How to Use the QuickReply.ai MCP in LangChain

Q: Can LangChain agents trigger drip campaigns using this QuickReply.ai Alternative?

Yes. You just call triggerdripcampaign inside your agent's tool execution block. It works as a standard step in your chain.

Chain your WhatsApp messaging logic directly into LangChain workflows for precise, multi-step automated customer engagement.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect QuickReply.ai MCP to LangChain

Create your Vinkius account to connect QuickReply.ai to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup QuickReply.ai with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Sequence your messaging logic

Build complex chains where `trigger_journey_event` acts as the primary signal for your messaging flow. Your agent evaluates the context and decides when to fire these events based on previous chain outputs. Pass data between tools without manual intervention. The output from `fetch_campaign_stats` flows directly into your logic to determine if you need to fire a follow-up `trigger_drip_campaign`.

Direct WhatsApp message control

Send session messages or pre-approved templates using `send_session_message` and `send_template` within your agent's execution loop. This MCP Server gives your chain full access to the WhatsApp Business API. Your agent handles the logic for message selection. It checks your requirements and selects the right template to ensure compliance and high engagement rates.

Native MCP Server integration

Connect this MCP Server to your LangGraph pipeline using standard adapters. Your agent treats these messaging tools as first-class citizens in its reasoning process. Everything happens inside your existing infrastructure. You get full control over the execution order without relying on external dashboard triggers.

Setup guide

Set up QuickReply.ai 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 QuickReply.ai 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({
    "quickreplyai-alternative-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 QuickReply.ai transactions"
    })
    print(result["messages"][-1].content)

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