO MCP in LangChain

You pass your Flow XO API key as an environment variable when initializing the MultiServerMCPClient. The client securely attaches this credential to every tool call, including `list_workflows` and `trigger_webhook`, so your agent never exposes raw keys.

Yes, your agent can read the conversation history, analyze the user's mood, and execute `toggle_workflow` to pause or resume specific sequences. It uses the output of the sentiment analysis step to determine whether to enable or disable the automation.

LangChain handles rate limits through its standard runnable retry configurations. If `send_push_message` or `get_user_details` hits a rate limit, the chain backs off and retries the tool call according to your defined policy.

Yes. You can query a local database for a user ID, then pass that ID to `get_user_details` or `list_user_history` within the same LangGraph execution. This lets you merge internal business data with live chatbot profiles.

Your message history, retrieved by `list_user_history`, stays strictly within your local LangChain runtime and the Vinkius sandboxed environment. Vinkius runs this MCP Server in an isolated container, ensuring your chatbot user logs are never stored or leaked to third parties.

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