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AI chatbot for website with conditional execution for cost efficiency

Amjid AliAmjid Ali
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2/3/2026
n8n
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AI Chatbot with Conditional Execution for Cost Efficiency

Description

This n8n workflow implements an AI-powered chatbot that only runs when a chat is initiated on a website. By introducing a conditional step, the workflow ensures that AI tokens are not consumed unnecessarily, making it a cost-efficient and resource-optimized solution.

The chatbot, named Sophia, serves as an interactive assistant for SyncBricks. It helps users with guest posting services, YouTube review videos, IT consultancy, and online courses while collecting user details step by step. The chatbot ensures that inquiries are properly logged and confirmed before proceeding to AI-driven responses.

This template is ideal for businesses, service providers, and content creators who want to optimize AI token usage while delivering personalized, interactive engagement with their users.

Features

  1. Conditional Execution – The AI chatbot only activates when a chat is initiated, avoiding unnecessary API calls.
  2. AI-Powered Conversations – Uses Google Gemini AI to generate human-like responses.
  3. Step-by-Step Data Collection – Ensures structured user input, requesting name, email, and request type sequentially.
  4. Memory Buffer for Context Awareness – Maintains conversation context using a window buffer memory system.
  5. Multiple Service Offerings – Supports inquiries related to:
    • Guest Posting Services
    • YouTube Review Videos
    • Online Courses on Udemy
    • IT Consultancy Services
  6. Automated Confirmation Messages – After collecting user details, sends a confirmation message summarizing the request.

How It Works

  1. Chat Message Trigger

    • The workflow starts only when a chat message is received from the website.
    • This ensures no AI token is consumed unless a user initiates a chat.

  2. Condition Check: Is Chat Input Provided?

    • The workflow checks if chat input is non-empty.
    • If the chat input is empty, the workflow stops, ensuring no unnecessary API usage.
    • If a message is detected, the chatbot continues processing.

  3. AI-Powered Chat Response

    • The chatbot, Sophia, generates personalized responses using Google Gemini AI.
    • AI ensures structured conversation flow by collecting:
      • User’s Full Name
      • Email ID
      • Request Type

  4. Memory Buffer for Context Retention

    • A Window Buffer Memory system stores chat history and retrieves previous responses to ensure context-aware conversations.

  5. Response Optimization

    • Checks memory to avoid asking the same question twice.
    • If details are already provided, Sophia moves directly to processing the request.

  6. Confirmation & User Engagement

    • After collecting the required details, Sophia summarizes the request as follows:
      • "Got it [Name], your request is [Request Type]. I will be sending the details to your email ID: [Email]. Hold on while I send confirmation."

  7. Final Confirmation Message

    • Ensures the user receives a proper acknowledgment of their inquiry.

Prerequisites

Before using this workflow, make sure you have:

  1. n8n Instance (Cloud or Self-Hosted)
  2. Google Gemini API Key (For AI-generated responses)
  3. Webhook Integration (To trigger the chatbot from your website)

Use Cases

  1. Businesses & Enterprises – AI-powered lead qualification for services.
  2. Bloggers & Content Creators – Automated guest post inquiry handling.
  3. YouTube Influencers & Educators – AI chatbot to promote courses and review services.
  4. Marketing Agencies – Lead generation chatbot without excessive AI token consumption.
  5. E-Commerce & Consulting Services – AI-driven personalized customer engagement.

Nodes Used in This Workflow

  1. Chat Trigger (Webhook) – Initiates only when a user sends a chat message.
  2. Conditional Check (If Node) – Ensures AI is only used when a chat is initiated.
  3. AI Agent (Google Gemini AI) – Generates intelligent chatbot responses.
  4. Memory Buffer (Context Retention) – Stores user inputs for context-aware conversations.

Important

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Creator Information

Developed by: Amjid Ali
Website: SyncBricks
Email: amjid@amjidali.com
LinkedIn: Amjid Ali
YouTube: SyncBricks

Support & Contributions

If you find this workflow helpful, consider supporting my work:

Donate via PayPal

For full courses on n8n, visit:

Course by Amjid

Final Thoughts

This n8n workflow ensures optimal AI token usage while engaging users with an intelligent chatbot. By integrating conditional execution, it prevents unnecessary API calls, making it cost-effective and efficient for businesses looking to automate chat-based customer interactions.

Let me know if you need any modifications!

AI Chatbot for Website with Conditional Execution for Cost Efficiency

This n8n workflow provides a robust and cost-efficient AI chatbot solution for websites. It leverages LangChain agents and Google Gemini to respond to user queries, with a conditional execution strategy to optimize API usage and associated costs.

Description

This workflow automates the process of responding to incoming chat messages on a website using an AI agent. It intelligently decides whether to process a chat message through the AI agent based on a predefined condition, ensuring that AI resources are only utilized when necessary, thereby improving cost efficiency.

What it does

  1. Listens for Incoming Chat Messages: The workflow is triggered whenever a new chat message is received, acting as the entry point for user interactions.
  2. Conditionally Processes Messages: It evaluates each incoming chat message against a specified condition. This allows for flexible logic, such as only processing messages that meet certain criteria (e.g., specific keywords, user roles, or message length).
  3. Initializes Chat Memory (if condition met): If the condition is met, a simple memory buffer is initialized to maintain context for the AI agent during the conversation. This ensures the chatbot can remember previous turns in the conversation.
  4. Utilizes Google Gemini Chat Model (if condition met): The Google Gemini Chat Model is configured as the underlying language model for the AI agent, providing advanced conversational capabilities.
  5. Engages AI Agent (if condition met): An AI Agent (LangChain Agent) is activated to process the chat message, using the configured memory and language model to generate a relevant response.

Prerequisites/Requirements

  • n8n Instance: A running instance of n8n (self-hosted or cloud).
  • LangChain Nodes: Ensure the LangChain nodes are installed and enabled in your n8n instance.
  • Google Gemini API Key: An API key for the Google Gemini Chat Model, configured as a credential in n8n.
  • Chat Integration: A chat platform or website integration that can send messages to the n8n "Chat Trigger" node (e.g., a custom webhook, a specific chat platform integration).

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Gemini API key as a credential in n8n.
  3. Configure "When chat message received" Node:
    • Ensure this node is correctly integrated with your website's chat system to receive incoming messages.
  4. Configure "If" Node:
    • Define the condition(s) under which the AI agent should be invoked. This is crucial for cost efficiency. For example, you might check for specific keywords in the message or only process messages during business hours.
  5. Configure "Simple Memory" Node:
    • (Optional) Adjust memory settings if needed, though the default "Simple Memory" is usually sufficient for basic conversational context.
  6. Configure "Google Gemini Chat Model" Node:
    • Select your Google Gemini API Key credential.
    • (Optional) Adjust model parameters like temperature, top_k, etc., to fine-tune the AI's responses.
  7. Configure "AI Agent" Node:
    • Ensure the "Simple Memory" and "Google Gemini Chat Model" are correctly linked.
    • (Optional) Add specific tools to the AI agent if it needs to perform actions beyond just chatting (e.g., fetching data, making API calls).
  8. Activate the Workflow: Once configured, activate the workflow to start processing chat messages.

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