Curated

(5/5)

Enhance Customer Chat by Buffering Messages with Twilio and Redis

Step 2. Buffer Incoming Messages [Learn more about using Redis](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.redis) New messages are captured into a list. After X seconds, we get a fresh copy of this list If the last message on the list is the same as the incoming message, then we know no new follow-on messages were sent within the last 5 seconds. Hence the user should be waiting and it is safe to reply. But if the reverse is true, then we will abort the execution here.

Integrations Used (6)

Workflow Structure (13 nodes)

100%

Twilio Trigger

Redis

Add to Messages Stack

Wait

Wait 5 seconds

Redis

Get Latest Message Stack

Should Continue?

Langchain.memory Manager

Get Chat History

Langchain.memory Buffer Window

Window Buffer Memory1

Set

Get Messages Buffer

Langchain.agent

AI Agent

Twilio

Send Reply

No Op

No Operation, do nothing

Langchain.lm Chat Open Ai

OpenAI Chat Model

Langchain.memory Buffer Window

Window Buffer Memory

Node Types:

default

action

logic

trigger

transform

Requires credentials

Prerequisites

Required Credentials (3)

OpenAI

Used by: OpenAI Chat Model

Docs

Redis

Used by: Add to Messages Stack, Get Latest Message Stack

Docs

Twilio

Used by: Twilio Trigger, Send Reply

Docs

Multiple services required

This workflow needs 3 different credentials. Make sure you have accounts for all services before importing.

Complexity

Complex

Advanced features used. Requires n8n experience.

Setup time: ~40 min

Score: 55/100

Conditional Logic

4 Integrations

When to Use This Workflow

Real-time Response

React instantly when events happen - new orders, form submissions, or API calls.

Zero delay between trigger and action for time-sensitive workflows.

Time Savings

Replace repetitive manual tasks with reliable automation.

Reclaim hours every week for higher-value work.

Error Reduction

Eliminate human error from routine processes with consistent automation.

Improve accuracy and reliability across your workflows.

workflow.json

{
  "meta": {
    "instanceId": "26ba763460b97c249b82942b23b6384876dfeb9327513332e743c5f6219c2b8e"
  },
  "nodes": [
    {
      "id": "d61d8ff3-532a-4b0d-a5a7-e02d2e79ddce",
      "name": "OpenAI Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        2660,
        480
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "8gccIjcuf3gvaoEr",
          "name": "OpenAi account"
...