This article provides a complete, practical walkthrough of the Supabase Stickynote 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:
   Automated A/B Prompt Testing for AI Agents Using n8n, Supabase, and OpenAI
   
   Meta Description:
   Learn how to implement A/B testing for large language model prompts in conversational AI with n8n. This guide uses Supabase for session tracking and OpenAI's GPT for AI responses, enabling data-backed improvements to user interactions.
   
   Keywords:
   A/B testing, n8n workflow, OpenAI GPT, Supabase, langchain, AI prompt testing, conversational AI, LLM testing, chatbots, machine learning experimentation, session-based prompt assignment
   
   Third-Party APIs Used:
   - Supabase API (for storing and retrieving session assignments)
   - OpenAI API (for generating language model responses)
   - PostgreSQL via Supabase (for chat memory persistence)
   
   —
   
   Article:
   
   A/B Prompt Testing for AI Agents with n8n: A Low-Code Approach to Smarter Conversations
   
   In today’s AI-powered world, crafting the perfect prompt for a large language model (LLM) can significantly influence the quality of its output. Particularly in customer support, virtual assistants, and other chat-based applications, small variations in phrasing can lead to vastly different user experiences. But how do you know which prompt performs best?
   
   Enter A/B testing—an industry-standard method for comparing two variants against a specific goal. Thanks to the open-source automation platform n8n, developers and non-technical users alike can now perform A/B prompt testing for LLMs without writing complex backend code. This article breaks down an n8n workflow that seamlessly integrates Supabase and OpenAI to run A/B tests in chat interactions by randomly assigning users to different prompt experiences and persisting choices across sessions.
   
   🌐 How It Works
   
   This workflow initiates when a message is received through n8n’s built-in chat interface. It checks if the session (user conversation) already exists in the Supabase table split_test_sessions. Here's a step-by-step breakdown of how the system responds intelligently by selecting a prompt variant:
   
   1. Message Trigger:
   The trigger node listens for incoming chat messages.
   
   2. Session Check:
   Using the Supabase API, the workflow checks whether the current session ID is already stored in the database. If it is, the session has already been assigned a prompt.
   
   3. Random Assignment:
   If the session is new, the workflow randomly assigns it to either the baseline or the alternative prompt. This assignment is stored in the Supabase database along with the session ID.
   
   4. Set Prompt Text:
   Two prompt values are hardcoded in a "Define Path Values" node—for instance: "The dog's name is Ben" (baseline) and "The dog's name is Tom" (alternative). Based on the session’s assignment, the corresponding prompt is selected.
   
   5. Send To LLM:
   The chosen prompt is injected as a system message and sent to OpenAI’s GPT model using n8n’s langchain-AI Agent node. This ensures that every response from the language model adheres to the pre-defined experimental logic.
   
   6. Persist Chat Memory:
   To maintain context across interactions, the chat memory is stored in a PostgreSQL table, using a chat history node specifically designed for Postgres and langchain compatibility.
   
   🔧 Setup Requirements
   
   Before deploying this workflow, a few key configurations are necessary:
   
   - In Supabase, set up a table named split_test_sessions with the columns:
     - session_id (text)
     - show_alternative (boolean)
   
   - Add credentials for:
     - Supabase API
     - OpenAI API
     - PostgreSQL (via Supabase connection)
   
   - Customize the "Define Path Values" node to experiment with different prompt wordings.
   
   📊 Why A/B Testing for Prompts Matters
   
   Most LLMs are prompt-sensitive—small changes in instructions can alter tone, accuracy, and output relevance. By running A/B tests like this, engineers and product teams can gather insights into:
   
   - Which prompt elicits more helpful or accurate responses
   - How user engagement varies between prompts
   - Whether certain prompt styles better suit target user personas
   
   This workflow ensures that each chat session remains consistent in its experience (users won’t switch between prompt types mid-conversation), making it easy to analyze performance per variant.
   
   🔄 Next Steps and Customizations
   
   Once this basic A/B test is running, you can expand its capabilities in several ways:
   
   - Add analytics to track success metrics (e.g., session length, user satisfaction scores).
   - Modify the random assignment logic to A/B/C for three or more experiments.
   - Test different model parameters—e.g., temperature or model choice—instead of prompt content.
   - Schedule periodic prompt rotations or refresh rates to prevent long-term bias.
   
   📎 Final Thoughts
   
   By coupling n8n’s low-code workflow automation with the power of OpenAI models and Supabase’s real-time database, it becomes possible to iterate on LLM experience as quickly and reproducibly as a website test. Whether you're designing a friendly chatbot or tuning a virtual legal assistant, A/B testing via this workflow will ensure your LLM behavior is continuously improving based on real user interactions.
   
   Give it a try—and let your data shape your dialogue.

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.