This article provides a complete, practical walkthrough of the Http Googlesheets Automate Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating Payslip Data Extraction with Line Chatbot and Google Gemini in n8n
   
   Meta Description:
   Discover how to build a no-code Line chatbot that uses Google Gemini to analyze image-based payslips and extract structured financial data directly into Google Sheets using n8n.
   
   Keywords:
   n8n workflow, Line chatbot, Google Gemini, payslip OCR, image data extraction, AI chatbot, no-code automation, document processing, Google Sheets integration, langchain, Gemini AI, conversational AI
   
   Third-Party APIs Used:
   
   1. Line Messaging API – for receiving and replying to user messages sent via the Line app.
   2. Google Gemini (via LangChain plugin) – for both text and image-based AI processing.
   3. Google Sheets API – for writing extracted structured data to a Google Sheet.
   4. LangChain – to enable AI-driven conversation and image analysis logic inside n8n.
   
   —
   
   Article:
   
   Automating Payslip Data Extraction with Line Chatbot and Google Gemini in n8n
   
   In today's digital-first workflows, businesses and individuals are increasingly looking for ways to extract and automate data from unstructured formats like images. A common example is digitizing payroll slips shared as images—either snapshots or scans. Leveraging AI image understanding and automation tools has become essential, and that's where this n8n workflow shines.
   
   This article breaks down an intelligent and fully automated n8n workflow that allows users to interact with a chatbot on Line Messenger—either by sending a text or an image of a payslip. From there, the system processes the message, extracts important financial data using Google Gemini, and automatically stores it in Google Sheets. The best part? This automation requires no hand-written OCR logic or backend infrastructure.
   
   Let’s dive into what makes this workflow unique and powerful.
   
   🏗️ Overview of the Workflow
   
   This n8n workflow titled "Line_Chatbot_Extract_Text_from_Pay_Slip_with_Gemini" serves as a full-cycle automation system that:
   
   1. Connects with the Line Messaging API to receive user inputs.
   2. Classifies those inputs as either text or image messages.
   3. For images, uses AI to analyze and extract specific structured data.
   4. Replies to the user with the parsed data.
   5. Logs the data into a Google Sheet for record-keeping or further processing.
   
   🧠 Intelligent Message Classification
   
   The first leg of the workflow uses a webhook to receive POST requests from the Line Messaging API. It listens for incoming events and classifies the message type with a Switch node. This classification distinguishes between text messages and image uploads.
   
   - If it’s a text message, the workflow processes it via a LangChain-powered conversational agent linked to Google Gemini.
   - If it’s an image (e.g., a payslip photo), a different path is followed, invoking image analysis logic.
   
   📷 Payslip Image Processing via AI
   
   When a user sends a payslip image, the workflow carries out these steps:
   
   1. Downloads the image using the Line API.
   2. Sends the binary image to the Google Gemini model through LangChain.
   3. Informs Gemini with a custom prompt to extract only the required fields: Status, From, To, Date, and Amount from the payslip.
   
   The beauty of using Gemini (specifically the Gemini 2.0 Flash experimental model) is its advanced multi-modal capabilities. This means it understands both natural language and images—no need to write traditional OCR routines.
   
   🎯 Structured Data Output Format
   
   The AI model is prompted to return data in a JSON format:
   
   {
     "Status": "Paid",
     "From": "ABC Corp",
     "To": "John Doe",
     "Date": "2024-05-31",
     "Amount": "$3,250.00"
   }
   
   This JSON data is cleaned using smart string replacements to strip out formatting markers and make it ready for display and storage.
   
   📝 User Feedback and Data Storage
   
   After Gemini processes the image:
   
   - The formatted result is sent back to the user via the Line chat as a human-readable text response.
   - Simultaneously, the workflow parses this JSON output and appends the data as a new row in a connected Google Sheets document.
   
   This not only reinforces immediate feedback to the user but also helps in logging and accounting over time.
   
   📌 AI Memory and Context Tracking
   
   Conversation persistence is enabled using LangChain’s Window Buffer Memory. This allows the agent to remember user inputs over a window of time and respond with contextual awareness, making the chatbot feel more intelligent and responsive.
   
   💡 Why This Matters
   
   Traditionally, automating data extraction from images demands integrating OCR libraries, parsing engines, and backend logic—a steep entry barrier for non-tech users. With this approach leveraging n8n, Google Gemini, and LangChain, anyone can automate intelligent document understanding using only visual tools and pre-built AI models.
   
   This system has practical applications beyond payslips—extending to invoices, shipping labels, receipts, or even business cards. Use cases could span HR automation, small business accounting, or digital assistants for administrative professionals.
   
   🚀 Final Thoughts
   
   This n8n workflow is more than just a chatbot; it represents the next generation of no-code AI agents. By blending Google Gemini’s multi-modal AI with conversational flows through Line and seamless Google Sheets integration, the result is a frictionless, intelligent automation pipeline.
   
   Whether you're a solo entrepreneur, HR professional, or automation enthusiast, this workflow shows just how powerful and accessible AI-driven automation has become.
   
   —
   
   By leveraging advanced AI through accessible platforms, daily tasks like data extraction no longer require technical teams—just smart design and the right tools.
   
   Ready to build your own AI automation? Start with this workflow and embrace the future of intelligent interactions!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.