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 Yelp Review Extraction and Summarization Using n8n, Bright Data, and Google Gemini
   
   Meta Description:  
   Learn how to automate the extraction and summarization of Yelp business reviews using Bright Data's web scraping API, Google Gemini's powerful LLM, and the n8n automation platform.
   
   Keywords:  
   n8n workflow, Yelp review automation, Google Gemini, Bright Data API, AI summarization, structured data extraction, LangChain, LLM automation, web scraping, AI engineering
   
   Third-Party APIs Used:
   
   1. Bright Data API (for web scraping Yelp pages)
   2. Google Gemini (models/gemini-2.0-flash-exp) via Google PaLM API (for LLM-based processing and summarization)
   3. Webhook.site (for testing and receiving the final structured and summarized output)
   
   Article:
   
   —
   
   In today’s data-driven world, automating the extraction and analysis of customer feedback can save countless hours and lead to more informed decision-making. Yelp, being a rich source of customer sentiment and business reviews, presents an incredible opportunity for businesses, analysts, and developers willing to harness the power of automation and AI.
   
   This article explores a practical n8n workflow titled “Extract & Summarize Yelp Business Review with Bright Data and Google Gemini,” which seamlessly scrapes Yelp reviews using Bright Data, structures and summarizes the data using Google’s Gemini LLM, and routes the output for further use—all without writing a single line of code.
   
   Let’s break down this workflow and see how the combination of no-code tools and advanced AI models brings the future of data analysis within reach.
   
   🧠 Overview of the Workflow
   
   At a high level, this n8n workflow performs the following:
   
   1. Initiates the process manually.
   2. Sends a configured Yelp URL (e.g., restaurants in San Francisco, sorted by rating) to Bright Data for scraping.
   3. Uses Google Gemini to analyze and structure the raw review data.
   4. Summarizes the structured data into digestible insights.
   5. Merges the original structured output with the summary.
   6. Sends this final result to a webhook endpoint for display or storage.
   
   📦 Tools and APIs in Action
   
   Before we dive further, here’s a quick glance at the technologies used:
   
   - 🔍 Bright Data API — to send requests to scrape Yelp review pages programmatically.
   - 🧠 Google Gemini (via Google PaLM API) — for leveraging advanced language model capabilities for both structuring raw text and generating summaries.
   - 🔧 Webhook.site — as a placeholder endpoint to receive the final processed output.
   - 📊 LangChain Integration in n8n — bridges LLMs and structured data manipulation for more control and precision.
   
   ⚙️ Step-by-Step Breakdown
   
   1. Manual Trigger
   The workflow begins with a Manual Trigger node in n8n, which offers flexibility during testing or fine-tuning.
   
   2. Set the Yelp URL and Bright Data Zone
   Using a Set node, the Yelp target URL and Bright Data zone credentials are configured. For example, this demo workflow uses the following URL to fetch top-rated restaurants in San Francisco:
   ```
   https://www.yelp.com/search?find_desc=Restaurants&find_loc=San+Francisco%2C+CA&sortby=rating
   ```
   
   3. HTTP POST to Bright Data
   An HTTP Request node posts this configuration to Bright Data's scraping service to retrieve raw response data, simulating a human browser interaction. The data is returned in its original HTML or raw format.
   
   4. LLM-Powered Data Structuring
   This raw data is then piped through the Google Gemini-powered “Structured Data Extractor,” where the LLM parses it into a predefined JSON schema. The schema includes structured fields like:
   - Restaurant name
   - Location
   - Average rating
   - Review count
   - Individual review details (rating, date, reviewer name, and text)
   
   A LangChain-compatible OutputParser ensures that Gemini's response conforms strictly to the defined format.
   
   5. Summarization Chain
   Next, the structured review data is summarized using another Gemini model through a LangChain Summarization Chain. This chain prompts the model to generate a concise and coherent digest, making the data practical for executive insights or dashboards.
   
   6. Merging Outputs
   With both structured data and natural language summary available, the Merge node combines these into a single object.
   
   7. Final Output to Webhook
   Finally, the merged response is POSTed to a webhook endpoint (e.g., webhook.site), where it can be visualized, processed, or even exported to a reporting tool of your choice.
   
   📋 Real-Life Applications
   
   This architecture opens up multiple use cases where Yelp review data can be turned into valuable business intelligence:
   
   - 🧾 Competitive Analysis: Compare customer sentiment across competing restaurants or service providers.
   - 📈 Trend Detection: Monitor recurring complaints or praise to track shifting customer expectations.
   - 💬 Social Listening: Pull customer quotes for marketing or product optimization.
   
   🚀 Why This Matters
   
   What once required custom scripts and manual data wrangling is now an elegant no-code solution powered by intelligent LLM models. By leveraging n8n’s workflow automation and integrations with Bright Data and Google Gemini, you build a robust pipeline that can scale and adapt to various review sources.
   
   Moreover, LangChain’s integration with n8n introduces modular control, ensuring that prompts, parsing, and summarization remain customizable depending on input complexity and required output fidelity.
   
   🔐 Things to Note
   
   - Replace the placeholder Yelp URL and ensure the correct Bright Data zone is configured.
   - The webhook used is for demo/testing purposes and should be replaced with your own endpoint in production.
   - Google Gemini's API usage may incur costs; make sure to review your API quota and billing.
   
   🌐 Final Thoughts
   
   This workflow beautifully demonstrates how modern automation not only scrapes data but transforms it into structured, meaningful, and actionable insights using AI. From setup to execution, n8n combined with LLMs makes it possible for individuals or teams without programming expertise to build powerful review analysis pipelines in just minutes.
   
   As automation and AI integration continue to evolve, workflows like this offer a glimpse into how intelligent systems can empower data-driven strategies across industries.
   
   —
   
   Want to try the workflow yourself? Set up an n8n instance, plug in your Bright Data and Google Gemini credentials, and start transforming raw review data into smart summaries today!

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.