This article provides a complete, practical walkthrough of the Manual Supabase Automation Triggered 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:  
   Building a Smart Supabase-Powered Chatbot Workflow Using n8n and Gemini 2.0
   
   Meta Description:  
   Discover how to build a context-aware chatbot using n8n, Supabase, and Google’s Gemini 2.0 language model. This automated workflow enables dynamic user engagement with session-based memory and custom data storing.
   
   Keywords:  
   n8n workflow, Supabase chatbot, Postgres automation, Google Gemini 2.0, chatbot memory, AI automation, LangChain n8n, Supabase database, Google Palm API, WhatsApp chatbot
   
   Third-party APIs Used:
   
   1. Google Palm API (for Gemini 2.0 Flash model)
   2. Supabase API (for data update and storage)
   3. PostgreSQL (Supabase-hosted Postgres database)
   
   Article:
   
   Creating Context-Aware Automations with n8n, Supabase, and Google Gemini 2.0
   
   In the evolving world of intelligent automation and AI-integrated workflows, building a chatbot that not only responds to user inquiries but also remembers past interactions can take user experience to the next level. Thanks to platforms like n8n, these kinds of sophisticated workflows are now achievable without deep backend coding.
   
   In this article, we’ll break down a ready-made n8n workflow designed to do just that — manage chatbot inputs dynamically, store user context in Supabase (using Postgres), and leverage the power of Google’s Gemini 2.0 Flash language model for smart interaction. Let's take a look at how it all connects together.
   
   🧠 Workflow Overview
   
   The workflow is titled "Supabase Setup Postgres" and is composed of six main nodes that together accomplish:
   
   - Capturing user input manually for testing.
   - Passing chat input to a Gemini 2.0 model for intelligent response generation.
   - Leveraging Supabase Postgres as a memory layer for conversation context.
   - Updating Supabase records with user metadata, enriching the dataset.
   - Serving as a reference template for scalable, AI-powered automation.
   
   🔗 Step-by-Step Breakdown
   
   1. Manual Trigger Node
   The workflow kicks off with a Manual Trigger called "When clicking ‘Test workflow’". This allows the user to test it manually inside the n8n editor.
   
   2. Set Sample Input Variables
   Next, a Set node defines three key input variables:
   - session_id: 491634502879
   - name: Genn Sverster
   - chatInput: wie gehts dir? (German for “how are you?”)
   
   This simulates a real-world incoming message from the user, providing a unique session ID and the user’s name.
   
   3. GeminiFlash2.0 Node – AI-Powered Chat Processing
   Here’s where Google’s Gemini 2.0 Flash model comes into play. Integrated via LangChain's n8n implementation, this node interprets the chat input using cutting-edge NLP capabilities from Google’s Palm API.
   
   4. Supabase Postgres – Memory Context Store
   To make the chatbot more intelligent and context-aware, the workflow implements Supabase Postgres as a “memory” node. It reads and stores chat history using the session_id as a unique key and maintains a context window of 20 messages. This ensures that the AI receives relevant background information during each session, improving its responses over time.
   
   5. Sample Agent – The AI Assistant in Action
   The input chat text and session context are then passed to a LangChain agent node named "Sample Agent." Configured with a system message of “You are a helpful assistant,” the agent crafts personalized replies using both the context from memory and the latest input. This simulates a thoughtful assistant who “remembers” previous parts of the conversation.
   
   6. Update Additional Values in Supabase
   Finally, this node scans the Postgres-hosted Supabase table whatsapp_messages3 for entries that:
   - Match the session_id
   - Have a NULL name field
   
   It then updates these rows with the stored user name (e.g., Genn Sverster). This ensures that even if future queries arrive without an explicit user name, Supabase provides consistent context across all flows.
   
   💡 Why This Matters
   
   This workflow goes beyond simple one-off bots and touches on the future of conversational AI. Message history, session tracking, and real-time database updates mean the bot can evolve into a personalized virtual assistant that grows smarter over time. With the use of Gemini 2.0 and Supabase’s fast, scalable Postgres backend, developers bridge the gap between cloud-native databases and AI modeling.
   
   🛠️ Tools and Technologies Powering the Workflow
   
   - n8n: The low-code workflow automation tool that stitches everything together.
   - Supabase: An open-source Firebase alternative offering hosted Postgres, authentication, and real-time subscriptions.
   - Gemini 2.0 Flash (via Google Palm API): A high-speed, high-efficiency large language model that excels in chat scenarios.
   - LangChain: Powers seamless interoperability between memory, language models, and agents.
   
   🔚 Final Thoughts
   
   This n8n workflow is a perfect blueprint for developers aiming to create smarter, session-based chatbots. Whether you’re handling customer support, automating internal communication, or building robust digital assistants, this approach combines modular components into a coherent and intelligent system.
   
   By integrating Supabase’s database capabilities with Gemini’s LLM technology, the potential of your chat applications is only limited by imagination. Try deploying this workflow yourself, and bring your chatbot logic into the future!

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.