AI Agents

Automated workflow

39 nodes 213 129 Automatic trigger
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Workflow Description

An intelligent automated workflow that manages customer conversations across multiple channels using an AI agent powered by Gemini, maintains conversation context, and retrieves relevant information from a vector database.

How it works

  1. 1.Receive incoming message via Chat Trigger or webhook from customer platform
  2. 2.Process message through intelligent agent using Gemini for understanding and generating responses
  3. 3.Search Vector Store Qdrant for contextually relevant information and knowledge base entries
  4. 4.Maintain conversation history and context using memory buffer window
  5. 5.Apply conditional logic through switch nodes to route different query types appropriately
  6. 6.Send response back to customer through the connected channel or application

Use cases

  • Intelligent customer support agent that answers common questions and retrieves answers from a knowledge database
  • Multi-channel conversation management on Twitter/X and chat platforms while preserving full conversation context
  • Automated analysis and summarization of extended customer conversations for insights and quality improvement

Requirements

  • Active API keys for Google Gemini service and configured Qdrant vector database instance
  • Connected social media and messaging platform accounts with proper authentication credentials
  • Historical customer data available for model fine-tuning to improve response quality and accuracy

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.

Apps Used

AI Agent Chat Trigger Gemini Memory Vector Store Twitter/X

Details

Trigger Automatic trigger
Nodes 39
Apps 6
Views 213
Downloads 129

How to Use

  1. 1.Click "Download Template"
  2. 2.Open your n8n dashboard
  3. 3.Go to Workflows > Import from File
  4. 4.Select downloaded file and configure credentials

Nodes Used (39)

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Query Classification

Agent

#1

Switch

Switch

#2

Factual Strategy - Focus on Precision

Agent

#3

Analytical Strategy - Comprehensive Coverage

Agent

#4

Opinion Strategy - Diverse Perspectives

Agent

#5

Contextual Strategy - User Context Integration

Agent

#6

Chat

Chat Trigger

#7

Factual Prompt and Output

Set

#8

Contextual Prompt and Output

Set

#9

Opinion Prompt and Output

Set

#10

Analytical Prompt and Output

Set

#11

Gemini Classification

Gemini Model

#12

Gemini Factual

Gemini Model

#13

Gemini Analytical

Gemini Model

#14

Chat Buffer Memory Analytical

Memory Buffer Window

#15

Chat Buffer Memory Factual

Memory Buffer Window

#16

Gemini Opinion

Gemini Model

#17

Chat Buffer Memory Opinion

Memory Buffer Window

#18

Gemini Contextual

Gemini Model

#19

Chat Buffer Memory Contextual

Memory Buffer Window

#20

Embeddings

Gemini Model

#21

Sticky Note

Sticky Note

#22

Sticky Note1

Sticky Note

#23

Sticky Note2

Sticky Note

#24

Sticky Note3

Sticky Note

#25

Concatenate Context

Summarize

#26

Retrieve Documents from Vector Store

Vector Store Qdrant

#27

Set Prompt and Output

Set

#28

Gemini Answer

Gemini Model

#29

Answer

Agent

#30

Chat Buffer Memory

Memory Buffer Window

#31

Sticky Note4

Sticky Note

#32

Sticky Note5

Sticky Note

#33

Respond to Webhook

Webhook

#34

Sticky Note6

Sticky Note

#35

When Executed by Another Workflow

Execute Workflow Trigger

#36

Combined Fields

Set

#37

Sticky Note7

Sticky Note

#38

Sticky Note8

Sticky Note

#39