This article provides a complete, practical walkthrough of the Postgres Webhook Create 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:
   Automated Realtime Meeting Insights with n8n, OpenAI, and Recall.ai
   
   Meta Description:
   Discover how to build a real-time AI meeting assistant using n8n, OpenAI, Recall.ai, and Supabase. Automate transcription, generate notes, and enhance productivity with live meeting insights.
   
   Keywords:
   AI meeting assistant, n8n workflow, real-time transcription, OpenAI assistant, Recall.ai integration, Supabase automation, meeting transcription, AI summary generation, AssemblyAI, workflow automation
   
   Article:
   
   Creating a Real-Time AI Agent for Meetings with n8n, Recall.ai, OpenAI, and Supabase
   
   In today’s fast-paced work environment, staying focused during virtual meetings can be challenging—especially when balancing participation with note-taking or action-item tracking. That’s where real-time AI-powered meeting assistants step in.
   
   Thanks to powerful automation tools like n8n, developers now have access to open-source, no-code/low-code platforms to create intelligent bots that can join meetings, transcribe conversations in real time, and instantly generate summaries using AI models like OpenAI's GPT.
   
   In this article, we’ll explore how a comprehensive workflow—built in n8n—leverages Recall.ai, OpenAI, and Supabase to automate the process of joining meetings, transcribing discussions, processing insights, and storing useful notes.
   
   Let’s walk through how it works.
   
   🧠 The Goal: Automate Real-Time Meeting Intelligence
   
   The workflow aims to:
   - Automatically join and transcribe meetings in real-time.
   - Store structured dialogue data.
   - Trigger AI-based summarization and note generation.
   - Archive everything for future retrieval.
   
   This powerful solution eliminates the need for manual notetaking, reduces post-meeting work, and ensures nothing important gets missed.
   
   🔌 Key Third-Party APIs Used
   
   1. Recall.ai – For joining online meetings (Zoom, Google Meet, etc.) and performing real-time transcription.
   2. OpenAI API – To process transcripts and generate summaries or extract notes with GPT-based assistants.
   3. AssemblyAI – Speech-to-text engine used by Recall for turning audio into text.
   4. Supabase – A PostgreSQL-based backend to store structured input/output data records.
   
   ⚙️ How the Workflow Works — Under the Hood
   
   The workflow has two main scenarios:
   
   🏁 Scenario 1: Bot Initialization
   
   1. Meeting URL Provided:
      A Google Meet or other supported URL is defined in the workflow via a Set node.
      
   2. Recall.ai Bot Created:
      A POST request is sent to the Recall API to spin up a bot that joins the meeting and listens in.
   
   3. OpenAI Thread Created:
      In parallel, a new "assistant thread" is created using the OpenAI Assistants v2 API.
   
   4. Data Record Stored in Supabase:
      All relevant IDs (Recall bot ID, OpenAI thread ID, and meeting URL) are saved into a Supabase table under the column "input." An empty "output" object is initialized to store later results from the session.
   
   📡 Scenario 2: Real-Time Transcription Handling
   
   Once the Recall bot is in the meeting, it transcribes audio in real-time and sends it via webhook to n8n.
   
   1. Webhook Triggered:
      A webhook node listens for incoming transcription payloads from Recall.ai.
   
   2. Insert Transcription Part:
      Each snippet (sentence or speech chunk) is parsed and appended to the corresponding data record's “dialog” array in Supabase, organized with an order number and timestamp.
   
   3. Conditional Keyword Check:
      A conditional node scans speech for a keyword (e.g., "Jimmy"). If spoken, it can activate additional automated interactions—such as querying the OpenAI assistant.
   
   4. Query OpenAI Assistant:
      If a trigger word is found, a prompt is sent to the Assistant, passing the relevant dialog context and resuming the ongoing OpenAI thread.
   
   5. Generate and Save Note:
      Based on GPT’s response (such as a summary or action item), the assistant’s result is parsed and stored back into Supabase under a “notes” section.
   
   📦 Supabase as a Data Repository
   
   Supabase acts as a unified container storing:
   - All metadata related to the transcription.
   - Transcript logs as structured JSON arrays.
   - Auto-generated notes correlated with time/order indexes for easy retrieval.
   
   The database structure allows filtering and retrieval of notes or transcript parts by order, timestamp, speaker, or trigger keywords.
   
   ✨ Additional Features
   
   - Real-Time Memory Support: OpenAI’s Assistants v2 thread allows the GPT agent to maintain memory across multiple inputs.
   - Silence/Bot Detection: Recall’s auto-leave configuration ensures bots exit meetings when no participants remain or silence persists.
   - keyword triggers: Configure automation logic to launch summaries or task creation when specific phrases are spoken in the meeting.
   
   💡 Why This Matters
   
   This automation becomes invaluable for sales teams, project managers, or researchers who regularly attend remote meetings. By combining transcription, real-time analysis, and secure storage, this n8n workflow saves hours of manual review time, prevents missed information, and enables smarter decisions.
   
   🔧 Setup Steps Recap
   
   1. Set up API keys for Recall, OpenAI, and Supabase.
   2. Create a Supabase table structured with `input` and `output` (both JSONB).
   3. Use the Create Recall bot node to initiate the bot in a meeting.
   4. Create an OpenAI assistant thread to store ongoing AI interactions.
   5. Set up a webhook to receive timestamped meeting transcriptions.
   6. Store transcriptions into Supabase using `jsonb_set`.
   7. Automatically prompt AI summaries using keyword detection.
   8. Archive notes back into Supabase as structured outputs.
   
   📽️ Ready to Try? Watch the Setup Tutorial
   
   For a complete visual guide, you can [watch the 10-minute setup walkthrough video](https://www.youtube.com/watch?v=rtaX6BMiTeo).
   
   👤 Author
   This workflow was created by Mark Shcherbakov from the [5minAI Community](https://www.skool.com/5minai) — a passionate group focused on building time-saving automation using AI and no-code tools.
   
   —
   
   With the ability to automate real-time transcription and generate AI-enhanced notes, this workflow is a leap forward in productivity for any organization relying on virtual meetings. Try it, customize it, and let your meetings work harder for you.
   
   Keywords:
   n8n AI automation, real-time meeting assistant, GPT summaries, AssemblyAI transcription, meeting transcript bot, Supabase database, Recall.ai workflow integration, OpenAI Assistants v2
   
   Third-party APIs Used:
   - Recall.ai
   - OpenAI API (Assistants v2)
   - AssemblyAI (via Recall)
   - Supabase API (PostgreSQL)

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.