This article provides a complete, practical walkthrough of the Manual Stickynote Automation 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 Google Search Data Extraction and Summarization with n8n, Bright Data, and Google Gemini
   
   Meta Description:  
   Learn how to automate Google Search result extraction and summarization using n8n, Bright Data’s Web Scraper API, and Google’s Gemini AI model. This workflow demonstrates advanced data processing through AI agents and real-time webhook integration.
   
   Keywords:  
   n8n automation, Google Gemini, Bright Data, Google Search scraper, AI summarization, search result automation, data extraction, LLM integration, webhook, Google SERP automation, intelligent agents, n8n workflow examples
   
   Third-Party APIs Used:
   
   1. Bright Data Web Scraper API (https://api.brightdata.com/request)
   2. Google Gemini (PaLM) API (models/gemini-2.0-flash-exp)
   3. Webhook.site (https://webhook.site/...)
   
   Article:
   
   In today’s data-driven world, the ability to rapidly extract, interpret, and act on search engine data can give organizations a significant competitive edge. Fortunately, with tools like n8n—a powerful open-source workflow automation tool—developers and data engineers can build low-code automations that leverage AI models and external data sources to streamline information retrieval processes.
   
   In this article, we’ll explore an n8n workflow template titled “Google Search Engine Results Page Extraction with Bright Data.” This workflow automates the process of querying Google Search, extracting key information, summarizing findings using a large language model (LLM), and forwarding a structured response to a configurable webhook endpoint.
   
   Let’s dive into how this is accomplished.
   
   The Problem It Solves
   
   Conducting a manual Google Search for data collection tasks can be laborious and inconsistent. Whether you’re doing SEO research, competitive analysis, or market intelligence gathering, you typically want structured and summarized data—quickly.
   
   This workflow enables:
   - Automatic execution of a Google Search query.
   - HTML parsing and conversion of raw search result pages into usable, textual data via Bright Data.
   - Summarization and contextualization using Google’s Gemini AI model.
   - Final expert formatting and delivery via webhook.
   
   Workflow Components and Flow
   
   Here’s how each piece fits into the automation:
   
   1. Triggering the Workflow  
   The workflow begins with a manual trigger node ("When clicking ‘Test workflow’") that activates the process. This is ideal for testing or manually launching search jobs.
   
   2. Setting Search Parameters  
   Using the “Set Google Search Query” node, users define the search term (e.g., “Bright Data”) and the designated scraper zone specific to Bright Data’s infrastructure.
   
   3. Querying Google Search via Bright Data  
   The "Perform Google Search Request" node uses Bright Data’s Web Scraper API to send a POST request to Google Search via a proxy network. The API simulates a user search and returns raw HTML results formatted according to the "raw" setting.
   
   4. Extracting Clean Textual Data  
   With the help of the “Google Search Data Extractor” (an Information Extraction node), the noisy HTML, CSS, and JavaScript are stripped, leaving behind meaningful textual content. A system prompt guides the AI to behave like an HTML extraction expert.
   
   5. Summarizing Results with an LLM  
   The output from the extractor is passed to the “Summarization Chain,” which leverages a Google Gemini model to summarize the search results. This step distills lengthy pages of data into concise, meaningful highlights.
   
   6. Intelligent Formatting via AI Agent  
   Next, the “Google Search Expert AI Agent” formats the summarized data into a structured response. By defining the agent’s role explicitly (“You are an expert Google Search Expert…”), the AI can tailor its output precisely to the intended downstream use.
   
   7. Sending Results via Webhook  
   Finally, the “Webhook HTTP Request” node sends the formatted and summarized search results to a preset Webhook URL. This decouples the search workflow from the consuming service, allowing flexibility in how the data is stored or displayed in other systems.
   
   AI Capabilities in Action
   
   What truly elevates this workflow is its integration of artificial intelligence at multiple levels:
   
   - Textual extraction using an AI-driven Information Extractor guided by a system prompt.
   - Summarization via the “Summarization Chain” using Gemini (PaLM) LLMs.
   - Role-based reasoning from the expert agent to contextualize and structure the final output.
   
   By blending Bright Data for search scraping and Google's Gemini as the thinking engine, n8n orchestrates an intelligent, end-to-end workflow that removes human dependency from repetitive, data-heavy querying tasks.
   
   Customization & Extensibility
   
   Two sticky notes within the workflow outline important usage considerations:
   - Set your actual Google search query and zone before deployment.
   - Update the webhook endpoint to reflect your system or service URL.
   
   Additionally, developers can tweak the prompts to yield different summary styles or extract customized types of information depending on domain-specific needs (e.g., extract just prices, dates, metadata, etc.).
   
   Conclusion
   
   This n8n workflow is a glimpse into the power of combining automation with artificial intelligence. From raw HTML to structured JSON summaries, it showcases a complete data pipeline that can be used out-of-the-box or customized to meet business-specific demands.
   
   Whether you’re in SEO, analytics, business intelligence, or AI development, automating your Google Search input/output cycle has never been easier—offering speed, accuracy, and extensibility.
   
   By leveraging Bright Data’s robust scraping capabilities and the reasoning power of Google Gemini, users gain more than just data—they gain insight.
   
   Download or fork this workflow today and reimagine how your applications interact with the world’s information.

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.