This article provides a complete, practical walkthrough of the Webhook Respondtowebhook 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

   Sure! Here is a complete article based on the provided n8n workflow:
   
   ---
   
   ✅ Title:
   “Build Your Own AI Chat Agent to Analyze Google Search Console Data with n8n, OpenAI, and PostgreSQL”
   
   ✅ Meta Description:
   Learn how to create a powerful AI assistant using n8n that chats with your Google Search Console data, powered by GPT-4o, storing conversation history in PostgreSQL, and customizing API calls dynamically using natural language.
   
   ✅ Keywords:
   n8n, OpenAI, GPT-4o, PostgreSQL, Google Search Console, AI agent, chat assistant, SEO insights, workflow automation, natural language API, Supabase, AI and SEO, marketing automation, Search Analytics API, data chatbot
   
   ✅ Third-Party APIs Used:
   - OpenAI API (for GPT-4o conversational AI)
   - Google Search Console API (for performance data retrieval)
   - PostgreSQL (via Supabase or self-hosted, for chat memory persistence)
   - OAuth2 (for Google API authentication)
   
   ---
   
   📝 Article: 
   
   # Build Your Own AI Chat Agent to Analyze Google Search Console Data with n8n, OpenAI, and PostgreSQL
   
   Imagine having an AI assistant that can chat with you about your website’s performance using Google Search Console data—offering insights on-top queries, pages, and search performance, all via natural language. With n8n, OpenAI's GPT-4o, and PostgreSQL working together, that vision is now a reality.
   
   This article breaks down a complete n8n workflow that creates an intelligent interface between you and your Search Console reports, turning keyword analysis and performance monitoring into a conversational experience.
   
   ---
   
   ## 🌐 What This Workflow Does
   
   This workflow builds an AI-powered dialog system for Search Console inside n8n, featuring:
   
   - An authenticated webhook that receives user input (chat messages)
   - OpenAI’s GPT-4o to process the request and generate conversational responses
   - A PostgreSQL backend that stores chat history, giving the AI long-term memory
   - Dynamic interactions with Google Search Console to fetch websites or custom SEO performance data
   - Contextual formatting of results into easy-to-read markdown tables
   - Data handling logic to personalize and verify user requests
   
   ---
   
   ## 🧩 Key Components of the Workflow
   
   ### 1. 🟩 Webhook for Receiving Chat Input
   At its core, the workflow begins with an authenticated **POST webhook** that receives two critical fields:
   - `chatInput`: the user's conversational query
   - `sessionId`: a unique identifier to retain context between chat interactions
   
   This ensures every conversation remains consistent—even across multiple messages.
   
   ### 2. 🧠 Set Fields & Context Enrichment
   Right after the webhook, a "Set" node standardizes and enriches the incoming request:
   - Sets the current date (`date_message`) for time-based queries
   - Extracts chatInput and sessionId for AI processing
   
   This structured format makes it easier for the AI agent to understand and act on the user's message.
   
   ### 3. 🤖 AI Agent Powered by GPT-4o
   The centerpiece of this workflow is the powerful AI agent node using OpenAI’s GPT-4o model. Configured with a detailed prompt and context memory, the AI agent knows:
   - How to respond in a friendly, natural tone
   - When to fetch website properties
   - How to confirm assumptions (like date ranges)
   - To format data in markdown tables
   - To avoid using technical jargon
   
   💡 Tip: The workflow uses GPT-4o by default, but can be switched to a cheaper alternative like GPT-4o-mini.
   
   ### 4. 🗃️ Chat Memory with PostgreSQL
   Every response and user message is stored in a PostgreSQL table named `insights_chat_histories`. This enables a memory context window of previous messages, improving coherence across longer interactions.
   
   Want a quick setup? Use Supabase to spin up a hosted PostgreSQL instance.
   
   ### 5. 🔌 Tool Calling: Invoking Search Console APIs
   The AI agent is equipped with a tool named `SearchConsoleRequestTool`. This dynamically calls a sub-workflow based on user intent.
   
   There are two tool triggers:
   - `website_list`: fetches the list of properties available through your authorized Search Console account
   - `custom_insights`: issues a query to retrieve specific performance metrics (pages, devices, countries, etc.)
   
   ### 6. 🔄 API Request Construction
   The AI agent constructs a JSON payload directly from the conversation. Fields include:
   - Property URL
   - Date range
   - Dimensions (e.g., page, query, device)
   - Row limits and start rows
   
   The set node pre-processes these parameters for exact adherence to the [Search Console API specs](https://developers.google.com/webmaster-tools/v1/searchanalytics/query).
   
   ### 7. 📊 Fetching & Formatting Results
   Two HTTP request nodes perform the actual data retrieval:
   - One fetches the list of websites
   - One fetches search analytics insights
   
   Results are converted into arrays (`searchConsoleData`) and returned as a structured response to the AI agent, which formats them into user-friendly markdown tables for the final reply.
   
   ### 8. ↩️ Responding to the User
   The AI agent sends the final output directly through the “Respond to Webhook” node. Optional hooks are available for visualizing the data (e.g., plotting a chart or embedding analytics into a dashboard).
   
   ---
   
   ## ⚙️ Setup Tips & Security Considerations
   
   ### 1. 🔐 Use OAuth2 Authentication Correctly
   Authenticating with Google Search Console API requires properly scoped OAuth2 credentials. Refer to [this guide](https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/#configure-your-oauth-consent-screen) for configuring the consent screen and selecting the correct scopes.
   
   Screenshots provided in the workflow offer a visual configuration reference using `https://www.googleapisapis.com/auth/webmasters`.
   
   ### 2. 🔒 Protect Your Webhook
   As this webhook is publicly accessible, the workflow includes Basic Auth for protection. Depending on your use case, you may wish to use OAuth2 or JWT for more robust security.
   
   ---
   
   ## ✨ Real-World Applications
   
   This setup is ideal for:
   - SEO teams wanting simple natural-language access to performance metrics
   - Marketing dashboards needing conversational insight layers
   - Agencies running multiple web properties
   - Automating routine analytics queries without technical dashboards
   
   📷 Bonus: The workflow comes with a visual example of an actual chat session showing how data is interpreted and displayed.
   
   ---
   
   ## 🚀 Final Thoughts
   
   By combining the ease of n8n’s visual workflow builder with OpenAI’s natural language understanding and Google’s powerful SEO data, this solution brings next-gen automation to your fingertips.
   
   Ready to give your website data a voice? Start chatting with your Search Console insights today.
   
   ---
   
   Let me know if you'd like this converted into a tutorial, downloadable JSON file guide, or short video!

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.