How to Use the MoEngage MCP in LangChain

Q: Can my LangChain agent automatically segment users using MoEngage?

Yes, your agent queries listsegments to identify the right audience and then uses that ID to trigger targeted actions. It makes the decision based on your defined chain logic.

Build event-driven chains in LangChain by connecting your agent directly to MoEngage campaign and customer data.

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Connect MoEngage MCP to LangChain

Create your Vinkius account to connect MoEngage 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 MoEngage with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Trigger personalized push notifications with LangChain

Your agent uses `send_push` to fire targeted messages based on live data flowing through your chain. It handles the payload construction so you don't have to manually format request objects. This keeps your agent loop moving without context switching. You define the logic in your chain, and the MCP Server executes the delivery instantly.

Process bulk user events within LangChain pipelines

Use `track_events_bulk` to push high-volume interaction data directly from your processing nodes into your growth stack. It batches the records to keep your pipeline throughput high and latency low. Your agent decides when to flush these events based on the chain state. This ensures your customer profiles stay current without blocking the primary reasoning flow.

Manage customer lifecycle data inside LangChain

Your agent calls `upsert_customer` or `get_customer` to read and write profile state as part of a multi-step decision task. It maps the returned JSON directly into your agent's memory for immediate use. This allows your chain to pivot strategies based on real-time segment membership. You see exactly how the agent updates individual records in your LangSmith traces.

Setup guide

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

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Common questions about MoEngage MCP in LangChain

LangSmith captures every tool invocation automatically. You can inspect the inputs and outputs of `check_moengage_status` or any other function directly in your trace dashboard.

Yes, your agent queries `list_segments` to identify the right audience and then uses that ID to trigger targeted actions. It makes the decision based on your defined chain logic.

The server is stateless, but you maintain context by using the client session features. This allows your LangChain agent to remember previous interactions with the API.

Wrap your tool calls in standard error-handling blocks within your chain. If a call fails, the agent receives the error message and can decide whether to retry or move to a fallback step.

The server uses ephemeral connections for every request. We only handle the specific customer profile data you pass through the tools, and no data is stored on our end.

Use it with your favorite AI tools

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